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Reducing inequalities and poverty: Insights from Multidimensional Measurement Sabina Alkire 16 October 2012, 4 th OECD Forum, New Delhi PowerPoint PPT Presentation


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Reducing inequalities and poverty: Insights from Multidimensional Measurement Sabina Alkire 16 October 2012, 4 th OECD Forum, New Delhi. Motivation. Measurement: usually income or consumption data. Trends: reflect trends in nutrition, services, education?

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Reducing inequalities and poverty: Insights from Multidimensional Measurement Sabina Alkire 16 October 2012, 4 th OECD Forum, New Delhi

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Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Reducing inequalities and poverty:

Insights from Multidimensional Measurement

Sabina Alkire

16 October 2012, 4th OECD Forum, New Delhi


Motivation

Motivation

Measurement: usually income or consumption data.

Trends: reflect trends in nutrition, services, education?

No: direct and lagged relationships are more complex

Hence additional indicators required to study change.


Why multidimensional measures

Why Multidimensional Measures?

Unidimensional measures such as MDGs are essential: consumption poverty, primary school attendance, malnutrition, immunization, housing, drinking water, etc.

Value-added of multidimensional measures

1) joint distribution of deprivations (what one person experiences)

a) focus on poorest of the poor

b) address interconnected deprivations efficiently

2) signal trade-offs explicitly: open to scrutiny

3) provide an overview plus an associated consistent dashboard


Why not

Why not?

Won’t an ‘overview’ index lose vital detail and information?

Aren’t weights contentious and problematic?

How to contextualise the measure?


Why not1

Why not?

Won’t an ‘overview’ index lose vital detail and information?

AF methodology: can be broken down by dimension, group.

Aren’t weights contentious and problematic?

How to contextualise the measure?


Why not2

Why not?

Won’t an ‘overview’ index lose vital detail and information?

AF methodology: can be broken down by dimension, group.

Aren’t weights contentious and problematic?

Weights are set anyway: budgets, policies, human resources.

Sen: the need to set weights is no embarrassment

Measures should be made robust to a range of plausible weights

How to contextualise the measure?


Why not3

Why not?

Won’t an ‘overview’ index lose vital detail and information?

AF methodology: can be broken down by dimension, group.

Aren’t weights contentious and problematic?

Weights are set anyway: budgets, policies, human resources.

Sen: the need to set weights is no embarrassment

Measures should be made robust to a range of plausible weights

How to contextualisethe measure?

The dimensions, cutoffs and weights can be tailor-made.


Multidimensional poverty index mpi

Multidimensional Poverty Index (MPI)

The MPI implements an Alkire and Foster (2011) M0 measure that can use ordinal data. It was introduced by Alkire and Santos (2010) and UNDP (2010) for 100+ countries

A person is identified as poor in two steps:

1) A person is identified as deprived or not in 10 indicators

2)A person is identified as poor if their deprivation score >33%


How is mpi computed

How is MPI Computed?

The MPI uses the Adjusted Headcount Ratio M0:

His the percent of people who are identified as poor, it shows the incidence of multidimensional poverty.

Ais 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.

.

Formula: MPI = H × A


Useful properties

Useful Properties

Subgroup Consistency and Decomposability

Enables the measure to be broken down by regions or social groups.

Dimensional Breakdown

Means that the measure can be immediately broken down into its component indicators. - Essential for policy

Dimensional Monotonicity

Gives incentives a) to reduce the headcountand

b) the intensity of poverty among the poor.


Changes in the global mpi from 2011 mpi update alkire roche seth 2011

Changes in the Global MPIfrom 2011 MPI UpdateAlkire, Roche, Seth 2011


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Changes over time in MPI for 10 countries

  • MPI fell for all 10 countries

  • Survey intervals: 3 to 6 years.

Multidimensional Poverty Index (MPI)


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

How and How much?

Ghana, Nigeria, and Ethiopia


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Let us Take a Step Back in Time

Ethiopia

2000

Nigeria

2003

Ghana

2003


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

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

Ethiopia

2000

Ethiopia

2005

Nigeria

2003

Nigeria

2008

Ghana

2003

Ghana

2008


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Nigeria 2003-2008 (Reduced H more than A)

Ethiopia

2000

Ethiopia

2005

Nigeria

2003

Nigeria

2008

Ghana

2003

Ghana

2008


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Ghana 2003-2008 (Reduced A and H Uniformly)

Ethiopia

2000

Ethiopia

2005

Nigeria

2003

Nigeria

2008

Ghana

2003

Ghana

2008


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

PathwaystoPovertyReduction


Performance of sub national regions

Performance of Sub-national Regions


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Ethiopia’s Regional Changes Over Time

Harari

Addis Ababa


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Nigeria’s Regional Changes Over Time

North Central

South South


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Looking Inside the Regions of Nigeria…


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Nigeria: Indicator Standard Errors


An indian example a lmost mpi 1999 2006 alkire and seth in progress

An Indian ExampleAlmost MPI 1999-2006Alkire and Seth In Progress


India almost mpi over time

India: Almost-MPI over time

  • We use two rounds of NationalFamilyHealthSurveysfortrendanalysis

  • NFHS-2 conducted in 1998-99

  • NFHS-3 conducted in 2005-06

  • Lessinformationisavailable in the NFHS-2 dataset; so wehavegeneratedtwostrictly comparable measures, withsmallchanges in mortality, nutrition, and housing.


How did mpi decrease for india

How did MPI decrease for India?


How did mpi decrease for india1

How did MPI decrease for India?


Absolute reduction in acute poverty across large states

Absolute Reduction in Acute Poverty Across Large States

Significant reduction in all states except Bihar, MP and Haryana.

We combined Bihar and Jharkhand, Madhya Pradesh and Chhattishgarh, and Uttar Pradesh and Uttarakhand


Change in mpi by caste

Change in MPI by caste

MPI Poverty decreased least among the poorest. The STs (8.5% population share) are the poorest, but the change is lowest for them and for OBCs, who have a higher pop share. STs saw almost no reduction of mortality or undernutrition.

MPI Poverty decreased most for SC and ‘None’.

Disparity Increases


Change in mpi by caste1

Change in MPI by Caste

Change in Censored Headcount Ratio

Least change in Mortality and Nutrition among ST


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Ultra Poor: Changing Both Deprivation and Poverty Cutoffs

No Deprivations

MPI z Cutoffs

MPI POOR

Severely

Poor

Ultra z Cutoffs

Deprived

Not Severe

Ultra Poor

Deprivation Score

k cutoffs

50%

33%


Inequality among the poor india 1999 2006 alkire and seth

Inequality Among the PoorIndia 1999-2006 Alkire and Seth


Multidimensional poverty reduction in india 1999 2006

Multidimensional Poverty Reduction in India, 1999-2006

  • Multidimensional poverty declined across India, with an 8% fall in the percentage of poor.

  • But disparity among the poor may have increased

  • Progress has been slowest for STs, for hh with uneducated head of household, for Bihar MP and Rajasthan, and for Muslims.

  • Subgroup decomposable indicators of inequality among the poor may be constructed, and their precise trends tracked.

  • We are unable to update these results: new data are unavailable for India since 2005/6.


Reducing inequalities and poverty insights from multidimensional measurement sabina alkire 16 october 2012 4 th oecd forum new delhi

Why MPI post-2015, & National MPIs?1. Birds-eye view – trends can be unpackeda. by region, ethnicity, rural/urban, etcb. by indicator, to show compositionc. by ‘intensity,’ to show inequality among poor2. New Insights: a. focuses on the multiply deprived b. shows joint distribution of deprivation. 3. Incentives to reduce headcount and intensity.4. Flexible: you choose indicators/cutoffs/values5. Robust to wide range of weights and cutoffs


Ultra poverty deprivation cutoffs subset of mpi poor that are most deprived in each dimension

Ultra-poverty Deprivation CutoffsSubset of MPI poor that are most deprived in each dimension


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