1 / 35

Reducing inequalities and poverty: Insights from Multidimensional Measurement Sabina Alkire 16 October 2012, 4 th OE

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?

garin
Download Presentation

Reducing inequalities and poverty: Insights from Multidimensional Measurement Sabina Alkire 16 October 2012, 4 th OE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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.

  3. 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

  4. Why not? Won’t an ‘overview’ index lose vital detail and information? 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? 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?

  7. 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.

  8. 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%

  9. 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

  10. 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.

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

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

  13. How and How much? Ghana, Nigeria, and Ethiopia

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

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

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

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

  18. PathwaystoPovertyReduction

  19. Performance of Sub-national Regions

  20. Ethiopia’s Regional Changes Over Time Harari Addis Ababa

  21. Nigeria’s Regional Changes Over Time North Central South South

  22. Looking Inside the Regions of Nigeria…

  23. Nigeria: Indicator Standard Errors

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

  25. 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.

  26. How did MPI decrease for India?

  27. How did MPI decrease for India?

  28. 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

  29. 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

  30. Change in MPI by Caste Change in Censored Headcount Ratio Least change in Mortality and Nutrition among ST

  31. 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%

  32. Inequality Among the PoorIndia 1999-2006 Alkire and Seth

  33. 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.

  34. 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 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

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

More Related