# Blattman on Institutions

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Chris Blattman has put up a very nice response to my institutions posts, including a number of good points on what the institutions literature in economics tends to overlook. You should go read that right now.

# The Skeptics Guide to Institutions – Part 4

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The final installment of my series on the empirical institutions literature. Quick summary of the prior posts:

1. Part 1: cross-country studies of institutions are inherently flawed by lack of identification and ordinal institutional indexes treated as cardinal
2. Part 2: instrumental variable approaches – settler mortality included – are flawed due to bad data and questions and more identification problems.
3. Part 3: historical studies show that there is path dependence or a poverty trap, but not that institutions themselves are central to underdevelopment

You have to be very careful with what you conclude from the institutions literature or from my three posts. We are dealing with empirics here, so we are not able to make any definitive statements. There is a null hypothesis, and we either reject or fail to reject that null.

So what is that null hypothesis? For the institutions theory, as with any theory, the correct null hypothesis is that it is wrong. Specifically, the null hypothesis is “institutions do not matter”. What does the empirical institutions literature tell me? I cannot reject that null. We do not have sufficient evidence to reject the idea that institutions do not matter.

But failure to reject the null is not the same as accepting the null. Having failed to reject the null, I cannot conclude that institutions do *not* matter. They may matter. All the other reading and thinking I’ve done on this subject suggests to me that they *do* matter. But the existing empirical evidence is not sufficient to strongly reject the null that they do *not*. As I said in the last post, there may be a working paper out there right now that offers a real definitive rejection of the null.

Given the empirical evidence, then, I’m uncomfortable making broad pronouncements that we have to get institutions “right” or “improve institutions” to generate economic development. We do not have evidence that this would work.

Further, I’m not sure that even if that mythical working paper did appear to solidly reject the null that the right advice would be to “improve institutions”. I say this because even the institutions literature tells you that it is impossible to make an exogenous change to institutions. Acemoglu and Robinson did not lay out a theory of what constitutes good institutions, they laid out a theory of why institutions are persistent. Their work shows that being stuck in the bad equilibrium is the result of a skewed distribution of economic power that grants some elite a skewed amount of political power. The elite can’t credibly commit to maintaining reforms, and the masses can’t credibly commit to preserving the elite’s position, so they can’t come to an agreement on creating better institutions (whatever those might be).

The implication of the institutions literature is that redistributing wealth towards the masses will lead to economic development (and vice versa, that redistributing it towards the elites will slow economic development). Only then will the elite and masses endogenously negotiate a better arrangement. You don’t even have to know precisely what “good institutions” means, as they will figure it out for themselves. The redistribution need not be explicit, but may arise through changes in technology, trade, or population.

Douglass North has the same underlying logic in his work. It was only with changes in the land/labor ratio favoring workers in Europe that old institutions disintegrated (serfdom) and new institutions arose (secure property rights).

A good example is South Korea. In 1950, Korea was one of the poorest places on earth, falling well below many African nations in terms of development. It had also been subject to colonization by Japan from 1910 to 1945. Korea had the same history of exploitive institutions as most African nations.

So why didn’t South Korea get stuck in the same trap of bad institutions and under-development as Africa? One answer is that is had a massive redistribution of wealth. In 1945, the richest 3 percent of rural households owned 2/3 of all land, and about 60 percent of rural households had no land. This should have led to bad institutions and persistent underdevelopment. (See Ban, Moon, and Perkins, 1980, if you can find a copy).

But starting in 1948 South Korea enacted wholesale land reform. By 1956, only 7 percent of farming households were tenants, and the rest owned their land. According to the FAO Agricultural Census of 1962, South Korea had *zero* farms larger than 5 hectares. Not a small number, not just a few, but *zero*. Agricultural land in South Korea, probably the primary source of wealth at that point, was distributed with incredible equity across households.

According to North or Acemoglu and Robinson, this redistribution changed the relative power of elites and masses. It would have allowed them to reach a deal on “good institutions”, or at least would have made the elite powerless to stop the masses from enacting reforms. South Korea got good institutions in part because it changed the distribution of wealth. [Good institutions for economic growth don’t appear to overlap with good institutions for personal freedom, though – South Korea was a dictatorship until 1988.]

The point is that even if we acknowledge that “institutions matter”, that does not imply that we can or should propose institutional reforms to generate economic development. It’s a mistake to think of ceteris paribus changes to institutions. They are not a thing that we can easily or independently alter. If they were, then they wouldn’t be *institutions* in the way that Douglass North uses the term.

If you want to generate economic development, the implication of the institutions literature is that you have to reform the underlying distribution of economic power first. Once you do that institutions will endogenously evolve towards the “good” equilibrium, whatever that may be.

[But the distribution of economic power *is* an institution, you might say. Okay, sure. Define institutions broadly enough and it will become trivially true that institutions matter. Defined broadly enough, institutions are the reason my Diet Coke spilled this morning, because gravity is an “institution governing the interaction of two masses in space”.]

# The Skeptics Guide to Institutions – Part 3

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This is the third in a series of posts regarding the institutions literature. The first two posts dealt with original cross-country work on institutions and the attempt to identify the effects using settler mortality.

The third generation of institutions work is, in large part, a response to the empirical problems of the first 2 generations. These new papers avoid vague measurement of “institutions” by drilling down to one very specific institution, and do their best to avoid identification problems by looking for natural experiments that give them good reason to believe they are looking at exogenous variation in the institution.

The following are some good examples of this third generation. There are others that I haven’t listed, but these are ones I talk specifically about in class:

• Dell (2010). Household consumption and child health are lower in areas in Peru and Bolivia subject to the Spanish mita – forced labor in mines – than in areas just outside the mita.
• Nunn (2008). The number of slaves taken from an African country is negatively related to income per capita today.
• Banerjee and Iyer (2005). Agricultural output and investments in education and health are currently lower in areas of India where the British invested property rights in landlords as opposed to cultivators.
• Iyer (2010). Areas of India subject to direct British colonial rule have lower investments in schooling and health today than areas ruled indirectly through Indian governors.
• Michalopoulos and Papaioannou (2013). Pre-colonial ethnic political centralization in Africa is related to current levels of development within Africa.

So, problem solved, right? We’ve got solid empirical evidence that institutions matter. Not necessarily.

What these papers demonstrate is that economic development is persistent. If you like, they are evidence that there are poverty traps. If something happens to knock you below some threshold level of development – slaving activity, the mita, arbitrary borders, bad landlords – then you can’t get yourself out of that trap. You are too poor to invest in public goods like human capital or infrastructure because you are spending all your money just trying to survive. So you stagnate. Pushing you into the trap was the result of an “institution”, if we call these historical experiences institutions, but it isn’t institutions that keep you poor, it’s the poverty itself that prevents development.

Take Dell’s paper. She does not have evidence that the mita reduced living standards while it existed, she has evidence that contemporary development in the area covered by the mita is lower, roughly two hundred years after the mita was abolished. Dell shows that education is lower and road networks are less dense in mita areas than in their close neighbors. So what explains the historical persistence? One possibility is that there was some other institutional structure left behind by the mita that limited development. But we have no evidence of any institutional difference between the mita areas and others. We simply know that the mita areas are poorer, and that could be evidence of a poverty trap rather than any specific institution.

The papers on India have a similar flavor. The British no longer are in charge in India, but there are some differences today related to how they did govern. With regards to the effects of direct British, we don’t actually know what the channel is leading to the poor outcomes. We just know that there is an effect. With regards to the effect of landlords or cultivator property rights, this isn’t about institutions, it’s about the distribution of wealth.

Think of the question this way. What specific policy change do any of these papers suggest would lead to economic development? “Don’t get colonized, exploited, or enslaved by Europeans” seems like it would be hard to implement retroactively.

Of the papers I listed, probably the strongest evidence that institutions actually matter is the Michalopoulos and Papaioannou work using African ethnicities. Geographic homelands of ethnicities cross national boundaries, and one can measure the economic development in one of these homelands by using satellite data on lights at night. What MP (I’m not spelling those again) find is that ethnicities that had stronger political centralization prior to being colonized – they had political systems beyond simple chief-led villages – are rich today relative to other ethnic groups within the same nation. But this still leaves unanswered what specifically about pre-colonial ethnic political centralization has been transmitted to current populations. The policy implication for development here is just “be descended from a more coherent political unit”.

Those same authors have another paper, by the way, that looks at the question from the other direction. They look within an ethnicity that spans a national border. Does the economic development level of the two parts depend on the national-level institutions? No. Measures of national-level institutions like those discussed in Part 1 have no explanatory power for development differences between the two parts of a partitioned ethnicity.

Understanding how a country/region/ethnicity got poor is not the same thing as understanding what will make them rich. “Institutions mattered” is different from “institutions matter”. I think the better conclusion from the 3rd generation of institutions research is that economies can fall into poverty traps from which escape is difficult if not impossible. Would better institutions allow these places to escape these traps? I don’t think we can say that with any confidence, partly because we have no idea what “better institutions” means.

I think the right null hypothesis regarding existing institutions is that they likely solving a particular issue for a particular group. Let’s call this the Elinor Ostrom hypothesis. I don’t think that the existing empirical institutions literature has provided sufficient evidence to reject the null at this point. Certainly not to the point that we can pinpoint the “right” institutions with any confidence.

Could I be wrong to be this skeptical? Absolutely. We may come up with concrete definitions of institutions that we can measure and use empirically. There may be research in the works right now that gives some definitive evidence that “institutions matter” for development, in the present, and that appropriately tweaking them will generate growth. If so, hallelujah. But until then, I remain skeptical.

# The Skeptics Guide to Institutions – Part 2

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This is the second of a series of posts on the empirical institutions literature that I am covering in my graduate growth and development course. In Part 1, I looked at how the 1st generation of this literature misused cross-country measures of institutions in their poorly identified regressions.

The second generation of empirical institutions work attempted to deal with the endogeneity problem in the standard “regress income per capita on institutions” regression of the 1st generation.

The dividing line between 1st-generation and 2nd-generation studies isn’t that bright. I used Mauro (1995) as an example of 1st-generation institutions work, but that paper uses ethnolinguistic fractionalizaton as an instrument for corruption. Hall and Jones (1999) look at measures of institutional quality instrumented with latitude and the percent of the population that speaks Western European languages. These instrumental variable (IV) strategies are generally dismissed, for the reason that few people believe ethnolinguistic fractionalization, latitude, or European language speaking have affects on income per capita *only* through institutions. In other words, these papers seem to fail on the second requirement of an IV, which is that the instrument has no separate correlation with the dependent variable.

The big event in the 2nd generation of literature was the arrival of Acemoglu, Johnson, and Robinsons (2001) using “settler mortality” as an instrument of institutional quality. They propose that the quality of institutions in a colony was a function of how deadly that colony was for European settlers. The idea is that in places where Europeans died quickly (Sub-Saharan Africa, Central America), they did not want to stay, and therefore installed extractive institutions to suck as many resources out of the colony before they caught some deadly disease. In places like the US or New Zealand, where they did not die, Europeans stayed. They therefore installed good, inclusive institutions.

The heart of the argument here is that institutions in colonies were exogenously determined by Europeans, and thus we have a clean empirical “natural experiment” that will yield a good estimate of the effect of institutions on economic development. AJR is widely cited, and the settler mortality instrument has been used in any number of other papers (I’ve refereed at least 5 or 6 myself in the last 10 years) since their paper came out.

But there are significant issues with the whole empirical strategy. There are four problems with their estimates that I usually think about:

1. They are still using an arbitrary measure of institutions as a continuous variable. The measure of institutions in AJR (2001) is “expropriation risk”, and every country is coded from 0 (high risk) to 10 (no risk). See the prior post for why index of institutions like this are useless. In short, the numbers have no meaning, but AJR treat them as if they do. A 10 does not mean that a US citizen is half as likely to be expropriated than a Bangladeshi (a 5.14). Going from Honduras (5.32) to Tunisia (6.45) is not necessarily the same thing as going from Mexico (7.50) to India (8.27). Their measure of institutions doesn’t measure “institutions”.

2. It is nearly impossible to believe that their instrument (settler mortality) has no separate correlation with the dependent variable (income per capita). Settler mortality arises from putting Europeans unadapted to different climates into those climates. Since the Europeans all come from a pretty similar climate zone, that means that settler mortality is essentially picking up the intensity of the tropical disease environment. While the Africans, Asians, or Americans they colonized may have been adapted to those diseases in the sense that they were no longer deadly, it doesn’t mean those diseases had no effect on those populations. Places that Europeans died are also places that tend to have incredibly poor agricultural conditions – lack of frost, overly heavy rains, and poor soils. Europeans dying at alarming rates is simply a proxy for bad geographic conditions. And no, the fact that AJR control for latitude, temperature, and humidity is not the same thing as controlling for agricultural conditions. You can hold those three things constant and have wildly different outcomes depending on soil, altitude, wind patterns, rainfall patterns, etc.. etc..

3. The estimated effect of institutions doesn’t make sense. Their IV results show a coefficient for institutions that is twice as large as the OLS coefficient. This is problematic. The whole reason we want IV estimates is because we think there is some kind of endogeneity between income per capita and institutions – specifically, that higher income leads to better institutions. This implies that the basic correlation of institutions and income per capita is biased *upwards*, or the OLS results are too big. But when they run IV, they get even bigger effects for institutions. This implies that income per capita has a *negative* effect on institutions, and that is hard to believe.

What about measurement error? We know that if institutions are measured with noise, then the OLS coefficient will be attenuated, or biased towards zero. But classic measurement error, as this would be, implies that there is some true “expropriation risk” out there in the world, and what we have is the true value plus some random error. But you can’t have this kind of measurement error when the numbers for expropriation risk are absolutely arbitrary. There is no *real* number to measure. The “expropriation risk” is precisely measured in the sense that it precisely measures the arbitrary index established by the Political Risk Services. So I don’t buy the measurement error argument.

In the end, the simplest explanation for why their IV results are larger than the OLS is that there is a correlation of their instrument with the error term. We know settler mortality is negatively related to expropriation risk. If settler mortality is independently and negatively related to income per capita, then the IV results are going to be larger than the OLS [for the math-inclined, beta(IV) = beta(OLS) + Cov(error,mort)/Cov(inst,mort) and that ratio of covariances is positive because the two terms are negative].

4. The data are probably wrong. David Albouy’s paper is the central reference here. Let me review the main issues. First, of the 64 observations, they do not have settler mortality data for 36 of them. For those 36, they infer a value from some other country. This inference could be plausible, but in many cases is not. For example, they use mortality data from Mali to infer values of mortality for Cameroon, Uganda, Gabon, and Angola. Gabon is mostly rainforest, and about 2300 miles away from Mali, a desert or steppe.

Second, the sources vary in the type of individuals used to make mortality estimates. Most relevantly, in some countries the mortality rates of soldiers on campaign are used, and in others the mortality rates of laborers on work projects. In both cases, mortality rates are outliers relative to what settlers would have experienced. Most importantly, the use of the higher mortality rates from campaigning soldiers or laborers is correlated with poor institutions. That is, AJR use artificially high mortality rates for places with currently bad institutions. Hence their results are already baked in before they go to run regressions.

Albouy’s paper shows that making any of a number of equally plausible assumptions about how to code the data will eliminate the overall results. Both the first stage – the relationship of mortality to institutions – and the second stage – the relationship of institutions to income per capita – become insignificant under any number of reasonable alterations of the AJR data.

So in the end the settler mortality evidence that institutions matter just does not stack up. It certainly does not have the kind of robust, replicable features we would like in order to establish the importance of something like institutions for development. If you want to argue that institutions matter, then by all means do so, but the AJR evidence is not something you should cite to support your case.

Next up I’ll talk about why 3rd generation empirical studies of specific institutions aren’t actually about institutions, but about poverty traps.

# The Skeptics Guide to Institutions – Part 1

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I’m starting a run of several lectures on institutions in my growth and development course. By revealed preference, so to speak, I take the institutions literature seriously. But there are some issues with it, and so I’m going to teach this literature from a particularly skeptical viewpoint and see what survives. These posts are going to sound very antagonistic as I do this, which isn’t completely fair, but makes it more fun to write.

This first post has to do with the cross-country literature on institutions. The 1st-generation of this research (Mauro, 1995; Knack and Keefer, 1995; Hall and Jones, 1999; Easterly and Levine, 2003; Rodrik et al, 2004; Acemoglu and Johnson, 2005) regressed either growth rates or the level of income per capita on an index of institutional quality along with other controls. In general, this literature found that institutions “matter”. That is, the indices were statistically significant in the regressions, and the size of the coefficients indicated big effects of institutions on growth or income per capita.

These results are the prima facie evidence that institutions are a fundamental driver of differences in development levels. The significance combined with the large absolute values of the estimate effects indicated that even small changes in institutions had a big impact on GDP per capita. We’ll get to talking about questions of whether in fact these are well-identified regressions in a future post. For now, let’s just take these regressions as they are.

The first big issue with this literature is that all the indices of institutions used are inherently arbitrary, and yet are used as if they have a strict numerical interpretation. (see Hoyland, et al, 2012; Donchev and Ujhelyi, 2014) This is easiest to talk about by using an example.

Let’s take the 7 point index for “constraint on the executive” used by Acemoglu and Johnson in their 2005 paper. 1 is “not so many constraints” and 7 is “lots and lots of constraints”. There are more official definitions of these categories. They comes from the Polity IV database, and I will concede that it is coded up by smart, reasonable people. I have no argument with how each individual country is coded. Minor quibbles about how we rank constraints on executives are not going to overturn the results of the regressions using this to measure institutions.

But does Australia (7) have seven times as many constraints at Cuba (1)? Does the one-point gap between Luxembourg (7) and South Korea (6) have a similar meaning to the one-point gap between Liberia (2) and Cuba (1)? Using this as a continuous variable presumes that the index values have some actual meaning, when all they are is a means of categorizing countries.

So what happens if you use the constraint on executive scores simply as categorical (i.e. dummy) variables rather than as a continuous measure? You’ll find that all of the action comes from the category for the 7’s (Western developed countries) relative to the 1’s (Cuba, North Korea, Sudan, and others). That is, the dummy variable on the 7’s indicates that their income per capita is statistically significantly higher than income per capita for the 1’s. Country’s with 2’s, 3’s, 4’s, and 5’s are not significantly richer than 1’s (2’s, 3’s, and 4’s are actually estimated to be *poorer* than 1’s). Country’s with 6’s have marginally significant higher income than 1’s. The finding is that having Western-style social-democracy constraints on executives is what is good for income per capita, but gradations in constraints below that are essentially meaningless.

But there is a more fundamental empirical problem once we use constraints on executive to categorize countries. Regressions are dumb, and don’t care that we have a particular interpretation for our categories. They just load *any* differences in income per capita onto those categorical variables. The dummy variable for category 7 countries captures the average income per capita difference between those countries and the category 1 countries. There might be – and certainly are – a number of things that distinguish North Korea from the U.S. beyond constraints on the executive, and the dummy is picking all those up as well. Even if I control for additional factors (geographic variables, education levels, etc.. ) we cannot possibly control for everything, in part because the sample is so small that I can’t include a lot of variables without losing all degrees of freedom. Empirically, the best I can conclude is that Western-style social democracies are different from poor countries. Well, duh. One aspect of that may be constraints on executives, but we cannot know that for sure.

Other indices of institutions are just as bad. The World Bank Governance indicators, commonly used, include sub-indices of “Governance”, “Accountability”, and “Voice”. Okay, and….what do I do with that? You want to tell me Governance is good in Switzerland and bad in Uganda, I guess I’d have to agree with you, not having any specific experience to draw on. But if I ask you what exactly you mean by that, what kind of answer would I get? These governance indicators are based on surveys of perceptions of the quality of institutions. The institutions that get coded as “good” are the institutions people find in rich countries, because those must be good institutions, right? These measures are inherently endogenous.

This problem holds to some extent even for modern measures of institutional quality like the Doing Business indicators. These have the virtue of measuring something tangible – the number of days necessary to start a business, for example – but it isn’t clear that this should enter linearly to a specification. Does going from 146 to 145 days to start a business have the same effect as going from 10 to 9? Why should it? Is there a threshold we should worry about, like getting the number of days under 30? And just because we can measure the number of days to register a business, does that mean it is important, or that it constitutes an “institution”?

Reading the cross-country empirical institutions literature is the equivalent of watching studio analysis of NFL games. You have a bunch of people “in the game” of economics sitting around making un-refutable statements that sound plausible, but have essentially zero content. “He’s got a real nose for the ball”. Okay, meaning what? How does one improve ones nose for the ball? Is there a machine in the weight room for that? Is this players nose better than that players nose? How could you compare? “Good institutions” is the equivalent of “having a nose for the ball”. It’s plausibly true, but impossible to quantify, measure, or define.

Another big problem with the empirical cross-country institutions work is courtesy of Glaeser et al (2004). Their point is that our institutional measures are generally measuring outcomes, not actual institutional differences. One example is Singapore, which scores (and scored) very high on institutional measures like risk of expropriation and constraints on executives. Except under Lee Kwan Yew, there were no constraints. He was essentially a total dictator, but happened to choose policies that were favorable to business, and did not arbitrarily confiscate property. But he *could* have, so there is no actual institutional limit there. The empirical measures of institutions we have are not measuring deep institutional, but transitory policy choices.

That leaves us with the whole issue of incredibly small sample sizes, often times in the 50-70 country range, eliminating the possibility of controlling for a number of other covariates without losing all degrees of freedom. And don’t forget publication bias, which means the only things we see in the literature are the statistically significant results that got thrown up in the course of running thousands of regressions with different specifications and measures of institutions.

In short, it may be that institutions do matter fundamentally for development. But the cross-country empirical literature is not evidence of that. There is a fundamental “measurement-before-theory” issue in this field, I think. We don’t know what we should be measuring, because we don’t have any good definition of an “institution”, much less a good theory of how they work, arise, collapse, or mutate. So we end up flinging things that sound “institution-ish” into regressions, without knowing what we are actually measuring.

Next up will be 2nd-generation cross-country empirical work that uses instrumental variables. Spoiler alert: those don’t work either.

# Perfect Competition is Bad for Growth

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You have to be careful in confusing “free markets” with “perfect competition”. By “free markets”, I think we mean free entry for new firms and/or products into the market. We don’t want restrictions on innovators from bringing their ideas to the market. We typically *assume* that free entry exists in economic models, but one thing that holds back development may be the absence of this free entry (think red tape and bad institutions).

But we don’t want “perfect competition” even if we do want “free markets”. One of the counter-intuitive things that comes up in growth courses is that perfect competition is not conducive to rapid growth. The story here involves a few steps

• Growth is ultimately driven by innovation
• People will innovate if they have incentives to innovate
• The incentive to innovate comes from economic profits
• Profits only exist when the innovator or firm has some market power

Innovators and/or firms need to charge a price greater than marginal cost to earn profits, otherwise there will be no incentive to innovate, and ultimately no growth. If you allow competitors to copy innovations they will drive the price down to marginal cost, eliminating profits and incentives for innovation. We want free entry of new firms with market power, but not free entry of imitators who produce perfect competition.

But perfect competition does maximize the combined consumer and producer surplus from a given product. So there is a tension here. Perfect competition maximizes the output of *existing* products, but minimizes the output from *potential* products. Think of it this way, if we decided that we had all the types of goods and services that we could ever want, then we’d want to enforce perfect competition. We would nullify every patent, and let competition take over to maximize the output of those existing goods and services. Nullifying patents (or any other kind of intellectual property) would crush the incentives to innovate, and we’d never get any new products.

This means that it is not obvious what the right policy is for intellectual property rights and/or competition in general. It depends on your long-run perspective. You can trade off long-run growth for a higher level of current output by canceling intellectual property rights. Or you can trade off current output for a higher long-run growth rate by enforcing property rights strictly, and probably instituting even stronger ones.

There is no *right* answer here, because it depends on your time preferences. But extreme answers are probably unlikely to be optimal for anyone. Strict perfect competition – allowing imitators to ensure P=MC – isn’t good because it prevents us from getting new products. Super strong market power – limiting each good to being produced by a perpetual monopolist, say – would shrink the availability of every existing product, even if it makes the incentive to innovate huge.

# Latitude and Income per Capita in Comparative Development

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New paper out by Holger Strulik and Carl-Johan Dalgaard (who I predict is at this moment taking a smoke break). The paper looks at the reversal of the latitude/income relationship over history, and propose a physiological reason for it.

For starters, if you are familiar at all with basic development statistics, then you probably know that latitude and income per capita are positively correlated. The farther away from the equator you get (higher latitude) the richer you get. This works going north or south. South Africa is richer than Nigeria, for example, and Chile is richer than Ecuador. Dalgaard and Strulik have a nice graph showing this relationship holds not only for all countries, but also within Europe.

The first really interesting fact in the paper is that this gradient reverses if you look at pre-Industrial Revolution data. For 1500 CE, there is a negative relationship, and countries that are closer to the equator are richer. Again, this also holds within Europe. I had some vague concept that it probably reversed in the whole sample, but the within Europe evidence is really fascinating.

Around 1500, the Mediterranean countries in Europe were better off compared to their Northern neighbors. An aside: Were the Greeks and Italians of 1500 tsk-tsk-ing their profligate Teutonic cousins for their lazy attitudes and lack of robust economic institutions? Discuss.

Anyway, the latitude/income reversal, and the fact that it holds up within Europe, are both by themselves the kinds of stylized facts that you should cram into your head when thinking about comparative development.

But given that you have crammed that information in there, you probably have several questions. (1) Why are hot places rich in 1500, and cold places poor? (2) What changed to make the cold places rich today?

Dalgaard and Strulik take a swipe at these questions, focusing on the physiology involved in hot and cold places. There thesis rests on “Bergmann’s Rule”, which is a biological regularity noted in 1847. Bergmann’s rule states that average body mass of organisms rises as they get farther from the equator. This holds for people as well as animals. People generally have higher body mass farther from the equator (and no, that’s not just because of Wisconsin. I kid. Sort of.).

Why does Bergmann’s rule hold? Surface area to mass ratios. Big people have lower surface area to mass ratios, so they are more thermally efficient in cold climates. Thus the optimal body type for high, cold, latitudes is large, while for places closer to the equator small body types are optimal to maximize surface area to mass in order to radiate heat.

Now, large bodies have an additional feature. They require a lot of fuel (food), in particular for mothers when pregnant. Big women having big babies means using a lot of food. Thus people in cold latitudes were able to have fewer babies, given a supply of food, than their peers in equatorial regions. So we have bigger populations in equatorial regions and smaller ones in cold latitudes prior to the IR. Big populations mean more innovation in almost any type of growth model you write down, so equatorial regions had more innovation during the pre-IR era, and hence were richer.

But, eventually even the cold latitudes are going to innovate far enough to get the point of inventing technologies that rely on human capital. And the cold climate physiology gives them a natural tendency to favor quality over quantity of kids. Thus families in higher latitudes are going to more easily adopt the human capital using technology. This then starts a feedback effect, where by having a few, high-education kids means they can use the human-capital technology. Which raises income per capita. Which leads to further investment in kids at the expense of family size, and cold latitudes enter the Demographic Transition ahead of equatorial regions.

The reversal is inevitable in their model, given the initial physiological difference between latitudes. The physiological story is also consistent with differences in marriage patterns and child birth patterns between Europe and much of Asia in the pre-IR era.

They use Europe as an example, and how the latitude/income relationship holds today. But it holds in the U.S. as well. Is the income per capita of states in the U.S. consistent with the implied physiological differences between different areas of Europe, Africa, or Latin America due to population composition?

This paper predicts a reversal, but this reversal has to happen “just so” to avoid becoming a-historical. That is, the reversal has to happen just before the equatorial countries (China, India, various iterations of Islamic empires) become sufficiently rich to colonize Europe, snuffing out their development. This leaves Europe to effect the reversal, and go out to colonize the rest of the world. Did Europe get lucky here, or is there some reason that those places don’t become colonizers? Luck might be the answer, as you’ve got plenty of close-run things in European history [the Mongols turning back, Lepanto, Vienna].

The last thing that comes to mind here is that for this physiological difference to have such persistent effects, the family patterns it determines must become either (a) genetically rooted into populations or (b) some deeply ingrained in culture as to be permanent. Fertility behavior is mutable. For it to continue to be a reason for lack of development in equatorial regions you need some strong force keeping people locked into the “bad” preference for lots of kids. What is that force?

# What He Said…

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

When I grow up, I want to be Lant Pritchett. He gives an excellent summary of issues that I’ve tried to get my head around before.

Lant’s big question is “Are interventions being evaluated important for development?”. I think the answer is no. The interventions – bed nets, micro-credit, deworming – are crucially important for poverty alleviation. But that does necessarily have anything to do with economic development.

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

Do we care if productivity growth is “broad-based”, meaning that all sectors or firms tend to be getting more productive? Or is it better to have a few sectors or firms experience massive productivity increases, even at the expense of other sectors? Think of it as an allocation problem – I’ve got a fixed amount of resources to spend on R&D, so should I spread those out across sectors or spend them all in one place?

The answer depends on how willing we are to substitute across the output of different types of goods. If we are willing to substitute, then it would be better to just load up and focus on a single sector. Make it as productive as possible, and just don’t consume anything else. On the other hand, if we are unwilling to substitute, then we would prefer to spread around the productivity growth so that all sectors produce goods more cheaply.

That’s the intuition. Here’s the math, which you can skip past if you’re not interested. Let the price people will pay for output from sector ${j}$ be ${P_j = Y_j^{-\epsilon}}$, so that ${\epsilon}$ is the price elasticity (in absolute value). As ${\epsilon}$ goes to one, demand is inelastic, and the price is very responsive to output. As ${\epsilon}$ goes to zero, demand is elastic, and in fact fixed at ${P_j = 1}$.

There are ${J}$ total sectors. Each one produces with a function of

$\displaystyle Y_j = \Omega_j Z_j^{1-\alpha} \ \ \ \ \ (1)$

where ${\Omega_j}$ is a given productivity term for the sector. ${Z_j}$ is the factor input to production in sector ${j}$. ${Z_j}$ can capture labor, human capital, and/or some physical capital. Raising it to ${1-\alpha}$ just means there are diminishing marginal returns to moving factors into sector ${j}$. There is some total stock of ${Z}$, and units of ${Z}$ are homogenous, so they can be used in any sector. So you could think of an element of ${Z}$ being a laptop, and this can be used by someone to do work in any sector. If ${Z}$ is labor, then this says that workers are equally capable of working in any sector. There are no sector-specific skills.

Now we can ask what the optimal allocation of ${Z}$ is across the different sectors. By “optimal”, I mean the allocation that maximizes the total earnings of the ${Z}$ factor. Each sector is going to pay ${w}$, the “wage”, for each unit of ${Z}$ that it uses. What maximizes total earnings, ${wZ}$?

Within each sector, set the marginal product of ${Z_j}$ equal to the wage ${w}$, which each sector takes as given. This allows you to solve for the optimal allocation of ${Z_j}$ to each sector. Intuitively, the higher is productivity ${\Omega_j}$, the more of the input a sector will employ. If we put the optimal allocations together, we can solve for the following,

$\displaystyle wZ = \left(\sum_j \Omega_j^{(1-\epsilon)/(1-(1-\alpha)(1-\epsilon))}\right)^{1-(1-\alpha)(1-\epsilon)} Z^{1-\alpha} \ \ \ \ \ (2)$

which is an unholy mess. But this mess has a few important things to tell us. Total output consists of a productivity term (the sum of the ${\Omega_j}$ stuff) multiplied through by the total stock of inputs, ${Z}$. Total earnings are increasing with any ${\Omega_j}$. That is, real earnings are higher if any of the sectors get more productive. We knew that already, though. The question is whether it would be worth having one of the ${\Omega_j}$ terms be really big relative to the others.

The summation term over the ${\Omega_j}$‘s depends on the distribution of the ${\Omega_j}$ terms. Specifically, if

$\displaystyle \frac{1-\epsilon}{1-(1-\alpha)(1-\epsilon)} > 1 \ \ \ \ \ (3)$

then ${wZ}$ will be higher with an extreme distribution of ${\Omega_j}$ terms. That is, we’re better off with one really, really productive sector, and lots of really unproductive ones.

Re-arrange that condition above into

$\displaystyle (1-\alpha) > \frac{\epsilon}{1-\epsilon}. \ \ \ \ \ (4)$

For a given ${\alpha}$, it pays to have concentrated productivity if the price elasticity of output in each sector is particularly low, or demand is elastic. What is going on? Elastic demand means that you are willing to substitute between sectors. So if one sector is really productive, you can just load up all your ${Z}$ into that sector and enjoy the output of that sector.

On the other hand, if your demand is inelastic (${\epsilon}$ is close to one), then you are unwilling to substitute between sectors. Think of Leontief preferences, where you demand goods in very specific bundles. Now having one really productive sector does you no good, because even though you can produce lots of agricultural goods (for example) cheaply, no one wants them. You’d be better off with all sectors having similar productivity levels, so that each was about equally cheap.

So where are we? Well, I’d probably argue that across major sectors, people are pretty unwilling to substitute. Herrendorf, Rogerson, and Valentinyi (2013) estimate that preferences over value-added from U.S. sectors is essentially Leontief. Eating six bushels of corn is not something I’m going to do in lieu of binge-watching House of Cards, no matter how productive U.S. agriculture gets. With inelastic demand, it is better to have productivity in all sectors be similar. I’d even trade off some productivity from high-productivity sectors (ag?) if it meant I could jack up productivity in low-productivity sectors (services?). I don’t know how one does that, but that’s the implication of inelastic demand.

But while demand might be inelastic, that doesn’t mean prices are necessarily inelastic. If we can trade the output of different sectors, then the prices are fixed by world markets, and it is as if we have really elastic demand. We can buy and sell as much output of each sector as we like. In this case, it’s like ${\epsilon=0}$, and now we really want to have concentrated productivity. I’m better off with one sector that is hyper-productive, while letting the rest dwindle. If I could, I would invest everything in raising productivity in one single sector. So a truly open economy that traded everything would want to load all of its R&D activity into one sector, make that as productive as possible, and just export that good to import everything else it wants.

Now, we do have lots of open trade in the world, but for an economy like the U.S. the vast majority of GDP is still produced domestically. So we’re in the situation where we’d like to spread productivity gains out across all sectors and/or firms.

Part of productivity is the level of human capital in the economy. If aggregate productivity is highest when productivity improvements are spread across lots of sectors, then we want to invest in broad-based human capital that is employable anywhere. That is, we don’t want to put all our money into training a few nuclear engineers with MD’s and an MBA, we want to upgrade the human capital of the whole range of workers. I think this is an argument for more basic education, as opposed to focusing so heavily on getting a few people through college, but I’m not sure if that is just an outcome of some implicit assumption I’ve made.