What other databases and measurements are out there? Sabina Alkire, ESCAP, October 2017

Transcription

1 What other databases and measurements are out there? Sabina Alkire, ESCAP, October 2017

2 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.

3 What other measure?

4 Methodology for the National and Global MPIs 4 1. Select Indicators, Cutoffs, Values 4. Use: MPI, Incidence Intensity & Composition 3. Identify who is poor Education Education 2. Build a deprivation score for each person 33%

5 MPIs jointly analyse multiple SDGs Example: Global Multidimensional Poverty Index (MPI)

6 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

7 MPIs: National or Comparable

8 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.

9 Chile 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

10 Existing Data Global MPI

11 Online Global MPI Data: National Urban-Rural Disaggregation Subnational Disaggregation Changes Harmonised All MPIs ever published Age Disaggregation National MPI, H, A, (k=20%, 33%, 50%) Destitution, Inequality among poor Pop, GNI, HDI, $1.90, $3.10, national poverty, missing indicator % deprived in each indicator Uncensored & Censored % contribution of each indicator Standard errors Sample size

12 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 Total number of countries with published changes over time Afghanistan Armenia Azerbaijan Bangladesh Bhutan Cambodia China India Indonesia Kazakhstan Kyrgyzstan Laos Maldives Myanmar Nepal Pakistan Philippines Tajikistan Thailand Timor-Leste Turkmenistan Uzbekistan Vanuatu Viet Nam : 215 regions : 18 countries

13 Global MPI in ESCAP 24 countries - 12 DHS surveys - 9 MICS survey - 1 DHS/MICS - China = CFPS 2014; India = IHDS 2011/ (2) 2007 (1) 2009 (1) 2009/10 (1) (2) 2011/12 (2) 2012 (3) 2012/13 (1) 2013 (1) 2013/14 (1) 2014 (5) 2015 (1) 2015/16 (3) 3 countries: countries: /12 6 countries: 2012=13/14 9 countries: indicators: 19 countries 9 indicators: 4 countries 8 indicators: 1 country

14 Kazakhstan Armenia Turkmenistan Kyrgyzstan Thailand Uzbekistan China Maldives Azerbaijan Viet Nam Philippines Tajikistan Indonesia Bhutan Nepal Vanuatu Myanmar Cambodia Laos India Bangladesh Pakistan Afghanistan Timor-Leste Global MPI in ESCAP 3.7 billion people of whom 837 million are poor (22.6%)

15 Share of MPI poor in ESCAP by major regions 16% MPI poor 0.3% MPI poor 84% MPI poor S Asia East Asia and the Pacific Europe and Central Asia South Asia

16 60% Multidimensional poverty in 24 ESCAP countries: a comparison between Headcount ratio (H) and Intensity (A) Pakistan 50% Philippines Myanmar Laos Afghanistan Bangladesh Timor-Leste 40% Bhutan China Viet Nam Indonesia NepalCambodia Tajikistan Vanuatu Azerbaijan Thailand Kyrgyzstan Uzbekistan Armenia Maldives Turkmenistan Kazakhstan India 30%

17 Average Intensity of Poverty (A) MPI also varies greatly across subnational regions 75% within a country e.g. Indonesia 70% 65% 60% 55% Indonesia 50% 45% 40% 35% 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of People Considered Poor (H)

18 Average Intensity of Poverty (A) MPI also varies greatly across subnational regions within a country e.g. Indonesia 75% 70% 65% 60% 55% Indonesia Papua 50% West Papua West Sulawesi Maluku North Sulawesi East Nusa Tenggara Southeast Sulawesi 45% North Maluku East Java Central Kalimantan West Nusa Tenggara Banten 40% Bali Aceh Riau West Java East Kalimantan 35% DKI Jakarta DI Yogyakarta 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of People Considered Poor (H)

19 Average Intensity of Poverty (A) MPI also varies greatly across subnational regions within a country e.g. Indonesia 75% 70% 65% 60% 55% Indonesia Papua 50% West Papua West Sulawesi Maluku North Sulawesi East Nusa Tenggara Southeast Sulawesi 45% North Maluku East Java Central Kalimantan West Nusa Tenggara Banten 40% Bali Aceh Riau West Java East Kalimantan 35% DKI Jakarta DI Yogyakarta 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of People Considered Poor (H)

20 Afghanistan 2015/16: MPI H rates vary subnationally

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22 Afghanistan 2015/16 56% Nepal 2014: 29% Bhutan 2010: 28% Pakistan 2012/13 44% India 2011/12: 41% Bangladesh 2014: 41%

23 Subnational MPI: poorest 24 ESCAP regions Urozgan (Afghanistan) Nooristan (Afghanistan) Oecussi (Timor-Leste) Ermera (Timor-Leste) Badghis (Afghanistan) Ainaro (Timor-Leste) Kandahar (Afghanistan) Viqueque (Timor-Leste) Bobonaro (Timor-Leste) Balochistan (Pakistan) Liquica (Timor-Leste) Badakhshan (Afghanistan) Ghor (Afghanistan) Aileu (Timor-Leste) Lautem (Timor-Leste) Laghman (Afghanistan) Takhar (Afghanistan) Saravane (Laos) Baucau (Timor-Leste) Heart (Afghanistan) Manufahi (Timor-Leste) Samangan (Afghanistan) Sar-E-Pul (Afghanistan) Wardak (Afghanistan)

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25 Myanmar (2016)

26 The composition of MPI can inform policy. Here, Indonesia 2007 (previous period) 100 Assets Fuel Flooring Water Sanitation Electricity Mortality Attendance Schooling

27 Annual Absolute Change in % MPI.010 MPI Reduction West Papua.005 Papua North Sulawesi Central Kalimantan Central Sulawesi West Nusa Tenggara West Sulawesi Jambi Jakarta East Kalimantan South East Sulawesi National Bangka-Belitung North Maluku ogyakarta Special Aceh Region Bali South Kalimantan Gorontalo Riau Central Java West Java Kepulauan Bangka- Lampung Belitung North Sumatra East Java West Kalimantan South Sumatra South Sulawesi Bengkulu Maluku East Nusa Tenggara Reduction in MPI.015 West Sumatra Banten Multidimensional Poverty Index (MPI) at initial year Good /Good

28 Annual Absolute Change in % MPI MPI Reduction West Papua Papua North Sulawesi Central Kalimantan Central Sulawesi West Nusa Tenggara West Sulawesi Jambi Jakarta East Kalimantan South East Sulawesi National Bangka-Belitung North Maluku Yogyakarta Special Aceh Region Bali South Kalimantan Gorontalo Riau Central Java West Java Kepulauan Bangka- Lampung Belitung North Sumatra East Java West Kalimantan Reduction in MPI South Sumatra Bengkulu Maluku East Nusa Tenggara South Sulawesi West Sumatra Banten Good /Good Multidimension Poverty Index (MPI) at initial year 28

29 Annualized Absolute Change in proportion who is poor and deprived in... 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 Nutrition Child Mortality Years of Schooling Attendance Cooking Fuel Sanitation Water Electricity Floor Assets 29

30 MPI vs. Income poverty MPI Incidence $1.25 Incidence

31 Annual Absolute Change in MPI Disaggregating by Ethnic Group - Benin Multidimensional Poverty Index at initial year Size of bubble is proportional to the number of poor in first year of the comparison. Dendi Peulh Yoruba Bariba Bétamaribe Fon Poorest ethnic group saw no change in MPI.. Adja Yoa and Lopka

32 Annual Absolute Change in MPI Disaggregating by Ethnic Group - Kenya Meru Size of bubble is proportional to the number of poor in first Poorest ethnic year of the comparison Kamba Mijikenda/Swahili group reduced MPI Kikuyu 0.20 Luhya Multidimensional Poverty Index at initial year Kisii Luo Kalenjin the fastest Somali

33 States (Significance) [MPI-I in 1999] Absolute Reduction in MPI I by Social groups Slower progress for STs and Muslims Muslim () [0.32] Hindu (*) [0.306] Christian () [0.196] Sikh (*) [0.115] Religion ST (*) [0.458] SC (*) [0.378] OBC (*) [0.301] General (*) [0.229] Caste Absolute Change (99-06) in MPI-I Rural (*) [0.368] Urban (*) [0.116]

34 Across 103 countries and 5.4 billion people Half the MPI poor people are children Leave No One Behind

35 Policy Matters

36 New National MPIs launched as official statistics since September 2015 El Salvador MPI based on the protagonists of poverty (2015) Costa Rica MPI aligns allocation with national goals (2015) Ecuador MPI reflects political commitment to Buen Vivir (Feb 2016) Pakistan MPI reflects the Vision 2025, in detail (June 2016). Chile MPI-2 includes dimension of environment & networks (2016) Honduras MPI includes work and informs targeting (August 2016) Mozambique MPI shows trends from /15 (Oct 2016) Armenia MPI reflects complexity & persistence (November 2016) Panama annual MPI profiles high disparity subnationally (June 2017) Dominican Republic innovative MPI with digital divide (June 2017) 36

37 New National MPIs launched as official statistics since September 2015 El Salvador MPI based on the protagonists of poverty (2015) Costa Rica MPI aligns allocation with national goals (2015) Ecuador MPI reflects political commitment Multidimensional to Buen Vivir (Feb 2016) Poverty Pakistan MPI reflects the Vision 2025, in detail Peer (June Network 2016). Chile MPI-2 includes dimension of environment ( & networks has (2016) 53 participating countries. Honduras MPI includes work and informs targeting (August 2016) Mozambique MPI shows trends from /15 The 2017 meeting (Oct 2016) is by Armenia MPI reflects complexity & persistence China; (November the 2018 meeting 2016) is Panama annual MPI profiles high disparity hosted subnationally by South (June Africa. 2017) Dominican Republic innovative MPI with digital divide (June 2017) 37

38 Policy makers are using national or global MPIs to: 1. Complement monetary poverty statistics 2. Track poverty over time (official statistics) 3. Allocate resources by sector and by region 4. Target marginalized regions, groups, or households 5. Coordinate policy across sectors and subnational levels 6. Adjust policies by what works (measure to manage) 7. Leave No One Behind see the poorest & track trends 8. Be Transparent so all stakeholders engage NGOs, Private Sector etc, all parts of government.

39 Pakistan: the poorest district reduced MPI most 15% 10% Starting MPI value vs Absolute Reduction of MPI Ziarat Killa Abdullah 5% D.I. Khan Pishin Chagai Barkhan Badin Islamabad 0% Chakwal Jaffarabad Kohistan Sheikhupura % Gujrat Zhob -10% Lahore Karachi Sialkot Rajanpur Jhal Magsi -15% -20% -25% Rawalpindi Jhelum Kasur Attock Haripur T.T. Singh Chitral Khairpur Naushehro Feroze Malakand Nasirabad Buner Leave No One Behind Loralai Kalat Khuzdar Killa Saifullah -30% -35% Larkana Dadu Musakhel

40 An MPI offers: a Headline, Disaggregation & Interlinkages to inform integrated action to complement monetary measures 40