Global Poverty and the Multidimensional Poverty Index (MPI ...strive.lshtm.ac.uk/sites/strive.lshtm.ac.uk/files/Alkire STRIVE... · Global Poverty and the Multidimensional Poverty

Global Poverty and the

Multidimensional Poverty Index

(MPI) 2014

Sabina Alkire

STRIVE, November 2014

What is the Global MPI?

• The Global MPI is an internationally comparable index

of acute poverty for 100+ developing countries.

• It was co-designed by OPHI and UNDP’s HDRO,

and was launched in 2010 in the Human Development

Report (HDR), where it has been published since.

• OPHI updated the global MPI in 2011, 2013 and 2014.

• The MPI uses the Alkire-Foster methodology (2011)

• The MPI methodology is being adapted for national

poverty measures – using indicators and specifications

that best reflect each policy context.

MPI

METHODOLOGY

Data: Surveys (Global MPI 2014)Details in: Alkire, Conconi and Seth (2014)

Demographic & Health Surveys (DHS - 52)

Multiple Indicator Cluster Surveys (MICS - 34)

World Health Survey (WHS – 16)

Additionally we used 6 special surveys covering urban Argentina

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

(ENNVM/LSMS), Occupied Palestinian Territory (PAPFAM), and

South Africa (NIDS).

Constraints: Data are 2002-2013. Not all have precisely the same

indicators.

Dimensions, Weights, Indicators

Build a deprivation

score for each personNathalie faces multiple deprivations in health

and living standards

Identify who is poor

Global MPI: A person is multidimensionally poor if

they are deprived in 33% or more of the dimensions.

Nathalie’s deprivation score is 67%

Compute the MPI (Alkire-Foster)

The MPI is the product of two components:

1) Incidence ~ the percentage of people who

are poor, H.

2) Intensity ~ the average percentage of

dimensions in which poor people are

deprived A.

MPI = H× A

Alkire and Foster Journal of Public Economics 2011

MPI 2014 FINDINGS

10

Across 108 countries & 5.4 billion people

30% of people are poor

=1.6 billion people

Aggregates

use 2010

population

data

11

Headline results

12

Disaggregated Data

13

Composition of

Poverty

14

Composition by region

The MPI is like a high resolution lens…

Breakdown by Indicator & Region


You can zoom in


You can zoom in

and see more

Of the 1.6 billion MPI poor people, 29% live in Sub-

Saharan Africa, and 52% in South AsiaTotal Population

MPI Poor

East Asia & the Pacific,

34.7%

South Asia, 29.7%

Sub-Saharan Africa, 14.4%

Latin America & Caribbean,

9.5%

Europe & Central Asia,

7.5%

Arab countries, 4.2%

East Asia & the Pacific,

14.6%

South Asia, 52.0%

Sub-Saharan Africa, 28.8%

Latin America & Caribbean,

1.9%

Europe & Central Asia,

0.7%

Arab countries, 2.1%

Most MPI poor people (71%) live in

middle-income countries

2010 Population Data

High Income,

3.4%Low

Income, 13.3%

Lower Middle Income,

44.1%

Upper Middle Income,

39.2%

Total Population by Income Category High

Income, 0.2%

Low Income,

28.9%

Lower Middle Income,

58.3%

Upper Middle Income,

12.7%

MPI Poor Population

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Nig

erE

thio

pia

Mal

iB

urk

ina

Fas

oL

iber

iaG

uin

eaSom

alia

Buru

ndi

Cen

tral

Afr

ican

Rep

ublic

Guin

ea-B

issa

uSen

egal

Co

ngo

, D

emo

crat

ic R

epublic

of

the

Sie

rra

Leo

ne

Ben

inU

gan

da

Mo

zam

biq

ue

Rw

anda

Tim

or-

Les

teM

adag

asca

rM

alaw

iA

fgh

anis

tan

Tan

zan

ia, U

nit

ed R

epub

lic

of

Zam

bia

Ch

adM

auri

tan

iaG

amb

iaC

ote

d'I

vo

ire

Ind

iaY

emen

Ban

glad

esh

To

go

Hai

tiK

enya

Cam

ero

on

Cam

bo

dia

Nep

alP

akis

tan

Nig

eria

Co

ngo

, R

epub

lic

of

Nam

ibia

Zim

bab

we

Les

oth

oSao

To

me

and P

rin

cip

eL

ao P

eop

le's

Dem

ocr

atic

Rep

ub

lic

Gh

ana

Van

uat

uD

jib

outi

Bh

uta

nG

uat

emal

aB

olivia

, P

luri

nat

ion

al S

tate

of

Sw

azilan

dG

abo

nN

icar

agua

Ho

nd

ura

sM

on

golia

Ind

ones

iaP

hilip

pin

esP

arag

uay

Taj

ikis

tan

Ch

ina

Iraq

South

Afr

ica

Mo

rocc

oP

eru

Guya

na

Est

on

iaT

urk

eyE

gyp

tSuri

nam

eT

rinid

ad a

nd T

ob

ago

Syr

ian

Ara

b R

epub

licC

olo

mb

iaSri

Lan

ka

Aze

rbai

jan

Mal

div

esK

yrgy

zsta

nB

eliz

eD

om

inic

an R

epub

lic

Hun

gary

Cro

atia

Vie

t N

amC

zech

Rep

ub

licA

rgen

tin

aM

exic

oB

razi

lJo

rdan

Uzb

ekis

tan

Ecu

ado

rU

kra

ine

Mo

ldo

va,

Rep

ub

lic

of

Uru

guay

Th

aila

nd

Lat

via

Mo

nte

neg

roO

ccup

ied P

ales

tin

ian T

erri

tory

Alb

ania

Russ

ian

Fed

erat

ion

Tun

isia

Geo

rgia

Mac

edo

nia

, T

he

form

er Y

ugo

slav

Rep

ub

lic

of

Un

ited

Ara

b E

mir

ates

Bo

snia

an

d H

erze

go

vin

aA

rmen

iaK

azak

hst

anSer

bia

Bel

arus

Slo

vak

iaSlo

ven

ia

Per

cen

tage

of

the

Po

pula

tio

n

MPI and $1.25/day poverty rates

MPI Poor $1.25 a day

Incidence and Intensity by Country

Namibia

Brazil

Argentina

Indonesia

Guatemala

Ghana

Lao

Nigeria

Tajikistan

ZimbabweCambodia

Nepal

Bangladesh

Gambia

TanzaniaMalawi

Rwanda

Afghanistan

Mozambique

Congo DR

Benin

Burundi

Guinea-Bissau

Liberia

Somalia

Ethiopia Niger

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)

Percentage of People Considered Poor (H)

Poorest Countries, Highest MPI

China

India

The size of the bubbles

is a proportional

representation of the total

number of MPI poor in

each country

MPI varies subnationally too

Namibia

Brazil

Argentina

Indonesia

Guatemala

Ghana

Lao

Nigeria

Tajikistan

ZimbabweCambodia

Nepal

Bangladesh

Gambia

TanzaniaMalawi

Rwanda

Afghanistan

Mozambique

Congo DR

Benin

Burundi

Guinea-Bissau

Liberia

Somalia

Ethiopia Niger

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)


Poorest Countries, Highest MPI

High Income

Upper-Middle Income

Lower-Middle Income

Low Income

China

India

The size of the bubbles

is a proportional

representation of the total

number of MPI poor in

each country

23

30%

35%

40%

45%

50%

55%

60%

65%

70%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)


Nigeria

24

Abia

Adamawa

Anambra

Bauchi

Bayelsa

Benue

Borno

Cross River

Ebonyi

Edo

Enugu

FCT (Abuja)

Gombe

Imo

Jigawa

Kaduna

Kano

Katsina

Kebbi

Kogi

Kwara

Lagos

Nasarawa

Niger

OgunOsun

Oyo

Plateau

Sokoto

Taraba

Yobe

Zamfara

30%

35%

40%

45%

50%

55%

60%

65%

70%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)


Nigeria

MPI also varies greatly across subnational regions

within a country – e.g. Cameroon

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)



within a country – e.g. Cameroon

Incidence: 6.5% to 86.7%

Intensity: 36.4% to 62.3%

Cameroon

Adamaoua

Centre (Excluding Yaoundé)

Douala

Est

Extrême-Nord

Littoral (Excluding Douala)

Nord

Nord-Ouest

OuestSud

Sud-Ouest

Yaoundé

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)



within a country – e.g. Bangladesh

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)



within a country – e.g. BangladeshIncidence: 44.5% to 61.9%

Intensity: 44.5% to 53%

Bangladesh Chittagong

Dhaka

Khulna

Rajshahi

Rangpur

Sylhet

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rage

Inte

nsi

ty o

f P

ove

rty

(A)


CHANGES OVER TIME

- Coverage:

- 34 countries, from every region; both LICS and MICS

- 338 sub-national regions

- covering 2.5 billion people

- Rigorously comparable MPI values, with std errors/inference

- Annualized changes compared across countries

30

And how has MPI gone down?

Nepal 2006

Nepal 2011

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Ave

rag

e I

nte

nsi

ty o

f P

ove

rty (

A)

Incidence - Percentage of MPI Poor People (H)

How MPI decreased in Nepal 2006-11Reduction in both H and A

Decomposition By Region

(or social group) – shows H, A, inequalities

Subgroup Decompositions

Subgroup Decompositions (regional)

35

How did MPI go

down?

Monitor each

indicator

Indicator Changes by region (Nepal)

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

An

nu

ali

zed

Ab

solu

te C

ha

ng

e

in p

rop

ort

ion

wh

o i

s p

oo

r an

d d

ep

rive

d i

n..

.

Nutrition

Child MortalityYears of SchoolingAttendance

Cooking FuelSanitation

Water

Electricity

Floor

Assets

Disaggregating by subnational region

- Poverty significantly decreased in 208 of the 338 subnational

regions, which house 78% of the poor.

- Ten countries reduced all MPI indicators significantly:

Bolivia, Cambodia, Colombia, the Dominican Republic,

Gabon, India, Indonesia, Mozambique, Nepal, and Rwanda;

- Eight countries reduced poverty in all subnational regions:

Bangladesh (2007-11), Bolivia, Gabon, Ghana, Malawi,

Mozambique, Niger and Rwanda.

- In nine countries the poorest region reduced poverty the

most: Bangladesh (2007-2011), Bolivia, Colombia, Egypt,

Kenya, Malawi, Mozambique, Namibia and Niger.

Adja

Bariba

Dendi

Fon

Yoa and Lopka

Bétamaribe

Peulh

Yoruba

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

An

nu

al

Ab

solu

te C

han

ge i

n M

PI T

Multidimension Poverty Index (MPIT) at initial year

Reduction in

MPI

Size of bubble is proportional to

the number of poor in first year of

the comparison.

Disaggregating by ethnic group - Benin

.

Adja

Bariba

Dendi

Fon

Yoa and Lopka

Bétamaribe

Peulh

Yoruba

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

An

nu

al

Ab

solu

te C

han

ge i

n M

PI T


Reduction in

MPI



the comparison.

Disaggregating by ethnic group - Benin

.

Kalenjin

KambaKikuyu

Kisii

Luhya

Luo

Meru

Mijikenda/Swahili

Somali

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

An

nu

al

Ab

solu

te C

han

ge i

n M

PI T


Reduction in

MPI



the comparison.

Disaggregating by ethnic group - Kenya

Kalenjin

KambaKikuyu

Kisii

Luhya

Luo

Meru

Mijikenda/Swahili

Somali

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

An

nu

al

Ab

solu

te C

han

ge i

n M

PI T


Reduction in

MPI



the comparison.

Disaggregating by ethnic group - Kenya

Poorest ethnic

group reduced

MPI the fastest.

They are catching

up.

MPI vs. Income poverty

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

MPI Incidence $1.25 Incidence

MPI vs. Income poverty

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

MPI Incidence $1.25 Incidence

If progress was only measured by reducing income

poverty, the tremendous gains of Rwanda, Ghana,

and Bolivia would have been less visible.

Other Global MPI 2014 Studies

- Rural-Urban poverty: MPI is mainly rural

- Inequality among the poor: How to measure and track it.

- Destitution: a subset of the MPI poor are destitute; who are

they, and how have they changed?

NATIONAL MPIs

MPI: Two kinds ~ both useful

Internationally comparable:- Like $1.25/day poverty measures

- We propose an MPI 2015+, that reflects the SDGs

- A better measure will use better data

- MPPN developed SDG-related survey modules for piloting


National MPIs: - Vital for policy

- Reflect National Definitions

- Official measures in Mexico, Colombia, Philippines, Bhutan


Internationally comparable:Example: The Global MPI estimated and analysed by OPHI and published by UNDP’s HDRO can be compared across 108 countries. Facilitates ‘lessons learned’ across countries.

- Like $1.25/day and $2/day poverty measures & MDGs

- Useful for policy analysis, but limited national ownership

Context-Specific: Example: National MPIs reflect national contexts and priorities. They guide policies like targeting and allocation and monitor changes

- Like National income poverty measures

- Useful for policy but can’t be compared internationally

MPI in National Settings

Official National MPIs

Colombia

Mexico

Bhutan

Philippines

Other national applications underway.

Some Policy Applications of MPIs:

• Track poverty over time (official statistics)

• Compare poverty by region, ethnicity, rural/urban

• Monitor indicator changes (measure to manage)

• Coordinate different policy actors

• Target the marginalized

• Geographic targeting

• Household beneficiaries

• Evaluate policy impacts

The Multidimensional Poverty Peer Network

Launched in June 2013 at University of Oxford with:

• President Santos of Colombia

• Ministers from 16 countries in person

• A lecture from Professor Amartya Sen

• Aim: South-South support for National MPIs & an improved Global MPI 2015+ engaging research.

MPPN has 30 countries plus 10 international

agencies in 2014

Supported by the German Federal Ministry for Economic

Cooperation and Development (BMZ)

The MPPN agenda

- Support National MPIs that inform powerful policies

- Suggest an improved Global MPI 2015+ that reflects the SDGs (acute & moderate poverty versions)

- Strengthen the data sources for MPI metrics

Why Measure? Action ‘with vigour’Coordination ~ Policy Design ~ Monitoring ~ Targeting ~ Allocation

“Positive changes have often occurred and

yielded some liberation when the remedying

of ailments has been sought actively and

pursued with vigour”

Jean Dreze and Amartya Sen India: An Uncertain Glory 2013

www.ophi.org.uk/

multidimensional-poverty-index

http://www.ophi.org.uk/multidimensional-poverty-index

People and Stories

Policy Briefings

Infographics

Interactive DataBank with Maps

Academic Paper Drafts for Comment

Data Tables

Thanks! From the OPHI-MPI Team

OPHI Research Team: Sabina Alkire (Director), James Foster (Research Fellow), John Hammock (Co-Founder and

Research Associate), Adriana Conconi (coordination MPI 2013/14), José Manuel Roche (coordination MPI 2011, 2013), Maria Emma

Santos (coordination MPI 2010), Mauricio Apablaza, Paola Ballon, Mihika Chatterjee, Bouba Housseini, China Mills, Suman Seth, Ana

Vaz, Gaston Yalonetzky, Diego Zavaleta.

Data analysts and MPI calculation: Ivan Gonzalez de Alba, Aparna John, Usha Kanagaratnam, Saite Lu, Maria

Mancilla Garcia, Christian Oldiges, Felipe Roa-Clavijo and Quang Van Tran.

Special contributions: Ana Vaz (Quality Checks and Changes over Time), Alejandro Olayo-Mendez (Research Assistance),

Putu Natih, John Hammock, Bouba Housseini and Vanita Leah Falao (new Ground Reality Check field material), Suman Seth (design and

programming for Inequality among the Poor and Destitution analyses).

Communication Team: Paddy Coulter (Director of Communications), Emma Feeney (Research Communications Officer),

Heidi Fletcher (Web Manager), Moizza B Sarwar (Research Communications Assistant), and Cameron Thibos (Design Assistant).

Administrative Support: Laura O’Mahony (OPHI Project Coordinator), Natasha Francis (OPHI Project Assistant)

OPHI estimate the MPI, the UNDP Human Development Reports also publish it

and we are grateful to our colleagues in HDRO for their support.

Global Poverty and the Multidimensional Poverty Index (MPI ...strive.lshtm.ac.uk/sites/strive.lshtm.ac.uk/files/Alkire STRIVE... · Global Poverty and the Multidimensional Poverty

Documents