MPI and Multidimensional Poverty Analysis What we knew before joining West Bank and Gaza Poverty Assessment team. Nobuo Yoshida April 21, 2011

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1 MPI and Multidimensional Poverty Analysis What we knew before joining West Bank and Gaza Poverty Assessment team Nobuo Yoshida April 21, 2011

2 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?

3 Multidimensional Poverty Index (Alkire-Foster method) n k c k c average M k c if 0 k c if c k c w 1 where z d w c z x if 0 z x if 1 z d n 1 i 0 i 0 i 0 i i i 0 i m 1 j j m 1 j ji j i j ji j ji ji = = = = = < = = = > = ) ( )) ( ( ) ( ) ( * ) ( n refers to population, z a vector of cutoffs for each dimension, k a cutoff for the weighted average of deprivation Weighted average of dummies of each indicators

4 Critical Reviews 1. Estimation of weights 2. Robustness of results against weighting

5 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

6 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

7 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).

8 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?

9 Depth and Width (interaction) Width/Interaction Depth Consumption Poverty Education deprivation Health deprivation Unemployment

10 Overlaps of deprivations by Venn Diagram for Brazil s case 61.6 E H 3.0 L

11 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

12 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

13 Now let s move to Nandini s presentation

14 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).

15 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

16 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

17 Nobuo Yoshida and Nandini Krishnan April 21, 2011

18 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? Total West Bank Gaza Poverty headcount rates, West Bank and Gaza:

19 West Bank Gaza Consumption Education Labor Refugee status Consumption Education Labor Refugee status 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

20 West Bank Gaza No deprivation Multiple deprivations Single deprivation No deprivation Multiple deprivations Single deprivation 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

21 S _ Poor = β + β E + β L + β C + β R + β EL + β EC + β CL + β RC + β RE + β CLE + β RLE + β RCL + β RCE + β ALL + ε β RL 9 Valuations of Dimensions of Deprivation in Subjective Poverty West Bank Gaza E (dropped) 0.00 L C R EL (dropped) EC CL RC RL RE (dropped) CLE (dropped) RLE RCL RCE (dropped) (dropped) (dropped) All Source: PECS Note: 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.

22 Gaza, 2009 Intensity of Deprivation Multiple Deprivations Incidence of Deprivation 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% E L C R EL EC CL RC RL RE CLE RLE RCL RCE All

23 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