The Glacial Speed of Institutional Change

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I just finished reading “The Long Process of Development” by Jerry Hough and Robin Grier. The quick response is that you should read this book. If that’s enough, then go get it. All the rest of this post is just some of my reactions to the book.

The basic idea of HG is to trace out how long it took England and Spain (and by extension, their colonies Mexico and the U.S.) to evolve the elements of “good institutions” that we think promote economic growth. Clearly the process went faster in some of these places than others, but the point is that it took centuries regardless of who we are talking about.

HG look at the development of an effective state in England through history. For them, England gets a minimally effective state with Henry VII in 1485. His victory in the War of the Roses (and in particular his ruthless elimination of others with claims to the throne) gave him a government that had at least some control over the entire area of England and Wales. So is that when England has good institutions? No, not really. From that point, it is another two hundred and four years until the Glorious Revolution and what we might call the beginnings of constitutional monarchy. All good? Not quite. It is another one hundred and forty three years before the Reform Act of 1832 generates the barest seeds of what might be called inclusive institutions. Even if you think that England in 1832 had “good institutions” for economic development, that was three hundred and forty-seven years after England got a functioning central government. If we lower our sights and say that the Glorious Revolution had given England the “good institutions” necessary for economic development, then that was still two hundred years after England got a functioning central government.

The second major example used by HG is Spain. By 1504, Isabella had acquired a kingdom that essentially looks like modern Spain in geographic reach. She was the monarch of Castile, the Moors had been forced out of Granada, and she had brought Aragon into the kingdom by marrying Ferdinand. HG then document that despite this geographic reach, the government of Spain was not an effective central government in the way that Henry VII or VIII had over England. Even Philip II’s reign in the late 1500’s did not consolidate government in a way that seems consistent with his numerous foreign military activities. HG argue that Spain was about 200 years behind England, and only reached an effective central government around 1700. It would be arguably another 280 years after that before Spain got what we would call “good institutions”.

Regardless of the exact historical case study, HG’s point is that developing modern institutions the support sustained economic growth takes centuries, even in one case – England – where all the breaks kept going their way.

What is the point of this regarding development and growth? HG suggest that a large number of developing countries have a central government with the capabilities roughly equal to those of Henry VII. Many of them began as separately defined states only in the 1960’s, and in the subsequent fifty years have perhaps gained the ability to extend their powers of taxation and coercion to all corners of their geographic area. In places like Afghanistan, they cannot even do that.

Asking, expecting, or advising these countries to adopt “good institutions” is to ask them to skip between two and five centuries of institutional evolution in one leap. Developing countries evolving their own stable institutional structures that support economic growth is going to be long, ugly, and likely violent – just like it was in every single currently rich country. HG’s work says that institutions are not just another technology. While you can play catch-up relatively easily with technology (e.g. adopting mobile phones without landline networks), you cannot do the same with institutions.

Further, institutional development is always going to involve some coercion. Some group is going to have to be dragged kicking and screaming into the new institutional arrangement. HG clearly reject the idea that new social contracts will spontaneously get re-negotiated as circumstances change, as in the old North and Weingast interpretation of the Glorious Revolution in England. In contrast they accept the more Mancur Olson-ian view, that social contracts are whatever the dude with the gun says they are. The only way to accelerate the development process is to accelerate the concentration of coercive power with one group/party/coalition. From that perspective, the problem with the U.S. attempts at state building in Afghanistan and Iraq was not that they intervened, but that this intervention was half-assed and ended before the job was done. If you are going to intervene, pick a winner and then make sure they win. Trying to equalize power across different factions is precisely the wrong thing you should do to encourage institutional development. That is me spinning the argument out to a logical extreme, but it makes the point.

A last mild critique of HG is that it has a fault similar to most other work on institutions. It does not define what a “good institutions” are. We know that England and the U.S. have them now, and that Spain seems to have them at least since after Franco. We know that England had “good” institutions in or around the 1800’s, and Spain apparently didn’t. And we know that England and Spain had “bad” institutions before the 1500’s. So it must be that institutional evolution takes somewhere between three and five centuries? But what precisely is it that England and Spain have today that they didn’t in 1500? What is a good institution?

HG are more clear than many on this point. They consciously limit themselves to examining whether a central government has effective control of taxation and violence within its borders. But of course, what does effective control mean? What does taxation mean – what’s the difference between a tribute, a donation, expropriation, and a tax? Does control of violence simply mean that all the people coercing others wear the same uniform?

This critique doesn’t eliminate the value of reading the book. The general point about the long time lags in the evolution of institutions (good or bad) is excellent. It is hard to fight time compression when reading history, and HG make clear that the institutions literature needs to get far more serious about that fight.

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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?

Did We Evolve the Capacity for Sustained Growth?

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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.

Great Britain and Laissez Not-so-Faire Economics

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I recently finished State, Economy, and the Great Divergence by Peer Vries. It’s a comparison of the activities of the state in Great Britain and China in the period running up to and including the Industrial Revolution, roughly 1650-1850.

Vries critiques the standard view on the role of the state and the divergence between these two places, encapsulating that view in the following:

In the Smithian interpretation of British economic history, that fits in quite neatly with the Whig interpretation of Britains overall history, the primacy of Britain and its industrialization are by and large regarded as the culmination of a long process in which Britains economy increasingly became characterized by free and fair competition and in which government increasingly tended to behave according to Smithian logics.
….
For those who endorse them, the predicament of imperial China, that it did not industrialize, has always been quite easy to explain. They only need to refer to the fact that China was characterized by some kind of oriental despotism. This notion has a long pedigree whose beginnings can be traced back at least to Marco Polo.

The alternative that Vries proposes is that China is far more “Smithian” than Great Britain in this period, in the sense that it operated a very hands-off government that mainly served to provide some subsistence insurance to its population, while Great Britain had a relatively large, intrusive, and active government managing its economy and actively interfering in the process of industrialization. With regards to the idea that Great Britain enjoyed a meaningful advantage in institutions (re: property rights) after the Glorious Revolution, Vries has this to say:

I, moreover, see no concrete direct links between changes in property rights and the emergence of modern economic growth during industrialization in Britain, or rather I do not see any major changes in that respect just before and during take off. In several respects property rights in Britain after 1688 were not better protected, as a strengthened central government had acquired more power to interfere with them on the basis of national interest. More in general, one has to realize that, as will be discussed later on, the history of Western Europe was not exactly lacking examples of expropriation and that well protected, entrenched property rights including patents can also be an obstacle to growth.

Vries then spends a good portion of the rest of the book laying out the evidence on government expenditures, taxes, employment, and transfer payments to support the idea that Great Britain had a much more intrusive state than China in this period.

I’ll leave you to the book for the full details, but here are some essential highlights. Taxes per capita in Great Britain were approximately 20 times higher than in China. As a percent of GDP, the figure depends on exactly your preferred source for GDP data, but taxes were again much higher in Great Britain (3-5 times higher depending on the measure). Further, taxes were rising in both per capita and percent of GDP terms over this entire period in Great Britain, while they were essentially flat in China. Finally, the government in China never ran deficits in this entire period. If you are familiar with the history of Great Britain, then you know that government debt as a percent of GDP was essentially zero in 1689, right after the Glorious Revolution. From there it rose steadily, reaching a peak of almost 250% of GDP after the Napoleonic wars. It wasn’t until after 1850 that debt fell back below 100% of GDP. In terms of the number of government officials, Vries cites data that China had between 20,000 and 30,000 civil servants in the 18th century. Great Britain had an equal amount, for a population roughly 30 times smaller. Great Britain spent a much larger fraction of GDP on welfare and poor relief than China ever did.

Drawing on the excellent War, Wine, and Taxes by John Nye, Vries also talks about the attitude of Britain towards free trade:

In the 1820s, for example, the average tariff rate for imported manufactured goods was between 45 and 55 per cent. It was only after 1850, and even then only quite temporarily, that Britain really became a free-trading nation. Overall, its tariffs in the first half of the nineteenth century were so high, higher for example than in France, and continued to be high for so long that any explanation of the first industrial revolution by reference to the existence or emergence of a free-trade economy is extremely improbable. When Britains economy took off, the country definitely was not a free trader in matters of international trade.

Compared to Britain, China was much closer to a free trade nation, declining to interfere or promote imports or exports actively.

Vries wraps up his argument with

This book maintains that the historical evidence now is so heavily in favour of industrial and military policies successfully encouraging long-term economic development in England, admittedly through far more complex means than simply setting tariffs to encourage domestic manufactures, that the burden of proof falls on neoclassical economics, not on the historic record.

Mercantilism, as practiced throughout this period in Great Britain, was not simply a fascination with collecting gold. The British government actively looked to strengthen manufacturing (of imported raw materials) and used military and naval power to open markets with that purpose in mind. To do this it taxed heavily, borrowed heavily, and spent heavily.

What to make of this? There is no necessary link between strict laissez-faire policies and growth. The first industrial nation in the world was anything but laissez-faire, and it intervened far more deeply into its economy than China, which functioned in some sense as the idealized “night watchman” state of Adam Smith. There is little to no evidence that government “just getting out of the way” leads to development. The interventions Great Britain did make certainly resulted in massive monopoly rents to small groups of people at times. So let’s not go overboard in the other direction and conclude that massive state interventions are necessary or optimal. But it is valuable knowing just how un-laissez-faire Britain was during this period.

Why did Britain take off even with all this government interference? Vries doesn’t say this explicitly, but I think his answer is partly that large-scale industrialization has big fixed costs. I want two things before I undertake big fixed investments: a large market and low risk. The British government used the high taxes to fund a military that could ensure large markets around the world, and could ensure that those markets remained open so I could earn enough to pay off my fixed cost. That military (directly or by proxy) could also actively ensure that other markets did not develop competitive industries, again ensuring that I could earn enough to make the fixed costs worth it. Without the market size and low risk, maybe British capitalists are not willing to create the large-scale industries that drove the IR. In that sense, the large size of government was necessary to the industrialization of Britain.

Plows were the Robots of the 13th Century

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Jury duty this morning, which meant lots of quiet reading time and in the end no *actual* jury duty (yeah for settlements!).

I am reading Rural Economy and Country Life in the Medieval West, by Georges Duby. I came across the following description of how the development of improved harnesses and plows in the Medieval period displaced a large fraction of rural labor (p. 116):

On the other hand, manual laborers without draught animals underwent no technical progress and sustained no rise in yields: on the contrary there was a relative fall in their living conditions…..That the increased value of farming equipment strengthened the hold of the wealthy over the peasantry cannot be denied….Everywhere the lord maintained his authority over his men by helping them to acquire livestock or by threatening them with its confiscation. When in some provinces in the thirteenth century servitude was born anew and flourished, it was the need to acquire agricultural equipment, efficient though costly, which led poorer peasants to bind themselves into dependence. The same needs held them in servitude, for although they had the right to decamp….they could do so only…by giving up their plough animals. In fact because of this, agricultural growth appears to have been a very powerful agent of social differentiation.

A couple of things struck me about the passage. First, the analysis of the disruption caused by the introduction of a new technology embodied in capital goods (plows, harnesses, and horses) sounds similar to some worries regarding the introduction of robots. With capital owned by only a few, those without capital become dependent on the wealthy and have their living standards driven down. Second, innovation favors those with the skills to work with the new technology. Skilled ploughmen – who only got that way by having a team of horses and a plough to begin with – were the high human capital workers of their day.

Mainly, though, it is just an interesting example of how the same issues with innovation, technology, and displacement have been occurring forever. The question of what happens when robots are plentiful is not a question unique to robots, it is a question about how we adapt to disruptive technology. The evidence suggests that whoever owns the technology or the capital associated with it will use it as leverage over those who do not, just like always.

By the way, I think the lady next to me in the jury room would have looked less shocked if I had told her I was reading a porn magazine.

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.

Friday Growth Links

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Things that I pretend I will have smart things to say about in the future:

  • Lemin Wu on whether we are thinking about Malthusianism correctly. A general point from this paper is that once you stop thinking of output as a single homogenous good, how you choose to weight different goods in your GDP/real wage/utility calculation matters a lot for your conclusions.
  • David Autor will take your job. Or something like that. I lost the thread of this article when I came upon this sentence: “Mr. Autor—who always sports a single gecko-shaped silver earring, his trademark symbol also pasted on his iPhone—says the fear has outpaced reality.”?!?!?
  • Ogilvie and Carus handbook chapter on institutions and economic growth in historical perspective. Whenever economic historians write something called “XXXXX in historical perspective”, the punch line is that “XXXXXX is wrong”. They do not definitively ruin the institutions/growth relationship, but do provide a lot of needed skepticism regarding the relationship. If you are going to argue that institutions matter for growth, then you have to do so in more of a case-by-case basis, and not using crude measures in cross-country regressions. I feel like I’ve heard that before
  • I like this Krugman post from earlier this week. Expanding education is not necessarily the answer to inequality. The idea that education levels are the key to higher wages is very useful for employers. Few people can or will leave work for 2 or 4 years to increase their education once they are working, and so they are willing to accept the low wages they currently have.
  • Ortman, Cabaniss, Sturm, and Bettencourt on settlement scaling and increasing returns in ancient society. They look at pre-Hispanic Mexico and find that the larger the settlement sizes/cities, the larger the monuments they built. Not surprising. But the relationship indicates IRS, which is. A ten-fold increase in settlement size led to a greater than 10-fold increase in the scale of monuments built.
  • Jane Humphries and Jacob Weisdorf look at women’s wages in England from 1260-1850. Female servant wages did not appear to be affected by the Black Death, which is problematic for the theory that the plague led to the emergence of the European Marriage pattern (see here). Women’s wages also did not track with men’s during the run-up to the Industrial Revolution, which may be problematic for the theory that high wages were part of the explanation for adoption of labor-saving technology (see here). Stupid data, always ruining things for everyone.

What I read on the plane rides to and from DC this week.

  • Medieval Technology and Social Change, Lynn White Jr. Probably best known for the “plows changed social structure” thesis. This is incredibly readable, and is less a definitive argument regarding technology and social change than a very nicer primer on the basic idea.
  • Mohammed and Charlemagne, by Henri Pirenne. A classic on the influence of Islam on the West. The first part of the book establishes that despite all the invasions of Huns, Goths, and the like, the areas of the Roman Empire remained fundamentally “Roman” throughout. The true disruption of Roman culture didn’t take place until Islam restructured the Mediterranean world. Like White’s book, very readable.
  • Annihilation, by Jeff VanderMeer. Fiction. First of a trilogy about unnamed scientists exploring the mysterious Area X. It sets up so much, I hope he can pull off a meaningful conclusion. Please don’t be like Lost. Please don’t be like Lost. Please don’t be like Lost….

For Chris Blattman, who needs kids book suggestions. These should work for 4-6 year olds, and were approved by my 11 and 9 year old as books they enjoyed a lot.

  • Magic Treehouse books. Kids explore different times, places, ideas in each book. As a bonus, they do companion non-fiction “Fact Tracker” books with more information on the topic.
  • Junie B. Jones. Barbara Park absolutely nails exactly how little kids talk and act. I loved reading these to my girls.
  • Roald Dahl. These are definitely too advanced for 4-6 year olds to read themselves, but we found they could start paying attention long enough to listen to a whole chapter. You have to stop sometimes to explain what is going on, or remind them the next night what is happening in the book, but reading James and the Giant Peach is soooooo much better then reading If You Give a Mouse a Cookie again, and again, and again.
  • Fancy Nancy. Shorter and good for helping them start to read. I resisted early on, but Fancy Nancy (including her many sequels) kind of grows on you. Perhaps that is just a coping mechanism.