Did We Evolve the Capacity for Sustained Growth?

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

I posted a few pieces (here and here) recently on genetics and growth. The Economist even picked up on Justin Cook’s work on lactose tolerance and development. Justin’s work on both lactose and the HLA system are about very specific genes, while the other research I mentioned is about genetic heritability of certain behaviors associated with growth, without specifying any particular genes.

There is another line of research on evolution and growth pioneered by Oded Galor and Omer Moav. They propose that natural selection over different types of individuals could have led to the onset of sustained economic growth. In particular, they focus on selection over preferences for the quantity and quality of kids. This is very much the second kind of research I mentioned above; it does not identify some specific gene that matters for growth, it suggests a mechanism through which selection could have operated. The original paper is linked here, but they have a nice summary article here that explains the logic without all the math.

Let’s be careful about terminology here. Evolution in general requires both mutation and natural selection. GM is really about natural selection, not mutation. They take as given the presence of two types of people in the population. “Rabbits” like to have large families, but do not invest much in their kid’s human capital. “Elephants” have a few kids, but invest a lot in those kids. Their theory is about the proportions of those types change over time due to economic forces, and eventually how a rising prevalence of Elephants leads to a speed-up in technological change. Yes, at some point there must have been a mutation that led to the differentiation between the types, but we can think of that as happening well back in history. They don’t propose that some mutation occurred at some specific year or a specific place to make this all work.

How does the underlying logic work? In the early Malthusian period, with very low income per capita, the Elephants actually have the evolutionary advantage. Why? In the Malthusian world, everyone is so poor that higher income leads to higher fertility no matter your type. Each Elephant kid has high human capital, and thus relatively high fertility compared to Rabbits. So the proportion of Elephants tends to increase in the population. And a higher proportion of Elephants means that average human capital is rising over time.

As the human capital rises, so does the pace of technological progress. At first this doesn’t do much, as the growth of technology is not sufficient to overcome the force of Malthusian population pressure. But eventually there is high enough human capital that technological change happens so rapidly that people reach the upper limit on fertility rates, and choose to spend any additional income on increasing their kids human capital rather than having more kids. This is the tipping point where human capital and technological change go into a virtuous cycle. Higher human capital leads to higher technological change, which leads to higher human capital, etc.. etc.. and you have sustained growth. Once this occurs, the relationship of income and fertility flips to become negative – the richer you are the fewer kids you have, just the opposite of the Malthusian period. This flip in sign is not unique to their explanation based on natural selection, the same type of flip is central to the general unified growth model in Galor and Weil.

After this transition point, the evolutionary advantage also flips to Rabbits. Why? Because the fertility rates decline with income, and as Elephants are richer due to their human capital, they have fewer kids than Rabbits. So Rabbits begin taking up a larger and larger proportion of the population. But everyone is already relatively rich, so this doesn’t mean that human capital levels are low generally. There is sufficient human capital to sustain technological progress.

Do we know if this exact mechanism is what generated sustained growth? No. To establish that you’d have to identify the precise genes that govern preferences for quantity/quality of kids and show that they varied within the population over time in a manner consistent with the GM model. But there are little bits and pieces of circumstantial evidence that work for GM. Greg Clark’s Farewell to Alms documents his research showing that in fact richer families tended to have more kids in pre-Industrial Revolution England. This fits with the selection mechanism proposed by GM. Similarly, Galor and Marc Klemp have a working paper out on the reproductive success of families in 17th and 18th century Quebec (a place and time with particularly detailed records), and the data shows that it was families with moderate fertility rates that actually had the most kids in subsequent generations, not those with the higher fertility rates. Again, it fits the selection mechanism proposed by GM for the Malthusian era.

Note that even if it isn’t true genetic differences in preferences for quantity/quality, you still need to have selection working for population composition to matter for sustained growth. Let’s say that quantity/quality preferences are purely cultural, passed on from parents to kids imperfectly but with some fidelity over time. Then the GM mechanism could still hold up, but it would be the cultural spread of preferences for high quality that generated the take-off, not the spread of specific genes.

There are reasons to be skeptical about this explanation, just as you should be skeptical about any hypothesis. But don’t dismiss it on the basis that natural selection moves far too slowly for this to have mattered for human populations. Galor and Moav have a number of very telling examples regarding the speed of selection within populations over just a few generations. The classic story is peppered moths during the Industrial Revolution. Peppered moths tend to be white, with little black spots on them – hence the name. But there are black varieties. With the rise of coal in the UK black moths became far more prevalent, as they were harder to spot for predators against the blackened sides of buildings. Within a few years the population jumped from predominantly white to predominantly black. And then flipped back to white when clean air regulations came into force. Given the variation in the population already exists, natural selection can take place very quickly to change population composition. So imagining that human population composition could change substantially over hundreds or thousands of years is reasonable.

Last, does GM mean that generating growth in poor countries is doomed to failure because their genetic composition is “wrong”? No. GM is a story about the rise of sustained growth at the global level. Suggesting that poor countries need to get their genetic mix right in order to grow is like suggesting that they need to adopt steam engines and telegraphs before they can step up to gas engines and mobile phones. The question of how to catch up to the frontier is an entirely different question than explaining how we got a frontier in the first place.

Genetic Origins of Economic Development

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

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:

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.

Populations, not Nations, Dictate Development

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

One of the more intriguing empirical regularities in recent growth research involves population origins. Rather than thinking about rich and poor countries, work by Louis Putterman and David Weil tells us to think about rich and poor population groups (Europeans and Native Americans, for example). Countries are rich if their population is made up of rich population groups, and vice versa. The U.S. is rich because it has lots of European descendants, and relatively few Native American descendants. Mexico, in contrast, is relatively poor because it has a few European descendants but lots of Native American descendants.

The interesting aspect of these findings is that they suggest we are looking at the wrong units of observation, so to speak, in studying economic growth and development. We should be studying the characteristics of population groups, not countries, and looking at the characteristics that make those groups prosperous relative to others.

I pulled two sets of results out of the survey by Spolaore and Wacziarg (2013), which is a great introduction to this material if you want more depth. The first are regressions of output per worker in 2005 on either years since agriculture first began or years of “state history” (i.e. how long organized political regimes have existed) for each country. Columns (1) and (3) show that the country-level measures of agriculture or state history are not relevant. But if you weight the years since agriculture began or state history by population composition, you get a different story. As an example, the weighted state history for the U.S. is a weighted average of the state history of England, Germany, Italy, etc.. (quite long) as opposed to the state history of North America (quite short).

The length of time that populations have had settled agriculture and organized states is highly correlated with output per worker today. Countries that have more history with economic organization are richer today.

Spolaore and Wacziarg’s next table shows that even holding those features constant, the share of Europeans in the population of a country is highly correlated with output per worker today. The upshot is that Europeans and their descendants are rich (as a group), wherever they are in the world, but not so for other population groups. See Easterly and Levine (2012) for more robustness checks on this result.

This idea that some population groups are the source of economic success leads to reactions that run from raised eyebrows to accusations of racism. But let’s be very clear that this finding regarding population groups implies nothing about any kind of inherent superiority to Europeans as a group.

We need only a few things to hold for these patterns to arise:

• First, economic organization has to be subject to some kind of cumulative process. Whether you want to call it tacit knowledge, acculturation, or learning-by-doing, successful economic organization must be something that cannot just be snatched out of the ether. Each generation builds upon the prior’s organization to become a little more advanced.
• Second, that cumulative knowledge is passed on more easily the more closely related – culturally, linguistically, genetically – are two groups. The English and French can benefit from each others accumulating knowledge more easily than the English and Chinese for example.
• Finally, you need Europe to “get started” earlier than other regions.

With those three elements, you get Europeans with an advantage today in economic organization. They simply got rolling earlier than other areas with figuring things out, and because it is much easier for Europeans to learn from Europeans, they maintain this early advantage over long periods of time.

Further, because economic organization is something accumulated within a cultural group, it moves with them. Hence the United States gains the benefits of the long European history with economic organization, while Mexico does not to the same extent.

Does that mean European-descended places are permanently entrenched as the richest places in the world? It might. The outcome depends on whether other population groups can improve their economic organization faster than Europeans. And this in turn depends on how fast the organization ideas of Europeans spill over or get transmitted to other groups. If other population groups are both learning on their own *and* are acquiring new ideas from Europeans, then they should be catching up. Maybe slowly, but they should catch up.

On the other hand, there could be some kind of increasing returns to scale here, with Europeans getting even better and better at economic organization as they get richer. Combine that with slow spillovers, and the European population lead could not only persist, but widen as time goes on.

If you want to avoid this spiral of divergence, then this literature implies three possible actions. (1) import Europeans, (2) export your people to European places, or (3) assimilate European culture.

Not sure of many places that are actively trying to recruit European settlers (although Paul Romer’s whole charter city thing sort of falls in this arena). Lots of developing country citizens do actively try to export themselves to European countries every year.

The last one is probably the most controversial. We can’t really tell people in poor countries to “act European”? The whole point is that European culture is this accumulated body of tacit knowledge that is not readily translatable. So how would you actually “assimilate European culture” even if you wanted to? It can obviously happen over time – there are 736 Kentucky Fried Chicken outlets in South Africa – but is this something you can actively manage?

Finally, this means the really interesting question is: how did Europeans get a head start in the first place? The research that Spolaore and Wacziarg review suggests that the advantages go back deep in time. It could be the nature of their agricultural endowments (as in Jared Diamond), or their optimal mix of diversity across groups (Ashraf and Galor), or pure un-adultered luck.

Regardless, studying development in light of this research implies studying population groups or cultures as the units of analysis, rather than confining ourselves to borders that may not have any information content about the economic organization of the populations inside of them.

Age Structure, Experience, Productivity…… and France!

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

Miles Kimball posted a link to a relatively old Scott Sumner post that was discussing a Paul Krugman post from 2011. Which means I am only about 3 years behind, which is good, because I would have estimated I was about 5 years behind.

Anyway, Scott’s post deals with some facts about France. Namely, while GDP per capita in France is only roughly 70% of the U.S. level, GDP per hour worked is essentially equal to that in the U.S. French workers are just as productive per hour as U.S. workers, but just work fewer hours in aggregate.

There are generally two responses to this. The optimistic one: “The French have made a decision to spend their high productivity by taking more vacations and retiring earlier, leading to lower GDP per capita, but probably higher utility.” The pessimistic one: “The French labor system is so mucked up by taxes and regulations that despite being as productive per hour as the U.S., firms do not find it profitable, and workers do not find it desirable, to have more hours provided.”

It’s non-obvious which view is correct. Scott’s post makes two great points, though, about how to think about this. The first is one that I’m not going to deal with here. Comparing France to the U.S. is not an apples to apples comparison. The U.S. is better compared to the EU, or at least Western Europe, as a whole. French productivity looks much worse when compared to New England or the Mid-Atlantic as a region, and only looks good in comparison to the U.S. because the U.S. includes Mississippi and Alabama (which I will arbitrarily call the Sicily and Greece of Europe). It’s a great point.

The second idea that Scott talks about is whether we should be impressed by French output per hour being as high as the U.S. In France, the high youth unemployment rate and early retirement rate mean that the employed population is concentrated in the 30-55 age range. If this age range tends to be particularly productive compared to other age groups, then shouldn’t French output per hour be much higher than in the U.S., where we employ lots of sub-30 and over-55 workers?

Jim Feyrer has a paper from a few years back that looks precisely at the relationship of age structure and measures of productivity. What he finds is that the most productive group of workers are those aged 40-49. An 1% increase in the number of those workers (holding other age groups constant) is associated with about a 0.2% increase in productivity. Ages 50-plus imply lower productivity, but the statistical significance is low. Ages under 39, though, are significantly negative for productivity. Jim uses these relationships to partly explain the productivity slowdown in the US during the 1970s, when the Baby Boomers were filling up the labor force and were still under 40, meaning they were relatively low productivity.

But the results speak to this French question that Scott poses as well. By employing so few under 39-year-olds, France is essentially only using the very high productivity workers in the economy. Thus their GDP per hour is likely inflated by that fact, and their workers are not necessarily just as productive as those in the U.S. What you’d want is some kind of equivalent measure for the U.S. to make this concrete. What is the age-structure-adjusted GDP per hour worked in the U.S. and France? Based on Jim’s results, the U.S. would be ahead in that comparison.

This is related to the well-known result in labor economics that wages rise with labor market experience, but at a decreasing rate. That is, people’s wages always tend to rise with experience, but once you hit about 25-30 years of experience (meaning you are somewhere between 40-55 most likely, the increase gets close to zero. You can see a bunch of these wage/experience relationships in a paper by Lagakos, Moll, Porzio, and Qian, who compare the relationship across countries. One of the features of the data is that in rich countries (like France and the U.S.) the wage/experience relationship is really, really steep when experience is below 10 years. In other words, wages are particularly low for people who have little labor market experience, like young workers aged 18-25.

The U.S. tends to employ a lot more 18-25 year olds as a fraction of our labor force than France. Even prior to 2007, unemployment among those under 25 was roughly 20% in France, and only 10% in the U.S., see here. So the U.S. is employing far more workers that have not yet hit the sweet spot in labor market experience and their wages are very low. On the assumption that wages are some indication of how productive workers are, this means that the U.S. employs proportionately more low-productivity workers. So, again, France’s measured GDP per hour should really be higher than the U.S. level if in fact France and the U.S. have similar productivity levels.

Scott’s point is that we can’t take the equivalence between France’s and the U.S.’s GDP per hour at face value. This doesn’t necessarily mean that the pessimistic view noted above is correct. France could well be making some kind of optimal decision to take lots of leisure time and retirement. But that decision is not one made with the same “budget constraint” as the U.S. – France is very likely not as productive as the U.S.

If you do want to subscribe to the pessimistic viewpoint, then you could argue that not only have French regulations mucked up the labor market, but they have also given the statistical illusion of high productivity. Hence, France is in fact much worse off than the U.S. Even if they fixed their labor market, their GDP per capita would not reach U.S. levels.

Bad Population Reporting

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

So I spotted this article in the Guardian, by one Damian Carrington, who gives us an example of how not to write about new research. The article is about the release of this paper in Science, by Gerland et al.

Let’s take a little walk through the article to see how Mr. Carrington mangles nearly everything important about this research.

1. The title of the article is “World population to hit 11bn in 2100”. The Gerland et al article is about using Bayesian techniques to arrive at confidence intervals for the size of global population, meaning that their article is about how much uncertainty there is in a population projection. The entire point of their work is that statements like “World population to hit 11bn in 2100” are stupid because they do not tell you about the uncertainty in that estimate.
2. “A ground-breaking analysis….” is how the Gerland et al article is introduced. I’m sure Gerland and his co-authors are very capable scholars. But this is not ground-breaking analysis. How do I know that? Because they base all their work on the existing 2012 United Nations Population Projections, so they do not fundamentally change our estimates of future population. What they do add is the Bayesian analysis to give more accurate confidence intervals to those United Nations projections. This technique was developed by some of the co-authors a while ago, see this paper. *That* technique could arguably called ground-breaking, but the current Science paper is not.
3. “The work overturns 20 years of consensus that global population, and the stresses it brings, will peak by 2050 at about 9bn people.” What consensus? The UN’s 2012 population projection that this Science article is based on predicts that population will be 11 billion by 2100, and that it will still be growing at that point. The UN population projection in 2010 also predicted population would be 11 billion in 2100. Population projects by the UN from around 2000 suggest that population would hit 9 billion in 2050, but never said it would max out there. The UN just didn’t project out populations past 2050 back then.
4. “Lack of healthcare, poverty, pollution and rising unrest and crime are all problems linked to booming populations, he [Prof. Adrian Raftery, U. of Washington] said.” Mr. Carrington does not feel compelled to support these statements by citing any evidence that (a) the links exist and (b) are causal. I’d like to think that Prof. Raftery at least tried to provide this kind of evidence, but we don’t know.
5. “The research, conducted by an international team including UN experts, is published in the journal Science and for the first time uses advanced statistics to place convincing upper and lower limits on future population growth.” Statistics? No – advanced statistics. See the difference? One is more advanced. Convincing? Convincing of what? That upper and lower limits exist? Of what relevance is it that the team was international? Do the advanced statistics require people with different passports to run the code? This is just such a ridiculous sentence. The stupid, it burns us.
6. “But the new research narrows the future range to between 9.6bn and 12.3bn by 2100. This greatly increased certainty – 80% – allowed the researchers to be confident that global population would not peak any time during in the 21st century.” They didn’t increase certainty at all. The prior UN projections were mechanical, and had no uncertainty associated with them at all. Gerland et al have created confidence intervals where none existed. This didn’t increase certainty, it quantified it.

By the way, it took me all of about 10 minutes on the Google machine to find the references I just cited here, and to look up the old UN projections. And I didn’t use any special PhD kung-fu to do this. So I don’t want to hear “well, science is hard for the layman to understand”. This is click-bait crap reporting, period.

Solitary, Poor, Nasty, Brutish, Short…and Happy?

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

In response to my recent post on geography/institutions, Thornton Hall posted some comments that led us to go back and forth regarding whether pre-historic/pre-agricultural humans led “long, happy lives” (Hall) or relatively short, dismal lives (me). Yes, this is tangential to the whole geography/institutions thing. It’s the internet, what do you want?

Hall pointed me towards several sources of evidence regarding longevity among hunter-gatherers to support his contention that these humans lived a relatively long time (70-ish years). The main source is a paper by Gurven and Kaplan (2007, direct link here) that as it turns out I had hiding on my hard drive anyway.

Gurven and Kaplan (GK) survey the collected evidence on life expectancies on early hunter-gatherers (much of which is based on reviews of current indigenous populations of h-g’s like the !Kung). I don’t have any reason to dispute the data presented by GK, as I’m not an anthropologist, nor have I read sufficiently in this literature to have any opinion on the sources. My response to Hall’s claims regarding the “long, happy lives” of pre-historic hunter-gatherers is based entirely on how that evidence is interpreted.

Let’s take figure 3 from the GK survey. This shows age-specific life-expectancies for different populations of hunter-gatherers, a hypothetical pre-historic population, and a population of wild chimps. What do we see?

First, life expectancy at birth is very low. These are the e_0 terms shown below the graph itself. This is what you typically think of when you hear “life expectancy” – how many years do we expect a newborn to live? This ranges as low as 27 for the Hiwi, as high as 42 for the Tsimane, but the inferred value for pre-historic populations is only 20. A newborn in a pre-historic society would – on average – live about 20 years.

This is mainly due to very severe infant and child mortality. Pre-historic babies were very likely to die before their first birthday, and making it to 5 years old is unlikely. But, conditional on making it to 5, life-spans could be quite long. So in the figure, you see that life expectancy at age 5 is almost 50 for some societies (so they would life to roughly 55) and 25 for the prehistoric society (so the 5-year old would life to 30).

Similarly, if you make it to 20 years old, you could expect to live another 40 years (so you’d be 60) in many of these populations, and another 20 years (so you’d be 40) in the hypothetical prehistoric society. Let’s just focus on the relatively high expectancies for the moment. These life expectancies tell us that a very lucky few hunter-gatherers will live long, happy lives. If you can survive to 20, you can expect to live to 60. If you can make it to 30, you can expect to live to about 70.

My claim that life is short for hunter-gatherers is based on the fact that a huge swath of the population dies by the age of 5. If I ignore them, then sure, average life expectancy is high. But that is like saying average wages in the U.S. are really high if I ignore people who are poor.

Second, the modal age of death has essentially no information in it. The GK survey, in their figure 4, shows the proportion of all deaths occurring at each age. The dark line is for hunter-gatherers, and it has a modal age of death (the highest point on the curve) of about 70 years.

This does not mean that most people die at age 70. If you look at the y-axis, you’ll see that it implies only about 1.6% of all deaths are at age 70. GK refer to this, and note that even with a model age of death of 70, roughly two-thirds of all deaths in the hunter-gatherer society will occur before age 70. Look at the distribution of deaths for the U.S. in 2002 in the same figure. See how the proportion of deaths at ages 15-35 is almost zero. Deaths do not really start to ramp up in the U.S. until age 55 or 60. For hunter-gatherers, there is a consistent chance of dying of about 1.2-1.5% from age 15 to age 65. You are far more likely to die at an age less than 65 in a hunter-gatherer society than you are in the U.S. today.

A last point on this figure is that it refers to ages of death, conditional on reaching age 15, which goes back to my first point. If you only look at the select population of individuals who make it past infant and child mortality, then yes they have the potential to live long periods of time. But that is ignoring the fact that a big chunk of the population will die by the time they are 15.

Final point, which refers to the “happy” part of “happy, long live”. I have no idea how happy these hunter-gatherers really were. It may have been a joyous life for them, perhaps far happier than we are today. I have no way of telling you otherwise.

But let me suggest two negatives that the early hunter-gatherers would have to overcome on their way to bliss. One, they had to witness a brutal rate of infant and child mortality. Every time they had a child, the likely outcome was that this child would be dead within 1 year. If it made it 1 year old, then there’s a slim chance the kid makes it to 5. You would have buried more kids than you ever saw married off. Oh, and let’s not forget that those kids led unhappy lives, dying early, likely from some kind of infectious disease.

Two, according to table 5 from the GK paper, 17% of all deaths for those under 60 were from violence. A similar 17% of deaths for those under 15 were from violence, either homicide or warfare. Close to one-in-five deaths occurred on the end of a spear, knife, arrow, or whatever weapon was at hand. One in five. For comparison, in the U.S. in 2010 (p. 11) only 0.4%, or 1 in 250, deaths are from homicide.

So I don’t buy that hunter-gatherers had it made compared to modern people. They died at astonishing rates at early ages, and a massive fraction of those deaths were through violence. Hobbes may have been wrong about life being “solitary”, as my guess is that you stuck as close as possible to your trusted family network, but “poor, nasty, brutish, and short” is a good first approximation.

Technology and Scale

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

This is a neat write-up by Matt Ridley regarding some research done by anthropologists Michelle Kline and Rob Boyd (ungated original paper here). They collected information on marine foraging technology used by 10 different Pacific Islander tribes at the time they first met Western explorers/colonizers. According to Ridley they assigned scores not only for the number of tools but also for their complexity. “A stick for prying clams from the reef, for example, counted as one techno-unit, whereas a bamboo crab trap with a baited lever counted as 16, because it comprised 16 working parts, each a technology in its own right.” The actual paper can give you a more detailed idea of the method.

The big take-away is that the higher the population, the more complex the technology being used. Hawaii, with 275,000 people, had seven times as many tools and those were of twice the complexity of those in Malekula, which only had 1,100 people. Further, the size of the network mattered. Island tribes that had more connections with other tribes also tended to have more tools and tools of higher complexity.

This is precisely what goes into our standard models of technological innovation. We tend to say something like , so that the growth rate of technology is increasing in population, as the anthropologists found, but decreasing in the level of technology itself. Moreover, that population L need not be limited to a country, but is really the population of those economies that are integrated enough to share ideas. Regardless, the idea that technological change is positively related to population size can seem counter-intuitive the first time you encounter it. But Kline and Boyd’s study gives a really nice demonstration of the power of scale. Simply put, more people means more chances for someone to have an “Aha!” moment, and more people tinkering around with existing ideas.

A model like this has the implication that long-run technological change is proportional to the rate of population growth (of integrated economies). In other words, long-run living standards depend positively on the population growth rate. Population growth may instill some drag on living standards because of fixed resources and/or lower capital/labor ratios, but ultimately the positive effect of population growth on technology wins out.

I’m absolutely saving this paper to use next time I teach growth at any level.