CFAR-m Presentation English

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Summary

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1. C ompos ite indic a tors and ranking

4. S ome examples of implementing C FAR -m

2. Traditiona l methods for c ons truc ting c ompos ite indic a tors and their w eaknes s

3. The C FAR -m a lg orithm


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1. Composite indicators and ranking

A C ompos ite Indic a tor is an ag reg ate index that s ummarizes a larg e amount of information g iven by s ing le indic ators .

Competitivity (Global Competitivity Index - FEM) Country risk (ICRG-PRS group) Well-being (Health System Achievement Index-WHO) Environment (Environmental Sustainability Index- WEF) Governance (The Corruption Perceptions Index - Transparency International) Innovation (Technology Achievement Index- UN)

C ompos ite Indic ators are inc reas ing ly being us ed to meas ure multidimens iona l performanc e and to rank c ountries , firms , c lients , ins titutions , etc ., in many fields , s uc h as :

What is it ?…

2 reas ons , bas ic a lly :

1- C omplexity of modern ec onomy : jus t one, or a s et of s ing le indic a tors is not enoug h any more.

2- Development of IC Ts : it means that a hug e mas s of information has to be proc es s ed

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A real interest …

1. Composite indicators and ranking

Demand for, and produc tion of C ompos ite Indic ators are rapidly g row ing .

Google search results for "composite indicators"


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2. Traditional methods for constructing composite indicators and their weakness A great number …

Most used weighting schema in aggregation methods:

E qua l w eig hts

Weig hts bas ed on s ta tis tic a l models

Da ta E nvelopment Ana lys is (DE A) P rinc ipa l C omponent Ana lys is (PC A) U nobs erved C omponents M odels (U C M )

B udg et a lloc ation

Weig hts bas ed on the s ta tis tic a l qua lity of da ta

S tandard devia tion

Weig hts bas ed on experts ’ opinions


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Drawbacks of traditional methods :

They are exogenous They are linear

They lose information

They offer a no positive capability to assist decision-making processes

Many problems…

2. Traditional methods for constructing composite indicators and their weakness

An orig ina l method bas ed on artific ia l intellig enc e for the c ons truc tion of c ompos ite indic ators tha t a llow s to perform relevant ranking .

The w eig hting s c hema of s ing le indic ators is g enerated throug h a learning proc es s , from informationa l c ontent of the variables thems elves and their interna l dynamic s .

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3. The CFAR-m algorithm

Innovation

Our solution …

C -FAR m w orks in three s tag es that a re s truc tura lly c ombined :

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S tag e 1 : Firstly, it carries out a c la s s ific a tion (self-organization) of objects (records, points, cases, samples, entities, or instances) through a lea rning proc es s that takes into account interactions between the attributes (variables, fields, characteristics, or features) in homog eneous c lus ters .

3. The CFAR-m algorithmOur solution …

Preliminary stage : Preparing the data base

Stage 1 : Classification

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S tag e 2 : S ec ondly, an appropria te w eig hts vec tor is g enerated for eac h objec t.



Stage 2 : Generating weights: one vector is defined for each object

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S tag e 3 : Thirdly, w eig hts vec tors a re applied to the orig ina l data to c ompute C FAR -m c ompos ite indic ators and fina lly to c a rry out the overa ll ranking of objec ts .



Stage 3 : Computing the composite indicators and rankingthe objects

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Stage 3 : Computing the composite indicators and rankingthe objects



Preliminary stage : Preparing the data base


3. The CFAR-m algorithm

Our s olution is bas ed on an orig ina l tec hnique tha t us es neura l netw orks and, unlike exis ting methods , pres ents the follow ing c harac teris tic s :

Objectivity There is no manipula 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 solution to the main aggregation problem.

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S pecificity E ac h objec t has a s pec ific equation to compute its composite indicator.

Decision support I t a llow s perform ing of s imula tions and therefore, can help to decide on appropriate actions and corrections.

4. Some examples of implementing C-FARm

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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)


Case study 1 : Computing a CFAR-m Human Development Index

(comparison with the UNDP aggregation methodology based on equal weights)

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4. Some examples of implementing C-FARm


H ea lth

Educ ation

S tandard of L iving

HDI

Dimens ions

In its firs t Human Development Report (1990), the U nited N ations Development P rog ram (U N DP) introduc ed a new index : H uman Development Index (HD I).

H D I is intended to s ummarize in one meas ure three dimens ions of the development proc es s : long evity, educ ationa l a tta inment, and s tandard of living .

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V ariables (bas ic indic a tors )

Life expec tanc y a t birth

A dult literac y ra te

Primary, s ec ondary and tertiary s c hooling enrolment ra tios

G DP per c apita



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To c ompute the HD I, U N DP c ons ider the s imple averag e (equa lly w eig hted s um) of the tree dimens ions .

The three dimens ions have the s ame w eig ht

Life expec tanc y index

E duc ation index

G DP

index



… … , thus , c omparis ons among different c ountries /reg ions a re c a rried out.

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The main arguments against HDI:

Important dimens ions a re not c ons idered (freedom, human rig hts ,

g overnanc e, etc .)

H D I is hig hly c orrela ted to the G DP (0,89 ac c ording to M ac G illivray,

1991).

The three dimens ions a ls o a re hig hly c orrela ted to the G DP

Weig hting of the three dimens ions is too s ubjec tive

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“The best known macro-indicator in the world is probably the Human Development Index (HDI) developed by the United Nations Development Program. 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 unjustified 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-indicatorsAndrew Sharpe (2004)

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The main critics made to the HDI :

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S tag e 1 : C ountry c la s s ific a tion


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Weig hting s c heme differs from one c ountry to another : C FAR -m is non-linear

Life expec tanc y index

Educ a tion index

G DP

index


S tag e 2 : G enera ting w eig hts : one vec tor is defined for eac h c ountry

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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 :

Intens ity and S ig n

Weights are generated automatically through a learning process from the database :

Objec tivity

S tag e 2 : G enera ting s pec ific w eig hting s w ith C FAR -m

The ranking of C FAR -m w ill be both objec tive and relevant.


S tag e 3 : C omputing a C FAR -m Human Development Index and ranking c ountries

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HDI dimensions - Year 2005 CFAR-m's results for year 2005

Countries topping the list

Life expectancy

indexEducation

indexGDP

index Country

CFAR-m rankt

CFAR-m rank minus

UNDP rank

Ic eland 0.941 0.978 0.985 IS L 1 0

N orway 0.913 0.991 1.000 N OR 2 0

Aus tra lia 0.931 0.993 0.962 AU S 3 0

C anada 0.921 0.991 0.970 C AN 4 0

I reland 0.890 0.993 0.994 IR L 5 0

S w eden 0.925 0.978 0.965 S WE 6 0

United S ta tes 0.881 0.971 1.000 U S A 7 5

S w itzerland 0.938 0.946 0.981 C HE 8 1

Japan 0.954 0.946 0.959 JPN 9 1


S tag e 3 : C omputing a C FAR -m Human Development Index and ranking c ountries

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HDI dimensions - Year 2005 CFAR-m's results for year 2005

Countries closing the list

Life expectancy

index

Education index

GDP index

Country CFAR-m rank

CFAR-mrank

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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C FAR -m as a dec is ion s upport s olution

As w eig ht s c hemas a re s pec ific , it a llow s to perform s imula tions


Number of ranks gained in overall ranking

Life expectancy index

Impact on the rank of an improvement 0.1 in one dimension (here, for North African countries)

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Education index






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Education indexGDP index







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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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The " Ins titutiona l profiles " databas e

It gathers a whole set of indicators characterizing the institutions of 85 developed and emerging countries

Each variable is weighted according to its standard deviation

132 variables

Information ag g reg a tion

proc es s

9 governance indicators


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9 g overnanc e indic a tors

1 : Political institutions

2 : Public order

3 :Perfomance of Administration

4 :Efficiency of free markets

5 :Prospective and planning

6 : Security of transactions

7 : Regulation

8 : Foreign openness

9 : Social cohesion

Information ag g reg ation

proc es s

132 variables

85 c

ount

ries


The " Ins titutiona l profiles " databas e

Gathers a whole set of indicators characterizing the institutions of 85 developed and emerging countries

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1st dimension's case : ″political institutions″


S tag e 1 : C ountry ranking

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Components of the 1st dimension re. political institutions

R eminder : in the M IN E FI 's method, the w eig ht of one variable c omes from its s tandard devia tion

The component that weighs the most in the computation

The component that weighs the least in the computation

H ow leg itimate a re thos e w eig hting s ? And w hat about the fac t tha t they apply to a ll c ountries ?


S tag e 2 : G enerating s pec ific w eig hts w ith C FAR -m

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S tag e 2 : G enerating s pec ific w eig hting s w ith C FAR -m

There is no empiric a l manipula tion. Weig hting s a re proc es s ed us ing the s ole information embedded in the variables .

Kuwait


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There is no empiric a l manipula tion. Weig hting s a re proc es s ed us ing the s ole information embedded in the variables .

Kuwait


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1st Dimension: "Political Institutions"Countries

topping the list CFAR-m ranking

MINEFI ranking

Ranking spread

Sweden 1 1 0France 2 3 -1New Zeland 3 2 1Spain 4 6 -2Canada 5 4 1Germany 6 5 1Norway 7 7 0USA 8 15 -7Italy 9 12 -3India 10 9 1Czech Rep. 11 8 3Ireland 12 11 1Senegal 13 16 -3Brazil 14 18 -4Israel 15 21 -6Hong Kong 16 26 -10Greece 17 10 7Hungary 18 14 4Argentine 19 19 0



S tag e 3 : C omputing a C FAR -m G overnanc e Index and ranking c ountries

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Ranking according to CFAR-m Governance IndexCountries

topping the listCountries

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

Onc e a ll dimens ions of the ins titutiona l profile have been c omputed w e have proc es s ed w ith the fina l a g g reg a tion, produc ed a C FAR -m indic a tor for eac h c ountry and then a g loba l rank ing , whic h the M IN E FI c ould not c omplete !


Ra

nks

gain

ed in

the

wor

ld ra

nkin

g

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C FAR -m is a va luable dec is ion s upport

Dimensions of the "institutional profile" when affected with a 10% increase

This is the dimens ion tha t a llow s to

prog res s the quic ker in the ranking


Publ

ic o

rder

Perf

. of

Adm

in.

Perf

. of f

ree

mar

kets

Pros

pect

ive

& p

lann

ing

Secu

rity

of

tran

sact

.

Reg

ulat

ion

Soci

al

cohe

sion

Fore

ign

open

ness

Polit

ical

in

stitu

tions

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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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Proc es s :

E c onomic variables

Information ag g reg a tion

proc es s

R is k indic ator

"B lac k box"Generally, there is no indication about the computation

method


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E ac h s ub-indic a tor is c ompos ed w ith s evera l fac tors too :

Applic a tion to thehe PR S G roup's International Country Risk Guide

The IC R G breaks the c ountry ris k into 3 s ub-c las s es :

C ompos ite indic a tor :

C ountry-ris k indic ator

S ub-indic a tor #1 :

Politic a l ris k


E c onomic ris k


Financ ia l ris k


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E very s ub-indic ator is a c ompos ite its elf :


Politic a l R is k

12 factors Score (max)

A Government's stability 12B Social and Economic environment 12C Investment environment 12D Internal conflicts 12E External conflicts 12F Corruption 6G Military's influence on policy 6H Influence of religions on policy 6

I Law and regulation 6J Ethnic lobbying 6K Democratic responsibility 6M Administration and stability of the

institutions4

Total 100



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5 factors Score (max)

A GDP per capita 5B GDP growth 10C Inflation rate 10D Balance of payments (% of GDP) 10E Current account (% of GDP) 15

Total 50


E c onomic ris k



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Financ ia l ris k5 factors Score (max)

A External debt (% of GDP) 10B Cost of external debt (% of GDP) 10C Current account (% of goods and

services exports)15

D International net liquidity (months of import funding)

5

E Exchange rate stability 10Total 50



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M eas uring the politic a l-ris k fac tor for year 2006Country Govern

ment's stability

Social and

Economic

environment

Investment

environment

Internal conflicts

External conflicts

Corruption

Military's influence on policy

Influence of

religions on policy

Law and regulation

Ethnic lobbying

Democratic

responsibility

Administration

and stability of the institutions

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

……….

C ountry-ris k indic a tor

Politic a l ris k E c onomic ris k Financ ia l ris k


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S tag e 1 : C ountry ranking


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R eminder : PR S G roup methodolog y Weig hting of eac h variable defined by experts Weig hting s a re the s ame w hatever the c ountry

1st dimension factors re. political institutions

12 factors

V1 Government's stability V7 Military's influence on policy

V2 Social and Economic environment

V8 Influence of religions on policy

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


S tag e 2 : defining s pec ific w eig hting s w ith C FAR -m

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N ot a ll c ountries have the s ame w eig hting s : it s how s that C FAR -m is a non-linear proc es s



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N ot a ll c ountries have the s ame w eig hting s : it s how s that C FAR -m is a non-linear proc es s


S tag e 2 : defining s pec ific w eig hting s w ith C FAR -m


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M eas uring the country-ris k fac tor for year 2006

CFAR-m results PRS Group results

Countries topping the

list Ranking Country Ranking Country Spread

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


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M eas uring the c ountry-ris k fac tor for year 2006


Countries in the

middle of the list

Ranking Country Ranking Country Spread

……… ……… ……… ………

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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M eas uring the country-ris k fac tor for year 2006


Countries closing the list

Ranking Country Ranking Country Spread

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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