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 TeasersJason Collins@GNXP Blogview

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I’m not sure how much drive-by traffic gnxp is continuing to receive, but figured it worthwhile to post a note about my latest working paper, which explores whether male signalling may have a role in driving economic progress. The abstract:

Sexual Selection, Conspicuous Consumption and Economic Growth

The evolution by sexual selection of the male propensity to engage in conspicuous consumption contributed to the emergence of modern rates of economic growth. We develop a model in which males engage in conspicuous consumption to send an honest signal of their quality to females. Males who engage in conspicuous consumption have higher reproductive success than those who do not, as females respond to the costly and honest signal, increasing the prevalence of signalling males in the population over time. As males fund conspicuous consumption through participation in the labour force, the increase in the prevalence of signalling males who engage in conspicuous consumption gives rise to an increase in economic activity that leads to economic growth.

I’ve posted some background to the paper over at Evolving Economics. I’ve also received some interesting feedback, including this post on The Conversation by Rob Brooks.

Finally, I have posted on SSRN an update to my paper examining the Galor-Moav model in which economic growth is triggered by the interplay between technological progress and an inherited preference for quality or quantity of children. I posted about it on gnxp mid-last year. The revision carries the same story as the original paper, but is tighter and cuts out some of the flotsam.

(Republished from by permission of author or representative)
• Category: Science 
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*This is a cross post from Evolving Economics.

Evidence from twin studies implies that economic and political traits have a significant heritable component. That is, some of the variation between people is attributable to genetic variation.

Despite this, there has been a failure to demonstrate that the heritability can be attributed to specific genes. Candidate gene studies, in which a single gene (or SNP) is examined for its potential influence on a trait, have long failed to identify effects beyond a fraction of one per cent. Further, many of the candidate gene results fail to be replicated in studies with new samples.

An alternative approach to genetic analysis is now starting to address this issue. Genomic-relatedness-matrix restricted maximum likelihood (GREML – the term used by the authors of the paper discussed below) is a technique that looks to examine how the variance in traits can be explained by all of the SNPs simultaneously. This approach has been used to examine height, intelligence, personality and several diseases, and has generally shown that half of the heritability estimated in twin studies can be attributed to the sampled SNPs.

A new paper released in PNAS seeks to apply this approach to economic and political phenotypes. The paper by Benjamin and colleagues shows that around half the heritability in economic and political behaviour observed in behavioural studies could be explained by the array of SNPs.

The authors used the results of recent surveys of subjects from the Swedish Twin Registry, who had their educational attainment, four economic preferences (risk, patience, fairness and trust) and five political preferences (immigration/crime, foreign policy, environmentalism, feminism and equality, and economic policy) measured. The GREML analysis found that for one economic preference, trust, the level of variance explained by the SNPs was statistically significant, with an estimate of narrow heritability of over 0.2. Two of the political preferences, economic policy and foreign policy, had narrow heritability that was statistically significant, with heritability estimates above 0.3 for each of these. The authors noted that as the estimates are noisy and GREML provides a lower bound, the results are consistent with low to moderate heritability for these traits.

Educational attainment was also found to have a statistically significant result, although the more precise measurement of educational attainment and the availability of this data across all subjects made that result more likely.

This result is corroboration of the evidence from twin studies and provides a basis for believing that molecular genetic data could be used to predict phenotypic traits. However, one interesting feature of the GREML method of analysis is that after conducting this analysis with one sample, the data obtained does not assist in predicting the traits for someone out of the sample. This technique shows the potential of molecular genetic data without directly realising those results.

As a comparison, the authors examined whether any individual SNPs might predict economic or political preferences, but found none that met the significance test standard of 5×10-8. Such a high level of significance is required to reflect the huge number of SNPs that are being tested.

The authors also conducted the standard comparison between monozygotic (identical) and dizygotic (fraternal) twins, which resulted in heritability estimates consistent with the existing literature, although with a much larger sample than typically used. Looking through the supplementary materials, the major surprise to me was that the twin analysis suggests that patience has low heritability, with a very low correlation between twins and almost no difference between monozygotic and dizygotic twins (in fact, for males, dizygotic twins were more similar).

The authors draw a few conclusions from their work, many which reflect the argument in a Journal of Economic Perspectives article from late last year. The first and most obvious is that we should treat all candidate gene studies with caution. Hopefully some journals that insist on publishing low sample size candidate gene studies will pay attention to this. Where they are going to be conducted, you need very large samples, and significantly larger than are being used in most studies being published.

Meanwhile, they are still hopeful that there can be a contribution from genetic research, particularly if the biological pathways between the gene and trait can be determined. This might include using genes as instrumental variables or as control variables in non-genetic empirical work. The use as instrumental variables does require, however, some understanding of the pathways through which the gene acts as it may have multiple roles (that is, it is pleiotropic). They also suggest that the focus be turned to SNPs for which there are known large effects and the results have been replicated.

On element of analyses of political and economic preferences that makes me slightly uncomfortable is the loose nature of these preferences. For one, the manner in which they are elicited from subjects can vary substantially, as can the nature of the measurement. Take the 2005 paper by Alford and colleagues on political preferences, which canvassed 28 political preferences. Many of the views are likely to change over time and be highly correlated with each other. And why stop at 28?

As a result, it may be preferable to take a step back and ensure that data on higher level traits are collected. I generally consider that IQ and the big five personality traits (openness, conscientiousness, agreeableness, extraversion and stability) are a good starting point and are likely to capture much of the variation in political and economic preferences. For example, preferences such as patience are likely to be reflected in IQ, while openness captures much of the liberal-conservative spectrum of political leaning. Starting from a basis such as this may also give greater scope for working back to the biological pathways.

The Social Science Genetics Association Consortium is doing some work in harmonising phenotypes across large samples. Hopefully their work will lead in this direction.

(Republished from by permission of author or representative)
• Category: Science • Tags: Genomics 
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I have added a new page over at Evolving Economics with a suggested reading list for those interested in the intersection of economics and evolutionary biology. The list is here.

The list is a work is progress, and I plan to update it as new sources emerge or are suggested (or when I realise what oversights I have made). I also intend to constrain it to the best sources, rather than being a complete list on every thought on the topic.

I am interested in suggestions from gnxp readers, so please let me know if you have any thoughts. Comments can also be made at the bottom of the reading list page.

(Republished from by permission of author or representative)
• Category: Science • Tags: Books 
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A cross post from Evolving Economics:

The heritability straw man has copped another bashing, this time in the Journal of Economic Perspectives. In it, Charles Manski picks up an old line of argument by Goldberger from 1979 and argues that heritability research is uninformative for the analysis of policy.

Manski starts by arguing that heritability estimates are based on the assumption that there is no gene-environment correlation. Manski writes:

The assumption that g and e are uncorrelated is at odds with the reasonable conjecture that persons who inherit relatively strong genetic endowments tend to grow up in families with more favorable environments for child development.

Any review of discussions of heritability, whether in the peer-reviewed literature or the blogosphere, will show that his claim is generally false. The proviso that the heritability estimate is only relevant to the existing environment is usually threaded through any discussion of heritability.

It is true that gene-environment covariance can affect estimates of heritability. Yet this does not mean that existing estimates have no value, nor that there are not methods that seek to account for the covariance. For example, the use of comparisons between misdiagnosed identical twins and actual identical twins allows for bounded estimates of heritability to be developed (pdf).

Manski’s broader claim, adopted directly from Goldberger, is that even if you knew the heritability of a trait, it tells you nothing about social policy. Manski uses Goldberger’s eyeglasses example as an illustration:

Consider Goldberger’s use of distribution of eyeglasses as the intervention. For simplicity, suppose that nearsightedness derives entirely from the presence of a particular allele of a specific gene. Suppose that this gene is observable, taking the value g = 0 if a person has the allele for nearsightedness and g = 1 if he has the one that yields normal sight.

Let the outcome of interest be effective quality of sight, where “effective” means sight when augmented by eyeglasses, should they be available. A person has effective normal sight either if he has the allele for normal sight or if eyeglasses are available. A person is effectively nearsighted if that person has the allele for nearsightedness and eyeglasses are unavailable.

Now suppose that the entire population lacks eyeglasses. Then the heritability of effective quality of sight is one. What does this imply about the usefulness of distributing eyeglasses as a treatment for nearsightedness? Nothing, of course. The policy question of interest concerns effective quality of sight in a conjectured environment where eyeglasses are available. However, the available data only reveal what happens when eyeglasses are unavailable.

Manski and Goldberger may be correct that the heritability estimate is uninformative as to the efficacy of distributing eyeglasses, but it is useful in assessing other policy responses to the problem and the trade-offs between them. Is it possible to prevent the eyesight loss in the first place? Is that policy cheaper and more effective than eyeglasses? If the heritability estimate was zero, you would look to the environmental causes and ask whether the eyesight problem is more appropriately dealt with by addressing the cause rather than by distribution of eyeglasses.

There is no shortage of other areas where heritability estimates might add value. Heritability estimates can inform whether it is an effective use of resources to make sure that everyone has a university degree or is over six-foot tall. Is everyone putty in the hands of the policy maker, or are there some constraints? On a personal level, Bryan Caplan’s use of heritability in Selfish Reasons to Have More Kids is a useful input to his parenting strategy. For me, the most salient example of the usefulness of heritability research comes from examination of the heritability of IQ among children. Among high socioeconomic status families, the heritability tends to be high. Among low socioeconomic status families, it is significantly lower. This suggests that there is significant room to improve the outcomes of the children at the bottom of the socioeconomic ladder in the early years of their life (assuming those changes have effects that persist into adulthood). Increasing heritability of IQ might be evidence that environmental disadvantages are being ameliorated and opportunity equalised.

The latter part of Manski’s paper turns to the use of genes as covariates in statistical regressions. Regression identifies statistical association and not causation, which appears to be an important point in attracting Manski to this use. Noting the wealth of data being created and the possibility of observing changes in the effect of genes as the environment changes, Manski considers that these regression exercises may assist in examining how genes and environment interact.

I don’t disagree with Manski, but at present, genome association studies have plenty of issues. First, there is the missing heritability problem. To date, the magnitude of the identified effect of genes on most traits accounts for a miniscule proportion of the trait’s heritability. This points to the important role played by heritability research to provide direction to research on genes as covariates. It also indicates that until these genes are found, heritability estimates will be more informative for social policy.

A second issue is that with 30,000 odd genes and the ability to test so many of them for correlation with traits, many are found to have a statistically significant relationship through chance. As blogged about recently by Razib, this is shown when people seek to replicate earlier results – such as when it was found that most reported genetic associations with general intelligence are probably false positives (pdf).

Finally, genome based research is now feeding back into estimates of heritability. From a recent paper:

We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits.

Despite all the critiques about methodology, most new studies confirm that the old “methodologically poor” heritability estimates were in the right ballpark. The problem is not that the estimates are not useful, but rather that they are not used.

Manski, C. (2011). Genes, Eyeglasses, and Social Policy Journal of Economic Perspectives, 25 (4), 83-94 DOI: 10.1257/jep.25.4.83

(Republished from by permission of author or representative)
• Category: Science • Tags: Genetics, Heritability 
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*A cross-post from Evolving Economics

There is another interesting topic in this month’s Cato Unbound, with Michael Shermer arguing in the lead essay that human nature is best represented by the libertarian political philosophy.

Shermer (rightly) spends most of the essay shooting down the blank slate vision of humans that underpins many policies on the left, and suggests that moderates on both the left and right should accept a “Realistic vision” of human nature. He then simply states that the libertarian philosophy best represents this vision. Unfortunately, Shermer provides no explanation about why that might be the case, and in particular, does not detail why libertarianism might better reflect human nature than conservatism.

In the first response to Shermer’s essay, Eliezer Yudkowsky puts Shermer’s argument as such:

[B]ecause variance in IQ seems to be around 50% genetic and 50% environmental, the Soviets were half right. And that this, in turn, makes libertarianism the wise, mature compromise path between liberalism and conservatism.

Yudkowsky’s response to this argument is spot on:

In every known culture, humans experience joy, sadness, disgust, anger, fear, and surprise. In every known culture, these emotions are indicated by the same facial expressions. …

Complex adaptations like “being a little selfish” and “not being willing to work without reward” are human universals. The strength might vary a bit from person to person, but everyone’s got the same machinery under the hood, we’re just painted different colors.

Which means that trying to raise perfect unselfish communists isn’t like reading Childcraft books to your kid, it’s like trying to read Childcraft books to your puppy.

The Soviets were not 50% right, they were entirely wrong. They weren’t quantitatively wrong about the amount of variance due to the environment, they were qualitatively wrong about what environmental manipulations could do in the face of built-in universal human machinery.

Shermer’s argument was a change from the line of reasoning that I have heard from him before, which is that if the left understood that capitalism is an emergent system like evolution, they would be more accepting of it. I find that argument even less convincing. My understanding of evolution provides one of the strongest challenges to my libertarian leanings – evolution is full of wasteful competition for relative status and what is good for the individual is often not good for the group.

The weakness of these arguments is probably reflected in the deeper rationale for Shermer’s libertarianism. As Yudkowsky questions, is human nature the real reason for Shermer’s libertarianism?

Would Michael Shermer change his mind and become a liberal, if these traits were shown to be 10% hereditary?

… Before you stake your argument on a point, ask yourself in advance what you would say if that point were decisively refuted. Would you relinquish your previous conclusion? Would you actually change your mind? If not, maybe that point isn’t really the key issue.

Yudkowsky’s answer to the question of why he is a libertarian is similar to mine:

When I ask myself this question, I think my actual political views would change primarily with my beliefs about how likely government interventions are in practice to do more harm than good. I think my libertarianism rests chiefly on the empirical proposition—a factual belief which is either false or true, depending on how the universe actually works—that 90% of the time you have a bright idea like “offer government mortgage guarantees so that more people can own houses,”someone will somehow manage to screw it up, or there’ll be side effects you didn’t think about, and most of the time you’ll end up doing more harm than good, and the next time won’t be much different from the last time.

A human nature thread could underlie some of this explanation, with the nature of individuals in government and bureaucracy shaping the outcomes from government intervention. However, an understanding of human nature, in itself, does not settle the case for libertarianism. It may provide some support, but it provides just as many challenges.

(Republished from by permission of author or representative)
• Category: Science 
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**This is a cross-post from my blog Evolving Economics

In my last post, I discussed Oded Galor and Omer Moav’s paper Natural Selection and the Origin of Economic Growth. As I noted then, my PhD supervisors, Juerg Weber and Boris Baer, and I have written a discussion paper that describes a simulation of the model.

In the discussion paper we consider the entry of people into the population that have a low preference for child quality – i.e. they weight child quantity more highly. Entry could be through migration or mutation. We show that if people with a low enough preference for quality enter the population, their higher fitness in the modern growth state can drive the economy back into Malthusian conditions.

To show this, we simulated a version of the model which had present at a low level in the initial population a genotype with a very low preference for educating their children (I refer to them as the strongly quantity-preferring genotype). This strongly quantity-preferring genotype has a similar fitness to other genotypes that do not educate in the Malthusian state, and declines in prevalence while the quality-preferring genotype increases.

Once the economy takes off into the modern growth state, the strongly quantity-preferring genotype has the highest fitness as it dedicates the lowest proportion of its resources to educating its children. The strongly quantity-preferring genotype increases in prevalence until, eventually, the average level of education plummets, undermining technological progress. The world returns to a Malthusian state, with high population growth eroding the income benefits of all earlier technological progress.

The following chart shows the rate of growth of population, technological progress and income per person. The first 70 to 80 generations look like the base model simulation I described in my earlier post. However, after that point, technological progress plummets to zero. For the next 150 or so generations, population growth is positive, which can occur as per person income is above subsistence. Eventually, population growth drives income down to subsistence levels.

In the next figure, you can see that the strongly quantity-preferring genotype, genotype c, grows from being a negligible part of the population to being over 90 per cent . It is this change in population composition that drives the return to Malthusian conditions (you can also see the small peak in quality-preferring types around generation 48 that kicks off the Industrial Revolution). The strongly quantity-preferring genotypes educate their children far less than the other genotypes, depressing technological progress.

There is no escape from the returned Malthusian conditions. The quality-preferring genotype will have a fitness advantage in this new Malthusian state and will increase in prevalence. Whereas that caused a take-off in economic growth the first time, this time there is no take-off. The strongly quantity-preferring types, which now dominate the population, cannot be induced to educate their children. They simply breed faster to take advantage of any technological progress spurred by the small part of the population that is educating their children.

This result could also be achieved by introducing the strongly quantity-preferring genotype into the simulation at other points in time. If it occurs after the Industrial Revolution, the timing of the return to Malthusian conditions will occur later. However, short of restricting the range of potential quality-quantity preferences, there is no way to avoid the return to Malthusian conditions in this version of model. The strongly quantity-preferring genotypes will always have a fitness advantage when income is above subsistence and their population growth will drive income back down to subsistence levels.

There are, of course, a few possible interpretations of this result. The model or assumptions may be missing an important element (or at the extreme are wrong). Humans may only have quality-quantity preferences in the growth promoting range. Or possibly, modern levels of economic growth are only transient.

(Republished from by permission of author or representative)
• Category: Economics, History, Science • Tags: Genetics 
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**This is a cross-post from my blog Evolving Economics

As I have focussed my PhD research on the link between evolution and long-term economic growth, for months I have meant to blog on the core paper in this area, Natural Selection and the Origin of Economic Growth by Oded Galor and Omer Moav. I have held off writing this post pending finalisation some of my own related work, which I have now done.

This paper is somewhat of an outlier as I’m not aware of any other paper that models the Industrial Revolution as a result of natural selection (apart from a soon to be published paper by Galor and Michalopoulos). There is another paper by Zak and Park that examines population genetics and economic growth (a topic for another blog post) but they do not directly tackle the Industrial Revolution. In A Farewell to Alms, Greg Clark notes that Galor and Moav’s paper reignited his interest in this topic.

Galor and Moav’s paper is based on a model that has two types of people in the population. Each of these types has a different, genetically inherited preference for quality or quantity of children. The quality-preferring genotype wants their children to have higher human capital, so they invest more in their education, while the quantity-preferring genotype is more interested in raw numbers.

During the long Malthusian era in which both genotypes struggle to earn enough to subsist (i.e. during the thousands of years leading up the Industrial Revolution), the quality-preferring genotypes have a fitness advantage. As the quality-preferring genotypes are of higher quality, they earn higher wages. These higher wages are more than enough to cover education expenses, so they are also able to have more children than the quantity-preferring genotypes.

This fitness advantage leads the quality-preferring genotypes to increase in prevalence. As this occurs, technological progress increases, as the average level of education in the population drives technological progress. This in turn increases the incentive to invest in education, creating a feedback loop between technology and education.

As this goes on, the population grows. Per capita income does not increase as any technological progress is balanced out by population growth, which is the central problem of the Malthusian world.

Eventually, the rate of technological progress gets high enough to induce the quantity-preferring genotypes to invest in education. When this happens, the average level of education jumps, boosting technological progress and causing the Industrial Revolution.

During this process, the population growth rate changes. Up to the time of the Industrial Revolution, population growth increases with technological progress. However, when the level of technology leaps with the Industrial Revolution, the level of education becomes so high that population growth drops dramatically. Everyone is investing more into education than raw numbers of children.

From an evolutionary perspective, the Industrial Revolution also changes the selection pressure in the model. After the Industrial Revolution, the quality-preferring genotypes invest so much into education that they have lower fertility than the quantity-preferring genotypes. They then reduce in prevalence, their fitness advantage erased.

Galor and Moav paper work through the dynamics of the model using phase diagrams. It is not particularly easy or intuitive to see the processes working together in their paper, so my two PhD supervisors and I have just put out a discussion paper that describes simulations of the model – and shows the dynamics in a form that is easier to visually comprehend. In the chart below, you can see the dramatic jump in technological progress around generation 45 of the simulation, with per capita income growth also jumping at that time. Meanwhile, population growth drops to zero.

This second chart shows the change population composition. The quality-preferring genotype (genotype a) steadily increases in prevalence through to the Industrial Revolution, peaking at just under 5 per cent of the population. Afterwards, it is selected against.

This change in selection pressure has an interesting implication. While natural selection is the trigger of the Industrial Revolution, the population composition before and after the transition is the same. There is no difference in population composition between developed and undeveloped countries. The only time there is a difference in population composition is during the transition, when the quality-preferring genotypes peak.

In some ways, the natural selection occurring in Galor and Moav’s model is a sideshow to the main event, the quality-quantity trade-off. In a similar model by Galor and Weil, a scale effect triggered the Industrial Revolution – that is, the concept that more people leads to more ideas, so technological progress increases with population growth. I am sure that other triggers could be substituted.

That highlights the point where I am not convinced that the model is true (to the extent that a model can be). As far as human evolution relates to economic growth, I expect that inherent quality is more important (and by quality, I mean economically useful qualities) than the quality-quantity trade-off. The Industrial Revolution was possible because higher quality people were selected for in the lead-up. Higher quality people had more children as they were genetically of higher quality and they passed their high quality genes to these children. The investment in quality is choosing a high quality mate.

If quality is inherent, a high-quality person should have as many children as possible and this would have little effect on quality. For a man of low resources, his larger problem is convincing a woman to mate with him and not deciding on the right quantity-quantity mix.

The other thing that I should note is that, like most economic models, Galor and Moav’s model includes consumption with no clear evolutionary rationale (an issue I have discussed in an earlier post). Why do people in the model consume more than subsistence? If some people chose to focus all excess consumption into raising children they would come to dominate the population. This might be justified as being something to which the population has not yet adapted, but that explanation does not satisfy me.

Having made these quibbles, the model is still an impressive feat. It would not have been an easy task to create a model with technological progress, population and per capita income all following a path that resembles the last few thousand years of economic growth. There are some further issues and extensions to the model that we explore in the discussion paper I referred to above, but I’ll talk about them in my next post.

Galor, O., & Moav, O. (2002). Natural Selection and the Origin of Economic Growth The Quarterly Journal of Economics, 117 (4), 1133-1191 : 10.1162/003355302320935007

(Republished from by permission of author or representative)
• Category: History, Science • Tags: Genetics 
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Bryan Caplan has a simple recommendation. Have more kids. If you have one, have another. If you have two, consider three or four. As Caplan spells out in his book, Selfish Reasons to Have More Kids, children have higher private benefits than most people think. Research shows that parents can take it easy, as there is not much they can change about their children. He also argues that there are social benefits to a higher population, with more people leading to more ideas, which are the foundation of modern economic growth.

Despite being someone who is about to face the number of children question, I am not sure that I am the target audience for Caplan’s book. I don’t mean that Caplan wouldn’t recommend to me that I have more children. Rather, as someone who has thought a lot about evolution and economics and having read many of the giants on whose shoulders Caplan stands (particularly Judith Rich Harris and Julian Simon), I didn’t learn a lot from the book. As Caplan ran through the examples of twin studies showing all the different facets of a child’s personality or life outcomes that a parent has no influence over, I found myself wanting more meat and analysis. I felt similarly about his arguments for a larger population.

Having said that, and recognising that I am not the target audience, most readers would probably learn a lot. Caplan provides a fun, easy to read book that gives a great, swift overview of his case. This is the book I’ll be giving to parents, grandparents and friends who have heard me go on about twin studies and genetics. I particularly like it that Caplan gives some practicality to the swathes of findings about trait heritability.

I felt that the largest shortcoming of the book was that it does not address the third factor affecting outcomes for the child – non-shared environment. While heritability explains some of the variation in a child’s traits and outcomes, and nurture generally explains close to nothing, Caplan does not explore the research into non-shared environment. Instead, he puts the variation down to free will:

So far, researchers have failed to explain why identical twins – not to mention ordinary siblings – are so different. Discrediting popular explanations is easy, but finding credible alternatives is not. Personally, I doubt that scientists will ever account for my sons’ differences, because I think their primary source is free will. Despite genes, despite family, despite everything, human beings always have choices – and when we can make different choices, we often do.

Caplan states that several of his friends call his belief in free will his “most absurd belief”. While I don’t know all of Caplan’s beliefs, for the moment I will agree with his friends. In Judith Rich Harris’s The Nurture Assumption, she explored what this non-shared environment might be. In her case, she argued for the effect of peers. What bothered me most with Caplan’s take on free will was not that he did not agree with Harris’s suggestion, but rather, his “it’s all too hard” approach. Unlike Caplan, I expect that over the next few years we will add even further to the explanations for how non-shared environment influences children.

When Caplan came to addressing potential reasons why family size has decreased over the last 60 years, I wanted to hear his arguments in more depth. Take Caplan’s take on Gary Becker’s argument that as women now earn more, they have to give up more income to have kids:

This explanation sounds good, but it’s not as smart as it seems. Women lose more income when they take time off, but they also have a lot more income to lose. They could have worked less, earned more, and had more kids. Since men’s wages rose, too, staying home with the kids is actually more affordable for married moms than ever. If that’s too retro, women could have responded to rising wages by working more, having more kids, and using their extra riches to hire extra help.

It sounds neat, but Caplan assumes that the income effect, which would tend to increase the number of children, dominates the substitution effect, which would tend to decrease the number. It is perfectly plausible for the substitution effect to dominate and women to decide to have fewer children, but Caplan does not address this. He might be right, but as there is no depth to his discussion, it is hard to judge the strength of his argument.

Caplan does point out that in the United States, fertility bottomed out in the 1970s. This occurred despite further increases in income and Caplan uses this as evidence against any income based hypothesis. But the people having children in the 1970s are different to the people having children now. For those women who chose to have no children in the 1970s and possibly responded most strongly to the income effect, they did not contribute to the gene pool and any heritable predisposition has disappeared with them. It is the children of larger families that are having children today. Second, the net fertility rate in the United States is substantially affected by recent immigrants.

Caplan’s preferred view on the decline in fertility is that we have gained a small amount of foresight, allowing us to see the negative effects of early childhood, but not gained enough foresight to note the benefits of children when they are older. There might be some truth to this, but I expect that the other factors that Caplan dismisses are also relevant.

One point where I disagree with Caplan is around his statement that men and women see eye to eye on the number of children they wish to have. Caplan considers that this puts to bed any arguments around women having increased bargaining power. While Caplan’s statistic is true in the most basic sense, the number of children that a man or woman want are a function of a number of things. The main one of these is who the other parent will be. If a woman is paired with the man of her dreams she is likely to want more children than if she is married to a guy who showed promise but has gone nowhere. While Caplan notes that condoms have been widely available since the end of World War II, the pill gave women extra power to decide who exactly the parent is. There is some interesting scope for sexual conflict here.

When it comes to policy prescriptions arising from his position, Caplan explicitly opposes natalist policies to increase birth rates. Caplan states:

After natalists finish lamenting low birthrates, they usually get on a soapbox and demand that the government “do something about it.” There are two big reasons why I refuse to join their chorus. First, while I agree that more kids make the world a better place, I oppose social engineering – especially for such a personal decision. When people are deciding how many children to have, government ought to mind its own business.

Instead, Caplan suggests that grandparents replicate the natalist incentives privately. Given this, it is interesting that Caplan drifts into supporting natalist tax credits in his recent Cato Unbound essay (as I have commented on here). I prefer his arguments for the use of private incentives from his book than his more recent encouragement of government action.

*This is a cross-post from my blog Evolving Economics.

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• Category: Science • Tags: Books, Genetics 
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Like the level of selection debate, the debate about what heritability means has a life of its own. The latest shot comes from Scott Barry Kaufman who argues (among other things) that:

The heritability of a trait can vary from 0.00 to 1.00, depending on the environments from which research participants are sampled. Because we know that genes play some role in the development of any trait, the precise heritability estimate doesn’t matter in a practical sense.

Heritability depends on the amount of variability in the environmental factors that contribute to a trait. The problem is that our understanding of the factors that contribute to the development of human traits in general — and to IQ in particular — is currently so deficient that we typically do not know if the environmental factors important in the development of a particular trait are stable across testing situations, vary somewhat across those situations, or vary wildly across those situations.

In his conclusion he states:

At the very least, heritability tells us how much of the variation in IQ can be accounted for by variation in genetic factors when development occurs in an exquisitely specific range of environments. However, David S. Moore has argued that even this is not significant when we realize that the magnitude of any heritability statistic reflects the extent of variation in unidentified non-genetic factors that contribute to the development of the trait in question.

(HT: Bryan Caplan)

Through his post, Kaufman constructs a series of paper tigers, tears them down and implies that because the extreme case does not hold, we should be wary of heritability estimates. I did not find much to disagree with in his examples, but the I differed on the conclusions we should draw.

So, where I do not agree – first, the heritability estimate does matter. While I don’t think it is hugely important whether the heritability of IQ in a specific sample is 0.5 or 0.6, it is important whether the measured heritability is 0 or 0.6. As Caplan notes in his post:

My money says, for example, that the average adult IQ heritability estimate published in 2020 will exceed .5.

I think that Caplan is right (although I might have stated some conditions about the relevant sample), and Kaufman’s argument overstates how finely tuned the environment needs to be to get a meaningful heritability estimate. Heritability estimates of a sample of children growing up in extreme poverty might be much lower (or zero) but as is found again and again, once the basic requirements of a child are met, heritability estimates for IQ are consistently above 0.4. We can construct arguments that in each study there are different gene-environment interactions and so on, but if genes weren’t important in variation in IQ and the gene-environment interactions weren’t consistent to some degree, why would such consistent heritability results (and correlation between parent and child IQ) be found?

Further, these results matter. They suggest that poverty is affecting the IQ of some children, and policies could be tailored to cut this disadvantage. For children not subject to deficient environments, the high heritability of IQ should influence policies such as those for education. Children are different and the education system should take this into account.

Implicit in Kaufman’s post was the “its all too complex” argument. Social and biological sciences are complex (which is why I find them interesting). However, if we fully accepted Kaufman’s argument that “our understanding of the factors that contribute to the development of human traits … is currently so deficient that we typically do not know if the environmental factors important in the development of a particular trait are stable across testing situations”, it would put into question most of the data analysis in economics, sociology and biology. Econometrics operates on the idea of all other things being equal.

Fortunately, Kaufman has not taken the Gladwell-esque approach of suggesting that we forget about genetic factors. Kaufman suggests further research into how nature and nurture are intertwined. If it is all too complex, we should start unwinding the complexity. However, I believe that, in the meantime, this complexity does not mean that we should throw out all the results that have previously been obtained.

**This is a cross-post from my blog Evolving Economics.

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• Category: Science • Tags: Genetics 
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As I noted in my recent post on Malcolm Gladwell’s Outliers, Gladwell ignored the possibility that traits with a genetic component, other than IQ, might play a role in determining success. His approach reminded me of a useful paper by Samuel Bowles and Herbert Gintis from 2002 on the inheritance of inequality. Bowles and Gintis sought to explain the observed correlation between parental and child income (a correlation of around 0.4) by examining IQ, other genetic factors, environment, race and schooling.

As an example of the consequences of the transmission of income. Bowles and Gintis cited a paper by Hertz which showed that a son born to someone in the top decile of income had a 22.9 per cent chance of attaining that decile himself, compared to a 1.3 per cent chance for someone born to parents in the bottom decile. Conversely, a child born to parents in the top decile had only a 2.4 per cent chance of finishing in the lowest decile compared to over 31.2 per cent for those born to bottom decile parents.

As Gladwell did, Bowles and Gintis started their examination with IQ. To calculate the inheritance of income through genetically inherited IQ, Bowles and Gintis considered the correlation between parent IQ and income, the heritability of IQ from parent to child and the correlation between IQ and income for the child. Breaking this down, Bowles and Gintis used the following steps and estimates:

1. The correlation between parental income and IQ is 0.266.

2.If the parents’ genotypes are uncorrelated, the genetic correlation between the genotype of the parents and of the child is 0.5. This can be increased with assortive mating (people pairing with people more like themselves) to a maximum of one (clones mating). Bowles and Gintis use 0.6.

3.The heritability of IQ is 0.5.

4. The correlation between child income and IQ is 0.266. Multiplying these four numbers together gives the intergenerational correlation of income due to genetically based transmission of IQ. I think there is a mistake in the calculations used by Bowles and Gintis, as they find an intergenerational correlation of 0.01, where I calculated 0.02. This leads to genetically inherited IQ variation explaining 5.3 per cent of the observed intergenerational correlation in income. Regardless of the error, this is a low proportion of the income heritability. (After I wrote this post I did a google search to find if someone had spotted this error before – and they had – on a earlier Gene Expression post on this same paper.)

I would have used some slightly higher numbers, but pushing the numbers to the edges of feasible estimates, such as increasing the correlation between income and IQ to 0.4, the genetically based correlation between parent and child IQ to 0.8 and the degree of assortive mating so that parent-child genotype correlation is 0.8 only yields an intergenerational correlation of 0.10. Genetically inherited IQ would account for approximately 26 per cent of the observed intergenerational correlation.

Unlike Gladwell, Bowles and Gintis then asked what role other genetic factors may play. By using twin studies, which provide an estimate of the degree of heritability of income (using the difference in correlation between fraternal and identical twins) and the degree of common environments of each type of twin, Bowles and Gintis estimated that genetic factors explain almost a third (0.12) of the 0.4 correlation between parent and child income. Loosening their assumptions on the degree of shared environments by identical twins compared to fraternal twins (i.e. assuming near identical environments for both identical and fraternal twins) can generate a higher estimate of the genetic basis of almost three-quarters of the variability in income.

From this, it seems that genetic inheritance plays an important role income transmission between generations. The obvious question is what these factors might be. I expect that patience or ability to delay gratification must play a role, although I would expect that there would be a broad suite of relevant personality traits. I would also expect that appearance and physical features would be relevant. Bowles and Gintis do not take their analysis to this point.

The authors finished their analysis with some consideration of other factors, and conclude that race, wealth and schooling are more important than IQ as a transmission mechanism of income across generations (although as the authors noted, they may have overestimated the importance of race by not including a measure of cognitive performance in the regression). That conclusion may be fair, but as they had already noted, there is a substantial unexplained genetic component.

This highlights the paper’s limitation, as once the specific idea that heritability of IQ is a substantial cause of intergenerational income inequality has been dented, the identification of other (but unknown) genetic factors leaves open a raft of questions about income heritability. Using Bowles and Gintis’s conservative estimates, we still have 25 per cent of income heritability being put down to genetic factors without any understanding of what these traits are and the extent of the role they play.

In their conclusion, Bowles and Gintis touch on whether policy interventions might be based on these results. They are somewhat vague in their recommendations, but suggest that rather than seeking zero intergenerational correlation, interventions should target correlations that are considered unfair. They suggest, as examples, that there are large majorities supporting compensation for inherited disabilities while intervention for good looks is not appropriate.

One thing I find interesting in an analysis of heritability such as this is that over a long enough time horizon, to the extent that someone with a trait has a fitness advantage (or disadvantage), the gene(s) behind the trait will move to fixation (or be eliminated) as long as heritability is not zero. The degree of heritability is relevant only to the rate at which this occurs and only in a short-term context. The obvious question then becomes (which is besides the point of this post) whether IQ currently yields a fitness advantage. Over a long enough time period, variation will tend to eliminate itself and Bowles and Gintis would be unable to find any evidence of IQ heritability affecting income across generations.

**This a cross-post from my blog Evolving Economics, which is my usual blogging home.

Bowles, S., & Gintis, H. (2002). The Inheritance of Inequality Journal of Economic Perspectives, 16 (3), 3-30 DOI: 10.1257/089533002760278686

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• Category: Science • Tags: Genetics 
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It is a bit over a year since Geoffrey Miller wrote this piece foreshadowing a crisis in conscience by human geneticists that would become public knowledge in 2010. The crisis had two parts: that new findings in genetics would reveal less than hoped about disease and that they would reveal more than feared about genetic differences between classes, ethnicities and race.

Now that we are through 2010 with no crisis (that I was aware of – is this crisis still happening in private?), I thought I’d revisit Miller’s suggestion that geneticists would show more than feared about class, ethnic and race differences.

At the time I first read the article, I found it hard to characterise this information as something to fear. As Miller identifies, it would be a consequence of some interesting progress:

Once enough DNA is analysed around the world, science will have a panoramic view of human genetic variation across races, ethnicities and regions. We will start reconstructing a detailed family tree that links all living humans, discovering many surprises about mis-attributed paternity and covert mating between classes, castes, regions and ethnicities.

This sounds good to me. To understand the way genes spread as people migrated and mixed across the world will be to gain an important insight into human history.

Miller then points out that some people may be troubled when researchers start to identify genes that create physical and mental differences between populations and identify when those genes arose. Millers states:

If the shift from GWAS [genome wide association studies] to sequencing studies finds evidence of such politically awkward and morally perplexing facts, we can expect the usual range of ideological reactions, including nationalistic retro-racism from conservatives and outraged denial from blank-slate liberals.

But it is not all bad. He closes with:

The few who really understand the genetics will gain a more enlightened, live-and-let-live recognition of the biodiversity within our extraordinary species—including a clearer view of likely comparative advantages between the world’s different economies.

Reading that last sentence, the title to the article and the first paragraph appear over inflated. People will always misuse information and there will be another body of people who will make great use of it.

Looking back at Miller’s article from the vantage point of 2011, I am not sure much has changed. If anything, there has been a slow trickling of some of these ideas into spaces where they are starting to add value. GWAS studies are filling the journals and the store of population genetic data is increasing quickly. While most blank slaters continue to ignore it and the retro-racists use bits as they see fit, some of us are ploughing through it to learn something new.

Although Miller barely touches on it, the economic idea in that last sentence is interesting. If GWAS and sequencing studies result in different skills and comparative advantages being identified across the world’s populations and economies, research into economic development could be vastly changed. However, I am not convinced that we are particularly close to obtaining that sort of information. As I noted in my last post, it seems that we are some distance from taking the load of genetic information and the associated picture of human evolutionary history and being able to link it to characteristics that matter economically.

**This is a cross-post from my blog Evolving Economics.

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• Category: Science • Tags: Genetics 
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The History and Geography of Human Genes has probably influenced the way I think about human evolution more than any other book. Even though it is getting old at a time when masses of population genetic data are being accumulated, a flip through the maps depicting the geographic distribution of genes provides a picture that is available in few other places.

It was only a matter of time before some economists grabbed this population genetic data and in particular, this work by L Luca Cavalli-Sforza, Paolo Menozzi and Alberto Piazza, to see whether it could shed any light on economic development. In a paper published (in one of the top economics journals) in 2009, Enrico Spolaore and Romain Wacziarg have taken data on genetic distance from the The History and Geography of Human Genes and asked whether it is correlated with differences in income between countries.

Before examining the data, Spolaore and Wacziarg proposed a model. Take an initial population that branches into two sub-populations each time period, with genetic distance between the two populations being the time since they had a common ancestor. Each sub-population has a transmitted characteristic which is represented by a number. This characteristic mutates either up or down with a 50 per cent probability each generation, so it follows a random walk. As a result, the difference in characteristics (or vertical distance) between two populations is a function of their genetic distance, with the vertical characteristics more likely to have “walked” apart as the time since the shared ancestor increases.

Next, the authors introduce technology. They assume that when a sub-population develops a new technology, other sub-populations’ ability to adopt that technology is a function of their vertical distance from the population at the technological frontier. If technology determines income, then the difference in income between two populations is the size of the relative vertical distance from the population that is at the frontier, which in turn is related to the genetic distance. The core insight from this model is that relative genetic distance and not absolute genetic distance should have a higher correlation with differences in technology.

While I am not sure this model adds much to the initial intuition, it does serve a useful purpose in that it looks to link genetic distance with income differences through differences in vertical characteristics. If genetic distance and income differences had been directly linked (positing that genetic distance is the barrier, as has been proposed within populations by Ashraf and Galor), we would not be left with the interesting question of what these characteristics are.

On the flip-side, Spolaore and Wacziarg have produced a model in which differences in vertical characteristics are a function of random drift, rather than selection. This is a touch unsatisfying, but it is hard to see how the authors could otherwise have produced the model without a theory about what those characteristics are. The model is also agnostic about how one country may develop technology as the authors assume transmitted characteristics do not have any effect on productivity. Introducing a theory of technological development could have been interesting as if certain traits make technological development more likely, there would be two effects creating the income difference – the higher probability of technological progress coupled with the barriers to diffusion.

With model in hand, Spolaore and Wacziarg turned to the population genetic data. Taking data on from 42 world populations, they matched it to countries (for which they have economic data) using information on the ethnic composition of those countries. This formed the basis of determining the genetic distance between countries. They also took a set of European population data (of 26 populations) which would allow them to do a European analysis. The regressions had to depart from the model and test the link between genetic distance and income differences directly as the data does not tell us anything about the vertical characteristics of the population.

The authors completed a mountain of regressions in analysing the data, so here are some of the headline findings. Taking the United States as the world technological frontier in 1995 (a fair assumption), the authors regressed genetic distance against the log of income and, as expected, found that income was negatively correlated with average genetic distance from the United States population. Genetic distance also had reasonably high explanatory power, accounting for 39 per cent of the variation in the sample. The chart below gives the picture. Throwing a range of other explanatory variables into the analysis such as geography and linguistic and religious differences did not materially change this result.

Spolaore and Wacziarg then ploughed deeper into the statistical analysis by creating 9,316 pairs of countries (from 137 countries) for the world sample and 325 pairs (based on 26 countries) for the European sample and assessed the link between genetic distance and income difference. When they use this broader set of pairs, as opposed to the simple comparison with the United States technological frontier, the degree of variation accounted for by genetic distance decreases, although the genetic distance still has a material effect. For example, one standard deviation change in genetic distance accounts for 16.79% of a standard deviation change in income difference when genetic distance alone is entered into the regression.

The authors also examined a range of other factors, such as Jared Diamond’s thesis about differences in geography and domesticable plants and animals. While including these factors in the analysis reduced the explanatory power of the genetic difference measure, the significance remained. The data also allowed some analysis of earlier time periods, which was in fact easier as most countries’ populations were more ethnically uniform in, say, 1500. At for the later dates, the relationship still held.

Given the agnosticism of Spolaore and Wacziarg on what the vertical characteristics driving income differences are, I hope this paper triggers some deeper examination of what is going on. What are the microeconomic mechanisms driving this result? What are the vertical characteristics that are relevant? And going the next step from Spolaore and Wacziarg’s model, how has selection affected these characteristics? Without the characteristics being subject to selection, the change in characteristics would be fairly slow. These slow changes are then hypothesised to create a substantial barrier to technological diffusion even though the populations have been separated a relatively short period. I would suggest that selection is required.

The authors suggest that more research on peaceful and non-peaceful interaction between societies may be useful to tease out the mechanisms that they have proposed. I agree that research may be interesting, but it leaves open the question which the model ignores – how did some countries get that technological lead in the first place. Do these vertical characteristics play a role in that? Asking why others did not follow does not seem as interesting as asking why some countries got the lead in the first place.

**By way of quick introduction, I am a PhD student in Western Australia and blog at Evolving Economics. I’ll be cross-posting the odd piece that might be of interest to readers (of which this is the first).

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• Category: History, Science • Tags: Genetics 
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