Multidimensional Poverty Measurement: The Way Forward?

Transcription

1 Multidimensional Poverty Measurement: The Way Forward? James E. Foster The George Washington University and OPHI NAS Food Security Workshop February 16, 211

2 Why Multidimensional Poverty? Missing Dimensions Just low income? Capability Approach Data Tools Conceptual framework More sources Unidimensional measures into multidimensional Demand Governments and other organizations

3 Hypothetical Challenge A government would like to create an official multidimensional poverty indicator Desiderata It must understandable and easy to describe It must conform to a common sense notion of poverty It must fit the purpose for which it is being developed It must be technically solid It must be operationally viable It must be easily replicable What would you advise?

4 Not So Hypothetical 26 Mexico Law: must alter official poverty methods Include six other dimensions education, dwelling space, dwelling services, access to food, access to health services, access to social security 27 Oxford Alkire and Foster Counting and Multidimensional Poverty Measurement 29 Mexico Announces official methodology

5 Continued Interest 28 Bhutan Gross National Happiness Index 21 Chile Major conference (May) 21 London Release of MPI by UNDP and OPHI (July) 21 Colombia Major conference (July) Washington DC World Bank (several), IDB, USAID, CGD OPHI Workshops on: Missing dimensions; Weights; Country applications; Applications to governance, quality of education, corruption, fair trade, and targeting; Robustness

6 Our Proposal - Overview Identification Dual cutoffs Deprivation cutoffs Poverty cutoff Aggregation Adjusted FGT Background papers Alkire and Foster Counting and Multidimensional Poverty Measurement forthcoming Journal of Public Economics Alkire and Santos Acute Multidimensional Poverty: A new Index for Developing Countries OPHI WP 38

7 Review: Unidimensional Poverty Framework Goal Sen 1976 identification and aggregation Poverty measure P(.) Variable income consumption or other aggregate Identification poverty line unchanged since Rowntree Aggregation Foster-Greer-Thorbecke 1984 see also Foster, Greer, and Thorbecke 21 - forthcoming Journal of Economic Inequality

8 Review: Unidimensional Poverty Example Incomes y = (7,3,4,8) Poverty line z = 5 Deprivation vector g = (,1,1,) Headcount ratio P = μ(g ) = 2/4 Normalized gap vector g 1 = (, 2/5, 1/5, ) Poverty gap = P 1 = μ(g 1 ) = 3/2 Squared gap vector g 2 = (, 4/25, 1/25, ) FGT Measure = P 2 = μ(g 2 ) = 5/1 Decomposable across population groups WB Policy implications Bourguignon and Fields 199

9 Multidimensional Data Matrix of achievements for n persons in d domains

10 Multidimensional Data Matrix of achievements for n persons in d domains y =

11 Multidimensional Data Matrix of achievements for n persons in d domains y = z ( ) Cutoffs

12 Multidimensional Data Matrix of achievements for n persons in d domains y = z ( ) Cutoffs These entries fall below cutoffs

13 Deprivation Matrix Replace entries: 1 if deprived, if not deprived y =

14 Deprivation Matrix Replace entries: 1 if deprived, if not deprived g =

15 Normalized Gap Matrix Matrix of achievements for n persons in d domains y = z ( ) Cutoffs These entries fall below cutoffs

16 Normalized Gap Matrix Normalized gap = (z j -y ji )/z j if deprived, if not deprived y = z ( ) Cutoffs These entries fall below cutoffs

17 Normalized Gap Matrix Normalized gap = (z j -y ji )/z j if deprived, if not deprived g 1 =

18 Squared Gap Matrix Squared gap = [(z j -y ji )/z j ] 2 if deprived, if not deprived g 1 =

19 Squared Gap Matrix Squared gap = [(z j -y ji )/z j ] 2 if deprived, if not deprived g 2 =

20 Identification g = Matrix of deprivations

21 Identification Counting Deprivations g = c

22 Identification Counting Deprivations Q/ Who is poor? g = c

23 Identification Union Approach Q/ Who is poor? A1/ Poor if deprived in any dimension c i 1 c g =

24 Identification Union Approach Q/ Who is poor? A1/ Poor if deprived in any dimension c i 1 c Difficulties g =

25 Identification Union Approach Q/ Who is poor? A1/ Poor if deprived in any dimension c i 1 c Difficulties g = Single deprivation may be due to something other than poverty (UNICEF) 2 4 1

26 Identification Union Approach Q/ Who is poor? A1/ Poor if deprived in any dimension c i 1 c Difficulties g = Single deprivation may be due to something other than poverty (UNICEF) Union approach often predicts very high numbers - political constraints 2 4 1

27 Identification Intersection Approach Q/ Who is poor? A2/ Poor if deprived in all dimensions c i = d g = c

28 Identification Intersection Approach Q/ Who is poor? A2/ Poor if deprived in all dimensions c i = d Difficulties g = c

29 Identification Intersection Approach Q/ Who is poor? A2/ Poor if deprived in all dimensions c i = d g = Difficulties Demanding requirement (especially if d large) c

30 Identification Intersection Approach Q/ Who is poor? A2/ Poor if deprived in all dimensions c i = d g = Difficulties Demanding requirement (especially if d large) Often identifies a very narrow slice of population c

31 Identification Dual Cutoff Approach Q/ Who is poor? A/ Fix cutoff k, identify as poor if c i > k g = c

32 Identification Dual Cutoff Approach Q/ Who is poor? A/ Fix cutoff k, identify as poor if c i > k (Ex: k = 2) c 1 1 g 2 =

33 Identification Dual Cutoff Approach Q/ Who is poor? A/ Fix cutoff k, identify as poor if c i > k (Ex: k = 2) c g = Note Includes both union and intersection

34 Identification Dual Cutoff Approach Q/ Who is poor? A/ Fix cutoff k, identify as poor if c i > k (Ex: k = 2) c g = Note Includes both union and intersection Especially useful when number of dimensions is large Union becomes too large, intersection too small

35 Identification Dual Cutoff Approach Q/ Who is poor? A/ Fix cutoff k, identify as poor if c i > k (Ex: k = 2) c g = Note Includes both union and intersection Especially useful when number of dimensions is large Union becomes too large, intersection too small Next step - aggregate into an overall measure of poverty

36 Aggregation g = c

37 Aggregation Censor data of nonpoor g = c

38 Aggregation Censor data of nonpoor g (k) = c(k)

39 Aggregation Censor data of nonpoor g (k) = c(k) Similarly for g 1 (k), etc

40 Aggregation Headcount Ratio g (k) = c(k)

41 Aggregation Headcount Ratio g (k) = c(k) Two poor persons out of four: H = ½ incidence

42 Critique Suppose the number of deprivations rises for person 2 g (k) = c(k) Two poor persons out of four: H = ½ incidence

43 Critique Suppose the number of deprivations rises for person 2 g (k) = c(k) Two poor persons out of four: H = ½ incidence

44 Critique Suppose the number of deprivations rises for person 2 g (k) = c(k) Two poor persons out of four: H = ½ incidence No change!

45 Critique Suppose the number of deprivations rises for person 2 g (k) = c(k) Two poor persons out of four: H = ½ incidence No change! Violates dimensional monotonicity

46 Aggregation Return to the original matrix g (k) = c(k)

47 Aggregation Return to the original matrix g (k) = c(k)

48 Aggregation Need to augment information g (k) = c(k)

49 Aggregation Need to augment information deprivation share g (k) = c(k) c(k)/d 2 / 4 4 / 4

50 Aggregation Need to augment information g (k) = deprivation share intensity c(k) c(k)/d 2 2 / / 4 A = average intensity among poor = 3/4

51 Aggregation Adjusted Headcount Ratio Adjusted Headcount Ratio = M = HA g (k) = c(k) c(k)/d 2 / 4 4 / 4 A = average intensity among poor = 3/4

52 Aggregation Adjusted Headcount Ratio Adjusted Headcount Ratio = M = HA = μ(g (k)) g (k) = c(k) c(k)/d 2 / 4 4 / 4 A = average intensity among poor = 3/4

53 Aggregation Adjusted Headcount Ratio Adjusted Headcount Ratio = M = HA = μ(g (k)) = 6/16 =.375 g (k) = c(k) c(k)/d 2 / 4 4 / 4 A = average intensity among poor = 3/4

54 Aggregation Adjusted Headcount Ratio Adjusted Headcount Ratio = M = HA = μ(g (k)) = 6/16 =.375 g (k) = c(k) c(k)/d 2 / 4 4 / 4 A = average intensity among poor = 3/4 Note: if person 2 has an additional deprivation, M rises

55 Aggregation Adjusted Headcount Ratio Adjusted Headcount Ratio = M = HA = μ(g (k)) = 6/16 =.375 g (k) = c(k) c(k)/d 2 / 4 4 / 4 A = average intensity among poor = 3/4 Note: if person 2 has an additional deprivation, M rises Satisfies dimensional monotonicity

56 Aggregation Adjusted Headcount Ratio Observations

57 Aggregation Adjusted Headcount Ratio Observations Uses ordinal data

58 Aggregation Adjusted Headcount Ratio Observations Uses ordinal data Similar to traditional gap P 1 = HI HI = per capita poverty gap = headcount H times average income gap I among poor

59 Aggregation Adjusted Headcount Ratio Observations Uses ordinal data Similar to traditional gap P 1 = HI HI = per capita poverty gap = headcount H times average income gap I among poor HA = per capita deprivation = headcount H times average intensity A among poor

60 Aggregation Adjusted Headcount Ratio Observations Uses ordinal data Similar to traditional gap P 1 = HI HI = per capita poverty gap = headcount H times average income gap I among poor HA = per capita deprivation = headcount H times average intensity A among poor Decomposable across dimensions after identification M = j H j /d

61 Aggregation Adjusted Headcount Ratio Observations Uses ordinal data Similar to traditional gap P 1 = HI HI = per capita poverty gap = headcount H times average income gap I among poor HA = per capita deprivation = headcount H times average intensity A among poor Decomposable across dimensions M = j H j /d Axioms - Characterization via freedom

62 Adjusted Headcount Ratio Note M requires only ordinal information. Q/ What if data are cardinal? How to incorporate information on depth of deprivation?

63 Aggregation: Adjusted Poverty Gap Augment information of M using normalized gaps g 1 (k) =

64 Aggregation: Adjusted Poverty Gap Augment information of M using normalized gaps g 1 (k) = Average gap across all deprived dimensions of the poor: G = ( )/6

65 Aggregation: Adjusted Poverty Gap Adjusted Poverty Gap = M 1 = M G = HAG g 1 (k) = Average gap across all deprived dimensions of the poor: G = ( )/6

66 Aggregation: Adjusted Poverty Gap Adjusted Poverty Gap = M 1 = M G = HAG = μ(g 1 (k)) g 1 (k) = Average gap across all deprived dimensions of the poor: G = ( )/6

67 Aggregation: Adjusted Poverty Gap Adjusted Poverty Gap = M 1 = M G = HAG = μ(g 1 (k)) g 1 (k) = Obviously, if in a deprived dimension, a poor person becomes even more deprived, then M 1 will rise.

68 Aggregation: Adjusted Poverty Gap Adjusted Poverty Gap = M 1 = M G = HAG = μ(g 1 (k)) g 1 (k) = Obviously, if in a deprived dimension, a poor person becomes even more deprived, then M 1 will rise. Satisfies monotonicity reflects incidence, intensity, depth

69 Aggregation: Adjusted FGT Consider the matrix of squared gaps g 1 (k) =

70 Aggregation: Adjusted FGT Consider the matrix of squared gaps g 2 (k) =

71 Aggregation: Adjusted FGT Adjusted FGT is M 2 = μ(g 2 (k)) g 2 (k) =

72 Aggregation: Adjusted FGT Adjusted FGT is M 2 = μ(g 2 (k)) g 2 (k) = Satisfies transfer axiom reflects incidence, intensity, depth, severity focuses on most deprived

73 Aggregation: Adjusted FGT Family Adjusted FGT is M α = μ(g α (τ)) for α > g α (k) =.42 α 1 α.4 α.17 α.67 α 1 α

74 Aggregation: Adjusted FGT Family Adjusted FGT is M α = μ(g α (τ)) for α > g α (k) =.42 α 1 α.4 α.17 α.67 α 1 α Satisfies numerous properties including decomposability, and dimension monotonicity, monotonicity (for α > ), transfer (for α > 1).

75 Weights Weighted identification Weight on first dimension (say income): 2 Weight on other three dimensions: 2/3 Cutoff k = 2 Poor if income poor, or suffer two or more deprivations Cutoff k = 2.5 (or make inequality strict) Poor if income poor and suffer one or more other deprivations Nolan, Brian and Christopher T. Whelan, Resources, Deprivation and Poverty, 1996 Weighted aggregation Weighted intensity otherwise same

76 Caveats and Observations Identification No tradeoffs across dimensions Fundamentally multidimensional Need to set deprivation cutoffs Need to set weights Need to set poverty cutoff across dimension Aggregation Neutral Ignores coupling of disadvantages Not substitutes, not complements Discontinuities

77 Sub-Sahara Africa: Robustness Across k Burkina is always poorer than Guinea, regardless of whether we count as poor persons who are deprived in only one kind of assets (.25) or every dimension (assets, health, education, and empowerment, in this example). (DHS Data used) Batana, 28- OPHI WP 13

78 Advantages Intuitive Transparent Flexible MPI Acute poverty Country Specific Measures Policy impact and good governance Targeting Accounting structure for evaluating policies Participatory tool

79 Revisit Objectives Desiderata It must understandable and easy to describe It must conform to a common sense notion of poverty It must fit the purpose for which it is being developed It must be technically solid It must be operationally viable It must be easily replicable What do you think?

80 Thank you

81 Thank you

82 Illustration: USA Data Source: National Health Interview Survey, 24, United States Department of Health and Human Services. National Center for Health Statistics - ICPSR Tables Generated By: Suman Seth. Unit of Analysis: Individual. Number of Observations: 469. Variables: (1) income measured in poverty line increments and grouped into 15 categories (2) self-reported health (3) health insurance (4) years of schooling.

83 Illustration: USA Profile of US Poverty by Ethnic/Racial Group

84 Illustration: USA Profile of US Poverty by Ethnic/Racial Group

85 Illustration: USA Profile of US Poverty by Ethnic/Racial Group

86 Illustration: USA Profile of US Poverty by Ethnic/Racial Group

87 Illustration: USA Profile of US Poverty by Ethnic/Racial Group

88 Illustration: USA Profile of US Poverty by Ethnic/Racial Group

89 Illustration: USA