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What Other Databases and Measurements Are out There?

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What Other Databases and Measurements Are out There? What other databases and measurements are out there? Sabina Alkire, ESCAP, October 2017 The MPI in the Sustainable Development Goals • The Global SDGs, adopted on 25 Sept 2015, address poverty in all its forms and dimensions, opening official space for Multidimensional Poverty Indices. • The first SDG target (1.1) is to end $1.90/day monetary poverty. The second target (1.2): to halve multidimensional poverty. A number of countries are reporting global MPI. • MPIs are used: a) to provide a disaggregated picture so as to leave no one behind; b) to inform multi-sectoral policy; c) to see where overlapping inequalities hit the hardest. What other measure? 4 Methodology for the National and Global MPIs 1. Select Indicators, Cutoffs, Values 3. Identify who is poor 4. Use: MPI, Incidence Intensity & EducationEducation Composition EducationEducation 33% 2. Build a deprivation score for each person MPIs jointly analyse multiple SDGs Example: Global Multidimensional Poverty Index (MPI) MPI: methodology Statistical methods include: Standard errors and confidence intervals for all statistics Statistical inference for all comparisons Validation for component indicators, alone and jointly Robustness tests for cutoffs and weights Axiomatic properties include: Subgroup decomposability and Subgroup consistency Dimensional breakdown, Dimensional monotonicity Ordinality, Symmetry, Scale and replication invariance, Normalization, Poverty and Deprivation Focus, Weak Monotonicity, and Weak Deprivation Re-arrangement Alkire Foster Seth Santos Roche Ballon OUP 2015 MPIs: National or Comparable Comparable MPIs (Global MPI, ECLAC MPI) - Like $1.90/day and $3.10/day poverty measures - Can also compare countries (& subnational groups, over time) - Could track SDG-1: halve poverty in its many dimensions; - Useful to compare progress and learn from each other. National MPIs: Tailor made for policy - Reflect National Priorities - Compute as official national statistics - Vital for policy: target, coordinate, monitor - Comparable over time, groups, provinces Ecuador Panama Chile Existing Data – Global MPI Online Global MPI Data: National National • MPI, H, A, (k=20%, 33%, 50%) • Destitution, Inequality among poor Urban-Rural Disaggregation • Pop, GNI, HDI, $1.90, $3.10, national Subnational Disaggregation poverty, missing indicator • % deprived in each indicator Changes – Harmonised • Uncensored & Censored All MPIs ever published • % contribution of each indicator Age Disaggregation • Standard errors • Sample size Global MPI in ESCAP – 24 countries Number of countries that are ESCAP members : 53 countries Number ESCAP countries with internationally comparable survey data : 24 countries Number ESCAP countries with comparable subnational data : 15 countries Total number of subnational regions with comparable data : 215 regions Total number of countries with published changes over time : 18 countries Afghanistan Maldives Armenia Myanmar Azerbaijan Nepal Bangladesh Pakistan Bhutan Philippines Cambodia Tajikistan China Thailand India Timor-Leste Indonesia Turkmenistan Kazakhstan Uzbekistan Kyrgyzstan Vanuatu Laos Viet Nam Global MPI in ESCAP – 24 countries - 12 DHS surveys - 9 MICS survey - 1 DHS/MICS - China = CFPS 2014; India = IHDS 2011/12 • 2006 (2) • 2007 (1) 3 countries: 2006-2007 • 2009 (1) • 2009/10 (1)- 6 countries: 2009-2011/12 • 2010 (2) 6 countries: 2012=13/14 • 2011/12 (2) • 2012 (3) 9 countries: 2014-16 • 2012/13 (1) • 2013 (1) • 2013/14 (1) 10 indicators: 19 countries • 2014 (5) 9 indicators: 4 countries • 2015 (1) • 2015/16 (3) 8 indicators: 1 country Global MPI in ESCAP – 3.7 billion people of Kazakhstan 0.001 Armenia 0.001 whom 837 million are poor Turkmenistan 0.001 Kyrgyzstan 0.002 (22.6%) Thailand 0.003 Uzbekistan 0.008 China 0.017 Maldives 0.018 Azerbaijan 0.021 Viet Nam 0.029 Philippines 0.052 Tajikistan 0.054 Indonesia 0.066 Bhutan 0.119 Nepal 0.126 Vanuatu 0.129 Myanmar 0.134 Cambodia 0.146 Laos 0.174 India 0.191 Bangladesh 0.196 Pakistan 0.230 Afghanistan 0.295 Timor-Leste 0.360 0.000 0.050 0.100 0.150 0.200 0.250 0.300 0.350 0.400 Share of MPI poor in ESCAP by major regions 16% MPI poor 0.3% MPI poor 84% MPI poor East Asia and the S Asia Pacific Europe and Central Asia South Asia Multidimensional poverty in 24 ESCAP countries: a comparison between Headcount ratio (H) and Intensity (A) 60% Pakistan Laos Timor-Leste Afghanistan 50% Philippines Myanmar Bangladesh Bhutan China India Viet Nam Indonesia NepalCambodia 40% Tajikistan Vanuatu Azerbaijan Thailand Kyrgyzstan Uzbekistan Armenia Maldives Turkmenistan Kazakhstan 30% 0 10 20 30 40 50 60 70 80 MPI also varies greatly across subnational regions 75% within a country – e.g. Indonesia 70% 65% 60% Indonesia 55% 50% 45% 40% Average of Intensity Average (A) Poverty 35% 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of People Considered Poor (H) MPI also varies greatly across subnational regions 75% within a country – e.g. Indonesia 70% 65% 60% 55% Indonesia Papua West Papua West Sulawesi 50% Maluku East Nusa Tenggara North Sulawesi Southeast Sulawesi 45% North Maluku East Java Central Kalimantan West Nusa Tenggara Banten 40% Bali Aceh Riau West Java East Kalimantan Average of Intensity Average (A) Poverty 35% DKI Jakarta DI Yogyakarta 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of People Considered Poor (H) MPI also varies greatly across subnational regions 75% within a country – e.g. Indonesia 70% 65% 60% 55% Indonesia Papua West Papua West Sulawesi 50% Maluku East Nusa Tenggara North Sulawesi Southeast Sulawesi 45% North Maluku East Java Central Kalimantan West Nusa Tenggara Banten 40% Bali Aceh Riau West Java East Kalimantan Average of Intensity Average (A) Poverty 35% DKI Jakarta DI Yogyakarta 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of People Considered Poor (H) Afghanistan 2015/16: MPI H rates vary subnationally Afghanistan 2015/16 56% Nepal 2014: 29% Bhutan 2010: 28% Pakistan 2012/13 44% Bangladesh 2014: 41% India 2011/12: 41% Subnational MPI: poorest 24 ESCAP regions Urozgan (Afghanistan) 0.340 Nooristan (Afghanistan) 0.611 Oecussi (Timor-Leste) 0.508 Ermera (Timor-Leste) 0.497 Badghis (Afghanistan) 0.466 Ainaro (Timor-Leste) 0.444 Kandahar (Afghanistan) 0.437 Viqueque (Timor-Leste) 0.410 Bobonaro (Timor-Leste) 0.406 Balochistan (Pakistan) 0.402 Liquica (Timor-Leste) 0.387 Badakhshan (Afghanistan) 0.387 Ghor (Afghanistan) 0.384 Aileu (Timor-Leste) 0.379 Lautem (Timor-Leste) 0.373 Laghman (Afghanistan) 0.369 Takhar (Afghanistan) 0.361 Saravane (Laos) 0.359 Baucau (Timor-Leste) 0.357 Heart (Afghanistan) 0.353 Manufahi (Timor-Leste) 0.353 Samangan (Afghanistan) 0.350 Sar-E-Pul (Afghanistan) 0.341 Wardak (Afghanistan) 0.340 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 Myanmar (2016) The composition of MPI can inform policy. 0.250 0.200 Here, Indonesia 2007 (previous period) 0.150 0.100 0.050 0.000 100 Assets 90 Fuel 80 70 Flooring 60 Water 50 Sanitation 40 30 Electricity 20 Mortality 10 Attendance 0 Schooling 0.010 MPI Reduction 2007-12 West Papua 0.005 Papua North SulawesiCentral Kalimantan Central Sulawesi West Nusa Tenggara West Sulawesi 0.000 Jambi 0.00 JakartaEast Kalimantan0.05 South0.10 East Sulawesi 0.15 0.20 0.25 National Reduction Bangka-Belitung North Maluku Yogyakarta Special Aceh in MPI Bali South Kalimantan Gorontalo Region Riau -0.005 Central Java West Java Kepulauan Bangka- Lampung Belitung North Sumatra East Java West Kalimantan -0.010 Bengkulu South Sumatra Maluku East Nusa Tenggara South Sulawesi Annual Absolute Change in % MPI in Change Absolute Annual -0.015 West Sumatra Banten Multidimensional Poverty Index (MPI) at initial year Good /Good -0.020 27 0.010 MPI Reduction 2007-12 West Papua 0.005 Papua North SulawesiCentral Kalimantan Central Sulawesi West Nusa Tenggara West Sulawesi 0.000 Jambi 0.00 JakartaEast Kalimantan0.05 South0.10 East Sulawesi 0.15 0.20 Reduction in 0.25 National Bangka-Belitung North Maluku MPI Yogyakarta Special Aceh Bali South Kalimantan Gorontalo Region Riau -0.005 Central Java West Java Kepulauan Bangka- Lampung Belitung North Sumatra East Java West Kalimantan -0.010 Bengkulu South Sumatra Maluku East Nusa Tenggara South Sulawesi Annual Absolute Change in % MPI in Change Absolute Annual -0.015 West Sumatra Banten Good /Good -0.020 Multidimension Poverty Index (MPI) at initial year 28 Nutrition Child Mortality Years of Schooling Aceh North Sumatra West Sumatra Riau Jambi South Sumatra Bengkulu Lampung Bangka-Belitung Kepulauan Bangka-Belitung Jakarta West Java Central Java Yogyakarta Special Region East Java Banten Bali West Nusa Tenggara East Nusa Tenggara 1 Attendance 0.5 Cooking 0 Fuel Sanitation -0.5 -1 Water -1.5 Electricity Annualized Absolute Change Absolute Annualized -2 Floor -2.5 -3 Assets in proportion in... anddeprived who is poor -3.5 29 MPI vs. Income poverty 2 1 0 -1 -2 -3 -4 -5 -6 MPI Incidence $1.25 Incidence -7 -8 Disaggregating by Ethnic Group - Benin 0.010 Multidimensional Poverty Index at initial year 0.005 Size of bubble is proportional to the number of poor in first year of Peulh the comparison. Dendi 0.000 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 MPI Bétamaribe -0.005 Yoruba Bariba Fon -0.010 Adja Yoa and Lopka -0.015 Poorest ethnic group saw no -0.020 change in . -0.025 MPI. Annual Absolute Change in Change Absolute Annual -0.030 -0.035 Disaggregating by Ethnic Group - Kenya 0.010 Multidimensional Poverty Index at initial year Meru 0.005 Size of bubble is proportional to the number of poor in first Poorest ethnic year of the comparison. 0.000 Kamba Mijikenda/Swahili group reduced MPI 0.00 0.10 Kikuyu 0.20 Luhya0.30 0.40 0.50 0.60 0.70 0.80 MPI the fastest.
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