Income Inequality Impacts ofExtreme Weather Events and Disasters

Jaimie Kim B. Arias1, Rosalina Palanca-Tan2

1 Associate Professor, University of the Philippines Los Baños
2 Professor, Ateneo de Manila University

地球温暖化に伴う苛酷な異常気象や大規模自然災害がもたらす損失を過去のデータから解析すると、貧困国や途上国では低所得層ほど深刻な打撃を受けやすいことがわかった。

Abstract

This paper examined the effect of natural disasters on income inequality using panel data of 124 countries, covering
the period 1986 to 2015. The fixed effects model was used in the analysis. It was found that for low and lower
middle-income countries, the occurrence of natural disasters increased income inequality (measured in terms of theGini coefficient). Meanwhile, for high income countries, the study found evidence that natural disasters reduces
inequality.
Keywords

income inequality, disasters

Introduction

The study of natural disasters has reached the interest of economists in recent years because of the enormous economic and social consequences associatedwith these events. Focus of research has been on assessing impacts, valuing damages, and identifying factors
and policy options that maximizes social welfare orleads to the minimization of their economic costs. The
topic is all the more relevant because the turn of the newmillennium showed a peak in the occurrence of natural
hazards. Moreover, there is a dire prediction that extreme
weather events associated with typhoons, floods, heatwaves, and droughts may increase in frequency andintensity as a consequence of global climate change(Intergovernmental Panel on Climate Change 2013).
On a micro-level, exposure to these natural hazards
can lead to the loss and destruction of properties,
damage to agriculture and aquaculture, income losses,
displacement, morbidity and deaths. On a macro-level,
it may lead to slower economic growth, production inefficiencies, massive migration, increase in poverty, anddirect monetary losses. An insurance company, Swiss

Re, estimated that the total economic losses that the
global economy sustained from both man-made andnatural disasters have reached USD 306 billion in 2017,about 60% higher than the estimated losses the prioryear (Swiss Re 2017). This is despite the fact that thefrequency of disaster occurrence actually declined from348 events in 2016 to 291 events in 2017 (CRED, D.
Guha-Sapir n.d.).
Based on the Centre for Research and Epidemiology
of Disasters (CRED) database, about 14,441 natural
disaster events occurred from 1900 to 2017 all over theworld. Majority of these disaster events were experienced in Asia (40%), followed by Americas (25%),
Africa (18%), Europe (12%) and Oceania (5%).
Although much has been understood about the natureof the economic and social costs of natural disasters, forinstance, how it affects economic productivity, economic growth, and even migration, studies on theincome distribution impacts of disasters and extreme
weather are still very limited. It is the goal of thisresearch paper to contribute to the limited literature on
the subject matter through an empirical study using

193

Jaimie Kim B. Arias, et al.

cross-national panel data. Specifically, the paper aims todetermine whether natural disasters and extreme
weather experience significantly contributes to income
inequality. The study employed a panel regression analysis using a fixed effects error correction model. The
Driscoll/Kray Standard Errors were used as basis fortesting the individual hypothesis of whether natural
disaster experiences are statistically significant determinants of the income Gini coefficient of selected countries. The study made use of an unbalanced panel data of
124 countries covering a maximum period of 30 years(1986 to 2015).

Review of Literature

Although studies on the interaction between disasters and income are quite extensive, studies on the relationship between disasters and inequality are still quite
limited. Studies that looked into the inequality impacts
of weather extremes or disasters are mostly confined toa local level or within country level of analysis and only
a few have utilized cross-national panel data. Most of
the studies were from a developing country setting, andthe findings are quite diverse.
A study conducted in the Philippines by Datt &Hoogeveen (2000) employed regression analysis to look
into the effect of El Niño and other economic shocks on
consumption, poverty and inequality in the Philippines
using data from the 1997/98 Annual Poverty Indicators
Survey. The study found that the El Niño phenomenon
played a major role in predicting consumption andincome, and that it had a regressive impact on inequality. The authors attributed this to the different levels ofvulnerability among households. Some are more resilient to shocks, while others are less so. It was found thatownership of land made households more susceptible to
El Niño.
Bayani-Arias and Palanca-Tan (2017) used data fromthe 2009 and 2012 Philippine Family Income andExpenditure Survey (FIES) to conduct decomposition
analysis of the provincial income and expenditure Theilinequality index grouped according to exposure totyphoons. It was found that the group with the highest
exposure to typhoons experienced worsening of both194

income and expenditure inequality, while the group withthe least exposure showed no change in expenditure
inequality and an improvement in income equality. The
study works under the premise that different households
have different attributes, different adaptive capacities,
and therefore different vulnerabilities and thus extreme
weather will have varying impacts on their income andconsumption, thereby worsening inequality.
In Mozambique, Silva, et al. (2015) conducted a study
that relates weather patterns associated with near normal
rainfall, tropical cyclones and flooding, and drought tochanges in inequality and polarization. The study utilized 2005 and 2008 National Agricultural Survey andconducted decomposition analyses of the Gini index
and Duclos-Esteban-Ray (DER) polarization index.
Silva et al. (2015) found that weather shocks can exacerbate existing economic income and divisions withinsocieties, but they also found that in some cases inequality and polarization can decline after an extreme event.
Reardon & Taylor (1996) examined the impacts of an
agroclimatic shock on income inequality in Burkina
Faso. The study applied income-source decompositionsof the Gini coefficient and the Foster-Greer-Thorbecke(FGT) poverty index before and after a severe drought.
The study used survey data collected by ICRISAT fromfarm households in the period 1983/84 and 1984/85. The
authors noted that the inequality impacts of a weather
shock depend on the extent to which incomes are diversified and on the costs of diversifying income in responseto the shock. They found that off-farm income increases
inequality and fails to shield poor households against
agro-climatic shocks.
In Ethiopia, Thiede (2014) used the 2005 and 2011
Ethiopia Demographic and Health Survey to determine
the impact of drought on livestock inequality index
using the triple difference approach. Thiede (2014)
found that rainfall deficits have an equalizing effect on
the livestock inequality within some Ethiopian communities, although the results vary from region to region.
On the other hand, the estimated effects of rainfall deficits on asset inequality within communities are
non-significant.
Keerthiratne and Tol (2017) examined the effects of

6th AFC Best Papers

natural disasters (in terms of the percentage of the population affected) on the district level Gini Coefficientand Theil index in Sri Lanka. The study applied various
regression models including panel fixed effects estimator, Ordinary Least Squares regression and system
GMM estimators. The study found that contemporaneous natural disasters and their immediate lags decrease
district level income inequality, but they have no significant effects on expenditure inequality. Moreover, thestudy found that natural disasters seem to worsen seasonal and agricultural income inequality while having
an equalizing effect on non-seasonal and non-agricultural income. They explained that natural disasters
reduced income inequality because those with higher
income suffers higher damages from natural disasters
than the poor. Those with lower income are able toeasily diversify their income sources in the aftermath ofdisasters (Keerthiratne and Tol 2017).
A study conducted in the United States looked into
the income inequality effects of hurricane Katrina(Shaughnessy, White and Brendler 2010). The study
predicted the impacts by constructing Lorenz Curves
and estimating Gini coefficients from three income density functions particularly the Lognormal density function, Log-logistic density function, and the Singh-Maddaladensity function pre- and post- Katrina. The study foundthat inequality in New Orleans declined after Katrina,
but it was not clear in the paper what possible mechanisms were responsible for this phenomenon.
In contrast, a study conducted in Vietnam found thatnatural disasters seem to worsen expenditure andincome inequality (Bui, et al. 2014). The study used datafrom the 2008 Vietnam Household Living Standard
Survey and applied regression analysis corrected for
fixed effects and endogeneity bias.
Yamamura (2013) examined the effects of natural
disaster frequency on the country level Gini coefficientutilizing panel data of 86 countries covering the period
1965 to 2004. The fixed effects regression model wasused wherein the dependent variable was the change in
the Gini coefficient from past to current year, while theexplanatory variables include the Gini coefficient of theprevious year, current and past disaster frequency(lagged 3 periods), GDP per capita, and land size. In
addition, an Ordinary Least Squares regression model
was also undertaken which included the following control variables: institutional conditions, legal origin andsocio-economic heterogeneity, ethnic and religious heterogeneities, and geographical dummies representing
Asia, Africa, South America. Results of the study
showed that natural disasters widened income inequality in the short term; but the effect seem to disappear in
the medium term. Yamamura attributed these findings
to the possibility that governments attempt to attenuate
the impacts of disasters by means of redistribution insubsequent years. Hence, the recovery will also be
accompanied by the reduction of income inequality.

Methodology

In this study, a framework was developed to explain
the link between natural disaster exposure and inequality. It is hypothesized that natural disasters have a direct
effect on the relative incomes of the different sectors
and also have an indirect effect via change in relative
demand and consequently, relative factor prices.
The model considers a two sector economy comprised of a weather vulnerable sector (V) and a non-vulnerable sector (N), both of which are utilizing two
inputs, skilled labor (S) and unskilled labor (U). It is
assumed that the vulnerable sector is intensive in theuse of unskilled labor, while the non-vulnerable sector isintensive in the use of skilled labor. The above is a plausible assumption because informal, traditional, andagriculture-based industries which are considered more
vulnerable to weather shocks rely more on unskilledlaborers than on skilled laborers.
Suppose a disaster or weather shock occurs, the vulnerable sector is expected to suffer a greater decline intheir output than the non-vulnerable sector, given thesame weather shock. Consequently, those employed in
the vulnerable sector will earn lower average incomesthan those employed in the non-vulnerable sector.
From the premise that the vulnerable sector is more
intensive in the use of unskilled laborers which now has
a lower average income than skilled laborers because of
the disaster, this will result in the widening of income

195Jaimie Kim B. Arias, et al.

gap and hence can lead to the worsening of inequality.
For the indirect impact, since output from the two
sectors are expected to decline due to the disaster shock,aggregate output and income of the economy will also
decline. When income declines, demand for both “vulnerable” and “non-vulnerable goods” will also decline.
Depending on the relative importance of the two goodsin the consumption process, the relative demand for the
goods can change, and consequently, so will the relative
output price.
According to the Stolper-Samuelson theorem, if relative output price changes, so will the relative factor
price. The theorem predicts that if the relative price ofthe skilled labor-intensive good increases, the relative
wage of skilled workers will also increase. On the other
hand, if the relative price of the unskilled labor-intensive good increases, the relative wage of unskilled workers will increase.
How relative output price changes will depend upon
the relative importance of each good in domestic
demand. If the marginal propensity to consume (mpc)
for the vulnerable good is greater than the mpc for the
non-vulnerable good, there will be a greater decline in
the demand for the vulnerable good, and hence the relative price of the vulnerable good in terms of the non-vulnerable good will decline (i.e. the absolute price of thevulnerable good will decline more than the absoluteprice of the “non-vulnerable good). This will lead to alower returns (wages) to the factor (unskilled labor) used
intensively in the production of the vulnerable good andconsequently will worsen inequality.
On the other hand, if the mpc for the non-vulnerable
good is higher than that for the vulnerable good, there
will be a greater decline in the demand for the non-vulnerable good, and hence will result in a lower relative
price for the non-vulnerable good. The factor used
intensively in this sector (skilled labor) will earn lower
returns, and hence equality may improve.
The link between relative wages (ω) and the Gini
coefficient is presented below. The Gini coefficient iscomputed through the Lorenz curve. It is equal to thearea between the Lorenz curve and the uniform distribution line divided by the area under the uniform distri-

196

bution line.
Equation 1

Following Goderis & Malone (2011), the measure ofincome inequality can be represented as a ratio of total
wage income earned by skilled workers over total wage
income earned by unskilled workers.
Equation 2

Substituting equation 2 into equation 1 yields,
Equation 3

G then is shown to be a non-linear increasing functionof I. Hence, an increase in exposure to weather and disaster shocks which increases the relative wage of skilled
versus unskilled, increases the inequality ratio I, andconsequently increases G. However, if such weather
shock reduces the relative wage of unskilled workers, theinequality ratio I will decline, consequently reducing G.
The present study hypothesizes that high income
countries and low income countries will experience different inequality impacts from disasters. The direct
impact of disasters is expected to be inequality-increasing in the immediate-term for both high and low income
countries as the localized impacts will have a disproportionate impact on affected areas/sectors. However, theindirect impact will differ depending on a country’s
income category. This arises from the premise that richcountries have a higher propensity to consume skilledlabor-intensive goods while poor countries have a higher
propensity to consume unskilled labor-intensive goods.
Following this assumption, it is hypothesized that asnatural disaster exposure increases and aggregate

6th AFC Best Papers

income declines, the relative demand for unskilledlabor-intensive goods will decline in the poor country,
causing the relative wage of unskilled labor to decline,
hence worsening inequality. For the rich country, however, as the natural disaster exposure increases andaggregate income declines, the relative demand forskilled labor-intensive goods will decline, causing therelative wage of skilled labor to decline, hence improving equality.
In this study, secondary data collected and publishedby reputable international agencies were used in theanalysis. For the inequality variable, the study used theStandardized World Income Inequality Database (Solt2008). The mean of the market Gini coefficient wasused. This is the estimate of the Gini index of inequality
in “equivalized” (square root scale) household pre-tax
and pre-transfer income. For the disaster/extreme
weather variable, the source of data is the Centre for
Research on the Epidemiology of Disasters’ (CRED)
Emergency Events Database (EM-DAT) (CRED, D.
Guha-Sapir n.d.). The indicator used is the frequency ofoccurrence of natural disasters. According to CRED,
for a disaster to be included in the database any one of
the following criteria must be fulfilled: ten (10) or more
people reported killed; hundred (100) or more people
reported affected; declaration of a state of emergency;
and call for international assistance. In addition, several
control variables were also included: Real GDP per
capita (GDP) and its square; trade openness (Trade)which is measured as the total value of export and importas a percentage of GDP; and total land area (Land).
The initial dataset, which contains 192 countries, hasa high number of missing data. Countries that have lessthan 10 years of observations were then dropped arriving at a final dataset of 124 countries covering the period
1986 to 2015. All in all, there were 21 low-income countries, 35 lower middle-income countries, 34 upper middle-income countries, and 34 high income countries.
The study used panel data regression techniques toestimate the effect of natural disasters on inequality. All
estimations were done using the Stata 12 Statistical
Package. The regression models used in the study isgiven in equation 4 and equation 5.

Equation 4

Equation 5

Because different income levels are expected to sustain different inequality impacts from disasters, twoseparate regressions were undertaken, one for the low
and lower middle income countries sub-sample, andanother for the upper middle and high-income countries
sub-sample.

Results and Discussion

The study’s main hypothesis is that natural disasteroccurrence has a different effect on income inequality
depending on the income categorization of countries.
For lower income countries the expected relationship is
positive. As the frequency of natural disasters increases,
the Gini coefficient also increases which means that
income inequality worsens. For higher income countries, the expected relationship may be positive or negative depending on which mechanism is stronger, whether
the direct impact or the indirect impact. As mentioned,
two separate regressions were undertaken, one for low
and lower middle-income countries (denoted as GroupLow), and another for upper middle and high-income
countries (denoted as Group High).
The diagnostic tests employed (Table 1) revealed thatthe fixed effects model is appropriate rather than thepooled OLS or the random effects model. For the Group
High, the Wald-test of joint significance rejects the null
hypothesis that the dummy variables for all years are197

Jaimie Kim B. Arias, et al.

equal to zero. However, individual test of significanceshows that not all the coefficients for these time dummies were statistically significant. Hence, both the oneway fixed effects model and the two-way fixed effects
model are presented in the analysis. On the other hand,for Group Low, the analysis failed to reject the null
hypothesis that the year dummies are not jointly equal
to zero at 5% level of significance. However, since thep-value is still less than 0.10, both the one-way fixed
effects and the two-way fixed effects specifications werestill presented.
Table 1. Summary of the diagnostic tests used in panel
regression analysis
Group
Low

Group
High

Wald joint test of significance forcountry dummies(FE versus OLS)

0.000

0.000

Wald joint test of significance for year
dummies(one-way versus two-way)

0.060

0.008

Hausman test (FE versus RE)

0.005

0.000

Pesaran’s test of cross-sectional
independence for one-way FE model

0.021

0.555

Pesaran’s test of cross-sectional
independencefor two-way FE model

0.873

0.192

Modified Wald test for group-wiseheteroscedasticity
For one-way FE model

0.000

0.000

Modified Wald test for group-wiseheteroscedasticity
For two-way FE model

0.000

0.000

Wooldridge test for autocorrelation inpanel data

0.000

0.000

Diagnostic Test

The p-value of 0.021 in the Pesaran’s test indicatesthe presence of cross-sectional dependence in the oneway FE model for the lower income group sub-sample.
Meanwhile, the null hypothesis of weak cross-sectional
dependence was rejected for the two-way FE model for
the Group Low sub-sample and all specifications for theGroup High sub-sample. Meanwhile, the results of the198

modified Wald test and Wooldridge test denote the presence of heteroskedasticity and first order autocorrelation for all specifications. Because of the presence ofcross-sectional dependence, heteroscedasticity, and firstorder autocorrelation, the standard errors by Driscoll/
Kray were used in the analysis.
The Fisher-type panel unit root test failed to strongly
reject the hypothesis that all panels for the Gini coefficient contain no unit roots. To address this, two specifications of the FE model were used in the analysis. One
specification contains the lag of the Gini coefficient as
an explanatory variable, while the second uses the firstdifference of the Gini coefficient as the dependent variable. The variable natural disaster occurrence was foundto be stationary using the same test.
The result of the panel regression analysis show thatfor Group Low, natural disasters have a worsening
impact on inequality (Table 2). The coefficients, however, were found to be statistically significant only fordisasters lagged three years to disasters lagged five. In
the one-way fixed effects model with the level Gini
coefficient as the dependent variable, the estimatedimpact of disasters lagged three years was modest at
0.011. This means that a one unit increase in disaster
three years prior increases the Gini coefficient by 0.011
Gini points, cet. par. The other specifications, particularly
the two-way FE with level Gini as dependent variable,
as well as the one-way and two-way FE with first differenced Gini as the dependent variable, had very similar
results. The coefficient for natural disasters lagged three
periods, ranges from 0.011 to 0.014, while for the natural
disaster variable lagged four periods was about 0.11.
Meanwhile, the natural disaster variable lagged five
periods had an estimated coefficient that ranges between
0.008 and 0.010. The results suggest that the magnitudeof the impact seems to wane over the years.
For the control variables, real GDP per capita, trade
openness, and land endowment were found to be statistically significant in the one-way FE models for the low
and lower-middle income group. The signs and values of
the coefficients are consistent for all specifications.
The study found a U-shape relationship between real
per capita GDP and inequality. This was determined by6th AFC Best Papers

Table 2. Results of the panel regression analysis for Group Low (low and lower middle-income countries)
Gini

D.GiniOne-way FE

Two-way FE.995***.988***

One-way FE

Two-Way FE

Gini
L1.
Natural disaster
L1.

.006

.009

.006

.009

L2.

.006

.009

.006

.009

L3.

.011*

.014*

.011*

.014*

L4.

.011*

.012*

.011*

.011*

L5.

.010*

.010*

.009*

.008*

Real per capita GDP-.001**-.000-.001**-.000

Real per capita GDP squared

6.5e-08*-4.1e-10

6.4e-08*

2.9e-09

Trade openness-.002*-.001-.002*-0.001

Land area

1.2e-06

7.4e-078.1e-07**-7.9e-10* p<0.05; ** p<0.01; *** p<0.001

taking the first and second order partial derivative of theequation with respect to the variable real per capita
GDP. This result is consistent with Bahmani-Oksooee,
Hegerty, & Wilmeth (2008) and Angeles-Castro (2006).
Inequality may decline as the economy expands because
expansion increases the demand for workers. And with
fixed supply of labor, firms would then eventually hire
down along the line of workers towards the less skilled
and less preferred laborers. This increases the probability of finding employment, as well as increases wage
rates in the lower strata, which then reduces inequality(Treas 1983). This relationship, however, is in effectonly up to a certain level of income. Further expansion
may eventually lead to the worsening of inequality as
growth encourages technological advancements which
increase productivity and the wage premium of skilledworkers, which will then increase inequality.
Furthermore, trade openness was found to have an
equalizing effect on the income of low and lower middle-income countries. The coefficient of the variable
was significant for the one-way FE specifications of themodel. The coefficient estimates of -0.002 suggests thatas the share of total exports and imports to GDP
increases by one percentage point, the Gini coefficientdeclines by about 0.002 cet. par. Theory relating trade

openness with inequality points to an ambiguous relationship between the two, depending on the factor
endowment of countries as well as the degree of technology transfer facilitated by trade. The result seems to
conform to the theory that for lower income countries,
trade openness tends to reduce income inequality.
Lower income countries are relatively more abundantwith unskilled labor. Therefore, they will tend to export
more of the good that are intensive with the use of
unskilled labor. Opening up to trade will increase therelative demand for the unskilled-labor intensive good
produced by the local economy because of the expansionof market opportunities offered by the world market.
This will lead to an increase in the relative demand forunskilled labor, and consequently increases their relative factor earnings, thereby reducing inequality.
Finally, land resource endowment was found to havea significant and positive effect on the Gini coefficientin the one-way FE model with first-differenced Gini as
the dependent variable. Land size was included in themodel to control for possible scale effects. Land area
was used instead of population because population may
be endogenous, while the former is exogenous. The estimate suggests that larger countries tend to have higher
levels of inequality.

199

Jaimie Kim B. Arias, et al.

Table 3. Results of the panel regression analysis for Group High (upper middle and high-income countries)
Gini

D.GiniOne-way FE

Two-way FE.999***.994***

One-way FE

Two-Way FE

Gini
L1.
Natural disaster
L1.

-.005

-.002

-.005

-.003

L2.

-.011*-.009-.011*

-.009

L3.

-.007

-.006

-.007

-.006

L4.

-.006

-.003

-.006

-.003

L5.

-.011*-.007-.011*-.007

Real per capita GDP-.001***-1.3e-06-4.4e-05**

6.2e-07

Real per capita GDP squared

3.8e-10**1.2e-103.7e-10*8.7e-11

Trade openness-.003***-0.001-.003***-0.001

Land area

4.3e-07*2.1e-074.1e-07***8.6e-08

Table 3 above summarizes the regression results forthe upper middle and high-income sub-sample (GroupHigh). The coefficients for natural disasters were negative for all specifications. However, the coefficients were
statistically significant only for the variable disasters
lagged two years and five years for the one-way FE
model. The results suggest that disasters have no immediate impact on inequality in upper middle and high-income countries and that disasters have an
equality-enhancing effect in the medium term.
The negative relationship between natural disasteroccurrence and the Gini coefficient is consistent withthe assumption of the study that richer countries have ahigher propensity for skilled-labor intensive goods.
When disasters reduce aggregate income, relative
demand for skilled-labor intensive goods will also
decline. This will reduce the relative demand forskilled-labor embodied in the good and consequently
reduce their earnings relative to lower paid unskilled-labor, thereby improving inequality.
For the control variables, real GDP per capita and its
square, as well as trade openness were statistically significant in the one-way FE models. The estimated coefficients of real GDP per capita and its square also suggest
a U-shape relationship with inequality, which is similar to
the findings in the lower income sub-sample. Moreover,200

the coefficients for trade openness and the size of land
were also found to be statistically significant in the one
way-FE model. The sign of the coefficient was negative
for trade, and positive for land area which is similar to
the findings for the lower income country group.

Summary and Conclusion

The study examined the impact of natural disasters
on income inequality by analyzing data from 124 countries, from 1986 to 2015, using panel regression techniques. Specifically, the one-way and two-way fixed
effects models with Driscoll/Kray standard errors wereused.
Earlier studies had varying findings on the possible
impact of natural disasters on inequality. Some havefound a positive relationship implying that natural disasters worsen inequality, while others found a negative
relationship, implying that natural disasters areincome-equalizing.
Most studies that were reviewed utilized single country data for their analysis. The paper by Yamamura(2013) was the only study found which analyzed inequality impacts using cross-country panel data. The study
covered 86 countries covering the period 1965 to 2004.
Yamamura (2013) found that natural disasters havewiden inequality in the short-term, but this effect6th AFC Best Papers

eventually disappears in the medium term. Yamamura’s
study therefore assumes that the effect of natural disasters will be the same regardless of the level of income of
the country. The present study deviates from this notion
and conducted separate regressions for low and lower
middle-income countries (Group Low) and upper middle
and high-income countries (Group High). The results ofthe fixed effects regression analysis showed that theoccurrence of natural disasters has a significant impact
on inequality, but the effect was only felt in themedium-term.
The study found evidence that natural disasters tend
to worsen inequality in low and lower middle-income
countries. This magnifies the urgency of mainstreaming
disaster risk reduction and providing safety nets for vulnerable groups in the economy. This may be in the formof skills development, health investments or directtransfers. Moreover, this further amplifies the importance of constructing disaster-resilient infrastructuresand promoting disaster-resilient production methods tominimize the social cost of natural disasters.
For upper middle and high-income countries, it was
found that natural disasters can be equality-improving
in the medium-term. However, this improvement inequality may be due to the reduction in income of thosein the high-income strata, instead of the desired scenario that the income of those in the low-income sector
is rising and catching up. The implication is that disasterrisk reduction should still be a priority in high income
countries.
For the control variables, the study found a U-shape
relationship between real Gross Domestic Product(GDP) per capita and inequality, while trade had a negative relationship.

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