Humanitarian aid: short term immediate relief vs
long term rebuilding
Geethanjali Selvaretnam; Kannika Thampanishvongy; David Ulphz
School of Economics and Finance, University of St Andrews, KY16 9AL
October 15, 2010
Abstract
This paper focuses on investigating the factors that in‡uence the amount of aid
which countries receive when they su¤er destruction by natural disasters. We make
a distinction between the humanitarian aid that is given as immediate relief to help
victims survive and the aid that is given with a long term purpose to help re-build
their livelihoods. First we present a simple model to analyse this problem, followed
by an empirical investigation.
JEL: C01, O12, O19, Q54
Key words: Humanitarian aid, Natural disaster
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1
1
Introduction
During the last few decades, there has been a heightened awareness of natural disasters
around the world. In 2008 alone there were approximately 400 natural disasters a¤ecting
about 220 million people1 . The severity with which natural disasters a¤ect people and
economies has prompted researchers to study this issue from several angles. Some studies
examine natural disaster hotspots, to understand the type and extent of disaster risks
faced by di¤erent geographic regions (Center for Hazards and Risk Research at Columbia
University). Dilley et al (2005) highlight the following information. It is estimated that
3.4 billion (more than half of the world’s population) live in areas which are exposed to
at least one signi…cant hazard ). Based on disaster relief data provided by the United
nations O¢ ce for the Coordination of Humanitarian A¤airs (OCHA), it is reported that
the total relief costs between 1992 to 2003 were US$ 2.5 billion. Similarly, based on the
data on emergency loans and reallocation of existing loans to meet disaster reconstruction
needs during 1980 to 2003 provided by the World bank, the total emergency lending and
loan reallocation during that time period were US$ 14.4 billion.
Aid-…nance literature is ‡ourished with studies that investigate the determinants and
e¤ectiveness of foreign aid in general. Particularly they assess the impact of development
aid on recipient counries. In recent years, however, attention has shifted more towards
sector-speci…c research on the allocation and e¤ectiveness of particular types of aid. Our
paper contributes to the strand of literature on humanitarian aid.
1
Emergency Events Database (EM-DAT), maintained by the Centre for Research on the Epidemiology
of Disasters (CRED) available on http://www.emdat.be/
2
Humanitarian aid in response to a natural disaster could be broadly categorised into
two types: (1) immediate relief in the form of food, clean water, clothes, shelter, medical
supplies, personnel etc to help the victims survive the immediate aftermath of the disaster
and alleviate their su¤ering (2) assistance to help rebuild the victims’ livelihood which
has been a¤ected by the disaster (i.e. homes, transport facilities, hospitals, schools, shops,
…shing boats, farms, estates, personal …nancial losses etc).
In this paper, we use a theoretical framework as well as an empirical analysis to study
the determinants of the amount of immediate humanitarian aid relief and the long term
humanitarian aid towards re-building that is given. How do donors decide to allocate the
humanitarian aid - to which countries to give and whether to give for immediate relief or
longer term re-building projects? Once a country is struck by a natural disaster, altruistic
donors would be driven to disburse the humanitarian aid based on factors re‡ecting not
only the scale of su¤ering but also by concerns over e¤ectiveness. The determinants of
immediate relief could be quite di¤erent from the determinants of humanitarian aid for
longer term re-building. This angle of study distinguishing between these two types of
humanitarian aid has not been done before, either in a theoretical framework or through
empirical analysis. This would be an interesting and important addition to the existing
strands of literature on humanitarin aid.
In the empirical literature on disaster relief, there are few papers that study the determinants of disaster relief or humanitarian aid. Among these is Olsen et al. (2003), a paper
that investigates the determinants of humanitarian aid basing on a qualitative and quantitative analysis. They …nd that there are three key factors that determine the amount
of humanitarian aid disbursed by the donors, namely the intensity of media coverage, the
3
degree of donors’political and security interest and the strength of humanitarian NGOs
and international organisations presence in a speci…c country a¤ected by humanitarian
emergency.
Stromberg (2007) conducted a formal econometric analysis on the characteristics of
countries that are vulnerable to disasters and the determinants of whether or not the
humanitarian aid is given by the donors and the targets of international aid to disaster
victims. In the …rst part of the paper, Stromberg conducted a regression analysis on the
determinants of the magnitude of the disaster measured in terms of the base-ten logarithm
of the number killed using data on 3200 natural disasters that occurred between 19802004. He …nds that disasters may be less severe in high-income countries with e¢ cient
and accountable governments and countries with lower economic inequality.
In the second part of his paper, Stromberg studies whether number of people killed
or a¤ected by natural disasters, the level of GDP and the degree of publicity, colonial
connections, common language and close proximity between the a¤ected countries and the
donor countries play a signi…cant role in determining whether or not bilateral emergency
aid is given by the donors. The results he obtained show that colonial history is clearly
important: having a common colonial history increases the probability of getting disaster
relief. Colonial history is also of importance for the amount of relief, when relief is
provided. Moreover, donors give more humanitarian aid to countries with a common
language. More distant countries are less likely to receive relief. Humanitarian aid is
clearly increasing in the importance of the trade partner. Finally, Stromberg …nd little
evidence that the measures of government friendliness are of importance for disaster relief.
Fink and Redaelli (2009) use data about the way …ve main donor countries responded
4
to 400 natural disasters. This empirical analysis of bilateral aid concluded that humanitarian aid is determined by political and strategic interests of donors - signi…cant determinants being close proximity, availability of crude oil and being former colonies. Raschky
and Schwindt (2009) also empirically show that donors are in‡uenced by strategic interests
such as availability of oil and trade relationships.
In this, our paper, we …rst construct a simple theoretical framework that allows us to
study the determinants of two types of humanitarian aid given to a country hit by natural
disaster: (1) immediate humanitarian relief given to victims who are very badly a¤ected
and are in need of basic human needs to be ful…lled to help them survive the aftermath
of the disaster and (2) longer term aid to help rebuild the livelihood of those who have
been …nancially a¤ected. The determinants we are interested in are (i) degree of recipient
country’s development; (ii) corruption and red tape; (iii) severity of disasters captured by
risk of victims being killed; (iv) severity of disasters captured by …nancial loss, becoming
homeless, jobless etc.
We then go on to the empirical section where the theoretical predictions are tested,
doing a panel data analysis of all the countries a¤ected by natural disasters over the period
1992 - 2008. Our empirical analysis allows us to study the determinants of disaster relief.
Instead of basing our empirical analysis on the bilateral emergency aid, in this paper, we
examine what are the factors that drive the total amount of emergency relief disbursed
by all relevant donors to the recipient country once the disaster strikes the recipient
country. In particular, we investigate whether factors such as severity of disaster, level
of income, and level of corruption or red tape play a signi…cant role in determining the
amount of humanitarian and development aid. As in Dudley and Montmarquette (1976)
5
which shows that the probability of granting development aid decreases with income per
capita but increases with population size of the recipient country, we would like to check
whether these two factors play a similar role in determining the amount of disaster relief
the recipient country receives.
The remainder of the paper is structured as follows. In Section 2, we present our theoretical framework. Section 3 is devoted for presenting the empirical analysis, containing
sources of data, empirical methodology and empirical results, while Section 4 concludes.
2
Theoretical Model
There are n countries that are struck by a natural disaster in a given period of time.
Following this, a humanitarian agency with a budget H has to decide how to allocate the
available funds to support each country that was a¤ected by the natural disaster.
Each country k = 1; : : : ; n is described by the following variables. The population is
Nk . The state of development is such that in the absence of a disaster, consumption per
head would be ck . Let the value placed by the humanitarian agency on an individual’s
getting consumption c
0 be given by,
u(c) =
Since it is assumed that
8
>
>
<
>
>
:
c1
1
;
> 0;
log c;
6= 1
:
(1)
=1
> 0, the value is strictly increasing in c and displays
diminishing marginal value and so inequality aversion. The parameter
measures the
agency’s concern about the plight of the destitute –in other words its degree of inequality
aversion. Higher the
, the more inequality averse and caring towards the poor, the
6
agency.
Assume that if a natural disaster occurs in a country, then a fraction
lation are at risk of dying, where 0 <
k
< 1. Of these, a fraction
k;
0<
k
of the popu-
k
< 1; actually
do die immediately. Dying is captured by assuming that individual consumption drops
to 0 generating individual utility u(0). Notice that if
then u(0) =
1. Higher the
k
and
k,
< 0 then u(0) = 0 while if
0
higher the severity of the disaster in destroying
lives.
Assume that in addition to the
fraction 'k ; 0 < 'k < 1
k,
k
proportion who are at risk of dying, there is also a
who are also a¤ected by disaster. While at no risk of dying,
their consumption is reduced to a fraction
no disaster where 0 <
Lower the
k,
k
k
of what it would have been had there been
< 1. Therefore their consumption would be reduced to
k ck .
higher the severity of the disaster in destroying wealth, home and earning
capacity.
The humanitarian agency has to decide how much to give each a¤ected country as
immediate humanitarian aid to help those who are at risk of dying, and long term aid to
rebuild the lives of those who are a¤ected.
Let xk be the immediate relief allocated to the proportion
k
(1
k) ;
who are alive
but at risk of dying unless helped. Let yk be the rebuilding aid allocated to the 'k
proportion who are a¤ected by the natural disaster, but are not at risk of dying. The
total amount of these two types of aid given to the n countries cannot exceed the budget,
H. The budget constraint for the humanitarian agency is given by (2).
7
n
X
Nk [
k
(1
k ) xk
+ 'k yk ]
(2)
H:
k=1
Assume that because of corruption, a fraction
k;
0<
< 1; of the humanitarian aid
k
that is spent in country k fails to reach its intended recipient. For those who face the risk
of dying but do not die instantly, immediate humanitarian aid provides consumption per
capita of xk
u((1
u(
k ck
0 in the form of medicine, food, clothing, shelter etc, so generating utility
k ) xk ).
+ (1
Those who are a¤ected but not face the risk of dying, end up with utility
k ) yk )
each if they receive humanitarian aid yk to help re-build their lives.
Social welfare in country k is therefore,
8
>
>
<
k [ k u(0) + (1
k )u((1
W k = Nk
>
>
: +'k u( k ck + (1
k ) yk ) + (1
k ) xk )]
k
9
>
>
=
>
;
'k )u(ck ) >
:
(3)
The …rst term is the value placed on those who are a¤ected so badly that they are
at a risk of dying. Of this proportion,
have u((1
k ) xk ).
k
are already dead and the balance (1
k)
The second term is the value received by the proportion 'k who are
a¤ected by the natural disaster but not at the risk of dying. The last term is the value
of u(ck ) that remain unchanged for the proportion who are una¤ected by the natural
disaster.
The humanitarian agency is altruistic towards the victims of the natural disaster.
The problem of the humanitarian agency is therefore to choose xk ; yk ; k = 1; 2; ::::n to
maximise the total welfare of the victims of the natural disaster, which is given by (4).
M ax
xk ;yk
n
X
k=1
Nk f k (1
k )u((1
k ) xk )
8
+ 'k u(
k ck
+ (1
k ) yk )g ;
(4)
n
X
subject to
Nk [
k
(1
k ) xk
+ 'k yk ]
H:
k=1
The Lagrangian function, L, for this problem is,
L =
n
X
k=1
Nk f k (1
0
k )u((1
n
X
B
+ B
@H
k=1
2
6
Nk 6
4
k
k ) xk )
(1
+ 'k u(
k ) xk
+'k yk
k ck
+ (1
(5)
k ) yk )g
31
7C
7C :
5A
The …rst order conditions for this problem are given by (6), (7) and (8) below, with
being the lagrange multiplier.
dL
= Nk k (1
dxk
k )((1
dL
= Nk ' k (
dyk
dL
=
d
k ck
H
k ) xk )
+ (1
n
X
Nk [
(1
k ) yk )
k
k)
(1
(1
Nk k (1
k)
k ) xk
Nk ' k
!
+ 'k yk ]
k=1
k)
0;
0; xk
0; yk
k
0:
0:
0:
(6)
(7)
(8)
Before giving the results of the problem solving (6), (7) and (8), let us de…ne J as
follows:
J =
n
X
Nj
j (1
j )xj
+ 'j 1
j=1
Nk f k (1
k )xk
9
+ 'k yk g :
j
yj
(9)
2
The aid agency will optimally choose xk and yk as given by (10) and (11) respectively.
xk =
yk =
(H J)
Nk
'k
(1
+
k (1
k)
k ck
k)
+ 'k
k k ck (1
(H J)
Nk
(1
k (1
k)
(10)
;
k)
k)
.
+ 'k
(11)
The total amount of immediate aid given to country k is given by Xk ; which is xk
multiplied by the number of people who are given this amount.
Xk = Nk
k
(1
(H J)
Nk
k)
k (1
'k
(1
+
k)
k ck
k)
+ 'k
!
.
(12)
The total amount of rebuilding aidis given by Yk ; which is yk multiplied by the number
of people receiving this amount, so long as it is positive:
Yk = M ax
2
(
0; Nk 'k
k k ck (1
(H J)
Nk
(1
k (1
k)
k)
+ 'k
k)
!)
Problem is solved as follows:
(6)=(7) ) yk = xk
k ck
(1
k)
Substitute in (8) )
H
J
= Nk
xk
=
yk
=
k (1
'k
(1
k )xk
k ck
H J
Nk
+
k (1
H J
Nk
k ) + 'k
+ '(1k k ck)
k
k (1
k)
'k
(1
+ 'k xk
k)
+ 'k
10
k ck
(1
k)
k ck
k)
.
(13)
Notice that Xk > 0, while Yk
0. This means that every country gets immediate
humanitarian aid, but a country will only get rebuilding aid (i.e. Yk > 0) if
Nk
k k ck (1
k)
< (H
k) .
J) (1
If we denote the number of people who die in country k as Dk = Nk
(14)
k k,
then we can
rearrange (12) and (13) as follows:
Xk = D
Yk =
k (1
(H
(H J)
Nk
k)
k
k (1
J) 'k
Dk (1
k (1
k)
+
'k
(1
k ck
k)
k)
+ 'k
k)
'k
(1
k
!
;
(15)
k ck
k)
+ 'k
:
(16)
If we denote the number of people who are at risk of dying in country k as Rk =
Nk
k
(1
k ),
then we can rearrange (12) and (13) as follows:
' k k ck !
(H J)
+
N
(1 k )
k
Xk = Rk
;
k (1
k ) + 'k
Yk =
(H
Rk '(1k
J) 'k
k (1
k)
+ 'k
(17)
k ck
k)
:
(18)
Results
Using the (15) - (18), which show the total amount given to country k as immediate
relief aid and rebuilding aid respectively, we get the following predictions:
1. Immediate humanitarian aid is positively related to the number of people who die,
dXk
dDk
> 0:
11
2. Immediate humanitarian aid is positively related to the number of people who are
at the risk of dying,
dXk
dRk
> 0:
3. Re-building humanitarian goes down with number of dead,
compared to
(1
More speci…cally,
'k k ck
(1 k )
k)
k
dXk
.
dDk
k (1
k )+'k
dXk
dDk
=
(1
k)
dYk
dDk
'
c
(H J)
+ k k k
Nk
(1 k )
k (1
k
k )+'k
< 0, but is low
, whereas
J) (1
k ).
=
k ck
>
.
4. Not all countries will get re-building humanitarian aid. Yk = 0 if Dk (1
(H
dYk
dDk
k)
k
Factors that will mean that country k gets no rebuilding aid, Yk :
High level of corruption indicated by a high
k;
Already quite well developed indicated by a high ck ;
Not being too severely a¤ected by the disaster indicated by a high
k
(less
k
indicates more …nancial loss and being made homeless).
5. On the same token, re-building humanitarian aid goes down if the country is already
rich with a high ck and if it is not too a¤ected, indicated by a high
k.
Once
k ck
reaches a too high level, it will choke o¤ re-building aid altogether. This is because
re-builiding is not so much of an emergency and it indicates that the country can
look after itself.
dYk
d k
k
< 0; dY
< 0: Moreover, this reduction is higher when number
dck
of people who die and are at risk of dying are higher. This is because the funds
are transferred to immediate relief. This is why increase in ck and
increase in immediate relief:
dXk
d k
k
> 0; dX
> 0:
dck
12
k
result in an
Note that
dYk
=
dck
dYk
=
d k
dYk
=
d k ck
'k k
;
(1
k (1
k)
k)
(1
'k ck
k ) 'k ck
Dk
= Rk
;
(1
k (1
k)
k)
(1
'k
k ) 'k
Dk
= Rk
:
(1
k (1
k)
k)
Dk
(1
k ) 'k k
=
Rk
(19)
6. Looking from another angle, increasing …nancial loss will result in aid being diverted
from immediate relief aid to re-building aid:
dYk
d k ck
< 0 and
dXk
d k ck
> 0.
7. The more corrupt the country the more immediate humanitarian aid it gets,
dXk
d k
> 0.
The more corrupt the country the less rebuilding humanitarian aid it gets,
dYk
d k
< 0.
This is explained by the fact that the aid agency is more altruistic and caring
towards the poorer, in this case, who are the most destitute and in danger of dying
if not for the immediate relief. Therefore, the agency gives more to compensate
for the amount that will not reach the victims due to corruption. However, in the
case of longer term relief to those less in need, it diverts the funds elsewhere when
corruption increases.
3
Empirical Analysis: Determinants of Disaster Relief
3.1
Data and Empirical Methodology
After the natural disaster struck the countries, the international relief from other countries
could play a crucial role. Given that during the past few years and at present, the aid bud13
gets of many major donors have stagnated or declined due to global economic slowdown,
the donors have encountered greater di¢ culty in gaining additional resources to expend
on overseas emergencies. Such …nancial limitations on international relief intervention
are also faced by many major donor government departments that deal with emergencies.
Given such …nancial constraint and the large humanitarian stakes, one wants to ensure
that the money from international relief e¤orts is given where it can do most good. In
this section, we conduct a formal empirical analysis, which aims to investigate the factors
that determine the amount of disaster relief –short-term and long-term commitments –
the recipient countries receive.
3.1.1
Sources of data
Disaster Relief. Thus far, the two key sources of disaster relief data are the Development
Assistance Committee (DAC) of the OECD and the Financial Tracking System (FTS) of
the UN O¢ ce for the Coordination of Humanitarian A¤airs. The DAC reports the annual
spending on emergency aid by donor-recipient country pair, but this data includes emergencies other than those caused by natural disasters. In March 2010, the new database on
aid activity was made available on the public domain. The Project-Level Aid (hereafter,
PLAID) developed by William and Mary University and Brigham Young University. The
coverage of this dataset includes information on every individual project committed by
both bilateral and multilateral aid donors during 1973-2009. This database also provides
detailed coding for a variety of additional factors which makes it possible for us to obtain
data on disaster relief for emergencies caused by natural factors only as well as categorise
the disaster reliefs into two types: short-term and long-term disaster reliefs. In this pa14
per, we make use of data on disaster relief from PLAID, which is publicly available at
www.aiddata.org
Natural disasters and their consequences. Data on the occurrences of natural disasters
and the damages caused by them are obtained from the Emergency Events Database (EMDAT), maintained by the Centre for Research on the Epidemiology of Disasters (CRED)
at the University of Louvain. This data is freely available on the public domain3 . The
URL for this database is http://www.emdat.be/. This EM-DAT database can be used
for …nding data for the following explanatory variables: …nancial loss, number of people
killed, number of people a¤ected by natural disasters (including persons injured and/or
rendered homeless) and number of countries a¤ected by natural disasters at the same
time.
GDP per capita and population. Data on GDP per capita and population size are
made available by the United Nations Statistics Division. The URL for this UNSTAT
database is http://unstats.un.org/unsd/snaama/dnllist.asp.
3.1.2
Empirical methodology
Why do donor countries provide disaster relief? One possible reason behind provision of
disaster relief is to save lives and reduce human su¤ering from natural disaster. Under
this motivation, the donors should be driven to provide greater relief to larger disasters
and to low-income countries since such countries have limited resources to mitigate the
3
Two other sources of data on natural disasters are Sigma from Swiss Re and NatCat from Munich
Re; however, these two sources are maintained by private insurance companies and not available in the
public domain.
15
e¤ects of natural disasters. In this section, we investigate these hypotheses. However,
there are other factors that explain why donor countries give disaster relief which are
not looked at in this paper, such as media coverage, economic or political interest of the
donors, geographical distance, sharing of common language as well as sharing common
colonial past. In a similar spirit as an analysis on general aid ‡ows, Alesina and Dollar
(2000) and Stromberg (2007) investigate the role of these factors on the determination of
disaster relief by the donor countries.
In this subsection, we present the empirical methodology which would help us to …nd
answers to the following questions. Is the amount of disaster relief received by the a¤ected
countries related to the scale of …nancial damage caused by the natural disaster? Do the
donors tend to cluster the disaster relief where it will have the largest impact on the victims
in terms of saving lives and reducing su¤ering? Do poor-resource countries receive more
disaster relief? Do these relationships di¤er between short-term and long-term disaster
reliefs?
Our empirical analysis on disaster relief covers all the countries struck by natural
disasters during 1992-2008. We use the panel data analysis to study the determinants of
the amount of disaster relief disbursed by the donor countries. A …xed-e¤ect model for
disaster relief during 1992-2008 is given by
DritST =
0
+
+
1 F inlossit
5 P opit
+
2 Killed
+ ai + uit ;
16
+
3 Af f ected
+
4 Gdpcapit
(1)
and
DritLT =
0
+
+
1 F inlossit
5 P opit
+
2 Killed
+
3 Af f ected
+
4 Gdpcapit
(2)
+ ai + uit ;
where DritST and DritLT refer to short-term and long-term disaster relief, respectively. In
categorising the disaster relief data into short- and long-term, we base our considerations
on the long descriptions, which come with the PLAID disaster relief data. The broad
criteria used in our classi…cation are as follows: short-term disaster relief refers to the
essential assistance o¤ered to the victims of natural disasters to ensure their survivals,
usually takes the form of distributions of food, water, medical supplies, and provision
of temporary shelters etc. On the other hand, long-term disaster relief refers to donors’
supports in the reconstruction and rehabilitation programmes in the countries a¤ected
by natural disaster, as well as investments in disaster prevention and preparedness programmes.
The subscript i denotes di¤erent countries and t = 1992; :::; 2008 denotes the time
period. The variable F inlossit denotes the amount of …nancial losses from the natural
disasters, Killedit represents the number of peopled killed by natural disasters, Af f ectedit
captures the number of people a¤ected by natural disasters (including those injured and/or
rendered homeless), Gdpcapit denotes the GDP per capita, P opit denotes the number of
population, and ai is the country …xed e¤ect, which include factors that are roughly
constant over the 16 years period.
17
3.2
Results from Empirical Analysis
This subsection is devoted to present the preliminary results from our empirical analysis
discussed in the previous subsection. Table 1 presents the preliminary results from our
…xed e¤ect estimations. In the …rst and second columns, the dependent variables are
variables for short-term and long-term disaster reliefs, respectively. The explanatory
variables are the amount of …nancial losses, the number killed, the number a¤ected, the
real GDP per capita and the size of population.
Short-term Disaster relief
665:2247
Long-term Disaster relief
11318:95
Financial losses (units: million)
(201:5245)
(1037:99)
35:81736
805:0236
(38:4444)
(195:3361)
7:563771
517:7723
(10:8645)
(55:15134)
Number killed
Number a¤ected (units: million)
176:3574
1056:277
(262:5614)
(1703:79)
152247:7
290096:4
Real GDP per capita
Population (units: million)
(21413:2)
R-squared (within)
0.0444
(110302)
0.1755
Table 1: Disaster relief and humanitarian needs
* signi…cant at 90 percent, ** signi…cant at 95 percent, *** signi…cant at 99 percent
18
We begin our discussion with the results presented in the …rst column of Table 1,
which based the short-term disaster relief data. The estimated coe¢ cient for …nancial
losses from natural disaster is 665:2247, and is statistically signi…cant at 99 percent. This
indicates that, holding other factors constant, an increase in the amount of …nancial losses
from natural disaster by 1 million US dollars is associated with an increase in the amount
of short-term disaster relief by 665:2247 US dollars. This seems to be quite small, suggesting about the importance of …nancial losses in donors’disaster relief allocation decision.
The estimated coe¢ cients for Killed and Af f ected are positive, but not statistically signi…cant. These, nevertheless, suggest that, holding other factors constant, an increase in
number killed or a¤ected by natural disaster is associated with an increase in the amount
of disaster relief disbursed by the donors. The estimated coe¢ cient for P op is 152; 247:7
and is statistically signi…cant at 99 percent, which suggests that, holding other factors
constant, an increase in the population size by one million is associated with an increase in
the disaster relief by 152; 247:7 US dollars. The estimated coe¢ cient for Gdpcap is positive
but not statistically signi…cant. Nevertheless, this suggests that a relatively resource-rich
country tends to receive more disaster relief than relatively resource-poor country4 .
Next, we turn our attention to the results presented in the second column of Table
1, which are based on the long-term disaster relief data. We …nd that all explanatory
variables except Gdpcap are found to be statistically signi…cant. The estimated coe¢ cient for F inloss is 11; 318:95 and is statistically signi…cant at 99 percent. This result
4
According to the empirical prediction in the development aid literature like Dudley and Montmar-
quette (1976), the probability of granting development aid increases with population size of the recipient
country but decreases with the GDP per capita.
19
indicates that, holding other factors constant, an increase in …nancial losses from natural
disaster by 1 million US dollars is associated with an increase in long-term disaster relief by 11,318.95 US dollars. The estimated coe¢ cients for Killed and Af f ected carry
the expected signs and are statistically signi…cant at 99 percent, suggesting that, holding
other factors constant, an increase in the number killed or a¤ected by the natural disaster
is associated with an increase in the amount of long-term disaster relief. This con…rms to
us one of the humanitarian motives of the donors, i.e. donors give more disaster relief to
countries with higher fatalities. The estimated coe¢ cients for P op is 290; 096:4, which is
consistent with our expectation: an increase in the population size by one million allows
the recipient countries to attract more long-term disaster reliefs by 290; 096:4 US dollars.
The estimated coe¢ cient for Gdpcap is positive but not statistically signi…cant.
The above preliminary empirical results help shed some light on the factors that enter
into the donors’ decision making with regards to disaster relief. The scale of …nancial
damages resulted from natural disaster and population size play an important role in
determining the amount of both short- and long-term disaster relief given by the donors.
We …nd no evidence that the resource-poor countries receive more short- and long-term
disaster relief. Last but not least, our results show that the donors tend to cluster the
long-term disaster relief where it will have the largest impact on the victims in terms of
saving lives and reducing su¤ering; however, no similar evidence is found for short-term
disaster relief.
20
4
Conclusion
We have analysed the factors that in‡uence the amount of humanitarian aid received by
countries which are hit by natural disasters. This analysis was carried out both theoretically and empirically, drawing a distinction between the amount received as immediate
relief and what is received for long term purposes. The theoretical model predicts that
the amount of immediate disaster relief increases with the severity of the natural disaster, as measured by the number of people a¤ected, killed and …nancial loss. Level of
corruption increases immediate relief while reduces long term aid. This can be explained
as the donor being more altruistic towards those who are in immediate need to survive.
Therefore, higher the amount lost to corruption, the more is given to ensure the survival
of the victims. On the other hand, longer term aid would be better used in a country with
lower corruption which would utilise better what is received. If the a¤ected country is
richer, is less severely a¤ected and has high level of corruption, it will receive less aid for
longer term re-building purposes. Empirical analysis to test the theoretical predictions is
still at a preliminery stage, and therefore we cannot say anything conclusive at this point.
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