1.
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2.
Summary
1. C o m po s ite indic a to rs a nd ra nk ing
2. T ra ditio na l m etho ds fo r c o ns truc ting
c o m po s ite indic a tors a nd their w ea k nes s
3. T he C FA R -m a lg o rithm
4. S om e ex a m ples of im plem enting C FA R -m
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3.
1. Composite indicators and ranking
What is it ?…
A C o m po s ite I ndic a tor is a n a g reg a te index tha t s um m a rizes a la rg e a m o unt
o f info rm a tion g iven by s ing le indic a tors .
C om pos ite I ndic a to rs a re inc rea s ing ly being us ed to m ea s ure
m ultidim ens iona l perfo rm a nc e a nd to ra nk c ountries , firm s , c lients ,
ins titutions , etc ., in m a ny fields , s uc h a s :

Competitivity (Global Competitivity Index - FEM)

Country risk (ICRG-PRS group)
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
masque
Well-being (Health System Achievement Index-WHO)

Environment (Environmental Sustainability Index- WEF)

Governance (The Corruption Perceptions Index - Transparency
International)

Innovation (Technology Achievement Index- UN)
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4.
1. Composite indicators and ranking
A real interest …
D em a nd for, a nd produc tion o f C om pos ite I ndic a to rs a re ra pidly g ro w ing .
2 rea s o ns , ba s ic a lly :
Google search results for "composite indicators"
1- C om plex ity o f m o dern
ec ono m y : jus t one, o r a s et
of s ing le indic a tors is not
enoug h a ny m o re.
2- D evelopm ent o f I C T s : it
m ea ns tha t a hug e m a s s o f
inform a tio n ha s to be
pro c es s ed
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2. Traditional methods for constructing composite indicators and
their weakness A great number …
Most used weighting schema in aggregation methods:
W eig hts ba s ed o n s ta tis tic a l m o dels

E qua l w eig hts

D a ta E nvelopm ent A na lys is (D E A )

P rinc ipa l C o m po nent A na lys is (P C A )

U no bs erved C o m po nents M o dels (U C M )
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W eig hts ba s ed o n ex perts ’ o pinio ns
masque

B udg et a llo c a tio n
W eig hts ba s ed o n the s ta tis tic a l qua lity o f da ta

S ta nda rd devia tion
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2. Traditional methods for constructing composite indicators and
their weakness Many problems…
Drawbacks of traditional methods :
They are exogenous
They are linear
They lose information
They offer a no posle style des sous-titres du
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masque
decision-making processes
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3. The CFAR-m algorithm
Our solution …
A n orig ina l m ethod ba s ed on a rtific ia l intellig enc e for the
c o ns truc tion of c om pos ite indic a to rs tha t a llow s to
perform releva nt ra nk ing .
Innovation
T he w eig hting s c hem a o f s ing le indic a tors is
g enera ted thro ug h a lea rning proc es s , from
inform a tiona l c ontent of the va ria bles
them s elves a nd their interna l dyna m ic s .
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3. The CFAR-m algorithm
Our solution …
C -FA R m w ork s in three s ta g es tha t a re s truc tura lly
c o m bined :
S ta g e 1 : Firstly, it carries out a c la s s ific a tio n (self-organization) of
objects (records, points, cases, samples, entities, or instances)
through a lea rning pro c es s that takes into account interactions
between the attributes (variables, fields, characteristics, or features)
in ho m o g eneo us c lus ters .
Preliminary stage : Stage 1 :
Preparing the data base Classification
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3. The CFAR-m algorithm
Our solution …
Stage 1 : Stage 2 :
Classification Generating weights: one vector is defined
for each object
S ta g e 2 : S ec ondly, a n a ppropria te w eig hts vec tor is
g enera ted for ea c h o bjec t.
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3. The CFAR-m algorithm
Our solution …
Stage 2 : Stage 3 :
Generating weights: one vector is defined Computing the composite indicators and
for each object rankingthe objects
S ta g e 3 : T hirdly, w eig hts vec tors a re a pplied to the
orig ina l da ta to c om pute C FA R -m c om pos ite indic a to rs
a nd fina lly to c a rry out the overa ll ra nk ing o f objec ts .
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3. The CFAR-m algorithm
Our solution …
Preliminary stage : Stage 1 :
Preparing the data base Classification
Stage 3 : Stage 2 :
Computing the composite indicators Generating weights: one vector is
and rankingthe objects defined for each object
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3. The CFAR-m algorithm
O ur s olution is ba s ed on a n orig ina l tec hnique tha t
us es neura l netw ork s a nd, unlik e ex is ting m etho ds ,
pres ents the follo w ing c ha ra c teris tic s :
Objectivity T here is no m a nipula tion of w eig hts . The
Weights used to aggregate single indicators are generated
automatically from the database through a learning
process. Our model provides a fundamental s olution to the
main aggregation problem.
S pecificity E a c h objec t ha s a s pec ific equa tion to
compute its composite indicator.
Decis ion s upport I t a llow s perform ing of s im ula tio ns and
therefore, can help to decide on appropriate actions and
corrections.
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4. Some examples of implementing C-FARm
Case study 1 : Computing a CFAR-m Human Development Index
(comparison with the UNDP aggregation methodology based on equal weights)
Case study 2 : Computing a CFAR-m indicator Governance Index
(comparison with the MINEFI-France aggregation methodology using weights based on
statistical quality of data )
Case study 3 : Computing a CFAR-m Country Risk Index
(comparison with the PRS Group aggregation methodology based on expert opinion)
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14.
4. Some examples of implementing C-FARm
Case study 1 :
Computing a CFAR-m Human Development Index
(comparison with the UNDP aggregation methodology based on
equal weights)
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15.
Case study 1 : Computing a CFAR-m Human Development Index
I n its firs t Human Development R eport (1990), the U nited N a tio ns
D evelo pm ent P ro g ra m (U N D P ) intro duc e d a new index : H um a n
D evelo pm ent I ndex (H D I ).
H D I is intended to s um m a rize in o ne m ea s ure three dim ens io ns o f
the develo pm ent pro c es s : lo ng evity, educ a tio na l a tta inm ent, a nd
s ta nda rd o f living .
D im ens io ns V a ria ble s (ba s ic indic a to rs )
H ea lth L ife ex pec ta nc y a t birth
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masque A dult litera c y ra te
E duc a tio n
HDI P rim a ry, s ec o nda ry a nd tertia ry
s c ho o ling enro lm ent ra tio s
S ta nda rd of L iving
G D P per c a pita
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Case study 1 : Computing a CFAR-m Human Development Index
T o c o m pute the H D I , U N D P c o ns ider the s im ple a vera g e
(equa lly w eig hted s um ) o f the tree dim ens io ns .
T he three dim ens io ns ha ve the s a m e w eig ht
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L ife ex pec ta nc y E duc a tio n GDP
index inde x inde x
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17.
Case study 1 : Computing a CFAR-m Human Development Index
… … , thus , c o m pa ris o ns a m o ng different c o untries /reg io ns a re
c a rried o ut.
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18.
Case study 1 : Computing a CFAR-m Human Development Index
The main arguments against HDI:
I m po rta nt dim ens io ns a re no t c o ns idered (freedo m , hum a n rig hts ,
g o verna nc e, etc .)
H D I is hig hly c o rrela ted to the G D P (0,89 a c c o rding to M a c G illivra y,
1991).
T he three dim ens io ns a ls o a re hig hly c o rrela ted to the G D P
W eig hting o f the three dim ens io ns is to o s ubjec tive
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Case study 1 : Computing a CFAR-m Human Development Index
The main critics made to the HDI :
“The best known macro-indicator in the world is probably the Human
D evelopment Index (HD I) developed by the United Nations D evelopment
P rogram. It has been severely criticized for combining together indicators of
income, health and education to create a composite index, both on the grounds
that the weights are arbitrary and unjus tified and on the grounds that the
three components of the index are highly correlated and hence give redundant
results ”
Literature Review of Frameworks for Macro-
indicators
Andrew Sharpe (2004)
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20.
Case study 1 : Computing a CFAR-m
Human Development Index
S ta g e 1 : C o untry c la s s ific a tio n
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21.
Case study 1 : Computing a CFAR-m Human Development Index
S ta g e 2 : G enera ting w eig hts : one vec to r is defined fo r ea c h
c o untry
W eig hting s c hem e differs fro m o ne c o untry to a nother : C FA R -m is non-
linea r
L ife ex pec ta nc y E duc a tio n GDP
index index index
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22.
Case study 1 : Computing a CFAR-m Human Development Index
S ta g e 2 : G enera ting s pec ific w eig hting s w ith C FA R -m
Weights are generated automatically through a learning
process from the database :
O bjec tivity
Each country has a specific equation to compute its
development index :
S pec ific ity
CFAR-m allows the identification, for each country,
of the dimension that most influenced the
calculation of its index, and therefore its ranking :
I ntens ity a nd S ig n
T he ra nk ing of C FA R -m w ill be both
objec tive a nd releva nt. 22

23.
Case study 1 : Computing a CFAR-m Human Development Index
S ta g e 3 : C om puting a C FA R -m H um a n D evelo pm ent I ndex a nd ra nk ing
c o untries
HDI dimensions - Year 2005 CFAR-m's results for year 2005
Countries Life Education GDP Country CFAR-m rank
topping the list expectancy index index CFAR-m
index minus
rankt
UNDP rank
I c ela nd 0.941 0.978 0.985 IS L 1 0
N o rw a y 0.913 0.991 1.000 NOR 2 0
A us tra lia 0.931 0.993 0.962 AUS 3 0
C a na da 0.921 0.991 0.970 CAN 4 0
I re la nd 0.890 0.993 0.994 IR L 5 0
S w eden 0.925 0.978 0.965 S WE 6 0
U nite d S ta te s 0.881 0.971 1.000 US A 7 5
S w itzerla nd 0.938 0.946 0.981 CHE 8 1
J a pa n 0.954 0.946 0.959 JPN 9 1
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Case study 1 : Computing a CFAR-m Human Development Index
S ta g e 3 : C om puting a C FA R -m H um a n D evelo pm ent I ndex a nd ra nk ing
c ountries
HDI dimensions - Year 2005 CFAR-m's results for year 2005
Countries closing Life Education GDP Country CFAR-m CFAR-m
the list expectancy index index rank rank
index minus
UNDP rank
Burundi 0.391 0.522 0.325 BDI 169 2
Central Afr. Rep. 0.311 0.423 0.418 CAF 170 1
Mozambique 0.296 0.435 0.421 MOZ 171 1
Guinea-Bissau 0.347 0.421 0.353 GNB 172 3
Chad 0.423 0.296 0.444 TCD 173 3
Mali 0.469 0.282 0.390 MLI 174 1
Sierra Leone 0.280 0.381 0.348 SLE 175 2
Burkina Faso 0.440 0.255 0.417 BFA 176 0
Niger 0.513 0.267 0.343 NER 177 3
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25.
Case study 1 : Computing a CFAR-m Human Development Index
C FA R -m a s a dec is io n s uppo rt s o lutio n
A s w eig ht s c hem a s a re s pec ific , it a llow s to perfo rm
s im ula tions
Impact on the rank of an improvement 0.1 in one dimension
(here, for North African countries)
Life expectancy
index
Number of ranks gained in overall ranking
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26.
Case study 1 : Computing a CFAR-m Human Development Index
C FA R -m a s a dec is io n s uppo rt s o lutio n
A s w eig ht s c hem a s a re s pec ific , it a llow s to perfo rm
s im ula tions
Impact on the rank of an improvement 0.1 in one dimension
(here, for North African countries)
Education index
Life expectancy
index
Number of ranks gained in overall ranking
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27.
Case study 1 : Computing a CFAR-m Human Development Index
C FA R -m a s a dec is io n s uppo rt s o lutio n
A s w eig ht s c hem a s a re s pec ific , it a llow s to perfo rm
s im ula tions
Impact on the rank of an improvement 0.1 in one dimension
(here, for North African countries)
GDP index
Education index
Life expectancy
index
Number of ranks gained in overall ranking
27

28.
Case study 2 :
Computing a CFAR-m Governance Index
(comparison with the MINEFI-France aggregation methodology
using weights based on statistical quality of data)
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29.
Case study 2 : Computing a CFAR-m Governance Index
T he " I ns titutio na l pro files " da ta ba s e
It gathers a whole set of indicators characterizing the institutions
of 85 developed and emerging countries
132 I nfo rm a tio n 9
variables a g g reg a tio n governance
pro c es s indicators
Each variable is weighted according to its
standard deviation
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30.
Case study 2 : Computing a CFAR-m Governance Index
T he " I ns titutio na l pro files " da ta ba s e
Gathers a whole set of indicators characterizing the institutions of
85 developed and emerging countries
9 g o verna nc e
132 variables indic a to rs
1 : Political institutions
2 : Public order
85 countries
3 :Perfomance of Administration
I nfo rm a tio n 4 :Efficiency of free markets
a g g reg a tio n
proc es s 5 :Prospective and planning
6 : Security of transactions
7 : Regulation
8 : Foreign openness
9 : Social cohesion
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31.
Case study 2 : Computing a CFAR-m
Governance Index
S ta g e 1 : C ountry ra nk ing
1st dimension's case : ″political institutions″
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32.
Case study 2 : Computing a CFAR-m Governance Index
S ta g e 2 : G enera ting s pec ific w eig hts w ith C FA R -m
R em inder : in the M I N E FI 's m ethod, the w eig ht o f o ne va ria ble
c o m es fro m its s ta nda rd devia tion
The component The component
that weighs the that weighs the
most in the least in the
computation computation
Components of the 1st dimension re. political institutions

H ow leg itim a te a re tho s e w eig hting s ?

A nd w ha t a bo ut the fa c t tha t they a pply to a ll c o untries ?
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33.
Case study 2 : Computing a CFAR-m Governance Index
S ta g e 2 : G enera ting s pec ific w eig hting s w ith C FA R -m
T here is no em piric a l m a nipula tio n. W eig hting s a re pro c es s ed us ing the
s o le inform a tio n em bedded in the va ria bles .
Kuwait
Components of the 1st dimension re. political institutions
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34.
Case study 2 : Computing a CFAR-m Governance Index
S ta g e 2 : G enera ting s pec ific w eig hting s w ith C FA R -m
T here is no em piric a l m a nipula tio n. W eig hting s a re pro c es s ed us ing the
s o le inform a tio n em bedded in the va ria bles .
Kuwait
Components of the 1st dimension re. political institutions
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35.
Case study 2 : Computing a CFAR-m Governance Index
S ta g e 2 : G enera ting s pec ific w eig hting s w ith C FA R -m
1st Dimension: "Political Institutions"
Countries CFAR-m MINEFI Ranking
topping the list ranking ranking spread
Sweden 1 1 0
France 2 3 -1
New Zeland 3 2 1
Spain 4 6 -2
Canada 5 4 1
Germany 6 5 1
Norway 7 7 0
USA 8 15 -7
Italy 9 12 -3
India 10 9 1
Czech Rep. 11 8 3
Ireland 12 11 1
Senegal 13 16 -3
Brazil 14 18 -4
Israel 15 21 -6
Hong Kong 16 26 -10
Greece 17 10 7
Hungary 18 14 4
Argentine 19 19 0
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36.
Case study 2 : Computing a CFAR-m Governance Index
S ta g e 3 : C om puting a C FA R -m G o verna nc e I ndex a nd ra nk ing
c o untries
O nc e a ll dim e ns io ns o f the ins titutio na l pro file ha ve bee n c o m puted w e ha ve pro c e s s ed w ith
the fina l a g g re g a tio n, pro duc ed a C FA R -m indic a to r fo r ea c h c o untry a nd the n a g lo ba l ra nk ing ,
w hic h the M I N E FI c o uld no t c o m ple te !
Ranking according to CFAR-m Governance Index
Countries Countries
topping the list closing the list
1 Sweden 76 Nigeria
2 Ierland 77 Cameroon
3 Israel 78 Yemen
4 Spain 79 Ouzbekistan
5 Canada 80 Mauritanie
6 Norway 81 Egypt
7 Italy 82 Syria
8 Germany 83 Iran
9 Portugal 84 Ivory Coast
10 Hungary 85 Chad
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37.
Case study 2 : Computing a CFAR-m Governance Index
C FA R -m is a va lua ble dec is ion s upport
T his is the dim ens io n
tha t a llow s to
pro g res s the quic k er
in the ra nk ing
Ranks gained in the world ranking
Security of
openness
Prospective
& planning
transact.
Foreign
Public order
cohesion
Perf. of free
Perf. of
Admin.
Social
Regulation
institutions
markets
Political
Dimensions of the "institutional profile" when affected with a 10% increase
37

38.
Case study 3
Computing a CFAR-m Country Risk Index
(comparison with the PRS Group aggregation methodology based on
expert opinion)
38

39.
Case study 3 : Computing a CFAR-m Country Risk Index
P ro c es s :
E c ono m ic I nfo rm a tio n R is k
a g g reg a tio n
va ria bles pro c es s indic a to r
"B la c k bo x "
Generally, there is no indication about the computation
method
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40.
Case study 3 : Computing a CFAR-m Country Risk Index
A pplic a tion to thehe P R S G ro up's International C ountry Risk Guide
T he I C R G brea k s the c ountry ris k into 3 s ub-c la s s es :
C om pos ite indic a to r :
C ountry-ris k indic a to r
S ub-indic a to r #1 : S ub-indic a to r #2 : S ub-indic a to r #3 :
P o litic a l ris k E c o nom ic ris k Fina nc ia l ris k
E a c h s ub-indic a to r is c o m po s ed w ith s evera l fa c tors to o :
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41.
Case study 3 : Computing a CFAR-m Country Risk Index
A pplic a tion to thehe P R S G ro up's International C ountry Risk Guide
E very s ub-indic a tor is a c om pos ite its elf :
12 factors Score (max)
S ub-indic a to r #1 :
P o litic a l R is k A Government's stability 12
B Social and Economic environment 12
C Investment environment 12
D Internal conflicts 12
E External conflicts 12
F Corruption 6
G Military's influence on policy 6
H Influence of religions on policy 6
I Law and regulation 6
J Ethnic lobbying 6
K Democratic responsibility 6
M Administration and stability of the 4
institutions
Total 100
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42.
Case study 3 : Computing a CFAR-m Country Risk Index
A pplic a tion to thehe P R S G ro up's International C ountry Risk Guide
S ub-indic a to r #2 : 5 factors Score (max)
E c o nom ic ris k
A GDP per capita 5
B GDP growth 10
C Inflation rate 10
D Balance of payments (% of GDP) 10
E Current account (% of GDP) 15
Total 50
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43.
Case study 3 : Computing a CFAR-m Country Risk Index
A pplic a tio n to thehe P R S G ro up's International C ountry Risk Guide
S ub-indic a to r #3 : 5 factors Score (max)
Fina nc ia l ris k
A External debt (% of GDP) 10
B Cost of external debt (% of GDP) 10
C Current account (% of goods and 15
services exports)
D International net liquidity (months 5
of import funding)
E Exchange rate stability 10
Total 50
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44.
Case study 3 : Computing a CFAR-m Country Risk Index
C o untry-ris k indic a to r
P o litic a l ris k E c onom ic ris k Fina nc ia l ris k
M ea s uring the po litic a l-ris k fa c tor for yea r 2006
Country Govern Social Invest Internal External Corrup Military's Influence Law and Ethnic Democrat Adminis
ment's and ment conflicts conflicts tion influence of regula lobbying ic tration
stability Econo environ on policy religions tion responsib and
mic ment on policy ility stability
environ of the
ment institu
tions
Albania 8.5 5.5 8.0 10.0 11.0 1.0 5.0 5.0 2.5 4.5 5.0 2.0
Algeria 9.6 5.8 9.1 8.9 10.0 1.5 3.0 2.5 3.0 3.5 4.5 2.0
Angola 9.6 2.0 7.9 9.3 11.0 2.0 2.0 4.0 3.0 3.0 2.0 1.0
Argentina 10.2 5.2 6.6 10.0 10.0 2.5 4.5 6.0 2.5 6.0 4.5 3.0
Armenia 8.4 4.0 8.0 8.6 7.6 1.5 3.5 5.0 3.0 5.5 3.0 1.0
Australia 10.3 9.7 12.0 9.3 9.6 4.6 6.0 6.0 5.5 4.0 6.0 4.0
……….
44

45.
Case study 3 : Computing a CFAR-m
Country Risk Index
S ta g e 1 : C ountry ra nk ing
45

46.
Case study 3 : Computing a CFAR-m Country Risk Index
R em inder : P R S G ro up m etho do log y
 W eig hting o f ea c h va ria ble defined by ex perts
 W eig hting s a re the s a m e w ha tever the c o untry
12 factors
V1 Government's stability V7 Military's influence on policy
V2 Social and Economic V8 Influence of religions on policy
environment
V3 Investment environment V9 Law and regulation
V4 Internal conflicts V10 Ethnic lobbying
V5 External conflicts V11 Democratic responsibility
V6 Corruption V12 Administration and stability of the
institutions
1st dimension factors re. political institutions
46

47.
Case study 3 : Computing a CFAR-m Country Risk Index
S ta g e 2 : defining s pec ific w eig hting s w ith C FA R -m
N ot a ll c o untries ha ve the s a m e w eig hting s : it s how s
tha t C FA R -m is a no n-linea r proc es s
1st dimension factors re. political institutions
47

48.
Case study 3 : Computing a CFAR-m Country Risk Index
S ta g e 2 : defining s pec ific w eig hting s w ith C FA R -m
N o t a ll c ountries ha ve the s a m e w eig hting s : it s how s
tha t C FA R -m is a no n-linea r proc es s
1st dimension factors re. political institutions
48

49.
Case study 3 : Computing a CFAR-m Country Risk Index
M ea s uring the c o untry-ris k fa c to r fo r yea r 2006
CFAR-m results PRS Group results
Countries Ranking Country Ranking Country Spread
topping the
list
1 Finland 1 Finland 0
2 Iceland 2 Luxembourg 1
3 Luxembourg 3 Iceland -1
4 Sweden 4 Ireland 1
5 Ireland 5 Sweden -1
49

50.
Case study 3 : Computing a CFAR-m Country Risk Index
M ea s uring the c o untry-ris k fa c tor for yea r 2006
CFAR-m results PRS Group results
Countries Ranking Country Ranking Country Spread
in the
middle of
the list
……… ……… ……… ………
68 Saudi Arabia 68 Saudi Arabia 0
69 El Salvador 74 El Salvador -5
70 Guatemala 80 Guatemala -10
71 Ghana 67 Ghana 4
72 Brazil 76 Brazil -4
……… ……… ……… ………
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51.
Case study 3 : Computing a CFAR-m Country Risk Index
M ea s uring the c o untry-ris k fa c tor fo r yea r 2006
CFAR-m results PRS Group results
Countries Ranking Country Ranking Country Spread
closing
the list
135 Haiti 135 Ivory Coast 1
136 Ivory Coast 136 Haiti -1
137 Serbia 137 Congo, RD 3
138 Montenegro 138 Iraq 1
139 Iraq 139 Serbia -2
140 Congo, RD 140 Montenegro -2
141 Somalia 141 Somalia 0
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52.
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