Using Data, Networks and Complexity to Study Trade, Aid, Economics

Let me get a quick admission out of the way, before I launch into this review of the freshly-published World Development article Using Census Data to Explore the Spatial Distribution of Human Development by IƱaki Permanyer: I don’t know much about human development indices. I know what a Gini coefficient is (in brief, a number which is close to unity if a few people have all the money, and close to zero if the money’s more-or-less evenly spread) but I haven’t read much from the long bibliography of this paper about the ins and outs of various ways of measuring human development. So, there’s a possiblity that I’m going to be over-enthusiastic about this approach compared to many other papers which I simply haven’t read.

But I must say, I’m hugely excited by the methods proposed by Permanyer in this paper.

In summary, he discusses a method of using very simple questions in the Mexican census to proxy for such unknowables as health, education and standard of living. He goes on to show that these proxies perform well in comparison to other, more difficult to obtain, metrics and that they allow a comparison between municipalities in human development terms.

The really nice thing about his method is that it doesn’t at any point rely on self-reported income or health; metrics which are famously inaccurately reported.

Instead he constructs an asset index to proxy for material welfare and a simple child mortality stat to proxy for health. These are incredibly simple to gather, and are not subject to misreporting.

A whole page of the paper is dedicated to the justification of using an asset index instead of income, included the hand-wringing worry that

…asset indices have been criticized because they might not correctly capture differences between urban and rural areas. Since many assets are cheaper, more easily available and more desirable in urban areas, urban households might appear to be wealthier than their rural counterparts.

This seems like a strange concern to me, since the whole point of using an asset index is to get away from traditional money-based definitions of wealth. As Amartya Sen (who seems oddly under-cited in this paper) would doubtless argue: if the assets in question add to the capabilities of the respondent, and the respondent happens to live in an area where the asset is cheaply available, then surely the respondent is indeed wealthier. This strong argument seems like an obvious omission to me.

The paper goes on to report the results of calculating these new human development indices using census data from 1990, 2000 and 2010 (data which the author needed “a special permit” to access) and the results are truly gripping. He shows a couple of fantastic choropleth maps (something which I’ve never seen in an economics paper!) and is able to calculate, using the fabulously titled kernel estimation, the change in distribution of these indices throught Mexico across each of the three census periods. This is where the real magic of the paper lies: the distribution graphs are wonderfully informative (they summarise tens of millions of data points into 6 curves!) and tell a powerful story of the success, and origins of, Mexico’s growth programmes over the last 20 years.

Finally, let me add that the paper is lucidly and well written (despite some English oddities which could have easily been ironed out at copy-editing: Permanyer insists on continually using the clunky construction that a method “…allows to…” do something cool) and that there is a nice example of disarming honesty.

Everyone in research, to some extent, bases their methodological decisions on what data are available but Permanyer makes this explicit:

The choice of municipality as unit of analysis has been basically determined by data constraints.

Great stuff, and a cracking good read the paper is too.

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