Decomposition and Changes over time. Sabina Alkire (OPHI), 28 Feb 2011

Transcription

1 Decomposition and Changes over time Sabina Alkire (OPHI), 28 Feb 2011

2 Decomposition of AF Method The AF method can be decomposed by Population subgroups Dimensions and Indicators

3 Example of Population Subgroups Income Education Health Person 1 X Person Person 3

4 Example of Dimension Subgroups Income Education Health Person 1 X Person Person 3

5 Key Issues While Decomposing by Population Subgroup Sample Design & Representativeness Relative size & internal diversity of different groups Salient characteristics for policy

6 Example: Nepal Regions Regions Population Share (%) MPI Contribution to Overall Poverty (%) Eastern Central Western Mid-western Far-Western Nepal = ( )/0.350

7 Example: Decomposing India by Region MPI Values Bihar In Kerala India 16% of the population is MPI poor; in Bihar it is 81%. Kerala

8 Global MPIs MPI ranges 0 to 0.64 Consider countries where MPI > 0.32

9 These countries have an MPI > 0.32

10 These countries have an MPI > 0.32

11 These countries have an MPI > 0.32 Here is India They include Nepal; Rest are African countries

12 There are 26 African Countries Focus: poor people India is a very populous country. And we know that MPI varies a lot within countries Where do the people whose MPI > 0.32 live? We take India as an example of a large country, and decompose it to see where such people live within India.

13 Total Population of India compared with pop of 37 African countries (Millions, 2007)

14 Which Indian States MPI > 0.32?

15 What States MPI > 0.32?

16 Average Intensity of Poverty (A) Visual comparison: Size = Number of Poor 421M MPI poor in India; 410M in Africa 0.70 Niger Cote D'Ivoire Nigeria Madhya Pradesh Bihar Ethiopia Africa India 0.55 West Bengal DRC Zambia Uttar Pradesh Percentage of MPI Poor People (H)

17 Compare MP & DRC

18 Curious? Compare like with like; Think about numbers of people Madhya Pradesh, India DR Congo Population M 62.50M MPI MPI Headcount 69.5% 73.2% Avg Intensity 56% 53.7%

19 Zoom in further: How are they poor?

20 Nutritional Deprivations contribute very most School Attendance, Electricity, and Water Zoom in further: How are they poor?

21 Let Decomposition by Indicators The censored headcount of years of schooling indicators is 50% or 0.5 Weights attached to the indicator is 1 out of 10 The M0 is 0.4 Then contribution of the indicator to M0 is ((1/10) 0.5) / 0.4 = or 12.5 percent

22 Example: Dimensional Decomposition of Nepal Weight = 1/6 Dimensions Indicators Eastern Central Nepal Years of Education Schooling % % % Child Enrolment % % % Health Standard of Living Child Mortality % % % Nutrition % % % Electricity % % % Toilet % % % Water % % % Floor % % % Cooking Fuel % % % Asset Ownership % % % M % % % The numbers in red are contribution to total M0. Weight = 1/18

23 Bangladesh: Contribution of Indicators to the MPI

24 Bangladesh: Contribution of Indicators to the MPI

25 TIME SERIES 1. Literature focus its attention in cross-sectional and internationally comparable results 2. Main Purpose: Decompose and evaluate changes of the MPI over the time. 3. Initial question: Is the MPI more stable than other poverty measures? 4. Second question: Can we decompose MPI changes? 5. Complement other measures in the design and evaluation on public policies.

26 Changes Over time MPI index or M 0 Variation of M 0 First Decomposition of M 0

27 Changes Over time

28 Bangladesh ( , k=3) M0 (a x h) Average Deprivation (a) Headcount Ratio (h) Human development index 42% 49% 53% 54% GDP per capita, PPP (U$ 2005) Life expectancy at birth (years)

29 Bangladesh ( , k=3) Education 60% 50% 40% % 20% 10% 0% Health Living Standard

30 Bangladesh (Education: ) 40% 39% 35% 30% 25% 20% 15% 10% 30% 26% 22% 10% 30% 28% 25% % 0% Enrolment Deprivation Schooling Deprivation

31 Bangladesh ( , k=3, yearly) Δ% M0 Δ% A Δ% H Δ%A x Δ%H % -1.67% -3.92% 0.20% % -0.54% -1.86% 0.04% % -2.12% -3.96% 0.25%

32 ΔMPI-Bangladesh ( , k=3) 1% 0% % % -1.7% -1.9% -2.1% -2% Δ%A x Δ%H -3% Δ% H -4% -3.9% -4.0% Δ% A -5% -6% -7%

33 Δ% Ave. deprivation - Bangladesh ( , k=3) 0.5% 0.0% 0.1% 0.1% -0.5% -0.7% -0.4% -0.2% -1.0% -0.4% -1.5% Health Living Standard -1.5% -0.6% Education -0.7% -2.0% -2.5%

34 Bangladesh Colombia Ethiopia Ghana India Morocco Nepal Nigeria Tanzania Vietnam Extension to 10 countries Country Period Years of School Enrolment Child Mortality Nutrition x x Electricity Toilet Water Floor x Cooking x - x - x Asset

35 Decomposition of M 0 for 10 countries and k=3 4% 2% 0% Ghana Vietnam India Nigeria Nepal Morroco Bangladesh Colombia Etihopia Tanzania -2% -4% -6% -8% -10% -12% %H* %A %A %H %M0

36 Decomposition of H for 10 countries and k=3 (yearly) 6% 4% 2% 0% -2% -4% -6% -8% -10% rr(t-a)* %Hr %Sr rr(t-a)* %Hr rr(t-a)* %sharer ru(t-a)* %Hu %Su ru(t-a)* %Hu ru(t-a)* %shareu %H

37 Decomposition of H for 10 countries and k=3 (yearly) 2.0% 1.0% 0.0% Ghana Bangladesh Vietnam India Nepal Morocco Colombia Ethiopia Tanzania Nigeria -1.0% -2.0% -3.0% %Aliving %Ahealth %Aedu %A

38 CONCLUSION & EXTENSIONS 1. For most countries analysed and k=3, multidimensional poverty decreased (except Tanzania) 2. In most countries, a key driver behind Δ%H is Δ%Hrural 3. In most countries, the determinants of Δ%A move in opposite directions (except Ethiopia) 4. In some countries poverty has decreased (or increased) for every value of k; in others it depends 5. This situation warrants considering partial orderings based on dominance conditions over k; i.e. checking for conditions under which poverty invariably rises or decreases for all k [1;D] 6. In most countries, most of the action in Δ%M0 seems to be driven by Δ%H

39 CONCLUSION & EXTENSIONS 1. Provide more information about temporal evolution of the indicator and its dimensions 2. The methodology allow us to decompose by geographic area, dimension and indicator. 3. The indicator (and its disaggregation) has the potential to complement other measures in the design and evaluation on public policies (perverse incentives for targeting?) Extensions: Applications to panel data (Chronic and Transient poverty)

40 Changes Over time