multidimensional progress in low and middle income contries · fon yoa and lopka bétamaribe peulh...

Multidimensional Progress in Low and Middle Income Contries

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 nationalpoverty measures – using better indicators for eachpolicy 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%

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 Metodología

Método de conteo(NBI)

Método axiomático

(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


You can zoom in


You can zoom in

and see more

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Why Multidimensional Poverty?It is different from monetary poverty

And different policies reduce it.

MPI Poor $1.25 a day

Income may not match other deprivations

Incidence and Intensity by Country

Namibia

Brazil

Argentina

Indonesia

Guatemala

Ghana

Lao

Nigeria

Tajikistan

ZimbabweCambodia

Nepal

Bangladesh

Gambia

TanzaniaMalawi

Rwanda

AfghanistanMozambique

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%

Aver

age

Inte

nsity

of

Pove

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

AfghanistanMozambique

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%

Aver

age

Inte

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of

Pove

rty (A

)


Poorest Countries, Highest MPI

High IncomeUpper-Middle IncomeLower-Middle IncomeLow Income

China

India

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

23

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35%

40%

45%

50%

55%

60%

65%

70%

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

Aver

age

Inte

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of

Pove

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%

Aver

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Inte

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of

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)


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%

Aver

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Inte

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of

Pove

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)


MPI also varies greatly across subnational regions 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

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Littoral (Excluding Douala)

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OuestSud

Sud-OuestYaoundé

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Aver

age

Inte

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of

Pove

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)


MPI also varies greatly across subnational regions within a country – e.g. Haiti

30%

35%

40%

45%

50%

55%

60%

65%

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

Aver

age

Inte

nsity

of

Pove

rty (A

)


Haiti

MPI also varies greatly across subnational regions within a country – e.g. Haiti

Aire Métropolitaine/Reste-

Ouest

Artibonite Centre

Grande-Anse

Nippes

Nord Nord-EstNord-OuestSud

Sud-Est

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

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

Aver

age

Inte

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of

Pove

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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?

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

Armenia 2005-2010Dominican Rep. 2002-2007

Bolivia 2003-2008Egypt 2005-2008

Peru 2008-2012Colombia 2005-2010

Nepal 2006-2011Jordan 2007-2009Ghana 2003-2008

Peru 2005-2008Indonesia 2007-2012Cambodia 2005-2010

Rwanda 2005-2010Gabon 2000-2012

Bangladesh 2004-2007Bangladesh 2007-2011

Tanzania 2008-2010Zimbabwe 2006-2010/11

Haiti 2006-2012Guyana 2005-2009Lesotho 2004-2009Uganda 2006-2011

Kenya 2003-2008/9Namibia 2000-2007

Zambia 2001/2-2007Nigeria 2003-2008

Mozambique 2003-2011Benin 2001-2006

Cameroon 2004-2011India 1998/9-2005/6

Ethiopia 2005-2011Ethiopia 2000-2005

Malawi 2004-2010Pakistan 2006/7-2012/13

Niger 2006-2012Senegal 2005-2010/11

Madagascar 2004-2008/9

Annualized % Relative Change

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

rage

In

ten

sity

of

Pov

erty

(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)

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

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...

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.

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Yoa and Lopka

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-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

nual

Ab

solu

te C

han

ge in

MP

I TMultidimension 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

.

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

nual

Ab

solu

te C

han

ge in

MP


Reduction in MPI


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

nual

Ab

solu

te C

han

ge in

MP


Reduction in MPI


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 & Income poverty trends: heterogeneous

‐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 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 MPIsColombia

Mexico

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.

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, suchas 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

UNGeneralAssemblySideEventSept2013:Pressrelease

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 Panoramasusing 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

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