Is poverty in our genes? From the comments

In response to the critique in Current Anthropology on Ashraf and Galor’s paper on genetic diversity and economic growth, C.W. writes:

1. The critique of the use of the McEvedy and Jones population density data is (as already noticed by the first comment) not reasonable.

McEvedy and Jones (1978) is the standard source for cross-country historical population estimates used in dozen of papers by economists, historians and economic historians. No Referee – as I now – did refuse this data source or demand authors to use another. Of course – as with every historical source – the uncertainty is relatively high and increases the farther backwards you go – this is because Ashraf and Galor use that of 1500 AD and put that of the other years in the appendix. Of course, it might be the case that the figures are not correct for some periods or regions. Does this make the source completely unreliable and useless? No, because there always will be measurement error – especially in historical measures. Additionally, Acemoglu et al. (2002) did conduct their empirical estimates based on different population estimates (like that of Bairoch etc.) and found no significant differences at all – at least for their regressions.

2. Pretty much the same holds true for the “timing of agricultural transition” variable from Putterman (2008). This is already a rough measure (nobody would argue that it is possible to figure out the exact date of the agricultural transition) and it might be incorrect for some countries. But, are the measurement errors systematic and how large is this error? As long as these questions cannot be answered appropriately, one should not dismiss this measure.

Of course, I know that actually the measure is criticized also by some economists (Acemoglu and Robinson e.g. in their new book and blog “Why Nations Fail”) and I think, there will be a new measure in future. But today the Putterman (2008) measure – already used in several papers – is the best and far most comprehensive collection of “timing of the Neolithic revolution” estimates available (as far as I know). Therefore it was an understandable choice to use this measure.

3. Again, the same is the case for the trust question from the WVS. This is actually the standard measure for generalized trust (i.e. general and unspecific trust of people in other people or strangers) used in almost every empirical study on trust by economist, sociologist or political scientists in the last 20 years.

It was – and is- heavily criticized by economist and others. As reaction, a significant amount of experimental economic papers test the validity of these question in field or laboratory experiments. Although no clear picture did emerge from these studies (in my opinion) the majority of the papers conclude that the question is valid , i.e. is correlated with actual trusting behavior in experiments. Furthermore, the question clearly is related to the amount of returned wallets in a so called “lost wallet game”.

Of course, the measure actually is not perfect and there are still economists who do not believe in studies based on this question from WVS. Nevertheless, it is the (only) standard measure established in the literature.

4. The author of the critique claim that distance to Addis Ababa would only be a proxy for genetic diversity on a continental scale and does only proxy broad global trends. They wrote:

“Ashraf and Galor’s description of the human pattern of global genetic diversity is consistently inaccurate, leading to concerns that the authors do not understand the data they are attempting to characterize. For example, they repeatedly contend that “migratory distance” to various settlements across the globe affected genetic diversity. This is misleading. The pattern of human genetic diversity they are referring to was primarily affected by the sequential series of founder effects that occurred during the peopling of the world; geographic distance is largely a proxy for these founder effects (Ramachandran et al. 2005). This proxy is accurate for roughly predicting global trends of genetic diversity on a continental scale but does not predict regional genetic diversity within continents. Human populations, stratified by heterozygosity, can be grouped into just four classes: Africa, West Eurasia, East Eurasia, and a fourth class comprising the remaining populations, nearly all of which have low heterozygosity. This class includes Native American populations. We prefer to use sequence data rather than genotype data to measure heterozygosity, as this avoids ascertainment issues involving the choice of SNPs used. Table S36 of Meyer et al. (2012), which used high coverage sequence data from 11 humans, shows the pattern clearly. In other words, genetic diversity varies on a continental scale, with Africa the most diverse, the Americas the least, and Eurasia having intermediate values. No amount of regression analysis and bootstrapping can alter the fact that, in essence, Ashraf and Galor are working with only four data points: Africa, Europe, Asia, and the Americas. This would be the case even if the raw data of Ashraf and Galor were perfect and free of noise.”

I read the paper of Ramachandran et al. (2005) and if one sticks to that paper, this is not true (I do not know how accepted and established the findings and methodology of Ramachandran et al. (2005) are within population genetics, but at least Ashraf and Galor stick primarily to them). Ashraf and Galor do extensively discuss and explain the findings and methodology of Ramachandran et al. (2005) and then – in my impression – do exactly the same as Ramachandran et al. (2005). They regress the genetic diversity measure on the distance to Ethiopia taking the migration patterns of humanity into account. This corresponds exactly to what Ramachandran et al. (2005) (Figure 4A) do. Additionally, they found evidence for a clear relation between pairwise genetic distances between the ethnic groups in their sample and the great-circle distance (with waypoints and without) between them (Figure 1 in Ramachandran et al. 2005). So, obviously, at least according to Ramachandran et al. the distance and diversity relation does not only hold for continents or broad global tendencies. Or do I get that wrong (I’m economist not anthropologist or biologist). I think the only point where Ashraf and Galor can be criticized here, is that they do rely on only one paper and do not discuss the probably existing other work in the area. But I do not know whether the other literature come to other conclusions than Ramachandran et al. (2005).

Nevertheless, I think the anthropologists do have some good points. First, the fact that genetic diversity between humans in general is remarkably low. This is a huge problem for all arguments highlighting that genetic diversity between humans might be important for xx or can explain this or that.

Second and probably most important, they correctly criticize that the causal story of the paper is really, really weak. To make causal claims about very general and unspecific advantages and disadvantages of genetic diversity within(!) populations and genetic diversity associated with diversity in “Junk DNA” is not very convincing. And then, even worse I do not see enough convincing arguments to link both diversity in “Junk DNA” and very general and broad advantages of genetic diversity (in non-neutral genes!) to trust or to the amount of scientific publications. (And why to choose exactly these two variables why not some other, related to cooperation and creativity?). For me, this is all far too unspecific and speculative.

In sum, I do not believe in the story of the paper. But nevertheless I think, nevertheless it is not that bad as postulated by the anthropologist. Actually, the empirical strategy and the arguments in the paper are much more complex, differentiated and careful than in most papers written by economists. They control for almost every imaginable factor, using different variants, measures and adjustments of their variables and data. They worked on the paper for a quite long time and I think they really believe their story. Oded Galor always has unconventional and deep ideas and I think he is one of the most creative and methodologically best trained economist alive. Even more, he is one of the few economists that is willing to do interdisciplinary work – something strongly necessary to solve some the problems mainstream economics have today.

To do interdisciplinary research is always a difficult undertaking. Blaming Ashraf and Galor for having the courage of trying it is neither helpful nor fair in my opinion.

You can find further conversation on the causation point below C.W.’s comment.

My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows:

  1. A summary of the paper methodology and findings
  2. Does genetic diversity increase innovation?
  3. Does genetic diversity increase conflict?
  4. Is genetic diversity a proxy for phenotypic diversity?
  5. Is population density a good measure of technological progress?
  6. What are the policy implications of the effects of genetic diversity on economic development?
  7. Should this paper have been published?

Other debate on this paper can also be found herehere and here.

2 thoughts on “Is poverty in our genes? From the comments

  1. C.W.

    I share your views that the criticism of the data on population density and trust used by Ashraf and Galor is unjustified. In addition, as you pointed out correctly, it appears that the anthropologist do not really understand the methodology of the Cavalli-Sforza team (Ramachandran et al.) used by Ashraf and Galor.

    I think you have been mislead by the anthropologist when you concluded based on their argument that: “To make causal claims about very general and unspecific advantages … associated with diversity in “Junk DNA” is not very convincing.”

    I should direct you to the relevant passage in the article:

    “It is relevant to note that the expected heterozygosity measure in the sample of 53
HGDP-CEPH ethnic groups is based on microsattelites, i.e., DNA loci in nonprotein-
coding regions of the human genome that do not directly result in phenotypic
expression. Therefore, this measure of observed genetic diversity has the advantage
of not being confounded by the forces of natural selection that may have operated
on these populations since their prehistoric exodus from Africa. Importantly, however,
the effects associated with heterozygosity in microsattelites capture the effects
of diversity in phenotypically-expressed genomic material since the serial-founder
effect, associated with the “out of Africa” migration process, is indeed reflected in
other dimensions of within-group diversity, including diversity in various craniometric
traits (Manica et al. Nature 2007).”

    This measure of observed genetic diversity has the advantage
of not being confounded by the forces of natural selection and at the same time being positively correlated with diversity in those genes that could affect innovativeness and trust.

    As argued by Ashrf and Galor

    “First, in an economy where the labor force is characterized by genetic heterogeneity in a wide array of traits, to the extent that some of these traits lead to specialization in task-oriented activities, higher diversity will increase productivity for society as a whole, given complementarities across different tasks. Second, in an environment in which only individuals with sufficiently high levels of cognitive abilities can contribute to technological innovation, greater variance in the distribution of these traits across the population will lead to higher productivity.”

  2. @ Evans

    Okay, i think i got the point. The data in the paper is correlated with “diversity in various craniometric
 taits”, this means it is correlated with differences in human morphology, or not? This is more specific than I supposed before (but I had to google what “craniometrics” is). This might be a staisfying thing for a first paper in this direction. Future research nevertheless should substitute the “various” in “…various craniometric
 taits” through “craniometric traits related to trust between individuals”. Then it would be fine.

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