This paper presents a new Multidimensional Poverty Index (MPI) for 104 developing countries. It is the first time multidimensional poverty is estimated using micro datasets (household surveys) for such a large number of countries which cover about 78 percent of the world´s population. The MPI … is composed of ten indicators corresponding to same three dimensions as the Human Development Index: Education, Health and Standard of Living. Our results indicate that 1,700 million people in the world live in acute poverty, a figure that is between the $1.25/day and $2/day poverty rates. Yet it is no $1.5/day measure. The MPI captures direct failures in functionings that Amartya Sen argues should form the focal space for describing and reducing poverty. It constitutes a tool with an extraordinary potential to target the poorest, track the Millennium Development Goals, and design policies that directly address the interlocking deprivations poor people experience.
Regarding the ‘Standard of Living’ indicators, the paper says:
The MPI considers and weights standard of living indicators individually. It would also be very important and feasible to combine the data instead into other comparable asset indices and explore different weighting structures. The present measure uses six indicators which, in combination, arguably represent acute poverty. It includes three standard MDG indicators that are related to health, as well as to standard of living, and particularly affect women: clean drinking water, improved sanitation, and the use of clean cooking fuel [emphasis added]. The justification for these indicators is adequately presented in the MDG literature. It also includes two non-MDG indicators: electricity and flooring material. Both of these provide some rudimentary indication of the quality of housing for the household. The final indicator covers the ownership of some consumer goods, each of which has a literature surrounding them: radio, television, telephone, bicycle, motorbike, car, truck and refrigerator.
The Economist (issue of 31 July) has a digestible one-page summary of this Working Paper A wealth of data: A useful new way to capture the many aspects of poverty, with a nice chart:
Here’s a bit of what The Economist has to say:
By and large, as the chart shows, countries’ poverty rates as calculated using the MPI differ quite a lot from those based on their $1-a-day rates. In India, for instance, many more people lack basic things, as measured using the MPI, than earn less than $1.25 a day. The opposite, however, is true of Tanzania, which is doing better at getting its people fed, housed and educated than its income-based poverty rate would suggest.
Since the MPI is calculated by adding lots of different things up, it is possible to work backwards and see what contributes the most to poverty in specific places. In sub-Saharan Africa, the material measures contribute much more to poverty than in South Asia, where the biggest contributor is malnutrition. The authors argue that having this information readily accessible makes it easier for development agencies and governments to decide what to focus on. The MPI also does a better job of uncovering long-term trends. Successful reforms in health or education increase earnings only many years into the future but will show up quickly in the MPI poverty rate.
Earlier this year there was another paper which questioned accepted statistics – this time on infant mortality: Global infant mortality: Correcting for undercounting by Rebecca Anthopolos and Charles M. Becker, both of Duke University, USA, which appeared in World Development (vol. 38, pp. 467–481). Here’s the Summary:
The UN Millennium Development Goals highlight the infant mortality rate (IMR) as a measure of progress in improving neonatal health and more broadly as an indicator of basic health care. However, prior research has shown that IMRs (and in particular perinatal mortality) can be underestimated dramatically, depending on a particular country’s live birth criterion, vital registration system, and reporting practices. This study assesses infant mortality undercounting for a global dataset using an approach popularized in productivity economics. Using a one-sided error, frontier estimation technique, we recalculate rates and concurrently derive a measure of likely undercount for each country.
So IMRs are higher than we previously thought (and they were bad enough then).
And, of course, there’s the big WatSan statistics ‘mess’: just ‘improved’ (sensu JMP) or ‘adequate’ (sensu UN-Habitat)? See blogs of 17 December 2008 and 14 January 2008.
►Who was it who said “Lies, damned lies and statistics”?! [If you really want to know, read this.]
