MPI, or Making Poverty Intricate
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Among several development-cum-deprivation measures used in HDRs, HDI (human development index) based on education (literacy, enrolment rate), health (life expectancy) and PPP per capita income are the most commonly cited. Since 1990 and even earlier, we have known poverty is multi-dimensional. It cannot be captured through simple measures, it is also a process. However, several poverty-related variables are correlated with one another. Therefore, even simple measures based on a few variables can provide a good enough picture.
While the academic purist can still complain about HDI (those complaints have declined over time), it succeeded in figuring in the policy discourse because it was simple. More complicated measures might have been academically more rigorous and sound, but I don't think they would have succeeded that much.
But perhaps because it is the 20th year, UNDP wanted to do something new. HDIs weren't that exciting. Scores and ranks changed a bit from year to year. That was all.
Therefore, in the 2010 version of HDR, we will have a multidimensional poverty index (MPI) developed by the Oxford Poverty and Human Development Initiative (OPHI). This won't replace HDI, but it will replace the human poverty index (HPI) used in HDRs. That's a fair point, since HPI was narrower than even HDI. But sample this quote from OPHI:
"The MPI uses 10 indicators to measure three critical dimensions of poverty at the household level: education, health and living standard in 104 developing countries. These directly measured deprivations in health and educational outcomes as well as key services such as water, sanitation, and electricity reveal not only how many people are poor but also the composition of their poverty.
"The MPI also reflects the intensity of poverty -- the sum of weighted deprivations that each household faces at the same time. A person who is deprived in 70% of the indicators is clearly worse off than someone who is deprived in 40% of the indicators."
This isn't just about variables, but also about normalization, weights and aggregate. I fear the average "policy-maker" will simply be lost. Using MPI, we have been told poverty in India is concentrated in Bihar, Chhattisgarh, Jharkhand, MP, Orissa, Rajasthan, UP and West Bengal. At the risk of being deliberately unfair to MPI, did we need MPI to tell us that?