Are many dimensions better than one?

Over at From Poverty to Power, Duncan Greene hosted a fiery debate about how best to measure poverty, sparked by the release of the UN’s new Multidimensional Poverty Index. 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.

Martin Ravallion comments on "We must know how many are suffering, so let’s make up numbers"

The following is a response from Martin Ravallion, director of the Development Research Group of the World Bank, on last week's Aid Watch post, We must know how many are suffering, so let’s make up numbers. Pull your head out of the sand Bill Easterly!

Faced with all these perceived “impossibilities,” Easterly and Freschi would apparently prefer to wait and see rather than take action when it is needed, based on the information available at the time. Forecasting is impossible in their eyes. What then is possible? The crisis will probably be over before we will no longer need to make forecasts or estimates to fill in for missing data. Counterfactual analysis of the impact of a crisis is also deemed to be “impossible,” even though the pre-crisis expectations for growth in developing countries are a matter of public record—hardly impossible to know! My Economix article last week defended forecasting against this type of analytic paralysis in the face of uncertainty.

Easterly and Freschi also suggest that the numbers coming from the international agencies are a muddle. Granted there are differences, but Easterly and Freschi have manufactured a good deal of the perceived muddle by mixing forecasts of different things made at different times (and hence with different information). As they could have readily verified, the 89 million figure quoted in the World Bank’s G20 paper is the estimated impact of the crisis on the number of people living below $1.25 a day by the end of 2010 based on our latest growth forecasts, as of mid 2009. “Impact” is assessed relative to the pre-crisis trajectories, as expected at the beginning of 2008.

The uncertainty about these numbers is, of course, acknowledged. But they appear to be the best estimates we can currently make given the information available.