metadata: authors: 0: author: Auffret,Philippe
count: Latin America,Caribbean
docna: 0: docna: High consumption volatility : the impact of
            natural disasters?
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Hydrology,Natural Disasters
teratopic: Poverty Reduction
abstracts: cdata!: A history of repeated external and
            domestic shocks has made economic insecurity a major concern
            across the Caribbean region. Of particular concern to all
            households, especially the poorest segments of the
            population, is the exposure to shocks that are generated by
            catastrophic events or natural disasters. The author shows
            that despite high consumption growth, the Caribbean region
            suffers from a high volatility of consumption that decreases
            household welfare. After presenting some empirical evidence
            that consumption volatility is higher in the Caribbean
            region than in the rest of the world, he makes some
            empirically testable inferences that help explain
            consumption volatility. The author develops a conceptual
            framework for analyzing the effects of catastrophic events
            on household and aggregate welfare. According to this
            framework, the volatility of consumption comes from
            production shocks that are transformed into consumption
            shocks mostly because of underdeveloped or ineffective
            risk-management mechanisms. Auffret conducts an empirical
            analysis of the impact of catastrophic events on 16
            countries (6 from the Caribbean region and 10 from Latin
            America) from 1970-99 and shows that catastrophic events
            lead to: 1) A substantial decline in the growth of output.
            2) A substantial decline in the growth of investment. 3) A
            more moderate decline in consumption growth (most of the
            decline is in private consumption, while public consumption
            declines moderately. 4) A worsening of the current account
            of the balance of payments.
repnme: repnme: High consumption volatility : the impact of
            natural disasters?
display_title: High consumption volatility : the
            impact of natural disasters
listing_relative_url: /research/2003/01/2128868/high-consumption-volatility-impact-natural-disasters
topicv3: Water m1326522 137,Crop Production m1326627 174,Earthquake m1503308 3154,Hurricane m1503314 3179,Flood m1503315 3216
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Economic Policy Sector (LCSPE)
no_of_pages: 40
author_keys: 0: author_key: 94425
model_extractions: **POLIcy RESEARCH WORKING PAPER** 2962

# Abstract

A history of repeated external and domestic shocks has made economic insecurity a major concern across the Caribbean region. Of particular concern to all households, especially the poorest segments of the population, is the exposure to shocks that are generated by catastrophic events or natural disasters.

Auffret shows that despite high consumption growth, the Caribbean region suffers from a high volatility of consumption that decreases household welfare. After presenting some empirical evidence that consumption volatility is higher in the Caribbean region than in the rest of the world, he makes some empirically testable inferences that help explain consumption volatility. The author develops a conceptual framework for analyzing the effects of catastrophic events on household and aggregate welfare. According to this framework, the volatility of consumption comes from production shocks that are transformed into consumption shocks mostly because of underdeveloped or ineffective riskmanagement mechanisms. Auffret conducts an empirical analysis of the impact of catastrophic events on 16 countries (6 from the Caribbean region and 10 from Latin America) from 1970-99 and shows that catastrophic events lead to:

* A substantial decline **in** the growth of output.

* A substantial decline **in** the growth of investment. * A more moderate decline in consumption growth (most of the decline is in private consumption, while public consumption declines moderately).

* A worsening of the current account of the balance of payments.

This paper-a product of the Economic Policy Sector Unit, Latin America and the Caribbean Region-is part of a larger effort **in** the region to assess the impact of catastrophic events on welfare. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kevin Tomlinson, room 14-403, telephone 202473-9763, fax 202-676-1494, email address ktomhnson@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pauffret@worldbank.org. January 2003. (33 pages) _he_ **_Policy Research Working Paper Series disseminates_** _the_ **_endings_** _of_ **_work in progress to encourage the exchange_** _of_ **_Ideas about developnient Issues. An objective_** _of_ **_the series is to get the findings out quickly, even If the presentations are less than fi@lly polished._** _The papers carry the names of the authors and should be cited accordingly The_ **_findings,_** _interpretations, and conclusions expressed In this paper are entirely those of the authors. They do not necessanly represent the_ **_view_** _of the World Bank,_ **_Its_** _Execftitie Directors, or the countries they represent._ Produced by the Research Advisory Staff
High Consumption Volatility: The Impact of Natural Disasters ?

Philippe Auffret*

* The initiative for this paper came from Orsalia Kalantzopoulos, Director of the Country Management Unit of the Caribbean Region, as an Economic and Sector Work. I want to thank Mauricio Carrizosa, Edgardo Favaro, Arnaud Marie Guinard, Ali Khadr, Alcira Kreimer, Auguste Kouame, Jacqueline Mora-Baez, John Pollner, Luis Serven and Jyoti Shukla for their comments on an earlier version of this paper. I also want to thank Amelia Santos and Maria Ivanova Reyes for their research assistance, and Stephanie Larson and Lincoln Sampong for editing the report.
# Table **of** Contents

- A. Aggregate Volatility in the Caribbean Region

- A. 1. Volatility of Consumption: An International Comparison

- A.2. Determinants of Consumption Volatility

- A.2. 1. A Theoretical Framework

- A.2.2. Empirical Evidence

- B. Effects of Natural Disasters on Macroeconomic Variables

- B. 1. A Theoretical Framework

- B. 1.1. Effects of Natural Disasters on Household Welfare

- B. 1.2. Effects of Natural Disasters on Macroeconomic Variables

- B.2. Empirical Evidence

- B.2. 1 Catastrophic Events: Occurrence, Impact and Evolution

- B.2.2. Effects of Natural Disasters on Macroeconomic Variables
# High Consumption Volatility: The Impact of Natural Disasters ?

The Caribbean region suffers from a high degree of economic volatility. A history of repeated external and domestic shocks has made economic insecurity a major concern across the region. Of particular concern to all households, especially the poorest segments of the population, is the exposure to shocks that are generated by catastrophic events or natural disasters.

After presenting some empirical evidence that consumption volatility is higher in the Caribbean region than in the rest of the world, the paper makes some empirically testable inferences that help explain consumption volatility (Section A), the paper develops a conceptual framework for analyzing the impacts of catastrophic events on household and aggregate welfare before testing the data empirically (Section B).

### _A. Aggregate_ **_Volatility in the Caribbean Region_**

## _A.]._ **_Volatility_** _of Consumption: An_ **_International Comparison_**

Despite a high level of consumption growth, the Caribbean region also suffers from a measurably high volatility of consumption (Box 1.1). Per capita consumption growth averaged 2.5 percent in the Caribbean region during the period 1960-97, comparable to average growth for the OECD countries and above that of all other regions with the exception of East Asia and the Pacific (Table 1.1). However, average per capita consumption volatility' in the Caribbean region is higher than in any other region of the world. Table 1.1 shows that the volatility of per capita consumption in the Caribbean region is four times higher than it is in industrialized economies, well above the levels of other regions like Asia, non-OECD Europe and Latin America.

The Caribbean region comprises different economies whose respective performances, in terms of consumption volatility, have been equally diverse (Table 1.1). The volatility of per capita consumption has been highest in Guyana and St. Lucia (with standard deviation exceeding 10 percent) and lowest in Grenada and Haiti (below 6 percent).

> The standard **deviation** of per capita consumption growth is usually referred to as the volatility of per capita consumption (See Box 1. 1).

4
<!-- Start of picture text --> Box 1.1: Defining and Measuring Consumption Volatility<br><!-- End of picture text --> <!-- Start of picture text --> In this report, we assume that per capita consumption follows a geometric Brownian motion with drift represented by<br>- =, d +a. dZwhere dZis a standard Brownian motion with expectation and variance equal to 0 and dt,<br>c<br>respectively." 11 The expected instantaneous per capita consumption growth is u while a' (resp. a) represents the<br>variance (resp. standard deviation) of consumption growth. The variable a is usually referred to as the volatility of<br>per capita consumption. Using Ito's lemma, it can be shown that d(Logc,) =(p 1 .a2J dt+a dZ. Hence,<br>c, =c0 .exp[ (P_.aJ)+a.[Z(t+l)-Z(t)]] and Log( '"±)=( -.a2.)+a.[Z(t+l)-Z(t)]. Consequently, the<br>variance of per capita consumption growth and the expected instantaneous consumption growth are respectively given<br>b'y ai=Var[Log(c"')] and u=E[Log(C'+i)]+! .awhere Varand E refer to the unconditional variance and<br>I C, )J L lc,) 2<br>expectation. This consumption process -- which has the advantage of not violating the assumption of non-negative<br>consumption -- can be derived as the optimal outcome in a general equilibrium model with constant return-to-scale<br>technologies defined by = a dt + s -dZ where K represents the stock of capital, dY is the instantaneous output<br>K<br>and the technological coefficients {a,s} are exogenously specified constants with s > 0 (Auffret, 2002).<br><!-- End of picture text --> The lognormal model is consistent with the properties of historical consumption data. Indeed, historical data show that both the skewness and kurtosis of per capita consumption growth are not statistically different from those of the nornal distribution (skewness and kurtosis of the normal distribution are 0 and 3 respectively). The Table below reports sample statistics for yearly per-capita consumption growth. The skewness estimates are negative for all regions but are not statistically different from 0. Indeed, since there are 30 observations, the standard error for the skewness estimate under the null hypothesis of normality is 6/30 = 045 .3 Also, yearly per-capita consumption growth have a kurtosis which, in most regions, is not significantly different from the one of the normal distribution. Indeed, the standard error for the kurtosis estimate is 4/3-0 = 0 89 .

Table: Per Capita Consumpti on Statistics: Regional Comparison <!-- Start of picture text --> Region Per Capita Consumption<br>(unweighted average) Growth (%) Variance Skewness Kurtosis<br>:1 __ E[Lg(5c`)-E[Lg(5C )]f [g( -E[g( C<br>Caribbean Region 2.45 0.0083 -0.19 0.58<br>Sub-Saharan Africa 0.90 0.0074 -0.21 1.78<br>Middle East & North Africa 1.60 0.0061 -0.07 1.13<br>Latin America 1.44 0.0035 -0.47 2.04<br>Europe and Central Asia 1.49 0.0030 -0.52 1.11<br>East Asia and Pacific 2.90 0.0018 -0.41 3.52<br>OECD 2.50 0.0006 -0.08 0.52<br><!-- End of picture text --> _'/_ The geometric Brownian motion is the prototypical process used m finance to model stock price movenents.

2/ This is equivalent to say that instantaneous per capita consumption growth follows a generalized Wiener process.

**3/** in large samples of normally distributed data, the skewness and kurtosis estimators are normally distnbuted with means 0 and 3 and vanances _6/T_ and 24/T , respectively (Stuart and Ord, 1987, Vol. 1).

_5_
Table 1.1: Consumption Path: Internatio nal Comparison

||Number<br>|Population<br>'|P|er CapitaConsump|tion|
|---|---|---|---|---|---|
|Countries/Regions|of<br>|(000,<br>||||
||Countries|1997)||||
||||Growth"<br>(%)|Standard<br>Deviation<sup>2</sup>'<br>(xIW)|Certainty-<br>Equivalent<br>Growth<sup>'</sup><br>(%)|
|Barbados||265|_1.51_|7.46|0.40|
|Belize||217|0.48|9.92|-149|
|Dommica||74|2.18|5.69|1.54|
|Dominican Republic||7,968|2.91|683|1.98|
|Grenada||96|349|5.31|2.92|
|Guyana||749|2.77|17.13|-3.10|
|<br>Haiti||7,492|-0.07|559|-0.69|
|Jamaica||2,554|107|8.61|-0.41|
|St.KittsandNevis||41|5.75|8.15|4.42|
|St Lucia||150|3.69|11.33|1.12|
|St.VincentandtheGrenadines||112<br>|3.43<br>|8.52<br>|1.98<br>|
|Trinidad andTobago<br>||1,278<br>|2.18<br>|8.17<br>|0.85<br>|
|Caribbean*|12|20,995|2.45|8.56|0.79|
|Argentina||35,672|1.48|5.75|0.82|
|Chile<br>||14,622<br>|3.01<br>|10.10<br>|0.97<br>|
|Colombia||40,042|2.45|2.44|2.33|
|ElSalvador||_5,911_|1.08|5.46|0.48|
|Honduras||5,939|068|3.55|0.43|
|Mexico||93,909|1.75|3.60|1.49|
|Nicaragua||4,680|-0.67|7.71|-186|
|Paraguay||5,085|2.81|6.00|2.09|
|Uruguay||3,265|1.39|5.73|0.74|
|Venezuela||22,777|0.44|4.65|0.00|
|LatinAmerica*|10|231,904|1.44|5.50|0.75|
|Sub-SabaranAfrica*|37|498,877|0.90|7.98|-0.58|
|Middle-East&NorthAfrica*|7|207,460|1.60|7.08|0.37|
|Europeand CentralAsia(NonOECD)*|6|291,422|1.49|4.93|0.89|
|EastAsiaandPaclflc*|8|1,633,758|2.90|3.92|2.55|
|France||58,208|2.45|138|2.41|
|Japan||126,091|4.05|2.53|3.93|
|UnitedKingdom||59,009|2.00|1.65|1.94|
|<br>UnitedStates||271,542|2.00|1.49|1.95|
|OECD|21|742,911|2.50|2.17|2.39|
|Memo(Weightedaverageusing1997population):||||||
|Caribbean|||1.55|7.47|0.43|
|LatinAmerica|||1.71|4.84|1.24|
|Sub-SaharanAfnca<br>,|||0.13|8.28|-1.24|
|Middle-East&NorthAfrica|||1.62|6.75|0.71|
|EuropeandCentralAsia<br>||.|0.15<br>|4.26<br>|-0.21<br>|
|EastAsiaandPacific<br>OECD|||4.63<br>2.59|5.26<br>1.94|4.07<br>2.52|

* Unweighted average.

"Refers to the expected instantaneous growth of per capita consumption defined as p in Box 1.1.

2' Refers to the standard deviation (or volatility) of per capita consumption growth defined as cf in Box 1.1.

3 Refers to the certainty-consumption growth defined as , in Box 1.2. We assume that p = 4 which is consistent with available empirical evidence.

Source: S[MA database (1960-97) 6
In the Caribbean region, the volatility of consumption undermines the welfare benefits of consumption growth. In terms of individual welfare, consumption volatility is very costly in the Caribbean region. Risk-adverse individuals prefer a smooth and stable consumption path to one which fluctuates, because their economic welfare depends positively on consumption growth but negatively on consumption volatility. Consequently, an individual would be willing to forfeit some percentages of consumption growth in order to eliminate the volatility of consumption and face a smooth consumption path, or what is referred to in Table 1.1 as the certainty-equivalent consumption growth. Specifically, a representative individual of the Caribbean region is willing to forego 1.66 percentage points of consumption growth on a sustainable basis - i.e. to accept an average consumption growth equivalent of 0.79 percent instead of 2.45 percent with a volatility of 0.0856 - in order to face a consumption path which does not fluctuate. In contrast, a representative individual of the Latin America region is willing to forego only 0.69 percentage point of consumption growth on a sustainable basis - i.e. to accept an average certaintyequivalent consumption growth of 0.75 percent instead of 1.44 percent with a volatility of 0.055. Furthermore, the low level of consumption volatility in OECD countries does not undermine the welfare benefits derived from consumption growth (Table 1.1). Although it is sometimes argued that international risk-sharing would decrease consumption volatility and hence increase individual welfare, this needs not be the case (Box 1.3).

**Box 1.2: Defining and Measuring Certainty-Equivalent Consumption Growth**

<!-- Start of picture text --> We assume that:<br>(i) individuals have time-separable expected utility function with constant risk-aversion preferences given by<br>u(c)=C -1 where p>O and pI is the coefficient of relative risk aversion or u(c)=Logc the limiting<br>case when p=l;<br>(ii) per-capita consumption follows a geometric Brownian motion with drift represented by dc dc = p* dt + e dZ and<br>defined in Box 1.1;<br>(iii) the rate of time preference is fi > 0 .<br>Per-capita consumption at any time t is given by c, =ci:exp I,-j_.a)+a.[Z(t+I)-Z(:)]]. Expected utility is<br>V(co,p,o.2.p)=Eo[ju(t). e-4 di] which can be rewritten V(c J, .e2,P)= 0 0. I l _2 _0 _)<br>Note that the same expected utility is obtained when per-capita consumption follows the deterninistic process<br>represented by c= (p- . p oa) dt = p,. di where . a -I- P. c'. We then define u, as the certainty-equivalent<br>consumption growth.<br><!-- End of picture text --> 7
## Box 1.3: Would International Risk-Sharing Increase Welfare?

> Some studies claim that intemational risk-sharing would decrease consumption volatility and hence increase country-

> specific welfare (See for example, De Ferranti et al., 2000, pp. 62-63). However, such analyses do not take into account the impact of intemational risk-shanng on consumption growth. Although intemational risk-shanng

> decreases consumption volatility, leading to some welfare gains, intemational risk-sharing may also simultaneously

> lead to a decline in consumption growth for some countnes, decreasing their welfare. As an extreme example, a

> country with a production growth of 5 percent and no volatility would not find it advantageous to share risks with another country which grows at I percent and is volatile.

In fact, most studies that seek to denve the benefits from intemational nsk-sharing use as examples developed economies with comparable levels of consumption growth and volatility but asymmetric shocks (van Wincoop, 1999; Kraay and Ventura, 2001). However, when these conditions are not met, intemational nsk-shanng does not systematically increase welfare.

# _A.2. Determinants of Consumption_ **_Volatility_**<sup>**_2_**</sup>

# _A.2.1. A Theoretical Framework_

> The general equilibrium framework, arguably the most important result of economic theory to date, shows that under the assumption of complete markets (i.e. fully developed financial and

> insurance markets), individuals are able to trade risk so that fluctuations in income do not result in fluctuations in consumption (Debreu, 1959; Arrow and Hahn, 1971). In such a framework,

> individuals have recourse to well-developed domestic and international financial and insurance markets to alleviate the impact of production shocks on consumption so that the remaining shocks

> to consumption are the global shocks which are not diversifiable away even internationally. However, in reality, fluctuations in income do translate into fluctuations in consumption if risk management mechanisms are not fully developed or effective (Chapter 2 provides a conceptual framework for risk management). Within this context, the volatility of consumption comes from production shocks which are translated into consumption shocks mostly because of inefficient risk-management mechanisms (Figure 1.1). The shocks to the production process may be

> domestic or external in origin. Domestic shocks can result from inadequate macroeconomic

> policies or from destabilizing events like civil unrest, armed insurrections or civil wars. External

> shocks can be in the form of international conflicts, natural hazards, terms of trade shocks or global shocks from worldwide booms and recessions.

> 2 The flagship report "Securing our Future" (World Bank, 2000) shows that terms of trade shocks and macroeconomic policies are important determinants of volatility. However, terms of trade shocks and macroeconomic policies need not have a direct impact on consumption volatility in the presence of well-developed financial and insurance markets that

> allow households to insure consumption against such shocks. We excluded these types of shocks in this section because they are not proximate determinants of consumption volatility. Indeed, these shocks (like catastrophic events) have an impact on production and affect consumption only to the extent that financial markets are not complete.

8
<!-- Start of picture text --> Figure 1.1: The Links between Production and Consumption Volatilities<br>Shocks<br>o Domestic<br>o Extemal<br>Production Volatility<br>Risk Management Mechanisms<br>Risk-Reduction Mechanisms:<br>e Risk Identification<br>o Risk Mitigation<br>o Risk Preparedness<br>Risk-Coping Mechanisms:<br>° Financial markets<br>r Insurance markets<br>• Counter-cyclical policies<br>o Others<br>Consumption Volatility<br><!-- End of picture text --> 9
# **_A.2.2._** _Empirical Evidence_

This section attempts to draw some inferences from the theoretical framework developed in the preceding section and to test them empirically.<sup>3</sup> A first inference from the theoretical framework is that consumption volatility can be expected to be larger in countries with high income volatility. This phenomenon is illustrated in Graph 1.1 which plots the volatility of per capita consumption against the volatility of per capita income. However, what is surprising is that countries typically lie above the 45-degree line where the volatility of consumption is higher than that of income (this is the case in 67 of the 102 countries and in only 1 of the 12 Caribbean countries considered).<sup>4</sup> This seems to indicate that the riskmanagement mechanisms seem to amplify shocks rather than help absorb them. Production volatility by itself accounts for about 40 percent of the observed variation in consumption volatility across countries, which implies that it is indeed a main factor which helps to explain the volatility of consumption (Table 1.2).

<!-- Start of picture text --> Graph 1.1: Consumption Volatility and Income Volatility<br>20<br>0 Conbbcan<br>+ + Others<br>0<br>Rep- I<br>+ 41 dg- t-~<br>U" ~~ ~~~~+ , + 0+<br>It ~+ +_ +<br>0<br>o 5 It S1 20<br>Volatility of Per Capita GDP<br>StSwfdod d.&H..o itt)<br>Sources SIMA database (102 countries included).<br><!-- End of picture text -->

**3De** Ferranti, 2000 and World Bank 2000 (a) present a series of stylized facts that help understand consumption and production volatulities.

" i autarky, consumption volatility and production volatility should be equal while consumption volatility should be less than production volatility under intemnational nosk-shanng.

10
A second inference is that if counter-cyclical policies are effective in reducing consumption volatility, the volatility of private consumption can be expected to be higher than that of total consumption. Graph 1.2 plots the volatility of per capita consumption against the volatility of per capita private consumption. Countries typically lie below the 45-degree line where the volatility of total consumption is lower than that of private consumption. Applicable to all regions and almost all countries, this observation indicates that governments indeed play some positive role in reducing total consumption volatility or in smoothing total consumption by providing more public goods during periods of low private consumption. In the Caribbean region, the volatility of total consumption is 8.6 percent, while that of private consumption is 10.6 percent. However, the countercyclical role of public consumption varies across countries. For example, public consumption is very effective in reducing private consumption volatility in Guyana and Trinidad and Tobago, while in Barbados and the Dominican Republic public consumption does not exhibit the same degree of smoothing.

<!-- Start of picture text --> Graph 1.2: Consumption Volatility and Private Consumption Volatility<br>25 -__ _ _ _ __ _____________________ ________<br>0 Caribbean<br>+ Others<br>20<br>45 d,pde h.e<br>o 0<br>o 5 lo0 1 5 20 25<br>Voladlty of Per Capita Private Consumptlon<br>tSitdi df i + x +s)<br><!-- End of picture text -->

**Source: SIMA data'oase (98 countnes included).**

A third testable inference is that on average consumption volatility can be expected to be higher in countries with less developed financial and insurance risk management mechanisms. Financial depth (monetary aggregate M2 divided by GDP) divided by the volatility of output is used as a proxy for the presence of financial risk coping mechanisms. This variable -- which increases as financial depth increases or production volatility decreases -- is used as a proxy for the availability of financial insoPuments in the presence of volatile output. Insurance depth (insurance premiums divided by GDP) divided by the volatility of output is similarly used as a proxy for the presence of insurance risk coping mechanisms. Graphs 1.3 and 1.4 illustrate the fact that financial and insurance instruments are important instruments of consumption smoothing, allowing individuals to transfer outputs across space and time. Each of these two variables by
itself accounts for about one-fourth of the observed variation in volatility across countries, which also indicates that they are key determinants of consumption volatility (Table 1.2).

<!-- Start of picture text --> Graph 1.3: Consumption Volatility and Risk-Coping Financial Mechanism<br>o Canbbean<br>|- OtheOs<br>2 ++<br>14 o o+ ++++|o=<br>+ +~~~<br>6 + 0+0 + 0<br>+ 0 +~~<br>6 +! + +<br>+ +0<br>0<br>0 to 20 30 40 so 60<br>(M I/DP)t/Vointility of Per Capita GDP<br>Source, SIMA database (91 countries included)<br>Graph 1. Consumption Volatility and Risk-Coping Insurance Mechanism<br>o Caribbean<br>+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+ Othoers<br>0<br>16<br>2 14<br>0~~~~. ~ ~ ~<br>+4<br>n~~I +++ + o<br>2 +<br>0 +~~~~<br>6<br>>~~~~~~~~~~~(nuac rmusG)YoaiiyoPrCpt D<br>Source, SIMA databas(ensranc Prm77nscountriesliy oiPerlapdteG)<br><!-- End of picture text -->

12
A fourth inference from the theoretical framework is that consumption volatility can be expected to be smaller in developed countries with developed risk-reduction mechanisms (risk identification, risk mitigation and risk preparedness) (see Chapter 2). Graph 1.5 illustrates the inverse relationship between consumption volatility and level of economic development. The level of economic development by itself accounts for about one-third of the observed variation in consumption volatility across countries, which implies that it is also an important factor in explaining the volatility of consumption (Table 1.2).

<!-- Start of picture text --> Graph 1.5: Consumption Volatility and Per Capita Income<br>20 _ ... _<br>0 Canbbea<br>+ Otbe.,<br>0<br>t6<br>14 +<br>12 + .<br>+<br>o + 00 c<br>O 9 +++ 0 +0<br>*5 + 0 - +' + +<br>4 ~~~~+<br>'4 + + +<br>0 ~~~~+ + + + + ++<br>3.00 000 I 0 15,001 2000 25.000 30.00 35. 40<br>-2<br>Per Capita GDP (US$, PPP adjusted)<br>Some SIMA database (100 countries included):-<br><!-- End of picture text --> A fifth inference is that smaller economies are expected to suffer from higher consunption volatility than larger economies. Smaller economies are less able to take advantage of economies of scale because their production is not diversified. As in the case of the Caribbean region, their size makes them vulnerable to the impact of hurricanes and other natural disasters. Also, large industrial economies may not suffer from significant output losses because regional or asymmetric shocks from disasters may be absorbed by economic activities in other regions and transfers across regions. This fact is summarized in Graph 1.6 which plots the volatility of per capita consumption against country size (as measured by the logarithm of the population). Country size by itself accounts for about one-third of the observed variation in consumption volatility across countries, which implies that it is also a main factor which explains the volatility of consumption (Table 1.2).

13
<!-- Start of picture text --> Graph 1.6: Consumption Volatility and Country Size<br>20<br>o Cnbbco,r<br>+ Other<br>18 -<br>0<br>16<br>14 +<br>o + + ++ + S +'¢++<br>0. +<br>to - +~~~+ +.'+ At 4<br>loit 12 14 16 10 20 22<br>Logarithm of Popuhatiou, t997<br>Sorce: SIMA database ( 102 countrnesincluded).<br><!-- End of picture text -->

> The above variables are tested empirically by regressing consumption variability on a constant,

> proxies of financial and insurance risk management mechanisms, per capita GDP and (the logarithm of) population (Regression 6 in Table 1.2). With the exception of the insurance mechanism proxy, the coefficients of each explanatory variable have the expected signs.

> However, the coefficients of the financial and insurance mechanism proxies and of per capita GDP are not statistically significant due mostly to multicollineary between them. When the

> insurance consumption mechanism proxy and per capita GDP are dropped, the coefficients of the remaining explanatory variables have all the expected signs and are statistically significant (Regression 7). The remaining three explanatory variables explain half of the volatility of

> consumption. When both the insurance and financial mechanism proxies are dropped, the

> coefficients of the remaining explanatory variables also have the expected signs and are statistically significant (Regression 8). The remaining three explanatory variables explain about

> 60 percent of consumption volatility.

14
Table 1.2: Determinants of Consumption Volatility <!-- Start of picture text --> Dependent Variable: Volatility o Per Capita Consumption"'<br>Exogenous Variables Regre ssions |<br>1 2 3 4 5 6 7 8<br>Constant 1.16 8.37 7.27 7.85 17.26 13.24 13 88 12.22<br>(I 82) (17 33) (16 82) (20.79) (6.60) (4.68) (6.15) (6 35)<br>Production Volatility 1.06 0.66 0.69 0.72<br>(8 44) (4 76) (4.94) (6.46)<br>Financial Depth/Production Volatility' -0.17 -0 07 -0 09<br>1(-5 22) (-1.65) (-2.89)<br>Insurance Depth/Production Volatility 4 ' -1.42 0.61<br>(-5.52) (1. 9)<br>Per Capita GDP -0.0002 -0.0001 -0.0001<br>(-7.37) (-1.76) (-4.67)<br>Log (Population) -0.71 -0.56 -0.62 -0.54<br>(-4.32) (-3.35) *(4.92) (-4.81)<br>Numberofcountries 101 90 76 100 102 66 1 100<br>R-squared 0.42 0 24 0.29 036 0.16 0 61 0.53 0.62<br>Adjusted R-squared 0.41 0.23 0.28 0.35 0.15 0.58 0.51 0 61<br>DW 1.75 2.13 1.59 2.10 1 38 207 231 2.07<br>Standard Deviation of Consumption Per Capita.<br>" Standard Deviation of Production Per Capita.<br><!-- End of picture text -->

" Ratio of M2/GDP to production volatility.

t Ratio of misurance preniumstGDP to production volatility. **3** Logarithm of 1997 population Note t-statistics given in parentheses. Sourem SIMA database of the World Bank.

# **_B._** _Effects of_ **_Natural Disasters on Macroeconomic Variables_**

# **_B.l. A Theoretical Framework_**<sup>**_5_**</sup>

This section develops a theoretical framework to assess the effects of natural disasters on individual and household welfare.

# **_B.1. 1._** _Effects of_ **_Natural Disasters on Household Welfare_**

The impact of natural disasters on household welfare may be classified into -three- categories: physical integrity, assets and income. Individual integrity is vulnerable to the consequences of natural disasters, inasmuch as immediate fatalities, debilitating injuries and health epidemics compromise the quality of life. The loss of lives permanently deprives households of productive members, orphans minor dependents and leaves a legacy of psychological and financial burdens. Additionally, incapacitating injuries limit one's ability to carry out full-scale productive functions and can mean prolonged hospitalization or rehabilitation and a significant decrease in a household's earning power. In the aftermath of geological or weather-related hazards, ruptured or overflowing sewage systems can disrupt or contaminate water supply to large areas, as well as increase the risk of water-borne diseases. Subsiding floods leave in their wake stagnant pools that quickly become a breeding ground for malaria-transmitting mosquitoes. In sum, overcrowded living quarters, inadequate disposal of waste and the environmental impact of disasters combine to incubate bacteria, viruses and other parasites that threaten public health and economic welfare.

Disasters lead to the loss of tools, dwellings and other income-generating fixed or liquid assets. Houses are particularly vulnerable to the damaging impact of earthquakes, high winds, volcanic eruptions, landslides and floods. Shops, factories and markets are not immune to the

> 5 This Section draws on Celine Charveriat, _Natural Disasters in Latin America and the Caribbean: An Overview of Risk._ October 2000. Inter-American Development Bank. Working Paper #434.

_15_
consequences of catastrophic occurrences. The loss of income from flooded arable land, damaged food crops and reduced agricultural production, may be temporary or permanent. Farmers can neither cultivate water-logged fields nor can flooded farms yield their expected produce. Flash floods and storm surges from hurricanes lead to increased salinity and decreased output of farmland. The loss of such perennial crops as coffee and bananas has long-term effects on a household's ability to replenish lost income or generate new sources of financial sustenance. For example, after Hurricane Mitch in 1998, the banana industry in Honduras was not expected to recuperate its lost volume of production until 2002.

The impact of natural disasters on household welfare depends primarily on the extent of the damage sustained by income-generating assets and on the period of disruption of flows of goods and services (Table 1.3). Invariably, the impact is more intense in the immediate aftermath of a disaster and may be absorbed relatively quickly afterward, as was the case in Honduras after Hurricane Mitch.

Table **1.3:** Effects of Natural Disasters on Household Welfare

|Welfare<br>**Indicators**|ExpectedPost-DisasterEffects|
|---|---|
|Physical<br>Integrity|Fatalities, incapacitatinginjuries, epidemics,poorsanitation, inadequateshelter,impaired public<br>safetyanddislodgingofwildlifefromnaturalhabitat|
|Assets|Decrease**in**agriculturalproduction,smallbusiness operations,access toroadwaysand<br>productiveactivities.|
|Income|Inabilitytowork,lossofcash cropsanddecreasedfoodcrops will mean reducedhousehold<br>earmings,decreaseinfoodconsumption and**inability**topurchasepreviouslyaffordablebasic<br>goodsandservices.|

16
# **_B.1.2._** _Effects of Natural Disasters_ **_on_** _Macroeconomic Variables_

This section develops a theoretical framework to assess the effects of natural disasters at the aggregate level (Table 1.4).

Natural disasters have direct effects on the stock of human and physical capital which, in turn, affects production, consumption, investment and the current account of the balance of payments. Natural hazards produce direct damages comprising total or partial destruction of housing, buildings, installations, machinery, equipment, means of transportation, storage and furniture, as well as damages to cropland, irrigation works and dams and the destruction of crops ready for harvesting. Calculating direct **damages** is difficult, especially in countries where assets are not registered. Moreover, some damages to the environment (for instance, erosion or total sedimentation that makes the land unsuitable for cultivation) as well as negative effects on human capital are difficult to assess.

Natural disasters also cause indirect damages, which refer to the loss of production of goods and services resulting from the destruction of the means of production. Indirect damages include loss of future harvests as a result of flooding of farmland or loss of perennial crops, loss of industrial output as a result of damages to factories or lack of inputs and increased transport costs due to destruction of roads or other transport infrastructure. Indirect damages continue to occur until reconstruction is completed and the entire production capacity is restored, which can take several years.

Natural disasters immediately reduce the amount of physical capital in an economy, which in turn reduces output. Consequently, natural hazards have an immediate negative impact on growth. Effects on economic sectors (agriculture, industry and services) depend on the nature of the catastrophic event. A hurricane may primarily affect agricultural production while an earthquake may destroy the industrial productive capacities. The disruption to the production process including transportation infrastructure translates into a decline in exports and imports increase as part of the reconstruction process. The decline in exports and the increase in imports lead to a deterioration of net exports - which usually translate into a deterioration of the balance of payments. The impacts of natural disasters on investment depend on the reconstruction effort. However, risk-coping measures (see Chapter 2) are likely to be insufficient in the short-term in restoring the investment to pre-shock level. Consequently, natural disasters can be expected to have an immediate negative effect on total investment. It can also be expected that private investments would decline more than public investment as the public sector may have more capacity to restore its investment capacity. Also, the Government can be expected to implement countercyclical policies leading to higher fiscal deficits. Higher fiscal deficit originates from a decline in tax revenues linked to the decline in production and from an increase in public expenditures to finance reconstruction. Inflation can be expected to increase in the afternath of a catastrophic event due to the disruption of the production and distribution processes and money creation to finance the reconstruction effort. It can be expected that the negative impact on production translates into a decrease in both private and public consumption although ex-post international financing together with the implementation of countercyclical fiscal policy may allow the public sector to be in a better position to maintain public consumption at or above preshock level. In fact, public consumption could theoretically increase in the aftermath of a catastrophic event as more public sector workers are hired to assist with the reconstruction effort. Long-term impacts are more difficult to predict because they depend closely on the mode and timing of the reconstruction effort. If lost capital is not replaced, there might be long-term negative effects.

17
**Table 1.4: Effects of Natural Disasters on Macroeconomic Variables**

|**MacroeconomicIndicators**|**Expected Post-DisasterEffect**|
|---|---|
|Physical Stock|Destructionofcapitalstock followedbyreconstruction.|
|1.Production(2+3+4)|Decreasefollowedbyincrease.|
|1.1Agriculture|Significantdrop**in**production(ifhurrcane,floodordrought).|
|1.2Industry|Decrease duetodisruptionoftransportation,reducedcapitalstock and production<br>capacities.|
|1.3Service|Decreaseduetodisruptionoftransportationandpaymentsystem.|
|2.Consumption|Decreasedueto declineinproduction|
|2.1Private|Decrease duetodeclineinproduction.|
|2.2Public|Decreasesomewhatmitigatedbyex-post intemationalfinancingand counter-<br>cyclical fiscalpolicy.|
|3.Investment|Decreaseininvestmentsintheaftermathofthedisaster.|
|4.Net ExportsofGoodsand<br>Services|Decreaseinexports duetodisruptionofproduction process includingtransportation<br>infrastructure.<br>Increaseinimportsaspartofthereconstructionprocess.|
|PublicFinances|Increaseinfiscaldeficit duetoadeclineintaxrevenueslinkedtothedeclinein<br>productionand theincreaseinpublic expenditures for reconstruction.|
|Inflation Rate|Increasecausedbythedisruptionofproductionanddistributionandincreasing<br>transportationcosts.|

Source. Adapted from Albala-Betrand (1993) and Downing, Holstoom and Tol (1999). As reported in Charveriat (2000), Table 1.2 p. 16.

18
# **_B.2._** _EmpiricalEvidence_

# **_B.2.1_** _Catastrophic Events: Occurrence, Impacts and Evolution_

Natural hazards have a devastating impact on Caribbean economies (Box 1.6). For the period 1970-99, the Caribbean region averaged 11.9 catastrophic events a year (Table 1.5). Cumulative damages from catastrophic events in the region for the period 1970-99 amounted to US$8.5 billion corresponding to a country average of US$605.3 million (12 percent of GDP), equivalent to US$20.18 million per annum on average. Five out of the thirteen countries for which data are available recorded cumulative economic losses above 25 percent of GDP during the period. Montserrat's staggering economic loss of 899 percent is an extreme example of the devastating impact that a natural disaster can inflict on a Caribbean island (Box 1.5). Between 1979 and 1995, the Dominican Republic, Jamaica, Montserrat, St. Kitts, Antigua and Barbuda suffered heavy losses in human lives, housing stock, territorial infrastructure and economic growth indicators (Box 1.6). Box 1.7 provides a more detailed analysis of the impacts of Hurricane Georges on the Dominican Republic in September 1998 as well as the policy responses.

19
# **Box 1.4: Definition of Selected Natural Hazards**

## **Geological Hazards**

**An earthquake is** a sudden tremor of the earth's substrata that may be caused by the movement of large masses of rocks or tectonic plates along fault lines in mountain ranges or mid-oceanic ridges.

A **tsunami** is a wave train or senes of waves generated in a body of water by an impulsive disturbance (such as earthquakes) that vertically displaces gigantic water columns. Tsunamis may reach a maximum run-up or above-sealevel height of 10, 20, or even 30 meters.

Slides are a downward slope movement of soil, rock, mud or snow because of gravity. One of the most common sources of slides **is** prolonged torrential downpours of rain or the accumulation of heavy snow. Mass displacement of large mud, snow or rocks can also be triggered by seismic waves.

Lahars are mudflows that are caused by the melting of the icecap by lava from a volcano or the downhill run-off of volcanic ash because of heavy rainfall.

A **volcanic eruption is** the process whereby molten lava, fragmented rocks or gases are released on to the earth's surface through a deep crater, vent or fissure.

## **Meteorological Hazards**

**Hurricanes** and **Tropical Storms** are large-scale, closed circulation system in the atmosphere with low barometric pressure and strong winds that rotate counter clockwise in the northern hemisphere and clockwise in the southem hemisphere.

Floods are a temporary inundation of normally dry land by overflowing lakes or rivers, precipitation, storm surges, tsunami, waves, mudflow, lahar. They may also be caused by the failure of water retaining structures, groundwater seepage and water-back up in sewer systems.

**Drought** is a lack or insufficiency of rain for an extended period that can cause a considerable hydrological imbalance and, consequently, water shortage, crop damage, stream-flow reduction and depletion of groundwater and **soil** moisture. It occurs when, for a considerable penod, evaporation and transpiration (the release of underground water into the atmosphere through vegetation) exceeds precipitation.

Forest fires are uncontrolled fires whose flames can consume trees and other vegetation of more than 6 feet (I .8m) in height. These often reach the proportions of a major conflagration and are sometimes begun by combustion and heat from surface and ground fires.

Source IDNDR (1992); Bell (1999); Swiss Re (1988, p. 16); Pidwimy (1999); and Encyclopedia Bntannica [http./www Bntannica.com].
**Table 1.5: Disaster Exposure Indicators, Caribbean**<sup>**Region(1970-1999)**</sup>

||**Disas**|**terOccurren**|**ce**|**Disaster**<br>**Fata**|**-Related**<br>**lities**|**Econom**|**icLosses**|
|---|---|---|---|---|---|---|---|
|**Country**|**Numberof**<br>**Catastrophic**<br>**Events**|**Occurrence**<br>**peryear**|**Occurrence**<br>**perkm2**<br>|**Fatalities**<br> <br>|**Fatalities**<br>**(per1000**<br>**inhabitants,**<br>**1995)**<br>|**Amount**<br>**(US$**<br>**million,**<br>**1998)**<br>|**Percentage**<br>**ofGDP**<br>**(Percentage,**<br>**1995)**<br>|
|Antigua&Barbuda|7|0.2|17.5<br>|7<br>|0.10<br>|105.7<br>|18.1<br>|
|Bahamas|4|0.l|0.4|5<br>|0.02<br>|290.4<br>|9.5<br>|
|Barbados|5|0.2|12.5<br>|3<br>|0.01<br>|148.4<br>|6.3<br>|
|Cuba|35|1.2|0.3<br>|181<br>|0.02<br>|578.0<br>|N/A<br>|
|Dominica|7|0.2|8.8|43<br>|0.60<br>|133.4<br>|55.0<br>|
|Dominican Republic|17|0.6|0.4|1839<br>|0.20<br>|2,657.2<br>|17.3<br>|
|Grenada|4|0.1|13.3<br>|0<br>|0.00<br>|30.1<br>|9.5<br>|
|Haiti|31|1.0<br>|1.1<br>|2031<br>|0.30<br>|288.7<br>|7.3<br>|
|Jamaica|19|0.6|2.6<br>|271<br>|0.10<br>|1,988.1<br>|29.3<br>|
|St.Kitts&Nevis|7|0.2|17.5|6<br>|0.20<br>|312.5<br>|116.5<br>|
|St.Lucia|8|0.3|13.3<br>|54<br>|0.30<br>|1,554.6<br>|272.3<br>|
|St.Vincent|9|0.3|22.5<br>|5<br>|0.04<br>|47.0<br>|16.5<br>|
|Trinidad&Tobago|8|0.3|1.6|9|0.01<br>|16.7<br>|0.3<br>|
|Montserrat'|5|0.2|50.0|43|3.40<br>|323.7|899.0|
|**Average**|**11.9**|**0.4**|**11.6**|**321**|**0.40**|**605.3**|**112.0**|
|**Memo:**<br>||||||||
|CentralAmerica<br>|**33.6**<br>|**1.1**<br>|**0.3**<br>|**9,184**<br>|**1.10**<br>|**3,868.0**<br>|**60.6**<br>|
|Latin America|**39.8**|**4.5**|**0.6**|**13,356**|**0.50**|**4,879.0**|**9.6**|

GDP, 97 est., CIA World Fact **Book.** N/A= Not available. Source: EM-DAT; World Bank Development Indicators.<sup>Reported</sup> in<sup>Charveriat (2000,p.38)</sup>

# **Box 1.5: Impact of a Volcano on a Caribbean Island: The**<sup>**Case of Montserrat, UK**</sup>

The Caribbean island of Montserrat, whose territory is<sup>0.6timesthesizeofWashingtonDC,andhas12,853</sup> inhabitants, constitutes an extreme example of the kind of<sup>impactthatdisasterscan haveonasmall,undiversified</sup> economy. In 1996, 81 percent of the island's economic activities<sup>was associated withthetournsmindustry.In1989,</sup> Hurricane Hugo caused an estimated US$240 million in<sup>damagesand leftaloss-to-output ratioof morethan600</sup> percent.' The hurricane also damaged or destroyed<sup>98</sup> percent<sup>of the housingstock.In1995,while the island was still</sup> recovering from Hugo (with growth rates between 0 and 2<sup>percentin1992,1993and1994),theSoufriere Hills</sup> Volcano became active again after being dormant<sup>for 350years,causing32fatalitiesandthe evacuationof 70 percent</sup> of the population. Volcanic activity, which peaked in 1996-97<sup>with severalviolent eruptions,subsided by March1998,</sup> leaving much of the island uninhabitable and unsuitable<sup>foragriculture.TheUKgovemmenthas committedabout</sup> US$100 million (equivalent to 300 percent of the island's<sup>GDP)to reconstruction.Despite this massiveassistance and</sup> the boom generated in the construction sector, GDP declined<sup>by18.5 percentin 1997realtermsand declned againin</sup> 1998.

'Calculation using 1996 GDP, CIA World Factbook Source CIA World Factbook 1999; DFID, 1999, pp. 60-61;<sup>Crowards, 1999, p. 18)</sup><sup>**in**Celine Charvenat 2000</sup> 21
|**Box1.6:ImpactofHurricanesonCaribbeanIslands**|
|---|

A few examples illustrate the impacts of hunicanes on the Caribbean region. In 1979, the Domrnican Republic was hit by Humcanes David and Frederick in a span of five days. Two thousand people died, 100,000 families were left homeless, and matenal damage across all sectors was estimated at one-third of 1979 GNP. Nearly 100,000 houses were destroyed, 37 percent of agricultural output was lost, and 85 percent of schools were damaged. Indirect effects included increases **in** fiscal deficit, and setbacks **in** health and education services. The direct effects of Hurricane Gilbert on Jamaica in 1988 amounted to US$956 million, with nearly half from losses in agriculture, tourism and industry, 30 percent in housing, and 20 percent in economic infrastructure. Economic projections had to be adjusted dramatically, based on expected losses in export eamings of US$130 million, and lost tourism earnings of over US$100 million. Instead of a forecasted GDP growth of 5 percent, a decline of 1.8 percent was experienced. Other changes induced by the disaster were: increases **in** inflation (30 percent), government expenditures (US$220 million), and the public sector deficit (from 2.8 percent to 10.6 percent of GDP). In September 1989, Hurricane Hugo's most severe damage was inflicted on Montserrat, leaving 10 fatalities, and a total damage estimate of US$240 million. Of the 98 percent of housing damaged by the storm, 50 percent was severely damaged and 20 percent totally destroyed. The port's concrete Jetty was destroyed and debris littered all island roads. The three **main** hotels were put out of business for at least four months. Agricultural crops were destroyed, and the fishing sector lost boats, buildings and ports. The total damage exceeded five years of GDP.

In August and September 1995 Hurricanes Luis and Marilyn hit the Leeward Islands with direct damage estimated at US$149 million in St. Kitts, US$254 million in Antigua/Barbuda and some US$ 175 million **in** estimated rehabilitation costs **in** Dominica. l These hurricanes caused widespread impacts over public, social and pnvate economic sectors.

l/bThese costs are different from those reported in Table 1.5 We were unable to reconcile these differences.

Source. CARICOM Working Party, 1996, pp. 6-7.

22
<!-- Start of picture text --> Box 1.7: Impact of Hurricane Georges: The Case of the Dominican Republic<br><!-- End of picture text --> Hurricane Georges passed over the Dominican Republic in September 1998 as a category 3 hurricane with winds reaching 130 miles per hour. The eye of the hurricane entered the southeastem portion of the country **in** the moming, traversed the country at approximately 6 mph on a northwest path and arrved at the border with Haiti m the evening, downgraded to category 1. The destructive winds destroyed housing, agricultural and industrial infrastructure, uprooted trees and destroyed crops, mainly in the eastem part of the country. The heavy rain was centered in the southem and southwestem areas of the country and led to floods and rivers overflowing with water and mud, which destroyed bridges, homes, and household and farming equipment; damaged roads, schools, health clinics and water supply systems; and washed away crops, including sugarcane, bananas, yucca, coffee and vegetables. Because of its diameter, the hurricane affected at least 70 percent of the country, equivalent to 34,000 square kilometers. The areas most affected were the eastem and southem portions of the country, including some important urban areas such as La Romana, San Pedro de Macoris, Santo Domingo and San Juan de la Maguana. Some of the principal tourist enclaves **in** the southeast were badly damaged, as well as cntical agricultural zones, such as the fertile San Juan valley and Cibao valley. The death toll stood at 235.

# **Economic and Social Impact**

A team from the Economic Commission for Latin America and the Caribbean (CEPAL) visited the Dominican Republic in the second half of October and estimated the total cost of direct damages at US$1.3 billion (8 percent of GDP). While the adverse impact of the hurricane on GDP growth and inflation was **limited,** the economic recovery led to a temporary widening of the fiscal and extemal current account deficits, largely reflecting increased spending on (mostly imported) basic needs and reconstruction materials.

- **Agriculture:** The most important sector of the economy, agnculture accounts for 16 percent of GDP. It sustained the greatest damage, from the winds that uprooted trees and plants and destroyed infrastructure, and the rains that caused the rivers, particularly **in** the south/southwest regions, to overflow, flood fields and destroy cultivated land. One third of all cultivated land was affected. The crops grown for intemal consumption were the most affected, particularly bananas and com. Total damages in the sector, including livestock and fishenes, were estimated at US$527 million.

- e **Transport:** The heavy rains caused serious damage to highways, roads and bridges. 20 percent of highways were affected, and 40 percent of local roads. 112 bridges were damaged, 55 of them destroyed. Total damages in the sector, including ports and airports, were estimated at US$292 million.

- *** Housing:** The wind destroyed roofs, windows and extemal installations, and the rains flooded many homes. The most affected provinces have some of the highest concentrations of poor in the country: Santo Domingo, Monsenor Nouel, San Juan de la Maguana and San Pedro de Macoris. A total of 171,000 of houses, or 10 percent of the total, were affected, while 49,000 houses, or 3 percent of the total, were completely destroyed. Total damages were estimated at US$232 million.

- * **Tourism:** The greatest damages were sustained in La Romana, La Altagracia and Juan Dolio. A total of 6,000 rooms were affected, at a estimated cost of US$149 mullion.

- o Electricity: Transmission and distribution networks sustained the greatest damages, pnmanly in the eastem part of the country. Total damages were estimated at US$46 million.

- * **Education:** 1,334 schools (28 percent of the total number of schools) were affected by the hurricane, of which 203 (4 percent of the total) were completely destroyed. As 443 of the largest schools were used as refuges for the homeless, the school year started several weeks late for close to 100,000 students. Total damages in the sector, which includes sports and recreational facilities, were estimated at US$69 million.

- **Health:** Damages in physical infrastructure were moderate - US$2 million in buildings and US$4 million in furmiture and equipment; the regions most affected were Santo Domingo, La Altagracia, San Pedro de Macoris and La Romana. The indirect cost of attending to emergency needs, estimated at US$17 million, has made the greatest impact on the sector, as the lack of running water, food and medicine and accumulation of solid wastes resulting in water pollution, increased the incidence of infectious diseases.

- * **Water & Sanitation:** As a consequence of flooding, the sector was adversely affected by damaged electricity lines, water **mains** and treatment plants, wells, access roads, distribution networks, windmnlls and emergency motors. Total damages were estimated at US$16.4 million.

- * **Irrigation:** The torrential rains damaged canals and electrical infrastructure. Damages were not fully quantified because some areas were **still** inaccessible, but the estimated total so far was US$8.8 million.

Source: Humcane Georges Emergency Recovery Project, World Bank, 1998 23
Despite the common Caribbean Basin location, each country under study exhibits peculiarities that can affect the level of vulnerability to a specific type of catastrophic event. Although generally perceived as hurricane and flood-prone, the Caribbean region is located astride major tectonic plates and is vulnerable to seismic activities as well. Graph 1.7 shows the Atlantic storm tracks from 1866 to 1995, while Graph 1.8 shows the tectonic plates of the Americas. Based on historical data, the Caribbean region can expect 2.5 storms every year. Fortunately, severe hurricanes, defined as category 4 and 5, are less common. Category 4 hurricanes can be expected to occur every fourth year and category 5 hurricanes every fifth year. The northern and eastern islands are more exposed to hurricanes than the southern islands. The Dominican Republic, Haiti, Jamaica, Antigua, Montserrat, St. Kitts and Nevis are situated within the hurricane belt while Trinidad and Tobago lie to the southernmost end of the Caribbean chain and are considered only minimally vulnerable to hurricanes. In terms of seismic risk, the Caribbean region is located on five tectonic plates and counts 250 of the world's 500 most active volcanoes. However, there are significant differences in exposure. Jamaica and the Dominican Republic are located on the cusp of or close to five tectonic plates. Trinidad and Tobago also have a high earthquake risk. A few islands have volcano risk, most notably Montserrat, where recent eruptions have caused almost cataclysmic damage.

**Graph 1.7: Atlantic Storm Tracks, 1866-1995**

<!-- Start of picture text --> M_ _. __W-S____ _ _ 2<br>74;, tA 2QU MIR R<br>I- to, 74, MP-K<br><!-- End of picture text --> 24
<!-- Start of picture text --> Gra h 1.8: Tectonic Plates of the Americas<br>Plate Aactc No<br>Malor ecoi plates of -he r-Plate<br>were a e by 97A2tarctica P str en,3 wh w s aa<br><!-- End of picture text -->

> theajos teenic plates **o.** the wuld.

> The occurrence of natural hazards has increased in the last century and accelerated more recently. Throughout the twentieth century, the Latin America and Caribbean region was hit by 1,309 catastrophes (Graph 1.9). During the period 1970-99, Latin America and the Caribbean region were affected by 972 catastrophic events, 43 of which were classified as major disasters (Table 1.6).6 Graphs 1.9 and 1.10O clearly reflect an increasing trend in the frequency of disasters over the past three decades.

**Graph 1.9: Occurrence of Natural Disaster Events in Latin America** and the Caribbean by Decade (1900-99)

<!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>1900 1910 1920 1930 1940 1950 1960 1970 1980 1990<br><!-- End of picture text --> Source: EM-DAT: The OFDA/CRED International Disaster Databasewww cred be/emdat -Universite Cathohque de Louvain - Brussels - Belgium

> **6** The impact of these events on macroeconomic variables is analyzed in the next section.

25
Graph 1.10: Annual Occurrence of Natural Disaster Events<sup>in</sup> Latin<sup>America</sup> and the Caribbean (1970-99) <!-- Start of picture text --> 80<br>70<br>60 A<br>50 -A<br>40- A z/<br>30<br>20<br>10<br>0<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br><!-- End of picture text --> 26
**Table 1.6: Catastrophic Events and Losses: Latin America and the Caribbean, 1970-99.**

|**Year**|<br>**Country**|<br>|<br>**DisasterType**<br>||I<br> <br>i<br> **Deaths**|<br> <br>**Casualties**<br>|I**Damages**<br>**1(1998,US$**<br>|
|---|---|---|---|---|---|
|||!|,||**million)**|
|1970|_______________________<br>Brazil|____________________________________<br>**I**Drought|___________ <br>N/A,|<br>10,000,000|0.4|
|1970|Peru|!Earthquake<br>i|<br>66,794|3,216,240<br>|2,225.0<br>|
|1972|Nicaragua|Earthquake|10,000|720,000|3<br>3,293.7|
|1973|Honduras|ILaLndslide|23,800i|1|N/A|
|i97i4|Honduras|HurricaneFifi<br>|8,000<br>70|730,000<br>i<br>|1,784.6<br>|
|.<br>1975|...<br>Brazil|I<br>i<br>wave||.<br>60,|1,817.0.<br>1,817.0|
|1976|Guatemala|Earthquake|23,000|4,993 000|2,8640|
|1978|IBrazil|!Drought|N/A<br>|N/A<br>|5,746.5|
|1979|iDominicanRepublic|HurcanesDavidandFrederick|124001|<br>1,554,000|336.8|
|1983|1Argentina|Flood|0|5,830,000'||
|193j|<br> Argentin-a|jFlood|0,|250,000|1,309.3|
|<br>1983|<br>Brazil|<br>Drouht|<br>20i|<br> <br>20,000,000|N/A|
|1983|Peru|!|34A?°|t°<br>|N/o<br>s<br>I68|
|1984<br>|Brazil<br>i|Flood<br>:<br>||700,000<br>i|1,68.3<br>|
|i984<br>-§8|Brazil<br>I<br>gezila|Flood<br>Fl<br>d|,<br>**10**|<br> <br>120400|1,568<br>15689|
|<br>1985|<br> <br>Argentina|<br> <br>lood|<br>12|<br>206,000|,.<br>1,969.4|
|1985|Chile|Earthquake|180i|<br>1,482.275|2,272.4|
|1985|Colombia|Volcano NevadodelRuiz|21,|12700|1,515.0|
|1985<br>|Mexico<br>|Earthquake<br>|8!776_|130,204<br>|6,059.8<br>|
|1986|ElSalvador|IEarthquake|,|770:000|2,231.0|
|1987|IColombia||Earthquake|It000|N/A||
|i98i|Ecuador,|Earthquake|4,000|227,000|1<br>1,003.6|
|1987|iEcuador|Tsunami|I1000|6,600|/A|
|i-988|!Brazil|Flood|2891|<br>3,020,734|1,378.4|
|1988|IJamaica||HurricaneGilbert|49|810,000|1,378.4|
|1988|Mexico|HurricaneGilbert|240|100,000|1<br>1,860.9|
|1988|St.Lucia|iHurricaneGilbert|45|/iA||<br>?i'8A|
|1989|Caribbean||HurricaneHugo|42|N/A7|90<br>4706N2|
|1991|ElSaivador<br>.|IEarthquake<br>I'||0<br>0<br>!<br>_N/A_<br>|I<br>N/A<br>|
|1993|jlMexico<br>.|!<br>TropicalStomsArlene&Beatrz|7|,<br>io0O|1000884.5|
|1994<br>|Haiti<br>|TropicalStormGordon|1,122|1,587,000|N<br>/A|
|1995|!USVirgin Islands|HurricaneManlyn|8|10,000|1,604.6|
|1996|<br>Mexico|<br>IDrought|0|N/A|1,247.1|
|1998|Argentina|<br>IElNilo,Flood<br>|I<br>19|360,000|2,500.0|
|1998|Brazil|IDrought<br>i|<br>|10,000,0006<br>|- 97.8<br>|
|1998|DominicanRepublic<br>~~~~~~~|iHurricaneGeorges<br>if <br>. <br> <br>.<br>I|288<br>tt|4515238<br>|2,193.4|
|1998|_<br>IEcuador|_<br>__<br>__<br><br>ElNiioFlood|,<br>322||2869.3|
|1998|iHfoniduras|HurricaneMitch|5,657|2,112,000|2,000.0|
|1998|Mexico|Flood||<br>506,744<br>|N/A<br>|
|1998I|Nicaragua|iHurricaneMitch<br>|2,447<br>|868,228|1<br>1,000.0|
|1998|<br>Peru|Flood<br> <br>|<br>1<br>3401<br>|<br>580,750j<br>|<br>1,200.0<br>|
|19999|'Colombia|Earthquake<br>|1,186|1,205,933|1<br>2,837.9|
|1999|Venezuela|<br>1Flood/DebrisFlows<br>||<br> <br>0,000|483,635|f1,957.2|

N/A= Not available.

Note' Casualties include those requinng emergency food, water, shelter, sanitation and medical assistance. Individuals affected by disaster-related health epidemics are also included under the same rubric. Source. Charv6nat, 2000, p.30 27
# **_B.2.2._** _Effects of Natural Disasters on Macroeconomic Variables_

This section analyzes the impact of catastrophic events on macroeconomic variables for a sample of 16 countries (6 from the Caribbean region and 10 from Latin America).<sup>7</sup> The impact of catastrophic events on macroeconomic variables (production, public and private consumption, investment and extemal balance<sup>8</sup> ) is estimated using dynamic panel data (DPD) models based on generalized methods of moments (GMM).<sup>9</sup> The catastrophic ev'ents considered are those described in Table 1.6. Catastrophic events are proxied by a variable (Cat) which takes the value of the costs of the damage (as percentage of GDP) in the year when the catastrophic events occur and zero otherwise.'° Additionally, changes in (log) income Ay, (log) consumption Ac and (log) investment Ainv are modeled as autoregressive processes. After experimentation, two lags of the dependent variables appear to be sufficient to capture their auto-regressive components.

Table 1.7 reports the equations for production, total consumption and investment. In each equation, the catastrophe variable (Cat) has the sign expected from Table 1.4 and is statistically significant at conventional level of confidence. A catastrophic event leads to a fall in output, consumption and investment growth. However, the coefficient of the catastrophe variable shows that most of the impact is on investment growth (-0.49) while the impact on total consumption growth (-0.09) is more moderate. Simulations based on these regressions indicate a worsening of the current account in the aftermath of a catastrophic shock.

> 7 Countnes included in the sample are Barbados, Bolivia, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Peru and Trinidad and Tobago. These countries were selected on the basis of the availability of information regarding the catastrophic events and the macroeconomic variables. Information on catastrophic events and damages are from Charvernat, 2000, p. 30 as reported in Table 1.6. Macroeconomic indicators (GDP, investment, and pnvate and public consumption) for the pernod 1960-98 are from an extended version of the Penn World Tables (Summer and Heston, 1992). Extemal balance is defined as the difference between production and the sum of consumption and investment. 9 The GMM estimator is based on first differencing, and controls for the endogeneity of the lagged dependent variable and the potential endogeneity of the other explanatory variables (see Arellano, 1993; Arellano and Bond, 1998).

> '° Costs of damage are those reported in the last column of Table 1.6. In the few cases when damages are not reported we used the average of the other catastrophic events that occurred in the country or the average costs of the catastrophic events of all countnes when information on other castastrophic damages was not available for this country. The results are robust to changes of specification. For example, we conducted the same regressions with a dummy variable which takes the value I in the year when a catastrophic event occurs (and 0 otherwise) and found very similar results.

28
|Table1.7:ImpactofCatastrophic<br>Dynamic PanelData(GMMEstimat<br>CountrySpecificEffects<br>Sample:1963- 1997<br>Total panel observations:540|EventsonEconom<br>or)|icVariables||
|---|---|---|---|
|Variable|GDP<br>**_AY,_**|TotalConsumption<br>Ac1|Investment<br>_Ainvy_|
|**14,-1**|**0.86...**<br>(3.I26|0.91"'<br>(3.13)|**1.32**<br>(2**35)**|
|_Wa,-2_|**_0.15_**<br>(2.42)|0.0'<br>(2.61)|-0.493<br>**(2.59)**|
|<br>_Wald 1_<br>test<br>_A in_<br>_v_<br>_<br>_<br>__<br>_<br>_<br>__<br>_<br>__<br>_<br>_<br>__|<br>[0.001;<br>__<br>_<br>_<br>__<br>_<br>__<br>_<br>**_**|<br>16.96;[0.420<br>**(2.14)**||
|_AinV1, 2_||-0.|1<br>4.8|
|Ac11I<br>|__<br>_ _ _ _ _ _ _ _ _<br> <br>|_(<br>. **3)_**<br>|_ _ _ _ _ _ _ _ _ _ __|
|Sargan test<br>_Cat,_|[0.9681<br>~**~~~~~~**<br>**(2.42)**|1.37<br>**~-0.49"*O09 **<br>**(3.67)**|[~~~~~~~~~~~~~~~~0.145<br>**-0.43"**<br>**(198)**|
||**DiagnosticSta**|**tistics**<br>correlaton3|.291.932.7|
|**Wald test**|**16.65..**<br>[0.0011<br>|1**6.96~**<br>[0.0001<br>|**3923..**<br>**[0.0001**<br>|
|Sargan test<br>**Sargan test**<br>~<br>~<br>~|~~~~~~~<br>~<br>[0.68!|~~~1.23**1.37**<br>**[0.1251**|**34.98**<br>[0V5|
|**la-orderserialcorreation**<br>__________**______**_________[0.0401|3.29|**21.91?**<br>-<br>**[0.000]**|.7<br>**[0.0101**|
|<br>2' <sup>1</sup>-orderserialcorTelation<br>|**029**<br>|<br>**071**|108|
|<br>|**.**<br>[0.7671|**.**<br>**[0.4771**|.<br>**[0.2811**|
|__________________________<br>Notes,<br>a.Figures**in**parentheses( )areabsolutet-ratios;<br>percent,**5**percentandIpercentlevelrespectively<br>b.**The**Waldtestisfor**thejomt**significanceofthe<br>c.TheSargantest**is**ofover-identifymg restriction<br>numberofover-identifyinginstruments.<br>d.ThetestsforIn**and2'"**orderofnoseral**corre**<br>Thep-valuesreport the**probabihty**ofrejecting<br>correlationif**the**time-varyingcomponentofthe|______<br>figuresinbrackets[<br>are<br>.<br>regressors.Here,**the**explan<br>s,that**is,**forthe**validity**of<br>**lation**areasymptoticallydis<br> thenull hypothesisof**ser**<br> ermor**termm **levelsisaser|<br>p-values.,*,**4**indicatethataco<br>atoryvanablesare**jointly**sigmficanta<br> thesetofmstrumentsandisdefined**as**<br>tributedasstandardnormal variables<br>**al**comlation,where thefirstdifferen<br>iallyuncorrelateddisturbance.1and|efficientissignificant**at**the10<br>tIpercentofsignificance.<br> Prob(**J**>**_Z,_**),wherep**is**the<br>**(see**ArellanoandBond,1991)<br>cmgwillinduce(MAI)**seral**<br>**2"d**orderofnoserial**correlation**|
|tests are relatedtothelagsoftheistruments(i.<br>laggeddependentvarable(except**the**catastiphe|e.**I,**and**C,**),wherethe<br> durnmy,whichis**an**exoge|instrumentsarethelaggedvaluesofth<br>nous shockto**the**system)|eexplanatoryvarables**and**the|

e The **GMM** estimations were performed by **using the** DPD model developed by Arellano and Bond (1991) Ox version 3.00 (Windows) for PcGive (C) by **.A.** Doornik (1994-2001).

**Soure** Estimated based on **an** extended version of **the** Penn World Tables 5.6 (Summer **and** Heston, 1992), **and** data on catastrophic events from Table 1.6.

Table 1.8 reports the equations for private and public consumption. In each equation again, the catastrophe variable (Cat) has the sign expected from Table 1.4 and is statistically significant at conventional level of confidence. Catastrophic events have a distinctive influence on private and public consumption growth. A catastrophic event leads to a fall in private consumption growth and a more moderate decline in public consumption growth. The absence of a larger decline in public consumption may result from public policies that aim at mitigating the impact of the event on overall consumption.

29
|Table1.8:ImpactofCatastrophicEventsonPublican<br>DynamicPanelData(GMMEstimator)<br>CountrySpecificEffects<br>Sample:1963- 1997<br>Totalpanelobservations:545|dPrivateConsumption|
|---|---|
|Vanable<br>Pnvate<br>Consumption<br>_Acp1_|Public<br>Consumption<br>_Acg,_|
|073...<br>AYI-I<br>_<br>__<br>_<br>__<br>__<br>__<br>_(3<br>26~|_0.85)_<br>.1|
|0.226<br>AYt-2<br>(2.43)<br>|________<br>_____|
|-0.08<br>Sargantest<br>[°997l8(0.44)||
|_Acg,,r_senalcorrelation<br>16.75-0<br> <br>022|16...<br> <br>|
|_Acg,-,_<br>~ ~~~~~-...<br>Cat,____________(4.93)|<br>-%005)_<br>(2.68)|
|_**_**______**_**<br>_____ _<br>________Diagnostic<br>Statistics<br>||
|Wald test<br>~ ~~~48.26L<br>[0.0001<br>|15.78"<br>(0.0561|
|Sargan test<br>~~~~14.14|7.97|
|I1'-orderserialcorrelation<br>_[0056]_<br>_O052_|[0.005<br>[0901|
|_._<br>2'd-order senalcorrelation<br>0.52<br>[0.601]<br>Notes.|.<br>0.91<br>[0364]|

a Figures in parentheses ( ) are absolute t-ratios; figures in brackets [ ] are p-values *, "' indicate t, that a coefficient Is significant at the 10 percent, 5 percent and I percent level respectively b The Wald test is for the joint significance of the regressors.

- c. The Sargan test is of over-identifying restrictions, that is, for the validity of the set of instruments and is defined as Prob( J > XX _),_ where p Is the number of over-identifying instruments

- d The tests for I" and 2'd order of no serial correlation are asymptotically distnbuted as standard normal vanables (see Arellano and Bond, 1991). The p-values report the probability of rejecting the null hypothesis of senal correlation, where the first differencing will induce (MAI) serial correlation if the time-varying component of the error term in levels is a senally uncorrelated disturbance. I' and **2,** order of no serial correlation tests are related to the lags of the instruments (i.e _L,_ and t-, ), where the instruments are the lagged values of the explanatory varables and the lagged dependent vanable (except the catastrophe dummy, which is an exogenous shock to the system).

- e. The GMM estimations were performed by using the DPD model developed by Arellano and Bond (1991) Ox version 3 00 (Windows) for PcGive (C) by J.A Doomik (1 994-200 1).

Source Estimated based on an extended version of the Penn World Tables 5 6 (Summer and Heston, 1992), and data on catastrophic events from Table 1 6.

30