28
https://doi.org/10.1007/s41885-017-0017-y ORIGINAL PAPER The Impact of Disasters on Inflation Miles Parker 1,2 Abstract This paper studies how disasters affect consumer price inflation. There is a marked heterogeneity in the impact between advanced economies, where the impact is neg- ligible, and developing economies, where the impact can last for several years. There are also differences in the impact by type of disasters, particularly when considering inflation sub-indices. Storms increase food price inflation in the near term, although the effect dissi- pates within a year. Floods also typically have a short-run impact on inflation. Earthquakes reduce CPI inflation excluding food, housing and energy. Keywords Disasters · Inflation JEL Classification E31 · Q54 Introduction Disasters caused by natural hazards have the potential to cause massive economic disrup- tion, and often are accompanied by a significant human toll. Recent examples of disasters include: earthquakes in Japan, Chile, Haiti and New Zealand in 2010 and 2011; the devas- tation of Vanuatu by Cyclone Pam in 2015; The 2011 floods in Thailand; ongoing drought This work was carried out while the author was employed by the Reserve Bank of New Zealand. The views expressed here are those of the author, and not necessarily those of the RBNZ or the European Central Bank. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s41885-017-0017-y) contains supplementary material, which is available to authorized users. Miles Parker [email protected] 1 Reserve Bank of New Zealand, 2, The Terrace, Wellington, New Zealand 2 Present address: European Central Bank, Sonnemannstrasse 20, Frankfurt am Main, Germany Received: 21 March 2017 / Accepted: 7 November 2017 / Published online: 29 November 2017 © Springer International Publishing AG, part of Springer Nature 2017 EconDisCliCha (2018) 2:21–48

The Impact of Disasters on Inflation - Springer · The Impact of Disasters on Inflation Miles Parker1,2 Abstract This paper studies how disasters affect consumer price inflation

Embed Size (px)

Citation preview

https://doi.org/10.1007/s41885-017-0017-y

ORIGINAL PAPER

The Impact of Disasters on Inflation

Miles Parker1,2

Abstract This paper studies how disasters affect consumer price inflation. There is amarked heterogeneity in the impact between advanced economies, where the impact is neg-ligible, and developing economies, where the impact can last for several years. There arealso differences in the impact by type of disasters, particularly when considering inflationsub-indices. Storms increase food price inflation in the near term, although the effect dissi-pates within a year. Floods also typically have a short-run impact on inflation. Earthquakesreduce CPI inflation excluding food, housing and energy.

Keywords Disasters · Inflation

JEL Classification E31 · Q54

Introduction

Disasters caused by natural hazards have the potential to cause massive economic disrup-tion, and often are accompanied by a significant human toll. Recent examples of disastersinclude: earthquakes in Japan, Chile, Haiti and New Zealand in 2010 and 2011; the devas-tation of Vanuatu by Cyclone Pam in 2015; The 2011 floods in Thailand; ongoing drought

This work was carried out while the author was employed by the Reserve Bank of New Zealand.The views expressed here are those of the author, and not necessarily those of the RBNZor the European Central Bank.

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s41885-017-0017-y) contains supplementary material,which is available to authorized users.

� Miles [email protected]

1 Reserve Bank of New Zealand, 2, The Terrace, Wellington, New Zealand

2 Present address: European Central Bank, Sonnemannstrasse 20, Frankfurt am Main, Germany

Received: 21 March 2017 / Accepted: 7 November 2017 / Published online: 29 November 2017© Springer International Publishing AG, part of Springer Nature 2017

EconDisCliCha (2018) 2:21–48

in California and the eruption of Eyjafjallajokull in Iceland in 2010. With greater concen-trations of population and activity in vulnerable regions, the incidence of economicallysignificant disasters is increasing (Cavallo and Noy 2011). Barro (2009) estimates the wel-fare cost of these rare, but extreme, events at 20 percent of output, far beyond the 1.5 percentestimated welfare cost of normal business cycle fluctuations.

Until recently, our understanding of the economic impact of disasters was limited.Progress has been made over the past decade in investigating the impact of disasters on out-put, with a number of authors systematically studying the impact across disaster type, levelof development and sectors of the economy (see, e.g. Noy 2009; Raddatz 2009; Loayza etal. 2012; Fomby et al. 2013; Felbermayr and Groschl 2014).

Yet while the impact on output is now better understood, the impact on prices hasreceived far less attention. Indeed, Cavallo and Noy (2011) in their recent survey of the lit-erature on disasters point to the effect on prices as being one of the main remaining gaps inour knowledge. Only a small handful of papers have carried out any investigation of priceimpacts, and most of them do so only as part as part of a broader case study of the impact ofa particular event, rather than a dedicated analysis of the impact on prices. Given the rangeof factors noted in the literature that affect the impact of disasters, caution should be exer-cised before extrapolating universal conclusions from single events. Even within countries,disasters of different magnitudes and types may have differing effects on inflation.

Section “How Disasters May Affect Prices” below reports the results from these priorstudies. Of them, Heinen et al. (2015) is worthy of mention as the only one that extends theanalysis beyond a single, or small number of similar events. They quantify the impact ofhurricanes and floods on inflation in 15 Caribbean economies, finding evidence of a positiveimpact from both in the month the disaster occurs.

This article extends the analysis of Heinen et al. (2015) in a number of importantdimensions. First, it also covers earthquakes and droughts, important disaster types that arecovered in the literature on output effects.1 Second, it extends coverage to 212 economies.As Heinen et al. (2015) note, the 15 Caribbean economies they study are particularly vul-nerable to diasters due to their small physical size, geographic isolation, high populationdensities, limited natural resources and reliance on imports. This particular vulnerabilitymay result in impact estimates that do not readily translate to other economies.

Third, as described below in Section “How Disasters May Affect Prices”, the impact ofdisasters on inflation may differ over time, as initial short-term disruption and shortagesmay give way to rehabilitation and a rebuild-led demand surge. Heinen et al. (2015) studythe impact on inflation in the initial three months following the disaster. This article extendsthat analysis to consider the first three years following the disaster. As results here show,there are prolonged impacts on inflation that can last a number of years, and may differ insign. For example, Heinen et al. (2015) find positive effects from hurricanes on food prices.The analysis here finds that that influence is relatively short-lived and is reversed beyondthe horizon of analysis of that earlier paper.

Finally, the analysis here considers the impact of disasters on major sub-componentsof CPI, allowing for a finer understanding of likely direct and indirect impacts on varioussectors of the economy. While Heinen et al. (2015) also look at subcomponents for theeconomies and disasters they study, less is known about the impact on sub-components forother disasters and economies.

1The estimation here additionally includes mass movements, insect infestations, extreme temperatures, vol-canoes and wildfires, but since those disasters combined are not found to have significant impacts we do notdiscuss these disaster types in any detail.

EconDisCliCha (2018) 2:21–4822

As a result of these important extensions, this article provides a systematic analysis ofthe impact of disasters on prices that is comparable to the well-known studies of the impacton output mentioned above. As such, it provides a significant step in closing one of theimportant remaining gaps in our knowledge of the effects of disasters. This article alsomakes a further contribution to the literature on the impact of disasters by using higherfrequency data. Quarterly GDP data is not widely available outside of the most developedcountries. Studies of the effects on output have focused on annual (or five-year average)GDP growth. Consumer prices are available at more frequent intervals, permitting the anal-ysis here to study at a more granular level the evolution through time of the impact ofdisasters.

There are a number of benefits in knowing the likely path for inflation following a dis-aster. Understanding the effect on prices provides monetary policy makers with greaterguidance on how to set policy in the immediate aftermath of the disaster. It can help withestimating the insurance costs for rebuild or cash settlement; it provides aid donors with ametric for determining the value of cash donations or gifts in kind; it assists fiscal authori-ties with calculating the future costs of the rebuild programme. Finally, the path for inflationhas implications for the exchange rate and capital account policies.

We combine two sets of data to undertake the analysis here. The first is the EM-DATdatabase on disasters widely used in the literature. This contains information on a widerange of disasters, including number of people killed, number of people affected and (lessfrequently) damage caused. This data set is widely used in the literature and is the only onewith widespread coverage that is publicly available. As a robustness check, we also under-take the analysis using the Ifo GAME database of geological and meteorological hazards,presented in Felbermayr and Groschl (2014).

The second data set is the consumer price data from Parker (2016). These data coverconsumer prices for 223 countries and territories over the period 1980-2012. We restrict oursample to those countries with at least 40 quarterly observations, resulting in 212 included inthe analysis here. The data include information on headline consumer prices, as well as sub-indices for food, housing, energy and the remainder of the index. The panel is not balanced,with coverage for the sub-indices less complete for less developed countries. Nonetheless,coverage of sub-indices far exceeds any other database for consumer prices.

Previous studies have highlighted a large heterogeneity in the impact of disasters onoutput, particularly between advanced and developing countries. The impact on inflationis similarly diverse. Disasters on average have negligible impact on inflation in advancedcountries, but typically increase inflation in developing countries. That said, the impact forsevere disasters (those in the upper quartile) is larger, and significant even in high incomecountries.

The impact of diasters on inflation differs by sub-index. The impact on food price infla-tion is in general positive, if short lived. The impact on housing and other sub-indices isin general negative. Differences in expenditure weights on these sub-indices will in partexplain the differences witnessed in headline inflation numbers by level of development.

Earthquakes do not significantly affect headline inflation, but do significantly reduce CPIinflation excluding food, housing and energy. Storms cause an immediate increase in foodprice inflation for the first six months, although this impact is reversed in the subsequenttwo quarters, resulting in no significant impact over the entire first year, or beyond. Floodsincrease headline inflation in the quarter that the flooding occurs in middle and low incomecountries, but have no significant impacts in subsequent quarters. In high income coun-tries, the impact on headline inflation is negative, although insignificant. Droughts increaseheadline inflation for a number of years.

EconDisCliCha (2018) 2:21–48 23

How Disasters May Affect Prices

As noted above, there has yet to be a systematic review of the impact of disasters on prices.Nonetheless, evidence from the literature on the impact on economic activity and a smallnumber of case studies provide some guide to the potential channels of impact.

Short-run Impacts

Disasters affect economic activity via a number of channels in the short run. The imme-diate direct impact of diasters can cause death and injury to people, and cause damage tobuildings, transport infrastructure and livestock. The destruction of harvests or housing cancreate shortages, pushing up the price of remaining food or houses. The size of the increasein prices may depend on market power of firms and perceptions of customers – it may notbe in the long-run interest of a firm to be seen to be profiteering from customers’ misery.Rotemberg (2005), p 835 notes examples of customer protests at prices increases followingthe 1994 earthquake in the Los Angeles area.2

Beyond the direct impact, other businesses and households may be indirectly affected,such as being unable to bring goods to market due to lack of transport infrastructure. Forexample, farmers may react to the shortage of feed caused by a drought by slaughteringlivestock. This could potentially reduce meat prices in the near term, but increase them inthe medium term as farmers act to rebuild livestock numbers once the drought has ended.If the disruption to economic activity is sufficiently large it may reduce demand for goodsand services from sectors not directly affected. This lower demand could reduce the pricesin these other sectors.

There are a number of papers that aim to quantify the impact of disasters on economicactivity. Noy (2009) examines the impact of 507 disasters over the period 1970-2003, find-ing a significant impact on GDP. The effect is greater for smaller and for less developedcountries. Higher per capita income, literacy rates and institutional capacity help to mitigatethe impact. The impact of disasters appears to differ by type of disaster. Raddatz (2009)finds that climatic disasters (storms, floods, droughts and extreme temperatures) have a sig-nificant negative impact on GDP, mostly in the year of the disaster. Other disasters are notfound to have a significant impact. Felbermayr and Groschl (2014) similarly find differen-tial impacts by type of disaster and level of development, using a database of geological andmeteorological events.

A number of authors also consider the impact of disasters on differing sectors of theeconomy. Loayza et al. (2012) find no significant effect on overall GDP using five-yeargrowth averages over the period 1961-2005, although droughts are negative and stormsand floods are positive. Droughts and storms negatively affect agricultural output, whereasfloods are positive. The authors suggest that this positive effect may derive from plentifulrainfall providing benefit to crops that outweighs the localised damage from flooding, andthe additional nutrients that aid the following season. Furthermore, cheaper electricity frommore abundant hydropower aids industry. Nonetheless, this positive effect disappears in thepresence of more severe flooding. Fomby et al. (2013) find that earthquakes affect agri-cultural production in developing countries, potentially a result of damaged infrastructure.Fomby et al. (2013) also find differing impacts of disasters depending on their severity.

2In some jurisdictions it is illegal to increase prices of certain goods, termed ‘price gouging’, in the immediateaftermath of a disaster (Gerena 2004).

EconDisCliCha (2018) 2:21–4824

Small-scale studies of individual disasters, or small groups, point to differing inflationimpacts by type of disaster. The most comprehensive study to date, Heinen et al. (2015),considers the impact of hurricanes and floods on the inflation rates of 15 Caribbean islands.Damaging hurricanes increased monthly headline CPI inflation by 0.05 percentage points,with a greater effect on impact on food prices. More damaging hurricanes have a proportion-ately higher impact on inflation, with the implied inflationary impact of the largest hurricanein their sample being 1.4 percentage points on monthly headline CPI inflation. Flooding hadan average 0.083 percentage point impact on inflation, with the implied largest effect 0.604percentage points. The impact of both hurricanes and floods takes place in the month of theevent, with no significant effects in subsequent months.

In terms of case studies of individual events or countries, Laframboise and Loko (2012)estimated that headline inflation increased by an additional 2 percent in Pakistan follow-ing the severe floods of 2010. Abe et al. (2014) find little increase in prices following theGreat East Japan earthquake of 2011. Reinsdorf et al. (2014) compare this earthquake withthe Chilean earthquake of 2010 using online data for supermarkets. Their data point to asharp fall in product availability in the immediate aftermath of both earthquakes, withoutconcurrent increases in price.

Doyle and Noy (2015) find no significant aggregate impact on New Zealand consumerprices from the Canterbury earthquakes of 2010 and 2011. At a disaggregated level, Parkerand Steenkamp (2012) and Wood et al. (2016) find large increases in rents and constructioncosts within Canterbury, consistent with restricted housing supply following the widespreaddestruction of the housing stock. Munoz and Pistelli (2010) investigate the impact on infla-tion of a small number of large earthquakes, by comparing inflation outturns with a forecastbased on information prior to the event. While they find that some earthquakes resulted inhigher inflation, it was by no means universal. Given their small sample of events they wereunable to explain the causes of this different response.

Kamber et al. (2013) study the impact of droughts on New Zealand, using measures ofrainfall and soil moisture deficit in a VAR framework. Their findings suggest a drought ofthe magnitude of that of early 2013 raises CPI food prices by around 1.0 - 1.5 percent. Inparticular, milk cheese and eggs prices increase by 3 percent, reflecting the importance ofdairy in domestic agriculture. Wholesale electricity prices increase by as much as 8 percentfollowing such a drought, as lower lake levels increase the cost of hydroelectricity, althoughthis cost increase does not appear to pass through to retail. Conversely, depressed economicactivity results in falling prices for other non-tradable sectors, resulting in no significantimpact on overall CPI. Buckle et al. (2007) similarly found no significant overall impact onconsumer prices from droughts in New Zealand.

Medium-run Impacts

There may be some longer-lasting impacts on prices beyond the immediate destruction anddisruption.Thedestructionof ports and infrastructuremaydisrupt imports, drivingup thepricefor those goods which are imported. Conversely, the lack of ports for export may lead to adomestic oversupply and price falls in goods normally exported. International investors mayalso choose to withdraw capital from a country recently hit by a disaster, pushing down on theexchange rate and increasing the cost of imports. Ramcharan (2007) finds that in flexible ex-change rate regimes, the real exchange rate depreciates by 10.25 percent in the year followinga windstorm. The exchange rate effect is uncertain, however, since domestic investorsrepatriating foreign investments could lead to an exchange rate appreciation; the yen appre-ciated sharply in the immediate aftermath of the 2011 Tohoku earthquake (Neely 2011).

EconDisCliCha (2018) 2:21–48 25

Over the medium term, as resources are allocated to damage and reconstruct destroyedbuildings and infrastructure there may be a ‘demand surge’, placing upward pressure onprices. Keen and Pakko (2011) calibrated a DSGE model to simulate the impact of Hur-ricane Katrina. In their simulation, the destruction of capital stock and temporary fall inproductivity causes firms to raise prices, resulting in higher inflation

However, this demand surge is not certain. The incidence of a disaster may cause revi-sions of people’s perception of disaster risk and cause outward migration. Boustan et al.(2012) find outward migration from areas affected by tornadoes, Hornbeck (2012) fromheavily eroded counties in the Dust Bowl era, and Hornbeck and Naidu (2014) documentsubstantial outward migration following the 1927 Mississippi floods. Coffman and Noy(2012) use synthetic control methods to estimate a 12 percent drop in population on theisland of Kauai in Hawaii, following Hurricane Iniki. The population of New Orleans fellsharply following Hurricane Katrina (Vigdor 2008), although the destruction of housingstock was far greater, resulting in higher house prices and rents. The destruction of disastersmay also create poverty traps where households are unable to regain previous wealth andincome (Carter et al. 2007). Such scenarios would put downward pressure on prices overthe medium term in areas affected by disasters, although it is less certain the extent to whichthis affects the overall national price level.

Taking the above factors into consideration, the overall impact of disasters on inflation isambiguous. The prior research on activity suggests there may be at the very least differencesin the impact of disasters on inflation: by type of disaster; between the short and mediumterm; by different sub-component of the inflation basket; by level of development, and;by severity of the disaster. The analysis that follows accounts for these differing potentialeffects in turn.

Data and Method

Disasters

The most widely used source for disasters is the EM-DAT database collected by the Cen-tre for Research on the Epidemiology of Disasters at the Universite catholique de Louvain.The database covers disaster events which meet one of the following criteria: ten or morepeople killed; 100 or more people affected (defined as people requiring immediate assis-tance during a period of emergency, i.e. requiring basic survival needs such as food,water, shelter, sanitation and immediate medical assistance); declaration of a state of emer-gency; or call for international assistance. Alongside the date of the disaster, the EM-DATdatabase also includes information on the number of people killed and the number of peo-ple affected. For a smaller set of disasters the database includes an estimate of the damagecaused.

It is worth noting that the EM-DAT database measures the ex post effects of disasters,which as shown in Noy (2009) and elsewhere depend on a number of country specificfactors such as institutions. The relevant institutional factors, for example good economicgovernance, may also affect inflation dynamics which can lead to endogeneity, and poten-tially biased coefficient estimates. Furthermore, it is known that coverage of disasters withinEM-DAT has increased over time (Cavallo and Noy 2011), although that applies more tothe coverage of during the 1960s and 1970s, which are not studied here. Moreover, accord-ing to Cavallo and Noy (2011), the increase in coverage appears to mostly relate to smalldisasters, which are less likely to have pronounced macroeconomic effects.

EconDisCliCha (2018) 2:21–4826

More recently, studies have attempted to proxy for disasters using data on the underlyingnatural hazard to determine the impact of disasters. For example, Heinen et al. (2015) use adataset of windspeed and precipitation for their study of windstorms and floods for a selectgroup of Caribbean islands. Felbermayr and Groschl (2014) use a large dataset of earth-quake magnitude, windspeed, temperature and precipitation. The aim of such datasets is toattempt to avoid the potential measurement error in the EM-DAT database described above.

But the use of data for underlying natural hazards is not itself without problems foreconomic studies seeking to estimate the impact of diasters on the wider economy. It isthe interaction of natural hazards with existent economic and demographic factors thatdetermines the ultimate severity. Take as an example two recent large earthquakes in NewZealand: the magnitude 7.8 Dusky Sound earthquake on 15 July 2009 and the magnitude6.2 Christchurch earthquake on 22 February 2011. Both occurred in the same country – infact on the the same island – and were close to each other in time. Clearly institutions didnot change much between the two earthquakes. Yet the former, much more powerful, earth-quake took place in an unpopulated area and caused little economic destruction. The lattertook place right on top of the country’s second largest city and caused damage estimated tobe 17 percent of national GDP (Wood et al. 2016).

Similarly, the concentration of ex ante hazard risks by country may mean exposed coun-tries put in place institutions to mitigate damage. The extent that the building code ensuresresilience to earthquakes – and the extent to which that code is enforced – may well deter-mine the impact on economic activity and inflation. A magnitude 6 earthquake is likely tohave less impact in Los Angeles, where the risk is known, than London, where earthquakesare rare and generally very weak. In the presence of such endogeneity, the estimated coeffi-cients may suffer from attenuation bias, which is to say smaller in magnitude than their truevalue, and less likely to be found significant.

Seen from the view point of economic impact, using country-level hazard data is not,therefore, itself without risk of measurement error. A second problem with ex ante hazarddata is the difficulty in assessing the relative impact across disaster types. It is not clear, forexample, that a two standard deviation wind speed event is of the same order of potentialimpact magnitude as, say, a two standard deviation temperature event.

Recognising that both measures have their shortcomings, we favour here the use of expostmeasures of disaster impact that can translate across disaster types, since the purpose ofthis paper it to consider the impact of disasters on inflation, rather than assessing the mag-nitude of disasters given ex ante hazards. For completeness, we re-estimate our equationsin Section “Alternative Measure – Underlying Natural Hazards”, using the ex ante hazardsdata from Felbermayr and Groschl (2014).

Only disasters with likely macroeconomic effects are considered here, namely: earth-quakes, storms, floods, droughts and other disasters (mass movements, insect infestations,extreme temperatures, volcanoes and wildfires). In order to estimate the effect of disasterson inflation, we require the quarter in which the disaster took place. The EM-DAT databasedoes not always have precise start dates for droughts (even to the three-month periodrequired) so as a consequence many droughts included in EM-DAT have been dropped fromthe analysis.

Even with these selection criteria, there are a large number of disasters in the EM-DATdatabase which are small relative to the overall size of the country and are unlikely to haveany discernable macroeconomic effects. To aid estimation, only disasters with at least majorimpact are considered in the analysis below. To estimate the severity of the impact of thedisasters we construct an impact variable for each disaster, calculated in a similar fashion toFomby et al. (2013).

EconDisCliCha (2018) 2:21–48 27

The impact variable used in this paper is:

IMP ′i,t = (EQIMPi,t , ST IMPi,t , FLIMPi,t , DRIMPi,t , OT IMPi,t )

′ (1)

where EQIMP , ST IMP , FLIMP , DRIMP and OT IMP represent the respectivetotal impact of earthquakes, wind storms, floods, droughts and other disasters. IMPi,t iscalculated as:

IMPi,t (k) =J∑

j=1

intensityki,t,j (2)

where

intensityki,t,j = 100 ∗ f atalitiesk

i,t,j + 0.3 ∗ total aff ectedki,t,j

populationi,t,j

, if intensity > 0.1

= 0 otherwise (3)

and J is the total number of each type-k events (k=1,2,3,4,5 and responds to earthquakes,wind storms, floods, droughts and other disasters respectively) that took place in each coun-try i in quarter t . The creation of IMPi,t can be described by the following steps. First, foreach disaster the intensity was calculated by dividing the number of fatalities and 30 per-cent of the total people affected by the population. Where this intensity is smaller than 0.1percent, the impact is set to zero (3). Then for each country, the total impact for each typeof disaster is calculated as the sum of the intensities of each such disaster that occurred ineach country for each quarter (2).

The criteria on disasters discussed above, together with the availability of consumer pricedata (see Section “Consumer Prices”), result in a total of 1349 disasters in 163 countries.Table 1 shows the incidence of disasters by type and by country development. We take theWorld Bank’s classification of High, Middle and Low income countries. We further splitHigh income countries into advanced and other high income countries. Following (Noy2009) we take advanced countries to be high income members of the OECD in 1990. Indeedall of these countries were members of the OECD before the period analysed here. Floodsand storms are the most frequently occurring disasters that meet the criteria for inclusion.Measured droughts are rare in advanced countries and other high income countries, withonly three in the sample, compared with 124 in middle income countries.

The impact on inflation is likely to depend on the size of the disaster. We follow Cavalloet al. (2013) and focus here on large disasters in the 75th and 90th percentiles. The 75thpercentile disaster is approximately the impact of Hurricane Earl on Antigua and Barbuda in2010. The hurricane affected around 6 percent of the population and did damage estimatedto be around 1 percent of GDP. The 90th percentile is approximately the impact of the2010 earthquake in Chile, which killed 562 people, affected 2.7 million (16 percent of thepopulation) and had estimated damages of 17 percent of GDP.

Consumer Prices

As noted in Section “How Disasters May Affect Prices” above, different types of disastersmay affect different prices, with the prices for food, housing (including rent) and energybeing the most commonly cited in the literature. Commonly used international databases,such as the International Financial Statistics of the International Monetary Fund and theWorld Development Indicators of the World Bank, typically contain information on just theoverall, headline CPI index. Information on the sub-indices is normally only available fromnational sources.

EconDisCliCha (2018) 2:21–4828

Table 1 Incidence of disasters

Earthquakes Storms Floods Droughts Other Total

Number

Advanced 17 14 9 2 5 47

Other high income 3 39 21 1 3 67

Middle income 47 288 433 124 29 921

Low income 6 57 155 90 6 314

Total 73 398 618 217 43 1349

75th percentile

Advanced 1.19 0.66 0.40 1.68 1.09

Other high income 1.26 0.28 0.94

Middle income 0.93 1.56 0.89 5.47 1.20 1.46

Low income 0.53 1.26 0.92 6.68 8.25 2.75

Total 0.95 1.33 0.87 5.79 1.95 1.64

90th percentile

Advanced 2.07 1.69 1.09 5.10 4.63

Other high income 3.89 0.53 3.67

Middle income 2.34 6.12 2.43 10.75 2.38 4.53

Low income 13.47 3.83 3.28 14.34 12.24 7.00

Total 2.63 4.37 2.48 12.00 5.10 4.99

Notes: countries within advanced and other high income groups set out in Table 7 in the Appendix. 75thand 90th percentile impact as calculated per (2) and (3). Measured in percent of population. Impact omittedwhere there are fewer than 5 events

The consumer price data used here are taken from the dataset in Parker (2016). Thisdataset contains CPI for 223 countries and territories on a quarterly basis for the period1980-2012. The series contained are the overall index (CPI) the sub-indices for food (CPIF),housing (CPIH), energy (CPIE), and all remaining items in the index (CPIxFHE). Coveragefor CPIH and CPIE is relatively sparse relative to the other indices, so a combined housingand energy index is also included (CPIHE) which has observations for a greater number ofcountries.

We drop countries for which there are fewer than 40 quarters of CPI data. This resultsin 212 countries with observations for headline CPI. The average number of quarters ofheadline CPI data per country is 105. Fewer countries have data for the sub-indices, and thelength of coverage is also typically shorter, particularly for less developed countries.

Method

To estimate the impact of disasters on inflation, we run a panel regression of the form:

πi,t =p∑

j=0

βjDi,t−j + μi + λt + νit (4)

where πi,t is quarterly log difference in CPI in country i in quarter t . We multiply theinflation rate by 100 to give coefficients that are in units of percentage points for ease of

EconDisCliCha (2018) 2:21–48 29

reading. Di,t is a vector of variables capturing the impact of disasters. The analysis thatfollows also considers the impact on the inflation rate for food, housing, energy and cpiexcluding food housing and energy, respectively π

fi,t , π

hi,t , π

ei,t , π

xf hei,t . We consider both the

impact of all disasters combined, and the five types of disasters (earthquakes, wind storms,floods, droughts and other) individually as described in Section “Disasters” above.

The parameters μi and λt are fixed effects for country and time respectively. The countryfixed effects capture the time invariant characteristics of each country that explain differ-ences in average inflation rates between countries. The time fixed effects capture globalfactors that affect all countries, such as global developments in output growth and commod-ity prices or the Great Moderation. The occurrence of disasters is assumed to be exogenous,and unaffected by current or previous values of CPI.

Previous studies of the impact of disasters on growth have tended to include a largenumber of controls, such as the saving rate, institutional quality, etc. to attempt to eliminateendogeneity. Yet as Dell et al. (2014) note in their review of the literature, this can lead to aproblem of “overcontrolling” if part of the impact of disasters works precisely through thesecontrols, which would result in an underestimate of the impact of disasters. Dell et al. (2014)recommend using the system of country and time fixed effects used here and dispensingwith other controls.

One potential problem with this estimation is that CPI data – particularly CPI food –is typically seasonal, which increases the variance of the underlying series. There are anumber of approaches to eliminate this seasonality. The first is to use a seasonal adjustmentprocess, the most widely used of which is the Census Bureau’s X12. However, X12 usesboth forward and backward looking filters, which violates the exogeneity assumption overCPI and disasters.

The use of country seasonal dummies for each quarter is also unsatisfactory for our pur-poses if disasters do have an impact on CPI, but are concentrated in particular quarters.Consider windstorms, whose incidence is for the most part concentrated to certain times ofthe year. In such cases, the seasonal dummy will absorb some of the true impact of disas-ters. Such quarterly dummies are also unsatisfactory if the seasonal pattern changes overtime.

Given these problems, we use quarterly regional seasonal dummies. We separate coun-tries into Northern Hemisphere (defined as those with capital cities north of the Tropic ofCancer), Southern Hemisphere (those with capitals south of the Tropic of Capricorn) andtropical countries (those whose capitals lie between the tropics). These seasonal dummiespermit varying seasonal patterns for those countries with pronounced (and opposite) sea-sons, as well as recognising the existence of weather patterns in the tropics where seasonaltemperature variations are reduced and there is often more than one growing season.3

Standard panel estimation assumes that the errors, νit , are not correlated cross-sectionally, i.e.:

ρij = ρji = corr(νit , νjt ) = 0 for i �= j (5)

However, such an assumption may not be valid when macroeconomic time series areused. Close trade ties and other economic interactions between spatially grouped countries

3For robustness, we also estimate using country seasonal dummies and using data seasonally adjusted usingX12 (not reported). The results using the seasonally adjusted data are qualitatively similar to those pre-sented here, although the impact of windstorms on food is no longer significant. As noted above, this lack ofsignificance is unsurprising.

EconDisCliCha (2018) 2:21–4830

are likely to result in positive cross-correlations. To test the null hypothesis of cross-sectional independence we use the Pesaran (2004) test, which is the most appropriategiven the unbalanced nature of the CPI dataset, and the large N relative to T. We obtain atest statistic of 205, which is significant evidence against the null of cross-sectional inde-pendence. The average absolute pairwise cross-sectional correlation is 0.302. A positivecross-sectional correlation results in substantial downward bias to the standard errors calcu-lated using standard panel estimation techniques. To account for this large cross-sectionalcorrelation, and any potential serial correlation, we use Driscoll and Kraay (1998) adjustedstandard errors in the estimations that follow.4

Results

This section describes the results from the regression described above in Eq. 4. We initiallyconsider the aggregate impact of all disasters combined on inflation. Given the potential forheterogeneity of impact, as discussed in Section “How Disasters May Affect Prices”, wethen analyse in turn the effects on inflation by type of disaster, by level of development andby severity of disaster. To verify the robustness of our findings, we also consider two alter-native specifications of impact – damage relative to GDP and considering just the numberof disasters rather than differentiating by impact.

Aggregate Impact of Disasters on Inflation

We first estimate (4) on the aggregate impact of all disasters, which is to say Di,t is the sumby country and by quarter of the impact across all types of disaster. We include up to 11 lags,since we find joint significance up to three years following the incidence of the disaster.Further lags are not individually or jointly significant. The individual coefficients from theestimation are included in Supplementary Table A1. To aid assessment of the impact ofa typical disaster, we multiply the coefficients by the impact value of the 75th and 90thpercentile disasters (see Table 1) to give the estimated effect on inflation of these disasters.These estimated impact results are shown in Table 2.

Our results estimate that a disaster in the 75th percentile would have a contemporaneous(i.e. quarter 0) impact on headline inflation of 0.27 percentage points (pp). There is a furthersignificant impact of 0.19pp on headline inflation in the quarter immediately following thedisaster (quarter 1). Since exact timing of effects may differ between individual disasters,we combine the coefficients for quarters 2 and 3. The impact at this horizon is positive,but insignificant. The combined impact on inflation of the 75th percentile disaster for thefirst year (quarters 0 through 3) is estimated to be 0.61pp. The impact over the second year(quarters 4 through 7) is estimated to be 0.91pp, although this is not significant. Finally,the impact over the third year (quarters 8 through 11) is significant, and estimated to be0.77pp.

Turning to the sub-indices, there is a positive and significant contemporaneous impact onfood prices of 0.17pp, and a similar impact in the first quarter following the disaster. How-ever, in the subsequent two quarters there is a negative and significant impact on inflation,

4The Pesaran (2004) test is carried out in Stata using the xtcsd command of Hoyos and Sarafidis (2006). Theestimation using Driscoll and Kraay (1998) adjusted standard errors is implemented using the xtscc commanddeveloped by Hoechle (2007).

EconDisCliCha (2018) 2:21–48 31

Table 2 Estimated inflation impact of disasters

Headline Food Housing Energy CPIxFHE

75th percentile

Quarter 0 0.266** 0.170 ** −0.062 −0.138 0.015

Quarter 1 0.190* 0.170* −0.051 −0.156 −0.097*

Quarters 2-3 0.151 -0.235* -0.245** -0.122 −0.125

Year 1 0.607* 0.104 -0.358** -0.415 −0.207*

Year 2 0.911 −0.030 −0.348* 0.153 −0.142

Year 3 0.770* 0.276 −0.132 0.203 0.034

90th percentile

Quarter 0 0.806** 0.514** −0.189 −0.418 0.045

Quarter 1 0.577* 0.515* −0.155 −0.472 −0.294

Quarters 2-3 0.457 -0.713* −0.743** −0.369 −0.378

Year 1 1.839 * 0.316 −1.087** −1.259 −0.627*

Year 2 2.763 −0.092 −1.057* 0.463 −0.432

Year 3 2.336* 0.838 −0.399 0.616 0.104

Observations 22471 18933 8191 9167 12639

R2 0.051 0.051 0.047 0.172 0.056

Notes: *, ** significant at 5 and 1 percent level respectively. CPIxFHE is consumer prices excluding food,housing and energy. Shows estimated impact for 75th and 90th percentile disaster. Underlying regressioncoefficients in Supplementary Table A1. Quarter 0 is the quarter the disaster takes place. Year 1 is quarters 0through 3 combined, year 2 is quarters 4 through 7, year 3 is quarters 8 through 11

such that the overall impact on food prices over the first year is insignificant and close tozero. There is no significant impact on food prices beyond the first year.

Housing inflation is significantly reduced in the aftermath of disasters, by 0.36pp and0.35pp in the first two years following the disaster. There is no significant impact on energyprices. CPI inflation excluding food, housing and energy is significantly lower in the after-math of disasters, by an estimated 0.21pp in the first year for the 75th percentile disaster.There is no significant impact beyond the first year. Table 2 also includes the estimatedfigures for the 90th percentile disaster. Since this involves multiplying the underlying coef-ficients by a larger impact coefficient, the overall pattern of effects and significance areunchanged from the 75th percentile case.

Strictly speaking, it is not possible to draw conclusions from the individual sub-indicesfor the overall impact on headline inflation. The samples differ for each sub-index becauseof the lack of availability of some sub-indices. In particular, the sub-indices for housingand energy are frequently unavailable outside of high income countries. There is also anoticeable difference in relative weights in the sub-indices between countries. For exam-ple, the weight of food in the index is around 10-15 percent in advanced countries, butfrequently exceeds 50 percent in low income countries (Parker 2016). For the purposes ofrobustness, we include the estimation results on a balanced panel of 78 countries over theperiod 1996-2012 for the sub-indices for food, the combined housing and energy sub-indexand CPIxFHE (see Supplementary Table A2). The sample in this balanced panel is heavilybiased towards high income countries, and the estimates are similar in nature to those forthis group of countries (see Section “Impact by Level of Development”).

EconDisCliCha (2018) 2:21–4832

Impact by Type of Disasters

As noted above in Section “HowDisasters May Affect Prices”, disasters have heterogeneousimpacts on activity, dependent on type. To test whether this finding also holds for inflation,we re-estimate (4) with separate impact variables of each type of disaster. The coefficientsfrom this estimation are shown in Supplementary Tables A3, A4, A5, A6 and A7. Theresults are summarised in Table 3. We again multiply the coefficients by the impact of the75th percentile disaster of the relevant type. The ‘other’ category of disasters has almostno significant coefficients, perhaps unsurprising given the diversity of disasters within thecategory, so these disasters are unreported in Table 3.

Earthquakes do not have a significant impact on headline or food inflation at any hori-zon. An earthquake in the 75th percentile is estimated to increase housing inflation in thefirst quarter after it takes place by 0.18pp and energy inflation in that quarter by 0.79pp.These increases appear to be unwound in subsequent quarters, with the estimated impactover the first year combined not significantly different from zero. CPI inflation excludingfood, housing and energy is significantly reduced by earthquakes in each of the three yearsfollowing the disaster, by 0.63pp, 0.45pp and 0.36pp respectively.

Storms are estimated to have a contemporaneous positive impact on headline inflation,and a positive impact the following quarter, although insignificant in both cases. The sec-ond quarter following the storm is negative and significant. Overall, the estimated impactfor the 75th percentile storm over the first year is 0.00pp. There is no significant impacton headline inflation in subsequent years. There is a significant impact on food price infla-tion during the first year. A 75th percentile storm significantly increases food price inflationby 0.16pp contemporaneously and by a further 0.22pp in the first quarter following thestorm. These increases are unwound in the subsequent two quarters, leaving the total esti-mated impact over the first year to be insignificantly different from zero. Storms reducehousing price inflation in the three years that follow, by 0.38pp, 0.64pp and 0.39pp respec-tively, although only the second year is significant. The impact on other sub-indices isinsignificant.

The 75th percentile flood is estimated to have a positive and significant contempora-neous impact on headline inflation of 0.38pp. There is estimated to be a positive impacton headline inflation throughout the first three years following the flood, although this isnot significant. Energy price inflation is estimated to be lower for the first year, beforerebounding in the following year. This would be consistent with plentiful rainfall lower-ing hydroelectric generation costs in the near term. The 75th percentile flood is estimatedto have no significant impact on food or housing price inflation. Inflation in the remain-der of the index is estimated to be lower by 0.33pp in the first year following theflood.

The 75th percentile drought is estimated to increase headline inflation by 1.36pp in thestart quarter, and by 1.30pp in the subsequent quarter.5 The impact on food price inflationis typically positive, although insignificant. The impact on housing and CPIxFHE priceinflation is negative, significantly so in the second and third quarters following the start ofthe drought.

5Note that unlike the other disasters considered here, droughts may continue for several quarters, indeedeven years. The 75th percentile drought is also much greater in impact than the 75th percentile of the otherdisasters.

EconDisCliCha (2018) 2:21–48 33

Table 3 Impact on inflation by type of disaster

Headline Food Housing Energy CPIxFHE

Earthquakes

Quarter 0 0.247* 0.282* 0.051 −0.233 −0.093

Quarter 1 0.055 −0.075 0.194* 0.704* −0.223**

Quarters 2-3 −0.052 −0.219 −0.296* −0.446 −0.312**

Year 1 0.250 −0.012 −0.051 0.025 −0.628**

Year 2 0.364 0.273 0.032 1.928 −0.449**

Year 3 0.218 0.389 −0.300 −0.725 −0.357*

Storms

Quarter 0 0.099 0.152* −0.088 −0.522 0.020

Quarter 1 0.060 0.220** −0.037 −0.316 −0.095

Quarters 2-3 −0.158 −0.334** −0.259** 0.000 −0.067

Year 1 0.001 0.038 −0.384 −0.839 −0.141

Year 2 −0.125 −0.009 −0.641** 0.075 −0.113

Year 3 −0.066 0.058 −0.386* 0.160 0.201

Floods

Quarter 0 0.382* 0.164 −0.075 −0.244 −0.147

Quarter 1 0.185 0.092 −0.072 −0.335 −0.153**

Quarters 2-3 0.347 −0.208 −0.006 −0.234 −0.033

Year 1 0.914 0.048 −0.154 -0.814 −0.333*

Year 2 1.650 -0.031 −0.332 1.211* -0.063

Year 3 1.464 0.255 −0.116 −0.047 0.045

Droughts

Quarter 0 1.369* 0.535 −0.079 0.278 0.139

Quarter 1 1.313* 0.441 −0.251 −0.272 −0.171

Quarters 2-3 1.951 −0.126 −0.762** −0.579 −0.647**

Year 1 4.634* 0.850 −1.092 −0.573 −0.679

Year 2 7.079 0.121 −0.682 −0.708 −0.207

Year 3 5.217** 1.753 −0.379 1.168 −0.134

Observations 22471 18933 8191 9167 12639

R2 0.057 0.054 0.051 0.175 0.062

Notes: *, ** significant at 5 and 1 percent level respectively. CPIxFHE is consumer prices excluding food,housing and energy. Shows estimated impact for 75th percentile disaster. Underlying regression coefficientsin Supplementary Tables A3 through A7. Quarter 0 is the quarter the disaster takes place. Year 1 is quarters0 through 3 combined, year 2 is quarters 4 through 7, year 3 is quarters 8 through 11

Impact by Level of Development

Previous research has highlighted that disasters have greater impact on activity in devel-oping economies than in advanced economies (Noy 2009; Raddatz 2009; Fomby et al.

EconDisCliCha (2018) 2:21–4834

2013). We investigate whether this finding holds for the impact on inflation by estimating(4) separately for advanced countries, other high income countries and for the remainingcountries. There are insufficient observations for low income countries, particularly for thesub-indices, to merit estimating these countries separately. The estimated impact for the75th percentile disaster in each country group is shown in Table 4. The underlying coeffi-cient estimates are provided in Supplementary Tables A8, A9 and A11. Given the relativelack of individual sub-indices for housing and energy in middle and low income countries,

Table 4 Impact of disasters by level of development

Advanced countries

Headline Food Housing Energy CPIxFHE

Quarter 0 0.005 0.054 −0.126 −0.050 −0.007

Quarter 1 −0.017 0.097* −0.018 −0.143 −0.034

Quarters 2-3 −0.076* −0.074 −0.080 −0.061 −0.049

Year 1 −0.088 0.076 −0.224 −0.254 −0.091*

Year 2 −0.054 −0.162* −0.251* −0.221 −0.017

Year 3 0.124 0.121 0.229 0.143 0.067

Observations 2783 2741 2167 2715 2591

R2 0.302 0.247 0.172 0.507 0.361

Other high income countries

Headline Food Housing Energy CPIxFHE

Quarter 0 1.067 0.226* −0.007 −2.427** 0.029

Quarter 1 1.152 0.296* 0.041 −0.514** −0.177

Quarters 2-3 0.745* 0.267 0.048 0.906* −0.055

Year 1 2.965 0.789* 0.082 −2.035* −0.204

Year 2 0.951* 0.920** 0.086 0.201 −0.189

Year 3 0.690* 0.072 0.077 −0.201 0.117

Observations 4887 4106 2486 2465 2852

R2 0.095 0.181 0.121 0.302 0.151

Middle and low income countries

Headline Food Housing & Energy CPIxFHE

Quarter 0 0.273** 0.184** 0.061 0.025

Quarter 1 0.183* 0.173* −0.048 −0.081

Quarters 2-3 0.164 −0.250* 0.053 −0.144

Year 1 0.620* 0.108 0.065 −0.200

Year 2 1.007 −0.025 0.000 −0.121

Year 3 0.832* 0.307 −0.080 0.053

Observations 14801 12086 7301 7196

R2 0.059 0.051 0.068 0.062

Notes: *, ** significant at 5 and 1 percent level respectively. CPIxFHE is consumer prices excluding food,housing and energy. Shows estimated impact for disaster in 75th percentile. Underlying regression coeffi-cients in Supplementary Tables A8, A9 and A11. Quarter 0 is the quarter the disaster takes place. Year 1 isquarters 0 through 3 combined, year 2 is quarters 4 through 7, year 3 is quarters 8 through 11

EconDisCliCha (2018) 2:21–48 35

we use the combined housing and energy sub-index that is more widely available in thesecountries.

Disasters do not have significant impact on either headline, food or energy price inflationin advanced countries. Housing price inflation is significantly lower in the second year afterthe disaster, by 0.25pp for the 75th percentile disaster. CPIxFHE inflation is significantlylower in the first year following the disaster, by 0.09pp.

In other high income countries, the 75th percentile disaster is estimated to increaseheadline inflation 2.97pp over the first year, but only the increase in quarters 2 and 3 is sig-nificant. There are significant increases in the second and third year after the disaster, by0.95pp and 0.69pp respectively. Food price inflation is significantly increased in the first twoyears following the disaster, conversely energy prices fall. There are no significant impactson the other sub-indices.

There are insufficient events to consider the impact by disaster separately for advancedand other high income countries, so Supplementary Table A10 considers the impact by typeof disaster for all high income countries. The 75th percentile earthquake in high incomecountries has no significant effect on headline inflation, but significantly reduces CPIxFHEinflation by 1.43pp in the first year, by 1.62 in the second year and by 1.61pp in the thirdyear. The 75th percentile storm has a positive impact on headline inflation. Over the firstyear, the estimated impact is 4.59pp, although this is insignificant. For the second and thirdyear the impact is positive and significant at 1.50pp and 0.96pp respectively. Food priceinflation is higher by 0.96pp in the first year and by 1.27pp in the second year.

For middle and low income countries, the 75th percentile disaster is estimated to increaseheadline inflation by 0.62pp in the first year, by (an insignificant) 1.01pp in the secondyear and by 0.83pp in the third year. By sub-component, food price inflation is significantlyhigher in the quarter that the disaster takes place and in quarter 1. But in the subsequent twoquarters, this higher inflation is partly reversed, such that the combined impact for the firstyear is insignificant. Disasters do not have significant impact on the other sub-componentsin middle and low income countries. Split by type of disaster (see Supplementary TableA12), earthquakes lower CPIxFHE inflation, and the 75th percentile drought has a large andpositive impact on headline inflation: 4.99pp in the first year, 7.34pp (insignificant) in thesecond year and 5.37pp in the third year.

Impact by Severity of Disaster

Given the heavily skewed distribution of disaster impacts, it is possible that there are non-linearities in their effect on inflation. We construct a series for the impact of severe disasters,SEV IMPt,i in an analogous fashion to Eqs. 2 and 3, but set the cutoff threshold to be the75th percentile of the distribution. Thus the upper quartile of disasters – 337 disasters in total– are classified as ‘severe’. We then estimate (4) including both IMPt,i and SEV IMPt,i

in the vector of impact variables, Di,t . The estimated coefficients on the IMPt,i variablesrepresent the impact of major disasters (those in the first three quartiles of disaster impact)on inflation. The coefficients on SEV IMPt,i capture any additional effect on inflation fromsevere disasters.

Given that the effect of disasters differs by level of development (Section “Impact byLevel of Development”), we estimate high income countries separate from middle and lowincome countries. Table 5 shows the estimated impact on inflation of a (severe) disaster inthe 90th percentile, split by level of development.

For high income countries, there is a significant positive impact on inflation in the firsttwo years following a severe disaster (Supplementary Table A14). The impact on housing

EconDisCliCha (2018) 2:21–4836

Table 5 Impact of severe disasters on inflation

High income countries

Headline Food Housing Energy CPIxFHE

Quarter 0 1.645 0.251 −0.476* −3.640 0.086

Quarter 1 2.029 0.717** −0.059 −0.331 −0.470

Quarters 2-3 0.322 −0.437 −0.600* 0.435 −0.235

Year 1 3.996* 0.530 −1.136** −3.536 −0.619

Year 2 0.888* 0.749 −0.744* −0.633 −0.662*

Year 3 0.395 −0.786 0.236 −0.133 −0.171

Observations 7670 6847 4653 5180 5443

R2 0.079 0.166 0.107 0.341 0.169

Middle and low income countries

Headline Food Housing & Energy CPIxFHE

Quarter 0 0.632* 0.563** 0.305 0.044

Quarter 1 0.491 0.453* −0.117 −0.277

Quarters 2-3 0.591 −0.517 0.193 −0.350

Year 1 1.714 0.499 0.381 −0.582

Year 2 3.232 −0.067 −0.004 −0.338

Year 3 2.675* 1.128 −0.041 0.263

Observations 14801 12086 7301 7196

R2 0.060 0.053 0.070 0.064

Notes: *, ** significant at 5 and 1 percent level respectively. CPIxFHE is consumer prices excluding food,housing and energy. Shows estimated impact for disaster in 90th percentile. Underlying regression coeffi-cients in Supplementary Tables A13 through A16. Quarter 0 is the quarter the disaster takes place. Year 1 isquarters 0 through 3 combined, year 2 is quarters 4 through 7, year 3 is quarters 8 through 11

price inflation is negative for the first two years. A split by disaster type is not worthwhilefor high income countries. There are only 23 severe disasters, and individual types are con-centrated in certain countries. For example, the four earthquakes are split evenly betweenChile and New Zealand, with the two New Zealand earthquakes taking place less than sixmonths apart.

For middle and low income countries, there are a number of significant coefficients onthe severe disaster variables. The reversal in the impact on headline and food price inflationtypically seen in quarters 2 and 3 is not as pronounced, and is no longer significant. Theimpact on the other sub-indices is more positive for severe relative to major disasters, sig-nificantly so in the third year following the disaster, although the aggregate impact of severedisasters on these sub-indices remains insignificant. The impact of severe disasters by typeof disaster in middle and low income countries is similar in pattern to that when all disastersare combined (Supplementary Table A17).

Alternative Measure – Underlying Natural Hazards

Previous studies of the impact of disasters have used a number of different metrics of impact,each of which has its own disadvantages. For the purposes of robustness, we re-ran the

EconDisCliCha (2018) 2:21–48 37

analysis using two other measures of disasters from the EM-DAT dataset that are commonlyused in the literature – number of disasters and damage caused as a share of GDP. Theresults for these measures are not shown here, but are qualitatively similar to the main resultsshown above.6

As noted earlier, the impact of disasters is complex, and is a function of a number ofinstitutional factors. These institutional factors may in turn also affect the inflationary pro-cess, raising concerns around potential estimation bias. Previous authors have also pointedto a number of omissions in the EM-DAT database (e.g. Cavallo and Noy 2011; Felbermayrand Groschl 2014), and there is the potential for bias in inclusion if developing coun-tries are more likely to declare a state of emergency in the hope of receiving additionalaid.

To address these issues, we carry out a final robustness check by using the Ifo GAMEdatabase of geological and meteorological hazards, presented in Felbermayr and Groschl(2014). This database measures the strength of the ex ante hazards – earthquake strength,wind speed, rainfall – rather than ex post measures of disaster impact. This has the advan-tage of being truly exogenous to the economic process. Conversely, ex ante hazards do notnecessarily translate directly to diaster potential - a strong earthquake that takes place in aremote, unpopulated area has smaller disaster potential than one that takes place at the sametime, in the same country, but in a more populated and economic active region. As a result,ex postmeasures can provide a more precise assessment of the impact, which is in turn moreuseful for determining impacts on economic variables.

We convert the monthly GAME data to quarterly frequency in a manner equivalent tothat taken by Felbermayr and Groschl (2014) for annual frequency. For earthquakes, we takethe maximum strength earthquake that takes place in the quarter. Since smaller earthquakesare unlikely to cause widespread damage, we discard the lower quartile, resulting in a cut-off value of 4 on the Richter scale. For storms, we take the maximum total wind speed inthe quarter. Again, the lower quartile is discarded, resulting in a minimum threshold of 35knots, which is rated 7 on the Beaufort Scale and rarely associated with significant damageor destruction.

For floods, we average across the quarter the monthly percentage deviation of precipita-tion from the long-run average. Where that quarterly average is below zero (i.e. precipitationis below average), we set the flood index to zero. For droughts, we carry out the same pro-cedure as for floods, but exclude all values above -50, which is to say precipitation must beless than half the long-run average to signal a drought. For ease of comparison of coeffi-cients, we multiply the drought index by -1, so that higher numbers represent a more severelack of precipitation. As noted above, the disadvantage of EM-DAT is that droughts are notalways provided with a precise date, so overall coverage is low for the analysis above. Theadvantage of the GAME dataset is that it provides much greater coverage, but the impactof sustained, multi-year events are less easily calculated than using the ex post impactsincorporated into EM-DAT.

Using the impact indices derived from the GAME dataset, we re-run (4). The results arepresented in Table 6. We standardise for the 75th percentile of the underlying natural hazard,in keeping with the main results.

6Regression results are available on request.

EconDisCliCha (2018) 2:21–4838

Table 6 Impact of natural hazards on inflation

Headline Food Housing Energy CPIxFHE

Earthquakes

Quarter 0 0.110 0.125 0.053 0.153 −0.120

Quarter 1 0.007 −0.077 0.063 0.420* 0.066

Quarters 2-3 0.181 −0.072 −0.267 0.142 −0.117

Year 1 0.298 −0.025 −0.151 0.715 −0.171

Year 2 −0.660 −0.074 −0.139 −0.158 −0.528*

Year 3 −0.115 −0.170 0.132 0.193 −0.231

Storms

Quarter 0 −0.173 0.034 −0.009 −0.710** −0.189

Quarter 1 −0.208 −0.028 0.155 −0.083 0.140

Quarters 2-3 −0.462 −0.424* −0.078 0.182 0.021

Year 1 −0.842 −0.419 0.068 −0.611 −0.028

Year 2 0.564 0.224 −0.333 −0.146 0.050

Year 3 0.787 0.084 −0.537 −0.192 0.361

Floods

Quarter 0 −0.075* −0.014 0.036 0.088 0.021

Quarter 1 0.044 0.071 0.041 −0.054 0.025

Quarters 2-3 0.015 0.088 0.044 −0.048 −0.033

Year 1 −0.015 0.145 0.121 −0.014 0.014

Year 2 −0.004 0.000 0.031 0.122 0.078

Year 3 0.089 0.113 0.015 0.091 0.113*

Droughts

Quarter 0 −0.067 −0.318 0.191 0.353 −0.034

Quarter 1 0.396 0.294 0.151 −0.074 0.210

Quarters 2-3 0.652 1.229** 0.009 −0.806 0.101

Year 1 0.980 1.205** 0.351 −0.526 0.277

Year 2 −0.161 −0.410 −0.762 0.415 −0.717

Year 3 0.743 0.466 −0.237 0.706 0.043

Observations 16875 13852 5806 6799 9090

R2 0.056 0.062 0.085 0.199 0.068

Notes: *, ** significant at 5 and 1 percent level respectively. CPIxFHE is consumer prices excluding food,housing and energy. Shows estimated impact for hazards in 75th percentile of distribution. Underlying regres-sion coefficients in Supplementary Tables A18, A19, A20 and A21. Quarter 0 is the quarter the hazard takesplace. Year 1 is quarters 0 through 3 combined, year 2 is s quarters 4 through 7, year 3 is quarters 8 through 11

Overall, the impact of disasters on inflation using the GAME database is qualitatively inline with the main results. Earthquakes have a short-term positive impact in Q1 on energyprices and overall put downward pressure on CPI excluding food, housing and energy,

EconDisCliCha (2018) 2:21–48 39

including by a significant 0.5pp in year 2. Storms have an initial upward pressure on foodprices, but this is unwound in quarters 2-3.

Droughts are found to have a significant, upward impact on food prices in the first year.The contrast with the main results lies with the likely difference in coverage of the twodatabases. GAME covers far more events, but the index does not pick up the impact ofprolonged, severe events to the same extent that EM-DAT does. Nonetheless, the overallconsistency of the results between the two measures of disasters – ex ante hazards and expost impact – provides comfort that the main results presented in Section “Results” arerobust.

Discussion of Results

That the impact of disasters on inflation varies so much by type, severity, location, and sec-tor of the economy is not surprising. Previous research has highlighted the varying impacton economic activity across those dimensions. As noted in Section “How Disasters MayAffect Prices”, disasters are complex and the impact on CPI is uncertain in both theshort run and the medium run given potential impacts on both supply and demand. Sincediffering types of disasters have varying impacts on crops, buildings and infrastructure,there is an inevitable difference on the effects on the various sub-indices of consumerprices. This section provides some candidate explanations for the diversity of resultsfound here, building on the results found here and on insights garnered from the existingliterature.

Earthquakes

Earthquakes cause significant destruction of physical capital, in terms of housing, businesspremises and infrastructure. There is also evidence of a reduction in agricultural outputin developing countries (Fomby et al. 2013). After the initial shock, reconstruction takesplace, bringing about an eventual lift in economic activity. Initial impacts therefore includeshortages of housing and potentially food.

The findings here suggest significant upward initial impact on overall prices and foodprices. This contrasts with the findings of Abe et al. (2014), Reinsdorf et al. (2014) andDoyle and Noy (2015) who do not find significant impacts when studying earthquakes inJapan, Chile and New Zealand. The contrast in findings can be explained by differences insample. These previous studies all involved high income countries, and the positive impactfound here is confined to middle and low income countries.

CPI housing prices (essentially rent) rise in the quarter after the earthquake, likely a resultof a reduction in housing stock putting pressure on rents for houses that remain habitable.This accords with the findings of higher rents by Parker and Steenkamp (2012) and Woodet al. (2016). The significant higher energy prices in the short term may reflect a reduc-tion in electricity generating capabilities if power stations are damaged in the earthquake.The damage to the Fukushima Daiichi nuclear power plant following the Great East Japanearthquake in 2011 is a recent example.

The other notable feature of earthquakes is the persistent negative impact on CPI exclud-ing food, housing and energy. This downward impact lasts for three years following theearthquake. The damage to capital stock and wealth appears to depress activity in these

EconDisCliCha (2018) 2:21–4840

sectors. This explanation is corroborated by Fomby et al. (2013), who find that the destruc-tion of capital stock and loss of labour supply cancels out the boost to activity fromreconstruction, resulting in no overall significant effect on output from earthquakes.

Storms

Storms similarly have the potential to damage buildings and crops, although horizontalinfrastructure such as roads is less prone to damage than from earthquakes. The findingshere are certainly consistent with short-term destruction of crops, although within a year theupward impact on food price inflation is unwound. Despite being large, the impact is notpersistent.

The impact on headline CPI in the initial quarter of 0.10 for a 75th percentile stormis insignificantly different from the average value of 0.05 found by Heinen et al. (2015).Referencing to just the 15 economies covered by that paper gives a somewhat closer figureof 0.087. The finding by Heinen et al. (2015) that food prices are more heavily affectedthan headline inflation is confirmed by the results here. Where the findings here divergefrom Heinen et al. (2015) is over the horizon that these positive impacts take place. In thatpaper, the effect on food prices is only positive. Yet as shown here, the duration of effectson food prices is short-lived, and is reversed beyond the horizon of analysis in Heinen et al.(2015).

CPI housing falls in the years following a storm. This appears at odds with a shortageof housing caused by destruction of the housing stock. The answer may lie in an overalldrop in household spending power from lower economic activity and lower wealth. It mayalso arise from outward migration from the affected area, as noted by Coffman and Noy(2012) and Vigdor (2008) in their studies of the impacts of respectively Hurricane Iniki andHurricane Katrina.

Floods

The damage from floods differs from earthquakes and storms in that it may be morelocalised, for example riverine floods are restricted to the immediate proximity of rivers.Depending on severity, flood damage to housing may be superficial rather than structural,although there is likely to be loss incurred to contents. Initial damage to crops may be off-set in future years by increases to fertility with some beneficial effects to agriculture in lateryears (Fomby et al. 2013).

There is evidence of an initial positive impact to overall CPI. In middle and low incomecountries, there is also an initial positive impact on food, likely a result of crop destruction.In high income countries, the potential benefits from greater agricultural production arereflected in a negative (albeit insignificant) impact on food prices in the years following theflood. This beneficial impact does not appear to occur with severe floods. Here, the damageto agricultural land, perhaps even including washing away topsoil, appears to outweigh anynutrient gain. Fomby et al. (2013) also find no beneficial effect from severe floods.

In terms of case studies for where the inflation impact of floods has been previouslyestimated, Laframboise and Loko (2012) estimate that the 2010 floods in Pakistan increasedheadline CPI by 2 percentage points. The model estimates here (noting that this is a severeflood in a middle income country) suggest a positive headline CPI impact of 1.6 percentagepoints for this event, around the order of magnitude experienced.

EconDisCliCha (2018) 2:21–48 41

Droughts

Droughts differ from the other types of diaster studied here insofar as they inflict little byway of physical damage to buildings or infrastructure. Nonetheless, they can have signif-icant impact on agricultural production, and can reduce reduce productive capital throughkilling livestock. Their impact can be particularly marked in developing countries wherea greater share of the population is in poverty and the share of food expenditure in totalconsumption is much greater.

As noted in Section “Disasters”, the EM-DAT dataset does not always have precise tim-ings for droughts, so the results from the GAME dataset provide a worthwhile addition. Thepositive effects on food prices and negative effects on other components of CPI found hereare consistent with droughts creating shortages in food and consumers therefore constrain-ing non-food expenditure. The results here are in line with the findings of Kamber et al.(2013) for New Zealand.

A further difference between droughts and the other main disaster types is the durationof impacts. Droughts maintain their impact on CPI into the third year, whereas the impact ofdisasters tends to be insignificant by that point. There are a range of potential explanationsfor this increased duration. First, droughts themselves may last over a number of years,with impact typically worsening as successive crops are devastated. Second, the impact ofdroughts can be more widespread: widespread rainfall may result in localised flooding, butdroughts can affect a large area. Finally, droughts are more prevalent in middle and lowincome countries, where the impact on inflation tends to be higher.

Level of Development and Severity of Disaster

The findings here point to a greater inflationary impact from disasters in developing coun-tries. This result is to be expected as a number of authors (e.g. Noy 2009; Raddatz 2009;Fomby et al. 2013) find that the impact of disasters on output is greater in these countries.It follows that the impact on inflation is likely to be greater.

Institutional quality was found by Noy (2009) to be one of the determining factors inthe severity of output loss from a disaster. Institutional quality – particularly central bankindependence – is also known to have a bearing on inflation outcomes, including inflationvolatility (See, among others, Cukierman et al. 1992; Alesina and Summers 1993; Klompand de Haan 2010). Where monetary policy is more credible, inflation expectations aremore likely to remain anchored, reducing the impact of the disaster on inflation. Sincecentral bank credibility tends to be positively correlated with development, a lower impactof inflation from a given disaster impact is likely on average in more developed economies.

The proportionately greater impact of larger disasters on inflation is also in line with thefindings of the literature on the effects on economic activity. Fomby et al. (2013) and Loayzaet al. (2012) both find that while some types of disasters (such as floods) can have overallpositive impacts on output, this positive effect disappears once disasters cross a certainseverity threshold. Larger disasters exceed an economy’s capacity to remain resilient.

Conclusion

This paper has analysed the impact of disasters on inflation, using a panel of consumerprice indices for 212 countries. It extends the literature by being the first to jointly model

EconDisCliCha (2018) 2:21–4842

earthquakes, storms, floods and droughts together, by expanding the coverage from singleevents or limited number of countries to as close as possible to the universe of countries,and also extends the timeframe of analysis beyond the very near term. As such, it provides acomprehensive analysis of the impact of disasters on inflation to match the existing literatureon the impact on output (e.g. Noy 2009; Raddatz 2009; Loayza et al. 2012; Fomby et al.2013; Felbermayr and Groschl 2014). The findings point to a considerable heterogeneity inthe impact of disasters by type of disaster, by sub-index of CPI, by level of developmentand by timing.

There is a clear differentiation in the inflation impact of disasters by level of devel-opment. The impact of disasters in advanced countries is for the most part insignificant,and even where there is a significant impact on a sub-index, its magnitude is negligi-ble. Conversely, the impact for less developed countries is more marked, with significanteffects on headline inflation persisting even three years post-disaster. That said, there isa significant impact in high income countries from severe disasters (those in the upperquartile).

In terms of sub-indices, the impact on food price inflation is in general positive, if shortlived. The impact on housing and other sub-indices is in general negative. Differences inexpenditure weights on these sub-indices will in part explain the differences witnessed inheadline inflation numbers by level of development.

Earthquakes do not significantly affect headline inflation, but do significantly reduce CPIinflation excluding food, housing and energy. Storms cause an immediate increase in foodprice inflation for the first six months, although this impact is reversed in the subsequenttwo quarters, resulting in no significant impact over the entire first year, or beyond. Floodsincrease headline inflation in the quarter that the flooding occurs in middle and low incomecountries, but have no significant impacts in subsequent quarters. In high income coun-tries, the impact on headline inflation is negative, although insignificant. Droughts increaseheadline inflation for a number of years.

The results here provide a starting place for future research into the impact on prices.Despite the overall uncertainty, they provide a number of lessons for policymakers whenconsidering the impact of disasters on inflation. First, movements in inflation, even if shortlived, can be substantial. In the context of developing countries where food expendituresoften exceed half the household’s budget, marked shifts in food prices can put notable pres-sure on poorer households nationwide, beyond just those located in the directly affectedarea.

Second, it is clearly important to focus on the entire general equilibrium effects ofincome, wealth and economic activity on prices, rather than focusing narrowly on disaster-induced shortages of goods. Indeed, beyond shortages in the most directly affected sectors(food and housing), disasters appear on balance to be deflationary. It follows that – par-ticularly over the medium term – the impact of indirect disruption and lower consumptionfrom reduced wealth tend to dominate any inflationary forces arising from rebuild activ-ity. This is the reverse of the findings in Keen and Pakko (2011) using a calibratedDSGE model, and as such the recommended tightening of monetary policy does notappear supported by real-world data. The results here instead suggest that forward-lookingcentral banks reacting to medium-term inflationary pressures should, if anything, loosenpolicy.

Acknowledgements The author thanks Ilan Noy, Eric Strobl and Paul Raschky for useful comments.

EconDisCliCha (2018) 2:21–48 43

Appendix: Country Classifications and Incidence of Disasters

Table 7 Country classification and incidence of disasters

Number of disasters Number of disasters

Major Severe Major Severe

Advanced

Australia 8 4 Austria 1 0

Belgium 0 0 Canada 0 0

Denmark 0 0 Finland 0 0

France 1 1 Germany 1 0

Greece 3 0 Iceland 0 0

Ireland 0 0 Italy 1 0

Japan 2 0 Luxembourg 0 0

Netherlands 1 0 New Zealand 0 2

Norway 0 0 Portugal 1 0

Spain 3 0 Sweden 0 0

Switzerland 0 0 United Kingdom 2 0

United States 4 1

Other high income

Andorra 0 0 Antigua and Barbuda 2 6

Aruba 0 0 Bahamas 6 0

Bahrain 0 0 Barbados 3 0

Bermuda 0 0 Brunei Darussalam 0 0

Cayman Islands 1 0 Chile 13 2

Croatia 0 0 Curacao 0 0

Cyprus 0 0 Czech Republic 2 0

Equatorial Guinea 0 0 Estonia 0 0

Faeroe Islands 0 0 French Polynesia 2 0

Guam 2 3 Guernsey 0 0

Hong Kong 0 0 Isle of Man 0 0

Israel 0 0 Jersey 0 0

Korea Rep 4 0 Kuwait 0 0

Latvia 0 0 Lithuania 0 1

Macau 2 0 Malta 0 0

New Caledonia 2 0 Northern Mariana Islands 1 0

Oman 1 0 Poland 1 0

Puerto Rico 2 0 Qatar 0 0

Russian Federation 2 0 San Marino 0 0

Saudi Arabia 0 0 Singapore 0 0

Sint Maarten 0 0 Slovakia 1 0

Slovenia 1 0 St Kitts and Nevis 2 2

Trinidad and Tobago 0 0 Uruguay 2 1

EconDisCliCha (2018) 2:21–4844

Table 7 (continued)

Number of disasters Number of disasters

Major Severe Major Severe

Middle income

Albania 3 3 Algeria 1 1

American Samoa 0 2 Argentina 7 3

Armenia 2 0 Azerbaijan 3 2

Belarus 1 0 Belize 2 5

Bolivia 14 6 Botswana 2 2

Brazil 13 0 Bulgaria 1 0

Cameroon 0 0 Cape Verde Islands 5 0

China, PR 69 16 Colombia 14 1

Congo 5 0 Costa Rica 13 4

Cote d’Ivoire 0 0 Djibouti 4 9

Dominica 3 3 Dominican Republic 5 2

Ecuador 11 1 Egypt 0 0

El Salvador 8 3 Fed. States Micronesia 2 2

Fiji 14 7 Gabon 1 1

Georgia 3 0 Ghana 4 3

Grenada 2 1 Guatemala 8 3

Guyana 0 5 Honduras 15 3

Hungary 2 0 India 35 10

Indonesia 5 0 Iran 5 1

Jamaica 5 4 Jordan 3 0

Kazakhstan 1 0 Kiribati 0 1

Kosovo 0 0 Kyrgyzstan 3 0

Lao, PDR 4 12 Lebanon 1 1

Lesotho 0 7 Libya 0 0

Macedonia, FYR 1 2 Malaysia 3 0

Maldives 1 2 Marshall Islands 1 1

Mauritania 6 2 Mauritius 3 0

Mexico 11 0 Moldova 3 1

Mongolia 2 6 Montenegro 2 0

Morocco 1 0 Namibia 5 4

Nicaragua 16 3 Nigeria 3 1

Pakistan 12 6 Palau 0 0

Palestinian territories 0 0 Panama 9 0

Papua New Guinea 13 1 Paraguay 13 3

Peru 16 3 Philippines 93 11

Romania 1 0 Samoa 2 4

Sao Tome et Principe 0 0 Senegal 8 1

Serbia 2 0 Seychelles 1 2

Solomon Islands 3 4 South Africa 5 1

EconDisCliCha (2018) 2:21–48 45

Table 7 (continued)

Number of disasters Number of disasters

Major Severe Major Severe

Sri Lanka 39 5 St Lucia 4 1

St Vincent and the Grenadines 3 2 Sudan 14 4

Suriname 1 1 Swaziland 1 4

Syria 1 0 Thailand 24 7

Tonga 5 2 Tunisia 2 0

Turkey 11 0 Tuvalu 1 1

Ukraine 3 1 Vanuatu 10 5

Venezuela 1 0 Viet Nam 46 6

Yemen 1 0 Zambia 4 5

Low income

Bangladesh 28 19 Benin 10 5

Burkina Faso 7 1 Burundi 4 1

Cambodia 6 8 Central African Republic 4 0

Chad 9 3 Comoros 1 6

Congo, DR 0 0 Ethiopia 10 1

Gambia, The 6 3 Guinea 4 0

Guinea Bissau 3 1 Haiti 16 4

Kenya 10 2 Liberia 2 1

Madagascar 19 4 Malawi 10 6

Mali 3 1 Mozambique 18 6

Myanmar 2 1 Nepal 12 2

Niger 6 5 Rwanda 5 0

Sierra Leone 0 1 Tajikistan 4 4

Tanzania 6 0 Togo 8 1

Uganda 9 0 Zimbabwe 4 2

References

Abe N, Moriguchi C, Inakura N (2014) The effects of natural disasters on prices and purchasing behaviors:the case of the great East Japan earthquake. RCESR Discussion Paper Series DP14-1 Research Centerfor Economic and Social Risks. Institute of Economic Research, Hitotsubashi University. http://ideas.repec.org/p/hit/rcesrs/dp14-1.html

Alesina A, Summers LH (1993) Central bank independence and macroeconomic performance: somecomparative evidence. J Money Credit Bank 25(2):151–162. https://ideas.repec.org/a/mcb/jmoncb/v25y1993i2p151-62.html

Barro RJ (2009) Rare disasters, asset prices, and welfare costs. Amer Econ Rev 99(1):243–64. https://ideas.repec.org/a/aea/aecrev/v99y2009i1p243-64.html

Boustan LP, Kahn ME, Rhode PW (2012) Moving to higher ground: migration response to natural disas-ters in the early twentieth century. Amer Econ Rev 102(3):238–44. https://ideas.repec.org/a/aea/aecrev/v102y2012i3p238-44.html

Buckle RA, Kim K, Kirkham H, McLellan N, Sharma J (2007) A structural VAR business cycle modelfor a volatile small open economy. Econ Modell 24(6):990–1017. http://ideas.repec.org/a/eee/ecmode/v24y2007i6p990-1017.html

EconDisCliCha (2018) 2:21–4846

Carter MR, Little PD, Mogues T, Negatu W (2007) Poverty traps and natural disasters in Ethiopia andHonduras. World Develop 35(5):835–856. https://ideas.repec.org/a/eee/wdevel/v35y2007i5p835-856.html

Cavallo E, Noy I (2011) Natural disasters and the economy - a survey. Int Rev Environ Resour Econ 5(1):63–102. http://ideas.repec.org/a/now/jirere/101.00000039.html

Cavallo E, Galiani S, Noy I, Pantano J (2013) Catastrophic natural disasters and economic growth. Rev EconStat 95(5):1549–1561. https://ideas.repec.org/a/tpr/restat/v95y2013i5p1549-1561.html

Coffman M, Noy I (2012) Hurricane Iniki: measuring the long-term economic impact of a natural disasterusing synthetic control. Environ Dev Econ 17:187–205

Cukierman A, Webb SB, Neyapti B (1992) Measuring the independence of central banks and itseffect on policy outcomes. World Bank Econ Rev 6(3):353–398. https://ideas.repec.org/a/oup/wbecrv/v6y1992i3p353-98.html

Dell M, Jones BF, Olken BA (2014) What do we learn from the weather? The new climate-economy literature. J Econ Literat 52(3):740–798. https://ideas.repec.org/a/aea/jeclit/v52y2014i3p740-98.html

Doyle L, Noy I (2015) The short-run nationwide macroeconomic effects of the canterbury earthquakes. NewZealand Econ Papers 49(2):143–156

Driscoll JC, Kraay AC (1998) Consistent covariance matrix estimation with spatially dependent panel data.Rev Econ Stat 80(4):549–560. https://ideas.repec.org/a/tpr/restat/v80y1998i4p549-560.html

Felbermayr G, Groschl J (2014) Naturally negative: the growth effects of natural disasters. J Develop Econ111(C):92–106. https://ideas.repec.org/a/eee/deveco/v111y2014icp92-106.html

Fomby T, Ikeda Y, Loayza NV (2013) The growth aftermath of natural disasters. J Appl Economet28(3):412–434. http://ideas.repec.org/a/wly/japmet/v28y2013i3p412-434.html

Gerena C (2004) States move to prevent post-disaster price gouging. Econ Focus (Fall) 8. https://ideas.repec.org/a/fip/fedrrf/y2004ifallp8nv.8no.4.html

Heinen A, Khadan J, Strobl E (2015) The inflationary costs of extreme weather. mimeo. Ecole PolytechniqueHoechle D (2007) Robust standard errors for panel regressions with cross-sectional dependence. Stata J

7(3):281–312. https://ideas.repec.org/a/tsj/stataj/v7y2007i3p281-312.htmlHornbeck R (2012) The enduring impact of the american dust bowl: short- and long-run adjustments

to environmental catastrophe. Amer Econ Rev 102(4):1477–1507. https://ideas.repec.org/a/aea/aecrev/v102y2012i4p1477-1507.html

Hornbeck R, Naidu S (2014) When the levee breaks: black migration and economic develop-ment in the American South. Amer Econ Rev 104(3):963–90. https://ideas.repec.org/a/aea/aecrev/v104y2014i3p963-90.html

Hoyos RED, Sarafidis V (2006) Testing for cross-sectional dependence in panel-data models. Stata J6(4):482–496. https://ideas.repec.org/a/tsj/stataj/v6y2006i4p482-496.html

Kamber G, McDonald C, Price G (2013) Drying out: investigating the economic effects of drought in NewZealand. Reserve Bank of New Zealand Analytical Notes series AN2013/02, Reserve Bank of NewZealand. http://ideas.repec.org/p/nzb/nzbans/2013-02.html

Keen BD, Pakko MR (2011) Monetary policy and natural disasters in a DSGE model. Southern Econ J77(4):973–990. http://ideas.repec.org/a/sej/ancoec/v774y2011p973-990.html

Klomp J, de Haan J (2010) Inflation and central bank independence: a meta-regression analysis. J Econ Surv24(4):593–621. https://ideas.repec.org/a/bla/jecsur/v24y2010i4p593-621.html

Laframboise N, Loko B (2012) Natural disasters; mitigating impact, managing risks. IMF Working Papers12/245, International Monetary Fund. http://ideas.repec.org/p/imf/imfwpa/12-245.html

Loayza NV, Olaberra E, Rigolini J, Christiaensen L (2012) Natural disasters and growth:going beyond the averages. World Develop 40(7):1317–1336. http://ideas.repec.org/a/eee/wdevel/v40y2012i7p1317-1336.html

Munoz E, Pistelli A (2010) Tienen los Terremotos un Impacto Inflacionario en el Corto Plazo? Evidenciapara una Muestra de Paises. Notas de Investigacion Journal Economia Chilena (The Chilean Economy)13(2):113–127. http://ideas.repec.org/a/chb/bcchni/v13y2010i2p113-127.html

Neely CJ (2011) A foreign exchange intervention in an era of restraint. Review (Sep) 303–324. https://ideas.repec.org/a/fip/fedlrv/y2011isepp303-324nv.93no.5.html

Noy I (2009) The macroeconomic consequences of disasters. J Develop Econ 88(2):221–231. http://ideas.repec.org/a/eee/deveco/v88y2009i2p221-231.html

Parker M (2016) Global inflation: the role of food, housing and energy prices. Discussion Paper DP2016/05,Reserve Bank of New Zealand

Parker M, Steenkamp D (2012) The economic impact of the Canterbury earthquakes. Reserve Bank NewZealand Bull Reserve Bank New Zealand 75:13–25. http://ideas.repec.org/a/nzb/nzbbul/sep201206.html

EconDisCliCha (2018) 2:21–48 47

Pesaran M (2004) General diagnostic tests for cross section dependence in panels. Cambridge WorkingPapers in Economics 0435. Faculty of Economics, University of Cambridge. https://ideas.repec.org/p/cam/camdae/0435.html

Raddatz C (2009) The wrath of God: macroeconomic costs of natural disasters. Policy Research WorkingPaper Series 5039, The World Bank. http://ideas.repec.org/p/wbk/wbrwps/5039.html

Ramcharan R (2007) Does the exchange rate regime matter for real shocks? Evidence from windstorms andearthquakes. J Internat Econ 73(1):31–47. http://ideas.repec.org/a/eee/inecon/v73y2007i1p31-47.html

Reinsdorf M, Hill R, Cavallo A, Cavallo E, Rigobon R (2014) Prices and supply disruptions during naturaldisasters. Rev IncomeWealth 60:S449–S471. http://ideas.repec.org/a/bla/revinw/v60y2014ips449-s471.html

Rotemberg JJ (2005) Customer anger at price increases, changes in the frequency of price adjust-ment and monetary policy. J Monetary Econ 52(4):829–852. http://ideas.repec.org/a/eee/moneco/v52y2005i4p829-852.html

Vigdor J (2008) The economic aftermath of hurricane katrina. J Econ Perspect 22(4):135–54. https://ideas.repec.org/a/aea/jecper/v22y2008i4p135-54.html

Wood A, Noy I, Parker M (2016) The Canterbury rebuild five years on from the Christchurch earthquake.Reserve Bank New Zealand Bull 79(3):1–16. https://ideas.repec.org/a/nzb/nzbbul/feb201603.html

EconDisCliCha (2018) 2:21–4848