How does the MPI measure up?

Duncan Green introduces us to the new Multi-Dimensional Poverty Index (MPI), developed by the Oxford Poverty and Human Development Initiative (OPHI):

The MPI brings together 10 indicators of health (child mortality and nutrition), education (years of schooling and child enrolment) and standard of living (access to electricity, drinking water, sanitation, flooring, cooking fuel and basic assets like a radio or bicycle). It’s thus a logical extension of its predecessor, UNDP’s pioneering Human Development Index, launched in the first Human Development Report back in 1990, which combined life expectancy, education (literacy + enrolment rates) and GDP per capita.

The measure, like the HDI, is part of an attempt to get a “better measure” of poverty, by including many non-income indicators. While I think most would agree that policymakers and researchers should always consider non-income indicators of welfare, does it make sense to average them out into a single index?

What precisely are we measuring when the HDI for a given country increases by .01? These questions always seem to lead back to the original indicators: “A advanced in rank because of education improvements” or¬† “B is lower than C despite being richer, because life expectancy in B is much lower.” Given that we need to unpack these indices to figure out what’s going on, why do we bother to pack them in the first place?

Duncan, always open for a healthy debate, has already posted a criticism of the MPI by Martin Ravallion of the World Bank, which questions the implicit values placed on different indicators when they are weighted:

The index is essentially adding up ‚Äúapples and oranges‚ÄĚ without knowing their relative price. When one measures aggregate consumption from household-survey data for the purpose of measuring poverty, as in the World Bank‚Äôs ‚Äú$1 a day‚ÄĚ measures, one relies on economic theory, which says that (under certain conditions) market prices provide the correct weights for aggregation. We have no such theory for an index like the MPI. A decision has to be taken, and no consensus exists on how the multiple dimensions should be weighted to form the composite index.

On closer scrutiny, the embedded trade-offs (stemming from the weights chosen by the analyst) can be questioned, and may be unacceptable to many people.  In the context of the HDI, I pointed out 15 years ago that by aggregating GDP per capita with life expectancy the HDI implicitly put a value on an extra year of life, and I showed that this value rises from a very low level in poor countries to a remarkably high level in rich ones (4-5 times GDP per capita).   If it was made clearer to users, I expect that they would question this trade-off embedded in the HDI.

The MPI index faces the same problem. How can one contend (as the MPI does implicitly) that the death of a child is equivalent to having a dirt floor, cooking with wood, and not having a radio, TV, telephone, bike or car?  Or that attaining these material conditions is equivalent to an extra year of schooling (such that someone has at least 5 years) or to not having any malnourished family member?  These are highly questionable value judgments. Sometimes such judgments are needed in policy making at country level, but we would not want to have them buried in some aggregate index.  Rather, they should be brought out explicitly in the specific country and policy context, which will determine what trade off is considered appropriate; any given dimension of poverty will have higher priority in some countries and for some policy problems than others.

One could continue to argue about the weights – but Ravallion’s argument will still stand. I fail to see why these indices amount to anything more than intellectual exercises – while the HDI has got us all thinking about other things than income, has it really been useful as a method of actually measuring development? Is the MPI likely to do any better with poverty?

Oxford Poverty and Human Development Initiative (OPHI)

3 thoughts on “How does the MPI measure up?

  1. Sam Gardner

    July 28, 2010 at 4:23pm

    It is a generalized problem in development to aggregate all kind of issues into one big, undefined jelly.

    Indeed, while air pollution is real and measurable, ‚Äôsustainable development‚Äô mains essentially nothing, and is in the end a synonym for ‚Äėmulti-dimensional poverty‚Äô. Implicit or explicit choices must be made between apples and oranges, child protection and child survival . Indeed, there are interactions: but apart from win-wins, there are also the win-lose or even lose-lose interactions. This is why these choices are made in a democracy during a democratic budgeting process, not in a technocratic planning exercise.

    On the other hand, if the indicators are relevant, one could just “teach to the test” and a kind of progress would be attained. A progress towards the goals of the technocrats that created the tests. This might be an actual improvement compared to the current way development priorities are defined.

  2. Jiesheng

    August 7, 2010 at 11:53pm

    The Truth is always out there for poverty. No one cna really tell who “poor” and who is “rich”. But that does not imply we discard new measurements immediately (as aidwatchers has done). The consequence would be to fall back on the dollar measurements or HDI.

  3. Will

    August 8, 2010 at 8:53am

    Is there any particular reason that value of a statistical life measurements are not used more often? I think that there are a lot of problems with calculating them, and I know a lot of people do not like the idea, but it does seem to be a methodology well-suited to getting around aggregation problems. Weighting for risk aversion, and measuring which countries and initiatives come closer to providing the most welfare enhancing effects (provided as either public or private goods) seems like a fairly reasonable way to investigate levels of satisfaction with conditions in general. Maybe this is more in line with Martin Ravallion’s point, but I thought most of this analysis used labor data, not GDP and life expectancy.

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