Multidimensional poverty: key results & perspectives for intergenerational analysis

1. Multidimensional poverty: key results & perspectives for intergenerational analysis Sabina Alkire OPHI, University of Oxford; 22 September 2014

2. Intergenerational Equity • Motivations (ethical, policy, measurement) • Data considerations (options, observations) • MD Measurement approaches: • Time series decomposed by identity markers • Adaptation of chronic multidimensional poverty, comparing IG poor with other poor groups • Intergenerational Disparity Index

3. Intergenerational Analysis: Motivation • Ethically, intergenerational equity is important, so children born into poverty are not condemned to remain. • Ethically, societies with equal opportunities reward effort • Equality of Opportunity literature & measurement • Butsocieties should also seek to eradicate absolute poverty, regardless of who has this condition • And societies may wish to curb excess inequality, regardless of its dynamic pattern. • So intergenerational analysis should accompany societal intertemporal analysis.

4. Intergenerational vs Societal Analysis • IA is a partial analysis, used alongside societal analyses • Intergenerational: Were today’s poor, poor in the previous generation • Societal: Did poverty decline? • Intergenerational: Does today’s distribution of well-being show strong social mobility – up & down? • Societal: Did inequality decline?

5. Intergenerational Poverty: Policy motivation • IA is policy relevantfor targeting and for policy design to address special conditions of the intergenerationally poor. • But there may be high poverty among other groups also (young families), whose condition also merits distinct policy attention. • So intergenerational policies accompany societal policies.

6. Data Considerations for Intergenerational Analysis • Long-term panel (ideal) • Chronically poor parents & chronically poor children • Data for parents and children from same survey • Volatility & content of parents’ & children’s poverty? eg. Are they the long-term poor in their generation? • Proxy for intergenerational identities (intertemp) • Representative by: race, ethnicity, region, • For all above: multiple variables in both periods • Monetary, Material, Educ., Health, Housing, Work.

7. Data Considerations for Intergenerational Analysis • The assumptions required to analyse intergenerational equity using imperfect data are substantial. • Accuracy of wealth proxy for chronic monetary poverty • Mismatch between chronic monetary poverty & material deprivation • The relevant variables often have ordinal data which affects measurement & analysis.

8. Data Considerations for Intergenerational Analysis • The assumptions required to analyse intergenerational equity using imperfect data are substantial. • The relevant variables often have ordinal data which affects measurement & analysis. • What can be done using existing data?

9. Time series with decompositions by identity markers

10. Background • Alkire, S., J. Foster, S. Seth, M.E. Santos, J.M. Roche, P. Ballon. Multidimensional Poverty: Measurement & Analysis,Forthcoming OUP 2015. • Alkire, S., Roche, J. M., and Vaz, A. (2014). ‘How Countries Reduce Multidimensional Poverty: A Disaggregated Analysis’. OPHI Working Paper 76, Oxford University. • Alkire, S., Apablaza, M., and E. Jung. (2014). ‘Multidimensional poverty measurement for EU-SILC countries’. OPHI Working Paper 74, Oxford University.

11. Notation • Notation: • and denote first and second generations • and are the achievement matrices for both periods • The same set of parameters is used across the two periods (deprivation cutoffs, weights, poverty cutoff) • Expressions are equally applicable to: • incidence (H), • intensity (A), • censored headcount ratios (), and • uncensored headcount ratios ().

12. Changes in M0, H and A • Absolute Rate of Change: is the difference in levels between two generations. • Relative Rate of Change: is the difference in levels across two generations as a percentage of the initial period. • Normally report and analyse both

13. Annualized Changes • Annualized Absolute Rate of Change: is the difference in levels across two periods divided by the difference in the two time periods. • Relative Rate of Change: is the compound rate of reduction per year between the initial and the final periods.

14. Identification: Who is poor? Construct deprivation scores: percentage of weighted deprivations. Identify as poor those deprived in at least given percentage of weighted indicators (here the poverty cutoffk is 33.33%)

15. Aggregation: Adjusted Headcount Ratio M0 • M0 is the product of two components: 1) Incidence ~ the percentage of people who are poor, or the headcount ratio H. 2) Intensity of people’s deprivation ~ the average share of dimensions in which disadvantaged people are deprived A. Alkire & Foster (2011) JPubE. M0 is used when some data are ordinal M0= H × A

16. In one period, we can see intragenerational

18. 69 countries decomposed: 780 subnational regions

19. Scope of study(Alkire Roche Vaz 2014) • Coverage: • 34 countries, from every region; both LICS and MICS • 338 sub-national regions • covering 2.5 billion people • Rigorously comparable MPI values, with std errors/inference • Annualized changes compared across countries & regions • Composition of poverty and its change by indicator. • All estimations by nation and subgroup available online

22. Societal trend in poverty

23. Subgroup Decompositions

24. Subgroup Decompositions (regional)

25. Disaggregating by subnational region • Poverty significantly decreased in 208 of the 338 subnational regions, which house 78% of the poor. • Eight countries reduced poverty in all subnational regions: Bangladesh (2007-11), Bolivia, Gabon, Ghana, Malawi, Mozambique, Niger and Rwanda. • In nine countries the poorest region reduced poverty the most: Bangladesh(2007-2011), Bolivia, Colombia, Egypt, Kenya, Malawi, Mozambique, Namibia andNiger. • For IG equity: decompose by identity markers that proxy a previous generations’ poverty

26. Using Subgroup decompositions: • The inter-temporal analysis would have to cover two generational periods, with the same variables. • It would need to be representative by identity markers that proxy high poverty in period 1. • Decompositions describe the extent to which societal poverty reduction was enjoyed by the previously poorest groups – which may illuminate mobility somewhat. • Demographic changes must be considered. • Examples illustrate methodology, but are not inter-generational spans of time.

27. Disaggregating by ethnic group - Benin .

28. Disaggregating by ethnic group - Benin . Poorest ethnic group saw no change in MPI. Worsening of inter-gen equity?

29. Disaggregating by ethnic group - Kenya

30. Disaggregating by ethnic group - Kenya Poorest ethnic group reduced MPI the fastest. Equity improved.

31. Decompose by AGE – EUSILC 2006 and 2012. Dark right is over 65

32. Dimensional composition of reduction in M0

33. Indicator Changes by region (Nepal)

34. Indicator Changes by region (Nepal) Analysis by identity markers makes visible changes in composition.

35. Dimensional Changes Compare changes in uncensored (raw) and censored headcount ratios

36. Different Subgroups in India

37. MPI vs. Income poverty: analyse both, or jointly

38. Adaptation of chronic multidimensional poverty to two-period intergenerational case

39. Background • Alkire, S., J. Foster, S. Seth, M.E. Santos, J.M. Roche, P. Ballon. Multidimensional Poverty: Measurement & Analysis,Forthcoming OUP 2015. • Alkire, S., Apablaza, M., Chakravarty, S., and Yalonetzky, G. (2014). ‘Measuring Chronic Multidimensional Poverty: A Counting Approach’. OPHI Working Paper 75, Oxford University.

40. With International data datawe can identify 4 dynamicgroups • Intergenerationallypoor • Fallinginto poverty • 3. Movingoutof poverty. • 4. Neverpoor • More formally, wegeneratetransition matrices showingtheprobability of entry andexit for H and A.

41. Panel data enables new analyses: • Howdo thefourgroupsdiffer – eitherdemographicallyor in thestructure of theirpoverty? • Povertytraps? Whatisthecomposition of povertyforthechronicpoor? Are anydimensionsalwaysdeprived? • Doesthecomposition of povertyforchronicpoorchange? Doeschronicpovertydecreaseover time? • Policysequences: whatchains do theycatalyze? Whichsequence of policies has highestimpact? • Howdoespovertyevolveacrossdifferentages? For differentsocial groupsand householdtypes?

42. Dynamic Subgroups – Panel Data • Assuming two period panel data with n individuals • Consider 4 mutually exclusive groups: • N: people who are non-poor in both periods • O: people who are poor in both periods (ongoing) • E_: people who are poor in but exit poverty • E+: people who are non-poor in but enter poverty

43. Changes in Headcount • What is the proportion of poor in period 1? • What is the proportion of poor in period 2? • What is the change in the proportion of poor?

44. Change in • Change in can be decomposed as follows:

45. Empirical ExampleThis illustrates the method only; data are not intergenerational Data: CASEN Represents four regions: 60% of Chile’s population Ordinal example presented Variables: Income, education, housing, employment

46. Ordinal M0

47. Patterns of Chronicity

48. Inter-temporal Dimensional Breakdown by dynamic subgroup

49. Intergenerational Parity Indices

50. Background • Alkire et al. (2013): Alkire, S., Meinzen-Dick, R., Peterman, A., Quisumbing, A. R., Seymour, G., and Vaz, A. (2013). ‘The Women’s Empowerment in Agriculture Index’. World Development, 53: 71–91. • [The Gender Parity Index].