Angus Maddison’s estimates of per capita GDP – from 1 AD through to the 2000s – are one of the most commonly used data sets in the examination of long-term economic growth. While Maddison passed away in 2010, a group of his colleagues created the Maddison Project, with the goal of continuing Maddison’s work.

The project has just produced one of its first major outputs, an update of the original Maddison dataset, including estimates of economic development across the world from 1 AD to 2010. A working paper describes the results, and you can download the new dataset (xlsx).

Maddison’s work has not been without its critics, and the working paper contains judgment on some of these points. In particular, Maddison’s estimates of the gap between Asian and European incomes before industrialisation are supported:

One of the central questions in this literature was whether the level of economic development (in terms of GDP per capita) in China (and India and Japan) before industrialization was comparable to Western Europe (Pomeranz 2000). Maddison’s estimates for that period have been criticized because they show an already substantial gap in real incomes between the different parts of EurAsia; in Western Europe the average GDP per capita was about 1200 dollars, whereas China and India were estimated at between 500 and 600 dollars. …

Summing up, a substantial amount of new work has been published in the past ten years which is generally consistent with the picture Maddison put forward in his 2001/2003 framework. The most severe criticisms at his estimates by Pomeranz (2000) and other specialists on Asian economic history, that he systematically underestimated real incomes in large parts of Asia in the 18th and early 19th century, has generally been proven wrong: detailed research by scholars working on India, Indonesia, Japan and China has shown that the magnitude of the real income gap as estimated by Maddison was about right. Another important result is that Maddison might have overestimated growth in Europe between 1300 and 1800, and that levels of real income were already quite high during the late Middle Ages.

As someone who uses Maddison’s datasets a bit, I’m pleased to see this work going on.

Working through my reading pile, I finally read this great 1966 article by James Crow – The Quality of People: Human Evolutionary Changes. For those unfamiliar with Crow’s work, it’s worth watching this piece from Wisconsin Public Television (HT: Steve Hsu). The introduction captures some of his achievements.

In the paper, Crow opens by discussing the prediction of future evolutionary trends for humans:

Prediction of future evolutionary trends is difficult because man himself plays such a decisive role. The largest influence in man’s future is man himself – the things that the individual and society do, intentionally or unwittingly. Yet, some trends are clear. Bacterial and protozoon diseases have been drastically reduced in many parts of the world. A few decades ago a gene producing a decreased susceptibility to smallpox would have had a great selective advantage. Now, in much of the world such a gene is of little value. We can expect that throughout the world selection for resistance to infection will become less and less important. …

The greater mobility of contemporary populations will also have genetic consequences. There is certain to be less inbreeding as persons tend to find mates away from their home environs. This should decrease the incidence of rare recessive diseases and cause some increase in general health and vigor – although the latter may not be measurable directly.

A second consequence of mobility may be enhanced degree of assortive marriage. The greater participation in higher education, the stratification of students by aptitude, the growth of communities with similar interests and attainments all can lead to increased correlations between husband and wife. Added to this is the greater range of choice created by affluence and mobility so that any inherent preferences for assortative marriage are more easily realized.

The effect of assortative marriage is to increase the population variability. There is already a high correlation in IQ between husband and wife, and this may well increase. To the extent that this trait is heritable there will be greater variability next generation than would otherwise be the case. This means more geniuses as well as more at the other end of the scale.

After discussing the rate of human evolution (in many dimensions slow) and the potential for selection in human societies as death rates decline (large enough that considerable selection can still occur), Crow moves to the question of eugenics. One of his more interesting points concerns the purpose of eugenics.

An immediate difficulty is to avoid the bias of our own society. What constitutes a good phenotype is not likely to be thought to be the same in Africa, China, and Greenland. …

Crow also foreshadows the challenges that the development of genetic technologies will present in the future.

It is clear that biological and chemical possibilities for influencing human evolution and development are certain to come, probably before we have thought them through. Eugenics could be a far more potent force in the future than previously. In the past it has been tolerated partly because it was not likely to make an appreciable genetic change. The early eugenics was genetically naive and was connected with various dubious and even tragic political movements. I think the time is here when the subject should be reopened and discussed by everyone – not just biologists – with a serious consideration of the consequences of misjudgments as well as the possibilities for good.

Over 40 years later, those possibilities are starting to crystallise (particularly with the rise of positive eugenics). The serious consideration is still to come.

Edge’s annual question for 2013 – What *should* we worry about? – has generated a bunch of interesting responses. First in the list is Geoffrey Miller’s response, Chinese eugenics. Miller writes:

When I learned about Chinese eugenics this summer, I was astonished that its population policies had received so little attention. China makes no secret of its eugenic ambitions, in either its cultural history or its government policies. …

The BGI Cognitive Genomics Project is currently doing whole-genome sequencing of 1,000 very-high-IQ people around the world, hunting for sets of sets of IQ-predicting alleles. I know because I recently contributed my DNA to the project, not fully understanding the implications. These IQ gene-sets will be found eventually—but will probably be used mostly in China, for China. Potentially, the results would allow all Chinese couples to maximize the intelligence of their offspring by selecting among their own fertilized eggs for the one or two that include the highest likelihood of the highest intelligence. Given the Mendelian genetic lottery, the kids produced by any one couple typically differ by 5 to 15 IQ points. So this method of “preimplantation embryo selection” might allow IQ within every Chinese family to increase by 5 to 15 IQ points per generation. After a couple of generations, it would be game over for Western global competitiveness.

Supposing that the Chinese are engaging in a eugenic exercise to boost IQ, and ignoring the potential moral implications such as coercion, should we be worried? Miller suggests it might be the end of Western global competitiveness, but off the top of my head I can see the following benefits:

1. Innovation would increase with the greater number of high-IQ people. As ideas are non-rivalrous, that innovation will benefit other countries.
2. Savings would increase (IQ is correlated with time preference), creating a greater capital stock. This capital stock can be invested in Western countries.
3. Given the strong link between IQ and economic growth, China’s economy would grow, creating a larger trading partner and greater demand for Western goods and services.
4. China would be a source of high-IQ immigrants.

Of course, we are already reaping these benefits from China. East Asians already have an average IQ above Western populations and China is a growing source of ideas, capital, demand for Western goods and services and high-IQ immigrants. If anything, we would be worried if Chinese IQ were dropping.

Miller notes at the end, however, that his real concern is the Western response:

The most likely response, given Euro-American ideological biases, would be a bioethical panic that leads to criticism of Chinese population policy with the same self-righteous hypocrisy that we have shown in criticizing various Chinese socio-cultural policies. But the global stakes are too high for us to act that stupidly and short-sightedly. A more mature response would be based on mutual civilizational respect, asking—what can we learn from what the Chinese are doing, how can we help them, and how can they help us to keep up as they create their brave new world?

The Western response will be interesting, but I do not expect that the response to actions in China will be the most important in this area. Rather, it will be the response to “positive eugenics” within Western Countries’ own borders as people increasingly take their genetic future into their own hands. If someone wishes to select the embryo with the highest predicted IQ, will they be allowed?

I had the following Mont Pelerin Society Special Meeting pointed out to me. It has a great bunch of speakers – Robert Boyd, Robin Dunbar, Leda Cosmides, Matt Ridley, Richard Wrangham, Pascal Boyer and Gary Becker among them. Not a bad location either, if you ignore the expense. Unfortunately, its for MPS members and their guests only.

Evolution, the Human Sciences and Liberty

What?

This Mont Pelerin Society Special Meeting has the objective to link the concept of evolution to freedom, reinforce the debate that opposes classical liberal society and statism using biology and anthropology as theoretical foundations, and to understand cultural evolution of open societies as a mean to escape from the tribal order.

The Universidad San Francisco de Quito (USFQ), from Ecuador, will host this world summit on its Galapagos campus (GAIAS) located on the island of San Cristóbal.

Why?

Friedrich Hayek asserted that: “cultural evolution is not the result of human reason consciously building institutions, but of a process in which culture and reason developed concurrently…”. The co-evolution of human preferences and institutions poses serious problems to anyone who promotes policies that supposedly will alter only one of the two. It is the old problem of culture versus institutions. Freedom, property rights, rule of law, how is it that all these elements evolved to promote peace and prosperity? Why some are more prominent only in some societies while in others they are almost inexistent? During this world summit, scholars with training in the natural and social sciences will gather to discuss the evolution of and the current challenges to freedom. Galapagos provides a unique environment for this; it inspired Charles Darwin, more than one hundred fifty years ago, to make his groundbreaking contributions to the biological sciences.

In response to the critique in Current Anthropology on Ashraf and Galor’s paper on genetic diversity and economic growth, C.W. writes:

1. The critique of the use of the McEvedy and Jones population density data is (as already noticed by the first comment) not reasonable.

McEvedy and Jones (1978) is the standard source for cross-country historical population estimates used in dozen of papers by economists, historians and economic historians. No Referee – as I now – did refuse this data source or demand authors to use another. Of course – as with every historical source – the uncertainty is relatively high and increases the farther backwards you go – this is because Ashraf and Galor use that of 1500 AD and put that of the other years in the appendix. Of course, it might be the case that the figures are not correct for some periods or regions. Does this make the source completely unreliable and useless? No, because there always will be measurement error – especially in historical measures. Additionally, Acemoglu et al. (2002) did conduct their empirical estimates based on different population estimates (like that of Bairoch etc.) and found no significant differences at all – at least for their regressions.

2. Pretty much the same holds true for the “timing of agricultural transition” variable from Putterman (2008). This is already a rough measure (nobody would argue that it is possible to figure out the exact date of the agricultural transition) and it might be incorrect for some countries. But, are the measurement errors systematic and how large is this error? As long as these questions cannot be answered appropriately, one should not dismiss this measure.

Of course, I know that actually the measure is criticized also by some economists (Acemoglu and Robinson e.g. in their new book and blog “Why Nations Fail”) and I think, there will be a new measure in future. But today the Putterman (2008) measure – already used in several papers – is the best and far most comprehensive collection of “timing of the Neolithic revolution” estimates available (as far as I know). Therefore it was an understandable choice to use this measure.

3. Again, the same is the case for the trust question from the WVS. This is actually the standard measure for generalized trust (i.e. general and unspecific trust of people in other people or strangers) used in almost every empirical study on trust by economist, sociologist or political scientists in the last 20 years.

It was – and is- heavily criticized by economist and others. As reaction, a significant amount of experimental economic papers test the validity of these question in field or laboratory experiments. Although no clear picture did emerge from these studies (in my opinion) the majority of the papers conclude that the question is valid , i.e. is correlated with actual trusting behavior in experiments. Furthermore, the question clearly is related to the amount of returned wallets in a so called “lost wallet game”.

Of course, the measure actually is not perfect and there are still economists who do not believe in studies based on this question from WVS. Nevertheless, it is the (only) standard measure established in the literature.

4. The author of the critique claim that distance to Addis Ababa would only be a proxy for genetic diversity on a continental scale and does only proxy broad global trends. They wrote:

“Ashraf and Galor’s description of the human pattern of global genetic diversity is consistently inaccurate, leading to concerns that the authors do not understand the data they are attempting to characterize. For example, they repeatedly contend that “migratory distance” to various settlements across the globe affected genetic diversity. This is misleading. The pattern of human genetic diversity they are referring to was primarily affected by the sequential series of founder effects that occurred during the peopling of the world; geographic distance is largely a proxy for these founder effects (Ramachandran et al. 2005). This proxy is accurate for roughly predicting global trends of genetic diversity on a continental scale but does not predict regional genetic diversity within continents. Human populations, stratified by heterozygosity, can be grouped into just four classes: Africa, West Eurasia, East Eurasia, and a fourth class comprising the remaining populations, nearly all of which have low heterozygosity. This class includes Native American populations. We prefer to use sequence data rather than genotype data to measure heterozygosity, as this avoids ascertainment issues involving the choice of SNPs used. Table S36 of Meyer et al. (2012), which used high coverage sequence data from 11 humans, shows the pattern clearly. In other words, genetic diversity varies on a continental scale, with Africa the most diverse, the Americas the least, and Eurasia having intermediate values. No amount of regression analysis and bootstrapping can alter the fact that, in essence, Ashraf and Galor are working with only four data points: Africa, Europe, Asia, and the Americas. This would be the case even if the raw data of Ashraf and Galor were perfect and free of noise.”

I read the paper of Ramachandran et al. (2005) and if one sticks to that paper, this is not true (I do not know how accepted and established the findings and methodology of Ramachandran et al. (2005) are within population genetics, but at least Ashraf and Galor stick primarily to them). Ashraf and Galor do extensively discuss and explain the findings and methodology of Ramachandran et al. (2005) and then – in my impression – do exactly the same as Ramachandran et al. (2005). They regress the genetic diversity measure on the distance to Ethiopia taking the migration patterns of humanity into account. This corresponds exactly to what Ramachandran et al. (2005) (Figure 4A) do. Additionally, they found evidence for a clear relation between pairwise genetic distances between the ethnic groups in their sample and the great-circle distance (with waypoints and without) between them (Figure 1 in Ramachandran et al. 2005). So, obviously, at least according to Ramachandran et al. the distance and diversity relation does not only hold for continents or broad global tendencies. Or do I get that wrong (I’m economist not anthropologist or biologist). I think the only point where Ashraf and Galor can be criticized here, is that they do rely on only one paper and do not discuss the probably existing other work in the area. But I do not know whether the other literature come to other conclusions than Ramachandran et al. (2005).

Nevertheless, I think the anthropologists do have some good points. First, the fact that genetic diversity between humans in general is remarkably low. This is a huge problem for all arguments highlighting that genetic diversity between humans might be important for xx or can explain this or that.

Second and probably most important, they correctly criticize that the causal story of the paper is really, really weak. To make causal claims about very general and unspecific advantages and disadvantages of genetic diversity within(!) populations and genetic diversity associated with diversity in “Junk DNA” is not very convincing. And then, even worse I do not see enough convincing arguments to link both diversity in “Junk DNA” and very general and broad advantages of genetic diversity (in non-neutral genes!) to trust or to the amount of scientific publications. (And why to choose exactly these two variables why not some other, related to cooperation and creativity?). For me, this is all far too unspecific and speculative.

In sum, I do not believe in the story of the paper. But nevertheless I think, nevertheless it is not that bad as postulated by the anthropologist. Actually, the empirical strategy and the arguments in the paper are much more complex, differentiated and careful than in most papers written by economists. They control for almost every imaginable factor, using different variants, measures and adjustments of their variables and data. They worked on the paper for a quite long time and I think they really believe their story. Oded Galor always has unconventional and deep ideas and I think he is one of the most creative and methodologically best trained economist alive. Even more, he is one of the few economists that is willing to do interdisciplinary work – something strongly necessary to solve some the problems mainstream economics have today.

To do interdisciplinary research is always a difficult undertaking. Blaming Ashraf and Galor for having the courage of trying it is neither helpful nor fair in my opinion.

You can find further conversation on the causation point below C.W.’s comment.

My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows:

1. A summary of the paper methodology and findings
2. Does genetic diversity increase innovation?
3. Does genetic diversity increase conflict?
4. Is genetic diversity a proxy for phenotypic diversity?
5. Is population density a good measure of technological progress?
6. What are the policy implications of the effects of genetic diversity on economic development?
7. Should this paper have been published?

Other debate on this paper can also be found here, here and here.

In my last post, I described Kremer’s O-ring theory of economic development. Kremer’s insight was that if production in an economy consists of many discrete tasks and failure in any one of those tasks can ruin the final output, small differences in skills can drive large differences in output between firms. This can lead to high levels of inequality as the high-skilled work together and are disproportionately more productive.

In a new paper in the Journal of Economic Behavior and Organization (although kicking around as a working paper for a few years), Garett Jones tweaks Kremer’s model to capture the observation that measures of worker skill are a stronger predictor of cross-country economic performance than of within-country differences in income.

Jones pictures an economy that comprises two sectors: an O-ring sector of the type described by Kremer; and what Jones calls a foolproof sector. The foolproof sector is not as fragile as the more complex O-ring sector and includes jobs such as cleaning, gardening and other low-skill occupations. The key feature of the foolproof sector is that being of low skill (which Jones suggests relates more to IQ than quantity of education) does not necessarily destroy the final product. It only reduces the efficiency with which it is produced. A couple of low-skill workers can substitute for a high-skill worker in the foolproof sector, but they cannot effectively fill the place of a high-skill O-ring sector worker, no matter how many low-skill workers are supplied.

In this economy, low-skill workers will work in the foolproof sector as these firms will pay them more than an O-ring sector firm. High-skill workers are found in both sectors, with their level of participation in each sector such that high-skill workers are paid the same regardless of which sector they work in (the law of one price).

Thus, within a country, firms will pay high-skill workers more than their low-skill counterparts, but not dramatically so. Their wage differential is determined by the difference in their outputs in the foolproof sector.

Across countries, however, things are considerably different. The highest skill workers in a country provide labour for the O-Ring sector. If they are low skilled relative to the high-skilled in other countries, their output in that fragile sector will be much lower. This occurs even for relatively small skill differences. Their income will reflect their low output, with wages also lower in the foolproof sector as high-skill workers apportion themselves between sectors such that the law of one price holds. The net result is much lower wages for workers in comparison to another country with a higher-skill elite.

This picture does depend on the relative proportions of the low and high skilled in the population. If there are too many low skilled people, the wage differences within a country may be greater as all the high-skilled will work solely in the O-ring sector and wages will not equalise. Some of the low skilled may even engage in a lower output O-ring sector. However, the general picture of a large gap in income between countries remains.

There are a couple of interesting things about this model. First, the low skilled can be productive. An additional low skilled person, such as a low-skill immigrant, does not drag everyone down or necessarily destroy a production process. The productivity of those in the O-ring sector remains unchanged. This is somewhat in line with the traditional economic story that everyone can provide value through their comparative advantage. That picture is, of course, incomplete. As Jones points out in other work, there may be other costs to the unskilled, such as lower cooperation or patience and political costs. But this model suggests is that we should look to the quality of the high skilled in the population, and worry less about whether there are too many at the bottom end.

The other point worth noting about Jones’s paper, as for Kremer’s, is that small differences matter. Large income differences are not evidence of large skill differences (although, obviously, they are also not evidence against). Conversely, small changes in skills can have large consequences for a nation’s wealth.

The latest issue of the Journal of Economic Behavior and Organization has a new paper by Garett Jones (ungated version here) on the  O-ring theory of economic development. Its been floating around as a working paper for a few years, so its nice to see it get a home. But before I post about that paper, I thought I’d revisit Michael Kremer’s classic 1993 paper on which Jones builds.

The name of the theory comes from the cause of the Challenger space shuttle explosion. In that case, a highly complex machine with thousands of components failed because one minor part, an O-ring, failed in the cold conditions. This is despite the remaining components being in order.

Kremer saw a similarity between what occurred in the Challenger case and what may happen in production in the economy. A company with a great product and service may fail due to bad marketing. An otherwise functional good may sell at a much reduced price due to a single defect. Kremer asked, if processes of this nature are the norm, what does this imply for economic development?

Kremer pictured firms that engage in production involving a series of tasks. Workers have different skill levels, which is represented by a probability that they properly perform the task. Even if workers have relatively high completion rates, small differences are costly. A firm employing a production process with 10 tasks and workers who complete their step with 95 per cent probability produces only 60 percent (0.95¹⁰) of the output of a firm with perfectly competent workers. It is also disastrous to have a weak link in the chain, as 9 fully competent workers paired with someone who messes up half the time will see their total output halved compared to a firm with the perfect ten.

This setup has some interesting implications. First, people will sort by skill as firms will find it worthwhile to employ people of the same competency. Those firms with the best workers will then attract the most capital. This will lead to large wage differentials, with the high-skilled workers paired together being much more productive than the low-skilled. Large differences in wages might also be observed across borders if there are differences in skill between countries.

If we assume that the tasks in a process are sequential, there are high costs to messing up at later stages of the production process as the work of everyone before is wasted. Kremer uses the example of Rembrandt, who finished off the face and hands of portraits after his assistants had done the easier work. The result of this assumption is that high skilled workers will be employed later in the production chain. If there is a lack of high-skilled workers, firms will focus on producing goods with short and easy production processes. Kremer suggests this may explain the higher share of primary production in poor countries, while rich countries will specialise in complex products.

Finally, Kremer asks what happens if firms cannot perfectly assess a worker’s skill. In that case, there will be imperfect matching between firms and workers, and firms will be less efficient. But if workers use education to both signal and increase skill, there are benefits from the improved matching and the significant output increase as mistakes drop. Kremer states that this provides a strong argument for subsidising education, as small increases in education and skill increase the returns to education and skill, causing a virtuous circle.

Kremer’s model also provides an argument for boosting IQ. Any measure that can systematically increase worker quality will have multiplicative effects. This matches the observation that boosting a person’s IQ increases their income, but boost the population’s IQ and the wealth gains are many times higher.

I tend to see Kremer’s model as a natural counterpart of another story about the benefits of high-quality workers. Modern growth is not primarily generated by people not messing up, but by people coming up with great ideas. If you place a bunch of innovative people together, there can be huge network effects. The cost of the low-skilled does not multiply in the same way as for Kremer’s model, but alternative assumptions about the strength of network effects of innovation for those high-quality people can generate similar disparities in wages. Firms pay the low skilled less simply because they have zero marginal product in those innovative processes.

Jones’s new paper has some relevance to that last point. I’ll post on his paper in the next few days. (That post on Jones’s paper can now be found here.)

Despite the common public brawls, a paper presented at the American Economic Association annual meeting by Gordon and Dahl shows high levels of consensus between economists on most economic issues. Based on questions to a 41 economist panel established by the Chicago Booth School of Business, on average only 6 per cent disagree with the “consensus” answer to a question, with around 25 per cent uncertain. This observation holds for what might be viewed as relatively controversial questions, including the effect of stimulus on jobs (although the phrasing of the questions could be changed to increase dispute – such as asking whether the benefits of the stimulus outweighed the costs. Some of the questions seem benign). In some areas the consensus is weaker, such as on labour issues where 17 per cent oppose the consensus and 29 per cent are uncertain, leaving a 3 to 1 ratio between those in the majority and those against.

There has been plenty of reaction to these findings in the blogosphere (such as Noah Smith and Paul Krugman), and Justin Wolfers’s comments on the role of  ideology are worth a look. But the question in my mind is what should we consider to be a high level of consensus?

As a benchmark, a raft of climate and earth scientist surveys put disagreement among climate scientists on whether humans are behind climate change at six per cent or less.

What if we posed some questions to biologists? Group selection seems an obvious area to generate a division, but my instinct is that most professional biologists would coalesce around the same view. Advocates of the general importance of group selection are still a small minority. If we expanded the question to include cultural group selection (and similarly increased the surveyed group), we might be closer to discovering a split. However, I suspect one of the features increasing consensus in the case presented by Gordon and Dahl is that it is a small group of high-profile economists from top institutions. We could probably also increase the measured level of dispute between economists by increasing the sample to include those less tied to the middle.

Evolutionary psychology might be fertile ground for a dispute, with there still some reluctance to apply evolutionary theory to the human mind. However, I expect that it would be specific evolutionary psychology hypotheses that would be more controversial and not the general concepts. Other possible areas of dispute might be the rate of recent human evolution and the question of race, which sees some disagreement among biologists (16 per cent in the minority), although anthropologists seem more divided. On that note, I would expect a stronger split if I started quizzing anthropologists about issues such as human nature. I am also  relatively confident I could predict which anthropology faculties would be on each side of the debate.

On a historical note, would Stephen Jay Gould have been able to rally more than 10 per cent of evolutionary biologists to his side in his famous debates with Dawkins or Maynard Smith? I suspect not (is this hindsight bias from the perspective of the victors?), although he might have succeeded at exceeding that benchmark in the sociobiology debates.

In this light, it’s probably right to consider the level of consensus in economics as being strong, but to note that some of the areas have less consensus than we’d normally see in a field such as biology. Compare the consensus in economics to anthropology, however, and economists could appear quite unified.

But what of the future? Gordon and Dahl noted that consensus is generally strongest where the area in question has a larger body of literature behind it. That makes sense, but for the areas of dispute (particularly the public disputes that created the perception of a rift between economists), I don’t expect to see much short-term change. I have never been very confident on the rejection of controversial theories in economics through data and Wolfers shows evidence of ideology behind the division for some issues, so I suspect that at a minimum we may need to wait for a few funerals to occur.

Justin Fox has asked whether the age of economic imperialism is coming to an end and whether economics may be vulnerable to imperialism itself:

Lately, though, I’ve found myself talking to and reading a little of the work of sociologists and political scientists, and coming away impressed with how adept they are in quantitative methods, how knowledgeable they are about economics, and how willing they are to challenge economic orthodoxy. …

Even anthropology, that most downtrodden of the social sciences, has been encroaching on economists’ turf. When a top executive at the world’s largest asset manager (Peter Fisher of BlackRock) lists Debt: The First 5,000 Years by anthropologist (and Occupy Wall Streeter) David Graeber as one of his top reads of 2012, you know something’s going on.

What’s going on is probably not the incipient overthrow of economics. As described by Lazear, its imperialistic power has in large part been the result of its uniformity of approach over the past half century. (That, and economists have actually been right about some things.) As best I can tell, there is no such methodological consensus in sociology, political science, anthropology, or history at the moment. But the economists’ consensus is wobblier than it’s been in a while (especially in macro), there is ample motive for insurrection, and the non-economists’ stores of intellectual ammunition are growing. Economics may well have reached the stage of imperial overstretch. Interesting times lie ahead.

As you might expect me to say, evolutionary biology will be part of the move into the economists’ turf. After forty years of evolutionary biology imperialism, starting in the sociobiology days of the 1970s, evolutionary biology is vital to much of psychology and anthropology. And the potential to reshape economics remains significant. My predictions of the areas of greatest effect are as follows:

1. Evolutionary psychology will form the bedrock on which behavioural economics sits, and will provide the basis for reconciling rational choice and behavioural economics approaches to decision making.
2. As economists become increasingly interested in the foundations of our economic preferences, such as risk or time preference, they will turn to the work already being done in the area (see my evolutionary biology and economics reading list for some examples).
3. Many areas of economic imperialism, such as Becker’s work on the economics of the family and crime, will be updated from an evolutionary perspective. Economics added much to these fields, but the work was incomplete.
4. Biology will be a source of the understanding of the economy as a complex system. This might include the study of the financial system as an ecosystem or broader consideration of when competition leads to positive outcomes.
5. Economic policy debates will gain more of an evolutionary flavour. The Evolution Institute is a part of that movement.

In addition, many economists underestimate how many tools and ideas used in economics are from other fields. I continue to run into economists who label John Maynard Smith as an economist, and who don’t consider how many of their statistical and mathematical tools were developed and used outside of economics. This is reflected in Sveriges Riksbank Prizes in Economic Sciences in Memory of Alfred Nobel  going to psychologists, political scientists and mathematicians. If anything, the skill of economists is recognising good tools and using them, with much economic imperialism the adoption of tools developed by others and claiming them as their own. But is an economist’s use of game theoretic tools developed by John Maynard Smith classed as economics or evolutionary biology? If an economist uses a tool from a field outside of economics in another field outside of economics, is it economics simply because the user labels themselves as an economist? (I often ask this last question about some of my work.)

As an aside, Fox credits the success of economic imperialism on the scientific foundations of economics, and quotes a passage from Edward Lazear’s 2002 QJE article on economic imperialism:

Economics is scientific; it follows the scientific method of stating a formal refutable theory, testing theory, and revising the theory based on the evidence. Economics succeeds where other social scientists fail because economists are willing to abstract.

Lazear’s description is more normative than positive, particularly in macroeconomics. What was the last macroeconomic theory that was generally rejected through the economics profession on the basis of data? (Or to make it more personal, when was the last time data changed your mind about an economic theory?) Fox hints at this problem, noting the multiple perspectives on the one-off global financial crisis, but the issue is broader. Ask a group of economists what causes business cycles. The consensus will be weak. The lack of consensus is not universal in economics, but where a divide exists, particularly on ideological lines, it is  rarely resolved through the scientific method.

Is Poverty in Our Genes? is the title of a new extended critique of Ashraf and Galor’s forthcoming American Economic Review paper on genetic diversity and economic development. Published in Current Anthropology, the critique is an extension of an earlier piece by a group of academics (mainly from Harvard) who argue that Ashraf and Galor’s work is false and undesirable.

The critique spends some time focusing on the data underlying Ashraf and Galor’s work, which provides a good reminder of the complexity of human migratory history. For example, the authors write:

Historical flaws also exist in Ashraf and Galor’s treatment of concepts of innovation in table A3. Here the achievements of the diverse populations at Cordoba are taken to stand for measures of “European” innovation at 1000 CE. It is misleading to use Cordoba as a measure of European success, given that it was ruled by North African Moors until 1236 CE. Likewise, it seems inconsistent to classify Constantinople as part of Europe in 1000 CE but part of Asia in 1500 CE (Ashraf and Galor 2013, table A3). It should also be remembered that Europe’s role in innovation is a very recent phenomenon. Indeed, if we are to look for traces of “innovation” according to Ashraf and Galor’s standards in Europe, archaeology has made it clear that agriculture was not independently invented in Europe, but rather spread there from the Near East (Bellwood 2006). One can also show that Renaissance Europe was heavily influenced by Greek and Arab thought (Lewis 2009; Saliba 2007). Clearly, there is a great deal of multicontinental interaction in the circum-Mediterranean region. If one excluded these data coming from the heavily African- and Middle Eastern–influenced Mediterranean region, population levels (and hence innovation levels, according to Ashraf and Galor) in Europe would be low compared to other areas of the world until the late medieval period (after 1470).

These are interesting arguments, but I’m not convinced that shifting a few data points will materially change the general findings. The more fruitful area of criticism is the causative mechanism. In that area, the authors make some interesting points about evidence from other species.

Ashraf and Galor’s theoretical model argues that genetic diversity can play a positive role in the expansion of a society’s “production possibility frontier” or its ability to innovate. In their appendix H, they use animal studies to justify this claim. They describe studies on insects that link genetic diversity to disease resistance and to several aspects of hive performance in honeybees (Seeley and Tarpy 2007; Tarpy 2003). The two bee studies cited by Ashraf and Galor correlate genetic diversity with bee foraging rates and hive temperature and indicate that disease susceptibility relates to inbreeding. Another cited insect study on fruit flies (Drosphila species) shows that genetic diversity helps increase resistance to environmental changes (Frankham et al. 1999). It is unclear how either of these relates to an ability to innovate. Perhaps Ashraf and Galor were inspired to use these data because there is no research demonstrating that genetic heterozygosity at the population level is associated with capacity to innovate.

In addition, these cross-species comparisons of genetic diversity seem to not take into account how genetic diversity varies widely among species. Humans are noted for having extremely low levels of genetic diversity compared to other animals, including our closest cousins, chimpanzees. In fact, some chimpanzee breeding groups, such as those in the Taï forests of West Africa, are estimated to have greater nucleotide diversity than the entire human species (Gagneux et al. 1999). It is important to put into perspective that the total amount of human genetic diversity is actually quite small compared to that found in other model organisms.

My posts on Ashraf and Galor’s paper on genetic diversity and economic growth are as follows:

1. A summary of the paper methodology and findings
2. Does genetic diversity increase innovation?
3. Does genetic diversity increase conflict?
4. Is genetic diversity a proxy for phenotypic diversity?
5. Is population density a good measure of technological progress?
6. What are the policy implications of the effects of genetic diversity on economic development?
7. Should this paper have been published?

Other debate on this paper can also be found here, here, here and here.