OPHIOxford Poverty & Human Development InitiativeDepartment of International DevelopmentQueen Elizabeth House, University of Oxford www.ophi.org.uk

Multidimensional poverty for monitoring development

progress

Outline–Multidimensional Poverty

Measurement–AF Methodology in breve– Illustration of metrics: MPI

Multidimensional Poverty Measurement

Why the surge in interest?

Motivation

“Human lives are battered and diminished in all kinds of different ways.”

Amartya Sen

Relevant Data

• “We are almost blind when the metrics on which action is based are ill-designed or when they are not well understood. For many purposes, we need better metrics.”

Political Demand

Stiglitz Sen Fitoussi: Commission on the Measurement of Economic Performance and

Social Progress

Policy Demand:Target the poorest: “Achieving the MDGs

will require increased attention to those most vulnerable.” MDG Report 2010

Address interconnections efficiently: “Acceleration in one goal often speeds up progress in others” Roadmap towards the Implementation of the MDGs 2010

Show changes directly & quickly: Monitoring & incentives

Plan and Evaluate PolicyTo identify & use the most effective kind and sequencing of policies.

AF Methodology:Since 2000, a surge in new

methodologies to measure multidimensional poverty.

AF method is based on the FGT, counting and basic needs traditions, & can use ordinal data.

It can also be decomposed into policy relevant and intuitive subindices.

The technology is flexible: you choose the dimensions, indicators, weights, & cutoffs.

Achievement Matrix

z = ( 13 12 3 1 ) Cutoffs

Dimensions

Persons

131120

01105.12

0572.15

14141.13

Y

AF in breve: Achievement Matrix

AF in breve: Deprivation Matrix

0=non-deprived1=deprived ‘count’

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

AF in breve: Dual Cut-off Identification

z = deprivation cutoffk = poverty cutoff

Deprivation Matrix Censored Deprivation Matrix, k=2

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

AF in breve: Aggregation

Deprivation Matrix Censored Deprivation Matrix, k=2

g0

0 0 0 0

0 1 0 1

1 1 1 1

0 1 0 0

0

2

4

1

g0(k)

0 0 0 0

0 1 0 1

1 1 1 1

0 0 0 0

0

2

4

0

M0 is the mean of the matrixThis matrix also generates H and ACensored Headcounts for each dimensionPercent Contributions for each dimensionAnd all of these for subgroups

– An international measure of acute poverty covering 104 developing countries in UNDP’s 2010 HDR.

– Complements income poverty measures by showing direct deprivations and their joint distribution

– A high resolution lens, using AF methodology

– Constrained by data availability

– Aims to encourage the development of better national and regional measures of multidimensional poverty

Multidimensional Poverty Index

(MPI) acute poverty in developing countries

1. Data for the MPI: Surveys

Demographic & Health Surveys (DHS - 48) Multiple Indicator Cluster Surveys (MICS - 35) World Health Survey (WHS – 19)

Additionally we used 2 special surveys covering Mexico and urban Argentina.

2. Dimensions Indicators & Weights of MPI

2. Data constraints

The MPI is deeply affected by the lack of comparable data. key indicators are not collected (stock, quality) • data for some dimensions are missing• missing values lead to sample size

reduction/biases • respondent(s) vary; individual level data is

sparse• surveys updated every 3-5 years, and in

different years • data exclude certain populations (elders,

institutionalized)• income/consumption surveys lack MPI

health indicators.

These can be addressed at a national level for national measures.

“Improving data gathering and its quality in all countries should be a central focus ...”

Bourguignon et al. 2008 page 6

3. Methodology: Alkire and Foster - Identification

A person is multidimensionally poor if they are deprived in 33% of the dimensions.

33%

3. Methodology Alkire and Foster: Aggregation

• We construct the MPI using the AF method:

• H is the percentage of people who are 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.

Formula: MPI = M0 = H × A

3. Methodology: MPI g0(k) matrixAdjusted Headcount Ratio = M0 = HA = .442

k=3.333 (have MPI for all k values)

Indicators c(k) c(k)/d

H = headcount = ¾ = 75% A = average deprivation share among poor = .59

= 59% HA = MPI = 0.442

0

0 0 0 0 0 0 0 0 0 0

1.67 1.67 1.67 1.67 .55 0 0 0 0 .55

0 1.67 0 1.67 .55 0 .55 .55 .55 0

0 0 0 1.67 .55 .55 .55 0 .55 .

)

5

(

5

g k

0

7.76

5.53

4.42

0

.776

.553

.442

Example: Tabitha

OPHI has done ground reality

checks in Kenya, Madagascar, Indonesia,

Bhutan, and India.

What’s new?Intensity:

The MPI and its related indices reflects each

household’s deprivation profile.

Stéphanie’s Intensity

Adil’s Intensity

Jiyem’s Intensity

Others

The MPI helps showWho they are (Headcount) & how they are poor (Intensity)

4. 2010 Results:

These results are for 104 developing countries, selected because they have DHS, MICS or WHS data since 2000. Special surveys were used for Mexico and urban Argentina.

They cover 78.5% of the world population (2007).

In 2011’s HDR this will be increased to 109 countries, and updated data are available for over 20 countries.

The MPI headcounts fall between $1.25 and $2.00/day,

but are quite different.

Arab States298.3

6%

Central and Eastern Europe and the

Commonwealth of Independent States

(CIS)4008%

East Asia and the Pacific1867.7

35%

Latin America and Caribbean

490.89%

South Asia1543.9

29%

Sub-Saharan Africa712.313%

Regional Distribution of the World's Total Population 2007 (millions)

Most poor people in the world by MPI live in

South Asia, followed by Sub-Saharan Africa.

Poor People

Total Population

Intensity tends to be highest with high Incidence

Nepal

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Ave

rage

Bre

adth

of P

over

ty (A

)

Percentage of People Considered Poor (H)

MPI = A x H Poorest Countries,Highest MPI

India

Pakistan

BangladeshIndonesia

China

Nigeria

Low incoHigh Income

Upper-MiddleIncome

Lower-Middle Income

Low Income

Niger

Ethiopia

DR CongoBrazil

Jordan

Vietnam

Decompose by region & ethnicity

• In Kerala India 16% of the population is MPI poor; in Bihar it is 81%.

India MPIKerala

Bihar

Keral

a

Punja

b

Tamil

Nadu

Mah

aras

htra

Gujar

at

Andhr

a Pra

desh

INDIA

Wes

t Ben

gal

Rajas

than

Chhat

tisga

rh

Jhar

khan

d0

0.1

0.2

0.3

0.4

0.5

0.6

MPI for Indian States/regions

Madhya Pradesh, India

DR Congo

Population 2007

69.97M 62.50M

MPI 0.39 0.39

MPI Headcount

69.5% 73.2%

Avg Intensity

56% 53.7%

Comparisons: Headcount plus

Intensity & Composition

Composition of Poverty: key for policy (equal MPIs)

DRC: Larger Std of Living Deprivations

Madhya Pradesh: Larger Malnutrition

IndiaMPI = 0.296 A = 53.5%

Cameroon0.29954.7%

Kenya0.30250%

Intensity – who is the poorest of all?

Pathways to Poverty Reduction

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

Bangladesh Ethiopia Ghana

Percent Variation in H (Δ%H) Percent Variation in A (Δ%A)

Interaction term (Δ%H* Δ%A)

Ghana and Bangladesh reduced H relatively more than A, Ethiopia the other

way round.

Bangladesh improved child enrolment, Ethiopia nutrition and water, Ghana

many at the same time.

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

Bangladesh Ethiopia Ghana

Per

cent

Var

iatio

n in

eac

h de

privat

ion

of th

e po

or

Assets

Cooking Fuel

Floor

Water

Sanitation

Electricity

Nutrition

Mortality

Child Enrolment

Schooling

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Point estimate

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Point estimate

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Point estimate

Time series show: Reduction in HeadcountReduction in IntensityChanges in each indicator’s censored headcountsChanges in percent contributions of each indicator

(Composition of poverty)

Time series can be used to:Understand how poverty evolves …

across time, regions, and dimensions. Evaluate policy (if natural experiments found)Observe shocks (positive or negative)Observe patterns (interconnections)

Media Coverage of the 2010 MPI

The Report was covered in over 60 countries, e.g. in:

• TIME Magazine• The New York Times• The Wall Street Journal• BBC• The Economist• The Guardian • The Financial Times• The Huffington Post• Foreign Policy • The Hindu• Christian Science Monitor• The Globe and Mail• The Times of India

Applications and Experiments Bhutan – Gross National Happiness index

released 2008 México – National multidimensional poverty

index 2009 Colombia: integrated into the National Plan in

2011. Chile: presentations and course (2 weeks);

under construction Iraq, Venezuela, Malaysia: presentations; trial

measures Bhutan: course taught (3 days) and trial

measures constructed Thailand: course taught (3 days) Egypt: course will be taught (6 days) El Salvador: course will be taught; trial

measures constructed EU: trial measures constructed using EU-SILC

data

www.coneval.gob.mx

Poverty Measurement Methodology

December, 2009

Degree of social cohesion

Territorial

What are the main features of the new methodology?

Social RightsDeprivations

Population

Wellb

ein

g

Incom

e

Current income per capita 50%

Social : 50%

• Education

•Health

•Social Security

•Housing

• Basic services

•Nutrition

03 2 1456

Social RightsDeprivations

Poverty Identification

EWL

With deprivations

EXTREME Multidimensional

Poverty

03

Moderate MultidimensionalPoverty

Vulnerable people by social

deprivations

Vulnerable people

by income

5 24 16

Ideal Situatio

n

MWL

$1,921.7 U$1,202.8 R $874.6 U

$613.8 R

Without

Deprivations

MULTIDIMENSIONALLY POOR

Economic wellbeing line

Minimum wellbeing line

MODERATE POVERTY 33.7%

36.0 million 2.3 Deprivation

Social RightsDeprivations

Wellb

ein

gIn

com

e

Vulnerable people by

income

Vulnerable by social

deprivations

Total Population 2008

18.3%19.5 million

33.0%35.2 million2.0 Deprivation average

03 2 1456

EXTREME POVERTY

averageaverage

10.5% 11.2 million 3.9 Deprivation

4.5%4.8 million

MODERATE POVERTY 36.5 %

2.5 millions 3.1 Deprivation

Social RightsDeprivations

Wellb

ein

gIn

com

e

Vulnerable people by

income

Vulnerable people by

social deprivation

s

Indigenous population 2008

1.2%.1 millions

20.0 %1.4 millions2.8 Deprivation average

03 2 1456

EXTREME POVERTY

averageaverage

39.2 % 2.7 millions 4.2 Deprivation

3.1%0.21 millions

European studies call for more panel research on multidimensional

poverty dynamics.

Source: Whelan Layte Maitre 2004 Understanding the Mismatch between Income Poverty & Deprivation

With Panel data we can identify different types of

poor people

1. Chronic poor – across many time periods

2. Churning going in and out of poverty

3. Falling into poverty4. Moving out of poverty.

With data from two periods we generate

transition matrices showing the probability of entry and exit for H and A. Apablaza & Yalonetzky

Panel data enables new analyses about chronicity and

poverty transitions: 1. How do the four groups differ – either

demographically or in the structure of their poverty?

2. Poverty traps? What is the composition of poverty for the chronic poor? Are any dimensions always deprived?

3. Does the composition of poverty for chronic poor change? Does chronic poverty decrease over time?

4. Policy sequences: what chains do they catalyze? Which sequence of policies has highest impact?

5. How does poverty evolve across different ages? For different racial groups and household types?