“We only use 10% of our brain”. I don’t know where that idea originated but it certainly took off as a popular meme – taxi drivers seem particularly taken with it. It’s rubbish of course – you use more than that just to see. But it captures an idea that we humans have untapped intellectual potential – that in each of us individually, or at least in humans in general lies the potential for genius.
Part of what has fed into that idea is the existence of so-called “savants” – people who have some isolated area of special intellectual ability far beyond most other individuals. Common examples of savant abilities include prodigious mental calculations, calendar calculations and remarkable feats of memory. These can arise due to brain injuries, or be apparently congenital. In congenital cases, savant abilities are often encountered against a background of the general intellectual, social or communicative symptoms of autism. (The portrayal by Dustin Hoffman in Rain Man is a good example, based on the late, well known savant Kim Peek).
A new hypothesis proposes that savantism arises due to a combination of autism and another condition, synaesthesia. Synaesthesia is commonly thought of as a cross-sensory phenomenon, where, for example, different sounds will induce the experience of particular colours, or tastes will induce the tactile experience of a shape. But in most cases the stimuli that induce synaesthesia are not sensory, but conceptual categories of learned objects, such as letters, numbers, days of the week, months of the year. The most common types involve coloured letters or numbers and what are called mental “number forms”.
These go beyond the typical mental number line that most of us can visualise from early textbooks. They are detailed, stable and idiosyncratic forms in space around the person, where each number occupies a specific position. They may follow complicated trajectories through space, even wrapping around the individual’s body in some cases. These forms can be related to different reference points (body, head or gaze-oriented) and can sometimes be mentally manipulated by synaesthetes to examine them more closely at specific positions.
The suggestion in relation to savantism is that such forms enable arithmetical calculations to be carried out in some kind of spatial, intuitive way that is distinct from the normal operations of formal arithmetic – but only when the brain is wired in such a way to take advantage of these special reprepsentations of numbers, as apparently can arise due to autism.
It has been proposed that the intense and narrowly focused interests typical of autism can lead to prolonged practice of these skills, which thus emerge and improve over time. While certainly likely to be involved in the development of these skills, on its own this explanation seems insufficient. It seems more likely that these special abilities arise from more fundamental differences in the way the brains of autistic people process information, with a greater degree of processing of local detail, paralleled by greater local connectivity in neural circuits and reductions in long-range integration.
Local processing may normally be actively inhibited. This idea has been referred to as the tyranny of the frontal lobes (especially of the left hemisphere), which impart top-down expectations with such authority that they override lower areas, conscripting them into service for the greater good. The potential of the local elements to process detailed information is thus superseded in order to achieve optimal global performance. The idea that local processing is actively suppressed is supported by the fact that savant abilities can sometimes emerge after frontal lobe injuries or in cases of frontotemporal dementia. Increased skills in numerical estimation can also, apparently, be induced in healthy people by using transcranial magnetic stimulation to temporarily inactivate part of the left hemisphere.
This kind of focus on local details, combined with an exceptional memory, may explain many types of savant skills, including musical and artistic ones. As many as 10% of autistics show some savant ability. These “islands of genius” (including things like perfect pitch, for example) are typically remarkable only on the background of general impairment – they would be less remarkable in the general population. Really prodigious savants are much more rare – these are people who can do things outside the range of normal abilities, such as phenomenal mathematical calculations. In these cases, the increased local processing typical of autism may not be, by itself, sufficient to explain the supranormal ability.
The idea is that such prodigious calculations may also rely on the concrete visual representations of numbers found in some types of synaesthesia. This theory was originally proposed by Simon Baron-Cohen and colleagues and arose from case studies of individual savants, including Daniel Tammett, an extraordinary man who has both Asperger’s syndrome and synaesthesia.
I had the pleasure of speaking with Daniel recently about his particular talents on the FutureProof radio programme for Dublin’s Newstalk Radio. (The podcast, from Nov 27th, 2010, can be accessed, with some perseverance, here). Daniel is unique in many ways. He has the prodigious mental talents of many savants, for arithmetic calculations and memory, but also has the insight and communicative skills to describe what is going on in his head. It is these descriptions that have fueled the idea that the mental calculations he performs rely on his synaesthetic number forms.
Daniel experiences numbers very differently from most people. He sees numbers in his mind’s eye as occupying specific positions in space. They also have characteristic colours, textures, movement, sounds and, importantly, shapes. Sequences of numbers form “landscapes in his mind”. This is vividly portrayed in the excellent BBC documentary “The Boy With the Incredible Brain” and described by Daniel in his two books, “Born on a Blue Day” and “Embracing the Wide Sky”.
His synaesthetic experiences of numbers are an intrinsic part of his arithmetical abilities. (I say arithmetical, as opposed to mathematical, because his abilities seem to be limited to prodigious mental calculations, as opposed to a talent for advanced calculus or other areas of mathematics). Daniel describes doing these calculations by some kind of mental spatial manipulation of the shapes of numbers and their positions in space. When he is performing these calculations he often seems to be tracing shapes with his fingers. He is, however, hard pressed to define this process exactly – it seems more like his brain does the calculation and he reads off the answer, apparently deducing the value based at least partly on the shape of the resultant number.
Daniel is also the European record holder for rembering the digits of the number pi – to over 20,000 decimal places. This feat also takes advantage of the way that he visualises numbers – he describes moving along a landscape of the digits of pi, which he sees in his mind’s eye and which enables him to recall each digit in sequence. The possible generality of this single case study is bolstered by reports of other savants, who similarly utilise visuospatial forms in their calculations and who report that they simply “see” the correct answer (see review by Murray).
Additional evidence to support the idea comes from studies testing whether the concrete and multimodal representations of numbers or units of time are associated with enhanced cognitive abilities in synaesthetes who are not autistic. Several recent studies suggest this is indeed the case.
Many synaesthetes say that having particular colours or spatial positions for letters and numbers helps them remember names, phone numbers, dates, etc. Ward and colleagues have tested whether these anecdotal reports would translate into better performance on memory tasks and found that they do. Synaesthetes did show better than average memory, but importantly, only for those items which were part of their synaesthetic experience. Their general memory was no better than non-synaesthete controls. Similarly, Simner and colleagues have found that synaesthetes with spatial forms for time units perform better on visuospatial tasks such as mental rotation of 3D objects.
Synaesthesia and autism are believed to occur independently and, as each only occurs in a small percentage of people, the joint occurrence is very rare. Of course, it remains possible that, even though most people with synaesthesia do not have autism and vice versa, their co-occurrence in some cases may reflect a single cause. Further research will be required to determine definitively the possible relationship between these conditions. For now, the research described above, especially the first-person accounts of Daniel Tammett and others, gives a unique insight into the rich variety of human experience, including fundamental differences in perception and cognitive style.
Murray, A. (2010). Can the existence of highly accessible concrete representations explain savant skills? Some insights from synaesthesia Medical Hypotheses, 74 (6), 1006-1012 DOI: 10.1016/j.mehy.2010.01.014
Bor, D., Billington, J., & Baron-Cohen, S. (2008). Savant Memory for Digits in a Case of Synaesthesia and Asperger Syndrome is Related to Hyperactivity in the Lateral Prefrontal Cortex Neurocase, 13 (5), 311-319 DOI: 10.1080/13554790701844945
Simner, J., Mayo, N., & Spiller, M. (2009). A foundation for savantism? Visuo-spatial synaesthetes present with cognitive benefits Cortex, 45 (10), 1246-1260 DOI: 10.1016/j.cortex.2009.07.007
Yaro, C., & Ward, J. (2007). Searching for Shereshevskii: What is superior about the memory of synaesthetes? The Quarterly Journal of Experimental Psychology, 60 (5), 681-695 DOI: 10.1080/17470210600785208
Review of “Braintrust. What Neuroscience Tells Us about Morality”, by Patricia S. Churchland
The question of “where morals come from” has exercised philosophers, theologians and many others for millennia. It has lately, like many other questions previously addressed only through armchair rumination, become addressable empirically, through the combined approaches of modern neuroscience, genetics, psychology, anthropology and many other disciplines. From these approaches a naturalistic framework is emerging to explain the biological origins of moral behaviour. From this perspective, morality is neither objective nor transcendent – it is the pragmatic and culture-dependent expression of a set of neural systems that have evolved to allow our navigation of complex human social systems.
The BBC have just finished a short (3-part) series of documentaries by Adam Curtis, under the general heading ‘All Watched Over by Machines of Loving Grace’. It’s impossible to describe them briefly, so I won’t try; suffice to say I found them fascinating but often exasperating with their wild leaps of logic. For GNXP readers the most interesting will probably be the last, which has a lot of material about W. D. Hamilton, George Price, and Dianne Fossey, as well as extraordinary archive footage from Central Africa. (I bet you never heard a BBC reporter casually referring to ‘jungle bunnies’ before.)
They are all available here for the next week, at least. Unfortunately I don’t know if this will be accessible outside the UK – some things are and some aren’t, usually for copyright reasons.
[Added: if you can't view it on the BBC iPlayer, the final part is currently available on YouTube - search YouTube for recent postings on 'Adam Curtis'.]
**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.
**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.
There is a paradox at the heart of behavioural and psychiatric genetics. On the one hand, it is very clear that practically any psychological trait one cares to study is partly heritable – i.e., the differences in the trait between people are partly caused by differences in their genes. Similarly, psychiatric disorders are also highly heritable and, by now, mutations in hundreds of different genes have been identified that cause them.
However, these studies also highlight the limits of genetic determinism, which is especially evident in comparisons of monozygotic (identical) twins, who share all their genetic inheritance in common. Though they are obviously much more like each other in psychological traits than people who are not related to each other, they are clearly NOT identical to each other for these traits. For example, if one twin has a diagnosis of schizophrenia, the chance that the other one will also suffer from the disorder is about 50% – massively higher than the population prevalence of the disorder (around 1%), but also clearly much less than 100%.
Recent evidence indicates that psychiatric disorders can arise from differences, literally, in how the brain is wired during development. Psychiatric genetic approaches are finding new mutations associated with mental illness at an amazing rate, thanks to new genomic array and sequencing technologies. These mutations include so-called copy number variants (deletions or duplications of sections of a chromosome) or point mutations (a change in the code at one position of the DNA sequence). At the recent Wiring the Brain conference, we heard from Christopher Walsh, Guy Rouleau, Michael Gill and others of the identification of a number of new genes associated with neurological disorders, epilepsy, autism and schizophrenia.
The emerging picture is that each of these disorders can be caused by mutations in any one of a large number of genes. Strikingly, many of these genes play important roles in neural development, with mutations affecting patterns of cell migration, the guidance of growing nerve fibres and their connectivity to other cells. Even more remarkable has been the observation that most such mutations predispose to not just one specific illness (such as schizophrenia) but to mental illness in general, with a strong overlap in the genetics of schizophrenia, autism, bipolar disorder, epilepsy, mental retardation, attention-deficit hyperactivity disorder and other diagnostic categories. These different categories may thus represent arguably distinct endpoints arising from common origins in neurodevelopmental insults.
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.
Via the Demography Matters blog, Russian birthrate seems to have recovered:
By 2009, the official TFR had risen to 1.537, 1.417 in urban areas and 1.900 in rural areas. Both urban and rural TFRs rose by about the same amount from 2000 to 2009, about 0.330. Vital statistics for 2010 were just released by the national statistics office, GOSKOMSTAT, also known as ROSTAT. The birth rate continues to rise but not as sharply in the past two years as it did in 2007 and 2008. One must wonder if the slower increase in the past two years suggests the birth rate revival may be running out of steam or that it may be due to the global recession. But natural decrease is now but one-fourth of what is was in 2000 and that is a truly dramatic turnaround. The TFR can be estimated at about 1.56 for 2010 although we must wait for the official TFR when it is released later this year. Births for January 2011 have also been released and those are down slightly from January 2010, 131,454 from 132,371. One month hardly defines a trend but I thought I’d pass that along.
This is still below replacement, but is substantially higher than the estimates from 2000, when the birth rate per woman bottomed out to roughly 1.2. At the time, everyone was extrapolating a near-certain birth spiral.
This brings to mind an article from Nature from a couple of years ago that argued that fertility follows a “J” curve with respect to human development. The graph plots fertility against human development (HDI) by country in two time periods:
That is, rather than fertility declining irreversibly with higher levels of development (which is what one might have thought in 1975, or in Russia through the 1990s); it appears that fertility seems to recover a bit at the highest levels of development. This doesn’t apply to all countries — Japan and Italy may have been left behind — but partially explains the relatively high fertility rate of, say, native-born Americans. Explaining the drop in fertility with rising development is easy; explaining the subsequent rise is a little tougher. I see two basic options:
1) It’s important that the measure here is HDI, as opposed to GDP/capital. What’s crucial is the level of female empowerment. Where women have the option to work and raise children, they frequently do so. Where they cannot as easily (Germany for instance, where a substantial cohort of women remain childless and attached to the workforce), women are simply forced to choose. It’s no coincidence that countries like Japan or Italy see plummeting fertility even at high levels of income.
2) This represents the optimal parenting strategy across income ranges. At Malthusian levels of income, additional income is spent on more children. As incomes rise, families start to face a “quantity/quality” tradeoff that leads to them invest more in fewer children. At yet higher levels of income, families are able to invest fully in multiple children.
It’ll be worth seeing whether some of the low-fertility countries out there today — particularly in Southern/Eastern Europe and Eastern Asia — recover. At some point, many countries will also start maxing out their HDI, and we’ll need another indicator. Perhaps people are reading Selfish Reasons to Have Children.
This concludes a series of posts on the work of George Price. For the most recent one, with links to the others, see here*. This final post covers the subject of group selection.
Price and Group Selection
The application of Price’s Equation to group selection, and the related problem of biological altruism, is largely responsible for the current interest in Price, as shown in Oren Harman’s biography. The controversy over group selection dates from the early 1960s, as discussed here*. Price attempted to cut through the controversy with a simple new approach. Using Price’s Equation, the overall change in frequency of a gene in a population between two generations can be broken down into two components, which I call the Covariance and Transmission terms. Price’s simple proposal was to identify the effect of group selection with the Covariance term, while selection on individuals (or genes within individuals) is covered by the Transmission term [Price, 1972, 488]. Price’s own work was cut short by his untimely death, but his approach received a boost when it was endorsed (with some qualifications) by W. D. Hamilton [Narrow Roads, vol.1, 333]. Yet it failed to attract much interest for another decade, and is still not generally accepted.
Read the rest of this entry »
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.
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
Back in 2004 I wrote a summary of Richard Dawkins’s oft-cited article ‘Twelve Misunderstandings of Kin Selection’. At that time the article was not, as far as I could see, available on the internet or in any easily accessible reprint. However, I have found that a free online pdf is now available, and anyone interested in the subject can read it here.
There is one last loose end to tie up before concluding my series on George Price.
In a previous post I discussed the meaning of altruism in biology, and the distinction between strong and weak altruism. With strong altruism the altruist obtains no benefit from its own actions, whereas with weak altruism it does, though less than other members of a group to which it belongs.
W. D. Hamilton showed that strong altruism cannot evolve if altruism is randomly distributed in each generation. By varying his key assumptions several authors have put forward models in which a population is divided randomly into groups, yet altruism still increases in frequency. For example, groups may be formed randomly, but the aggregate benefit of altruism increases disproportionately with the number of altruists in the group.
A more surprising result is that strong altruism may evolve if groups are formed randomly, but allowed to persist for more than one generation. A natural first suspicion is that this is simply due to kin selection. In small groups which persist for several generations, many of the members in later generations are likely to be close relatives. However, an ingenious recent study by Fletcher and Zwick shows that in some circumstances strong altruism can evolve even if benefits to close relatives are excluded. Read the rest of this entry »
Via Razib, I checked out Clive Finlayson’s The Humans Who Went Extinct. On the human migration to Australia, Finlayson writes:
The long-tailed macaque, primate beachcomber par excellence, can teach us another lesson. These monkeys have managed to establish viable populations on a number of remote islands over a wide area of south-east Asia. They even reached the Nicobar (south of the Andamans) and Philippine Islands, which were never connected to the mainland…
Nobody, to my knowledge, has suggested that these macaques had found ways of making canoes or other watercraft and they do not seem to have developed maritime navigation sk ills either. The simple combination of their habits, which often brought them close to drifting rafts, and chance allowed them to populate many distant islands. Yet when it comes to the dispersal of humans across these same islands and onto Australia the prerequisites in all accounts of the epic journeys are watercraft and navigation skills.
That is, rather than humans settling Australia via rafting across the long channel across from East Timor, Finlayson suggests that instead humans were washed out to sea onto New Guinea, more in the manner of other species which have managed the same trick. Presumably his argument would also apply to other early human island hopping events, such as on Crete. The tenor of the book is based on arguments such as this, which reject human triumphalism in favor of naturalistic primate comparisons.
To me, however, this adds to the puzzle of how there were Hippos in Madagascar. Contrary to popular impression, adult Hippos can neither swim nor float; they navigate in the water by pushing up from the bottom. Their daily dietary needs are quite large, involving the consumption of up to 150 lbs of grass a day.
So how on earth did they manage their way across the ~275 mi channel between Africa and Madagascar? This strait — at times miles deep — was sufficient to keep out the vast majority of African wildlife, preserving a largely endemic plant and wildlife. Humans never made it across before inventing watercraft. There are a few islands in between, which at the present day largely lack the fresh water needed to sustain viable hippo populations. Only a handful of mammals have made the transit in the last 50 million years, most of which were fairly small in size.
And it’s not like this was a freak event. Hippos made it to several Mediterranean islands, establishing dwarf island populations. If anyone has insight on this pressing question, do let me know in the comments.
Update: Welcome Instapundit readers! Please make sure to follow the very thorough discussion/debate over at Discover Blogs, where this has been cross-posted.
End Update
Over the past few days I’ve been very disturbed…and angry. The reason is that I’ve been reading Misha Angrist and Dr. Daniel MacArthur. First, watch this video:
In the very near future you may be forced to go through a “professional” to get access to your genetic information. Professionals who will be well paid to “interpret” a complex morass of statistical data which they barely comprehend. Let’s be real here: someone who regularly reads this blog (or Dr. Daniel MacArthur or Misha’s blog) knows much more about genomics than 99% of medical doctors. And yet someone reading this blog does not have the guild certification in the eyes of the government to “appropriately” understand their own genetic information. Someone reading this blog will have to pay, either out of pocket, or through insurance, someone else for access to their own information. Let me repeat: the government and professional guilds which exist to defend the financial interests of their members are proposing that they arbitrate what you can know about your genome. A friend with a background in genomics emailed me today: “If they succeed in ramming this through, then you will not be able to access your own damn genome without a doctor standing over your shoulder.” That is my fear. Is it your fear? Do you care?
In the medium term this is all irrelevant. Sequencing will be so cheap that it will be impossible for the government and well-connected self-interested parties to prevent you from gaining access to your own genetic information. Until then, they will slow progress and the potential utility of this business. Additionally, this sector will flee the United States and go offshore, where regulatory regimes are not so strict. BGI should give glowing letters of thanks to Jeffrey Shuren and the A.M.A.! This is a power play where big organizations, the government, corporations, and professional guilds, are attempting to squelch the freedom of the consumer to further their own interests, and also strangle a nascent economic sector of start-ups as a side effect.
You are so much more than your genes. So much more than that 3 billion base pairs. But they are a start, a beginning, and how dare the government question your right to know the basic genetic building blocks of who you are. This is the same government which attempted to construct a database of genetic information on foreign leaders. We know very well then who they think should have access to this data. The Very Serious People with a great deal of Power. People with “clearance,” and “expertise,” have a right to know more about about your own DNA sequence than you do.
What can you do? What can we do? Can we affect change? I don’t know, I can’t predict the future. But this is what I’m going to do.
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.
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 gnxp.com readers (of which this is the first).
I am writing a series of posts on the work of George Price. For the most recent one, with links to the others, see here I was planning next to cover Price’s treatment of group selection, but this raises side issues more conveniently dealt with separately. A previous post here considered what is meant by group selection. In the present post I look at definitions of altruism as used in biology. It has taken me a while to complete, partly because I found there is a lot of recent literature on the subject which I needed to digest. A valuable but difficult recent survey is here.
Read the rest of this entry »
Dale and Krueger have responded to Robin Hanson at his blog, which commented on their most recent paper. I’ve also commented on this paper, here.
Most of Dale and Krueger’s comments relate to the stability of estimates that suggest that women earn less after attending high-SAT Colleges. I don’t see particularly compelling evidence here either way, though Hanson is right to note that many of the estimates are consistent in nature. I was surprised by their comment, “The paper is not about gender differences from college selectivity, and we have little reason to suspect that there are such differences.” Well, all three drafts of this paper that are online emphasize the results for attending College on various subgroups — for instance, by race, parental education, and parental income. Surely gender is an equally interesting subgroup.
They do also address the selectivity question — that is, why the Barron’s selectivity measure was large and statistically significant in the working paper, but not used in the published paper. They argue that precise manner in which the Barron’s selectivity measures were coded made a huge difference, and the result was important only for one specification. I’m happy to accept this answer. But as far as the “grand conspiracy” is concerned, I’ll note that even the published paper did make a compelling case that both the identity of the school and tuition paid were hugely important in determining future income. This result, for various reasons, may still have been incomplete. Yet it was the basic message of the published paper, and it’s simply the case that the popular press did not emphasize that result. For the record, I don’t think there was any conspiracy here. But it is awfully easy to trumpet the counter-intuitive but pleasing result — the College you went to doesn’t matter!
Also on the Barron’s measure, Dale and Krueger argue:
“While we did report a 23% return associated with attending the most selective colleges (according to the 1982 Barron’s ranking) in our earliest working paper, these results were from our basic model–which does NOT adjust for student unobserved characteristics.”
Here is the relevant section from Table 7 of their working paper:
If you haven’t seen a regression table, this will be confusing. The dependent variable — what they’re testing the effects for — is a logarithm transformation of wage. They’re testing which of the variables listed on the left matter for that, and each column represents a different specification.
The first three columns select on men. The first one tests to see how these variables impact future wages, without taking into consideration other Colleges you applied to, or where you got in. This is the “basic model,” and the .0234 here next to “Most Competitive” corresponds to the 23% return they mention above (relative to the lowest category of selectivity). But skip over to column 3. This “self-revelation” model is designed to get at student unobserved characteristics. As the authors write:
“The effect of the Barron’s rating is more robust to our attempts to adjust for unobserved school selectivity than the average-school SAT score. Based on the straightforward regression results in column 1, men who attend the most competitive schools earn 23% more than men who attend very competitive colleges, other variables in the equation being equal. In the self-revelation model, the gap is 13 percent… [An] F-test of the null hypothesis that the Barron’s ratings jointly have no effect on earnings is rejected at the .05 level in the matched applicant model for men.”
Now, this was in response to Hanson’s point. Hanson picked up on the 23% number, and Dale and Krueger are right to note that’s a little high (and Hanson is right to concede). But note that the very next sentence reports results from a specification which does adjust for student unobserved characteristics; and it is also quite high.
Finally, I’ll note that while the authors emphasize the significance (or lack of significance) for individual estimates in individual years, my simple calculations suggest that the aggregate, pooled effect of their variables might be quite large in economic importance.
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