Poverty is one of my core areas of interest, and fortunately, work. Good to work on what one likes. I’ve written on the subject a few times before on this blog, about its databases and recent numbers. This time, I would like to talk a bit about Multidimensional Poverty Index.
“What is poverty?” is the million dollar question in the area of poverty studies. There are many ways to define it, such as the lack of income, or consumption, or nutrition and so on. Recently, conceptualization of poverty as deprivation across multiple dimensions has gained strength and acceptance. This concept, called multidimensional poverty, looks at a range of needs and services which are considered basic for humans, and defines poverty as the deprivation of people in those basic needs and services. One of such measures is the Multidimensional Poverty Index (henceforth MPI).
MPI was developed by the Oxford Poverty and Human Development Initiative (OPHI), based in the University of Oxford. It is currently estimated for more than 100 countries and is part of the Human Development Report since 2010. Click here for the OPHI website which has a tonne of resources on MPI.
MPI is composed of 3 dimensions and 10 indicators. The dimensions are health, education, and living standard. For an overview of the dimensions and indicators, have a look at the below image. For details, the one below it.
Equal weightage is given to the three dimensions, 33.3 percent each. Indicators under each dimension have equal weightage. This means that the indicators under education and health have a weightage of 16.7 percent each, which is half of 33.3 percent. However, the indicators under living standard have lesser weight, as 33.3 percent is split among 6 indicators, meaning each has a weightage of 5.6 percent. I don’t want to deep dive into estimation of MPI. Let me just put it simply that a household which is deprived in 33 percent of the indicators or more is considered multidimensionally poor.
One great thing about MPI is that it can be customized. If a country/state decides that these dimensions and/or indicators are irrelevant to it, it is more than free to modify the composition of MPI and include dimensions and/or indicators of their preference. For instance, Bhutan’s MPI has 3 dimensions and 13 indicators, while Colombia’s has 5 dimensions and 15 indicators.
There are a few good things about the MPI compared to the other measures of poverty, mainly the one-dimensional ones which look only at income or consumption. MPI gives an overall state of the household with regard to the amenities and services it has access to. Even if a household is above the poverty line as per the one-dimensional measure of income/consumption, it may lack access to toilet, electricity, or cooking gas or its members may not have gone to school, which are serious deprivations of the present time. MPI captures such deprivations, while income/consumption measures do not.
MPI also gives a picture of overlap of deprivations. For instance, what percentage of the population is illiterate and malnourished at the same time? How many persons live in impoverished houses and do not have access to electricity? MPI can answer such questions which can lead to solid policy measures.
However, many people are not fans of MPI. They argue that it is biased against urban poverty. For instance, people in urban areas may have to pay for water, living space, and using toilets, which is different from the rural case and is not accounted for in the MPI. Another problem is with “adding up” indicators which are totally different in nature. For example, in the estimation of deprivations, how can one “add” the death of a child and not having a car? How about the difference in value and importance people attach to these indicators? Unavailability of reliable data on a range of indicators is another pressing concern.
MPI may not be a perfect measure of poverty, but sure it looks better equipped than the one-dimensional ones. Considering its importance, it is proposed to be added as one of the indicators under Sustainable Development Goal 1, which aims to eradicate extreme poverty from the world by 2030. Hope this will turn out to be a big step in widening its acceptance and designing reliable data collection systems.
All rights of the above images rest with OPHI.