Genetic Origins of Economic Development

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I recently posted about the genetic component of savings behavior. The paper I reviewed there said that one could account for about 1/3 of variation in savings behavior by appealing to genetic differences. Whatever the authors of this study found (rightly or wrongly), they did not identify the gene(s) for savings. They identified the proportion of savings behavior that is correlated with some as-yet-unknown set of genes.

This is not atypical for a paper on economic or social outcomes and genetics. The findings support the idea that “genetics” explain some proportion of behavior, but this does not mean that we know the specific genes involved.

An entirely different kind of study is one where the researcher looks at a specific gene(s), with a known biological function, and examines whether this has a social or economic influence. I’m going to highlight two papers by Justin Cook, who has undertaken exactly this kind of research on genes and economic development.

Justin’s first paper is on disease resistance and development. There is a human leukocyte antigen (HLA) system, which is determined by a set of 239 genes. The HLA system identifies foreign pathogens so that your immune system can kill them. Within populations, there is a lot diversity in this system. That is, people vary in their alleles in the HLA system. At the population level, this is good, because this means that even if I cannot identify the pathogen (and hence die a horrific death), *your* body can identify it and survive to live another day. Populations that are very uniform in the HLA system are thus more susceptible to disease, as one bad bug (or mutation of that bug) can kill them off more effectively. So a lot of heterogeneity in the HLA system in your population is good for surviving diseases, as a population.

You can measure the HLA variation at ethnic-group levels, and then roll this up into HLA variation at country-group levels based on their underlying ethnic composition. This is what Justin does, and then looks at how life expectancy or mortality are related to it. Sure enough, Justin finds that in 1960 there is a significant relationship of HLA heterozygosity (i.e. variation in HLA alleles) and life expectancy across countries. But as you go forward in time, the relationship weakens. By 1990 the relationship has half the estimated strength, and by 2010 only one-fifth. Further, by 2010 the relationship is no longer statistically significant.

There are a couple of interesting implications of this result for thinking about genetics and development. First, it shows that genetics are not fate. Yes, having low HLA variation in a country was bad for life expectancy in 1960, but with the advent of the epidemiological transition after WWII, the effect starts to fall. With antibiotics, vaccinations, public health measures, etc.., the underlying HLA variation matters less and less for life expectancy.

Second, prior to the epidemiological transition, genetics could have played a (statistically) significant role in variation in living standards. Justin shows that HLA variation (which is good) is positively related to the years since the Neolithic revolution in your underlying population, and also positively related to the number of potential domesticable animals in your underlying population. Longer exposure to agriculture and animals generated benefits in dealing with disease, presumably because the populations were exposed longer and to more pathogens. (By “underlying population” I mean the ancestry-adjusted composition of your population today – so the US HLA variation depends mainly on European exposure to diseases). Thus places that had longer histories of civilization, by building up variation in HLA, would have enjoyed higher life expectancies and (assuming that living longer is good), higher living standards. You could spin this out further to speculate that places with higher life expectancies had greater incentives to invest in human capital and achieve even more gains in living standards historically.

The second paper is on lactose tolerance and development. Simply put, if you can digest milk, then you have an additional source of nutrition that lactose-intolerant people do not have. It changes the productivity of dairy-producing animals, making them a better investment. But no other mammal, and the vast majority of humans, do not produce lactase (the enzyme to break down lactose) beyond weaning from breast milk. At some point in time a sub-population of humans acquired a mutation that allowed them to keep producing lactase beyond weaning, meaning they could continue to consume dairy and use the nutrition available.

Justin backs out the ethnic composition of countries in 1500 (you can do this by using data on migration flows and known ethnic groups). He can then look at lactose tolerance in countries in 1500 by using the existing lactose tolerance of ethnic groups (which is presumed to not have changed much in 500 years). He finds that population density in 1500 is highly related to lactose tolerance in the population. This holds up even after you throw a lot of other controls into the specifications, including continent dummies – which is important in establishing that this is not just a proxy for some broader Asia/Europe difference.

Lactose tolerance acted like a Malthusian productivity boost, raising population density in 1500. Did this have long-run consequences for living standards? Maybe. Places that were densely population in 1500 tend to be relatively rich today, even if you control for their contemporary lactose tolerance levels. So through that channel, lactose tolerance may have helped push up living standards today. The story here would be something about dense populations having greater capacity for innovation, or density indicating broader potential for productivity increases.

I think what Justin’s papers show is that a useful way of thinking about genetics and development is in the sense of budget constraints. Gene(s) change the relative price of different activities or goods, which can alter social and/or economic outcomes, without implying that they make one person or population superior. People who can drink milk without getting sick are not making better decisions than people who cannot, they simply are less constrained in their budget set. Genes, in this sense, are just like geography, which creates different relative prices for populations in different areas. This is different than saying that genes “determine” behavior (e.g. a “patience” or “savings” gene) and that this creates variation in how people respond to an identical set of constraints.

Genetic Factors in Savings Behavior

NOTE: The Growth Economics Blog has moved sites. Click here to find this post at the new site.

There is a recent article by Henrik Cronqvist and Stephan Siegel on the origins of savings behavior (published in JPE, but link is for working paper). They use the Swedish Twin Registry, which gives them data on roughly 15,000 twins, and link that to the deep Swedish data on income, savings, employment, and other information. They use this to examine whether savings behavior has a genetic component. Essentially, they are asking whether genetically similar people (twins) have similar savings behaviors. Figuring this out is hard, as twins share not just genes but also share home environments.

To get around this, Cronqvist and Siegel use the differences between identical and fraternal twins to their advantage. Here is the basic idea. If genes matter for savings behavior, then identical twins should have a higher correlation of their savings behavior than fraternal twins because fraternal share (on average) 50% of their DNA while identical twins share 100%. On the other hand, twins of either type will experience similar environmental factors (i.e. parenting). That is, the assumption is that fraternal twins share 100% of the common environment, just like identical twins, and not just 50%.

You have to be careful. Savings behavior can be correlated across twins at 100%, and yet that doesn’t mean that genes matter. It may mean that two individuals raised in a similar environment share similar attitudes towards savings. So the absolute level of correlation is not important, but the pattern between identical and fraternal twins is. It is by comparing the correlations within the two groups that allow the authors to draw out the importance of genetics.

Here’s a crude first look at their data:
Cronqvist and Siegel 2015

You can see that identical twins do in fact have higher correlations in their savings rates than fraternal twins. Much of the remainder of the paper is confirming that this figure holds up with various controls included. Perhaps not surprisingly, it does hold up. You can argue with their exact measure of savings (changes in net worth divided by disposable income), but it is a measure used in other papers, and they are not trying to compare across countries so definitional issues in the dataset are less problematic.

The end result is that roughly 1/3 of variation in savings behavior can be accounted for by genetics (a little higher than this for men, and a little less for women). As an example, if you pulled two pairs of identical twins out of the population, you might find that Alice and Agnes saved 15% and 18% of their income, while Bob and Bubba saved 10% and 11%, respectively. About one-third of the difference in average savings (17.5% versus 10.5%) is due to genetic differences between the A girls and the B boys. The A family presumably has alleles that code to more patience on the “savings gene”, while the B family has alleles that code to less patience.

Maybe as interesting as the 1/3 number is that the share attributed to common family experience is essentially zero. Their paper supports a “nature” over “nurture” view on savings behavior. For completeness, the remaining 2/3 of variation in savings behavior is purely idiosyncratic. That is, 2/3 of Alice and Agnes’s higher saving rate is simply a result of Alice being Alice and Agnes being Agnes.

Do we know what or where “the savings gene” is? No. It is almost certainly not even a single gene, but rather some complex set of genes that combine to determine savings behavior. But what Cronqvist and Siegel establish is that it is reasonable to suspect that this complex set of genes actually exists.

From a growth perspective, research that examines heterogeneity in individual behaviors within economies is often useful in thinking about heterogeneity across countries. This is particularly true when you realize that much of the cross-country variation in economic development is driven by the composition of country’s population.

The Cronqvist and Siegel paper cannot tell us whether there are true genetic differences in savings behavior *between* different populations. The genetic variation in savings behavior within Sweden might be similar to genetic variation in savings behavior within Burundi, or Nepal, or Peru. But it opens up the possibility that there could be some genetic variation in savings behavior between countries. If there is a set of genes that code for savings (or patience, or long-run planning, or whatever) then it is certainly theoretically possible that populations vary as well.

Given the relative importance of population composition in accounting for differences in living standards, we cannot dismiss the idea that there is a genetic component involved. Note that this doesn’t mean that high-saving or low-saving populations are biologically different, any more than blue eyed populations and brown-eyed populations are biologically different. That is, high-savings populations are not super-patient mutants (who would make the worst X-men ever). They have a gene expression that may lead to higher savings rates.

There are starting to dribble into the research world studies that look at actual genetic differences across populations and the implication of those for economic development. We are no where close to a thorough accounting of the role of genetic variation in explaining development, but it is beginning to look as if we should accept that there is a meaningful role for it.