Summer School on Multidimensional Poverty Analysis

Transcription

1 Summer School on Multidimensional Poverty Analysis 1 12 August 2016 Beijing, China

2 Interpreting the MPI Results Adriana Conconi (OPHI)

3 Motivation

4 Policy Interest Why? 1. Intuitive easy to understand 2. Birds-eye view can be unpacked a. by region, ethnicity, rural/urban, etc. b. by indicator, to show composition c. by intensity to show inequality among poor 3. Adds Value: a. focuses on the multiply deprived b. shows joint distribution of deprivation. 4. Incentives to reach the poorest of the poor 5. Flexible: you choose indicators/cutoffs/values 6. Robust: to wide range of weights and cutoffs 7. Academically Rigorous

5 Choose the purpose Choose the unit Last week

6 then Choose the purpose Choose the unit Choose the dimensions Choose the indicators Choose the deprivation cut-off of each indicator Choose the weights Choose the poverty cut-off (poverty line) And finally...

7 STATA

8 Understanding

9 MD Poverty Statistics: What do you need to understand? k : poverty cutoff M 0 (sometimes called MPI) H : Headcount Ratio A : Intensity h j : Censored Headcount (each indicator) j : Percent Contribution (each indicator) Vulnerability and Severe MPI (All of the above, by subgroup)

10 Multidimensional Data Matrix of well-being scores for 4 persons in 4 dimensions Who is deprived in what? Income Years of Education Health Index Water No Ana X = Yes Chris Yes Adriana No John z = No

11 . tab water [aw=weight], miss Drinking Water Freq. Percent Cum piped into dwelling piped to yard/plot public tap/standpipe 1, tube well or borehole 1, dug well/protected well dug well/unprotected well protected spring unprotected spring rainwater tanker truck cart with small tank surface water (river/dam/lake/pond/stre bottled water other Total 5,

12 Achievement Matrix Matrix of well-being scores for 4 persons in 4 dimensions Who is deprived in what? Income Years of Education Health Index Water No Ana X = Yes Chris Yes Adriana No John z = No

13 Deprivation Matrix Replace entries: 1 if deprived, 0 if not deprived Income Years of Education Health Index Water Ana g 0 = Chris Adriana John z = No

14 . tab water hh_d_water [aw=weight], miss RECODE of water (Drinking Water) Drinking Water 0 1 Total piped into dwelling piped to yard/plot public tap/standpipe 1, , tube well or borehole 1, , dug well/protected we dug well/unprotected protected spring unprotected spring rainwater tanker truck cart with small tank surface water (river/ bottled water other Total 4, , ,119

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16 Deprivation Matrix Replace entries: 1 if deprived, 0 if not deprived Income Years of Education Health Index Water Ana g 0 = Chris Adriana John z = No

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18 Weighted Deprivation Matrix Replace entries: 1 if deprived, 0 if not deprived Income Years of Education Health Index Water g 0 = Ana Chris Adriana John z = No

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20 Counting Vector Income Years of Education Health Index Water C_vector Ana W_g 0 = Chris Adriana John

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22 Identification (Headcount ratio) W_g 0 = Income Years of Education Health Index Water c k_30 k_60 k_ H = 50% 25% 25%

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24 Intensity (censored c_vector among the Poor) W_g 0 = Incom e Years of Education Health Index Water c a_30 a_60 a_ A = 75% 100% 100%

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26 Adjusted Headcount Ratio (Censored C_Vector ) W_g 0 = Income Years of Education Health Index Water c m0_30 m0_60 m0_ M0 =

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28 Censored Headcount Ratios (k = 30%) W_g 0 = Income Years of Education Health Index Water c m0_

29 Censored Headcount Ratios k = 30% W_g 0 (k)= Income Years of Education Health Index Water c m0_

30 Censored Headcount Ratios k = 30% W_g 0 (k)= Income Years of Education Health Index Water c m0_ CH: 2/4 1/4 1/4 2/4 CH*w: 2/4*0.25 1/4*0.25 1/4*0.25 2/4* M0 = 0.375

31 Contribution each Indicator k = 30% Income Years of Education Health Index Water m0_30 CH: 2/4 1/4 1/4 2/4 2/4*0.25 1/4*0.25 1/4*0.25 2/4* M0 = Contrib. 2/4*0.25 1/4*0.25 1/4*0.25 2/4* Σ 100%

32 Interpretation

33 Global Multidimensional Poverty Index Identify someone as poor if he or she is deprived in 33% or more of the weighted indicators. Poverty cutoff k = 33% Grace s Deprivation Score 33%

34 MPI is Alkire & Foster s M 0 The M 0 is the product of two components: Formula: M 0 = H A Interpretation of M 0 (Example: 0.350): the percentage of deprivations poor people experience, as a share of the possible deprivations that would be experienced if all people were deprived in all dimensions.

35 Partial Indices H and A- The M 0 is the product of two components: Formula: M 0 = H A 1) Incidence ~ the percentage of people who are poor, or the headcount ratio H. 2) Intensity ~ the average deprivation score, or percentage of dimensions in which poor people are deprived A. Grace s Deprivation Score: 39%

36 MPI Censored Deprivation Matrix g 0 (k) g 0 (k) = MPI = H*A = k = 33% Indicators c(k) c(k)/d H = headcount ratio = 3/4 = 75% A = intensity = ( )/3 = 0.59 = 59% MPI = HxA =

37 Nepal s MPI MPI H 64.7% A 54.0% 37

38 Average Intensity of Poverty (A) 75% 70% 65% 60% 55% 50% 45% 40% 35% What MPI shows National level Nepal 2006 Nepal 2006, 64.7, % 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Incidence - Percentage of MPI Poor People (H)

39 75% MPI in Nepal % 65% Average Intensity of Poverty (A) 60% 55% Nepal % Nepal % 40% 35% 30% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Incidence - Percentage of MPI Poor People (H)

40 Nepal s MPI MPI H 64.7% 44.2% A 54% 49% 40

41 2013 MPI in South Asia MPI H A Maldives DHS Sri Lanka WHS Bhutan MICS Nepal DHS Pakistan DHS 2006/ India DHS 2005/ BangladeshDHS

42 Censored Headcount Ratios % of people who are poor and deprived in each indicator Nepal Censored Headcounts Ratios, Schooling 8.1 School attendance Child mortality 8.8 Nutrition Electricity Sanitation Water Flooring Fuel Assets

43 MPI Censored Deprivation Matrix g 0 (k) Censored Headcount Ratios: Go Vertical! No Weights! c(k) c(k)/d g 0 (k) = = h j Hint: The Censored Headcount can never exceed the Uncensored Headcount Ratios. Why?

44 MPI Censored Deprivation Matrix g 0 (k) % Contribution: Go Horizontal! Use Weights (as %)! % Contr = (h j * w j )/M 0 M 0 = g 0 (k) = (0.25 x 0.167)/0.442 = 9.4% Hint: The % Contribution of all 10 indicators add up to 100%

45 Decomposition by region (or social group) shows inequalities Regional MPI values

46 Changes in Censored Headcount Ratios to understand trends in poverty reduction

47 Changes in Censored H by region (Nepal) Annualized Absolute Change in proportion who is poor and deprived in Nutrition Child Mortality Years of Schooling Attendance Cooking Fuel Sanitation Water Electricity Floor Assets

48 Comparing countries Ethiopia 2000 Nigeria 2003 Ghana 2003

49 Comparing countries Multidimensional poverty Country MPI H A Range 0 to 1 % Population Average % of weighted deprivations Lao People's Democratic Republic Yemen India

50 Comparing countries Country Range 0 to 1 Multidimensional poverty MPI H A % Population Average % of weighted deprivations Number Population of vulnerable MPI poor to poverty (who people experience % intensity of Year deprivations) of the survey b Populatio severe po (with intensit than 50 Thousands % Population % Populat Lao People's Democratic Republic , Yemen , India , Different number of people

51 Comparing countries Country Multidimensional poverty MPI H A Population vulnerable to poverty (who experience % intensity of deprivations) Population in severe poverty (with intensity higher than 50%) Range 0 to 1 % Population Average % of weighted deprivations % Population % Population Lao People's Democratic Republic Yemen India TOTAL POOR Lao %

52 Policy Interest Why? 1. Intuitive easy to understand 2. Birds-eye view - can be unpacked a. by region, ethnicity, rural/urban, etc. b. by indicator, to show composition c. by intensity to show inequality among poor 3. Adds Value: a. focuses on the multiply deprived b. shows joint distribution of deprivation. 4. Incentives to reach the poorest of the poor 5. Flexible: you choose indicators/cutoffs/values 6. Robust to wide range of weights and cutoffs 7. Academically Rigorous

53 The input of researchers Figure out what issues are timely and relevant. Follow policy dialogues & get to know people. Keep asking why my measurement matters to a poor person. See your work from a users perspective Craft factoids Design metaphors Experiment: not everything works