The new index will complement a simpler method used in the UN Human Development Reports which relies on uniformly-weighted variables measuring life expectancy, education and income. The new method, created by researchers at the University of Oxford, combines ten different variables (including malnutrition, years of schooling, access to electricity and toilets, type of cooking fuel used, and others) and assigns them different weights.
The Oxford researchers say this is the first index covering most of the developing world to be created using micro datasets (ie household surveys), and that it is useful because it “captures a set of direct deprivations that batter a person at the same time.”
The MPI also captures distinct and broader aspects of poverty. For example, in Ethiopia 90 per cent of people are ‘MPI poor’ compared to the 39 per cent who are classified as living in ‘extreme poverty’ under income terms alone. Conversely, 89 per cent of Tanzanians are extreme income-poor, compared to 65 per cent who are MPI poor.
On Duncan’s blog, Martin Ravallion of the World Bank asks why we should add up different measures of poverty into a single index rather than getting the best data we can on individual measures, especially when weights assigned to those measures are likely to be arbitrary and controversial. (Gabriel Demombynes at the Africa Can…End Poverty blog also has a good summary of the discussion).
What is the point of creating ever more complex measures of poverty? For one, they draw attention to the importance of facets of poverty besides low income, like lack of access to education or clean water. But coming up with better measures of who is poor and how they are poor really matters if it helps allocate resources more effectively to those who need them most. It might be informative to understand why (for example) many more Ethiopians are poor under the new index than using the conventional, under-$1.25-a-day measure. But it’s hard to imagine how to find the answer without unraveling the many strands that make up the multidimensional index.
This blog frequently asks whether we should trust the figures we purport to know (for example: the malaria data cited over and over by the Gates Foundation; post-economic crisis poverty forecasts from Ravallion and colleagues; new maternal mortality figures reported in the Lancet). Aggregating different poverty measures together could also mask weaknesses in the data. Better then to measure and meet each type of deprivation separately, as best we can.
CORRECTION: In this year’s Human Development Report, the new index will be used as a complement to the existing Human Development Index, not as a replacement, as paragraph two originally stated.