Multidimensional Poverty For Monitoring Development Progress

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

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  • Multidimensional Poverty For Monitoring Development Progress

    1. 1. OPHIOxford Poverty & Human Development InitiativeDepartment of International DevelopmentQueen Elizabeth House, University of Oxfordwww.ophi.org.uk<br />Multidimensional poverty for monitoring development progress<br />
    2. 2. Outline<br />Multidimensional Poverty Measurement<br />AF Methodology in breve<br />Illustration of metrics: MPI<br />
    3. 3. Multidimensional Poverty Measurement<br />Why the surge in interest?<br />
    4. 4. Motivation<br />“Human lives are battered and diminished in all kinds of different ways.” <br /> Amartya Sen<br />
    5. 5. Relevant Data<br />
    6. 6. Political Demand<br />StiglitzSenFitoussi: Commission on the Measurement of Economic Performance and Social Progress<br />“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.”<br />
    7. 7. Policy Demand:<br />Target the poorest: “Achieving the MDGs will require increased attention to those most vulnerable.” MDG Report 2010<br />Address interconnections efficiently: “Acceleration in one goal often speeds up progress in others” Roadmap towards the Implementation of the MDGs 2010<br />Show changes directly & quickly:<br /> Monitoring & incentives<br />Plan and Evaluate Policy<br /> To identify & use the most effective <br /> kind and sequencing of policies.<br />
    8. 8. AF Methodology:<br />Since 2000, a surge in new methodologies to measure multidimensional poverty. <br />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. <br />
    9. 9. AF in breve: Achievement Matrix<br />
    10. 10. AF in breve: Deprivation Matrix<br /> 0=non-deprived <br /> 1=deprived ‘count’<br />
    11. 11. AF in breve: Dual Cut-off Identification<br />z = deprivation cutoff<br />k = poverty cutoff<br />
    12. 12. AF in breve: Aggregation<br />M0 is the mean of the matrix<br />This matrix also generates H and A<br />Censored Headcounts for each dimension<br />Percent Contributions for each dimension<br />And all of these for subgroups<br />
    13. 13. Multidimensional Poverty Index (MPI) acute poverty in developing countries<br />An international measure of acute poverty covering 104 developing countries in UNDP’s 2010 HDR. <br />Complements income poverty measures by showing direct deprivations and their joint distribution<br />A high resolution lens, using AF methodology<br />Constrained by data availability<br />Aims to encourage the development of better national and regional measures of multidimensional poverty<br />
    14. 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. <br />
    15. 15. 2. Dimensions Indicators & Weights of MPI<br />
    16. 16. 2. Data constraints<br />The MPI is deeply affected by the lack of comparable data. key indicators are not collected (stock, quality) <br /><ul><li> data for some dimensions are missing
    17. 17. missing values lead to sample size reduction/biases
    18. 18. respondent(s) vary; individual level data is sparse
    19. 19. surveys updated every 3-5 years, and in different years
    20. 20. data exclude certain populations (elders, institutionalized)
    21. 21. income/consumption surveys lack MPI health indicators. </li></ul>These can be addressed at a national level for national measures. <br />“Improving data gathering and its quality in all countries should be a central focus ...” Bourguignon et al. 2008 page 6<br />
    22. 22. 3. Methodology: Alkire and Foster - Identification<br />A person is multidimensionally poor if they are deprived in 33% of the dimensions.<br />33%<br />
    23. 23. 3. Methodology Alkire and Foster: Aggregation<br />We construct the MPI using the AF method:<br />His the percentage of people who are poor. It shows the incidence of multidimensional poverty.<br />Ais the average proportion of weighted deprivations people suffer at the same time. It shows the intensity of people’s poverty.<br />Formula: MPI = M0 = H × A<br />
    24. 24. 3. Methodology: MPI g0(k) matrix<br />Adjusted Headcount Ratio = M0 = HA = .442<br />k=3.333 (have MPI for all k values)<br /> Indicators c(k) c(k)/d<br />H = headcount = ¾ = 75%<br /> A = average deprivation share among poor = .59 = 59%<br /> HA = MPI = 0.442<br />
    25. 25. Example: Tabitha<br />OPHI has done ground reality checks in Kenya, Madagascar, Indonesia, Bhutan, and India. <br />
    26. 26. What’s new?<br />Intensity:<br />The MPI and its related indices reflects each household’s deprivation profile. <br />
    27. 27. Stéphanie’s Intensity<br />
    28. 28. Adil’s Intensity<br />
    29. 29. Jiyem’s Intensity<br />
    30. 30. Others<br />The MPI helps show<br />Who they are (Headcount) & how they are poor (Intensity)<br />
    31. 31. 4. 2010 Results:<br />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. <br />They cover 78.5% of the world population (2007).<br />In 2011’s HDR this will be increased to 109 countries, and updated data are available for over 20 countries. <br />
    32. 32. The MPI headcounts fall between $1.25 and $2.00/day, but are quite different.<br />
    33. 33. Total Population <br />Most poor people in the world by MPI live in South Asia, followed by Sub-Saharan Africa. <br />Poor People<br />
    34. 34. Intensity tends to be highest with high Incidence<br />Nepal<br />
    35. 35. Decompose by region & ethnicity<br />In Kerala India 16% of the population is MPI poor; in Bihar it is 81%.<br />Bihar<br />India MPI<br />Kerala<br />
    36. 36. Comparisons: Headcount plusIntensity & Composition<br />
    37. 37. Composition of Poverty: key for policy (equal MPIs)<br />
    38. 38. DRC: Larger Std of Living Deprivations<br />
    39. 39. Madhya Pradesh: Larger Malnutrition<br />
    40. 40. Intensity – who is the poorest of all?<br />Kenya<br />0.302<br />50%<br /> India<br />MPI = 0.296 <br />A = 53.5%<br />Cameroon<br />0.299<br />54.7%<br />
    41. 41. Pathways to Poverty Reduction<br />Ghana and Bangladesh reduced H relatively more than A, Ethiopia the other way round.<br />
    42. 42.
    43. 43. Bangladesh improved child enrolment, Ethiopia nutrition and water, Ghana many at the same time.<br />
    44. 44.
    45. 45.
    46. 46.
    47. 47.
    48. 48. Time series show: <br />Reduction in Headcount<br />Reduction in Intensity<br />Changes in eachindicator’scensoredheadcounts<br />Changes in percentcontributions of eachindicator<br /> (Composition of poverty)<br />Time series can be usedto:<br />Understandhowpovertyevolves … across time, regions, and dimensions. <br />Evaluatepolicy (if natural experimentsfound)<br />Observe shocks (positive ornegative)<br />Observe patterns (interconnections)<br />
    49. 49. Media Coverage of the 2010 MPI<br />The Report was covered in over 60 countries, e.g. in: <br />TIME Magazine<br />The New York Times<br />The Wall Street Journal<br />BBC<br />The Economist<br />The Guardian <br />The Financial Times<br />The Huffington Post<br />Foreign Policy <br />The Hindu<br />Christian Science Monitor<br />The Globe and Mail<br />The Times of India<br />
    50. 50. Applications and Experiments<br /><ul><li>Bhutan – Gross National Happiness index released 2008
    51. 51. México – National multidimensional poverty index 2009
    52. 52. Colombia: integrated into the National Plan in 2011.
    53. 53. Chile: presentations and course (2 weeks); under construction
    54. 54. Iraq, Venezuela, Malaysia: presentations; trial measures
    55. 55. Bhutan: course taught (3 days) and trial measures constructed
    56. 56. Thailand: course taught (3 days)
    57. 57. Egypt: course will be taught (6 days)
    58. 58. El Salvador: course will be taught; trial measures constructed
    59. 59. EU: trial measures constructed using EU-SILC data</li></li></ul><li>Poverty Measurement Methodology<br />December, 2009<br />www.coneval.gob.mx<br />
    60. 60. What are the main features of the new methodology?<br />Current income per capita 50%<br />Population<br />Social : 50%<br /><ul><li>Education
    61. 61. Health
    62. 62. Social Security
    63. 63. Housing
    64. 64. Basic services
    65. 65. Nutrition</li></ul>Wellbeing<br />Income<br />Territorial<br />Degree of social cohesion<br />Deprivations<br />4<br />3<br />6<br />2<br />5<br />0<br />1<br />Social Rights<br />
    66. 66. Moderate Multidimensional<br />Poverty<br />Poverty Identification<br />Without<br />With deprivations<br />Ideal Situation<br />Deprivations<br />Vulnerable people by social deprivations<br />Economic wellbeing line<br />$1,921.7 U<br />$1,202.8 R <br />EWL<br />MULTIDIMENSIONALLY POOR<br />Vulnerable people by income<br />$874.6 U<br />$613.8 R <br />MWL<br />EXTREME Multidimensional<br />Poverty<br />Minimum wellbeing line<br />Deprivations<br />5<br />4<br />6<br />1<br />2<br />3<br />0<br />Social Rights<br />
    67. 67. Total Population 2008<br />MODERATE POVERTY<br /> 33.7%<br />36.0 million<br /> 2.3 Deprivation<br />Vulnerable by social deprivations<br />33.0%<br />35.2 million<br />2.0 Deprivation<br />average<br />18.3%<br />19.5 million<br />Wellbeing<br />Income<br />Vulnerable people by income<br /> 10.5%<br />11.2 million<br /> 3.9 Deprivation<br />EXTREME POVERTY<br />4.5%<br />4.8 million<br />Deprivations<br />average<br />average<br />4<br />3<br />6<br />2<br />5<br />1<br />0<br />Social Rights<br />
    68. 68. Indigenous population 2008<br />MODERATE POVERTY<br /> 36.5 %<br /> 2.5 millions<br /> 3.1 Deprivation<br />Vulnerable people by social deprivations<br />20.0 %<br />1.4 millions<br />2.8 Deprivation <br />average<br />3.1%<br />0.21 millions<br />Wellbeing<br />Income<br />Vulnerable people by income<br /> 39.2 %<br /> 2.7 millions<br /> 4.2 Deprivation<br />EXTREME POVERTY<br />1.2%<br />.1 millions<br />Deprivations<br />average<br />average<br />4<br />3<br />6<br />2<br />5<br />1<br />0<br />Social Rights<br />
    69. 69. European studies call for more panel research on multidimensional poverty dynamics. <br /> Source: Whelan Layte Maitre 2004 Understanding the Mismatch between Income Poverty & Deprivation<br />
    70. 70. With Panel data we can identify different types of poor people<br />1. Chronicpoor – acrossmany time periods<br />2. Churninggoing in and out of poverty<br />3. Fallingintopoverty<br />4. Movingoutof poverty. <br />With data fromtwoperiodswegeneratetransition matrices showingtheprobability of entry and exitfor H and A. Apablaza & Yalonetzky<br />
    71. 71. Panel data enables new analyses about chronicity and poverty transitions: <br />How do thefourgroupsdiffer – eitherdemographicallyor in thestructure of theirpoverty? <br />Povertytraps? Whatisthecomposition of povertyforthechronicpoor? Are anydimensionsalwaysdeprived?<br />Doesthecomposition of povertyforchronicpoorchange? Doeschronicpovertydecreaseover time? <br />Policysequences: whatchains do theycatalyze? Whichsequence of policies has highestimpact?<br />Howdoespovertyevolveacrossdifferentages? Fordifferentracialgroups and householdtypes?<br />

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