Consequences of natural disasters on the country level using aggregate performance measures from time-series analysis:

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Abstract

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Policy Research Working Paper 4968

There is an ongoing debate on whether disasters cause

significant macroeconomic impacts and are truly a

potential impediment to economic development. This

paper aims to assess whether and by what mechanisms

disasters have the potential to cause significant GDP

impacts. The analysis first studies the counterfactual

versus the observed gross domestic product. Second, the

analysis assesses disaster impacts as a function of hazard,

exposure of assets, and, importantly, vulnerability. In a

medium-term analysis (up to 5 years after the disaster

This paper—a product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network

Vice Presidency—is part of a larger effort in the Network to disseminate the emerging findings of the forthcoming joint

World Bank-United Nations’ Assessment of the Economics of Disaster Risk Reduction.. Policy Research Working Papers

are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hochrain@iiasa.ac.at. We are

grateful to Apurva Sanghi, Reinhard Mechler and participants of the seminar at the World Bank held on this topic for

their suggestions and constructive comments.

event), comparing counterfactual with observed gross

domestic product, the authors find that natural disasters

on average can lead to negative consequences. Although

the negative effects may be small, they can become

more pronounced depending mainly on the size of the

shock. Furthermore, the authors test a large number of

vulnerability predictors and find that greater aid and

inflows of remittances reduce adverse macroeconomic

consequences, and that direct losses appear most critical.

NTRODUCTION

A small, but growing literature has emerged over the last few years on the

macroeconomic and development impacts of natural disasters. Interestingly, there is as

yet no agreement on whether disasters are important from a macroeconomic perspective,

and two positions can be identified. The first considers natural disasters a setback for

economic growth and is well represented by the following citation:

It has been argued that although individuals are risk-averse [to natural disasters risk],

governments should take a risk-neutral stance. The reality of developing countries suggests

otherwise. Government decisions should be based on the opportunity costs to society of the

resources invested in the project and on the loss of economic assets, functions and products. In

view of the responsibility vested in the public sector for the administration of scarce resources,

and considering issues such as fiscal debt, trade balances, income distribution, and a wide

range of other economic and social, and political concerns, governments should not act risk-

neutral (OAS, 1991).

The other position sees disasters as entailing little growth implications and consider

disasters and their reduction a problem of, but not for development (e.g. Albala-Bertrand,

1993, 2006; Caselli and Malhotra, 2004). These authors find natural disasters do not

negatively affect GDP and “if anything, GDP growth is improved” (Albala-Bertrand,

1993: 207). This paper can be understood as an attempt at reconciling this body of

literature. There are two entry points for the analysis. The first is to look at counterfactual

vs. observed GDP, the second entry point is to assess disaster impacts as a function of

hazard, exposure of assets (human, produced, intangible), and, importantly vulnerability.

Overall, the evidence reveals adverse macroeconomic consequences of disasters on

GDP. In a medium-term analysis, natural disasters on average seem to lead to negative

effects on GDP. The negative effects may be small, yet they can become more

pronounced depending on the size of the shock. We tested a large number of vulnerability

predictors and found that higher aid rates as well as higher remittances lessen the adverse

macroeconomic consequences, while capital stock loss is the most important predictor for

the negative consequences.

The paper is organized as follows. Section 2 reviews the literature on the macroeconomic

impacts of disasters and locates the proposed analysis within the disaster risk

management paradigm. In section 3, we present the data and methodology used for

projecting the economic impacts for a medium term horizon (up to 5 years after an

event), as well as the regression analysis used for identifying predictor variables

explaining potential impacts. Section 4 ends with a discussion of possible implications of

our analysis.

ITERATURE REVIEW

The literature on the macroeconomic effects of disasters can be divided into studies

looking into the short-to-medium term (1-5 years in economic analysis) and the longer

term (beyond 5 years), with almost all studies taking a shorter-term perspective. A key

response variable analyzed in this line of work is GDP. In principle, after a disaster event

the following trajectories may be distinguished (see figure 1) leading to no, positive or

negative follow-on effects.

GDP

Time

Disaster Event

Projected line without

disaster event

Negative long term

effect

Positive long term

effect

No long term effect

Fig. 1:

Possible trajectories of GDP after a disaster.

Source: Hochrainer, 2006

Two positions can be distinguished as shown in table 1. Position 1 broadly suggests the

post-disaster trajectory will fall short of the planned trajectory, while position 2 contends

that there is no negative effect beyond the first year and the planned GPD path can be

achieved or even surpassed.

Table 1:

Synopsis of macroeconomic perspectives on natural disasters

Position 1

“Natural disasters are setbacks for

economic growth”

Position 2

“Disasters have no effects on economic

growth”

Methodologies involving

•

Supply side focus

•

Model projections

•

Neoclassical intuition

•

Empirical evidence

Studies by Benson (various); ECLAC

(various); Otero and Marti, 1995; Crowards,

2000; Charveriat, 2000; Murlidharan and

Shah, 2001; Freeman et al., 2002; Mechler,

2004; Cuaresma, Hlouskova, and Obersteiner,

2004;

Hochrainer,

2006;

Noy,

2009;

Okuyama, 2009

Methodologies involving

•

Supply side and demand side

•

Empirical evidence

Studies by Albala-Bertrand, 1993, 2006;

Skidmore and Toya, 2002; Caselli and

Malhotra, 2004.

Source: Adapted from Zenklusen, 2007

The body of research subscribing to position 1 generally finds significant short-to-

medium-term macroeconomic effects (Otero and Marti, 1995; Benson, 1997a,b,c;

Benson, 1998; Benson and Clay, 1998, 2000, 2001; ECLAC 1982, 1985, 1988, 1999,

2002; Murlidharan and Shah, 2001; Crowards, 2000; Charveriat, 2000; Mechler, 2004;

Hochrainer, 2006; Noy, 2009) and considers natural disasters a barrier for development

in disaster-vulnerable developing countries.

ECLAC (various studies) has been conducting numerous case studies on disaster

impacts in Latin American countries since 1972. Otero and Marti (1995) summarized the

results and generally found serious shorter-term impacts as national income decreases, an

increase in the fiscal deficit as tax revenue falls, and an increase in the trade deficit as

exports fall and imports increase. Substantial longer term impacts on development

prospects, perpetual external and fiscal imbalances due to increased debt service

payments post-disaster and spending requirements, and negative effects on income

distribution were also found (ECLAC and IDB, 2000; Otero and Marti, 1995). They

generally hold that the significance of the impact depends on the size of the disasters, the

size of the economy and the prevailing economic conditions (Otero and Marti, 1995).

Benson (1997a,b,c) and Benson and Clay (1998, 2000, 2001) produced a number of case

studies on Fiji, Vietnam, the Philippines, and Dominica. The timeframe of this analysis

was mainly short-term, i.e. the period up to one year after a disaster. They detected severe

negative economic impacts, with agriculture being hit most strongly, an exacerbation of

inequalities, and reinforcement of poverty, however also finding it difficult to isolate

disaster impacts on economic variables from other impacts. Murlidharan and Shah (2001)

by means of a regression analysis analyzed a large data set of 52 catastrophes in 32

developed and developing countries with a the short-term focus (year before event

compared to year of event). They found catastrophes for all country income groups to

affect short-term growth very significantly. In the medium-term (average of two

preceding years compared to average of event and two following years), the effect on

growth was still significant. Over time, they detected impact on economic growth to

subside. They also discovered associations between disasters and the growth of external

debt, the budget deficit and inflation. Crowards (1999 discussed in Charveriat, 2000)

examined the impacts of 22 hurricane events in borrowing member countries of the

Caribbean Development Bank and found that GDP growth slowed by 3% points on

average post-event, but rebounded due to the increase in investment the following year.

He also detected large variations around averages.

This study could not be obtained and we rely on Charveriat (2000) as a secondary source.

Charveriat (2000) for most cases in

her disaster sample identified a typical pattern of GDP with a decrease in the year of an

event and a recuperation of the growth rate in the following two years due to high

investment into fixed capital. She detected the scale of short-term impacts to depend on

the loss-to-GDP-ratio and whether the event was localized or country-wide. For high-

loss-to-GDP ratios and country-wide events she found larger impacts. She found the

following crucial variables affecting the scale of aggregate effects: structure of the

economy and general conditions prevailing, the size of economy, the degree of

diversification and the speed of assistance of the international community. Another study,

Rasmussen (2004), is in accordance with above studies and for a cross-country sample

identified a median reduction of the growth rate by 2.2% points in the year of the event.

Raddatz (2007) generally assessed the role of external shocks (such as commodity price

fluctuations, natural catastrophe, and adverse influences from an international economic

environment) on output volatility of low-income countries. While he found external

shocks to explain a fraction of output variance, their contribution to output fluctuations

was dwarfed by more important contributors from internal sources such as level of

inflation, a possible overvaluation of the real exchange rate and large public deficits. Noy

(2009) took a look at the reduction of GDP growth rates for a large sample of disaster

events, for which while using a linear regression modeling approach he concluded that

the ability to mobilize resources for reconstruction as well as the financial condition of

the country are important predictors of GDP growth effects. As one of the few longer

term studies, Cuaresma et al. (2004) concluded that the degree of catastrophic risk has a

negative effect on knowledge spillovers between industrialized and developing countries.

Further, they suggested that only countries with relatively high levels of development

may benefit from capital upgrading through trade after a natural catastrophe.

There are only a few studies adopting position 2 and the key papers here are Albala-

Bertrand (1993) and to a lesser extent Caselli and Malhotra (2004). In (partial) contrast

to the above studies, Albala-Bertrand (1993) came to different conclusions and finds

himself partially in opposition to accepted views when analyzing impacts mainly on

developing countries. He first statistically analyzed part of the ECLAC data set discussed

above and found that natural disasters do not negatively affect GDP, public deficit and

inflation in the short to medium term. His findings on the trade deficit are in accordance

with ECLAC and other research. These findings he explains with a sharp increase in

capital inflows and transfers (private and public donations). He holds that natural

disasters do not lower GDP growth rates and "if anything, they might improve them"

(1993: 207). Albala-Bertrand also examined longer-term effects for a number of

developed and developing countries and found no significant long-term effects in

developed countries; he came to the conclusion that in developing countries aggregate

effects fade away after two years, but that some negative effects on income distribution

and equality persist. Overall, Albala-Bertrand considered disasters "a problem of

development, but essentially not a problem for development." (Albala-Bertrand 1993).

According to his analysis, while the number of deaths and people affected and the extent

of monetary losses are determined by the current state of a country's development,

disasters do not normally hinder long-term development, with the sole exception being

widespread droughts.

Albala-Bertrand (1993) started fruitful discussions about some assumptions and estimating issues in the

literature, and his findings were discussed and replicated by various other authors including Mechler
(2004) and Hochrainer (2006). For example, Hochrainer (2006) extended Albala-Bertrand’s sample to
85 disaster events in 45 countries and found GDP growth (on average) negatively affected in the disaster
year and no significant increases in growth for the subsequent post-disaster years, which implies that,
due to a lack of recovery, a net loss of GDP.

Further, Caselli and Malhotra (2004) based their analysis on

neoclassical growth theory and analyzed the losses in relation to country growth rates

after disaster events using a dataset of 172 countries for events between 1975 and 1996.

They concluded that their hypothesis that losses of labor and capital stock have no effect

on short-term economic growth could not be rejected. Finally, Skidmore and Toya (2002)

discovered a robust positive correlation between the frequency of natural disasters and

long-run economic growth after conditioning for other determinants, which they explain

by some type of Schumpeterian creative destruction.

Overall, while the balance of evidence and studies seems to imply that there are

adverse economic disaster effects in terms of the “negative” trajectory stylized above,

there are important “outliers” that merit more investigation. Another observation is that

the studies generally have a short-term focus, and in their analyses often do not go

beyond the year following an event. Finally, analyses generally compare key indicators of

interest after the fact to their pre-disaster states, rather than comparing the counterfactual,

i.e. the system without a shock, to the observed. The latter point seems important, as

important opportunity costs, e.g. in terms of economic growth foregone, are consequently

often not accounted for in analyses on the macro effects of disasters.

2.1

Economic effects and vulnerability

In order to set the stage for the analyses, we hold it important to locate the discussion

within the disaster risk management framework. The standard approach here is to

understand natural disaster risk as a function of hazard, exposure and (physical)

vulnerability (see figure 2). Hazard analysis entails determining the type of hazards

affecting a certain area with specific intensity and recurrence. Assessing exposure

involves analyzing the relevant elements (population, assets) exposed to relevant hazards

in a given area. Vulnerability is a multidimensional concept encompassing a large

number of factors that can be grouped into physical, economic, social and environmental

factors as outlined on the figure. We refer mostly to physical vulnerability as the

susceptibility to incurring harm of people and engineered structures leading to direct risk

in terms of people affected and, important from the perspective taken in this paper,

capital stock destroyed. As a consequence of such direct impacts, follow-on effects may

materialize leading to indirect potential and actual impacts. Economic vulnerability may

refer to the economic or financial capacity to absorb disaster events, e.g. the ability to

refinance asset losses and to recover quickly to a previously planned economic growth

path. It may relate to private households and businesses as well as governments, the latter

often bearing a large share of a country’s risk and losses. Based on assessments of

disaster risks and its determinants, risk management measures may be systematically

planned for risk reduction and risk transfer.

Fig. 2:

Conceptual framework used in this study for explaining economic risk due to natural

disasters

The literature on the economic impacts discussed above can be related to this framework,

and table 2 lists the key studies and general factors contributing to a discussion of

(macro) economic risk. Determinants of impacts and risk can be distinguished according

to (i) the type of natural hazard (hazard variable), (ii) geographical area and spatial scale

of impact (exposure), (iii) the overall structure of the economy, (iv) the stage of

development of the country, (v) prevailing socio-economic conditions, and (vi) the

availability of formal and informal mechanisms to share risks (the latter four variables

related to economic vulnerability).

It should be mentioned that in the studies discussed and our analysis, observed losses are used

for examining future economic consequences. However, when it comes to risk management,
losses should be based on probabilities and the discussion framed in terms of risk in order the
incorporate the full possible range of potential losses (and its probabilities) in the analysis.

Hazard

Exposure

Physical

Vulnerability

Direct losses (risk)

Produced capital

Human capital
Environmental capital

Socio-economic vulnerability

Risk Management

Economic

Consequences

GDP

Table 2:

Studies assessing macroeconomic consequences and economic vulnerability

to natural hazards.

Study

Vulnerability variables for predicting
economic impacts and risk

Response variables

Charveriat, 2000

•

Size of the economy, degree of
diversification and size of the
informal and agricultural sectors.

•

GDP

ECLAC and IDB,
2000;

Freeman et al.
2002;
Mechler,2004;
Hochrainer, 2006

•

Ability to refinance losses and
provide relief to the affected
population (financial vulnerability)

•

Availability of implicit (aid) and
explicit (insurance) risk sharing
arrangements

•

GDP, fiscal variables

Burton et al.,1993;
Kahn, 2005.

•

Income

•

Deaths due to natural
disasters

Benson and Clay,
2004

•

Structure of the economy

•

Size

•

Income level and stage of
development

•

Prevailing socioeconomic conditions

•

Total GDP annual change

•

Agricultural GDP annual
change

•

Non-Agric. GDP annual
change

Toya and
Skidmore, 2007

•

Educational attainment in population
aged 15 and over

•

Economic openness
(exports+imports)/GDP

•

Financial sector level of development
(M3/GDP)

•

Government consumption

•

Additional variables that determine
the deaths caused by disasters
(population, land area, disaster type).

•

Disaster-related deaths

•

Damages/GDP

Noy, 2009

•

Literacy rate

•

Quality of institutions

•

Per capita income

•

Openness to trade

•

Levels of government spending

•

Foreign exchange reserves

•

Levels of domestic credit

•

Openness of capital accounts

•

GDP

Raschky, 2008

•

Availability of financial risk sharing
institutions

•

GDP

Source: extended from Barrito, 2008.

All of the indicators used for explaining the response variables mentioned above are valid

candidates as proxies for hazard, exposure and vulnerability and most of them will be

used in the analysis in the next section.

SSESSING ECONOMIC DISASTER CONSEQUENCES AND RISK

In order to identify the macroeconomic effects of disasters, we suggest comparing a

counterfactual situation ex-post to the observed state of the system ex-post. This involves

assessing the potential trajectory (projected unaffected economy without disaster) versus

the observed state of the economy. This contrasts with observing economic performance

post-event and actual performance pre-event, as usually done in similar analysis. Our

analysis requires projecting economic development into a future without an event. The

approach is illustrated via the case of Honduras, which was heavily hit by Hurricane

Mitch at the end of 1998. In figure 3 absolute GDP with the event and projected GDP

without an event were estimated. The chart exhibits GDP growth to become negative in

the year after, then rebound later; yet, overall the net effect would seem to be a loss.

GDP in Honduras

5,000

5,500

6,000

6,500

7,000

7,500

8,000

1996

1997

1998

1999

2000

2001

2002

2003

2004

i
l
l
i
on co

nst

ant

00 U

Projected w/o event-ECLAC
Projected w/o event-IIASA
Observed

Fig. 3:

Observed GDP in Honduras with events vs. projected growth without events. Source:

Zapata, 2008; World Bank, 2007; own calculations

Note: Zapata (2008) uses a model based projection, IIASA projects growth statistically based on

pre-disaster observed GDP.

Using this approach for Honduras, a “GDP gap” as a follow-on consequence after the

hurricane can be identified. For example, in 2004, about 6 years after the event, this gap

can be considered to have, ceteris paribus, amounted to about 6% of potential GDP given

extrapolation of pre-disaster GDP with a 4-year average growth rate, and to 8.6% percent

based on the ECLAC projection.

In the following, similarly we compare GDP effects in terms of counterfactual vs.

observed trajectories by projecting absolute GDP into the future under the assumption of

a no disaster event scenario and comparing it with observed GDP values. A 5 year time

horizon is chosen as it is the minimum data requirement for estimating time series

projections into the future and reflects the trade-off between data requirements and

number of samples (the larger the sample the lower the time horizon). There are two

avenues for deriving the counterfactual: (i) running a (statistical or behavioral) economic

model without a disaster event, for which a large number of models calibrated to the

respective countries would be necessary; (2) using time series models. We adopt the

second option to eliminate as much possible business cycles in the dataset. We use

econometric models which seem to be able to handle empirically observed patterns,

which is important as a large number of the countries examined are of developing nature

and exhibit strong growth volatility.

3.1

Estimation methodology

We use autoregressive integrated moving average models, also called ARIMA(p,d,q)

(Box and Jenkins, 1976) for forecasting GDP into the future after the disaster event.

ARIMA modeling approaches are chosen because they are sufficiently general to handle

virtually all empirically observed patterns and often used for GDP forecasting (see for

example Abeysinghe and Rajaguru, 2004). While such a type of modeling may be

criticized for its black box approach (Makridakis and Wheelwright, 1989), it here serves

well due to the large number of projections to be made and the difficulty identifying

suitable economic model approaches, such as input-output models for all the different

countries within the sample and over a time period starting from 1965.

The ARIMA process

Recall, an autoregressive process of order AR(p) can be defined as

−



A moving-average process of order MA(q) may be written as

and an ARMA(p,q) process, with p autoregressive and q moving average terms can be

defined to be

−



where

φ and θ are parameters to be estimated and ε are white noise stochastic error

terms. Now, let t

y be a non-stationary series and define the first order regular difference

of t

y as

−

∆

or more generally using a back-shift operator denoted as

−

)

(

−

∆

An ARIMA(p,d,q) model can then be expressed as

)

(

)

)(

(

−

with

−



)

(

and

−



)

(

−



The Box-Jenkins methodology (Box and Jenkins, 1976) is applied for determining the

components of the ARIMA process; i.e. we test different ARIMA(p,d,q) models with p

and q to be smaller or equal 4 (due to the limited amount of data) and estimate

φ and θ

using Maximum likelihood techniques and the Akaike Information Criterion (AIC) as

well as diagnostic checks to detect a suitable model. The data requirements were set thus

that at least 5 observed data points are needed for projections into the future. This is the

smallest number of observations which are needed to estimate ARIMA(4,1,4) models

(however, the majority of the sample (greater 90 percent) has at least 10 data points).

Furthermore, all models are tested to be stationary (usually d=1 suffices to assure a

stationary process) and all series are demeaned. To include uncertainty in the projections,

also 95 percent confidence forecasts were calculated and analyzed.

Forecasts into the future are performed with the selected models and then compared

to the observed variables. Increases or decreases of GDP in future years are measured as

a percentage increase or decrease to baseline GDP (i.e., baseline =100) which is defined

to be GDP a year before the disaster event.

3.2

Data used

Furthermore, the differences between

observed values and projected ones are calculated and called Diff(t), which indicates the

percentage difference between the observed and projected value of GDP in year t. We

focus on projections with a medium term perspective (up to 5 years into the future). This

limitation is due to important data constraints for the ARIMA models within the sample

and increasingly large uncertainties beyond the medium-term time horizon.

Our sample consists of 225 large natural disaster events during 1960-2005. The sample is

based on information from two databases and was compiled by Okuyama (2009) with the

threshold for a large event defined arbitrarily to a loss exceeding 1 percent of GDP.

To decrease variance a logarithmic transformation of GDP was performed at the beginning.

In order to define the “event set” the threshold of stock losses is set as a share (1%) of flow effects (GDP).

While it would have been more systematic to define an asset threshold, yet we responded to the larger
intuitive appeal of using GDP as a denominator, and the fact that this threshold was also used by another
paper in the EDRR working paper series which we wanted to be in line with.

One

database is the open-source EMDAT disaster database (CRED, 2008) maintained by the

Centre for Research on the Epidemiology of Disasters at the Université Catholique de

Louvain. EMDAT currently lists information on people killed, made homeless, affected

and financial losses for more than 16,000 sudden-onset (such as floods, storms,

earthquakes) and slow-onset (drought) events from 1900 to present. Primary data are

compiled for various purposes, such as informing relief and reconstruction requirements

internationally or nationally, and data are generally collected from various sources and,

including UN agencies, non-governmental organizations, insurance companies, research

institutes and press agencies. The other database is the proprietary Munich Re NatCat

Service database, which mainly serves to inform insurance and reinsurance pricing.

This database contains fewer entries focusing on the about 300 largest events since 1950,

yet data exhibit a higher reliability as often crosschecked with other information. We

focus on the monetary losses (direct impacts or risk) listed in constant 2000 USD terms.

In both datasets, loss data follow no uniform definition and are collected for different

purposes such as assessing donor needs for relief and reconstruction, assessing potential

impacts on economic aggregates and defining insurance losses. We distinguish between

sudden and slow onset events. Key sudden-onset events are extreme geotectonic events

(earthquakes, volcanic eruptions, slow mass movements) and extreme weather events

such as tropical cyclones, floods and winter storms. Slow-onset natural disasters are

either of a periodically recurrent or permanent nature; these are droughts and

desertification.

We broadly associate the loss data with asset losses, i.e. damages to produced

capital. This is a simplification, as indirect impacts, such as business interruption, may

also be factored into the data. Yet, generally, at least for the sudden onset events, analysts

generally equate the data with asset losses, and an indication that this assumption can be

maintained is the fact that loss data are usually relatively quickly available after a

catastrophe, which indicates that flow impacts emanating over months to years are

usually not considered. Losses are compared to estimates of capital stock from Sanderson

and Striessnig (2009), which estimated stocks using the perpetual inventory method

based on Penn World table information on investments starting in 1900 and assuming

annual growth and depreciation of 4 percent.

3.3

Projecting disaster impacts on GDP

We project differences (in percent) between observed and projected GDP up to five years

after a disaster event. A negative value indicates a situation where the projection

surpasses the observation leading to a negative effect. Figure 4 charts out these

differences for the years 1 to 5. Due to the heterogeneity of the data, it is not very

surprising that the results are heavily skewed and as an average value the median should

be looked at.

Difference

(Year 1)

Difference

(Year 2)

Difference

(Year 3)

Difference

(Year 4)

Difference

(Year 5)

-45

-30

-15

156

104

145

114

120

108

120

114

108

122

106

114

106

102

Fig. 4:

Box-plots for differences between observed and projected GDP (in percent of

observed, baseline GDP in the event year)

The mean, median, standard deviation as well as the skewness coefficients for the whole

sample are shown in table 3.

Table 3:

Summary results for differences of observed and projected GDP levels

t+1

t+2

t+3

t+4

t+5

Mean

-1.27

-1.43

-1.68

-1.75

-2.02

Median

-0.53

-1.03

-1.86

-2.27

-3.98

Std. Dev

7.19

11.01

14.99

18.37

22.53

Skewness

-1.54

-0.76

-0.13

0.42

0.98

According to the skewness and standard deviation the results are asymmetric with a large

spread. The results, however, clearly indicate a trend. All post-disaster years show

negative values with an increasing “gap,” indicating that “on average” one can expect

negative economic follow-on consequences in the short-medium term, leading to a

median reduction of GDP of about 4% points (of baseline GDP in to) in year 5 after the

event.

We further test whether the differences are statistically different from zero and,

due to non-normality of the data, used the non-parametric one-sample Wilcoxon test

(table 4). The null hypothesis H0 is that the median is equal to zero, while the alternative

hypothesis H1 is that the median is smaller than zero. Table 4 shows the p-values for this

test using the (mean) projections.

Table 4:

p-values of the Wilcoxon test for differences to be smaller than zero (H1) and

H0: equal to zero.

t+1

t+2

t+3

t+4

t+5

p-value

0.0138

0.0379

0.0258

0.0171

0.0129

Hypothesis

Clearly, the null hypothesis is rejected for all post-disaster years, and therefore one can

conclude that there are significant negative follow-on effects. Furthermore, also 95

percent forecast confidence intervals to include uncertainty of the projections within the

analysis are used. Additionally, also sub-sample analysis to include uncertainty regarding

the influence of multiple occurrences of disasters is performed. The sub-sample is chosen

so that only events are considered with no other event (with losses higher than 1 percent

of GDP) occurring 5 years before and 5 years after the event considered in the sample.

Results related to this sub-sample corroborate our findings on the negative economic

consequences (details can be found in Appendix D).

3.4

Explaining the variation: vulnerability predictors

As a next step, we test key variables, particularly those relating to economic

vulnerability, as to their suitability as predictors for explaining the differences of

projected and observed GDP in year 5 post event. Based on the literature review and

discussion above, the following variables listed in table 5 are assessed.

Table 5:

Predictor variables used in the analysis

Predictors

Variables

Source

Direct impact and risk

Direct monetary losses

EMDAT, 2009, Munich Re,

2008 as compiled by

Okuyama, 2009

Losses in percent of GDP

Okuyama, 2009

Losses in percent of capital stock

Own calculations

Exposure

GDP

WDI, 2008

Capital stock

Sanderson and Striessnig, 2009

Total number of population

WDI, 2008

Hazard

Hazard type:

Storm, Flood, Earthquake,

Drought, others

EMDAT, 2008

Munich Re, 2008

Economic vulnerability

Indebtedness

WDI, 2008

Income level

WDI, 2006

Land area

WDI, 2008

Literacy rate

WDI, 2008

Aid

WDI, 2008

Remittances

WDI, 2008

Small island development state

(

SIDS)

WDI, 2008

In the following, we first use multivariate models, then employ general linear regression

modeling approaches (GLM) using fixed factors, covariates and mixed models as

independent variables and Diff(5) as the dependent variable.

We did not look at physical vulnerability factors (for example, the quality of building stock in an

economy) as predictors, as those do not seem to be of importance in isolation and are accounted for in
the direct impact variable.

First, exploratory analyses are performed (see tables A-1). Pearson correlation

analysis (which assumes a linear relationship) between the continuous variables and

Diff(5) leads to (highly) significant results with (log) capital stock losses (correlation of -

0.317, p-value 0.000). Interestingly, such a correlation cannot be found for GDP losses,

indicating that capital stock losses may serve as a better predictor. Furthermore, total

population (correlation of 0.200, p-value 0.013) as well as aid (in percent of capital

formation) are found to be significant (correlation of 0.187, p-value 0.032).

Descriptive statistics for Diff(5) within sub-groups according to the income,

indebtedness, SIDS and hazard type indicators are considered next (see tables A-2 to A-

6). Using the income indicator, the mean of Diff(5) for all sub-groups exhibits negative

values. Also, with regards to the indebtedness indicator, there are negative mean

(median) values. As to the type of hazard, storms and earthquakes as well as droughts (if

the median is looked at) show negative values. In addition, additional “layers” (or sub-

sub groups) are examined; however, the number of observations quickly becomes very

small, and therefore average values should be treated with caution. Results of Diff(5) for

the interaction of two indicators (which means 6 possible sub-groups) can be found in

tables A-6 to A-11. For example, low income in combination with high indebtedness

leads to more pronounced negative consequences. Overall, however, a general

interpretation of these results is difficult as no clear trend can be discerned. Therefore, we

use regression models in the following.

Multivariate regression model

A forward stepwise regression procedure to detect the most important independent

variables from table 5 for the dependent variable Diff(5) is employed. In the first round of

the iteration, the independent variables are each added to the starting model (i.e. intercept

only model), and the improvement in the residual sum of squares for each of these

resulting models is calculated. Next, for each model the p-value for the change in the sum

of squares is determined (based on the F-distribution). The variable associated with the

lowest p-value is the first model candidate. If the p-value is below 0.1 (significance at the

10% level), then this model is taken. In the next round, this model will be the starting

model and the subsequent rounds follow the same procedure as the first. The forward

procedure stops if the lowest candidate p-value in subsequent rounds is not lower than

0.1. Table 6 lists the initial model 1 and the final model 2 (all output tables for the full

regression model can be found in Appendix B).

Table 6:

Multivariate Regression results using a forward algorithm( Model=1:

Starting model, Model=2: Final model)

Model

Coefficients

(Unstandardized)

Standardized

Coefficients

p-value

Std. Error

Beta

Constant

Percent of Capital

stock loss (log)

3.254

-4.600

3.247

2.076

-0.317

1.002

-2.216

0.322

0.032

Constant

Percent of Capital

stock loss (log)

Remittances

-3.095

-5.934

1.946

4.276

2.086

0.897

-0.409

0.312

-0.724

-2.844

2.170

0.473

0.007

0.036

The final regression model is already reached at step 2, which indicates that the selected

variables already have good predictive power. Regarding the fit of the model, while not

very satisfactory from a predictive point of view (R square is around 19 percent), two

variables are significant at the 5 percent level: capital stock losses (p 0.007) and

remittances in the disaster year (p 0.036). While the capital stock loss variable has a

negative coefficient suggesting a larger direct shock will lead also to larger negative GDP

effects, the remittances parameter has a positive value suggesting that stronger

remittances inflow will decrease negative consequences. In line with the exploratory

analysis, the direct impacts variable (capital stock losses) seems to be a strong predictor.

To summarize, the size of the direct impact (losses) strongly predicts the magnitude

of follow-on effects. The fact that it significantly explains the variation in Diff(5), which

is based on the time series approach, seems to suggest some validity of the regression

results so far. However, interdependencies between variables are not used in this model

and are looked at next.

General linear regression model

A general linear regression modeling approach

, which also allows for inclusion of

interdependencies of several indicator variables, is used next. The model is restricted to

selected key variables first identified in the literature review, the further limited by the

exploratory analysis (partly presented already in the tables). The model has 4 fixed

factors (indicators), including country income group, indebtedness, countries relating to

SIDS and hazard type (see table 7).

Table 7:

Indicators used for the GLM regression

Name [abbreviation]

Value Label

Observations

Income [I_Income]

high income

middle income

low income

Indebtedness [debt]

Nan

less indebted

medium indebted

highly indebted

SIDS [I_SIDS]

Yes

118

Hazard [I_Hazard]

Storm

Flood

Earthquake

Drought

Other

GLM underlies most of the statistical analyses used in applied and social research due to its widespread

applicability. With general linear models many statistical tests can be handled as a regression analysis,
including t-tests and ANOVA (Analysis of Variance).

The covariates (continuous variables) are chosen based on table 2 and full order effects up to

level 2 are included, i.e. relationships between up to two fix factors (indicators) and one
covariate are explored within the model.

We thus define different sub-samples according to these indicator variables. For example,

the whole sample can be split by the income group indicator into 3 sub-samples, the high

income sub-sample (19 observations), the middle (94 observations) and low income sub-

samples (46 observations). As mentioned, the limitation of higher order effects is mainly

due to the decreasing number of observations within sub-groups. Table 8 shows the tests

for the different main factors as well as their interactions with the indicators.

Table 8:

GLM Findings: tests of between-subjects effects

Full

output details can be found in Appendix C.

Dependent Variable: Difference (year 5)

21220

531

6.446

.023

1337

16.243

.010

244

2.969

.145

.162

.704

764

9.284

.029

1802

21.888

.005

2230

27.093

.003

1849

22.467

.005

.238

.646

.971

.370

.003

.956

4108

2054

24.959

.003

.008

.931

1.174

.328

965

11.723

.019

653

7.932

.037

4155

519

6.310

.029

369

4.483

.088

106

1.291

.307

245

.991

.468

727

364

4.418

.079

698

8.475

.033

.063

.812

1805

451

5.482

.045

.998

.364

140

1.706

.248

.381

.702

412

22969

21632

Source
Corrected Model

Intercept

Literacy rate

Aid (capital formation)

Aid (percent of import and exports)

Capital Stock loss (log) [logCapLoss]

Aid (percent of GNI)

Remittances [Remit]

Capital Stock (log)

GDP (log)

Land Area (log)

I_debt * Remit

I_Income * Remit

I_SIDS * Remit

I_debt * I_Income * Remit

I_debt * I_SIDS * Remit

I_debt * I_Hazard * Remit

I_Income * I_SIDS * Remit

I_Income * I_Hazard * Remit

I_SIDS * I_Hazard * Remit

I_debt * logCapLoss

I_Income * logCapLoss

I_SIDS * logCapLoss

I_Hazard * logCapLoss

I_debt * I_Income * logCapLoss

I_debt * I_SIDS * logCapLoss

I_debt * I_Hazard * logCapLoss

I_Income * I_SIDS * logCapLoss

I_Income * I_Hazard * logCapLoss

I_SIDS * I_Hazard * logCapLoss

Error

Total

Corrected Total

Type I Sum of

Squares

Mean

Square

Sig.

R Squared = .981 (Adjusted R Squared = .829)

A least squares criterion is used to obtain estimates of the parameters models.

As to the model specification (table 8 bottom), the model itself is significant (p-value

0.021) with about 83 percent of the variation explained (R-square 0.829), which is quite

satisfactory. Significant variables (p-value smaller than 0.05) include aid (in percent of

import and exports), capital stock loss (logged), aid (in percent of GNI), remittances, and

interactions of capital stock losses and remittances with some of the other indicators, such

as indebtedness, income and hazard.

The parameter estimates in Appendix C for the dependent variables cannot be used

for interpretation purposes, because GLM models usually have systematic colinearity

between the dependent variables and therefore the impact of one single dependent

variable is not captured within the parameter estimate. Hence, the variables found to be

significant in table 8 are analyzed according to scatter-plots, profile plots as well as

comparisons of averages. In line with the observations made above the results lead to the

conclusion that especially the direct impact, measured in percent of capital stock loss

leads to negative long-term consequences. Remittances as well as various forms of aid

decrease the negative effects, however, not as strongly as direct losses. Unfortunately, it

has not been possible to refine the analysis with further sub-sub groups, such as looking

at country debt levels which seems promising, as the number of observations became too

small. Overall, we also find that in general natural disasters can be expected to entail

negative consequences in the medium term (five years after an event). As in the

multivariate regression, adverse macroeconomic effects can be related to the direct

impact in terms of asset losses. Higher aid rates as well as higher remittances (pre-

disaster) seem important in lessening the adverse macroeconomic consequences.

ISCUSSION

There is an ongoing debate on whether disasters cause significant macroeconomic

impacts and are truly a potential impediment to economic development. Given the

divergent positions, this analysis aimed at better defining a sort of “middle ground”

identifying circumstances under which disasters have the potential to cause significant

medium-term economic impacts. In a medium-term analysis, comparing counterfactual

GDP derived by time series analysis with observed GDP, natural disasters on average

lead to significant negative effects on GDP. The negative effects may be small, yet can

become more pronounced depending on the direct impact measured as a loss of capital

stock. Using regression analysis, we further test a large number of predictors and find that

higher aid rates as well as higher remittances importantly lessen the adverse negative

macroeconomic consequences, while direct capital stock losses had the largest effects in

causing adverse GDP effects. A number of other variables, such as country debt, seemed

promising in terms of explaining the variability of GDP, yet it was not possible to further

refine the analysis due to small number of observations. Beyond these findings, final

conclusions are difficult to draw and the uncertainty in loss data and socioeconomic

information has to be acknowledged. One reason is the challenge associated with

determining the size and type of impacts as well as identifying additional key predictors.

For example, particularly for middle and high income countries, capital stock losses

probably play a minor role and other variables such as human and natural capital

increasingly become important. Obvious steps for improving the analysis should thus

focus on increasing the sample size and quality of data generated, particularly as relates

to those lower income and hazard-prone countries supposed to be most vulnerable and of

highest interest for the analysis. Finally, another key extension of the analysis would be

to also look at disaster impacts on human and environmental capital and its economic

repercussions, in isolation as well as in conjunction with produced capital.

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Appendix A: Tables

Table A-1: Correlation matrix

Correlations

-.105

.051

-.128

-.142

-.184*

.200*

.098

-.092

.195

.528

.117

.083

.025

.013

.388

.653

155

150

149

155

-.105

-.102

-.052

.261**

.334**

-.099

.093

-.065

.195

.131

.466

.000

.152

.338

.689

155

220

199

193

210

108

.051

-.102

.242**

.174*

-.025

.693**

.107

-.035

.528

.131

.001

.014

.728

.000

.269

.832

155

220

199

193

210

108

-.128

-.052

.242**

.422**

.014

.101

.084

-.066

.117

.466

.001

.000

.846

.156

.399

.692

150

199

193

199

102

-.142

.261**

.174*

.422**

.948**

.035

.073

-.071

.083

.000

.014

.000

.628

.463

.666

150

199

193

199

102

-.184*

.334**

-.025

.014

.948**

-.023

.017

-.057

.025

.000

.728

.846

.000

.749

.864

.734

149

193

.200*

-.099

.693**

.101

.035

-.023

.028

-.044

.013

.152

.000

.156

.628

.749

.776

.789

155

210

199

193

210

105

.098

.093

.107

.084

.073

.017

.028

.112

.388

.338

.269

.399

.463

.864

.776

.629

108

102

105

108

-.092

-.065

-.035

-.066

-.071

-.057

-.044

.112

.653

.689

.832

.692

.666

.734

.789

.629

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Difference (year 5)

Loss in percent of GDP

Capital Stock

GDP

Loss in monetary terms

Loss in percent of
Capital Stock

Total Population

Literacy rate (percent of
adult)

Government Aid

Difference

(year 5)

Loss in

percent of

GDP

Capital Stock

GDP

Loss in

monetary

terms

Loss in

percent of

Capital Stock

Total

Population

Literacy rate

(percent of

adult)

Government

Aid

Correlation is significant at the 0.05 level (2-tailed).

Correlation is significant at the 0.01 level (2-tailed).

**.

Table A-1: Correlation matrix (continued)

Correlations

.187*

.132

.118

-.149

-.317**

.061

.107

.032

.162

.143

.064

.000

.494

.277

155

132

113

155

149

130

106

.187*

.763**

-.171*

.034

-.034

.813**

.009

.032

.000

.025

.661

.668

.000

.921

132

171

133

171

160

161

122

.132

.763**

-.147

.052

.049

.636**

.041

.162

.000

.081

.540

.572

.000

.656

113

133

142

133

136

121

.118

-.171*

-.147

-.203**

-.338**

-.137

-.195*

.143

.025

.081

.002

.000

.065

.016

155

171

142

220

193

183

152

-.149

.034

.052

-.203**

.714**

.208**

.355**

.064

.661

.540

.002

.000

.005

.000

155

171

142

220

193

183

152

-.317**

-.034

.049

-.338**

.714**

.100

.210*

.000

.668

.572

.000

.210

.015

149

160

133

193

160

133

.061

.813**

.636**

-.137

.208**

.100

.172*

.494

.000

.065

.005

.210

.049

130

161

136

183

160

183

132

.107

.009

.041

-.195*

.355**

.210*

.172*

.277

.921

.656

.016

.000

.015

.049

106

122

121

152

133

132

152

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Difference (year 5)

Aid (capitall formation)

Aid (percent of imports
and exports)

Land area

Loss in percent of
GDP (log)

Loss in percent of
Capital Stock (log)

Aid (% of GNI)

Remittances

Difference

(year 5)

Aid (capital

formation)

Aid (percent

of imports

and exports)

Land area

Loss in

percent of
GDP (log)

Loss in

percent of

Capital

Stock (log)

Aid (% of GNI)

Remittances

Correlation is significant at the 0.05 level (2-tailed).

Correlation is significant at the 0.01 level (2-tailed).

**.

Table A-1: Correlation matrix (continued)

Correlations

.117

-.065

-.177*

.043

.147

.428

.030

.598

155

154

150

155

.117

.833**

.618**

.624**

.147

.000

154

204

193

204

-.065

.833**

.837**

.593**

.428

.000

150

193

199

-.177*

.618**

.837**

.368**

.030

.000

150

193

199

.043

.624**

.593**

.368**

.598

.000

155

204

199

220

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Difference (year 5)

Capital Stock (log)

GDP (log)

Money loss (log)

Land Area (log)

Difference

(year 5)

Capital

Stock (log)

GDP (log)

Money

loss (log)

Land

Area (log)

Correlation is significant at the 0.05 level (2-tailed).

Correlation is significant at the 0.01 level (2-tailed).

**.

Table A-2: Diff(5) vs. Income

Difference (year 5) * Income level

Difference (year 5)

-10.0428

10.28454

-8.2346

-.610

-1.5493

28.08414

1.8748

.661

-.1570

21.37437

-4.1126

1.075

155

-1.7820

22.73418

-3.4932

.951

Income level
high income

low income

middle income

Total

Mean

Std. Deviation

Median

Skewness

Table A-3: Diff(5) vs. Debt

Difference (year 5) * Indebtedness

Difference (year 5)

-8.5480

12.31746

-7.4272

-.033

-.6998

26.53054

1.7900

.629

1.4293

33.09615

-8.4707

1.283

-1.5386

16.55988

-4.8505

.396

155

-1.7820

22.73418

-3.4932

.951

Indebtedness level
NanN

highly indebted

medium indebted

less indebted

Total

Mean

Std. Deviation

Median

Skewness

Table A-4: Diff(5) vs. SIDS

Difference (year 5) * SIDS

Difference (year 5)

114

-1.0722

21.51452

-2.5134

1.009

-3.7554

26.01534

-3.9810

.944

155

-1.7820

22.73418

-3.4932

.951

SIDS
no

yes

Total

Mean

Std. Deviation

Median

Skewness

Table A-5: Diff(5) vs. Hazard type

Difference (year 5) * Hazard type

Difference (year 5)

-3.2304

15.29672

-5.1644

1.287

2.5940

22.90447

3.0448

-.032

-3.6452

23.32322

-4.4723

.998

4.6507

31.28664

-5.4178

1.711

-17.4760

23.65540

-9.8835

-.427

155

-1.7820

22.73418

-3.4932

.951

Hazard type
Storm

Flood

Earthquake

Drought

other

Total

Mean

Std. Deviation

Median

Skewness

Table A-6: Diff(5) vs. Income vs. Debt.

Difference (year 5)

-9.8812

10.82718

-7.4272

-.679

-10.9044

8.45075

-11.5879

.362

-10.0428

10.28454

-8.2346

-.610

-1.1036

29.47572

3.3870

.603

-5.2039

12.89095

-8.1523

.716

-1.5493

28.08414

1.8748

.661

-3.2148

18.09280

-3.6352

.114

.0887

20.20344

.4068

.857

4.1931

38.78767

-10.0309

.991

-1.0084

16.79157

-4.5365

.331

-.1570

21.37437

-4.1126

1.075

-8.5480

12.31746

-7.4272

-.033

-.6998

26.53054

1.7900

.629

1.4293

33.09615

-8.4707

1.283

-1.5386

16.55988

-4.8505

.396

155

-1.7820

22.73418

-3.4932

.951

Indebtedness level
NanN

less indebted

Total

highly indebted

medium indebted

Total

NanN

highly indebted

medium indebted

less indebted

Total

NanN

highly indebted

medium indebted

less indebted

Total

Income level
high income

low income

middle income

Total

Mean

Std. Deviation

Median

Skewness

Table A-7: Diff(5) vs. Income vs. Hazard type

Difference (year 5) * Income level * Hazard type

Difference (year 5)

-9.9249

7.93491

-8.9508

-.233

-8.7336

7.81854

-12.3034

1.626

-15.3454

14.40122

-16.6655

.329

-4.5909

4.97845

-6.6197

1.529

.7820

-10.0428

10.28454

-8.2346

-.610

1.4656

9.33077

4.1589

-.602

4.5497

28.45303

7.6761

-.186

-11.8533

37.53206

3.5834

-1.538

.8741

39.00068

-7.8058

2.163

-34.2222

24.94859

-41.9039

1.528

-1.5493

28.08414

1.8748

.661

-4.0054

17.78638

-6.9198

1.482

2.7163

19.84732

1.7985

-.168

2.2814

22.53234

.6409

2.057

10.2610

29.30183

4.9994

1.213

-11.3851

21.02976

-7.6592

-.918

-.1570

21.37437

-4.1126

1.075

-3.2304

15.29672

-5.1644

1.287

2.5940

22.90447

3.0448

-.032

-3.6452

23.32322

-4.4723

.998

4.6507

31.28664

-5.4178

1.711

-17.4760

23.65540

-9.8835

-.427

155

-1.7820

22.73418

-3.4932

.951

Hazard type
Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Income level
high income

low income

middle income

Total

Mean

Std. Deviation

Median

Skewness

Table A-8: Diff(5) vs. Income vs. SIDS

Difference (year 5) * Income level * SIDS

Difference (year 5)

-11.0912

10.62976

-9.9112

-.457

-4.4515

6.98729

-2.1327

-1.329

-10.0428

10.28454

-8.2346

-.610

1.5991

21.89418

4.9307

.033

-9.5415

39.78641

-5.4178

1.464

-1.5493

28.08414

1.8748

.661

.0377

22.82711

-4.2906

1.281

-.6632

17.44403

-3.9810

-.339

-.1570

21.37437

-4.1126

1.075

114

-1.0722

21.51452

-2.5134

1.009

-3.7554

26.01534

-3.9810

.944

155

-1.7820

22.73418

-3.4932

.951

SIDS
no

yes

Total

yes

Total

yes

Total

yes

Total

Income level
high income

low income

middle income

Total

Mean

Std. Deviation

Median

Skewness

Table A-9: Diff(5) vs. Hazard vs. SIDS

Report

Difference (year 5)

-10.4743

11.15400

-8.2346

-.494

2.3679

15.35486

1.0816

.374

-8.5480

12.31746

-7.4272

-.033

.7537

23.01306

3.3392

.329

-3.7520

33.20379

-1.0841

1.013

-.6998

26.53054

1.7900

.629

6.2663

34.49705

-6.2215

1.500

-10.1795

29.49825

-11.5911

.481

1.4293

33.09615

-8.4707

1.283

-1.1866

17.74184

-5.4348

.300

-2.7030

12.38017

-3.9810

1.078

-1.5386

16.55988

-4.8505

.396

114

-1.0722

21.51452

-2.5134

1.009

-3.7554

26.01534

-3.9810

.944

155

-1.7820

22.73418

-3.4932

.951

SIDS
no

yes

Total

yes

Total

yes

Total

yes

Total

yes

Total

Indebtedness level
NanN

highly indebted

middle indebted

low indebted

Total

Mean

Std. Deviation

Median

Skewness

Table A-10: Diff(5) vs. Debt. vs. SIDS

Difference (year 5)

-10.4743

11.15400

-8.2346

-.494

2.3679

15.35486

1.0816

.374

-8.5480

12.31746

-7.4272

-.033

.7537

23.01306

3.3392

.329

-3.7520

33.20379

-1.0841

1.013

-.6998

26.53054

1.7900

.629

6.2663

34.49705

-6.2215

1.500

-10.1795

29.49825

-11.5911

.481

1.4293

33.09615

-8.4707

1.283

-1.1866

17.74184

-5.4348

.300

-2.7030

12.38017

-3.9810

1.078

-1.5386

16.55988

-4.8505

.396

114

-1.0722

21.51452

-2.5134

1.009

-3.7554

26.01534

-3.9810

.944

155

-1.7820

22.73418

-3.4932

.951

SIDS
no

yes

Total

yes

Total

yes

Total

yes

Total

yes

Total

Indebtedness level
NanN

highly indebted

medium indebted

less indebted

Total

Mean

Std. Deviation

Median

Skewness

Table A-11: Diff(5) vs. Debt. vs. Hazard

Difference (year 5)

-8.9454

9.11666

-6.3138

-1.191

-6.6942

7.78935

-10.3152

.548

-15.3454

14.40122

-16.6655

.329

-3.8723

15.37481

-6.6197

.333

.7820

-8.5480

12.31746

-7.4272

-.033

.9441

17.26948

2.6662

.951

4.6616

29.24047

7.6761

-.252

-7.9115

31.64260

3.7486

-1.811

2.7294

38.47571

-5.4178

2.081

-27.4645

26.36579

-37.2340

.369

-.6998

26.53054

1.7900

.629

-9.1192

3.59991

-10.0309

1.009

-7.6672

16.27240

-8.1523

.013

71.3230

12.6623

43.04089

-12.3435

1.152

-17.7618

43.65925

-17.7618

1.4293

33.09615

-8.4707

1.283

-7.4628

12.97722

-8.3785

1.456

6.9051

19.91528

6.3466

-.382

-2.7668

13.83809

-2.2192

.650

9.6128

13.16919

10.9818

-.443

-11.0247

15.71403

-9.8835

-1.098

-1.5386

16.55988

-4.8505

.396

-3.2304

15.29672

-5.1644

1.287

2.5940

22.90447

3.0448

-.032

-3.6452

23.32322

-4.4723

.998

4.6507

31.28664

-5.4178

1.711

-17.4760

23.65540

-9.8835

-.427

155

-1.7820

22.73418

-3.4932

.951

Hazard type
Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Storm

Flood

Earthquake

Drought

other

Total

Indebtedness level
NanN

highly indebted

medium indebted

less indebted

Total

Mean

Std. Deviation

Median

Skewness

Appendix B: Linear (forward) regression: Details

Table B-1: Model Summary

Model Summary

.317

.100

.080

21.03051

.435

.189

.151

20.19663

Model
1

R Square

Adjusted

R Square

Std. Error of

the Estimate

Predictors: (Constant), Loss in percent of Capital Stock
(log)

Predictors: (Constant), Loss in percent of Capital Stock
(log), Remittances

Table B-2: ANOVA

ANOVA

2171.570

4.910

.032

19460.421

442.282

21631.991

4092.124

2046.062

5.016

.011

17539.867

407.904

21631.991

Regression

Residual

Total

Regression

Residual

Total

Model
1

Sum of

Squares

Mean Square

Sig.

Predictors: (Constant), Loss in percent of Capital Stock (log)

Predictors: (Constant), Loss in percent of Capital Stock (log), Remittances

Dependent Variable: Difference (year 5)

Table B-3: Coefficients

Coefficients

3.254

3.247

1.002

.322

-4.600

2.076

-.317

-2.216

.032

-3.095

4.276

-.724

.473

-5.934

2.086

-.409

-2.844

.007

1.946

.897

.312

2.170

.036

(Constant)

Loss in percent of
Capital Stock (log)

(Constant)

Loss in percent of
Capital Stock (log)

Remittances

Model
1

Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

Sig.

Dependent Variable: Difference (year 5)

Table B-4: Excluded Variables

Excluded Variables

-.163

-.807

.424

-.122

.506

-.131

-.878

.385

-.133

.916

-.116

-.807

.424

-.122

1.000

-.221

-1.332

.190

-.199

.728

.312

2.170

.036

.314

.913

-.083

-.570

.572

-.087

.983

.043

.211

.834

.032

.512

.047

.319

.751

.049

.968

.102

.696

.490

.105

.954

.011

.075

.940

.011

.908

-.123

-.629

.533

-.097

.501

-.100

-.688

.495

-.106

.906

-.087

-.629

.533

-.097

.990

-.070

-.376

.709

-.058

.561

-.027

-.187

.853

-.029

.948

.034

.177

.861

.027

.512

.108

.756

.454

.116

.933

.169

1.183

.243

.180

.918

-.049

-.330

.743

-.051

.876

Capital Stock (log)

GDP (log)

Money loss (log)

Land Area (log)

Remittances

Aid (% of GNI)

Loss in percent of
GDP (log)

Aid (capital formation)

Aid (percent of
imports and exports)

Literacy rate (percent
of adult)

Capital Stock (log)

GDP (log)

Money loss (log)

Land Area (log)

Aid (% of GNI)

Loss in percent of
GDP (log)

Aid (capital formation)

Aid (percent of
imports and exports)

Literacy rate (percent
of adult)

Model
1

Beta In

Sig.

Partial

Correlation

Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), Loss in percent of Capital Stock (log)

Predictors in the Model: (Constant), Loss in percent of Capital Stock (log), Remittances

Dependent Variable: Difference (year 5)

Table C-2: Tests of between-Subject factors

Dependent Variable: Difference (year 5)

21220

531

6.446

.023

1337

16.243

.010

244

2.969

.145

.162

.704

764

9.284

.029

1802

21.888

.005

2230

27.093

.003

1849

22.467

.005

.238

.646

.971

.370

.003

.956

4108

2054

24.959

.003

.008

.931

1.174

.328

965

11.723

.019

653

7.932

.037

4155

519

6.310

.029

369

4.483

.088

106

1.291

.307

245

.991

.468

727

364

4.418

.079

698

8.475

.033

.063

.812

1805

451

5.482

.045

.998

.364

140

1.706

.248

.381

.702

412

22969

21632

Source
Corrected Model

Intercept

Literacy rate

Aid (capital formation)

Aid (percent of import and exports)

Capital Stock loss (log) [logCapLoss]

Aid (percent of GNI)

Remittances [Remit]

Capital Stock (log)

GDP (log)

Land Area (log)

I_debt * Remit

I_Income * Remit

I_SIDS * Remit

I_debt * I_Income * Remit

I_debt * I_SIDS * Remit

I_debt * I_Hazard * Remit

I_Income * I_SIDS * Remit

I_Income * I_Hazard * Remit

I_SIDS * I_Hazard * Remit

I_debt * logCapLoss

I_Income * logCapLoss

I_SIDS * logCapLoss

I_Hazard * logCapLoss

I_debt * I_Income * logCapLoss

I_debt * I_SIDS * logCapLoss

I_debt * I_Hazard * logCapLoss

I_Income * I_SIDS * logCapLoss

I_Income * I_Hazard * logCapLoss

I_SIDS * I_Hazard * logCapLoss

Error

Total

Corrected Total

Type I Sum of

Squares

Mean

Square

Sig.

R Squared = .981 (Adjusted R Squared = .829)

Table C-3: Parameter estimates

Parameter Estimates

Dependent Variable: Difference (year 5)

65.048

82.020

.793

.464

-145.791

275.886

-.394

.362

-1.088

.326

-1.324

.537

-.192

.242

-.796

.462

-.813

.429

.391

.424

.923

.398

-.698

1.479

-21.650

229.545

-.094

.929

-611.715

568.414

-.297

.959

-.309

.770

-2.762

2.169

-16.487

194.754

-.085

.936

-517.120

484.145

2.950

6.885

.428

.686

-14.749

20.649

-11.146

7.886

-1.414

.217

-31.417

9.124

11.320

4.287

2.640

.046

.299

22.340

273.337

372.066

.735

.496

-683.088

1229.762

122.872

232.243

.529

.619

-474.127

719.872

-245.669

506.327

-.485

.648

-1547.223

1055.886

24.322

202.097

.120

.909

-495.183

543.828

-65.834

125.906

-.523

.623

-389.487

257.818

-163.417

473.051

-.345

.744

-1379.433

1052.599

-271.442

380.054

-.714

.507

-1248.401

705.517

55.542

65.961

.842

.438

-114.015

225.099

-32.420

143.687

-.226

.830

-401.780

336.940

-130.722

238.276

-.549

.607

-743.229

481.786

12.886

544.168

.024

.982

-1385.943

1411.715

55.172

190.856

.289

.784

-435.439

545.784

-5.385

7.599

-.709

.510

-24.919

14.149

103.618

228.818

.453

.670

-484.576

691.812

306.871

481.720

.637

.552

-931.429

1545.171

178.903

159.026

1.125

.312

-229.887

587.692

-16.443

550.186

-.030

.977

-1430.741

1397.855

-168.587

197.815

-.852

.433

-677.087

339.913

-113.086

237.732

-.476

.654

-724.195

498.024

-89.034

114.835

-.775

.473

-384.226

206.159

-74.571

32.945

-2.264

.073

-159.258

10.116

59.254

34.853

1.700

.150

-30.339

148.848

-137.896

79.075

-1.744

.142

-341.166

65.373

137.943

193.275

.714

.507

-358.886

634.772

153.894

202.842

.759

.482

-367.529

675.317

161.206

199.402

.808

.456

-351.375

673.786

160.106

202.766

.790

.466

-361.121

681.334

-48.924

42.237

-1.158

.299

-157.498

59.649

114.522

102.978

1.112

.317

-150.192

379.235