Sabina Alkire, Director, OPHI, Oxford Poverty & Human Development Initiative, University of Oxford

1. OPHIOxford Poverty & Human Development InitiativeDepartment of International DevelopmentQueen Elizabeth House, University of Oxfordwww.ophi.org.uk Multidimensional poverty for monitoring development progress

2. Outline Multidimensional Poverty Measurement AF Methodology in breve Illustration of metrics: MPI

3. Multidimensional Poverty Measurement Why the surge in interest?

4. Motivation “Human lives are battered and diminished in all kinds of different ways.” Amartya Sen

5. Relevant Data

6. Political Demand StiglitzSenFitoussi: Commission on the Measurement of Economic Performance and Social Progress “We are almost blind when the metrics on which action is based are ill-designed or when they are not well understood. For many purposes, we need better metrics.”

7. Policy Demand: Target the poorest: “Achieving the MDGs will require increased attention to those most vulnerable.” MDG Report 2010 Address interconnections efficiently: “Acceleration in one goal often speeds up progress in others” Roadmap towards the Implementation of the MDGs 2010 Show changes directly & quickly: Monitoring & incentives Plan and Evaluate Policy To identify & use the most effective kind and sequencing of policies.

8. AF Methodology: Since 2000, a surge in new methodologies to measure multidimensional poverty. AF method is based on the FGT, counting and basic needs traditions, & can use ordinal data. It can also be decomposed into policy relevant and intuitive subindices. The technology is flexible: you choose the dimensions, indicators, weights, & cutoffs.

9. AF in breve: Achievement Matrix

10. AF in breve: Deprivation Matrix 0=non-deprived 1=deprived ‘count’

11. AF in breve: Dual Cut-off Identification z = deprivation cutoff k = poverty cutoff

12. AF in breve: Aggregation M0 is the mean of the matrix This matrix also generates H and A Censored Headcounts for each dimension Percent Contributions for each dimension And all of these for subgroups

13. Multidimensional Poverty Index (MPI) acute poverty in developing countries An international measure of acute poverty covering 104 developing countries in UNDP’s 2010 HDR. Complements income poverty measures by showing direct deprivations and their joint distribution A high resolution lens, using AF methodology Constrained by data availability Aims to encourage the development of better national and regional measures of multidimensional poverty

14. 1. Data for the MPI: SurveysDemographic & Health Surveys (DHS - 48) Multiple Indicator Cluster Surveys (MICS - 35)World Health Survey (WHS – 19)Additionally we used 2 special surveys covering Mexico and urban Argentina.

15. 2. Dimensions Indicators & Weights of MPI

17. missing values lead to sample size reduction/biases

18. respondent(s) vary; individual level data is sparse

19. surveys updated every 3-5 years, and in different years

20. data exclude certain populations (elders, institutionalized)

21. income/consumption surveys lack MPI health indicators. These can be addressed at a national level for national measures. “Improving data gathering and its quality in all countries should be a central focus ...” Bourguignon et al. 2008 page 6

22. 3. Methodology: Alkire and Foster - Identification A person is multidimensionally poor if they are deprived in 33% of the dimensions. 33%

23. 3. Methodology Alkire and Foster: Aggregation We construct the MPI using the AF method: His the percentage of people who are 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. Formula: MPI = M0 = H × A

24. 3. Methodology: MPI g0(k) matrix Adjusted Headcount Ratio = M0 = HA = .442 k=3.333 (have MPI for all k values) Indicators c(k) c(k)/d H = headcount = ¾ = 75% A = average deprivation share among poor = .59 = 59% HA = MPI = 0.442

25. Example: Tabitha OPHI has done ground reality checks in Kenya, Madagascar, Indonesia, Bhutan, and India.

26. What’s new? Intensity: The MPI and its related indices reflects each household’s deprivation profile.

27. Stéphanie’s Intensity

28. Adil’s Intensity

29. Jiyem’s Intensity

30. Others The MPI helps show Who they are (Headcount) & how they are poor (Intensity)

31. 4. 2010 Results: These results are for 104 developing countries, selected because they have DHS, MICS or WHS data since 2000. Special surveys were used for Mexico and urban Argentina. They cover 78.5% of the world population (2007). In 2011’s HDR this will be increased to 109 countries, and updated data are available for over 20 countries.

32. The MPI headcounts fall between $1.25 and $2.00/day, but are quite different.

33. Total Population Most poor people in the world by MPI live in South Asia, followed by Sub-Saharan Africa. Poor People

34. Intensity tends to be highest with high Incidence Nepal

35. Decompose by region & ethnicity In Kerala India 16% of the population is MPI poor; in Bihar it is 81%. Bihar India MPI Kerala

36. Comparisons: Headcount plusIntensity & Composition

37. Composition of Poverty: key for policy (equal MPIs)

38. DRC: Larger Std of Living Deprivations

39. Madhya Pradesh: Larger Malnutrition

40. Intensity – who is the poorest of all? Kenya 0.302 50% India MPI = 0.296 A = 53.5% Cameroon 0.299 54.7%

41. Pathways to Poverty Reduction Ghana and Bangladesh reduced H relatively more than A, Ethiopia the other way round.

43. Bangladesh improved child enrolment, Ethiopia nutrition and water, Ghana many at the same time.

48. Time series show: Reduction in Headcount Reduction in Intensity Changes in eachindicator’scensoredheadcounts Changes in percentcontributions of eachindicator (Composition of poverty) Time series can be usedto: Understandhowpovertyevolves … across time, regions, and dimensions. Evaluatepolicy (if natural experimentsfound) Observe shocks (positive ornegative) Observe patterns (interconnections)

49. Media Coverage of the 2010 MPI The Report was covered in over 60 countries, e.g. in: TIME Magazine The New York Times The Wall Street Journal BBC The Economist The Guardian The Financial Times The Huffington Post Foreign Policy The Hindu Christian Science Monitor The Globe and Mail The Times of India

51. México – National multidimensional poverty index 2009

52. Colombia: integrated into the National Plan in 2011.

53. Chile: presentations and course (2 weeks); under construction

54. Iraq, Venezuela, Malaysia: presentations; trial measures

55. Bhutan: course taught (3 days) and trial measures constructed

56. Thailand: course taught (3 days)

57. Egypt: course will be taught (6 days)

58. El Salvador: course will be taught; trial measures constructed

61. Health

62. Social Security

63. Housing

64. Basic services

65. NutritionWellbeing Income Territorial Degree of social cohesion Deprivations 4 3 6 2 5 0 1 Social Rights

66. Moderate Multidimensional Poverty Poverty Identification Without With deprivations Ideal Situation Deprivations Vulnerable people by social deprivations Economic wellbeing line $1,921.7 U $1,202.8 R EWL MULTIDIMENSIONALLY POOR Vulnerable people by income $874.6 U $613.8 R MWL EXTREME Multidimensional Poverty Minimum wellbeing line Deprivations 5 4 6 1 2 3 0 Social Rights

67. Total Population 2008 MODERATE POVERTY 33.7% 36.0 million 2.3 Deprivation Vulnerable by social deprivations 33.0% 35.2 million 2.0 Deprivation average 18.3% 19.5 million Wellbeing Income Vulnerable people by income 10.5% 11.2 million 3.9 Deprivation EXTREME POVERTY 4.5% 4.8 million Deprivations average average 4 3 6 2 5 1 0 Social Rights

68. Indigenous population 2008 MODERATE POVERTY 36.5 % 2.5 millions 3.1 Deprivation Vulnerable people by social deprivations 20.0 % 1.4 millions 2.8 Deprivation average 3.1% 0.21 millions Wellbeing Income Vulnerable people by income 39.2 % 2.7 millions 4.2 Deprivation EXTREME POVERTY 1.2% .1 millions Deprivations average average 4 3 6 2 5 1 0 Social Rights

69. European studies call for more panel research on multidimensional poverty dynamics. Source: Whelan Layte Maitre 2004 Understanding the Mismatch between Income Poverty & Deprivation

70. With Panel data we can identify different types of poor people 1. Chronicpoor – acrossmany time periods 2. Churninggoing in and out of poverty 3. Fallingintopoverty 4. Movingoutof poverty. With data fromtwoperiodswegeneratetransition matrices showingtheprobability of entry and exitfor H and A. Apablaza & Yalonetzky

71. Panel data enables new analyses about chronicity and poverty transitions: How do thefourgroupsdiffer – eitherdemographicallyor in thestructure of theirpoverty? Povertytraps? Whatisthecomposition of povertyforthechronicpoor? Are anydimensionsalwaysdeprived? Doesthecomposition of povertyforchronicpoorchange? Doeschronicpovertydecreaseover time? Policysequences: whatchains do theycatalyze? Whichsequence of policies has highestimpact? Howdoespovertyevolveacrossdifferentages? Fordifferentracialgroups and householdtypes?