Multidimensional Poverty Reduction in India 1998/99 to 2005/06: Where and How?

1. Multidimensional Poverty Reduction in India 1998/99 to 2005/06: Where and How? Sabina Alkire and Suman Seth The Development Studies Association Annual Conference Institute of Education, London 3 November 2012

2. Motivation Poverty measurement has been traditionally based on Per Capita Consumption Expenditure (PCE) Non-poor by PCE does not necessarily imply non-deprived in other indicators, such as • Basic services like health, education, sanitation, clean drinking water (Ahluwalia 2011) • Nutrition (National Family Health Survey 2005/06; HUNGaMA Survey Report, 2011)

3. Motivation Reduction in income poverty did not necessarily translate into improvement in other social indicators Source: Tendulkar (2009) and National Family Health Survey (NFHS)

4. Motivation Need for understanding the joint distribution of deprivations and distinguish those who are multiply deprived from those who are not Need for a complementary measure that can capture direct deprivations

5. Multidimensional Poverty Index (MPI) The method is an adaptation of Alkire and Foster (2011) which can deal with the binary or categorical data and was introduced by Alkire and Santos (2010) and UNDP (2010) A person is identified as poor using a counting approach in two steps 1) A person is identified as deprived or not in each dimension using a set of deprivation cutoff (z) 2) Based on the deprivation profile, a person is identified as poor or not Terms: deprived and poor are not synonymous

6. How is MPI Computed? The MPI uses the Adjusted Headcount Ratio: His the percent of people who are identified as poor, it shows the incidence of multidimensional poverty. Ais the average proportion of weighted deprivations people suffer at the same time. It shows the intensity of people’s poverty – the joint distribution of their deprivations. A person is identified as poor if deprived in 1/3 of ten weighted indicators (k = 1/3) . Formula: MPI = H × A

7. Useful Properties The MPI can be broken down into the headcount ratio (H) and the average deprivation score (intensity) among the poor (A) to understand how poverty has been reduced over time Population subgroup decomposition Breakdown of overall poverty by dimensions to understand their contribution

8. Data for Analysis over Time We use two rounds of NationalFamilyHealthSurveysfortrendanalysis NFHS-2 conducted in 1998-99 NFHS-3 conducted in 2005-06 Notall ten MPI indicators are available in the NFHS-2 dataset

9. Indicators for Comparison over Time

10. An Almost MPI for India (MPI-I) • Based on the indicators and dimensions we create a poverty index similar to the global MPI • We refer it as MPI-I • It takes a lower value than the global MPI for India because of the changes in indicators.

11. How Did Uncensored Deprivation in Indicators Change Over Time (raw)? Significant reduction in all deprivations except attendance. Highest reductions in housing, sanitation, water and electricity deprivations.

12. Change in MD Poverty Nationally for Different Poverty Cut-offs

13. How has Acute Poverty Decreased Nationally?

14. Absolute Reduction in Acute Poverty Across Large States Significant reduction in all states except Bihar, MP and Haryana. We combined Bihar and Jharkhand, Madhya Pradesh and Chhattishgarh, and Uttar Pradesh and Uttarakhand

15. Improvement in Poverty: H or A? Performance consistently strongest in Kerala, TN, & AP.

16. Comparison with Change in Income Poverty (p.a.)

17. Acute Poverty Across Castes/Tribes MPI Poverty decreased least among the poorest. The STs (8.5% population share) are the poorest, but the change is lowest for them and for OBCs, who have a higher pop share. MPI Poverty decreased most for SC and ‘None’. Disparity Increases

18. Distribution of Poor (k = 1/3) Across CastesWe see the % of ‘None’ decreased most, and that of SC, ST increased a bit and OBC increased quite a bit. Change in Population Share

19. Poverty for k = 50% Subset of poor for k = 1/3: each person’s intensity > 50% No Deprivations MPI-I z Cutoffs Poorbyk = 1/3 Poor by k = 1/2 Deprived Deprivation Score k cutoffs 50% 33%

20. Poverty for k = 1/2 Across States

21. Poverty for k = 1/2 Across other Subgroups

22. Ultra Poor: Changing Both Deprivation and Poverty Cutoffs No Deprivations MPI z Cutoffs Poor by k = 1/3 Poor by k = 1/2 Ultra z Cutoffs Deprived Ultra Poor Deprivation Score k cutoffs 50% 33%

23. Ultra-poverty Deprivation CutoffsSubset of MPI poor that are most deprived in each dimension

24. Deprivation in Ultra-Poverty Indicators(Raw Headcount Ratios)

25. Total Change in Deprivations of Ultra Poor across time (raw)

26. Ultra Poor in 1999 No Deprivations Only 7.1% of the population did not have any deprivations at all MPI z Cutoffs Poorby k = 1/3 is 56.4% 3.7% Poor by k =1/2 is 30.6% Ultra & k = 1/2 is 15.8% Ultra z Cutoffs 36.4% Deprived 22.1% Deprivation Score Ultra & k = 1/3 is 37.9% k cutoffs 50% 33%

27. Ultra Poor in 2006 No Deprivations 10.9% MPI z Cutoffs Poorby k = 1/3 is 48.3% 5.5% Poor by k =1/2 is 23.6% Ultra & k = 1/2 is 12.5% Ultra z Cutoffs 40.8% Deprived 19.2% Deprivation Score Ultra and k = 1/3 is 31.7% k cutoffs 50% 33%

28. Summary

29. Conclusion – i (of ii) We have compared multidimensional poverty MPI-I across a seven year period, matching the global MPI indicators as closely as possible Multidimensional poverty declined across India, with an 8% fall in the % of poor (or 1.17% p.a.). But disparity among the poor has increased Progress has been slowest for STs, for hh with uneducated head of household, for Bihar MP and Rajasthan, and for Muslims.

30. Conclusion – ii We also looked at two subsets of the MD poor: those with severe intensity (k = 1/2), and those with high depths of deprivations (ultra). They are not the same: most ultra poor are not poor for k = 1/2. Still 12.5 percent of the population experienced ultra poverty and also poverty for k= 1/2 We are unable to update these results: needed data are unavailable for India since 2005/6.