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Index-a-thon

Since we bashed Transparency International’s Corruption Perception Index, it just wouldn’t be right if we ignored the brand new Human Development Report which is, it just so happens, chalk full of indices. Fortunately, it’s not all bashing this time.  (We have a love-hate relationship with statistics.)

Just which indices are we talking about here? The alphabet soup of HDR acronyms includes the well-known Human Development Index (HDI) and, this year, adds several others such as an Inequality-adjusted HDI and a Gender Inequality Index as well as a new Multidimensional Poverty Index.  The idea behind these measures is to look at aspects of poverty like education and health that can’t be seen through the lens of traditional income growth statistics.

Bear with us for a quick breakdown (or just skip ahead for some bashing):

The HDI includes three dimensions—health, education, and income—measured from life expectancy, school enrollment rates, literacy, and GDP per capita.  The new MPI expands upon this concept even further by combining health and education indicators like child mortality, nutrition, years of schooling, and child enrollment with standard of living measures such as access to electricity, drinking water, sanitation, flooring, cooking fuel and basic assets like a radio or bicycle.  The goal in all of this is of course to look at holistic deprivations, not just income poverty—recognizing that “people’s lives cannot be measured simply in terms of money or income.”

As we mentioned before, throwing a bunch of different ingredients together with arbitrary weights of importance can be a recipe for a completely muddled version of reality.  Because weighting (or giving importance to indicators) is pretty arbitrary, UNDP weighs everything equally and also has a website where anyone can build their own development index to assign their own weights to whichever indicators they want.

UNDP has also responded to past criticisms by providing disaggregations of the indicators.  For example, income can be the sole driver behind a country’s HDI improvement (which, by the way, is exactly the case for China this year).  Since this obviously doesn’t paint an accurate picture of what is happening, the report lists countries’ HDI improvements accompanied by gains from non-income HDI.  This emphasizes the point that indices are often misrepresentations of reality if taken at face value—but at least UNDP is trying to get that message out there, too.

The MPI is another story altogether.  It has come under more fire because it aggregates  even more poverty indicators.  The pros? MPI can add more nuance to the MDGs by showing overlap between different dimensions—that is, it can figure out in how many ways an individual is poor.  This is important for comparing inequality among regions and even within countries.  The cons? For starters, it compares “apples and oranges” while implicitly putting value equivalencies on human lives.  And the need for homogeneous data means the indicators were drawn from less rich data sets.  Looking at it this way means it amounts to no more than an intellectual exercise since the most useful part of the data, as is the case with the China example above, comes from when it’s broken down, not mashed together.

Kudos to UNDP for highlighting the complexities of poverty but, as always with statistics, proceed with caution.  Or, in other words: “Math may be the language of the Devil, but statistics proves that reality really is what you make it.” (Stephen Colbert)

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