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? No: direct and lagged relationships are more complex Hence additional indicators required to study change. 2

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 3

Why not? Won’t an ‘overview’ index lose vital detail and information? Aren’t weights contentious and problematic? How to contextualise the measure? 4

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? 5

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? 6

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? The dimensions, cutoffs and weights can be tailor-made. 7

Multidimensional Poverty Index (MPI) The MPI implements an Alkire and Foster (2011) M 0 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? The MPI uses the Adjusted Headcount Ratio M 0 : H is the percent of people who are identified as 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 – the joint distribution of their deprivations.. Formula: MPI = H × A

Useful Properties 10 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 headcount and b) the intensity of poverty among the poor.

Changes in the Global MPI from 2011 MPI Update Alkire, Roche, Seth 2011

Changes over time in MPI for 10 countries MPI fell for all 10 countries Survey intervals: 3 to 6 years. Multidimensional Poverty Index (MPI)

How and How much? Ghana, Nigeria, and Ethiopia

Let us Take a Step Back in Time Ghana 2003 Nigeria 2003 Ethiopia 2000

Ethiopia: (Reduced A more than H) Ghana 2008 Nigeria 2008 Ethiopia 2005 Ghana 2003 Nigeria 2003 Ethiopia 2000

Nigeria (Reduced H more than A) Ghana 2008 Nigeria 2008 Ethiopia 2005 Ghana 2003 Nigeria 2003 Ethiopia 2000

Ghana (Reduced A and H Uniformly) Ghana 2008 Nigeria 2008 Ethiopia 2005 Ghana 2003 Nigeria 2003 Ethiopia 2000

Pathways to Poverty Reduction

Performance of Sub-national Regions

Ethiopia’s Regional Changes Over Time Addis Ababa Harari

Nigeria’s Regional Changes Over Time South North Central

Looking Inside the Regions of Nigeria…

Nigeria: Indicator Standard Errors

An Indian Example Almost MPI Alkire and Seth In Progress

India: Almost-MPI over time 25 We use two rounds of National Family Health Surveys for trend analysis NFHS-2 conducted in NFHS-3 conducted in Less information is available in the NFHS-2 dataset; so we have generated two strictly comparable measures, with small changes in mortality, nutrition, and housing.

How did MPI decrease for India? Change MPI-I * Headcount56.5%48.3%-8.2%* Intensity52.9%51.7%-1.2%

How did MPI decrease for India? 27

Absolute Reduction in Acute Poverty Across Large States 28 We combined Bihar and Jharkhand, Madhya Pradesh and Chhattishgarh, and Uttar Pradesh and Uttarakhand Significant reduction in all states except Bihar, MP and Haryana.

Change in MPI by caste 29 M 0 -99M Change H-99H-06 Change A-99A-06 Change Scheduled Tribe %73.2%-6.5%56.9%56.1%-0.8% Scheduled Caste %58.3%-10.4%55.0%52.8%-2.2% OBCs %50.8%-6.5%52.0%50.7%-1.2% None Above %32.7%-12.3%50.7%49.8%-0.9% Disparity Increases 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’.

Change in MPI by Caste 30 M 0 -99M Change H-99H-06 Change A-99A-06 Change Scheduled Tribe %73.2%-6.5%56.9%56.1%-0.8% Scheduled Caste %58.3%-10.4%55.0%52.8%-2.2% OBCs %50.8%-6.5%52.0%50.7%-1.2% None Above %32.7%-12.3%50.7%49.8%-0.9% Change in Censored Headcount Ratio Least change in Mortality and Nutrition among ST

Deprivation Score Ultra Poor: Changing Both Deprivation and Poverty Cutoffs 50% Deprived 33% No Deprivations MPI POOR MPI z Cutoffs Ultra z Cutoffs Not Severe k cutoffs Severely Poor Ultra Poor

Inequality Among the Poor India Alkire and Seth 32 Year M0M0 H (MPI) High Intensity High Depth Intense & Deep %30.6%37.9%15.8% % of MPI poor 54.2%67.1%28.0% %24.7%31.7%12.5% % of MPI poor 51.1%65.6%25.9% Change in MPI %-5.9%-6.2%-3.3%

Multidimensional Poverty Reduction in India, 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. 33

Why MPI post-2015, & National MPIs? 1. Birds-eye view – trends can be unpacked a. by region, ethnicity, rural/urban, etc b. by indicator, to show composition c. by ‘intensity,’ to show inequality among poor 2. 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/values 5. Robust to wide range of weights and cutoffs

Ultra-poverty Deprivation Cutoffs Subset of MPI poor that are most deprived in each dimension 35 IndicatorAcute Deprivation Cut-off‘Ultra’ Cutoff Nutrition Any adult or child in the household with nutritional information is undernourished (2SD below z score or 18.5 kg/m 2 BMI) 3SD or 17 BMI Child mortality Any child has died in the household Years of schooling No household member has completed five years of schooling No Schooling School attendance Any school-aged child is not attending school up to class 8 Electricity The household has no electricity Sanitation The household´s sanitation facility is not improved or it is shared with other households Anything except bush/field Drinking water The household does not have access to safe drinking water or safe water is more than 30 minutes walk round trip Unprotected well and 45 Minutes House The house is kachha, or semi-pucca and owns <1 acre or < 0.5 irrigated kaccha & no land Cooking fuel The household cooks with dung, wood or charcoal. Wood, grass, Crops, dung Assets The household does not own more than one of: radio, TV, telephone, bike, motorbike or refrigerator, and does not own a car or truck even one