Methodologies to integrate subjective and objective information to build well-being indicators

Methodologies to integrate

subjective and objective

information to build

well-being indicators

Filomena Maggino – Elena Ruviglioni

e-mail:

filomena.maggino@unifi.it

First of all

I would like to thank

Stefano Tarantola for his invitation and

for having encouraged me to present this

work on which we are working together.

the Joint Research Centre group for its

co-operation not only in preparing this

work: the idea to work on this topic was

– Relationships between the two components

Methodological issues in developing indicators

– Hierarchical design

Applied approach to integration

– Multi-stage multi-technique approach

Particular aggregation issues

– Construction of complex indicators

– Definition of macro-units

– Analytical approaches to integration

– Problems in selecting indicators

An Example in four stage

Final remarks

Conceptual framework

Three philosophical approaches

– The ability to select goods and

services that one desires

Æ economic indices

– Normative ideals

Æ social indicators

– Subjective experiences

Conceptual framework

Conceptual framework

└►

Observation approaches

└

►

Measures

└►

Operative models

└►

_{Methodological}

approaches for

managing the

complexity

Societal well-being should be assessed

through a multidimensional and

integrated approch

Quality of Life approach (

QoL

)

of Quality of Life (

QoL

)

Various definitions of QoL share a clear

definition between

• Objective components

• Subjective components

└►

Objective components at micro level

Objective components at macro level

Subjective components

Objective = descriptive

Subjective = evaluative

Social structure

Living conditions

Evaluations of living conditions

Subjective QoL, in terms of well-being

More

objective

More

subjective

Conceptual framework

Conceptual framework

It is impossible and undesirable to

consider one perspective separated from

the others

Integration represents the MOST valid

and complete approach in order to study

QoL

Interrelating and combining individual

living conditions and subjective well-being

components

└►

Two perspectives:

1. objective QoL at macro level can be considered an

antecedent with respect to subjective QoL

(subjective well-being).

¾ In this case, objective indicators (input) can be

interpreted in terms of contextual conditions that can

explain the subjective indicators (output)

2. objective QoL conditions at macro-level and

subjective QoL (perceptions) are independent;

perceptions are influenced by individual

characteristics and not by the objective living

conditions.

¾ In this case, subjective indicators (input) can be

considered as an important component driving the

improvement of objective conditions.

Zapf’s model:

→

↓

deprivation

adaptation

low

dissonance

well-being

high

Objective

living conditions

low

high

Subjective

well-being

Level of

→

↓

Conceptual framework

Costanza’s model:

→

↓

Conceptual framework

– Social Capital (SC)

• networks and norms that facilitate cooperative action

– Human Capital (HC)

• the knowledge and information stored in our brains, as

well as our labour

– Built Capital (BC)

• manufactured goods such as tools, equipment, buildings

– Natural Capital (NC)

• the renewable and non-renewable goods and services

provided by ecosystems

– Time (T)

Social epidemiology approach:

Epidemiology + behavioral sciences

in order to investigate

social determinants of

population distributions of

health, disease, and well-being

Methodological issues

Hierarchical design:

ELEMENTARY INDICATORS

Ð

LATENT VARIABLES

Ð

AREAS TO BE INVESTIGATED

Ð

CONCEPTUAL MODEL

COMPONENTS

Methodological issues

Hierarchical design is completed by

identifying the relations between:

– Latent variables

– Latent variables and the

corresponding indicators

– Elementary indicators

objective and subjective information

relies on

└►

Applied approach to integration

Definition of conceptual framework

Organizational context (system of indicators)

– Levels:

• micro

• macro

Perspectives of analysis:

– Aggregation of

• indicators (reflective or formative approach)

• units

– Integration of objective and subjective

characteristics

Applied approach to integration

Multi-stages multi-techniques approach

└►

Conceptual framework

▼

Definition of objective and subjective

components

▼

Conceptual perspective of integration

(CPI)

▼

→

↓

Stage I : Indicators aggregation

Perspective:

Creation of complex indicators by synthesizing

elementary indicators

Level of analysis:

From elementary indicators to synthetic indicators

Analytical issues:

Reflective indicators Æ scaling models

Formative indicators Æ composite indicators construction

→

↓

Stage II : Integration

Perspective:

Understanding relationships between objective

and subjective characteristics

Level of analysis:

Micro level

Analytical issues:

Different solutions (consistently with CPI)

→

↓

Stage III : Units aggregation

Perspective:

Creation of macro-units by synthesizing

elementary units

Level of analysis:

From micro units to macro units

Analytical issues:

Following homogeneity/functionality criteria

→

↓

Applied approach to integration

Stage IV : Integration

Perspective:

Understanding relationships between objective

and subjective characteristics

Level of analysis:

Macro level

Analytical issues:

Different solutions (consistently with CPI)

→

↓

(a)

construction of complex indicators

Observational units aggregation

(b)

definition of macro-units

Particular aggregation issues

(a)

Construction of complex indicators:

Reflective criterion

(Homogeneity)

Æ Synthetic indicator

Formative criterion

(Heterogeneity)

Æ Composite indicator

Æ Comprehensive/Summary indicator

Condensation Æ New synthetic values

Particular aggregation issues

(b)

Definition of macro units by

condensation:

Information Æ same level

Micro level Æ aggregation Æ proper scale

This problem involves both objective and subjective

indicators with different solutions.

not observable

subjective well-being

subjective

(i) population information

(ii) territory information

(i) individual living

conditions

objective

information

Macro

Micro

Level of observation

Particular aggregation issues

Aggregation of objective information

criteria

Compositional

– Information refers to population (observed at

individual level

Contextual

Particularly delicate (characteristics

non-cumulative) Æ ad-hoc aggregating criteria

Homogeneity

Functionality

Particular aggregation issues

Homogeneity:

Segmentation analysis

Partitioning analysis

Tandem analysis

Particular aggregation issues

Functionality:

Groups

Areas

Time periods

Particular aggregation issues

Analytical approaches to integration

Structural model approach

Multi-level approach

Life-course perspective

Composite indicators

1. Settlement/aggregation area sizes

2. Time frames

3. Population composition

4. Domains of life composition

5. Objective vs subjective indicators

6. Positive vs negative indicators

7. Input vs output indicators

8. Benefits and costs

9.

Measurement scales

10. Report writers

11. Report readers

12. Quality-of-life model

13. Distributions

14. Distance impacts

15. Causal relations

An example

Goal :

to illustrate the multi-technique

multi-stage characterization of the

proposed approach

By using:

subjective and objective data

Provided by:

– European Social Survey project

– Joint Research Centre (JRC – European

Commission)

An example

First stage:

– synthesis of basic indicators at individual

level

Second stage:

– understanding relationships between objective

and subjective characteristics at micro level

Third stage:

– synthesis of micro units

Fourth stage:

– understanding relationships between objective

and subjective characteristics at macro level

An example : first stage

B31 the national government

B30 present state of economy in country

0 (left) 10 (right) B28

placement on left-right scale

Self-placement

B12 the United Nations

B11 the European Parliament

B10 politicians

B9 the police

B8 the legal system

reflective 0 (no trust at all)

10 (complete trust) B7 country’s parliament Trust in Politics Model of measurement Scaling technique Item Nr Items Variable Area

E19 voluntary organizations E18 religion E17 work E16 politics E15 leisure time E14 friends formative 0 (extremely unimportant) 10 (extremely important) E13 family Values: important in life 0 (extremely dissatisfied) 10 (extremely satisfied) B29

how satisfied with life as a whole Life satisfaction 0 (extremely unhappy) 10 (extremely happy) C1

how happy are you Happiness

Subjective aspects

An example : first stage

1. living comfortably 2. coping 3. difficult 4. very difficult on present income F31

feeling about household’s income nowadays Income Socio-demographic profile D9 many/few immigrants from poorer countries outside Europe

D8 many/few immigrants from richer countries outside Europe

D7 many/few immigrants from poorer countries in Europe

D6 many/few immigrants from richer countries in Europe

D5 many/few immigrants of different race/ethnic group from majority reflective 1. allow many 2. allow some 3. allow a few 4. allow none to come and live here D4

many/few immigrants of same race/ethnic group as majority

Acceptance of immigration: allow Immigration and asylum issues Model of measurement Scaling technique Item Nr Items Variable Area

An example : first stage

0.6

IMP_W Work dimension

0.4

IMP_PL Private life dimension

0.6

TRUST_II International institutions COMPOSITE3

12

Personal life principles

0.4

IMP_C Caring dimension

0.7

SAT_NSS Satisfaction for national social

services

0.4

IMP_PL

Private life dimension Welfare dimension

₁₅

COMPOSITE2

0.8

TRUST_NS National security

0.8

SAT_NF Satisfaction for national foundations

0.6

IMP_AL

Active life dimension Public & political life

18

COMPOSITE1

0.8

TRUST_NP National politics Aggregated score Variance explained (%) Obtained component Item loading

Synthetic indicators

An example : second stage

0.95 3.13 -0.29 -3.19

Public & political life

COMPOSITE1 0.79 2.17 -0.47 -1.96

Non-acceptance of immigration

IMMIGR 0.98 2.24 -0.34 -2.30

Self-placement on left-right scale

B28

0.85 2.46 1.10 -1.14

Feeling about household’s income

nowadays

F31 0.93 1.34 -0.37 -3.74

Happiness

C1 0.97 1.31 -0.58 -3.10

Life satisfaction

B29 CLUSTER

1

(n=7369) SD max. mean min.

INDICATOR

0.91 3.15 0.10 -5.03

Personal life principles

COMPOSITE3 0.86 2.90 0.12 -4.32

Welfare dimension

COMPOSITE2 0.76 4.08 0.60 -2.50

Public & political life

COMPOSITE1 0.76 2.17 -0.64 -1.96

Non-acceptance of immigration

IMMIGR 0.92 2.24 0.26 -2.30

Self-placement on left-right scale

B28

0.63 2.46 -0.61 -1.14

Feeling about household’s income

nowadays

F31 0.59 1.34 0.48 -3.74

Happiness

C1 0.54 1.31 0.54 -3.10

Life satisfaction

B29 CLUSTER

2

(n=14855)

An example : second stage

0.97 3.07 -0.24 -5.71

Personal life principles

COMPOSITE3 0.94 3.85 0.48 -3.83

Welfare dimension

COMPOSITE2 0.90 2.36 -0.49 -3.85

Public & political life

COMPOSITE1 0.78 2.17 0.48 -1.96

Non-acceptance of immigration

IMMIGR 0.90 2.24 -0.46 -2.30

Self-placement on left-right scale

B28

0.68 2.46 -0.40 -1.14

Feeling about household’s income

nowadays

F31 0.58 1.34 0.54 -3.23

Happiness

C1 0.60 1.31 0.53 -3.10

Life satisfaction

B29 CLUSTER

3

(n=9703) SD max. mean min.

INDICATOR

An example : second stage

1.11 3.22 -0.11 -5.59

Personal life principles

COMPOSITE3 0.99 3.29 -0.54 -4.34

Welfare dimension

COMPOSITE2 0.99 3.61 -0.26 -3.47

Public & political life

COMPOSITE1 0.79 2.17 0.81 -1.96

Non-acceptance of immigration

IMMIGR 0.99 2.24 0.30 -2.30

Self-placement on left-right scale

B28

0.89 2.46 0.47 -1.14

Feeling about household’s income

nowadays

F31 1.04 1.34 -0.93 -3.74

Happiness

C1 1.00 1.31 -0.86 -3.10

Life satisfaction

B29 CLUSTER

4

(n=10418) SD max. mean min.

INDICATOR

An example : second stage

CLUSTER PLOTS

Cluster Parallel Coordinate Plots

1 COMPOSITE3 B28 COMPOSITE2 COMPOSITE1 C1 IMMIGR B29 F31 In de x of C as e -10 -5 0 5 2 COMPOSITE3 B28 COMPOSITE2 COMPOSITE1 C1 IMMIGR B29 F31 In de x of C as e -10 -5 0 5 3 B29 F31 4 B29 F31

Cluster Profile Plots

1 COMPOSITE3 B28 COMPOSITE2 COMPOSITE1 C1 IMMIGR B29 F31 2 COMPOSITE3 B28 COMPOSITE2 COMPOSITE1 C1 IMMIGR B29 F31 3 F31 4 F31

Medium-low Low

Medium-high High

Personal life principles COMPOSITE3 Low High Medium-high Medium-low Welfare dimension COMPOSITE2 Medium-low Low High Medium-low

Public & political life COMPOSITE1 High Medium-high Low Medium-low Non-acceptance of immigration IMMIGR Right Left Centre-right Centre-left self-placement on left-right scale B28 Some difficulties Comfortable Very comfortable Many difficulties Feeling about household’s income nowadays F31 Low High Medium-high Medium-low happiness C1 Low Medium-high Medium-high Medium-low life satisfaction B29

4

3

2

1

An example : second stage

-2

-1

0

1

Dim(1)

-1.5

-1.0

-0.5

0.0

0.5

1.0

D

i

m

(

2

)

50-64

voted yes

65 and more

cluster 2

male

31-49

cluster 3

cluster 4

female

cluster 1

less than 31

voted no

An example : third stage

42345 10418 9703 14855 7369 N 100.0 24.6 22.9 35.1 17.4 Total 1519 100.0 30.0 21.5 31.4 17.1 Slovenia SI 1999 100.0 8.2 17.5 63.0 11.3 Sweden SE 1511 100.0 47.5 11.8 12.9 27.9 Portugal PT 2109 100.0 32.6 11.5 17.1 38.8 Poland PL 2036 100.0 12.4 26.6 51.4 9.6 Norway NO 2364 100.0 17.0 25.0 50.7 7.4 Netherlands NL 1552 100.0 18.4 27.5 45.5 8.6 Luxembourg LU 1206 100.0 28.0 15.1 37.5 19.4 Italy IT 2497 100.0 22.3 19.0 26.1 32.6 Israel IL 2046 100.0 15.3 18.3 49.9 16.4 Ireland IE 1685 100.0 56.3 11.9 10.5 21.2 Hungary HU 2566 100.0 51.1 12.5 11.4 25.0 Greece GR 2051 100.0 23.0 32.3 32.5 12.2 United Kingdom GB 1503 100.0 33.3 28.9 25.4 12.4 France FR 2000 100.0 14.7 35.5 39.4 10.5 Finland FI 1728 100.0 27.5 20.9 31.1 20.5 Spain ES 1500 100.0 7.1 26.6 60.1 6.2 Denmark DK 2919 100.0 23.8 28.7 30.9 16.5 Germany DE 1360 100.0 35.1 13.8 23.8 27.4 Czech Rep. CZ 2040 100.0 8.8 22.9 57.5 10.9 Switzerland CH 1897 100.0 15.6 26.8 43.1 14.5 Belgium BE 2257 100.0 21.8 41.2 23.4 13.6 Austria AT Cluster 4 Cluster 3 Cluster 2 Cluster 1 Total N

An example : third stage

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

D

i

m

(

2

)

DK

CH

cluster 2

NO

NL

LU

FI

BE

IE

cluster 3

GB

AT

HU

GR

PT

cluster 4

cluster 1

CZ

IL

FR

SI

ES

IT

DE

0 10 20 30 40 50 60 70 % cluster 1 0 2 4 i n e q u alit y of i nc o me d DK NL LU NO FI SE FR AT CZ HU SI DE IE BE 0 10 20 30 40 50 60 70 % cluster 2 0 2 4 i n e q u alit y of i nc o me d HU AT CZ FR DE SI NODK SE NL IE LU BE FI r = 0.509 r = -0.437 0 10 20 30 40 50 60 70 % cluster 3 0 2 4 6 8 i n e q u alit y of i nc o me dsi tr i b uti o n PL PT HU GR CZ IT SE IEES SI AT FI GB FR DE LU BE NO DK NL 0 10 20 30 40 50 60 70 % cluster 4 0 2 4 6 8 i n e q u alit y of i nc o me dsi tr i b uti o n DK SE NO FI IE BE NL_ATLU HU GR PT CZ FR PL SI IT ES DE GB r = -0.350 r = 0.418

Final remarks

Goal :

– to illustrate the composite approach

through which integration between

objective and subjective information

Final remarks

The soundness of the approach and

its results :

–

the defined and adopted conceptual framework

assuming the correct perspective

to be

identified according to different objectives

i.

the aggregation of indicators and units

ii.

the integration of objective and subjective

information

iii.

the levels at which the previous objectives have

to be pursued

Final remarks

Restricted Goal :

– to illustrate and exemplify the multi-technique

multi-stage characterization of the proposed

approach.

Thanks to:

– Econometrics and Applied Statistics Unit

(EAS) at the Joint Research Centre of the

European Commission

step

of our study.

Our intention

(together with EAS –

JRC)

is that to continue

exploring

these datasets in order to provide

further results, especially in

longitudinal perspective.

Presentation designer: Marco Trapani