MPI and Multidimensional Poverty AnalysisWhat we knew before joining West Bank and Gaza Poverty Assessment team

Nobuo YoshidaApril 21, 2011

Multidimensional poverty analysis and Oxford-UNDP MPI In July, 2010, UNDP issued a press release

“Oxford and UNDP launch a better way of measuring poverty”◦ Oxford and UNDP published a new measure of

poverty: Multidimensional Poverty Index (MPI)

Is that true?

More important, what do we want to know from multidimensional poverty analysis?

Multidimensional Poverty Index (Alkire-Foster method)

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Weighted average of dummies of each indicators

Critical Reviews

1. Estimation of weights2. Robustness of results against weighting

1. Estimation of weights Ravallion (2011) and Decancq and Lugo

(2010) show ideally weights should reflect MRS of different dimensions

In reality, since it is difficult to know MRS; many other approaches are proposed◦ Equal/arbitrary weight◦ Principal component◦ Expert opinion weight◦ Shadow price◦ Stated preference approach◦ Revealed preference approach

Revealed preference approach Regress a welfare indicator on a bunch of

indicators and use the coefficients for weights◦ Regression results tell us what is affecting their

welfare status Challenges◦ Regression results are often unstable over time and

across areas◦ MPI is in the end a predictor of the LHS, like

subjective poverty◦ Why do we need the predictor if we have the original

variable? In our analysis with Brazil data, correlation between subjective

poverty and its predictor is just 0.33

2. Robustness of ranking against weighting We examined robustness of ranking against weighting An interesting trade-off between stability of ranking

and multidimensionality◦ If ranking is not sensitive to weighting, then information

from MPI can be summarized by one single indicator◦ If ranking is sensitive to weighting, results from MPI are

not robust!

“a multidimensional approach is called upon precisely because important dimensions of well-being are not strongly related” (Somarriba and Pena 2009).

What do we want know from multidimensional poverty analysis? MPI is not a goal Do we want to know the characteristics and

trends of non-monetary poverty?◦ Then, dash board approach (analysis on each

dimension independently) is enough Emerging conclusion from the second MPI

workshop◦ Interaction of multiple dimensions of poverty is

important◦ Many different ways of showing the interactions

What is an interesting way to show the interactions?

Depth and Width (interaction)

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Overlaps of deprivations by Venn Diagram for Brazil’s case

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How should we evaluate the pain from multiple problems? Implicit assumption in MPI◦ If one has problems in two dimensions, then

the person’s MPI is the sum of weights for the dimensions◦ For example, c=0.33*dH+0.33*dE+0.33*dL◦ Then, if you have both health and education

deprivations, your MPI score is 0.66

Do we think like this?◦ Multiple problems can be bigger than the sum

What we tried for West Bank and Gaza poverty assessment We wanted to think about an interesting

way to show◦ Interactions◦ Assessment of the interactions

Now let’s move to Nandini’spresentation

Principal Component Approach Decancq and Lugo (2010) refer a very

powerful quote

Principal components analysis will assign lower weights to dimensions that are poorly correlated, while one could argue that a multidimensional approach is called upon precisely because important dimensions of well-being are not strongly related

(Somarriba and Pena 2009).

Stated preference approach

Carry out a survey, asking individuals their preference over multiple dimensions of deprivations

Use population averages of these valuations for each dimension as a weight

Assumption: Each individual knows what dimension affects his/her living standard by how much

Equal weight“In terms of both absolute dollar values and the

rate of GDP growth needed to make up for lower longevity, the construction of the HDI assumes that life is far less valuable in poor countries than in rich ones”

Ravallion (1997)

“equal weighting as obviously convenient but also universally considered to be wrong."

Chowdhury and Squire 2006, p. 762

Nobuo Yoshida and Nandini KrishnanApril 21, 2011

Was the “recovery” in poverty rates in Gaza in 2009 reflected in other dimensions?

Does a broader measure of deprivation better capture the lack of improvement in Gaza? Can we better reconcile popular opinion with poverty estimates?

Can we learn anything more about the 2007 crisis?

What do Palestinians place value on in evaluating their own well being?

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2004 2005 2006 2007 2009

Total West Bank Gaza

Poverty headcount rates, West Bank and Gaza: 2004-2009

Consumption poor=1 Unemployed or Out of the

labor force=1

Less than secondary education or any member of household illiterate=1

Registered refugees=1

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

2004 2005 2006 2007 2009

West Bank

Consumption Labor

Education Refugee status

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

2004 2005 2006 2007 2009

Gaza

Consumption Labor

Education Refugee status

Small increase in population with at least one deprivation in 2007 in Gaza, large increase in those with multiple deprivations; Remain high in 2009 in Gaza

36

39

41

41

45

36

38

38

35

36

28

23

21

24

19

2004

2005

2006

2007

2009

West Bank

No deprivation Single deprivation

Multiple deprivations

16

18

18

16

13

44

39

46

33

45

39

43

36

51

42

2004

2005

2006

2007

2009

Gaza

No deprivation Single deprivation

Multiple deprivations

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Valuations of Dimensions of Deprivation in Subjective PovertyWest Bank Gaza

2004 2005 2006 2007 2009 2004 2005 2006 2007 2009E 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (dropped) 0.00L 0.00 0.52 0.00 0.41 0.36 0.47 0.51 1.00 0.00 0.00C 0.59 0.64 1.15 0.68 0.50 0.52 0.68 0.00 0.00 0.49R 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00

EL 0.00 0.38 0.00 0.54 0.29 0.63 0.00 (dropped) 1.00 0.53EC 0.58 0.62 1.02 0.59 0.38 0.00 0.00 1.45 1.26 1.18CL 1.22 1.00 2.08 1.20 0.91 0.84 1.15 0.94 0.60 0.49RC 0.78 0.58 1.27 0.75 0.85 0.72 0.47 0.00 0.37 0.63RL 0.50 0.54 0.00 0.31 0.66 0.32 0.40 0.64 0.48 0.42RE 0.00 0.00 0.00 0.00 0.00 0.32 0.00 (dropped) 1.61 0.00

CLE 0.64 1.12 1.08 0.84 1.00 0.84 1.24 (dropped) 1.04 0.69RLE 0.53 0.59 1.39 0.00 0.44 0.51 0.00 0.00 0.00 0.48RCL 0.91 1.08 0.00 1.22 0.89 0.89 1.26 2.63 0.87 0.81RCE 0.74 0.93 (dropped) (dropped) 0.69 1.02 1.20 (dropped) 0.78 1.22All 1.49 0.00 0.00 1.45 0.91 0.93 1.10 1.34 0.00 1.06

Source: PECSNote: Coefficients reported are from probit regressions by year and region of subjective poverty status and are normalized so as to be compatible with the creation of a broader poverty index. If a coefficient is not statistically significantly different from zero or negative, we assign zero values instead of the estimated coefficient. The constant terms are not reported here.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Incidence of Deprivation

Intensity of Deprivation

Gaza, 2009

E L C R EL EC CL RC RL RE CLE RLE RCL RCE All

Multiple Deprivations

A confirmation of the fragility of poverty decline in Gaza: While poverty rates fell by 16 percentage points between 2007 and 2009, the incidence of simultaneous multiple deprivations fell by only 3 percentage points.

In Gaza in 2007, as consumption poverty increased, households became increasingly vulnerable in the sense that they simultaneously suffered along multiple dimensions of deprivation

Linking subjective assessments of well-being to objective measures of deprivation Weights have an interesting interpretation: measure the value placed on

each dimension and its combination by people Multiple overlapping deprivations matter a lot more to people but are

volatile Useful for policy makers? In a region where consumption poverty rates

are relatively low in many countries, and arguably do not capture the whole story, a multidimensional approach linked to popular perception may provide insights…