Back in 2013, I wrote a post regarding a controversial claim that high genetic diversity, or low genetic diversity, is bad for the economy:
Two economics professors, Quamrul Ashraf and Oded Galor, wrote a paper, “The Out of Africa Hypothesis, Human Genetic Diversity, and Comparative Economic Development,” that is scheduled to appear in the American Economic Review. As Peyton has indicated, the paper is pretty silly and I’m surprised it was accepted in such a top journal. Economists can be credulous but I’d expect better from them when considering economic development, which is one of their central topics. Ashraf and Galor have, however, been somewhat lucky in their enemies, in that they’ve been attacked by a bunch of anthropologists who have criticized them on political as well as scientific grounds. This gives the pair of economists the scientific and even moral high ground, in that they can feel that, unlike their antagonists, they are the true scholars, the ones pursuing truth wherever it leads them, letting the chips fall where they may.
The real issue for me is that the chips aren’t quite falling the way Ashraf and Galor think they are. . . . Everybody wants to be Jared Diamond, that’s the problem. . . .
And in 2016 I followed up with a post, “Why is Africa so poor while Europe and North America are so wealthy?”:
Any claim that economic outcomes can be explained by genes will be immediately controversial. It can be interpreted as a justification of the status quo, as if it is arguing that existing economic inequality among countries has a natural, genetic cause. See this paper by Guedes et al. for further discussion of this point.
When the paper by Ashraf and Galor came out, I criticized it from a statistical perspective, questioning what I considered its overreach in making counterfactual causal claims . . .
My criticisms were of a general sort. Recently, Shiping Tang sent me a paper criticizing Ashraf and Galor from a data-analysis perspective, arguing that their effect goes away after allowing for a “Eurasia” effect . . . I have not tried to evaluate the details of Tang’s re-analysis because I continue to think that Ashraf and Galor’s paper is essentially an analysis of three data points (sub-Saharan Africa, remote Andean countries and Eurasia). It offered little more than the already-known stylized fact that sub-Saharan African countries are very poor, Amerindian countries are somewhat poor, and countries with Eurasians and their descendants tend to have middle or high incomes. . . .
In the meantime (actually, before my 2016 post), various experts have written more on the topic.
There’s this 2015 paper, “Heterogeneity and Productivity,” by Ashraf, Galor, and Klemp, which begins:
This research explores the effects of within-group heterogeneity on group-level productivity within a novel geo-referenced dataset of observed genetic diversity across the globe. It establishes that observed genetic diversity of 230 worldwide ethnic groups, as well as predicted genetic diversity of 1,331 ethnic groups, has a hump-shaped effect on economic prosperity, reflecting the trade-off between the beneficial and the detrimental effects of diversity on productivity. Moreover, the study demonstrates that variations in within-ethnic-group genetic diversity across ethnic groups contribute to ethnic and thus regional variation in economic development within a country.
Also in 2015, Noah Rosenberg and Jonathan Kang published a paper, “Genetic Diversity and Societally Important Disparities,” which begins:
The magnitude of genetic diversity within human populations varies in a way that reflects the sequence of migrations by which people spread throughout the world. Beyond its use in human evolutionary genetics, worldwide variation in genetic diversity sometimes can interact with social processes to produce differences among populations in their relationship to modern societal problems. We review the consequences of genetic diversity differences in the settings of familial identification in forensic genetic testing, match probabilities in bone marrow transplantation, and representation in genome-wide association studies of disease. In each of these three cases, the contribution of genetic diversity to social differences follows from population-genetic principles. For a fourth setting that is not similarly grounded, we reanalyze with expanded genetic data a report that genetic diversity differences influence global patterns of human economic development, finding no support for the claim. The four examples describe a limit to the importance of genetic diversity for explaining societal differences while illustrating a distinction that certain biologically based scenarios do require consideration of genetic diversity for solving problems to which populations have been differentially predisposed by the unique history of human migrations.
So, the economists go with a biological explanation for economic disparities, but the biologists disagree. Rosenberg summarizes in an email:
Something that might be considered different in this critique of Ashraf & Galor (2013) is that the critique is accompanied by detailed descriptions of several topics where genetic diversity has a clear impact on socially consequential variables that differ across populations (e.g. transplantation match probabilities), and by a discussion of the distinction that separates Ashraf & Galor (2013) from those other topics. We would hope that the line of work that computes correlations between economic variables and features of population-genetic data will recognize the distinction between fundamentally nonbiological uses of the population-genetic variables and scenarios where the utility of those variables is based in biology.
I don’t have anything new to add beyond my 2013 post, really. Anyone who’s interested can go to these papers and read more.
The post A couple more papers on genetic diversity as an explanation for why Africa and remote Andean countries are so poor while Europe and North America are so wealthy appeared first on Statistical Modeling, Causal Inference, and Social Science.