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.

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Geography is Kinda-Sorta Destiny

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I spent last weekend in Orlando with my wife and kids at Universal Studios. This had two effects. The first was to confirm everything I hate about large groups of people. The second was that it allowed me to read a number of books. So this is another post that is partly a book review.

I read Why the West Rules–for Now: The Patterns of History, and What They Reveal About the Future by Ian Morris. This is a book I was surprised I hadn’t already read. But nevertheless, I finally got around to it on the plane.

By itself, Morris’ book is fine. I think it falls in a grey area: it gets a little dense for a popular book, but isn’t thorough enough for an academic one. Parts of it are like reading a history textbook, where it becomes a list of events and names without a lot of context. I do like his summary of what drives history. “Change is caused by lazy, greedy, frightened people looking for easier, more profitable, and safer ways to do things. And they rarely know what they’re doing.”

The larger theme of the book is interesting. Morris stakes out a position that geography is really why the West “rules” at this point. Somewhat fixed characteristics like soil and general weather patterns ensured that Western Europe and China were bound to be relatively rich compared to most of the world. The additional advantages of western Europe were the relatively easy access they had to the geographic bonanza of the New World (which itself was due to the particular fact that Native Americans died from European diseases and not vice versa).

Given my not-overwhelming recommendation of Morris’ specific book, let me offer you some additional books that make the case for geography and/or biology being a major factor in economic development.

  1. Plagues and Peoples by William McNeill
  2. Guns, Germs, and Steel: The Fates of Human Societies by Jared Diamond
  3. The Wealth and Poverty of Nations: Why Some Are So Rich and Some So Poor by David Landes. (Not the whole book, but the early chapters focus on geography)
  4. The European Miracle: Environments, Economies and Geopolitics in the History of Europe and Asia by Eric Jones. (Probably my favorite in this list)

I could go on, but I run into the “wedding invitation” problem. If I recommend another book in which geography features strongly, like Empire of Cotton, I feel compelled to recommend the other 10 books that I find similar in scope or quality. Pretty soon we’re talking about a long list. So stick with these for now as your entree to the world of geography as a determinant of development.

Morris and these other authors are often accused of “geographic determinism”. This is often slung about as a kind of epithet, implying that the author means that world economic history had to come out *exactly* like it did because of geography. This bothers people because it seems to exonerate western Europeans from all the awful things they did along the way to becoming rich. It can also be easily twisted into arguments about how Europeans are superior to other races or groups of people.

But that is setting up straw men in place of what these authors actually say. The mistake is to think that by asserting geography matters, this denies any role for human agency. Geography sets the budget constraint, affecting the slope (i.e. relative cost of land versus labor) and intercept (i.e. how many people land can support). But people set the utility function, making the choices about production, consumption, and innovation. To say that geography matters for development is to say that incentives matter, that’s all. Geography creates some subtle, and some not so subtle, differences in the constraints facing people, and they react accordingly. They look for easier, more profitable, and safer things to do within their given geographic conditions.

It is also a mistake to think that geography implies that relative development levels must be constant over time. Certain geographic characteristics are fixed, for all intents and purposes; North American is closer to Europe than to China. But nearly all other characteristics that we could lump under “geography” change over the course of human history. Think of the climate, with little ice ages and the Medieval warm period. And technological changes can make geographic characteristics change in their influence on development. Think of oil.

Geography doesn’t say that some populations are supposed to be rich, that they deserve to be rich, or that they will always be rich. It says that it isn’t terribly surprising that they are rich right now. Imagine that we could rewind and rerun human history over and over and over again. Each time, set the clock back to 15,000 BC and then let things go. Each time, it would be different as all the millions of coin flips in history came up heads or tails. Geography means the coins are not fair. Europe, blessed with productive agricultural land, lots of internal waterways, access to oceans, etc. etc.. comes up heads 55% of the time. Africa, with tough agricultural conditions, a bad disease environment, and a lack of natural transport networks comes up heads only 45% of the time. Over those thousands of versions of history, it would tend to be the case that Europeans would be relatively rich.

So when these authors say “geography matters”, take that as a statement similar to saying that a coefficient in a regression “matters”. It’s a statistical statement that the coefficient on geography is significant, not that the R-squared of the regression is 100%.

Markets, Institutions, and Underpants

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The title of this post is my proposed re-naming of Sven Beckert’s Empire of Cotton: A Global History. Grabs the attention, right?

The short recommendation is that you should read this book if you are interested in economic history and growth.

The long recommendation is that Beckert’s is an entry in the “global history” genre, this time using cotton production, processing, and trade as the framing device. But it is not just another version of Salt: A World History, with a new commodity plugged in. Beckert actually has a larger point to make about how a “market” in a commodity is something that is created by people, sometimes explicitly and sometimes not. In that sense, this book is better at explaining how institutions shape economies than most books that are specifically about institutions.

A key component of the story is the recognition that the global market for cotton was created prior to the Industrial Revolution, as part of what Beckert somewhat awkwardly calls “war capitalism”. De Gama and Columbus created direct links between Europe, South Asia, Africa, and North America. Europeans then used a superior ability to coordinate firepower and capital to ship goods between these nodes. Cotton from India was sent to Africa for slaves or South-east Asia for spices. The slaves were sent from Africa to North America, the spices to Europe. One could refer to there being “markets” for these things, but only in the sense that Europeans were trading claims on these various people or goods amongst themselves.

Beckert separates the institutions of modern capitalism, which governed the intra-European trade, from the institutions of war capitalism, which governed European trade with non-Europeans. The former developed along the idealized lines of protected property rights, secure contracts, and so forth. The latter was about coercion and expropriation. The Europeans played “cooperate” with each other, so to speak, while playing “deviate” with the rest of the world. In Liverpool the English cotton brokers developed standards of quality, separated physical location in a warehouse from nominal ownership, and created futures contracts. In the American South planters enslaved millions in order to fulfill those contracts.

The consequences of the global market in cotton were far-reaching. The cotton factory, all spindles and chimneys, becomes the epitome of the Industrial Revolution. Beckert’s implied story about innovation in this industry is Allen-like. The major costs of cotton trade were in spinning and weaving, not in growing. So innovation occurs in Britain where those costs are particularly high. But cotton also has far more scope for innovation in processing than the other major crops. It may be natural that cotton production was innovated on. There just isn’t much innovation to do on sugar once it is refined. What are you going to do, make clothes out of it? This isn’t the book to use in an argument about factor prices versus the enlightenment in generating the IR.

The more interesting question that looms over Beckert’s book is whether slavery, or the coercion of labor in any form, was necessary for the growth of the cotton trade and Industrial Revolution. Here you have to be careful about wording. Necessary? No. It was certainly possible that the global cotton trade could have evolved in a different way, perhaps with India and Egypt remaining major exporters and the American South a patchwork of small-holding cotton farmers. But did slavery and the coercion of labor accelerate the development of the global cotton trade and likely the Industrial Revolution? The answer seems to be yes. Ceteris paribus, slavery and coercion made the IR happen sooner rather than later. I think that’s what Beckert would argue. I am leaning towards agreement with him, but I need some more information before I would come down hard one way or the other.

Probably the most compelling thing I learned reading the book is about the layers of institutions that exist within economies. Beckert makes clear that there is no such thing as “English institutions” (or any other) that are constant across all transactions. Institutions are a characteristic of two entities (states, people, firms) and any given pair of entities will have its own set of institutions. So Liverpool and New Orleans cotton brokers had one set of institutions, Liverpool and Manchester brokers had another, while Liverpool and Bombay brokers a third. In some cases those institutions are “good”, fostering cooperation and trust, while others are “bad”, involving coercion. As is typical, institutions are really central to studying growth, but measuring or quantifying institutions without being extremely specific about the exact parties involved is probably hopeless.

Research on Persistent Roots of Development

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A few papers of interest regarding the persistent effect of historical conditions (geographic or not) on subsequent development:

  1. Marcella Aslan’s paper on the TseTse fly and African development is now out in the American Economic Review. I believe I’ve mentioned this paper before, so go read it finally. Develops an index of suitability for TseTse flies by geography, then shows that within Africa higher TseTse suitability is historically associated with less intensive agriculture, fewer domesticated animals, lower population density, less plow usage, and more slavery (If you are queasy about using Murdock’s ethnographic atlas, then avoid this paper). Marcella shows that TseTse suitability is currently related to lower light intensity (everyone’s favorite small-scale measure of development), *but* this effect disappears if you control for historical state centralization. The idea is that the TseTse prevented the required density from forming to create proto-states, and that these places remain underdeveloped. Great placebo test in this paper – she can map the TseTse suitability index of the whole world, and show that it has no relationship to outcomes. The TseTse is a uniquely African effect, and she is not picking up general geographic features.
  2. James Ang has a working paper out on the agricultural transition and adoption of technology. Simple idea is to test whether the length of time from when a country hit the agricultural transition is related to their level of technology adoption in 1000 BCE, 1 CE, or 1500 CE (think “did they use iron?” or “did they use plows?”). Short answer is that yes, it is related. Places that experienced ag. transition sooner had more technology at each year. Empirically, he uses instruments for agricultural transition that include distance to the “core” areas of transition (China, Mesopotamia, etc..) and indexes of biological endowments of domesticable species (a la Jared Diamond, and operationalized by Olsson and Hibbs). The real question for this kind of research is the measure of technology adoption. We (meaning Comin, Easterly, and Gong) retrospectively code places as having access to technologies in different years. A worry is that because some places are currently poor (for non-agricultural reasons) the world never bothered to adopt their particular technologies, but that doesn’t necessarily mean they were technologically unsophisticated for their time.
  3. Dincecco, Fenske, and Onorato have a paper out on historical conflict and state development. The really interesting aspect here is how Africa differs from other areas of the world. Across the world and over history (meaning from 1400 to 1799) wars are associated with greater state capacity today. That is, places that were involved in conflicts in the past are now stronger states (measured as their ability to tax) than those without conflict. The basic theory is that wars allow states to concentrate their power. However, historical conflict is unrelated to current civil conflicts…except in Africa. In Africa, historical wars are correlated with current civil conflicts, and this is associated with poor economic outcomes today, so things are bad on multiple fronts. Here’s my immediate, ill-informed, off-the-cuff analysis: In non-African places, wars generated strong states who were able to use their power to completely and utterly eliminate ethnic groups or cultural groups that were alternative power centers. They don’t have armed civil conflicts today because the cultural groups that might have agitated conflict were wiped out or so completely assimilated that they don’t exist any more. In Africa, central states were just not as successful in eliminating competing cultural groups, so they remain viable sources of conflict. Africa’s problem, perhaps, was a lack of conclusive wars in the past.

Populations, not Nations, Dictate Development

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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).

Spolaore Wacziarg 2013 Fig 5

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.

Spolaore Wacziarg 2013 Fig 6

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.

Housing, Productivity, and Nerds

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A quick comment about Krugman’s last NYT op-ed about housing costs, mobility, and productivity. He starts with several facts that are, I think, uncontroversial at least in a broad sense.

1) On net, people are moving from NY and SF to Atlanta and Houston
2) Housing prices are higher in NY and SF than they are in Atlanta and Houston
3) Wages are higher in NY and SF than they are in Atlanta and Houston

Great. What Krugman concludes is that this represents a loss of productivity. The high wages in NY/SF must capture high productivity in those places compared to Atlanta/Houston, so the movement of people is lowering productivity. If housing prices were not so high in NY/SF, then people would not move, and they would stay in these those high-productivity areas – and perhaps people would even move from Atlanta/Houston back to NY/SF. So making housing more affordable in NY/SF would be a boost to productivity.

This argument is basically the slimmed down version of what Chang-tai Hsieh and Enrico Moretti study in a recent working paper that I saw presented at NBER this summer. Here’s the core concept, simplified from my casual reading of the Hsieh/Moretti paper, but hopefully capturing the basic idea. Each city has some productivity level. For the moment, let’s assume that productivity is highest in San Fran, and lower everywhere else. If each city had constant returns to scale in production and produced an identical good, then the optimal allocation of labor is for everyone to move to San Francisco, to work in the highest-productivity place. That allocation would maximize output.

Of course, this might not be the optimal allocation for welfare. The geographical space is limited, and furthermore there are lots of restrictions on building in the greater Bay area, so real estate prices would go to something approaching infinity if all of us moved there. For Hsieh and Moretti, and by extension Krugman, this what keeps us from achieving the allocation of labor that maximizes output. The sensitivity of housing prices to population is so high in the Bay area that it isn’t worth the higher wages to live there any more. So people leave for Houston and Atlanta. This raises movers welfare, but at the cost of lower overall output because we move farther away from the output-maximizing equilibrium.

[This, of course, is leaving out completely the concept that not everyone might want to live in San Francisco. There is a high danger of running into 49ers fans in San Francisco, which we can all agree is equivalent to getting a root canal. But in the Hsieh/Moretti set-up, people don’t have idiosyncratic preferences over cities, and it’s actually not crucial to their findings. This is also probably a good spot to mention that I live in Houston, but as you’ll see below I’m not going to argue that Houston or Atlanta are demonstrably better places to live than SF or NY. Preferences actually aren’t the story here.]

Let’s take the Hsieh/Moretti/Krugman setting as given. The implication is that we should act to lower the house price elasticity in SF (e.g. allow skyscrapers in Palo Alto), so that prices are not so sensitive to population, and then people can move from Atlanta and Houston back to SF where they will all be more productive. Output and welfare will rise.

Of course, there is an equivalent solution – move everyone in SF to Houston or Atlanta. The reason SF is the most productive city is not because of some fixed, inherent quality of the location at 37.78 degrees North, 122.41 degrees West. It’s certainly not because of it’s fantastic summer climate. San Fran is the most productive city because it so happened that a unique collection of nerds coalesced there starting in the 1960’s. More nerds were attracted to the bright, shiny things that the original nerds were making, and now I have an iPhone. But here’s the thing about nerds – they are easy to move. You can easily strap one to a dolly and wheel them anywhere you want.

If you want to maximize welfare in the Hsieh/Moretti/Krugman model, then you want the cost of housing to be relatively insensitive to city size. This allows all the people to congregate in the most productive city without driving up costs so much that people no longer want to live there. So you can either lower that sensitivity in places like NY or SF, or you can make Houston or Atlanta the most productive city. It’s non-obvious which is the right solution. Arguably, it is far easier to incent Bay area nerds to relocate south than it is to convince existing Bay area home-owners (a much larger group) to take a massive capital loss on their houses.

What do we make of the steady shift south, then? Perhaps for right now, it is productivity-lowering for all those people to leave San Fran and NY. But if housing prices remain so fantastically high in those places, then eventually Houston and/or Atlanta will become the high-productivity city, because at some point even the nerds will move. Maybe the right answer is to speed up this movement, not try to reverse it as Krugman suggests.

Cyclones and Economic Growth

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I finally got a chance to read through a recent paper by Solomon Hsiang and Amir Jina on “The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence From 6,700 Cyclones”. The paper essentially does what it says on the tin – regresses the growth rate of GDP on lagged exposure to cyclones for a panel of countries over the period 1950-2008. By cyclone, the authors mean any hurricane, typhoon, cyclone, or tropical storm in this period.

Hsiang and Jina 2014 Table 1

One thing I like about this paper is that they do not bury the lede. Table 1 in the introduction gives you an instant grasp of the magnitude of what they find. They compare the cumulative effect of various disasters on GDP. A storm at the 90th percentile in strength (based on wind speeds and/or energy) reduces GDP by 7.4% after 20 years, similar in size to a banking or financial crisis. This is a big effect. As a point of reference, 20 years after World War II Germany’s GDP was already back on it’s pre-war trend.

Hsiang and Jina 2014 Figure 9

We might think that this is due to particularly slow convergence rates following cyclones. That is, the cyclone is a big shock that pushes the economy below steady state, and then it simply takes a long time for the economy to recover back to that steady state. But Hsiang and Jina’s figure 9 shows that this isn’t the kind of trajectory we see in places hit by cyclones. The full effect of the cyclone isn’t felt until nearly 15 years after. So the cyclones appear to have long-lasting effects pushing economies below their pre-storm trends. This implies some sort of change in behavior – lowering savings/investment rates, increasing depreciation rates, lowering human capital accumulation, limiting technology adoption – something that puts a persistent drag on the level of GDP.

Hsiang and Jina 2014 Figure 22

Making things worse is that countries are hit by multiple cyclones over time, and the negative impacts of one cyclone (as in their figure 9) is then accumulated with the negative impact of other cyclones to really push down GDP. They do some counter-factuals with their estimated effects to see what growth would have looked like across countries if there had been no cyclones at all from 1950-2008. Their figure 22 shows the distribution of growth rates in panel A with and without cyclones, and panel B shows the implied growth rate of world GDP with and without cyclones. There’s a sizable effect, with world GDP growth being about 1.4% per year higher without these storms.

For particular countries, the effects can be startlingly large. Take the Philippines, which has one of the highest exposures to tropical cyclones of any country in the world. In Hsiang and Jina’s counter-factual, GDP per capita would be higher by 2,000%, making the Philippines just about as rich as the U.S. Believable? Maybe not, but it gives you a sense of how much the negative impacts of these cyclones build upon each other through continued exposure. For places like Jamaica, Madagascar, or the Philippines, exposure to cyclones constitutes a persistent negative shock to GDP per capita that is difficult to overcome.

Time for some skepticism. In estimating these effects, Hsiang and Jina use 20-year lags of exposure to cyclones to estimate their effects, and hence are able to create figures like those in their figure 9 above. But their evidence does not rule out long-run convergence back to trend. If the shock of a cyclone is felt over about 15 years, and it then takes 30 years to return to trend, Hsiang and Jina will not be able to identify that. They’d only be capturing the initial negative shock, and not the recovery. This matters because we want to know whether the cyclones have (a) permanently lowered the standard of living or (b) act as temporary (but perhaps long-lived) reductions in standards of living. To put it into regular language, we want to know if the response to a cyclone is “Screw it, I’m not going to bother building a new house at all” or “Crap, it sure is going to take me a long time to rebuild my house”.

Hsiang and Jina do look at how exposure effects GDP for different sub-samples based on how repeated their exposure is to cyclones. For countries in the lowest two quintiles of exposure to cyclones, the implied negative effects are very large (I’m having a hard time interpreting the scale on their figure 19, so I’m not sure of the exact magnitude). For the three top quintiles, though, the effects of cyclones are much smaller in estimated size. The estimated effects are negative, and statistically indistinguishable from the effects in their pooled sample. However, the effects are also statistically indistinguishable from zero in most cases – except for the highest exposure countries.

This doesn’t quite settle the matter, though. Even though any individual storm may not cause any statistically significant drop in GDP per capita for high-exposure countries, this does not mean that they are unaffected by storm exposure. They may have adopted option (a) above – the “screw it” response – and so have a permanently lower trend for GDP per capita. The Hsiang and Jina paper cannot tell us anything about this, because they are only estimating the short-run effect of exposure to any particular storm, not the long-run adaptation to being exposed (which is differenced out and/or slurped up by the country-level time trend in their regressions).

Regardless, the paper is an interesting read, the latest in an increasing number of studies on economic growth that use detailed-level geographic/climate/weather data. Seeing the effect of the shocks of these cyclones out to 20 years in table 1 is a little startling, and gives you some appreciation for how geographic shocks remain as pertinent as economic ones to growth prospects.