Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica
Seven key developmental risks for Latin America are isolated: a) ecology and deforestation, b) natural disasters, c) drugs and crime, d) political stability, e) access to clean drinking water and sanitation facilities, f) health of the population and g) education level of the population. Selected fa...
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| citation_txt | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica / M. Zgurovsky, V. Bakhtina // Систем. дослідж. та інформ. технології. — 2008. — № 4. — С. 33-40. — Бібліогр.: 15 назв. — англ. |
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| description | Seven key developmental risks for Latin America are isolated: a) ecology and deforestation, b) natural disasters, c) drugs and crime, d) political stability, e) access to clean drinking water and sanitation facilities, f) health of the population and g) education level of the population. Selected factors are aggregated into an integrated risk index for the twenty three Latin American countries. Countries most susceptible to the indicated set of the threats are identified.
Виділено сім ключових ризиків сталого розвитку Латинської Америки: 1) екологія та винищення лісів; 2) природні катаклізми; 3) наркотики та злочинність;4) політика та безпека; 5) обмежений доступ до питної води та санітарні умови; вектор загроз для 23 країн Латинської Америки. Ідентифіковано країни, найбільш вразливі з боку виділеної системи ризиків.
Выделены семь ключевых рисков устойчивого развития Латинской Америки: 1) экология и уничтожение лесов; 2) природные катаклизмы; 3) наркотики и преступность; 4) политика и безопасность; 5) ограниченный доступ к питьевой воде и санитарные условия; 6) здравоохранение; 7) уровень образования населения. Риски синтезированы в комплексный вектор угроз для 23 стран Латинской Америки. Идентифицированы страны, наиболее уязвимые со стороны выделенной системы рисков.
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© M. Zgurovsky, V. Bakhtina, 2008
Системні дослідження та інформаційні технології, 2008, № 4 33
UDC 316.42+504+330.34
COMPOSITION OF AGGREGATE DEVELOPMENTAL RISK
INDEX FOR SELECTED SUBSET OF COUNTRIES
IN LATIN AMERICA
M. ZGUROVSKY, V. BAKHTINA1
Seven key developmental risks for Latin America are isolated: a) ecology and defor-
estation, b) natural disasters, c) drugs and crime, d) political stability, e) access to
clean drinking water and sanitation facilities, f) health of the population and g) edu-
cation level of the population. Selected factors are aggregated into an integrated risk
index for the twenty three Latin American countries. Countries most susceptible to
the indicated set of the threats are identified.
INTRODUCTION
Ample research covers a wide diversity of developmental risks as major causes
for instability and crises in the world. World Economics Forum identified five
main domains of risks: Economics, Geopolitics, Environment, Society and Tech-
nology, and highlighted two new topics on the agenda – food and energy crises
[1]. At the same time, Human Development reports specifically emphasize the
climate change as the defining human developmental issue of our generation [2].
To confront the challenges on the global scale, it is important to consider the
differences among various regions, narrowing down the set of risks into a few
most substantial threats for particular continents. When the areas of concern are
targeted and quantified it is essential to integrate them into a standardized index to
be able to systematically isolate most vulnerable spots and develop corrective
measures. In this paper the authors attempt to separate specific developmental
threats for Latin America and estimate their impact on sustainability of the twenty
three selected countries.
The research and index aggregation are prepared based on “Sustainable de-
velopment gauging matrix” methodology [3] which allows to estimate the Sus-
tainability Index and global impact of the threats totality.
DEVELOPMENTAL RISK INDICATORS AND THEIR PROXIES FOR LATIN
AMERICA
The 21st century forces humanity face the risk of global climate change and its
consequences which include threats to biodiversity and ecosystems, escalating
probability of natural disasters, limitation of access to clean drinking water and
food, and impact on human health. The highest burden falls on the poorest coun-
1 The views expressed herein are those of the individual contributor and do not necessarily
reflect the views of IFC.
M. Zgurovsky, V. Bakhtina
ISSN 1681–6048 System Research & Information Technologies, 2008, № 4 34
tries. The climate change could be one of the major impediments to reaching Mil-
lennium Development Goals [4], and could have a direct bearing on development
prospects in the world [2]. Research of Global Risk Network demonstrated that all
domains of developmental risks are interrelated and have to be considered
jointly [1].
Latin America covers vast territories of land and represents one of major
sources of biodiversity on the planet. At the same time, substantial exploitation of
natural resources leads to deforestation, wild fires and destruction of natural re-
serves. Joint effort is needed to create effective policies to protect and nurture the
continent’s ecosystems, and to avoid detrimental consequences on the global
scale. [5] Therefore, Ecology and Deforestation and Natural Disasters are se-
lected as major threats for the continent.
According to the World Bank report, Latin America leads in a number of
Millennium Development Goals. At the same time it is the continent with the
world’s largest income inequality facing the challenge to meet the poverty
goal [6].
Another major problem in Latin America is crime with crime rates histori-
cally dominating the other regions. High level of inequality and urbanization es-
calate the level of crime and drug abuse [7], [8], [9]. Crime and Drugs factor is
considered as a separate risk for Latin America.
The region still faces significant political instability and lack of actionable
reforms in selected countries [5]. Political Stability is the next major risk.
Health of the Population and Access to Clean Drinking Water are important
factors where Latin America is on track to meet developmental goals [6],
[10], [11].
World Economic Forum on Latin America deemed the Education Level of
the Population to be the top priority to lay foundation for sustainable develop-
ment in the region [5].
In total, we distinguish seven specific risk factors which have the greatest
impact on Latin American countries. These factors are a) Ecology and Deforesta-
tion, b) Natural Disasters, c) Drugs and Crime, d) Political Stability, e) Access to
Clean Drinking Water and Sanitation Facilities, f) Health of the Population, and
g) Education Level of the Population as a factor which amplifies the conscious-
ness of the population to global problems and capability to make a change [4].
For each of the threats one or several proxies are assigned based on rele-
vance and data availability. CO2 Emissions per capita and deforestation rate are
used as a proxy for Ecology and deforestation (a). Disaster risk index is consid-
ered a measure for susceptibility to natural disasters (b). Three proxies are chosen
for Drugs and crime factor (c): Annual prevalence of cocaine and cannabis abuse,
and intentional homicide. Increase in urban population as percentage of total may
also cause crime increase as a result of migration of poor population to the cities
and limitation of access to basic needs. Health of the population (f) risk proxy at
the moment is limited to a risk of HIV infection and measured by percentage of
HIV affected population. It potentially can be expanded to other areas. Literacy
rate is added as a proxy for Education Level of the Population (g). Appendix 1
gives a detailed description of particular proxies and scale of the threats.
Twenty three Latin American countries are identified for the research. Data
points are normalized and aggregated. Aggregated vector of global threats is es-
Composition of aggregate developmental risk index for selected subset of countries …
Системні дослідження та інформаційні технології, 2008, № 4 35
timated for each country and remoteness of each country from the global set of
threats is assessed2. The countries are clustered based on their remoteness from
the entirety of threats.
SIMULATION RESULTS
The following eleven variables-threats are selected as input to the model:
a) HIV affected population (%) (HIV);
b) political stability (PSAW);
c) disaster risk index (DRI);
d) access to water supply, (AWS);
e) CO2 Emissions per capita (metric tons) (CO2);
f) deforestation rates (DR);
g) urban population (% ) (UP);
h) intentional homicide per 100,000 people (IH);
i) annual prevalence of cocaine abuse as % of population 15-64
(COCAINE);
j) annual prevalence of cannabis abuse as % of population 15-
64(CANNABIS);
k) literacy rate, adult total (% of people ages 15 and above (LR).
Let’s determine cumulative impact of the global threats (1–11) on various
Latin American countries and groups of countries. Based on quantitative inputs
for stand-alone variables-threats, we apply methods of cluster analysis with the
objective of separation of groups of countries with “close” characteristics with
respect to the totality of threats [3]. For each country let’s assign a vector
LR)CANNABIS,COCAINE,IH,UP,DR,CO2,AWS,DRI,PSAW,(HIV,=rT j
with the coordinates characterizing the degree of the corresponding threats (ta-
ble 1):
The resulting data on every threat are normalized so that the values belong to
an interval (0–1). For example, for a global threat LR :
minmax
min0 1
LRLR
LRLR=LR
−
−
− .
After normalizing for the remaining global variables-threats, we derive a
normalized vector
,IH,UP,DR,CO2,AWS,DRI,PSAW,(HIV 000000000 =rT j
),CANNAIBIS,COCAINE 00 LR .
Here the value of 0 denotes the maximum threat, and 1 corresponds to the
minimum threat. As a result, after normalizing, the closer the proximity of a
threat, the closer its numeric value to zero, and on the contrary, the further the
proximity of a threat, the closer its value to one.
2 The distance to the totality of threats is based on Minkovsky norm.
M. Zgurovsky, V. Bakhtina
ISSN 1681–6048 System Research & Information Technologies, 2008, № 4 36
T a b l e 1
R
an
k
C
ou
nt
ry
D
eg
re
e
of
re
m
ot
ne
ss
fr
om
to
ta
lit
y
of
g
lo
ba
l t
hr
ea
ts
H
IV
PS
A
V
D
R
I
A
W
S
LR
C
O
2
D
R
U
P IH
C
oc
ai
ne
C
an
na
bi
s
Very Low Risk
1 Uruguay 0.77 0.89 0.65 1.00 0.98 0.97 0.95 1.07 0.08 0.92 0.86 0.93
2 Costa Rica 0.66 0.94 0.69 0.98 0.98 0.95 0.95 0.35 0.38 0.91 0.81 0.96
3 Cuba 0.63 1.00 0.52 0.99 0.85 1.00 0.91 0.72 0.25 0.97 0.62 0.88
Low Risk
4 Brazil 0.57 0.87 0.48 1.00 0.85 0.89 0.94 0.34 0.16 1.00 0.67 0.99
5 Paraguay 0.55 0.92 0.40 0.99 0.71 0.94 0.98 0.29 0.42 0.82 0.86 0.92
6 Argentina 0.50 0.87 0.49 1.00 0.79 0.97 0.87 0.35 0.10 0.86 0.86 0.77
Medium Risk
7 Chile 0.49 0.94 0.67 0.98 0.92 0.96 0.85 0.47 0.12 0.98 0.14 0.48
8 Peru 0.48 0.87 0.32 0.97 0.75 0.88 0.97 0.39 0.27 0.92 0.67 0.90
9 Trinidad
and Tobago 0.47 0.09 0.47 1.00 0.86 0.98 0.09 0.37 0.88 0.83 1.00 0.69
10 Bolivia 0.47 0.98 0.31 0.98 0.77 0.87 0.97 0.35 0.36 0.96 0.09 0.75
11 Mexico 0.46 0.94 0.42 0.95 0.85 0.92 0.84 0.34 0.24 0.81 0.81 0.76
12 Barbados 0.45 0.62 0.72 1.00 1.00 1.00 0.83 0.41 0.47 0.89 0.52 0.30
13 Colombia 0.42 0.86 0.18 0.98 0.89 0.93 0.95 0.39 0.27 0.09 0.62 0.89
14 Dominican
Republic 0.42 0.71 0.53 0.98 0.79 0.87 0.90 0.41 0.33 1.00 0.57 0.69
15 Panama 0.41 0.78 0.52 0.99 0.87 0.92 0.93 0.39 0.29 0.86 0.43 0.66
16 Ecuador 0.41 0.94 0.32 0.96 0.71 0.91 0.93 0.20 0.37 0.73 0.43 0.87
High Risk
17 Nicaragua 0.36 0.96 0.41 0.75 0.75 0.77 0.97 0.20 0.41 0.81 0.52 0.86
18 Honduras 0.36 0.60 0.41 0.09 0.87 0.80 0.97 0.04 0.54 1.00 0.57 0.93
19 Belize 0.33 0.47 0.52 0.98 0.76 0.75 0.89 0.41 0.52 1.00 0.67 0.36
Very High Risk
20 Haiti 0.28 0.11 0.19 0.91 0.46 0.55 1.00 0.32 0.61 1.00 0.86 0.42
21 El Salvador 0.27 0.78 0.49 0.88 0.74 0.81 0.97 0.20 0.40 0.54 0.76 0.55
22 Guatemala 0.25 0.80 0.34 0.96 0.85 0.69 0.97 0.25 0.53 0.63 0.43 0.10
23 Venezuela 0.17 0.83 0.25 0.55 0.84 0.93 0.78 0.32 0.07 0.52 0.47 0.74
For each country lets assign a certain number jrT which represents
Minkovsky norm for a vector of normalized threats jrT 0 , where 3=p and
11=n :
3
1
30 )(∑
=
=
n
l
jlj rTrT .
Note, that generally, in the majority of practical cases, p is chosen to equal 2.
Increasing the parameter increases response (sensitivity) of the model to the
Composition of aggregate developmental risk index for selected subset of countries …
Системні дослідження та інформаційні технології, 2008, № 4 37
change of each subcomponent of jrT 0 , and vise versa, its decrease smoothens
(roughens) its sensitivity. That is why, based on data analysis from table 1, it is
recommended to increase parameter p from 2 to 3 to increase sensitivity of the
model to certain threats which have insignificant numerical values in comparison
to others but are extremely important in the risk context.
Let’s call number jrT 0 the degree of remoteness from the totality of threats
(1–11) for country j. Based on the computation of norms for the vector of threats
jrT for each country j [3] lets introduce a relation of order among country clus-
ters (table 1)
jkjk rTrTKK ≺ ≤⇔ .
The simulation results are provided in table 1.
Based on simulation results the set of twenty three countries is split into five
clusters: Very Low Risk, Low Risk, Medium Risk, High Risk and Very High
Risk.
Uruguay, Costa Rica and Cuba are falling to Very Low Risk category. The
common characteristics of these three countries are low HIV rates, relatively sta-
ble political situation, low drug prevalence and intentional crime, low emissions
level and deforestation rates.
It is interesting that Barbados fell into Medium Risk cluster while leading in
political stability, access to water supply, showing low deforestation rate and
quite high literacy rate. The reason is quite high HIV percentage, intentional
crime and drug prevalence.
Brazil, Paraguay and Argentina form the next cluster of Low Risk. These
countries are subject to approximately equivalent threats of natural disasters and
approximately equal level of political stability. Paraguay has the lowest access to
water supply but leads the cluster in terms of stopping the spread of HIV infection
and higher literacy rate.
Chili, Peru, Trinidad, Bolivia, Mexico, Barbados, Colombia, Dominican Re-
public, Panama and Ecuador represent Medium Risk cluster. All countries in me-
dium cluster represent low natural disaster risk and quite high literacy rate. Level
of political stability varies broadly from 0.31 to 0.72 Medium Risk cluster faces
increasing drug usage and deforestation rates. Among all countries Colombia
faces the highest intentional homicide rate and lowest political stability.
Nicaragua, Honduras and Belize are in High Risk Category.
El Salvador, Guatemala and Venezuela show the closest proximity to the
cumulated threats chosen. Mostly the result is influenced by one or a couple of
single factors from the whole set of threats. For example, Haiti has the highest
HIV percentage after Trinidad. It is one of the most turbulent countries at the con-
tinent compared to Venezuela and Colombia with lowest level of access to water
supply and literacy rate. All countries in the cluster have serious problems with
deforestation.
All variables-threats equitably contributed to the cumulated threats assess-
ment. Possible interpretation is that even if a certain country is lagging behind in
one or a couple of areas it can use its strengths and manage crisis efficiently. For
example, even if Colombia has the lowest level of political stability and the high-
M. Zgurovsky, V. Bakhtina
ISSN 1681–6048 System Research & Information Technologies, 2008, № 4 38
est homicide rate in the region, it has higher literacy rates, lower environmental
risks and adequate infrastructure, what leads to believe that if the focus is shifted
primarily to the domestic terrorism and homicide reduction, the situation will be
significantly improved.
SUMMARY
Based exclusively on the set of risk factors and variables-threats proxies chosen,
the results of the simulation do not look surprising. Aggregated index of global
threats is computed for all the threats collectively and takes into account country
current situation and the potential. The index allows to isolate the countries which
can reduce stand alone threats using their strengths and the most vulnerable
groups where all threats represent a problem.
The research can potentially take into account broader measures of political
risk, drugs and crime data. Another enhancement can be done using extended
health statistics.
A P P E N D I X 1 . Developmental Risks Proxies detail interpretation and scale
Risk Interpretation Scale Threat Di-
rection
HIV, % Percentage of population affected by HIV [0; 100]
Increase in
absolute
value
Political
Stability
and Absence
of Violence
(PSAW)
The indicator is a measure of "perceptions of the
likelihood that the government will be destabi-
lized or overthrown by possibly unconstitutional
and/or violent means, including domestic vio-
lence and terrorism." Low scores in this variable
indicate that citizens cannot count upon continu-
ity of government policy or the ability to peace-
fully select and replace those in power
[–2.5;2.5]
Decrease in
index value
(–2.5 worst
governance,
0 average,
2.5 best
governance)
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]. (Weighted av-
erage population (1980-2000), takes into ac-
count the actual population at the time the casu-
alties were recorded. E.g. if most of the disasters
happened in the early eighties, then the figure
reflects the average population at that time)
(0; +∞)
Increase in
absolute
value
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, pub-
lic standpipes, boreholes with hand pumps, pro-
tected dug wells, protected springs and rain-
water 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
[0;100]
Decrease
in absolute
value
Composition of aggregate developmental risk index for selected subset of countries …
Системні дослідження та інформаційні технології, 2008, № 4 39
source within one kilometer of the user's dwell-
ing. Access to water, does not imply that the
level of service or quality of water is "adequate"
or "safe"; these terms were replaced with "im-
proved" Index shown as % of population
CO2
Emissions
per capita
(metric tons)
CO2 emissions divided by midyear population (0; +∞)
Increase in
absolute
value
Deforestation
Rates
(DR)
Deforestation – permanent conversion of natural
forest area to other uses, including shifting culti-
vation, permanent agriculture, ranching, settle-
ments, and infrastructure development on an
average annual basis between 1990 and 2005. De-
forestated areas do not include areas lagged but
intended for regeneration, or areas degraded by
fuel wood gathering, acid precipitation and wild-
fires. Negative numbers indicate increase in for-
est area. Measures the rate of change
(–∞;+∞)
Increase in
absolute
value
Urban Popula-
tion, % Urban Population as % of total [0;100]
Increase in
absolute
value
Intentional
Homicide per
100,000 people
(IH)
Number of homicides per 100000 people (0; +∞)
Increase in
absolute
value
Annual Preva-
lence of Cocaine
abuse as % of
population
15–64
People consuming cocaine as % of
total population (0; +∞)
Increase in
absolute
value
Annual Preva-
lence of Canna-
bis abuse as %
of population
15–64
People consuming cannabis as % of
total population (0; +∞)
Increase in
absolute
value
Literacy rate,
adult total (% of
people ages 15
and above)
Shows % of people ages 15 and above (0; +∞)
Decrease in
absolute
value
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M. Zgurovsky, V. Bakhtina
ISSN 1681–6048 System Research & Information Technologies, 2008, № 4 40
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Received 15.10.2008
From the Editorial Board: the article corresponds completely to submitted
manuscript.
|
| id | nasplib_isofts_kiev_ua-123456789-12010 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1681–6048 |
| language | English |
| last_indexed | 2025-11-29T13:13:44Z |
| publishDate | 2008 |
| publisher | Навчально-науковий комплекс "Інститут прикладного системного аналізу" НТУУ "КПІ" МОН та НАН України |
| record_format | dspace |
| spelling | Zgurovsky, M. Bakhtina, V. 2010-09-13T17:18:54Z 2010-09-13T17:18:54Z 2008 Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica / M. Zgurovsky, V. Bakhtina // Систем. дослідж. та інформ. технології. — 2008. — № 4. — С. 33-40. — Бібліогр.: 15 назв. — англ. 1681–6048 https://nasplib.isofts.kiev.ua/handle/123456789/12010 316.42+504+330.34 Seven key developmental risks for Latin America are isolated: a) ecology and deforestation, b) natural disasters, c) drugs and crime, d) political stability, e) access to clean drinking water and sanitation facilities, f) health of the population and g) education level of the population. Selected factors are aggregated into an integrated risk index for the twenty three Latin American countries. Countries most susceptible to the indicated set of the threats are identified. Виділено сім ключових ризиків сталого розвитку Латинської Америки: 1) екологія та винищення лісів; 2) природні катаклізми; 3) наркотики та злочинність;4) політика та безпека; 5) обмежений доступ до питної води та санітарні умови; вектор загроз для 23 країн Латинської Америки. Ідентифіковано країни, найбільш вразливі з боку виділеної системи ризиків. Выделены семь ключевых рисков устойчивого развития Латинской Америки: 1) экология и уничтожение лесов; 2) природные катаклизмы; 3) наркотики и преступность; 4) политика и безопасность; 5) ограниченный доступ к питьевой воде и санитарные условия; 6) здравоохранение; 7) уровень образования населения. Риски синтезированы в комплексный вектор угроз для 23 стран Латинской Америки. Идентифицированы страны, наиболее уязвимые со стороны выделенной системы рисков. en Навчально-науковий комплекс "Інститут прикладного системного аналізу" НТУУ "КПІ" МОН та НАН України Теоретичні та прикладні проблеми і методи системного аналізу Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica Построение индекса интегрального риска устойчивого развития для избранного множества стран Латинской Америки Побудова індексу інтегрального ризику сталого розвитку для вибраної множини країн Латинської Америки Article published earlier |
| spellingShingle | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica Zgurovsky, M. Bakhtina, V. Теоретичні та прикладні проблеми і методи системного аналізу |
| title | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica |
| title_alt | Построение индекса интегрального риска устойчивого развития для избранного множества стран Латинской Америки Побудова індексу інтегрального ризику сталого розвитку для вибраної множини країн Латинської Америки |
| title_full | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica |
| title_fullStr | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica |
| title_full_unstemmed | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica |
| title_short | Composition of aggregate developmental risk index for selected subset of countries in Latin Аmerica |
| title_sort | composition of aggregate developmental risk index for selected subset of countries in latin аmerica |
| topic | Теоретичні та прикладні проблеми і методи системного аналізу |
| topic_facet | Теоретичні та прикладні проблеми і методи системного аналізу |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/12010 |
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