Multidimensional Progress in Low and Middle Income Countries

UNDP 7th Ministerial Forum in Latin America & CaribbeanSabina Alkire, 30 October 2014

Multidimensional

Progress

MEASURE TO MANAGE

From Measure to ToolExample: Global MPI

• The MPI is an internationally comparable index of acute poverty for 100+ developing countries.

• It was launched in 2010 in UNDP’s Human Development Report, and updated in 2011, 2013 and 2014.

• The MPI methodology is being adapted for national poverty measures – using better indicators for each policy context.

• A revised MPI 2015+ could be used to monitor extreme poverty in the SDGs

Data: Surveys (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

1. Build a deprivation

score for each person

Nathalie faces multiple deprivations in health

and living standards

2. 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%33%

3. 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

AF Metodolo

gía

Método de

conteo(NBI)

Método axiomáti

co(FGT)

10

Headline results

Headlines Help“While assessing quality-of-life requires a plurality of indicators, there are strong demands to develop a single summary measure.”

Stiglitz Sen Fitoussi Commission Report

12

H and A for every country

13

Disaggregated Data

14

Composition of Poverty

15

Composition by region

The MPI is like a high resolution lens…

Breakdown by Indicator & Region

The MPI is like a high resolution lens…

You can zoom in

The MPI is like a high resolution lens…

You can zoom in

and see more

Nig

er

Mal

i

Lib

eria

Som

alia

Cen

tral

Afr

ican

Rep

ubl

ic

Sen

egal

Sie

rra

Leo

ne

Uga

nda

Rw

anda

Mad

agas

car

Afg

han

ista

n

Zam

bia

Mau

rita

nia

Cot

e d'

Ivoi

re

Yem

en

Tog

o

Ken

ya

Cam

bodi

a

Pak

ista

n

Con

go, R

epu

blic

of

Zim

babw

e

Sao

Tom

e an

d P

rin

cipe

Gh

ana

Djib

outi

Gu

atem

ala

Sw

azila

nd

Nic

arag

ua

Mon

golia

Ph

ilipp

ines

Taj

ikis

tan

Iraq

Mor

occo

Gu

yan

a

Tu

rkey

Su

rin

ame

Syr

ian

Ara

b R

epu

blic

Sri

Lan

ka

Mal

dive

s

Bel

ize

Hu

nga

ry

Vie

t N

am

Arg

enti

na

Bra

zil

Uzb

ekis

tan

Ukr

ain

e

Uru

guay

Lat

via

Occ

upi

ed P

ales

tin

ian

Ter

rito

ry

Ru

ssia

n F

eder

atio

n

Geo

rgia

Un

ited

Ara

b E

mir

ates

Arm

enia

Ser

bia

Slo

vaki

a 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Why Multidimensional

Poverty?It is different from mon-

etary povertyAnd different policies re-

duce it.

MPI Poor

Perc

en

tag

e o

f th

e P

op

ula

tion

Income may not match other deprivations

Incidence and Intensity by Country

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

Tajikistan

Zimbabwe6592.7

11865.5

77494.1

1015.4

29483.910007.97472.2

18787.716681.4

46008.6

6830.3

7457.8

1230.3

3321.0

7820.6

76063.0 14188.3

Argentina

Indonesia

Guatemala

Ghana

Lao

Nigeria

Brazil

Namibia

Percentage of People Considered Poor (H)

Ave

rage

Inte

nsi

ty o

f P

over

ty (

A)

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

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

Tajikistan

Zimbabwe6592.7

11865.5

77494.1

1015.4

29483.910007.97472.2

18787.716681.4

46008.6

6830.3

7457.8

1230.3

3321.0

7820.6

76063.0 14188.3

Argentina

Indonesia

Guatemala

Ghana

Lao

Nigeria

Brazil

Namibia

Percentage of People Considered Poor (H)

Ave

rage

Inte

nsi

ty o

f P

over

ty (

A)

Poorest Countries, Highest MPIHigh Income

Upper-Middle In-come

Lower-Middle In-come

Low Income

China

India

The size of the bubbles is a proportional representation of the total number of MPI poor in each country

Here we see what intensity adds

23

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

35%

40%

45%

50%

55%

60%

65%

70%

Percentage of People Considered Poor (H)

Avera

ge I

nte

nsi

ty o

f P

overt

y (

A)

Nigeria

24

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

35%

40%

45%

50%

55%

60%

65%

70%

544.1

2184.7

623.7

4897.5

706.8

2429.0

3397.2

1225.6

1165.0

460.9

967.7

284.4

2128.4

689.2

4167.4

3530.5

6743.6

5388.9

3396.5

1476.3

985.6

279.8

1245.8

3187.2

633.2535.0

1620.1

1643.0

3727.4

1810.8

2302.8

3333.8

Percentage of People Considered Poor (H)

Avera

ge I

nte

nsi

ty o

f P

overt

y (

A)

Nigeria

69 countries

decompos

ed: 780 subnation

al regions

MPI also varies greatly across subnational regions within a country –

e.g. Cameroon

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

Percentage of People Considered Poor (H)

Avera

ge I

nte

nsi

ty o

f P

overt

y (

A)

MPI also varies greatly across subnational regions within a country –

e.g. CameroonIncidence: 6.5% to 86.7%Intensity: 36.4% to 62.3%

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

512.8

641.2

130.8

501.3

3500.0

177.3

1897.4

724.3

864.8187.5

444.9126.4

Percentage of People Considered Poor (H)

Avera

ge I

nte

nsi

ty o

f P

overt

y (

A)

MPI also varies greatly across subnational regions within a country –

e.g. Haiti

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

Percentage of People Considered Poor (H)

Avera

ge I

nte

nsi

ty o

f P

overt

y (

A)

Haiti

MPI also varies greatly across subnational regions within a country –

e.g. Haiti

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

1331.6

940.5 512.8

297.9

180.3

498.5 245.0310.0390.0

330.6

Percentage of People Considered Poor (H)

Avera

ge I

nte

nsi

ty o

f P

overt

y (

A)

Haiti

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?

Armenia 2005-2010

Bolivia 2003-2008

Peru 2008-2012

Nepal 2006-2011

Ghana 2003-2008

Indonesia 2007-2012

Rwanda 2005-2010

Bangladesh 2004-2007

Tanzania 2008-2010

Haiti 2006-2012

Lesotho 2004-2009

Kenya 2003-2008/9

Zambia 2001/2-2007

Mozambique 2003-2011

Cameroon 2004-2011

Ethiopia 2005-2011

Malawi 2004-2010

Niger 2006-2012

Madagascar 2004-2008/9

-20.0% -15.0% -10.0% -5.0% 0.0% 5.0%

Annualized % Relative Change

DR, Bolivia,

Peru and Colombia

had strong MPI

reduction relative to

start

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

35%

40%

45%

50%

55%

60%

65%

70%

75%

Nepal 2006

Nepal 2011

Incidence - Percentage of MPI Poor People (H)Ave

rag

e I

nte

nsi

ty o

f P

ove

rty

(A)

How MPI decreased in Nepal 2006-11

Reduction in both H and A

Decomposition By Region (or social group) – shows H, A, inequalities

Subgroup Decompositions

Subgroup Decompositions (regional)

36

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

Ann

ualiz

ed A

bsol

ute

Cha

nge

in p

ropo

rtio

n w

ho is

poo

r and

dep

rive

d in

...

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.

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

712.9762

512.6847

152.9799

2140.301

258.5993

379.8044

307.9244

540.256

Multidimension Poverty Index (MPIT) at initial year

An

nu

al

Ab

solu

te C

han

ge i

n

MP

IT

Reduc-tion in

MPI

Size of bubble is propor-tional to the number of poor in first year of the compari-son.

Disaggregating by ethnic group - Benin

.

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

712.9762

512.6847

152.9799

2140.301

258.5993

379.8044

307.9244

540.256

Multidimension Poverty Index (MPIT) at initial year

An

nu

al

Ab

solu

te C

han

ge i

n

MP

IT

Reduc-tion in

MPI

Size of bubble is propor-tional to the number of poor in first year of the compari-son.

Disaggregating by ethnic group - Benin

.

Poorest ethnic group saw no change in MPI.They are being left behind

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

2643.594

2187.622265.77

1173.195

2735.716

2355.76

782.827599999999

1173.459

1181.643

Multidimension Poverty Index (MPIT) at initial year

An

nu

al

Ab

solu

te C

han

ge i

n

MP

IT

Reduc-tion in

MPI

Size of bubble is propor-tional to the number of poor in first year of the compari-son.

Disaggregating by ethnic group - Kenya

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

2643.594

2187.622265.77

1173.195

2735.716

2355.76

782.827599999999

1173.459

1181.643

Multidimension Poverty Index (MPIT) at initial year

An

nu

al

Ab

solu

te C

han

ge i

n

MP

IT

Reduc-tion in

MPI

Size of bubble is propor-tional to the number of poor in first year of the compari-son.

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 & Income poverty trends: heterogeneous

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

If progress was only measured by reducing income poverty, the gains of Bolivia, Peru, and the Dominican Republic would have been less visible.

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

High Resolution Lens

• Break down by population subgroup – Province, State, Ethnicity, Social Groups

• Break down by indicators• Show (weighted) composition of

deprivations• Analyse changes across time• Analyse robustness, inclusive

growth, strategies

National MPIs & MPPN

MPI: Two kinds ~ both usefulInternationally comparable:

- Like $1.25/day poverty measures - Can compare regions, subnational,

groups, time- Could track SDG-1: poverty in its

many dimensions- Could measure both acute and

moderate poverty

MPI: Two kinds ~ both usefulNational MPIs:

- Reflects National Priorities- Vital for policy- Measure to Manage ~ Target,

Coordinate, M&E

MPI: Two kinds ~ both usefulInternationally 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 Action

Official National MPIsColombiaMexico Bhutan

Philippines

Other national applications underway.

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• http://www.ophi.org.uk/policy/policynetwork/

Supported by the German Federal Ministry for Economic Cooperation and Development (BMZ)

OPHI is Secretariat to the Multidimensional

Poverty Peer Network (MPPN) that had 22

countries when launched by Colombia’s President

Santos and Amartya Sen in June 2013.

• Angola, Bhutan, Brazil, Chile, China,

Colombia, ECLAC, Ecuador, El Salvador,

Dominican Republic, Germany, India,

Iraq, Malaysia, Mexico, Morocco,

Mozambique, Nigeria, OECD, the

Organization of Caribbean States, Peru,

Philippines, SADC,

Vietnam 2013

MPPN

Voices ActionCommitment Wisdom

Over 20 governments pressure UN to change how it measures poverty

Germany, Colombia and Mexico lead calls for a new poverty measure at side-event at the UN General Assembly on the Post-2015 Development Agenda

A global network of more than 20 governments and institutions are using a side-event at the UN General Assembly on 24 September to argue for a new multidimensional poverty index to stand alongside an income poverty measure. Why? Focussing on ending income poverty alone in the post-2015 development context overlooks policies that address other aspects of being poor, such as a lack of access to healthcare, quality schooling, housing, electricity and sanitation.

Research by the Oxford Poverty and Human Development Initiative (OPHI) at Oxford University, among others, shows startling discrepancies between income poverty and multidimensional poverty, which takes into account other factors. The Multidimensional Poverty Peer Network – which was founded by Colombia, Mexico and OPHI – will use the side-event to make a case for the UN to include a multidimensional poverty index, or MPI, alongside the $1.25/ day measure, to track progress towards nationally defined goals.

The MPI 2015+ would build on the global MPI published in the UN Development Programme’s flagship Human Development Reports, and would incorporate the most accurate indicators possible with new data post-2015. It would enable policymakers to identify more easily what poor people lack, and address interconnected aspects of poverty more effectively. Because it reflects improvements directly, the MPI2015+ also celebrates success and provides strong political incentives to reduce poverty.

UN General Assembly Side Event Sept 2013: Press release

Islamic Solidarity Fund for Development: “The Fund views poverty as a multi-

dimensional phenomenon, encompassing not only low income and consumption, but also low achievement in fundamental human rights including education, nutrition, primary health, water and sanitation, housing, crisis coping capacity, insecurity, and all other forms of human development.”

MPPN had a side-event at the Islamic Development Bank: June 2014

Economic Commission for Latin America (ECLAC): “Social Panorama of Latin America 2013

addresses poverty from a multidimensional perspective.”

This multidimensional perspective on poverty will be continued at a regional level in future Social Panoramas using the AF methodology.

Option: An ‘acute’ MPI + ‘moderate’ regional MPIs

Other Intersections:

SADC: use MPI as a standard indicator to compare SADC members

MEDSTAT: Use MPI to compare North African countries

UNDP LAC: Forthcoming regional report on poverty will present MPIs from MPPN member countries.

Organisation for Economic Cooperation and Development

(OECD): ● Create a new headline indicator to measure progress towards eradicating all forms of poverty, which could complement the current income-poverty indicator.

Executive Summary,Development Cooperation Report 2013: Ending Poverty

Sustainable Development Solutions Network (SDSN) May

2014: Goal 1: End Extreme Poverty including Hunger

End extreme poverty in all its forms (MDGs 1-7), including hunger, child stunting, malnutrition, and food insecurity. Support highly vulnerable countries.

Target 1a. End extreme poverty, including absolute income poverty ($1.25 or less per day).

Indicator 1: Percentage of population below $1.25 (PPP) per day

Indicator 2: Percentage of population in extreme multi-dimensional poverty— Indicator to be developed

MPPN Second Meeting Berlin 2014

Supported by the German Federal Ministry for Economic Cooperation and Development (BMZ)

25 Sept 2014 UNGA Side event

• Mexico, Colombia, Ecuador, S. Africa, Ecuador, Seychelles, China, Nigeria, Costa Rica, Indonesia, Honduras, OPHI, DR, & Germany

• 300 persons

• Effectiveness of National MPIs

• Importance of defining poverty as multidimensional

• Promote a Global MPI 2015+ in the SDGs

(300 participants)

MPPN has grown:October 2014

• Over 30 countries• 10 multilateral bodies• Circulated draft survey modules for

MPI 2015+• Side Event for UN Statistics

Commission 2015

62

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