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Current research identifies six key developmental risks for Africa: (a) vulnerability of infrastructure, (b) health, (c) education, (d) political and security risk, (e) vulnerability to natural disasters and (f) limitation of access to drinking water and sanitary facilities. Key risks are combined t...
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"author": "V. Bakhtina",
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| description | Current research identifies six key developmental risks for Africa: (a) vulnerability of infrastructure, (b) health, (c) education, (d) political and security risk, (e) vulnerability to natural disasters and (f) limitation of access to drinking water and sanitary facilities. Key risks are combined to an integrated risk measure and their impact on 42 African countries is analyzed. Six countries most susceptible to the indicated set of risks are isolated. |
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© V. Bakhtina, M. Zgurovsky, 2008
Системні дослідження та інформаційні технології, 2008, № 3 7
TIДC
ТЕОРЕТИЧНІ ТА ПРИКЛАДНІ ПРОБЛЕМИ І
МЕТОДИ СИСТЕМНОГО АНАЛІЗУ
УДК 316.42 + 504+330.34
QUANTIFICATION OF KEY DEVELOPMENTAL RISKS
IN AFRICA
V. BAKHTINA, M. ZGUROVSKY
Current research identifies six key developmental risks for Africa: (a) vulnerability
of infrastructure, (b) health, (c) education, (d) political and security risk, (e) vulner-
ability to natural disasters and (f) limitation of access to drinking water and sanitary
facilities. Key risks are combined to an integrated risk measure and their impact on
42 African countries is analyzed. Six countries most susceptible to the indicated set
of risks are isolated.
INTRODUCTION
In recent years, the concepts of Sustainable Development and Millennium Devel-
opment Goals (MDG) [1] are in the center of attention of the world community.
At the same time, reaching the target by 2015 could represent a major challenge.
Inter-agency working group, led by the United Nations, developed a system of
measures to track the progress towards the MDG implementation [2]. The latest
United Nations and the World Bank reports emphasize that “Africa is not on track
to achieve any of the Developmental Goals on time” and lags behind in poverty
reduction, human development and environmental sustainability [3]. In spite of
recent economic growth, Africa faces significant challenges in the areas of
healthcare, education, infrastructure and gender. According to the World Bank
data for 2006 [4] Africa contains 32 of 48 world’s poorest countries and 24 coun-
tries ranked lowest in human development. According to P. Collier [5], Africa is
likely to be a developmental problem in the future.
Ample research was done defining the developmental and sustainability
measures. Sustainable Development Gauging Matrix methodology [6] defines a
comprehensive framework which integrates economical, ecological and social
components of sustainable development to a unique sustainability index. The au-
thors attempt to compliment this methodology for Africa case and assess the Sus-
tainable Development from the risk perspective. Similar to [7], a major set of spe-
cific threats, which could impair development and represent major setbacks, are
separated, estimated and their impact is analyzed.
V. Bakhtina, M. Zgurovsky
ISSN 1681–6048 System Research & Information Technologies, 2008, № 3 8
DEVELOPMENTAL RISK INDICATORS AND THEIR PROXIES
Based on the United Nations, World Bank, P. Collier research, and World Devel-
opment reports we isolate six key risk factors-threats. These factors are
a) vulnerability of infrastructure, in particular, severe energy crisis;
b) health of the population, including availability of health facilities and
shortage of healthcare workers (current paper focuses on HIV/Aids infected popu-
lation);
c) education;
d) political and security risk;
e) vulnerability of the countries to natural disasters;
f) limitation of access to drinking water and sanitary facilities.
For each risk factor an intuitive quantifiable proxy which could be used as an
input to the model is considered. The exact description of variables used and their
definitions are provided in Appendix 1.
Ratio of energy production to energy use is considered as a proxy measure
for vulnerability of infrastructure factor (e). For some countries electrical outages
(in days) and WTTC. (World Travel and Tourism Counsil.) Infrastructure Index
(2001) are provided as supplementary data points. Factor (b), or health of the
population, is measured by percent of HIV infected population as of total country
population. Number of physicians per 1000 people is used as an additional factor
to fine-tune the model. Education (c) is measured by literacy rate data from WDI
(World Development Report.) [3]. Appendix 2 explains how missing data points
on various factors are approximated. Political and security risk (d) is approxi-
mated by political stability and absence of violence index developed by the World
Bank (2006) [8]. Vulnerability to natural disasters (e) can be evaluated with the
help of disaster risk index (DRI) and fine-tuned using deforestation rate. At last,
limitation of access to drinking water and sanitary facilities (f) is associated with
access to improved water supply variable (AWS).
Key data for 42 countries out of 56 African countries were added to the data-
set. For the analysis purposes Middle East (North Africa) was not excluded. It
should be noted that some partial data points were available for the remaining 14
countries but were not used. Absence of data could be an indication of a potential
risk for a country, and further work is required to collect the data.
SIMULATION RESULTS
Six main variables are introduced as inputs to the model:
Energy production to energy use (ENPRCONS).
Percent of HIV infected population (%HIV).
Literacy rate (LR).
Political stability and absence of violence (PSAV).
Disaster Risk Index (DRI).
Access to water supply (AWS).
Initially, all variables are normalized for 42 countries in Africa. As the next
step, a vector of Global Africa Risks (GAR) is formed to assess the cumulative
impact and the level of remoteness of the selected countries from the indicated
Quantification of key developmental risks in Africa
Системні дослідження та інформаційні технології, 2008, № 3 9
threats [7]. Minkovski norm and Vard clusterization agglomerative hierarchical
algorithm are used to measure the likelihood of crisis caused by the combined
series of threats. Results of a simulation are provided in table 1.
T a b l e 1 .
Po
lit
ic
al
S
ta
bi
lit
y
an
d
A
bs
en
se
o
f V
io
-
le
nc
e
In
de
x,
(W
G
I)
, 2
00
6
D
is
as
te
r R
is
k
In
de
x
(D
R
I)
, 2
00
3
A
cc
es
s t
o
w
at
er
su
pp
ly
, 2
00
3
Li
te
ra
cy
ra
te
, a
du
lt
to
ta
l (
%
o
f p
eo
pl
e
ag
es
15
a
nd
a
bo
ve
) H
um
an
D
ev
el
op
m
en
t
R
ep
or
ts
(U
N
),
20
07
En
er
gy
p
ro
du
ct
io
n
(k
t o
f o
il
eq
ui
va
le
nt
) /
En
er
gy
u
se
(k
t o
f o
il
eq
ui
va
le
nt
),
(D
D
P)
,
20
04
%
H
IV
Ra
nk
C
ou
nt
ry
PS
A
V
D
R
I
A
W
S
LR
EN
PR
C
O
N
S
%
H
IV
M
in
ko
vs
ki
N
or
m
a
fte
r
N
or
m
al
iz
at
io
n
M
ea
n
V
al
ue
s
of
T
re
at
s
Low risk
1 Egypt 0,387 0,997 0,945 0,714 0,095 1,000 0,751 0,690
2 Congo 0,369 1,000 0,510 0,847 0,987 0,880 0,745 0,765
3 Algeria 0,384 0,983 0,940 0,699 0,420 0,998 0,737 0,737
4 Comoros 0,518 0,982 0,920 0,560 0,000 0,997 0,717 0,663
5 Libya 0,589 1,000 0,715 0,842 0,391 0,992 0,711 0,755
6 Tunisia 0,584 0,997 0,800 0,743 0,065 0,997 0,704 0,698
High risk
7 Djibouti 0,509 0,949 1,000 0,375 0,000 0,924 0,697 0,626
8 Morocco 0,489 0,996 0,785 0,523 0,005 0,997 0,677 0,633
9 Botswana 0,769 0,996 0,950 0,812 0,045 0,388 0,674 0,660
10 Senegal 0,498 0,997 0,750 0,393 0,034 0,979 0,653 0,608
11 Niger 0,482 0,998 0,560 0,290 0,000 0,977 0,629 0,551
12 Lesotho 0,575 0,997 0,910 0,822 0,000 0,398 0,628 0,617
13 Benin 0,615 0,997 0,630 0,347 0,055 0,959 0,625 0,600
14 Gabon 0,565 1,000 0,700 0,840 0,596 0,827 0,623 0,755
15 Mali 0,547 0,999 0,600 0,240 0,000 0,962 0,622 0,558
16 Ghana 0,587 0,998 0,600 0,579 0,062 0,942 0,615 0,628
17 Sierra Leone 0,462 0,997 0,280 0,348 0,000 0,965 0,613 0,509
18 Zimbabwe 0,331 0,999 0,810 0,894 0,077 0,477 0,612 0,598
19 Eritrea 0,387 1,000 0,460 0,610 0,000 0,946 0,610 0,567
20 Burkina Faso 0,511 0,999 0,345 0,240 0,000 0,955 0,608 0,508
21 Gambia 0,578 0,991 0,620 0,420 0,000 0,947 0,608 0,593
V. Bakhtina, M. Zgurovsky
ISSN 1681–6048 System Research & Information Technologies, 2008, № 3 10
22 Dem. Rep.
Congo (Zaire) 0,125 1,000 0,450 0,672 0,086 0,930 0,604 0,544
23 South Africa 0,533 0,995 0,860 0,824 0,099 0,531 0,601 0,640
24 Burundi 0,300 1,000 0,650 0,593 0,000 0,921 0,600 0,577
25 Angola 0,453 1,000 0,380 0,674 0,504 0,920 0,600 0,655
26 Nigeria 0,184 0,999 0,530 0,691 0,193 0,912 0,596 0,585
27 Namibia 0,696 1,000 0,745 0,850 0,020 0,547 0,594 0,643
28 Togo 0,389 1,000 0,525 0,532 0,059 0,928 0,593 0,572
29 Rwanda 0,449 0,999 0,410 0,649 0,000 0,916 0,591 0,571
30 Guinea Bis-
sau 0,438 1,000 0,490 0,260 0,000 0,919 0,582 0,518
31 Cameroon 0,505 1,000 0,570 0,679 0,150 0,875 0,577 0,630
32 Somalia 0,045 0,943 0,290 0,380 0,000 0,979 0,575 0,440
33 Kenya 0,347 0,998 0,445 0,736 0,067 0,848 0,564 0,574
34 Tanzania 0,515 0,998 0,520 0,694 0,078 0,854 0,563 0,610
35 Ivory Coast 0,165 1,000 0,710 0,487 0,087 0,835 0,554 0,547
36 Liberia 0,324 0,999 0,610 0,519 0,000 0,860 0,553 0,552
Very High risk
37 Zambia 0,598 1,000 0,580 0,680 0,076 0,623 0,504 0,593
38 Central Afri-
can Rep 0,238 1,000 0,595 0,486 0,000 0,752 0,503 0,512
39 Sudan 0,149 0,213 0,710 0,609 0,139 0,961 0,435 0,464
40 Swaziland 0,520 0,901 0,620 0,796 0,000 0,222 0,403 0,510
41 Ethiopia 0,215 0,221 0,230 0,359 0,076 0,952 0,364 0,342
42 Mozambique 0,640 0,064 0,600 0,387 0,080 0,636 0,022 0,401
The algorithm allows separate three clusters denoted as Low Risk, High Risk
and Very High Risk.
North Africa clearly shows less susceptibility to the selected six risk factors.
Out of five Middle East (North Africa) countries available for the analysis, four
are grouped in a cluster with Low Risk (rank 1, 3, 5 and 6) relative to Sub-
Saharan Africa. Morocco appears to be the riskiest country in the North Africa
(ranked 8) mostly due to lower energy production and lower literacy rates in
comparison to Egypt, Lybia, Tunisia and Algeria. The authors plan to consider
North Africa in a separate research, perhaps, extending political risk, and adding
more granularity.
Low Risk cluster includes Congo and Comoros. Congo has the highest rate
of energy production, high literacy rate and very low disaster risk index. These
strong components are overweighting relatively weak political stability and access
to water supply components. Comoros looks stronger in relation to managing an
HIV threat and access to improved water supply, with relatively low natural disas-
ter risk.
Zambia, Central African Republic, Sudan, Swaziland, Ethiopia and Mozam-
bique compose a Very High Risk cluster. Mozambique, Sudan and Ethiopia are
most vulnerable to natural disasters in comparison to other countries. (DRI ex-
ceeds average countries by 300 times). In addition, Sudan is one of the countries
with least political stability in the dataset. The other variables look promising for
Sudan and show a reasonable potential to improve if the risks are paid special at-
Quantification of key developmental risks in Africa
Системні дослідження та інформаційні технології, 2008, № 3 11
tention to. In comparison to Sudan, Ethiopia and Mozambique need more support
struggling with the risks as they show low literacy rate, are not supplying enough
electricity to sustain the industry needs, and show higher percentages of HIV in-
fected population and the lowest number of medical workers per thousand of peo-
ple compared to the other countries.
Swaziland has one of the highest likelihood of natural disasters and the high-
est number of people infected by HIV (and only about one physician per 5000
people!). The political stability is average in comparison to other areas. Interest-
ingly, Swaziland is one of the two countries which added forest area during the
last years. Based on the data available, it is likely, that the government accentu-
ated the efforts on environmental sustainability.
Kenya is placed at the High Risk cluster and ranked the 33rd out of 42. It is
one of the riskier countries. Political stability is one of the lowest for Kenya (-
1.09 compared versus the median of about -0.52 for the sample). Kenya has a de-
scent capacity of energy production, but it covers only 81 percent of energy use.
In addition, about 84 days per year have electrical outages. Some effort should be
directed to manage the energy distribution. Approximately three percents of coun-
try population are HIV affected (and only about one doctor is available per 10000
people!) The numbers look striking and demonstrate how much effort should be
mobilized to alter the current situation.
The simulation results showed that out of forty two African countries con-
sidered for the analysis, six countries are most vulnerable to the indicated devel-
opmental risks. These countries are Zambia, Central Africa, Sudan, Swaziland,
Ethiopia and Mozambique. The research demonstrated that the natural disaster
risk contributed most to this cluster of countries. The results also show that the
combination of high political risk, energy production deficit, and problems with
HIV/AIDS placed the countries to higher risk categories.
The simulation clearly indicates that North Africa should be analyzed sepa-
rately.
SUMMARY
Africa faces serious challenges in attaining Millennium Development Goals
which draws a lot of international attention. Current situation calls for a special
effort to assist African countries in the most vulnerable areas and prevent critical
risks’ impact. We defined key developmental risks (developmental threats) for the
continent and combined them to an integrated risk measure. Based on the degree
of risk remoteness, African countries which are more vulnerable to the indicated
risk measure are isolated. Out of six risk components, natural disasters, political
risk, energy crisis and health issues contributed most to the existing ranking.
The research can potentially cover and refine the risks dataset and the results
can be further expanded to (a) create likely offsets to the risks, (b) contribute to
sustainable development of Africa and (c) be a supplementary tool to Millennium
Developmental Goals’ progress evaluation. Blending additional risk indices such
as more granular political risk variables, healthcare data information, and climate
related measures may add substantial granularity to the output.
V. Bakhtina, M. Zgurovsky
ISSN 1681–6048 System Research & Information Technologies, 2008, № 3 12
APPENDIX 1
Index
(Measure) Description Source
Politics and
Freedom:
Political
Stability and
Absence of
Violence
Index
(PSAV)
The Political Stability and Absence of Vio-
lence indicator is a measure of "perceptions
of the likelihood that the government will
be destabilized or overthrown by possibly
unconstitutional and/or violent means, in-
cluding domestic violence and terrorism."
Low scores in this variable indicate that
citizens cannot count upon continuity of
government policy or the ability to peace-
fully select and replace those in power.
http://info.worldbank.org/
governance/wgi2007/
http://papers.ssrn.com/sol3/
papers.cfm?abstract_
id=999979
Disaster Risk
Index
(DRI)
Measure of vulnerability of countries to
three key natural hazards: (1) earthquake,
(2) tropical cyclone, (3) flood.
Index is based on number of casualties as
% of weighted national population.
[killed per millions inhabitants].
http://gridca.grid.unep.ch/
undp/
Improved
access
to water
supply
(AWS)
The access to water supply is defined in
terms of the types of technology and levels
of service afforded. This included house
connections, public standpipes, boreholes
with hand pumps, protected dug wells, pro-
tected springs and rainwater collection;
allowance was also made for other locally-
defined technologies. "Reasonable access"
was broadly defined as the availability of at
least 20 liters per person per day from a
source within one kilometer of the user's
dwelling. Access to water, does not imply
that the level of service or quality of water
is "adequate" or "safe"; these terms were
replaced with "improved"
Index shown as % of population
http://gridca.grid.unep.ch/
undp/cntry_profile.php
Literacy rate,
adult total
(LR)
Shows % of people ages 15 and above
Human Development
Reports (UN)
http://hdrstats.undp.org/
countries/data_sheets/cty_
ds_BEN.html
WTTC Infra-
structure In-
dex, 2001
(WTTI)
Measure of the level of infrastructure de-
velopment based on: (1) the total length of
roads in a country compared with the ex-
pected length of roads, (2) the percentage
of the population with access to improved
sanitation facilities, (3) the percentage of
the population with access to improved
drinking water.
http://humandevelopment.
bu.edu/dev_indicators/
show_info.cfm?
index_id=227&data_type=1
HIV/Aids
Infected Total
Population,
2005 (%HIV)
Percentage of population affected by HIV http://www.globalhealthfacts.
org/topic.jsp?i=1
Quantification of key developmental risks in Africa
Системні дослідження та інформаційні технології, 2008, № 3 13
Energy pro-
duction to
Energy use
(ENPRCONS)
Energy production (kt of oil equivalent) as
percentage of Energy use
(kt of oil equivalent)
World Bank Data, 2004
Electrical
outages of
firms
(ENOUT)
Electrical outages of firms (average num-
ber of days per year), World Bank Data
World Bank Data, latest avail-
able 2003-2006
Physicians
per 100 peo-
ple (HWDI)
Physicians per 100 people. HWDI reflects
overall number of physicians per 1000 peo-
ple in each country
World Development Report,
latest data
Deforestation
Rate (DR)
Measuring the total rate of habitat conver-
sion. Change in forest area plus change in
woodland area minus net plantation expan-
sion for the 1990-2005 interval( the rate
lost in % of forest and woodland habitat).
("-" is a positive trend)
http://rainforests.mongabay.
com/deforestation/2000/
APPENDIX 2
Selected missing data points are approximated separately.
Literacy rate for Somalia is computed as average between male and female
rates for 2001. For Comoros literacy rate for 2005 is used. Gambia and Eritrea
literacy rates are approximated by youth literacy rates as of 1990.
For Ethiopia HIV statistical data provide low and high bounds. Average be-
tween low and high bounds is used as an approximation for HIV affected popula-
tion. World Health Report estimate is used to approximate percentage of HIV af-
fected population as of total population.
Based on [10], US Energy Administration Statistics country profiles, rates of
energy production to consumption are approximated by zero for the countries
where there is no natural gas, coal, electricity and no primary energy production
as of 2006. The assumption covered the following countries: Burkina Faso, Bu-
rundi, CAR, Comoros, Djiboti, Eritrea, Gambia, Guinea-Bissau, Lesotho, Liberia,
Mali, Niger, Rwanda, Sierra Leone, Somalia, Swaziland.
APPENDIX 3
Main Africa developmental risks (Initial data).
C
ou
nt
ry
W
TT
In
fr
as
tru
ct
ur
e
In
de
x,
2
00
1
Po
lit
ic
al
S
ta
bi
lit
y
an
d
A
bs
en
se
o
f
V
io
le
nc
e
In
de
x,
(W
G
I)
, 2
00
6
D
is
as
te
r R
is
k
In
de
x(
D
R
I)
, 2
00
3
A
cc
es
s t
o
w
at
er
su
pp
ly
, 2
00
3
Li
te
ra
cy
ra
te
, a
du
lt
to
ta
l (
%
o
f p
eo
pl
e
ag
es
1
5
an
d
ab
ov
e)
H
um
an
D
ev
el
o-
pm
en
t R
ep
or
ts
(U
N
),
20
07
En
er
gy
p
ro
du
ct
io
n
(k
t o
f o
il
eq
ui
va
-
le
nt
)/E
ne
rg
y
us
e
(k
t o
f o
il
eq
ui
va
le
nt
),
(D
D
P)
, 2
00
4
%
H
IV
El
ec
tri
ca
l o
ut
ag
es
o
f f
irm
s (
av
er
ag
e
nu
m
be
r o
f d
ay
s p
er
y
ea
r)
, (
D
D
P)
,
20
03
–2
00
6
Ph
ys
ic
ia
ns
p
er
1
00
0
of
p
eo
pl
e,
2
00
0-
20
05
D
ef
or
es
ta
tio
n
R
at
es
, 1
99
0–
20
05
, %
Algeria 56,2 -0,89 6 94 69,87 5,04 0,06 12,42 0,2 -3,60
V. Bakhtina, M. Zgurovsky
ISSN 1681–6048 System Research & Information Technologies, 2008, № 3 14
Angola 24,37 -0,51 0,1 38 67,4 6,05 2,01 87,27 0,1 3,10
Benin 23,77 0,38 0,9 63 34,7 0,66 1,03 77,33 0 9,1
Botswana NA 1,23 1,3 95 81,2 0,54 15,30 21,28 0,4 3,70
Burkina
Faso NA -0,19 0,2 34,5 24,00 0 1,13 9,61 0,1 2,80
Burundi NA -1,35 0,1 65 59,3 0 1,99 137,07 0 22,10
Cameroon 49,71 -0,22 0,1 57 67,9 1,80 3,12 12,94 0,2 8,40
Central Af-
rican Rep 29,4 -1,69 0,1 59,5 48,6 0,00 6,19 NA 0,1 1,40
Comoros NA -0,15 6,2 92 56,00 0,00 0,08 NA 60,00
Congo NA -0,97 0 51 84,68 11,84 3,00 NA 0,2 1,10
Dem. Rep.
Congo
(Zaire)
NA -2,31 0,1 45 67,2 1,03 1,74 177,97 0,1 3,10
Djibouti 65,44 -0,2 17,7 100 37,50 0,00 1,89 NA 0
Egypt 62,59 -0,87 1 94,5 71,41 1,14 0,01 13,91 0,5 0
Eritrea 13,41 -0,87 0 46 61 0,00 1,34 NA 0,1 4,30
Ethiopia 5,08 -1,82 272,6 23 35,90 0,91 1,21 NA 0 3,60
Gabon 29,34 0,11 0 70 84,02 7,15 4,34 NA 0,3 0,70
Gambia 29,45 0,18 3 62 42 0,00 1,32 NA 0,1 2,60
Ghana 39,55 0,23 0,7 60 57,9 0,75 1,45 NA 0,2 27,60
Guinea
Bissau 29,01 -0,59 0,1 49 26,00 0,00 2,02 110,24 0,1 8,10
Ivory
Coast NA -2,09 0,1 71 48,7 1,04 4,13 NA 0,1
Kenya 42,29 -1,09 0,8 44,5 73,6 0,81 3,79 83,60 0,1 2,00
Lesotho 61,3 0,16 1,1 91 82,2 0,00 15,04 19,06 0 69,20
Liberia NA -1,22 0,2 61 51,94 0,00 3,50 NA 0 22,30
Libya 57,08 0,24 0 71,5 84,2 4,69 0,20 NA 1,1 0
Mali 41,87 0,01 0,2 60 24,00 0,00 0,96 10,48 0,1 4,90
Morocco 50,98 -0,31 1,5 78,5 52,31 0,06 0,06 5,79 0,5 0,30
Mozam-
bique 30,51 0,52 327,5 60 38,7 0,96 9,09 NA 0 3,60
Namibia 58,92 0,83 0 74,5 85 0,24 11,32 19,17 0,3 9,30
Niger 20,85 -0,35 0,6 56 29,00 0,00 0,57 11,09 0 25,70
Nigeria 36,86 -1,99 0,2 53 69,12 2,32 2,20 NA 0,3 39,20
Rwanda 10,19 -0,53 0,3 41 64,9 0,00 2,10 NA 0 50,20
Senegal 47,37 -0,26 1,2 75 39,3 0,40 0,52 26,10 0,1 7,90
Sierra
Leone 12,71 -0,46 1 28 34,83 0,00 0,87 NA 0 17,70
Somalia NA -2,75 19,9 29 38,00 0,00 0,53 NA 13,90
South Af-
rica 65,65 -0,07 1,7 86 82,4 1,19 11,73 5,45 0,8 0,80
Sudan 42,43 -2,18 275,4 71 60,9 1,66 0,97 NA 0,2 11,60
Swaziland NA -0,14 34,8 62 79,6 0,00 19,45 28,61 0,2 -46,40
Tanzania 47,03 -0,17 0,8 52 69,4 0,93 3,65 60,64 0 37,40
Togo 24,69 -0,86 0 52,5 53,2 0,71 1,79 NA 0 16,40
Tunisia NA 0,21 1,1 80 74,30 0,78 0,09 NA 1,3 2,30
Zambia 50,06 0,29 0 58 68,00 0,92 9,43 NA 0,1 14,30
Zimbabwe 49,25 -1,18 0,5 81 89,36 0,92 13,07 NA 0,2 36,80
Quantification of key developmental risks in Africa
Системні дослідження та інформаційні технології, 2008, № 3 15
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The views expressed herein are those of the individual contributor and do not
necessarily reflect the views of IFC.
Received 15.05.2008
From the Editorial Board: the article corresponds completely to submitted manu-
script.
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| id | journaliasakpiua-article-108899 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-07-17T10:22:56Z |
| publishDate | 2008 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/7e/0c4c3781810e6bb1217c956b90cff27e.pdf |
| spelling | journaliasakpiua-article-1088992018-04-11T11:07:52Z Quantification of key developmental risks in Africa Определение основных рисков развития Африки Визначення основних ризиків розвитку Африки Bakhtina, V. Zgurovsky, М. Current research identifies six key developmental risks for Africa: (a) vulnerability of infrastructure, (b) health, (c) education, (d) political and security risk, (e) vulnerability to natural disasters and (f) limitation of access to drinking water and sanitary facilities. Key risks are combined to an integrated risk measure and their impact on 42 African countries is analyzed. Six countries most susceptible to the indicated set of risks are isolated. Выделены шесть основных рисков устойчивого развития Африки: 1) инфраструктура, 2) здравоохранение, 3) образование, 4) политика и госбезопасность, 5) природные катаклизмы, 6) ограниченный доступ к питьевой воде и санитарным средствам. Перечисленные риски объединены в понятие единого интегрального риска. Проанализировано их влияние на развитие 42 стран Африки. Выделены шесть стран, наиболее чувствительных к действию этого набора рисков. Визначено шість основних ризиків сталого розвитку Африки: 1) інфраструктура, 2) охорона здоров’я, 3) освіта, 4) політика та держбезпека, 5) природні катаклізми, 6) обмежений доступ до питної води і санітарних засобів. Ці ризики об’єднано в поняття єдиного інтегрального ризику. Проаналізовано їх вплив на розвиток 42 країн Африки. Виокремлено шість країн, найбільш вразливих до дії цього набору ризиків. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2008-09-22 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/108899 System research and information technologies; No. 3 (2008); 7-15 Системные исследования и информационные технологии; № 3 (2008); 7-15 Системні дослідження та інформаційні технології; № 3 (2008); 7-15 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/108899/103811 Copyright (c) 2021 System research and information technologies |
| spellingShingle | Bakhtina, V. Zgurovsky, М. Визначення основних ризиків розвитку Африки |
| title | Визначення основних ризиків розвитку Африки |
| title_alt | Quantification of key developmental risks in Africa Определение основных рисков развития Африки |
| title_full | Визначення основних ризиків розвитку Африки |
| title_fullStr | Визначення основних ризиків розвитку Африки |
| title_full_unstemmed | Визначення основних ризиків розвитку Африки |
| title_short | Визначення основних ризиків розвитку Африки |
| title_sort | визначення основних ризиків розвитку африки |
| url | https://journal.iasa.kpi.ua/article/view/108899 |
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