The Connection of Urbanization with Growth

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Paul Romer has a nice post up about how urbanization “passes the Pritchett test” for development. Pritchett’s test is that urbanization (in this case) is related both in the cross-section and the time-series to living standards, and positive shocks to urbanization are associated with higher living standards. So Romer argues that we should be studying urbanization as a route towards higher living standards in developing countries. This jives to some degree with his charter city concept, which proposes establishing new cities with functioning institutions in developing areas. There isn’t really anything to argue with about the rough correlations in the data. Urbanization is, and has been, associated with higher living standards for a long time.

But there are some subtleties in those relationships that mean simply urging everyone to flood into cities is not necessarily wise. Everything I’m going to talk about now is based on joint work of mine with Remi Jedwab, who studies urbanization in developing countries very deeply.

The first caveat I’ll point out is that the absolute pace of urbanization matters a lot. Moving an extra 10,000 people into a city in a year may improve productivity in that city and overall in the country. Moving 1,000,000 people into the same city in a year will probably generate such awful congestion costs that productivity in that city falls and country-level productivity may be lower. Remi and I lay out a simple model of this in a working paper that we have out (and which is being furiously revised right now). We show that if the absolute growth of city population is too large, then city wages will actually get pushed down due to the overwhelming congestion effects, even if there is some exogenous technological progress. That part isn’t incredibly shocking. What we then do is show that if population growth is endogenous, and rises as wages get lower, then too-rapid city population growth pushes a city into what we call a poor mega-city equilibrium. The city gets stuck with low wages and high population growth, and cannot overcome the congestion costs of that growth. We explain the arrival of poor mega-cities like Dhaka, Lagos, and Karachi as a kind of perverse result of the mortality transition after World War II, as it raised the absolute growth of cities beyond a critical threshold. Cities like these grow by 400,000 or 500,000 residents per year, while historically cities like New York or London only grew – at their peak – by maybe 200,000 per year. Urbanization that happens too rapidly can have counter-productive results.

The second caveat is that what drives urbanization matters. Remi and I, along with Doug Gollin, have a paper on urbanization and natural resources. If you look across countries, as Romer does, then there is a clear relationship of GDP per capita and urbanization rates. However, urbanization rates are not necessarily correlated with industry or tradable service production.
Urbanization and Industrialization

The figure above shows the lack of a firm relationship, and this shows up if you use just manufacturing, just manufacturing and finance, or some other reasonable definition of what constitutes tradable goods and services. There are lots of countries in the world that have high urbanization rates, but are not industrialized, and they tend to be resource exporters. And this isn’t just places like Dubai. Angola – a major oil exporter – has an urbanization rate equal to China’s. We document that natural resource exports are a significant driver of urbanization. We even have a neat little diff-in-diff type specification that looks at discoveries of resources and shows that urbanization rates jump in the decade after the discovery. Perhaps more important, though, we show that cities in places that urbanize because of natural resource booms have very different urban workforces than typical “industrial” urbanizers. Cities in places like Angola have a big percentage of their urban workforce in personal services and small-scale retail trade, and few people in industry or high-value services. This contrasts with China, where their urban workforce has a huge percentage of people in sectors that produce tradable goods or services (i.e. finance). The point is that urbanization is not homogenous. What drives urbanization matters, in that it determines what sectors people in those urban areas end up working in.

The last caveat kind of takes off from the second. Urbanization has been related to higher living standards over much of history, but that doesn’t mean it always will be. Remi and I did a survey paper on the relationship of urbanization and GDP per capita over time. Yes, they are positively related in every year we look at, going back to 1500. But that doesn’t mean that urbanization rates have increased primarily because countries have gotten richer.

Urbanization and GDP per Capita Over Time

What we see in the data is that urbanization rates have shifted higher at every level of GDP per capita over time. A country with GDP per capita of $1,000 had an urbanization rate of about 10-15% in 1500, but by 2010 a country with the same GDP per capita would be between 35-50%. Most urbanization over history has occurred not because of countries getting richer, but simply because urbanization has gone up everywhere. One implication is that the positive relationship of urbanization and living standards can only go down in the future. Rich countries are maxed at at urbanization rates of 100%. So if poorer countries continue to urbanize, then the relationship of GDP per capita and urbanization has to fall.

The over-arching point is that the positive relationship between urbanization and living standards we see in existing data is an equilibrium relationship, not necessarily a causal one. There are plausibly negative impacts of too-rapid urbanization on living standards. And Romer is careful in his post not to make any kind of strong causal claim. He thinks we should be studying urbanization more carefully to try and understand what exactly it is that generates the positive relationships. I’d strongly agree with that. I’d like to think that Remi and Doug and I have given some clues towards an answer, perhaps just by pointing out things that are not responsible for the positive relationship.

All Institutions, All the Time?

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Wolfgang Keller and Carol Shiue just released a working paper on “Market Integration as a Mechanism for Growth“. They are looking at growth in Germany during the 19th century, and proxy for growth by using city population growth, on the presumption that people only flood into cities that are booming economically. They examine the explanatory power of both market integration and institutions for city population growth, and hence for economic growth.

To measure market integration KS use the spread in wheat prices between pairs of cities. The smaller the spread, the more integrated the cities are. Larger price spreads indicate either high transportation costs and/or some kind of other barrier to transactions that keeps trade from reducing this spread. Why wheat? Because it is widely traded, homogenous, and they have good data on it.

For institutions, KS use three different measures, all binary indicators: abolition of guilds, equality before the law, and the ability to redeem feudal lands. The very good part about their measures are that they are binary, and this conforms to the historical situation. As Napoleon conquered German territories, he imposed some very specific institutional change in these places. So one can reasonably code a 0/1 variable for whether a specific city had abolished guilds, or had imposed equality before the law (that is, adopted the Napoleonic code), or allowed redemption of feudal lands. There is natural variation across German cities in when (or if) these institutional changes took place, based on Napoleon’s activity. (This empirical set-up is drawn from Acemoglu, Cantoni, and Robinson).

The binary indicators are fine as they are. But KS then do a bad thing, and average these measures. Regular readers of this blog know how I feel about arbitrary indexes of institutions, and averaging creates an arbitrary index. Their main specification averages the first two (guilds and legal equality). This effectively presumes that abolishing guilds and legal equality have precisely the same effect. A city that abolished guilds but did not adopt legal equality has an institutional level exactly equal to one that did not abolish guilds but did adopt legal equality. Why should this be identical in effect? These are clearly not institutional substitutes. They potentially have wildly different effects on economic activity. If you want to use different measures of institutions in this kind of study, then you should incorporate these measures separately in your regressions.

That gripe aside, what do KS do? First, they realize that if they just regress city population growth on their institutional measure and their measure of price gaps, then this is subject to all sorts of objections regarding endogeneity and omitted variables. So KS come up with instruments. They use a dummy for French rule to instrument for institutions, as only those places conquered by Napoleon necessarily adopted the institutional reforms (this is also the Acemoglu et al strategy). They then use a geographic measure of the slope of terrain surrounding a city as an instrument for market integration. This is because the cost of shipping by rail increases with the slope of the terrain (gravity is a bitch). They make an argument that both French rule and the slope characteristics are exogenous to city population growth, and serve as valid instruments.

They’re using IV, so you could also chuck rocks at the instruments and claim they don’t work. If you’re going to do that, you need to have some plausible story for why the IV’s aren’t exogenous. I don’t have a good story like that, so I’m going to take their IV strategy as solid.

What do they find? They find that city population is significantly and negatively related to market integration (price gaps) and insignificantly (but positively) related to institutions. Cities that had smaller price gaps with other cities, and so were more integrated into the wider economy, experienced more rapid city population growth over the 19th century. Cities with better institutions may have had higher city population growth, but the evidence is too noisy to know for sure. For future reference, their 2nd-stage regression has an R-squared of 68%, which includes the impact of city and year fixed effects. The regression also predicts 73% of the actual city growth in the mean city. So they have what I would consider a lot of explanatory power (although a bunch could just be due to fixed effects).

Here is where I start to get confused by the paper. I look at this and think, “Looks like institutions – at least the abolition of guilds and the Napoleonic code – didn’t have a big impact on city growth. Holding those institutions constant, more integrated cities grew faster.” But KS seem determined to find an interpretation of these results that preserves the primacy of institutions as an explanation for growth. They take this result and say it does not tell us about the relative importance of institutions, meaning those two or three very specific institutions of guild abolition, legal equality, and feudal redemption.

They argue that what you should really be doing is not looking at the lack of significance on institutions in this regression, but do some different counter-factuals. So they do two different regressions. They regress city population growth on market integration only, with market integration instrumented by only the geography instruments. This is their “mechanisms” model, and it is intended to capture just the pure effect of market integration. That specification yields an R-squared of 49%, and predicts 44% of actual city growth in the mean city. Again, these numbers include any influence of the city and time fixed effects, so this isn’t all due to market integration.

They then do the mirror image of this. They regress city population growth on institutions, instrumented with only the French rule instrument. This is their “institutions” model, and is intended to capture the pure effect of institutions. That gives them an R-squared of 15%, and predicts 13% of actual city growth in the mean city. Again, these numbers reflect the explanatory power of institutions and the city and time fixed effects.

Unsurprisingly, both of these separate regressions have less explanatory power than the combined specification. But it sure seems as if market integration is far more important that institutions, doesn’t it? The R-squared is 49% versus 15%, and remember that those both include the explanatory power of the city and time fixed effects. So it could well be that the explanatory power of institutions was zero, and the explanatory power of market integration is like 34%. (This is knowable, by the way, and I’d suggest they report the partial R-squared’s in the paper.)

KS press on, though, to keep institutions a central part of the story. They argue that we should view institutions as fundamental, and that institutions led to market integration, which led to further growth. In support of this, they use their first-stage results from the main specification. This shows that market integration is significantly related to both the French rule dummy and the geographic variables affecting rail costs. On the other hand, the institutions measure is only significantly related to the French rule dummy. From this, they conclude that “Institutional change led to gains in the integration of markets, but market integration did not, at least in the short run, affect institutions.” Institutions are more fundamental, so to speak.

I don’t think this follows from those first stages. Market integration is related to the French rule dummy, which is not a measure of institutions. It is a measure of whether the French ever ruled that particular city. It captures everything about French rule, not just those three particular institutional reforms. It captures, in part, whether Napoleon thought the city was worth taking over, and I would venture to guess this depended a lot on whether the city was well-connected with the rest of Germany. He needed to move troops around, so cities that were already well-integrated to other areas via roads would be particularly attractive. The French rule dummy does not tell me that institutions matter for market integration. They tell me that places conquered by Napoleon were better connected to other cities.

I’m not sure why it is so crucial to establish that these particular institutions in this time frame were important for growth. KS have a really cool paper here, with an impressive collection of data, an interesting time period to analyze, and a lot of results that stand up by themselves as interesting facts. Why shove it through the pin-hole of institutions?

I think KS could have easily written this paper as evidence that market integration matters more than the three institutions they study. And that would be okay. It doesn’t mean INSTITUTIONS don’t matter for growth, it means that guild abolition, legal equality, and feudal redemption were not important for growth. That leaves approximately an infinity of other institutions that could be important for growth. Given the ambiguous definition of institution, market integration is an institution itself, even if it depends on (gasp!) geography. Eliminating some institutions as relevant would be helpful at this stage, as the literature has to this point (miraculously?) found that every single institutional structure studied really matters for growth. Have we reached the point where publication requires finding each and every single institution relevant for growth?

Do You Have to Choose Growth or Development? Part Deux

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

In my last post I talked about Lant Pritchett’s recent article which questioned the focus of the World Bank on only the extreme poor. He wondered whether the Bank was the right partner for developing countries hoping to grow to middle- or high-income living standards. I agreed with Pritchett that poverty alleviation efforts are not capable of generating what we might consider transformative growth, meaning significant convergence towards rich countries.

I got a lot of very thoughtful responses to that post (and one or two unhinged ones). One response came from Emre Ozaltin, who had written an earlier reply to Pritchett. Emre pointed out on Twitter some research showing that health improvements had accounted for 11% of growth in a set of developing countries, suggesting that this means poverty alleviation efforts do lead to growth.

Twitter just isn’t sufficient to make a coherent reply, so here we go. Even if health interventions really were responsible for 11% of growth in developing countries, that is not the same thing as saying that health interventions can create fundamental economic development.

Comparative Growth
Consider the figure, which shows the time path of GDP per capita in the U.S., South Korea, and Tanzania from 1950 to 2010. In 1960, South Korea and Tanzania are roughly equivalent in living standards. They are both far poorer than the U.S., by a factor of about 10 to 1. In the subsequent decades, though, South Korea and Tanzania have entirely different experiences. South Korea has transformative growth, and the U.S. is ahead of South Korea now by a factor of 1.5 to 1. In contrast, Tanzania doesn’t experience much overall growth at all, and the U.S. is ahead by a factor of 42 to 1.

When I talk about and study economic development, I mean the study of what caused South Korea to make that dramatic leap to rich-country status. What will promote that transformation in poor countries? There is no evidence that health or education interventions could be the answer to that question.

When Emre is talking about health generating growth, he is talking about the contribution of health to that mild growth seen in the last twenty years or so in Tanzania. Yes, there was growth. Yes, health interventions were (maybe) responsible for 11% of that growth. But that growth has not been sufficient to do anything to transform Tanzania into the next South Korea.

It doesn’t mean that these efforts were a waste, or should be stopped. There is no binary choice between poverty alleviation and promoting transformative growth. I believe firmly that from a humanitarian perspective, we should act to alleviate the conditions of poverty in these countries as much as possible. At the same time, we should also be doing what we can to promote the transformation of these economies, so that they take off in the way South Korea did.

I think Pritchett is worried that the Bank is ignoring transformative growth in favor of focusing only on poverty alleviation. And I agree with him in general that one should not abandon promoting transformative growth. But I don’t know that we should worry specifically if the World Bank changes its focus.

From the World Bank perspective, if they do want to focus on poverty alleviation, mazel tov. But do not claim that this is because it is a more robust way to generate fundamental economic development, or because you have somehow “seen the light”. You are making a distinct choice to focus on the humanitarian aspect of development, and ignore the promotion of transformative growth.

Do You Have to Choose Growth or Development?

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

A number of posts/comments have been floating around the last few days that deal with the goals or the World Bank. Lant Pritchett published a piece that asks whether rich countries are in fact good partners for poor countries looking to develop. Pritchett is worried that rich-country development agencies (including the World Bank) have altered their focus from promoting overall economic development, and “defined development down” to be only about alleviating the conditions for the extremely poor – those earning less than $1 per day.

Pritchett suggests that the reason for this is a “post-materialist” attitude within rich countries. More crudely, you could say that this is another example of rich countries attempting to impose their goals/values/hopes onto developing countries. We in rich countries in general – and most likely highly paid development agency workers specifically – have the luxury of saying that material economic growth is not that important. Pritchett argues that this is to ignore the goals/values/hopes of actual people in those developing countries, who very much would like some material economic growth, please.

I’m very much on Pritchett’s side on this, with a caveat I’ll get to later in the post. I wrote a post back when I started this blog on defining development economics. I contrasted “development economics” with the “economics of poverty”. Development economics, to me, is the study of what allows countries to shift from poor, agricultural, rural economies to rich, industrial, urban economies. It is “development economics” in a classic sense, but today you’d probably call it “growth economics” or “macroeconomic development”.

The economics of poverty is about the constraints facing poor people in un-developed economies, how they cope with those constraints organically, and what kind of interventions will alleviate these constraints. The problem is that what I call the economics of poverty is what everyone else calls “development economics” – field studies and surveys in poor countries, running randomized control trials of interventions, and the like.

Pritchett is arguing, in my mind, for the World Bank to return to thinking about growth economics, or about development in the classic sense. Looking for projects like ports, roads, energy generation, and the like. Scale-intensive activities that need someone to coordinate the investment, and investments that will not take place organically because they are essentially public goods. Things that might allow or push economies into sustained growth.

Pritchett’s article generated a response in defense of the World Bank’s focus on poverty alleviation. The main example I know of is here, by Emre Ozaltin. He argues that Pritchett has a “growth fetish”, and that we have evidence that this does not lead to development. That’s debatable, but Ozaltin is correct that this is not an either/or decision. One does not have forgo poverty alleviation to focus on growth, or vice versa.

But Ozaltin also overstates the case for focusing on poverty alleviation. He says, “The sum of the activities in which we are engaged are not incidental to the challenge of development. They are development. For example targeting, investing in health and education, and doing so in multisectoral and coordinated ways, are all critical to growth.”

No, they are not. We have no convincing evidence that improving on those dimensions leads to growth. Yes, we have hundreds of well-designed studies showing how specific interventions improve health or education outcomes, but that does not mean they lead to economic growth. Acting to alleviate poverty is a noble, useful, moral activity. But you do not get sustained growth as a freebie on top of it. What Pritchett is arguing (I think. I’m putting words in his mouth here.) is that the Bank has presumed that their poverty alleviation efforts will generate growth as a byproduct. They haven’t, and most likely won’t. Growth is a distinct dimension of development different from poverty alleviation.

Now, here is my caveat to supporting Pritchett’s position. Who cares if it is specifically the World Bank that provides that infrastructure investment supporting economic growth? If the aims and goals of the World Bank have changed to poverty alleviation, fine. Let that be their focus, and the business of promoting growth can be left in the hands of other entities.

This has essentially already happened, and it isn’t clear why one should try to stop it. Tim Taylor had a nice post on the World Bank. In it, he links to research showing that the World Bank is rapidly running out of countries that qualify for its help. Further, official development assistance (ODA) is being dwarfed by foreign direct investment, remittances, and sovereign bonds as a source of investment funds for developing countries. Development banks such as the Inter-American Development Bank, the African Development Bank, the Asian Development Bank, and the new bank proposed by China are all in the business of lending for large infrastructure projects. Let them.

I think Pritchett is wasting his time here, trying to turn the World Bank to a new (actually, old) heading. The Bank is a gargantuan organization, and has reached the point where self-perpetuation is as important as the actual mission. This isn’t to trash the World Bank, it’s no worse than any other large organization on this front. But if the nature of the interventions that the Bank wants to undertake has changed, so be it. Argue instead for increased funding to the existing development banks. Argue for the US to drop its opposition to the Chinese-led development bank. It may be useful or best to separate the poverty alleviation and growth-promotion, anyway. But you need both. Poverty alleviation alone is not a robust path to long-run sustained economic development.

Tuesday Growth Links

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

Non-economics note: I finished the Souther Reach Trilogy (Annihilation, Authority , and Acceptance) by Jeff VanderMeer. I had been worried about a “Lost” situation, where the story doesn’t tie off nicely at the end, and….the books do not tie off nicely. BUT the writing is hypnotic, and the stories weave together so nicely, that I still recommend the books. If you cannot handle books without neat answers to their mysteries (and I normally cannot) then you should be careful of these books. But if you can give that kind of story a chance, these are a great place to start.

On to links that have been piling up over the last two weeks:

  • Great visualization of the spread of mega-cities over time. When you go to the site, notice that large cities appear first in relatively rich places (Europe, North America) and then slowly spread across the rest of world. Now, most mega-city growth is in particularly poor countries. Remi Jedwab and I have a paper we are working on right now regarding this rise of poor mega-cities. We link it to the change in mortality rates within cities after World War II. Historically, cities were deadly, and their growth was muted by the awful conditions. But with the epidemiological transition, cities became in many ways healthier than rural areas, meaning explosions of population growth, which ultimately let congestion outweigh the positive agglomeration effects of cities.
  • Actual data on the effect of robots! VoxEU post by Guy Michaels and Georg Graetz. They build a new dataset of information on industrial robots in use in 17 countries (OECD) by sector. They find that robot use is associated with higher labor productivity, wages, and total factor productivity, but no effect on labor’s share of output. They also find that robot use lowers the employment of low-skilled workers, and only a marginal effect on medium-skilled workers. They are studying industrial robots, and not necessarily the idealized general-purpose robot that seems to be the big worry of some, so their results are not immediately applicable to the future. But finally studying this in the data is a huge step. Original paper is located here.
  • Tim Harford on Luddites and their modern equivalents. A nice explainer of how Luddites were not anti-technology, and did not think that technology would result in aggregate loses of jobs. They were worried about losing their market power as skilled artisans.
  • This was floating around a lot on my Twitter feed. There was a severe bottleneck in the Y chromosome some time around 4-8 thousand years ago. What does that mean? It means the diversity of Y chromosomes in the human population dropped remarkably in that period, indicating that a relatively small number of men were fathering most children. The diversity of Y chromosomes recovers in most areas in the centuries that follow, indicating that more men are having children. So was it the onset of settled agriculture that led to this bottleneck, with a few males at the top of early agricultural civilizations able to dominate the pool of available females? Was early agriculture as bad for living standards, as has been suggested, so that many men were not healthy enough to have kids or have kids that survived?
  • Dated (from three weeks ago) but excellent post by Cardiff Garcia on the long lags between technology introduction and the effects on labor markets, using “Engel’s pause” in the 19th century to illustrate. Strongest point made here is that we have so few points of evidence regarding the effect of massive technology shifts on labor markets that trying to say anything firm about robots, AI, or anything else is almost impossible.
  • Tim Taylor on Paul Rubin on the mis-use of the idea of competition. The economy involves far more cooperation (implicit or explicit) than we like to give it credit for. Perfect competition is a non-existent, theoretical construct that is useful when writing models and you want to avoid talking about irrelevant things. But that doesn’t mean it is how things actually work, or how they should work. It definitely isn’t true that perfect competition would necessarily make an economy richer in the long run.
  • Many poor people in developing countries are poor in part because they live on really poor agricultural land. About 1.3 billion people are on what Edward Barbier and Jacob Hochard term less-favored agricultural land. Which of course leads to the big question regarding development. Is it better (or even possible) to improve that land, or is it better (or even possible) to get those people to leave those less-favored areas? If it’s the former, then your concern is more with technology and input provision (fertilizer, etc..). If it’s the latter, then your concern is more with property rights and compensating people who have to adapt to additional farmers showing up in their favored areas.
  • A good economic smack-down on trying to use market-based arguments against paying college athletes.
  • Kindle-to-Evernote script. Simple Python script that will suck up your Kindle highlights when you plug it into your computer and e-mail them to your Evernote account, organized nicely by book. Small tweaking necessary/possible to get things to your liking. But I cannot tell you how much I love this script. I want to give it a great big hug for saving me from having to go to my Amazon account all the time for notes.

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:
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.