The History and Geography of Human Genes has probably influenced the way I think about human evolution more than any other book. Even though it is getting old at a time when masses of population genetic data are being accumulated, a flip through the maps depicting the geographic distribution of genes provides a picture that is available in few other places.
It was only a matter of time before some economists grabbed this population genetic data and in particular, this work by L Luca Cavalli-Sforza, Paolo Menozzi and Alberto Piazza, to see whether it could shed any light on economic development. In a paper published (in one of the top economics journals) in 2009, Enrico Spolaore and Romain Wacziarg have taken data on genetic distance from the The History and Geography of Human Genes and asked whether it is correlated with differences in income between countries.
Before examining the data, Spolaore and Wacziarg proposed a model. Take an initial population that branches into two sub-populations each time period, with genetic distance between the two populations being the time since they had a common ancestor. Each sub-population has a transmitted characteristic which is represented by a number. This characteristic mutates either up or down with a 50 per cent probability each generation, so it follows a random walk. As a result, the difference in characteristics (or vertical distance) between two populations is a function of their genetic distance, with the vertical characteristics more likely to have “walked” apart as the time since the shared ancestor increases.
Next, the authors introduce technology. They assume that when a sub-population develops a new technology, other sub-populations’ ability to adopt that technology is a function of their vertical distance from the population at the technological frontier. If technology determines income, then the difference in income between two populations is the size of the relative vertical distance from the population that is at the frontier, which in turn is related to the genetic distance. The core insight from this model is that relative genetic distance and not absolute genetic distance should have a higher correlation with differences in technology.
While I am not sure this model adds much to the initial intuition, it does serve a useful purpose in that it looks to link genetic distance with income differences through differences in vertical characteristics. If genetic distance and income differences had been directly linked (positing that genetic distance is the barrier, as has been proposed within populations by Ashraf and Galor), we would not be left with the interesting question of what these characteristics are.
On the flip-side, Spolaore and Wacziarg have produced a model in which differences in vertical characteristics are a function of random drift, rather than selection. This is a touch unsatisfying, but it is hard to see how the authors could otherwise have produced the model without a theory about what those characteristics are. The model is also agnostic about how one country may develop technology as the authors assume transmitted characteristics do not have any effect on productivity. Introducing a theory of technological development could have been interesting as if certain traits make technological development more likely, there would be two effects creating the income difference – the higher probability of technological progress coupled with the barriers to diffusion.
With model in hand, Spolaore and Wacziarg turned to the population genetic data. Taking data on from 42 world populations, they matched it to countries (for which they have economic data) using information on the ethnic composition of those countries. This formed the basis of determining the genetic distance between countries. They also took a set of European population data (of 26 populations) which would allow them to do a European analysis. The regressions had to depart from the model and test the link between genetic distance and income differences directly as the data does not tell us anything about the vertical characteristics of the population.
The authors completed a mountain of regressions in analysing the data, so here are some of the headline findings. Taking the United States as the world technological frontier in 1995 (a fair assumption), the authors regressed genetic distance against the log of income and, as expected, found that income was negatively correlated with average genetic distance from the United States population. Genetic distance also had reasonably high explanatory power, accounting for 39 per cent of the variation in the sample. The chart below gives the picture. Throwing a range of other explanatory variables into the analysis such as geography and linguistic and religious differences did not materially change this result.
Spolaore and Wacziarg then ploughed deeper into the statistical analysis by creating 9,316 pairs of countries (from 137 countries) for the world sample and 325 pairs (based on 26 countries) for the European sample and assessed the link between genetic distance and income difference. When they use this broader set of pairs, as opposed to the simple comparison with the United States technological frontier, the degree of variation accounted for by genetic distance decreases, although the genetic distance still has a material effect. For example, one standard deviation change in genetic distance accounts for 16.79% of a standard deviation change in income difference when genetic distance alone is entered into the regression.
The authors also examined a range of other factors, such as Jared Diamond’s thesis about differences in geography and domesticable plants and animals. While including these factors in the analysis reduced the explanatory power of the genetic difference measure, the significance remained. The data also allowed some analysis of earlier time periods, which was in fact easier as most countries’ populations were more ethnically uniform in, say, 1500. At for the later dates, the relationship still held.
Given the agnosticism of Spolaore and Wacziarg on what the vertical characteristics driving income differences are, I hope this paper triggers some deeper examination of what is going on. What are the microeconomic mechanisms driving this result? What are the vertical characteristics that are relevant? And going the next step from Spolaore and Wacziarg’s model, how has selection affected these characteristics? Without the characteristics being subject to selection, the change in characteristics would be fairly slow. These slow changes are then hypothesised to create a substantial barrier to technological diffusion even though the populations have been separated a relatively short period. I would suggest that selection is required.
The authors suggest that more research on peaceful and non-peaceful interaction between societies may be useful to tease out the mechanisms that they have proposed. I agree that research may be interesting, but it leaves open the question which the model ignores – how did some countries get that technological lead in the first place. Do these vertical characteristics play a role in that? Asking why others did not follow does not seem as interesting as asking why some countries got the lead in the first place.
**By way of quick introduction, I am a PhD student in Western Australia and blog at Evolving Economics. I’ll be cross-posting the odd piece that might be of interest to gnxp.com readers (of which this is the first).