Economic theory tells us that trade makes the parties involved better off. Through trade, a person can specialise in the activity in which they have a comparative advantage. A person is better off even if they are trading with someone who is better than them at all activities. This is because the less productive person will still have a comparative advantage in some activities. By specialising, an individual can use income from the activity in which they have a comparative advantage to buy other goods and services.

Whether trade is beneficial is not as clear from the perspective of the genotype. To examine this, Gilles Saint-Paul authored a paper in which he examined how trade may affect the evolution of humankind.

Saint-Paul’s model involves a haploid population that has two productive activities – fight and defence. Haploid means that each loci, the specific location of the gene, has only one gene. Each person in the population has two unlinked loci, with the gene at the first locus determining fighting productivity and the gene at locus two determining productivity in defence. At each loci there can be one of two alleles, the higher productivity H or the lower productivity L.

This situation leads to four possible genotypes: HH, HL, LH and LL. HH has maximum productivity in both activities. HL is productive at fighting but not defence and so on.

First, Saint-Paul considers the situation where there is no trade and each person must do everything for themselves. In this case, the unproductive L alleles are eliminated and eventually the population consists only of HH genotypes. The LL types are eliminated as they are unproductive at both activities. The HL and LH genotypes are also eliminated as, while they are productive in one activity, they are unproductive in the other and must still provide for themselves in that unproductive activity.

Under trade, not everyone needs to be perfect. Each person only needs one H allele and they can then specialise in that activity and trade for the other service in which they have lower productivity. HL genotypes can specialise in fight and trade their fight for defence provided by someone else. Similarly, LH genotypes can specialise. However, both HL and LH genotypes cannot both exist in the population in the equilibrium as when they mate, they will produce some LL genotype children. As a result, HL and LH genotypes will have lower fitness than the HH genotypes until the L allele is completely eliminated from one locus. Once that occurs, all children will have H in one locus, ensuring they are a productive in one activity that they can specialise in. The population will end up a mix of HH and HL or, HH and LH.

Having looked at these two scenarios, the natural question is what would happen if a trading and a non-trading society were part of a larger population. Would one grow faster than the other? In equilibrium, the answer is no – both societies in equilibrium have the same fitness as all production is done by those with the productive H allele at the relevant loci. If both populations started from a mixed group consisting of all four genotypes, the group which trades would reach equilibrium first, so it would grow faster during transition. However, once the populations reach equilibrium, both grow at the same rate, so neither population is eliminated.

If environmental shocks are thrown into the mix, with these shocks changing whether the H or L allele is more productive, then the trading population might grow faster. This is because it maintains some diversity in the form of the L allele in at least one loci. When the productive advantage shifts, the non-trading society has only the newly unproductive alleles for each task. The non-trading society would then have lower fitness, although not directly from not trading, but rather from their non-trading-induced lack of genetic diversity.

One observation on this model is that it differs from the typical comparative advantage story told in economics. Economics shows that trade can make someone better off even if they are absolutely less productive at all possible activities. The genetic story tells us that even when there is trade, only those who have maximum productivity in at least one activity will be present in the equilibrium population. Trade may make a completely unproductive person better off in the short-term, but over the long-term, their unproductive alleles will be eliminated – totally in the case of no trade and from at least one loci in the case of trade.

I like the concept behind this model, but struggle to apply it to any examples. In societies with a history of exchange, do we see more genetic diversity? Conversely, are people in societies with little history of exchange productive across all areas which were relevant to their fitness? I am not sure there is evidence for either. More likely (or so I believe), have humans been trading in one form or another for so long that I am wasting my time looking for modern examples and that we should simply take Saint-Paul’s concept as one factor behind the diversity that we see today.

Having put it in the top ten books I had read in 2010 despite being only halfway through it then, I feel somewhat obliged to offer a review of Janet Browne’s Charles Darwin: Voyaging (or more accurately, some observations). I have now finished it, and I am pleased to say that it can stay in the Top 10 and that I don’t intend to make any late retractions.

Voyaging is the first volume of a two-part biography. Voyaging covers Darwin’s life to 1856, three years before the Origin of Species was published, which is the point where Darwin has decided to put his theory of natural selection into a book. The second volume, Charles Darwin: The Power of Place, takes off from there.

There were a number of features of Voyaging that I particularly enjoyed. Foremost was the way Browne placed Darwin within the context of the day’s science. Victorian science was an interesting mix of gentlemen naturalists and university scientists. From Darwin’s lectures in human anatomy in Edinburgh, consisting of dissections of recently snatched corpses, to his gentlemanly existence as a member of the Royal Society, Linnean Society and the Athenaeum Club, Browne provides reams of insights into the state of the science, the participants and their views. This allowed Browne to place Darwin’s insight into the context of existing debates on evolution and the rapid growth in other sciences such as geology.

In this context, Browne takes the reader through the creation of Darwin “the naturalist” as opposed to Darwin the country parson, idle sporting man or doctor. As Browne notes in the introduction, Darwin was no born naturalist. In his late teens and early twenties, he seems to have little direction and even on his return from the Beagle voyage, it seems that he could have easily slipped into a much simpler life than becoming a scientific giant. Through her understanding of Darwin’s correspondence (she was an associate editor of a project putting it together), Browne is able to offer a picture into how Darwin gained enough confidence in his ideas to dedicate a large part of his life to establishing the evidence behind them. Over the course of years, he slowly tested his thoughts with his closet friends (often in the most obtuse ways) as he finally built foundations to his theory. As we well know, it took over 15 years from the first indications to his closest friends of what he was thinking to the publishing of the Origin of Species, and even that was rushed by the letter he had received from Wallace.

The way in which Darwin established and used his massive network, largely from the seclusion of Downe house, is nothing short of incredible. Browne paints a beautiful picture of how Darwin sent an extraordinary number of letters to scientists, farmers, gentleman naturalists and anyone else who might be able to help with a litany of requests for samples, evidence and support. The evidence that fills the pages of the Origin of Species was largely sourced through this correspondence, which says something for Darwin’s ability to extract useful information and help from others. He had a gift for this from an early age, with his requests for insects from his friend Herbert carrying little recognition of Herbert’s deformed foot – and yet, Herbert happily complied.

Darwin does not stay unscathed in the book. It is clear that he is a creature of his times, despite his progressive attitudes towards some issues such as slavery. For example, he largely did not credit his fellow Beagle crew members who made significant contributions to Darwin’s work. For someone who relied so much on help from others, he was sometimes slow to share the credit. However, this did not seem to stem from malice or selfishness, but perhaps unawareness due to his relatively high station in society. When Darwin did feel that he owed something (such as for Herbert’s anonymous gift of a Coddington microscope), his gratitude lasted a lifetime.

Having read Volume I, I might take a break before Volume II. A 600 page biography has to contend with the rather large reading list. Interestingly, Volume II seems to get the biggest praise, so I look forward to when it is time to read it.

I have just listened to the recent Econtalk podcast with Arnold Kling on his new “paradigm”, Patterns of Sustainable Specialisation and Trade (PSST). On first thoughts, I am not convinced about the idea. If anything, the paradigm appears to need a lot more development – although reading Kling’s blog posts, he may agree. I felt that many of the stories involved too much hand-waving and not enough empirical backbone to be convincing.

I won’t go into the details of Kling’s paradigm – I suggest listening to the podcast or tracking through Kling’s posts at Econlog for background. His most recent one on PSST is here. But, I had a couple of initial thoughts.

First, this paradigm has many similarities to evolutionary economics. Nelson and Winter’s An Evolutionary Theory of Economic Change contains a lot of material on search, competition and organisation is along the same lines as that discussed by Kling. Naturally flowing from this, would agent based modelling or other evolutionary economic modelling techniques be useful in developing working models of Kling’s theory?

Second, and given that I am far from convinced as to whether this paradigm is correct (for example, can PSST explain the current high levels of unemployment), I was wondering what empirical evidence would sway me towards it. If we track workers who have become unemployed during this recession, the PSST paradigm would predict that a sizeable chunk of this group will go to new jobs created by entrepreneurs looking to take advantage of this cheap resource. Will this be the case? How many construction or manufacturing workers will end up in jobs in which they have a new comparative advantage, and how many will get employment doing almost the same thing they were doing before? We could apply a similar test to the employer side. Do firms hire back workers for positions that they previously dumped workers from, or is the hiring in new positions in new firms?

Update: A quick additional thought – what does this paradigm say about immigration? If the gates are opened and immigration levels jump, how long is the period of adjustment for entrepreneurs to be able to take advantage of this huge resource of presumably low-skilled labour?

As I posted a couple of months ago, a higher level of violence in a society may lead women to prefer more masculine appearing men. In such an environment, picking the healthiest appearing male is more important than the level of parental care the woman expects the man to give.

The latest issue of Evolution and Human Behavior has an article examining the link between female preference and violence, with Jeffrey Snyder and colleagues examining whether a woman’s fear of crime might be a predictor of her preference for “aggressive and formidable” mates. Unlike earlier research, which focused on actual violence levels, Snyder and colleagues’ targeted their hypothesis at the woman’s perception of her vulnerability to crime. This makes sense, as the need for an aggressive man is likely to be a function of both the level of crime and the woman’s ability to defend herself. The woman’s perception of her vulnerability should capture both of these elements.

Snyder and colleagues also framed the trade-off around outcomes to the women instead of reproductive outcomes. Instead of asking whether the aggressive man will deliver a healthy child or invest in parental care, the trade-off discussed concerned violence to the woman by the aggressive man versus the protection he can offer the woman.

Using three internet based studies of United States women, Snyder and colleagues supported their hypothesis through the discovery of a positive relationship between a woman’s perception of her vulnerability to crime and her preference for aggressive men. However, there was no or a very weak link between female mate preference and actual crime rates (which were determined by zip code). This is somewhat confusing, as it suggests that fear of crime may not be rationally based. Snyder and colleagues’ hypothesis would predict a weaker link between actual crime and preferences than between perceived vulnerability and preferences, but there should still be a link.

A further issue with the results is the low-level of effect that the perception of crime has. While it comfortably passes the significance tests, fear of crime can only explain (at most) 5.5 per cent, 7 per cent and 6 per cent of the variation in preferences for aggressive men across the three studies (and that is including other variables in some of the regressions including race, age, education and inequality). This suggests that while perceived vulnerability is significant in a statistical sense, it has very little predictive power.

Having mulled on this study for a couple of days, I am not sure what to make of it. I find the hypothesis attractive, but the absence of a link to actual crime leaves me with a large number of questions about the survey methods used and suggestions for follow-up research – a number of which were also noted by Snyder et al.

First, it would be useful to get some more variation in the sample. The study participants were highly educated, with less than ten per cent of the sample in each study having a level of education at high school level or below. As a result, there are likely to be very few in the sample who live in a violent area. This variation may be particularly important if the survey subjects are subject to levels of crime too low for protection from aggressive men to matter.

More variation could be introduced by including other countries or particularly high crime areas. In those countries or areas, a more masculine male may deliver much stronger benefits and be more strongly preferred.

A related observation is that for this study’s well-educated participants, the easier way to avoid crime would be to marry a rich man. As a result, women in this sample might want to avoid aggressive men. However, that may not be a feasible choice in Sudan or for some inner-city residents. Are these preferences stronger where the best response to violence is a violent response?

Second, and as suggested in the paper, finding out what these women actually do as opposed to their survey responses would be useful. Apart from the obvious benefits to seeing revealed preferences, this might also help to calibrate the responses. In each survey, the women were asked their responses to characterisations such as “bad-boy”, “broad shouldered” and “strong”. If a well-educated woman thinks an accountant with a motorcycle is a bad boy, that is probably a different level of masculinity compared to someone seeking physical protection from a real risk of crime.

I would also like to know more about the women in the survey. Are they in a partnership or married? How tall are they? Have they been a victim of crime? Does their fear of violence come from in the home or from people they know? How much property do they have which could be appropriated in a crime? This might help find some explanatory variables with some real predictive power. However, to test the basic hypothesis, we need a sample with more variation in the levels of violence and ideally, a sample in which we can observe real choices.

I don’t believe that this story sheds much light on my earlier ruminations on violence (most recent here) and the importance of a shift away from violence to allow characteristics such as hard work, intelligence and patience to be rewarded and spread through the population. It could be argued that as the study was conducted in a developed country, and among educated women in that country, we would expect violence to be a trait associated with low fitness. You would expect women to generally favour other traits, with the aggressive characteristics to be secondary and only accepted if they do not come at the cost of economically important traits. Again, to test this idea, we require more variation in the sample. We would need some of the sample to come from populations in which crime brings benefits to its purveyors and results in reproductive success.

Most of my interest in the use of biology in economics concerns humans being subject to the forces of selection like any other biological organism. With this starting point, it is natural to use many of the tools, models and methods of analysis that evolutionary biologists use.

But sometimes those models and tools are of value without the biological underpinnings. Evolutionary economics is one area where this is done, with the concepts of selection applied at the level of firms (as discussed in my last post).

Another instance of this crossover was in an article published by Andrew Haldane and Robert May, who have proposed that analysis of complexity and stability in ecosystems (dating from the 1970s) is useful in examining financial systems.

Haldane and May’s starting point was the recognition that complex ecosystems are not necessarily stable, with instability increasing with the number and strength of interactions. As an example, they noted recent work by Caccioli and colleagues which suggested that very strong fluctuations in the volume of trading in derivative markets could occur in a complex but complete market. As long as there is a positive premium to trading, banks will supply new financial instruments despite the lack of demand from non-banks. This expansion in derivatives comes at the cost of stability. There is no benefit to this expansion as market completeness has already been achieved.

Haldane and May developed a model which examined how banks may fail in response to a shock. In their model, each bank is linked to the same number of other banks and each has the same size of loans, capital reserves and ratio of loans to total assets. The more banks each bank is linked to reduces the number of failures following from the first bank failure, as the losses are spread more broadly. However, when later failures do occur, they will involve more banks. The model also showed the potential of small liquidity shocks to amplify through the system. Liquidity “hoarding” can have significant effects, as we saw in the recent crisis.

They also noted that their model reflected earlier work that had shown that as banks become increasingly homogeneous in their holdings (as they seek to cut their risks through diversification), the probability of the entire system collapsing increases. Once they are the same, the probability of one bank failing is the probability of all banks failing.

Haldane and May list a number of policy implications of their model. The first is that there is a broader role to minimum capital requirements for banks than simply reducing risk to each bank. Capital requirements could increase the entire system’s stability. Regulators should set capital limits with the broader systemic implications in mind.

A second implication concerned the goal of regulatory intervention. Typically, regulation might seek to reduce the probability of failure of all institutions to below a certain threshold. Haldane and May suggest that particular institutions that pose broad systemic risk should face higher regulatory requirements.

The most interesting suggestion concerned the desire to shape the topology of the financial system. As banks diversify, they became homogeneous. Accordingly, Haldane and May noted that a diversity objective of regulators may have merit. Trying to introduce “modularity” to prevent cascades through the entire system may also be desirable.

Nature published two responses to Haldane and May’s article: one in support of the use of such analysis by Thomas Lux, while Neil Johnson suggests that a model as simple as that used by Haldane and May will produce unreliable predictions that are only as robust as the assumptions used to prepare the model.

I do not have much sympathy for Johnson’s argument. While it is appropriate for models to contain health warnings about how broadly applicable the model is, models should by their nature have some simplicity. The question is whether any concepts are usefully illustrated. Haldane and May’s paper has several. Without a doubt, further work on these models by adding more elements and testing the robustness of the assumptions could be useful. That is often the way that science progresses. But to suggest that we cannot scale up a paper plane to a full-scale 747 does not mean that a paper plane can teach nothing about flight.

We may see more of these types of studies, or at least in Nature, as an editorial in the same issue suggested that where economic research has significant implications for fields such as behaviour, conservation biology, systems biology or physics, they would be happy to publish it. The editors suggested that this could benefit both economic science and natural science. My instinct is that economics has the most to gain.

As my research intersects economics and evolution, I have found it inconvenient that the term “evolutionary economics” is already taken. Evolutionary economics is an area of economics inspired by biological processes, with interactions between firms, industries and institutions examined using evolutionary methods. The economics is evolutionary by analogy.

I find the ideas in evolutionary economics attractive, which is natural given my interest in complex systems, out-of-equilibrium processes and the dynamic, emergent properties of economies. However, each time I read an evolutionary economics paper or book, I wonder if they are looking at the right level of selection. Should the agents in the models be firms or should they be the employees or managers of those firms (or their genes)? Putting it more bluntly, is evolutionary economics based on an inappropriate use of group selection?

The actions of firms in the lead up to the financial crisis provides an illustration. Could the web of financial firms and their interactions be usefully modelled without consideration of the range of incentives faced by employees and managers? Think Dick Fuld, his brinkmanship around saving Lehman brothers and the half a billion he was left with after it all went bad. An evolutionary economic model of this sector at the firm level might miss the major incentives (this could lead us to my previous question of what the objectives of these agents are).

So why we don’t start from a biological basis to begin with and then work up? Evolutionary economics would then become evolutionary in the truest sense. The flip side is that it is already difficult to model the interactions between firms. Adding more layers of employees, managers, creditors and shareholders may make the model more opaque, need a more complex set of assumptions and be more difficult to interpret. After all, the purpose of a model is to offer a set-up simple enough for analysis.

To assess what is the right balance, a fair starting point for analysis of an evolutionary economics paper is to ask whether the model would give the same predictions if an alternative, lower level of selection was examined? If not, it may be time for that evolutionary economic model to be evolutionary in fact and not by analogy.

An economist typically bases their economic models on an assumption that the economy is composed of agents who gain utility from consumption. From the beginning of the model, they take consumption to be the objective and all decisions by the agents aim to maximise their level of consumption within the budget constraint that they face.

While I recently posted on how most economists’ fixation on consumption might be biologically justified, I would like to approach the issue from another angle. To do that, it is worth going back a few years to a 1979 article by Paul Rubin and Chris Paul II on risk preferences.

Rubin and Paul’s starting point is the obvious step (from a biological perspective, not so obvious from an economic perspective) of defining utility as fitness. In their model, utility depends on the number of mates that each man gets.

They then asked what level of income would be required to obtain (or support) one mate. If the man’s income is not enough to attract a single mate, there is no utility from that income. All we have is an angry young man. Once the young man obtains one mate, it would take a very large increase in income to attract a second mate. However, losing a small amount of income and dropping below the threshold could cost them the mate they have.

If obtaining a mate, rather than consumption, is the objective, people would have preferences towards wealth that contrast with the way economists typically assume people react. Changes in wealth below the single mate threshold deliver no utility. An increase in wealth from below to above the threshold delivers a large jump in utility. Further wealth then delivers further incremental increases in utility. Contrast this with taking consumption as the objective, where each increase in wealth would deliver smooth increases in utility.

This difference in objectives can result in significant differences in the decisions taken. Rubin and Paul theorised that where a young male did not have a mate, that individual may be faced with a choice of either undertaking risky behaviour (from the perspective of expected wealth) and possibly accumulating enough wealth to acquire a mate, or undertaking wealth maximising (risk neutral) behaviour and having no mate with certainty. The young male’s action may seem irrational, but it is only through taking the risk that he can possibly reach the required wealth threshold. The safe, steady, low-paying job might normally deliver the highest expected wealth, but it might never be enough.

If this were the case, risk seeking young men would acquire more mates and leave more offspring, leading to the spread of that trait. Once they had attracted a mate, however, the incentives change. They would then seek to minimise risks as there would be little upside. They might lose the wealth necessary to keep their mate. Hence, older people are risk averse.

This risk profile is different to what would be expected from assuming a nice, stable relationship between utility and consumption. By thinking about what their objective might actually be, Rubin and Paul have developed a model which may have more predictive power and better reflect empirical evidence. It delivers a nice illustration that it is worth putting more thought into the assumptions underpinning each model, and at the most basic level, asking what the objective of the agents might actually be.

For several years, I have been relatively convinced that beyond a certain threshold, parenting does not matter. This belief came from two sources. The first was Judith Rich Harris’s book The Nurture Assumption in which she effectively argues that the children are socialised by other children, not their parents. The second was the concept that the variation attributable to genetic factors increases as you age (from memory I first read this in Matt Ridley’s The Agile Gene: How Nature Turns on Nurture – called Nature via Nurture in Australia). This implies that while environmental differences might affect the speed of development, the result is more robust to environmental factors.

I consider the idea that parenting does not matter to be relatively positive. As Harris argues in her book, instead of fretting about how you are shaping your children, you can relax, enjoy them and not worry that your actions could ruin their futures.

Adding to this picture, a new paper by Tucker-Drob and colleagues published in Psychological Sciences reports on a study on which 750 pairs of twins were tested at 10 months of age for mental ability. It was found that at 10 months, genes had a negligible influence on mental ability regardless of the socio-economic status of the family. However, the authors found that at the age of 2 years, genes have a measurable effect on variation in mental ability. This effect was most significant in the high socio-economic families, with genes accounting for around 50 per cent of the variation, while the effect of genes was negligible for those defined as low socio-economic status. I won’t go  further in the results here, but it is worth checking out Razib Khan’s deeper analysis of the paper.

This study supports the idea of larger genetic influence once a certain threshold is met and of increasing genetic influence as one ages. Razib and Jonah Lehrer noted that this study provides evidence that once the environmental variance is removed, the genetic variance remains. This comes from the diminishing marginal returns to investment in children. This makes sense, but the  question that hits me is why do rich parents bother with the effort they put into raising their children if, as Jonah suggests, rich parents don’t matter. You could write their actions off as being misguided as they chase increasingly low returns to their investment, but shouldn’t the revealed actions of these people tell us something.

So, here are a few suggestions to rationalise (or not) the investment, with varying degrees of plausibility. Some of these ideas are probably worth posts of their own down the track.

1. The old chestnut – people are irrational. We could use an evolutionary argument that we evolved in a Malthusian era in which humans had to dedicate a high proportion of their income to increasing child quality. Now that wealth is abundant, people are inclined to invest a similar proportion of their income in education despite it having severely diminishing returns. If this argument holds, we would expect that in this era of abundance, those who invest less in quality and direct those resources to quantity will grow in proportion of the population (this reflects the result of the Galor and Moav model).
2. The investment in children is a signal by the parents (conspicuous consumption), who still seek status.
3. The investment is a signal for the child. Those years in a top school may not deliver more educational benefit, but it looks good on the CV. This does assume, however, that the person looking at the CV believes that the investment matters.
4. Parents can influence the environmental factors that do matter. If you accept Harris’s argument that children socialise children, investment in attendance at a top private school might yield benefits (although I am not sure that a bunch of very rich class-mates is the what a child needs). However, you never know who your room mate at Yale might be.
5. The investment matters in other dimensions. Although a child may be of a certain intelligence or level of sociability no matter what you do, they will only be a pro-golfer or concert pianist if they put in their 10,000 hours of practice. I am not convinced that the rate of return from such investment is enough to underpin the huge level of parental investment that occurs. While it might have worked for Tiger Woods, how many other golf fathers are putting their kids through their paces?

Personally, I lean largely towards 2 and 3, with a dose of 1 thrown in – if status obtained through signalling does not lead to an increase in the quantity of children, the proportion of the population who do not have such a trait and invest in quantity rather than quality will grow.

Are there any other explanations I have missed out?

Since Bill Easterly stuck his head above the parapet last week and referred to complex systems in response to Paul Collier, the “complexity” community has been up in arms. In a quick reference to complexity, Easterly wrote:

A popular topic in the aid blogosphere this week was not about Haiti or Ivory Coast or south Sudan but about complex systems, i.e. systems that cannot be reduced to a simple mathematical or statistical model, where actions often have unintended effects.

He then (rightly in my opinion) questioned Collier’s ability to predict the consequences of supporting a coup, including Collier’s chain of predictions about the actions of various levels of Ivory Coast army officers. With what confidence can the course of a civil conflict be predicted? Not much if previous conflicts are any guide.

Although I agree with Easterly’s analysis, his use of complexity is out-of-place. First, complexity can be simple. As Philip Auerswald said:

[T]he core insight of the study of complexity …. is this: systems that are not just reduced to, but actually defined by, simple mathematical models, have the potential to generate extremely…well, complex behaviors.

For examples of this, I recommend Thomas Schelling’s Micromotives and Macrobehavior.

Second, and to me the most interesting point, comes from Auerswald’s likening of Easterly’s use of complexity to previous attempts by economists to tie complexity theory into Hayek’s concept of spontaneous order. While Hayek’s work provides some early thinking on complexity, Auerswald suggested that bringing these ideas together is a failed undertaking from the outset, with Hayek’s concern being about calculation in the presence of randomness, not the emergence of complexity in the absence of randomness. That distinction is important, and the problem identified by Hayek was fundamentally one of calculation (although the calculation problem was about more than just randomness).

However, there is a more important distinguishing feature. Hayek saw a benevolent force (or invisible hand) creating a more efficient system than humans could create by central planning. A complexity theorist might argue that there is no such benevolent force and that human interference may be required for an efficient outcome (this distinction was the central conclusion of a paper by Kilpatrick). If we look at Easterly’s argument under this distinction, it is much more Hayekian than of modern complexity theory.

The third point, and flowing directly from this distinction, is that if a system is complex, that (in itself) does not mean that we should not touch the system. We can see this in some of the work that has come out of the Santa Fe Institute. With concepts such as path dependence, increasing returns and out-of-equilibrium dynamics, one can argue that the current state of affairs is not ideal and a few “tweaks” might help. I don’t generally agree with that argument, but you need something more than “it’s too complex” to respond.

Fourth and finally, I don’t think this third point matters in the current debate. Complexity is not the major problem. As stated by David Ellerman in response to Easterly’s update on where the complexity debate is at:

The mistake in applying complexity theory to human relationships such as the education, management, development aid, and helping in general is that the basic problem is NOT that the human “systems” are complex, “messy,” nonlinear, etc. The basic problem …. is that success lies in achieving more autonomy on the part of the doers, and autonomy is precisely the sort of thing that cannot be externally supplied or provided by the would-be helpers. This is the fundamental conundrum of all human helping relations, and it is the basic reason, not complexity, why engineering approaches and the like don’t work.

Any theory that seeks to invoke human evolution as a factor in the Industrial Revolution needs to deal with how quickly humans can evolve and whether this rate of change is fast enough to be a factor.

I was recently browsing Gregory Clark’s web-page for his book A Farewell to Alms and came across a video of a seminar in 2007 involving Clark, Brad DeLong and Tyler Cowen. There were some interesting points throughout the session (and it is worth watching it all) but one interesting point was an argument by DeLong on the pace of evolution.

His argument was based on the following example. Suppose there is a patience gene in the population. Assume that each person with the patience gene has a two-thirds chance of being patient while those without the gene have a one-third chance. Of those who are patient, they have a two-thirds chance of being rich, versus one-third for the others. Finally, assume that those who are rich have a two-thirds chance of having children, while the rest have a one-third chance.

None of the numbers in the example seem implausible, although there is plenty of room to debate the specifics. Based on these numbers, DeLong noted that those with the patience gene have a 14 in 27 chance of having children, while those without have a 13 in 27 chance. DeLong translated this to a proportional growth rate of 1/27 or approximately 0.04 for those with the patience gene. Assuming 25 years per generation, it would take about 500 years to double the proportion of the patience gene in the population from, say, 1 per cent to 2 per cent.

The following table indicates how he came to that conclusion (the numbers are how many of each type):

DeLong took this slow rate of change to be a challenge for any genetically based theory of the Industrial Revolution. If those numbers were the last word, I would be inclined to agree. However, I would not rule out a scenario where a change in a relatively small part of the population could have large effects if s small group of individuals were responsible for a large proportion of innovation in an economy or there were positive feedback loops.

More importantly, a closer look at the numbers can change the assessment. The first issue is DeLong’s interpretation of his own example. While DeLong’s estimate of 0.04 for the rate of growth is approximately right when the population is composed of equal numbers of each genotype, it underestimates the growth rate when there are proportionally less patient genotypes. Take the situation where the population has 1 per cent patient genotypes. In such a case, the increase of patient genotypes is effectively their absolute growth rate as they make little difference to the total population. Therefore, they increase as a proportion of the population at a rate of 1/13, or approximately 8 per cent per generation.

This would see the patient genotypes increase to 2 per cent of the population in less than 10 generations, or around 250 years. They would quadruple their proportion of the population in 500 years. As their proportion grows, their proportional growth rate slows. However, an argument that patient genotypes increased from 5 to 20 per cent of the population over 500 years is certainly a basis for significant macroeconomic effects.

Further playing with the numbers gives us some other possibilities. If instead of using two-thirds, one-third as the basis of our calculations, we could use three-quarters-one quarter, giving the following:

These calculations yield us a proportional growth rate of 12 per cent when there is a low proportion of patient genotypes, and 6 per cent when the population is composed of around 50 per cent patient genotypes. That is a doubling in proportion every 6 generations or 150 years when there is a low proportions of patient genotypes.

Another alternative is to simply cut out a step and assume that patient genotypes have a two-thirds chance of being rich, while the others have a one-third chance. This dramatically increases the potential growth rates:

At low prevalence, the patient genotypes increase in proportion of the population at a rate of 25 per cent per generation. Every three to four generations, patient genotypes would double in proportion of the total population.

All of the above is fairly crude and open to debate. However, it seems to indicate that genetically based hypotheses about the Industrial Revolution are robust to this particular back of the envelope calculation.