Andrea Brandolini Banca d’Italia, Department for Structural Economic Analysis

Andrea BrandoliniBanca d’Italia, Department for Structural Economic Analysis

2012 ISFOL Conference “Recognizing the Multiple Dimensions of Poverty:

How Research Can Support Local Policies”Rome, 22 -23 May 2012

Strategies of multidimensional measurement

World Bank, World Development Report 2000/2001:Attacking Poverty

“This report accepts the now traditional view of poverty … as encompassing not only material deprivation (measured by an appropriate concept of income or consumption), but also low achievements in education and health. … This report also broadens the notion of poverty to include vulnerability and exposure to risk – and voicelessness and powerlessness” (italics added)

Background

0

4

8

12

16

20

24

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11

As a % ratio to "income poverty"

0

100

200

300

400

500

600

700

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11

Absolute number

Multidimensionality in poverty research

Source: author’s search of "exact phrase" in Google Scholar, 21 May 2012.

Multidimensional poverty

Multidimensional deprivation

Alkire & Foster “Counting and multidimensional povertymeasurement”, Journal of Public Economics, 2011

“Multidimensional poverty has captured the attention of researchers and policymakers alike due, in part, to the compelling conceptual writings of Amartya Sen and the unprecedented availability of relevant data.”

Background

Background

Europe 2020 strategy

Five headline targets for member states’ policies:“Reduction of poverty by aiming to lift at least 20 million people out of the risk of poverty or social exclusion”

Risk of poverty or social exclusion → multidimensionalPoor population comprises people …… either living in households with very low work intensity

(where adults work less than 20% of total work potential)… or at-risk-of-poverty after social transfers

(equivalised income below 60 % of national median) … or severely materially deprived

(at least 4 out of 9 deprivations owing to lack of resources)

• Multidimensionality in practice• Empirical strategies

– Do we want a single number?– Weighting– Functional form

• Health and income deprivation in France, Germany and Italy, 2000

• Conclusions

Outline

• Multidimensionality has intuitive appeal

• Problems arise in transforming intuition into hard data

• Not every indicator needs to be appropriate

E.g. “proportion of persons meeting friends or relatives less than once a month or never” (Eurostat 2000; Townsend 1979)

Infrequent meetings with friends may signal… weak social tiesbut also … preference for quietness … or passion for internet

Multidimensionality in practice

Multidimensional measurement without theory may be misleading

• What is needed?– Identification of relevant dimensions– Construction of corresponding indicators– Understanding of indicator metrics – Empirical strategies, i.e. tools to deal with

multidimensionality

Multidimensionality in practice

Empirical Strategies for Multiple Dimensions

Source: author’s elaboration based on Brandolini e D’Alessio 1998.

Item-by-itemanalysis

Supplementation strategy

Comprehensive analysis

Non-aggregative strategies

Aggregativestrategies

Vector dominance

Sequential dominance

Equivalencescales

Well-being indicator

Multivariate techniques

Multidimensional poverty indices

Counting approach

Social welfare approach

• Alternative strategies differ for extent of manipulation of raw data the heavier the structure imposed on data, the

closer to complete cardinal measure

• Focus on aggregate measures, i.e. multidimensional index or well-being indicator (both single number but …) Do we want a single number? Weighting Functional form

Empirical strategies

• Pros: communicational advantage single complete ranking more likely to capture

newspaper headlines and people’s imagination than multidimensional scorecards

(‘Eye-catching property’, Streeten on HDI)

• Cons: 1. different metrics2. informational loss3. imposed trade-offs (complements/substitutes)

Do we want a single number?

• Different weighting structures reflect different viewsSen ‘ranges’ of weights rather than single

set

• Alternatives:

– Equal weighting. Lack of information about ‘consensus’ view. But no discrimination.

– Data-based weighting. Frequency-based or multivariate techniques. Caution in entrusting a mathematical algorithm with a normative task

– Market prices. Existing for some attributes only, inappropriate for well-being comparisons

Weighting

(Old) HDI measured average achievement in human developments in a country as

where: Y = GDP per capita L = life expectancy at birth A = adult literacy G = gross school enrolment

Upper/lower bars = max/min

Replace prefixed minima and maxima and simplify

YY

YY

GG

GG

AA

AA

LL

LLHDI iiii

ilnln

lnln

3

1

3

1

3

2

3

1

3

1

39510ln05560001100022000560 .Y.G.A.L.HDI iiiii

Functional form

Iso-HDI Contours

45

50

55

60

65

70

75

80

85

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000

GDP per capita (PPP US$)

Life

exp

ecta

ncy

at b

irth

(yea

rs)

Japan

Russia

Argentina

Hungary

Kyrgyzstan

Higher HDI

Source: author’s elaboration on data drawn from UNDP (2005). All countries shown in the figure have similar values of the education index, comprised between 0.93 and 0.96.

1 year = $2,658 in Japan = $166 in Kyrgyzstan

Functional form

0

10

0 10z1

z2

x2

x1

Intersection poorH1,2

Union poor H1+H2–H1,2

Nonpoor

Union vs. intersection

Atkinson’s counting index: A = 2-κ(H1+H2) + (1–21-κ)H1,2

κ = 0 unionκ ↑ more weight on multiple deprivationsκ → ∞ intersection

0

10

0 10z1

z2

x2

x1

Iso-poverty contours for Bourguignon-Chakravarty measure

If θ → ∞ substitutability tends to 0, contours = rectangular curves

If θ=α=1 attributes are perfect substitutes and convex part becomes straight line

The higher relative to , the more the two attributes are substitutes

i

ii

z

xw

z

xw

nP 0,1max0,1max

1

2

22

1

112

• European Community Household Panel (ECHP)• All persons aged 16 or more• Two indicators:

– Health status: measured on a scale from 5 (very good) to 1 (very bad) and based on respondent’s self-perception

Health-poor = bad or very bad health– Household equivalent income

Income-poor = equivalent income < median

Health and income deprivation in France, Germany and Italy, 2000

Health and income deprivation(percentage values)

Source: author’ elaboration on ECHP data, Wave 8.

Health-poor

Income-poor

Health-poorand

income-poor

Health-pooror

income-poor

France 8.0 15.2 2.0 21.2

Germany 19.0 11.2 3.1 27.1

Italy 11.5 19.5 2.7 28.3

0.000

0.040

0.080

0.120

0.160

1 10 100 1000

=0.5, w=0.5

0.000

0.025

0.050

0.075

0.100

1 10 100 1000

=1, w=0.5

0.000

0.003

0.006

0.009

0.012

1 10 100 1000

=5, w=0.5

Bourguignon-Chakravarty index - Different parameter values

Source: author’ elaboration on ECHP data, Wave 8. Logarithmic scale for horizontal axes.

Italy

Germany

France


Bourguignon-Chakravarty index - Different weighting

0.000

0.040

0.080

0.120

0.160

0.00 0.25 0.50 0.75 1.00

=0.5, =2

0.000

0.020

0.040

0.060

0.080

0.00 0.25 0.50 0.75 1.00

=1, =2

0.000

0.003

0.006

0.009

0.012

0.00 0.25 0.50 0.75 1.00

=5, =2

Source: author’ elaboration on ECHP data, Wave 8.

from health only to income only

ItalyGermany

France


• Health-poor: score=1,2

• Consistent with cutoff at any value between 2 and 3 – cutoff = 3 (used above) possible values=1/3,2/3– cutoff = 2+ possible values=0,1/2

Contribution of health lower with 2+– Germany 0.0110 instead of 0.0232– France 0.0134 instead of 0.0195

• Agreement on identification of poor health status does not lead to unambiguous definition of poverty cutoff and then consistent with different values of index Serious shortcoming, as general problem for

any indicator in discrete space

Bourguignon-Chakravarty index

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10k

Atkinson’s counting index

Source: author’s elaboration on ECHP data, Wave 8.

Italy

GermanyFrance


A = 2-κ(H1+H2) + (1–21-κ)H1,2

κ = 0 unionκ ↑ more weight on

multiple deprivationsκ → ∞ intersection

• Measurement of poverty and inequality in a multidimensional space poses new problems relative to measurement in unidimensional spaces

• Understanding sensitivity of results to underlying hypotheses is crucial part of analysis

• But there is value added!

Conclusion

Thank you for your attention!

Andrea Brandolini Banca d’Italia, Department for Structural Economic Analysis

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