Although the debate it triggered has been going for a few months (see here, here, here and here.), Quamrul Ashraf and Oded Galor’s paper The ‘Out of Africa’ Hypothesis, Human Genetic Diversity, and Comparative Economic Development has been published in the February edition of the American Economic Review (for the latest ungated version, go here - although you can download the data and supplementary materials from the AER site without a subscription).
Over the next few weeks I will dissect parts of Ashraf and Galor’s argument, and look at some of the criticisms that people have made. As a start, however, I’ll present a basic description of the method and findings.
Ashraf and Galor’s hypothesis is that genetic diversity affects economic development through two pathways. First, genetic diversity has a positive role in development as it expands a population’s production possibility frontier. That is, the wider mix of traits available in the population means that there are more likely to be traits present that can advance and implement new technologies.
The second is a negative effect of genetic diversity, whereby heterogeneity increases distrust, thereby reducing cooperation. This increases the chance of conflict and generally reduces the level of social order in the population.
Ashraf and Galor use expected heterozygosity as their measure of genetic diversity, which is a measure of the probability that two randomly selected people from the population differ with respect to a given gene, averaged over the measured genes. Genetic diversity is affected by what is known as the founder effect. When a new population emerges from a larger population, such as when a group of humans migrate, they take only a subset of the genetic diversity available in the initial population. As humans migrated out of Africa and spread across the world, each new migration took a smaller set of the available diversity. Diversity tends to decline as we move from Africa to Europe to the Americas.
Depending on the relative strengths of the negative and positive effects of genetic diversity on economic development, this pattern may result in a hump-shaped relationship between the two. Populations with more extreme levels of diversity may suffer from insufficient diversity for pushing out the production frontier, or high levels of conflict due to dissimilar individuals.
Ashraf and Galor tested this hypothesis using genetic data from the Human Genome Diversity Cell Line Panel, which comprises 53 ethnic groups, each of which are believed to be native to the area in which they are found and relatively isolated from gene flow from other groups. As a result, they represent a reasonable measure of genetic diversity in those areas before modern-day mobility.
Ashraf and Galor recognised that this dataset is not very big. For example, it comprises only 2 populations from Oceania and 4 populations from the Americas. As a result, they also developed an index of predicted genetic diversity based on migratory distance for a larger group of 145 countries.
As their initial analysis is for 1500 CE, Ashraf and Galor use population density as the measure of development. In a Malthusian world, any improvements in technology that might improve living standards are quickly swallowed by population increases. Population grows to its carrying capacity. The Industrial Revolution was the first time in human history where this pattern was broken. Thus, per person income is a poor measure of technology in a Malthusian world as everyone is at subsistence. Technology only changes how many people can live on subsistence in a given area - hence the use of population density. Population density is also used for their analysis of 1 CE and 1000 CE (which is contained in the Web Appendix). For their analysis of 2000 CE they use income per person.
Ashraf and Galor ran regressions of genetic diversity against economic development using a range of control variables, including latitude, the percentage of arable land and the suitability of land for agriculture. They also use continent fixed effects as part of the controls, which should account for any unobserved continent specific factors. The interpretation of the use of continent fixed effects is that the findings hold within the continents.
Their first set of results using the smaller 53 ethnic groups found a hump-shaped relationship between genetic diversity and development, as would be predicted by the opposing costs and benefits to diversity. The size of the effect is such that a 1 percentage point increase in diversity for the least diverse society would increase population density by 58 per cent. A 1 percentage point decrease in diversity for the most diverse society would increase population density by 23 per cent. Ashraf and Galor also ran some tests to show that it is diversity and not migratory distance that is affecting development.
The headline results from their larger predicted diversity set is again a hump-shaped relationship between diversity and development for 1500 CE. A one percentage point increase in diversity for the least diverse society would increase population density by 36 per cent, while a one percentage point decrease for the most diverse society would increase population density by 29 per cent.
The analysis is then done for 2000 CE, with country diversity calculated by examining the mix of ethnicities that make up the country. Again, the hump shaped pattern holds. Given countries can be identified, these results have attracted some of the most attention. Ashraf and Galor summarise them as follows:
The direct effect of genetic diversity on contemporary income per capita, once institutional, cultural, and geographical factors are accounted for, indicates that (i) increasing the diversity of the most homogenous country in the sample (Bolivia) by 1 percentage point would raise its income per capita in the year 2000 CE by 41 percent; (ii) decreasing the diversity of the most diverse country in the sample (Ethiopia) by 1 percentage point would raise its income per capita by 21 percent; (iii) a 1 percentage point change in genetic diversity (in either direction) at the optimum level of 0.721 (that most closely resembles the diversity level of the United States) would lower income per capita by 1.9 percent; (iv) increasing Bolivia’s diversity to the optimum level prevalent in the United States would increase Bolivia’s per capita income by a factor of 5.4, closing the income gap between the United States and Bolivia from a ratio of 12:1 to 2.2:1; and (v) decreasing Ethiopia’s diversity to the optimum level of the United States would increase Ethiopia’s per capita income by a factor of 1.7 and thus close the income gap between the United States and Ethiopia from a ratio of 47:1 to 27:1. Moreover, the partial R2 associated with diversity suggests that residual genetic diversity explains about 16 percent of the cross-country variation in residual log income per capita in 2000 CE, conditional on the institutional, cultural, and geographical covariates in the baseline regression model.
The paper closes with a brief look at the evidence for the costs and benefits of genetic diversity, such as various measures of trust and innovation within countries. Ashraf and Galor show that there is some evidence that these relationships are in the right direction. I’ll delve into that evidence in more detail in later posts.
My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows:
A summary of the paper methodology and findings (this post)