Value Judgements in Multidimensional Poverty Measurement ... · methodology M ka =(ρ k,Ma) satisfies: decomposability, replication invariance, symmetry, poverty and deprivation focus,

Value Judgements in Multidimensional

Poverty Measurement Design

Sabina Alkire, Jose Manuel Roche and Maria Emma Santos

OPHI Workshop, Oxford, 28 June 2012

Two parts:

• Measurement methodology

• Value Judgements

– Constraints

– Complementary Analyses

– Uncertainty/Incompleteness

– Authority

Measurement methodology

• Why focus on a particular method? – Focused discussion

– Actual examples

– Practical Benefits

– Clarity

And note:

– Relevance to other methods during field-building

Multidimensional Poverty- our challenge:

• A government would like to create an official

multidimensional poverty indicator

• Desiderata

– It must understandable and easy to describe

– It must conform to “common sense” notions of poverty

– It must be able to target the poor, track changes, and guide

policy.

– It must be technically solid

– It must be operationally viable

– It must be easily replicable

• What would you advise?

Methodology

• Identification – Dual cutoffs

• Aggregation – Adjusted FGT

• All value judgements are assumed given for now:

– Purpose, Dimensions, Indicators, Cutoffs, Weights/Values etc

• Today we focus on one measure M0, which can be used with ordinal, categorical, binary, or cardinal data. It has been used extensively, for practical reasons.

Review: Unidimensional Poverty

Variable – income

Identification – poverty line

Aggregation – Foster-Greer-Thorbecke ’84

Example Incomes = (7,3,4,8) poverty line z = 5

Deprivation vector g0 = (0,1,1,0)

Headcount ratio P0 = m(g0) = 2/4

Normalized gap vector g1 = (0, 2/5, 1/5, 0)

Poverty gap = P1 = m(g1) = 3/20

Squared gap vector g2 = (0, 4/25, 1/25, 0)

FGT Measure = P2 = m(g2) = 5/100

Multidimensional Data

Matrix of well-being scores for n persons in d domains

Domains

Persons

y

13.1 14 4 1

15.2 7 5 0

12.5 10 1 0

20 11 3 1

Multidimensional Data

Matrix of well-being scores for n persons in d domains

Domains

Persons

z ( 13 12 3 1) Cutoffs

y

13.1 14 4 1

15.2 7 5 0

12.5 10 1 0

20 11 3 1

Deprivation Matrix

Replace entries: 1 if deprived, 0 if not deprived

Domains

Persons

y

13.1 14 4 1

15.2 7 5 0

12.5 10 1 0

20 11 3 1

Deprivation Matrix

Replace entries: 1 if deprived, 0 if not deprived

Domains

Persons

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

Normalized Gap Matrix

Normalized gap = (zj - yji)/zj if deprived, 0 if not deprived

Domains

Persons

z ( 13 12 3 1) Cutoffs

These entries fall below cutoffs

y

13.1 14 4 1

15.2 7 5 0

12.5 10 1 0

20 11 3 1

Normalized Gap Matrix

Normalized gap = (zj - yji)/zj if deprived, 0 if not deprived

Domains

Persons

g1

0 0 0 0

0 0.42 0 1

0.04 0.17 0.67 1

0 0.08 0 0

Squared Gap Matrix

Squared gap = [(zj - yji)/zj]2 if deprived, 0 if not deprived

Domains

Persons

g1

0 0 0 0

0 0.42 0 1

0.04 0.17 0.67 1

0 0.08 0 0

Squared Gap Matrix

Squared gap = [(zj - yji)/zj]2 if deprived, 0 if not deprived

Domains

Persons

g2

0 0 0 0

0 0.176 0 1

0.002 0.029 0.449 1

0 0.006 0 0

Identification

Domains

Persons

Matrix of deprivations

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

Identification – Counting Deprivations

Domains c

Persons

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

Identification – Counting Deprivations

Q/ Who is poor?

Domains c

Persons

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

Identification – Dual Cutoff Approach

Q/ Who is poor?

A/ Fix cutoff k, identify as poor if ci > k

Domains c

Persons

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1


Q/ Who is poor?

A/ Fix cutoff k, identify as poor if ci > k (Ex: k = 2)

Domains c

Persons

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1


Q/ Who is poor?

A/ Fix cutoff k, identify as poor if ci > k (Ex: k = 2)

Domains c

Persons

Note Includes both union (k = 1) and intersection (k = d)

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

Identification – The problem empirically

k = H

Union 1 91.2%

2 75.5%

3 54.4%

4 33.3%

5 16.5%

6 6.3%

7 1.5%

8 0.2%

9 0.0%

Inters. 10 0.0%

Poverty in India for

10 dimensions:

91% of population

would be targeted

using union,

0% using intersection

Need something in

the middle. (Alkire and Seth 2009)

Aggregation

Censor data of nonpoor

Domains c

Persons

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

Aggregation

Censor data of nonpoor

Domains c(k)

Persons

Similarly for g1(k), etc

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

Aggregation – Headcount Ratio

Domains c(k)

Persons

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

Aggregation – Headcount Ratio

Domains c(k)

Persons

Two poor persons out of four: H = 1/2

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

Critique

Suppose the number of deprivations rises for person 2

Domains c(k)

Persons


g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

Critique


Domains c(k)

Persons


0

4

3

0

0000

1111

1011

0000

)(0

kg

Critique


Domains c(k)

Persons


No change!

Violates ‘dimensional monotonicity’

0

4

3

0

0000

1111

1011

0000

)(0

kg

Aggregation

Return to the original matrix

Domains c(k)

Persons

0

4

3

0

0000

1111

1011

0000

)(0

kg

Aggregation

Return to the original matrix

Domains c(k)

Persons

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

Aggregation

Need to augment information deprivation shares among poor

Domains c(k) c(k)/d

Persons

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

2 / 4

4 / 4

Aggregation

Need to augment information deprivation shares among poor

Domains c(k) c(k)/d

Persons

A = average deprivation share among poor = 3/4

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

2 / 4

4 / 4

Aggregation – Adjusted Headcount Ratio

Adjusted Headcount Ratio = M0 = HA

Domains c(k) c(k)/d

Persons


g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

2 / 4

4 / 4


Adjusted Headcount Ratio = M0 = HA = m(g0(k))

Domains c(k) c(k)/d

Persons


g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

2 / 4

4 / 4


Adjusted Headcount Ratio = M0 = HA = m(g0(k)) = 6/16 = .375

Domains c(k) c(k)/d

Persons


g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

2 / 4

4 / 4

Aggregation: Adjusted Poverty Gap

Adjusted Poverty Gap = M1 = M0G = HAG

Domains

Persons

Average gap across all deprived dimensions of the poor:

G/

g1 (k)

0 0 0 0

0 0.42 0 1

0.04 0.17 0.67 1

0 0 0 0

Aggregation: Adjusted Poverty Gap

Adjusted Poverty Gap = M1 = M0G = HAG = m(g1(k))

Domains

Persons

Obviously, if in a deprived dimension, a poor person becomes

even more deprived, then M1 will rise.

Satisfies monotonicity

g1 (k)

0 0 0 0

0 0.42 0 1

0.04 0.17 0.67 1

0 0 0 0

Aggregation: Adjusted FGT

Consider the matrix of squared gaps

Domains

Persons

g2(k)

0 0 0 0

0 0.422 0 12

0.042 0.172 0.672 12

0 0 0 0

Aggregation: Adjusted FGT

Adjusted FGT is M = m(g(k))

Domains

Persons

Satisfies transfer axiom

g2(k)

0 0 0 0

0 0.422 0 12

0.042 0.172 0.672 12

0 0 0 0

Aggregation: Adjusted FGT Family

Adjusted FGT is Ma = m(ga(t)) for a > 0

Domains

Persons

Theorem 1 For any given weighting vector and cutoffs, the

methodology Mka =(ρk,Ma) satisfies: decomposability,

replication invariance, symmetry, poverty and deprivation

focus, weak and dimensional monotonicity, nontriviality,

normalisation, and weak rearrangement for a>0;

monotonicity for a>0; and weak transfer

for a>1.

ga (k)

0 0 0 0

0 0.42a 0 1a

0.04a 0.17a 0.67a 1a

0 0 0 0

International MPI

National Poverty

Mexico, Colombia

Well-being

Bhutan’s GNH

Adaptations

Empowerment

Energy

Governance

Some Applications

Examples

UNDP’s

2010 Human Development Report first published the MPI

(updated annually for countries with new data)

The MPI (UNDP 2010) • The MPI 2011 is an international index of acute

multidimensional poverty developed at OPHI

• In 2011 it covers 109 developing countries.

• It was launched in 2010 in the Human Development

Report, and updated in 2011, 2012

• It complements the $1.25/day poverty by bringing

more dimensions into view

• It is the first measure to reflect joint distribution of

disadvantages.

Data: Surveys Demographic & Health Surveys (DHS - 54)

Multiple Indicator Cluster Surveys (MICS - 32)

World Health Survey (WHS – 17)

Additionally we used 6 special surveys covering urban

Argentina (ENNyS), Brazil (PNDS), Mexico (ENSANUT),

Morocco (ENNVM), Occupied Palestinian Territory

(PAPFAM), and South Africa (NIDS)

Constraints: Data are 2000-2010, and not all have all 10

indicators.

MPI Dimensions Weights & Indicators

Measurement: Indicators & Cutoffs

• Health – Child Mortality: If any child has died in the family

– Malnutrition: If any interviewed adult in the family has

low Body Mass Index; if any child is more than 2 standard

deviations below the reference normal weight for age,

WHO standards) [WHS has male & female data but no

child data; MICS has child data but no adult data; DHS

has women 15-49 & child]


• Education

– Years of Schooling: if no person in the household

has completed 5 years of schooling

– Child Enrolment: if any school-aged child is out

of school, where school-aged is an eight year

period from the national starting age.


• Standard of Living

– Electricity (no electricity is deprived)

– Drinking water (MDG definitions)

– Sanitation (MDG definitions + not being shared)

– Flooring (dirt/sand/dung are deprived)

– Cooking Fuel (wood/charcoal/dung are deprived)

– Assets (deprived if do not own a car/truck and do

not own more than one of these: radio, tv,

telephone, bike, motorbike, or refrigerator)

Identification: Who is poor?

A person is multidimensionally poor if they are

deprived in 33% of the dimensions at the

same time.

33%

How do you calculate the MPI?

• The MPI uses the Alkire Foster method:

• H is the percent of people who are identified as poor,

it shows the incidence of multidimensional poverty.

• A is the average proportion of weighted deprivations

people suffer at the same time. It shows the intensity

of people’s poverty – the joint distribution of their

deprivations. The MPI is appropriate for ordinal data, and satisfies properties like subgroup consistency,

dimensional monotonicity, poverty & deprivation focus. MPI is

like the poverty gap measure – but looks at breadth instead –

what batters a person at the same time.

Formula: MPI = M0 = H × A

What is new? Intensity.

The MPI starts with each person, and constructs a

deprivation profile for each person.

Some people are identified as poor based on their joint

deprivations. The others are identified as non-poor.

• Most multidimensional poverty measures look at

deprivations one by one, not at the household level.

• Counting measures do look at coupled deprivations but only

provide a headcount, giving no incentive to target those

who are deprived in most things at the same time or to

reduce intensity.

53 53

54 54

55 55

56

57 57

58 58

59 59

60 60

61

Phuba

Deprived in 67% of dimensions.

It doesn’t tell the full story

But it gives some idea.

MPI – Key Results

Global Results:

These results are for 109 developing countries, selected

because they have DHS, MICS or WHS data since 2000.

Special surveys were used for Argentina, Brazil, Mexico,

Morocco, Occupied Palestinian Territory, and South Africa

They cover 5.3 billion people - 78.6% of the world’s population

Of these 5.3 billion people, 31% of people are poor.

That is 1.65 billion people. (2008 population figures taken from Population Prospects 2011; 2010 Revision).

Half of the world’s MPI

poor people live in

South Asia, and 29% in

Sub-Saharan Africa

MPI poor people by region

Total Population in 109 MPI countries

The MPI Headcount Ratios and the $1.25/day Poverty

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Nig

er

Ethi

opia

Mal

i

Cent

ral A

fric

an R

epub

lic

Buru

ndi

Libe

ria

Burk

ina

Faso

Gui

nea

Rwan

da

Moz

ambi

que

Sier

ra L

eone

Com

oros

DR

Cong

o

Uga

nda

Mal

awi

Beni

n

Tim

or-L

este

Sene

gal

Mad

agas

car

Tanz

ania

Nep

al

Zam

bia

Chad

Cote

d'Iv

oire

Gam

bia

Bang

lade

sh

Hai

ti

Togo

Nig

eria

Indi

a

Cam

eroo

n

Yem

en

Cam

bodi

a

Paki

stan

Keny

a

Lao

Swaz

iland

Repu

blic

of C

ongo

Gab

on

Leso

tho

Sao

Tom

e an

d Pr

inci

pe

Hon

dura

s

Gha

na

Djib

outi

Nic

arag

ua

Bhut

an

Gua

tem

ala

Indo

nesi

a

Boliv

ia

Peru

Vie

t N

am

Tajik

ista

n

Mon

golia

Iraq

Phili

ppin

es

Sout

h A

fric

a

Para

guay

Chin

a

Mor

occo

Esto

nia

Turk

ey

Egyp

t

Syri

an A

rab

Repu

blic

Colo

mbi

a

Sri L

anka

Aze

rbai

jan

Mal

dive

s

Kyrg

yzst

an

Dom

inic

an R

epub

lic

Hun

gary

Croa

tia

Mex

ico

Arg

enti

na

Braz

il

Jord

an

Uzb

ekis

tan

Ecua

dor

Ukr

aine

Mac

edon

ia

Mol

dova

Uru

guay

Thai

land

Latv

ia

Mon

tene

gro

Alb

ania

Russ

ian

Fede

rati

on

Arm

enia

Serb

ia

Bosn

ia a

nd H

erze

govi

na

Geo

rgia

Kaza

khst

an

Bela

rus

Slov

enia

103 of our 109 Countries have

income; only 71 have income

poverty data within 3 years of MPI.

Income data ranges from 1992-2008;

MPI from 2000-2010.

Intensity is highest in the poorest countries.

But there is variety…H in High-income countries 1-7%

H in High- and Upper Middle-income countries 1-40%

H in Middle- and High-income Countries 1-77%

H in Low-income Countries ranges from 5-92%

Ghana, Nigeria, and Ethiopia

Ethiopia’s Regional Disparities

Ethiopia

Ethiopia’s Regional Disparities

Addis Ababa

Somali

Afar

Harari

Dire

Dawa

Nigeria’s Regional Disparities

Nigeria

Nigeria’s Regional Disparities

South West

North East

Nigeria

Ghana’s Regional Disparities

Ghana

Ghana’s Regional Disparities

Greater

Accra

Northern Ghana

CHANGES OVER TIME

Ghana, Nigeria, and Ethiopia

Let us Take a Step Back in Time

Ghana

2003

Nigeria

2003

Ethiopia

2000

Ethiopia: 2000-2005 (Reduced A more than H)

Ghana

2008

Nigeria

2008

Ethiopia

2005

Ghana

2003

Nigeria

2003

Ethiopia

2000

Nigeria 2003-2008 (Reduced H more than A)

Ghana

2008

Nigeria

2008

Ethiopia

2005

Ghana

2003

Nigeria

2003

Ethiopia

2000

Ghana 2003-2008 (Reduced A and H Uniformly)

Ghana

2008

Nigeria

2008

Ethiopia

2005

Ghana

2003

Nigeria

2003

Ethiopia

2000

Pathways to Poverty Reduction

-6

-5

-4

-3

-2

-1

0

Ghana Nigeria Ethiopia

An

nual

ized

Ab

solu

te C

han

ge

in

th

e P

erce

nta

ge

Wh

o is

Po

or

and

Dep

rive

d in

...

Assets

Cooking Fuel

Flooring

Safe DrinkingWater

ImprovedSanitation

Electricity

Nutrition

Child Mortality

SchoolAttendance

Years ofSchooling

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Nigeria: Indicator Standard Errors

Ethiopia’s Regional Changes Over Time

Addis Ababa

Harari

Nigeria’s Regional Changes Over Time

South South

North Central

Inside the Regions of Nigeria

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

NorthCentral

NorthEast

NorthWest

SouthEast

SouthSouth

SouthWest

An

nual

ized

Ab

solu

te C

han

ge

in t

he

Per

cen

tage

Wh

o is

Po

or

and D

epri

ved in

... Assets

Cooking Fuel

Flooring

Safe DrinkingWaterImprovedSanitationElectricity

Nutrition

Child Mortality

Years ofSchoolingSchoolAttendance

Robustness checks

to ‘value judgements’

(choice of parameters)

Robustness to poverty cutoff

k= 20% to 40%

• 90% of the possible pairs of countries have a

dominance relation for k 2 to 4. That is, we

can say that one country is unambiguously

poorer than another regardless of whether we

require to be poor in 20, 30 or 40% of the

weighted indicators.

Robustness to Weights MPI Weights 1 MPI Weights 2 MPI Weights 3

Equal weights:

33% each

(Selected

Measure)

50% Education

25% Health

25% LS

50% Health

25% Education

25% LS

Pearson 0.992

Spearman 0.979

Kendall (Taub) 0.893

Pearson 0.995 0.984

Spearman 0.987 0.954

Kendall (Taub) 0.918 0.829

Pearson 0.987 0.965 0.975

Spearman 0.985 0.973 0.968

Kendall (Taub) 0.904 0.863 0.854

Number of countries: 109

MPI

Weights 2

50% Education

25% Health

25% LS

MPI

Weights 3

50% Health

25% Education

25% LS

MPI

Weights 4

50% LS

25% Education

25% Health

Robustness to Weights

Summary:

•High Correlations: 0.97 and above

•High Rank Concordance: 0.90 and

above

•85% of all possible pairwise

comparisons are robust

Complements monetary poverty measures

Gives a ‘high resolution’ lens on poor people’s lives

An overview and a ‘dashboard’

Changes over time – can change relatively quickly

Provides incentives to reduce intensity and incidence.

Can be used to identify the poorest

Adaptable for National Poverty Measures (or M&E)

Research and Policy: large agenda is ongoing

Uses of an MPI

Value Judgement 1: Selection of Data

- existing data

- internationally comparable

- legitimacy: MDGs

- malnutrition rather than income

- updated infrequently

Value Judgement 2: Selection of

Dimensions

Dimensions are ‘notional’ –

affect wts, communication

Follows precedent: HDI,

HPI

Possible because of data

Value Judgement 3: Selection of Indicators

Data constrained

Reflect MDGs (consensus)

Relatively Comparable

*note: technicalities in

construction are hardly

normatively justified but

matter a lot – e.g. hh

Value Judgement 4: Selection of Weights

Weights are ‘nested’ –

equal then equal.

This is easy to

understand, and often

used.

Robust to a range of

plausible weights

Most controversial.

1/6 on Health and

Education

Indicators

1/18 on standard

of living indicators.

Identification: Who is poor?

A person is multidimensionally poor if they are

deprived in 33% of the dimensions at the

same time.

33%

Colombia’s National MPI:

5 Dimensions, 15 Variables, Nested Weights

Educational

Conditions Childhood & Youth

Work Health Housing & Public

Services

Schooling

Illiteracy

School

Attendance

At the right

level

Access to

infant

services

No Child

Labour

Absence of

long-term

unemploy-

ment

Coverage

Access to health

care given a

necessity

Improved Water

Flooring

Overcrowding

Sanitation

Exterior

Walls

Formal work 0.1

0.2 0.2 0.2 0.2 0.2

0.05

0.1 0.1

0.04 Poverty cutoff =

33%

Territorial

Mexico’s National Measure: 6 social deprivations (1/12) + income (1/2)

Social Rights Deprivations

Population

We

llb

ein

g

Inco

me

Current income per capita

Six Social Rights: • Education

• Health

• Social Security

• Housing

• Basic Services

• Food

0 3 2 1 4 5 6

Social Rights

Deprivations

Mexico’s Identification:

poverty = (income + 1); extreme = (lower income + 3)

With Deprivations

EXTREME Multidimensional

Poverty

0 3

Moderate Multidimensional

Poverty

Vulnerable by social deprivations

Vulnerable by income

5 2 4 1 6

Ideal Situation

Without

Deprivations

MULTIDIMENSIONALLY POOR Basic Needs £

Food £

Income

102

Day 1:

Purpose of poverty measure

Dimensions to communicate measure

Particular Indicators used to measure poverty

Deprivation Cutoffs how much of each indicator?

Poverty Cutoff who is poor?

Values/Wts what are the relative weights of indicators?

Day 2:

Procedure: Who decides normative issues? What is the

appropriate role of poor people, governments, and

statistical or technical experts?

Plural Criteria: How should statistical, political, and

participatory input be coordinated in measurement design?

Thank you

www.ophi.org.uk

Purpose

105

Aim of each session - discuss how to: • make the value judgements inherent in this decision (options)

• balance normative, technical, & political issues (priority)

• update over time

Chair to summarize:

1. How would we suggest to those charged with designing the multidimensional

poverty measure that they undertake the three steps above?

2. What pressing research questions have been noted?

3. (does the question need to be reformulated or changed?)

Participants to contribute:

• Literature – please jot or email annotated biblio, stating why you propose each

and what you see it adding ([email protected])

• People / Projects with expertise or research (as above)

• Ideas that you feel like sharing in writing

mailto:[email protected]

106


For today, we presume that the value judgements pertain to the

design of a long-term official measure of multidimensional poverty.

The poverty measure will inform policy design, and reflect positive

change that can be influenced by public policy.

This is to be updated periodically (say every 2 years) using time series

data that are nationally representative and can be decomposed by

region and relevant social groups.

The survey design will take place after the measure is designed.

107


The purpose of the evaluative exercise shapes all choices, e.g.

National Poverty Measure – to span decades

Youth Poverty Measure – once, to profile youth issues

Targeting exercise – to benefit poorest of the poor

Monitoring measure – to track progress to given goals

International Comparisons – across nations

Community Development – show changes transparently

108


To some extent, the purpose, having been determined, shapes the

value judgements. Should these be taken ‘as given’?

E.g. a measure designed to monitor progress towards a national development plan

might systematically exclude public debate.

Should omission of public debate require justification? Space of resources?

E.g. a measure designed to document a given set of human rights from the

universal declaration might ignore cultural values.

How justify the ‘need’ for contextual vs comparable measures ?

E.g. a very rigorous measure designed to evaluate a small poverty intervention may

cost more than the intervention itself.

E.g. a measure run in a famine-prone area may be framed to exclude malnutrition

E.g. a measure may be designed to target 20% of people when 50% are destitute

Other early questions for a measure

1. Legal basis? (how endure across time)

2. How to update – Data / Survey; Frequency

3. Who will update (Institution; authority)

4. What Incentives it provides (ministries)

5. Political process of developing measure. 1. Public Consultations?

2. Expert Group – National Statistics & Economics

3. International/Regional Experts?

Dimensions

111


For today, we presume that the value judgements pertain to the

design of a long-term official measure of multidimensional poverty.

The poverty measure will inform policy design, and reflect positive

change that can be influenced by public policy.

This is to be updated periodically (say every 2 years) using time series

data that are nationally representative and can be decomposed by

region and relevant social groups.

The survey design will take place after the measure is designed.

112

Dimensions to communicate measure

Assume:

Dimensions are to be articulated in the space of

capabilities and functionings

Recall:

Dimensions are conceptual categories.

They do not appear in the ‘matrix’.

Each indicator belongs to one dimension.

They often help to set (nested) weights

Colombia’s National MPI:

Dimensions emerge from National Plan

Educational

Conditions Childhood & Youth

Work Health Housing & Public

Services

Schooling

Illiteracy

School

Attendance

At the right

level

Access to

infant

services

No Child

Labour

Absence of

long-term

unemploy-

ment

Coverage

Access to health

care given a

necessity

Improved Water

Flooring

Overcrowding

Sanitation

Exterior

Walls

Formal work 0.1

0.2 0.2 0.2 0.2 0.2

0.05

0.1 0.1

0.04 Poverty cutoff =

33%

Territorial

Mexico’s National Measure: Dimensions named by law

Social Rights Deprivations

Population

We

llb

ein

g

Inco

me

Current income per capita

Six Social Rights: • Education

• Health

• Social Security

• Housing

• Basic Services

• Food

0 3 2 1 4 5 6

115

Dimensions to communicate measure Observations:

- Fits into ‘selection of capabilities’ (as broad categories)

- via public debate, consensus instruments, researcher choice,

legal/institutional mandates, empirical studies, data (MPI)

- Clearest issue in theory & practice

Concerns:

- Priority: Don’t other value judgements matter ‘more’?

- Timing: planning ex ante vs response to a clear measure

Practical Issues:

- How update?

- How combine?

- How document?

Indicators ? more powerful; least discussed ?

117

Indicators – More powerful than dimensions?

Constraints:

Need to come from some data source (functionings?)

Finance & politics constrains content, periodicity, quality

Some Considerations are not purely normative:

• data exist or could exist;

• stock vs. flow

• individual vs. household vs cty

• comparability across all ages/ethnicities

• higher quality vs lower quality indicators (£ & survey)

• statistical associations across indicators

• can be changed by public policy

• Frequent usage (national or international);

literature review; discussion with experts;

other indicators. IPM-OPHI Internacional,

NBI, ICV y Sisbén III.

1. Indicators can be affected by public

policies.

2. Availability of information (in the survey of

Quality of Life in Colombia).

Precision of the sample

to estimate the

variable – estimated

coeff of variation <15%.

*EL DANE utiliza:

0-7: Estimación precisa

8-14: precisión aceptable

15-20 ó 15-25: Precisión regular y por lo tanto

se debe utilizar con precaución

Selection of Indicators (Variables)

Colombia’s MPI

Criteria for variable

selection Criteria to validate

variables

Mexico’s Multidimensional Poverty

Presentations report the 6 dimensions,

and do not share indicators or cutoffs.

Do indicators matter - other than for

experts?

Justification of Indicators

• Links to and proxy the dimensions/capabilities

– E.g. water. health/asset/dimension/gender

– E.g. indicators for health capabilities?

– In practice, rarely discussed; rarely debated.

– Arguably indicators of functionings (BMI, Ed) or their proxies

• Technical issues often rule:

– Accuracy, measurement error, expense, non-response

– Tracks changes in poverty over place and time

– Large debates even when clear analysis: stunting vs undernutrition

120

Justification of Indicators

• How frame the problem & debate?

– Conceptual categorization? (e.g. water)

– Best proxy for definition of capability/dimension?

– Choice of priority among technical criteria?

– Take actual issues one by one (e.g. time use)

– Is normative input ‘essential’ vs ‘possibly helpful’

– Should the ‘choice of dimensions’ become ‘choice of indicators?’

• But too technical for public debate?

121

Cutoffs

z ( 13 12 3 1) Cutoffs

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

Recall: two cutoffs. Both clearly vj.

Deprivation Cutoffs zj: if a person is deprived in each indicator

Poverty cutoff k, a person is poor if ci > k (Ex: k = 2)

Indicators Indicators c

Persons

y

13.1 14 4 1

15.2 7 5 0

12.5 10 1 0

20 11 3 1

Deprivation Cutoffs zj: if a person is deprived in each indicator

Poverty cutoff k, a person is poor if ci > k (Ex: k = 2)

Indicators Indicators c (k)

Persons

z ( 13 12 3 1) Cutoffs

0

4

2

0

0000

1111

1010

0000

0

g

Recall: two cutoffs

y

13.1 14 4 1

15.2 7 5 0

12.5 10 1 0

20 11 3 1

Deprivation Cutoffs:

Clearly are value judgements:

How much is enough not to be deprived?

– Example: Income Poverty Line

– Example: MPI – MDG indicators

Clearly matter fundamentally:

- Affect ‘effective weights’

- Define possibility to be identified as poor

- Empirically, can be greater sensitivity

Justification of deprivation cutoffs

• Technical (although disputed)

– E.g. safe water. Particular bugs absent (response codes)

– E.g. malnutrition. Z scores and reference groups

– Statistical properties

• Political & Legal

– Promised / Required (e.g. compulsory education, plan)

• Constraints & Challenges:

– Diversity – individual & group

– Knowledge of data concerns & analyses

– Knowledge of possibilities

– Comparability (rural-urban; climatic zones)

126

Poverty Cutoffs:

Clearly a value judgment: How much is enough to be poor?

– Reflects purpose (targeting vs national measure)

– Often political interest

This is a new step – so not many precedents.

Has been set

• To match particular headcount ratio

• To reflect participatory or subjective assessments

• To match legal definition (Mexico)

• To match statistical ‘gaps’ in data points (Bristol)

The number of MPI deprivations experienced by those who were income poor,

and those who perceived themselves to be poor, was compared with the number

of deprivations among the non-income and non-subjective poor.

Poverty Cutoff – Colombia.

Median Average

People who perceive themselves to be poor 5.0 5.0

Income poor people 5.1 5.2

Income poor people who perceive self as poor 5.4 5.6

Those who don’t perceive themselves as poor 3.0 3.2

Those who are not income poor 3.0 3.2

All people 3.8 4.1

Median and Average number of deprivations 2008

Fuente: Cálculos DNP-SPSCV, con datos de la ECV2008

A non-poor person on average has 3 deprivations, which suggests that a low value of k would capture

deprivations that were not related to or sufficient to identify poverty.

Social Rights

Deprivations

Mexico’s Poverty Cutoffs:

poverty = (income + 1); extreme = (lower income + 3)

With Deprivations

EXTREME Multidimensional

Poverty

0 3

Moderate Multidimensional

Poverty

Vulnerable by social deprivations

Vulnerable by income

5 2 4 1 6

Ideal Situation

Without

Deprivations

MULTIDIMENSIONALLY POOR Basic Needs £

Food £

Income

Weights

Weights (Values)

• Some critics have focused on the weights – Claiming they cannot be set in a defensible way

– Claiming disputes on weights undermine legitimacy of measure

– Prefer a ‘mechanical’ route – eigen vectors/regression coefficients/prices

• Thus far, insufficient guidance

– Yes, weights are normative, and not embarrassing to set

– Yes, we will disagree hence need a plausible range of weights, but:

– Weights are also a function of deprivation cutoffs / headcounts

– Weights are also influenced by association among indicators

– Weights vary across person/group: combine or apply individually?

131

Weights (Values)

• Literature is large (review paper) – Is it sufficient to explain the options, strengths & weaknesses?

– Is there anything that can be ruled out

• Thus far, insufficient guidance

– Yes, weights are normative, and not embarrassing to set

– Yes, we will disagree hence need a plausible range of weights, but:

– Weights are also a function of deprivation cutoffs / headcounts

– Weights are also influenced by association among indicators

– Weights vary across person/group: combine or apply individually?

132

Equal (nested) weights

• Most commonly used approach

• Advocated for policy communication

(Atkinson)

• Equal weights represent value judgments

• Example: 1. BMI, years of school (0.5)

2. BMI, yrs school, caloric intake, anaemia, (0.25)

Field Studies: Participatory FGD

–The Participants:

–Identified the focal problems of poverty

–Ranked the dimensions of poverty (weights)

–Identified ‘cutoffs’ – who is poor?

–Provided feedback on the 3 trial measures

135

Another community: FGD

Ruepisa: Ranking

Most important Electricity

Land

Sanitation

Health

Drinking Water

Next most Education

Housing

Third Income / Money

Fourth Animal

Assets

Documents

Value Judgements in Multidimensional Poverty Measurement ... · methodology M ka =(ρ k,Ma) satisfies: decomposability, replication invariance, symmetry, poverty and deprivation focus,