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COMPOSITE INDICATORS OF WELL-BEING

BASED ON PRINCIPAL COMPONENTS

- QUALITY APPRAISAL

Kaja MalešičJože RovanLea Bregar

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CONTENT

� Introduction� Outline of Analysis� The set of indicators: concept and selection� The set of indicators: limitations� Composite indicators of well-being� Cluster analysis of well-being� Evaluation of composite indicators based on principal components� Conclusions � Open issues

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�� a sustainable increase in wella sustainable increase in well--being is a key strategic goal of modern being is a key strategic goal of modern societiessocieties

� well-being is not evenly distributed within countries

� AIM: to identify differences in well-being by municipalities in Slovenia

� BASIC ANALYTIC UNITS: - municipalities in Slovenia (193 by status in 2005)- in Slovenia municipalities provide local public services and represent

the level of government closest to people

INTRODUCTION

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HIERARCHICAL PROCEDURE

MAJOR

PRINCIPAL COMPONENTS

GROUPS OF MUNICIPALITIES

A SET OF INDICATORS (demographic, economic, social,

environmental)

DATA PREPARATION (normalization, extreme outliers, values 0,

imputation of missing values, standardization)

PRINCIPAL COMPONENT ANALYSIS

CLUSTER ANALYSIS


NON-HIERARCHICAL PROCEDURE

COMPARISON OF RESULTS

OUTLINE OF ANALYSIS

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THE CONCEPT OF WELL-BEING:� definition: a state of being happy, healthy and prosperous� complex, abstract and multidimensional nature

SELECTION OF INDICATORS:� objective components of well-being � criteria: data quality of European Statistics; the emphasis is on

relevance, accuracy and data availability

� 49 indicators selected at the municipality level (4 demographic, 8 economic, 33 social and 4 environmental)

THE SET OF INDICATORS: CONCEPT AND

SELECTION

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THE SET OF INDICATORS: CONCEPT AND

SELECTION

Indicators of the level of living(14)

ENVIRONMENTAL INDICATORS

(4)

Indicator of crime(1)

Educational indicators(5)

Indicator of municipal administration

(1)

DEMOGRAPHIC INDICATORS

(4)ECONOMIC

INDICATORS (8)

Leisure time indicators(3)

COMPOSITE INDICATOR OF WELL-BEING

Indicators of individual well-being

(4)

Health indicators(5)

SOCIAL INDICATORS(33)

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ORIENTATION ON SECONDARY DATA

SMALL SIZE OF MANY MUNICIPALITIES� data availability

- some data are not available:• social indicators: sample sizes of surveys are not adequate for the

municipalities’ level• environmental indicators such as air, noise, ground pollution

- methodology limitations (e.g. life expectancy)� the event is too rare for comparison (e.g. number of physicians per

capita)

ADJUSTMENTS� proxy measures� average variable values of several years

THE SET OF INDICATORS: LIMITATIONS

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PRINCIPAL COMPONENT METHOD� the aims: to reduce the dimensionality in the data set and to enable easier

interpretation� three principal components were retained (45.6 % of the total variance)

- 1. component of economic and social advancement (26.9% t.v.)- 2. component of family well-being and non-urban territories (9.7% t.v.)- 3. component of demographically endangered territories (9.0% t.v.)

COMPOSITE INDICATORS OF WELL-BEING� municipalities with highest values: Trzin, Šempeter-Vrtojba, Horjul, Škofljica,

Sežana � municipalities with lowest values: Rogašovci, Kuzma, Zavrč, Šalovci, Hodoš


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� WARD’S HIERARCHICAL METHOD� K-MEANS NON-HIERARCHICAL METHOD

3 4 2 1

MUNICIPALITIES OF MODERATE

WELL-BEING

ECONOMICALLY AND SOCIALLY

SUPERIOR MUNICIPALITIES

MUNICIPALITIES OF LOW

WELL-BEING

MUNICIPALITIES OF BALANCED WELL-BEING

average values of indicators 70 municipalities 33,5% population

high values of indicators 15 municipalities

36,5% of population

high values of indicators 56 municipalities

22,4% of population

low values of indicators 52 municipalities 7,6% population

193 MUNICIPALITIES

CLUSTER ANALYSIS OF WELL-BEING

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-2-1,5

-1-0,5

00,5

11,5

2

Mean

Group 1

-2-1,5

-1-0,5

00,5

11,5

2

Me

an

Group 2

-2-1,5

-1-0,5

00,5

11,5

2

Me

an

Group 3

-2-1,5

-1-0,5

00,5

11,5

2

DE

01-popincrD

E02-labm

igrD

E03-ageind

DE

04-deprateE

C01-valuead

EC

02-exportE

C03-invest

EC

04-company

EC

05-enterpE

C06-com

newE

C07-service

EC

08-agricultS

O01-earning

SO

02-dwlow

nS

O03-dw

lareaS

O04-dw

lmin

SO

05-phoneS

O06-w

aterS

O07-car

SO

08-carluxS

O09-unem

plS

O10-unem

plwS

O11-unem

plyS

O12-unem

plpS

O13-assiscsh

SO

14-assisrecS

O15-students

SO

16-educatS

O17-eduteach

SO

18-edupersS

O19-educhild

SO

20-agedecS

O21-earlydec

SO

22-doctorS

O23-healthw

SO

24-healthpS

O25-tim

eS

O26-sports

SO

27-cultureS

O28-crim

eS

O29-adm

inS

O30-abortion

SO

31-suicideS

O32-divorce

SO

33-fertilityE

N01-sew

ageE

N02-w

asteE

N03-envbuild

EN

04-envinv

Me

an

Group 4

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CLUSTER ANALYSIS OF WELL-BEING:

FOUR GROUPS OF MUNICIPALITIES

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PROs CONs

Can summarize complex and multidimensional issues

Enable ranking of units

Are easier to interpret than many separate indicators

Ease communication with general public

The selection of basic indicators, weights and aggregation is subjective to some extent

May disguise certain issues

Dimensions that are data abundant may be overrepresented, dimensions with lack of data may be underrepresented

Composite indicators as a tool for measuring complex phenomena

EVALUATION OF COMPOSITE INDICATORS


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COMPOSITE INDICATOR

OF WELL-BEING

CLUSTERING

STRENGHTS Enables ranking of units as unidimensional composite indicator

Enables ranking by each principal component

Formation of groups on the basis of whole information – all observed indicators

WEAKNESSES Partial loss of information

Composite indicator enables ranking, but it does not form groups of units

Aggregate nature of composite indicator may mask certain issues

Large number of indicators may blur out the differences among groups

No ranking of units

EVALUATION OF COMPOSITE INDICATORS


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� composite indicators show substantial differences in the level of well-being between west and east of Slovenia;

� cluster analysis has revealed 4 groups of municipalities, where the first two are top well-being groups, but differing in the type of well-being;

� 52 municipalities of low-well being (7.6% of population, 17% of the country territory);

� combined use of the two analytical approaches contributed to validity and interpretability of the composite measure of well-being.

CONCLUSIONS

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� dependency of composite indicators on the selection of basic indicators;

� problems connected with availability and relevance of environmental indicators;

� suitability of municipality as a basic unit due to small size and heterogeneity concerning population, area etc.

OPEN ISSUES

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