The Unz Review: An Alternative Media Selection
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Last Friday I asked this question of Andrew Roberts, whose one volume biography “Churchill: Walking with Destiny” has been described as the best single-volume life of Churchill ever written. It is marvellous to be able to question someone who had actually read all Churchill’s school reports. On a broader front, Roberts had the benefit of recently released documents, including being the first Churchill biographer to be given unfettered access to the whole of the Queen’s late father King George VI’s wartime diaries. What did the King know, you may wonder? The whole lot, it seems. Churchill had an audience with the King every week during the entire war, and told him everything that was on his mind that week, including every single secret: the plans for D day, secret actions abroad, everything. The King wrote it all down in his diaries. Why was Churchill willing to disburse himself thus, when they were not exactly soul mates on many matters, including the Abdication? Churchill said he spoke to the King because he was the only man in Britain who was not after his job. He also judged, correctly, that the King would keep his trap shut.

I asked Andrew Roberts the question because so many people, possibly to attack the notion of intelligence or scholastic ability being predictive of later life success, revel in the notion that Churchill was a dunce at school. The moral of that story, it would seem, is that dunces rise as far as swots, so damn the swots, and exams count for nothing.

Was Churchill really no good at school?

Answer: he was in the top third of his class in all subjects, and towards the top in History and English. He was also a rebel, which caused him trouble, but was to stand him in good stead in his later political life.

Roberts writes (pages 16 following):

It is rare for anyone to depict themselves as less intelligent than they genuinely are, but Churchill did so in his biography “My Early Life” in 1930, which needs to be read in the context of his colourful self-mythologizing rather than as strictly accurate history. His school reports utterly belie his claims to have been an academic dunce. Those for St George’s Preparatory School in Ascot, which he entered just before his eight birthday in 1882, record him in six successive terms as having come in the top half or top third of the class.

Churchill was regularly beaten as St George’s, but this was not because of his work – his History results were always “good” “very good” or “exceedingly good” – but because his headmaster was a sadist described by one alumnus as “an unconscious sodomite” who enjoyed beating young boys on their bare bottoms until they bled. Ostensibly the reason for these fortnightly beatings derived from Churchill’s bad conduct, which was described as “very naughty” “still troublesome” “exceedingly bad” “very disgraceful” and so on. “He cannot be trusted to behave himself anywhere” wrote the headmaster, but “He has very good abilities.”

Churchill’s stay at St Georges was one long feud with authority. Churchill’s very good abilities included an excellent memory. He learned his Latin first declensions by heart.

His capacity for memorizing huge amounts of prose and verse stayed with him for life, and would continue to astonish contemporaries well into old age. Many were the occasions that he would quote reams of poetry or songs or speeches half a century after having learned them.

He was drawn to long Shakespeare soliloquies, but also to much of the repertoires of popular music hall performers. At his next school, in Hove, Churchill read voraciously, especially epic tales of heroic, often imperial, adventures. He came first in Classics, third in French, fourth in English, and near or at the bottom of the entire school for conduct. He remained unpunctual all his life.

Churchill claimed to have not learned any Latin or Greek at Harrow, but his school reports show that that was untrue. Furthermore, at fourteen he got a prize for reciting without error 1,200 lines of Macaulay’s Lays of Ancient Rome. He could quote whole scenes of Shakespeare’s plays and had no hesitation in correcting his masters if they misquoted.

Why did Churchill underplay his abilities? The answer is simple: if you boast about your abilities people will hate you; if you claim to be a fool they will be charmed by your modesty and by the abilities they detect in you. Always help the voter believe himself to be brighter than he is.

Does this false modesty explain why palpably clever people often claim to be not much different from the average? Probably. Whenever some famous figure in science tries to cheer us all up by confessing that they failed at school, or developed very late, I wonder if they are simply showing that they are clever enough to realize that the clever thing to do is to avoid being judged “too clever by half”.

In the spirit of empirical enquiry, we should request their entire series of school reports, and any further test results and higher education achievements. Without those we have no need to believe stories simply designed to make us feel good.

 
• Category: Science 
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It is generally agreed that the Wechsler tests are one of the best measures of intelligence, and can be considered the gold standard. That is hardly surprising, because they cover 10 subtests and take over an hour, sometimes an hour and a half, for a clinical psychologist to administer. This gives the examiner plenty of opportunity to see the fine grain of individual responses, to probe within the limits allowed by the manuals to make sure that the person has every chance to reveal what they know, and to observe the way in which the person handles objects on non-verbal tasks. Watching block design is a window into how a person thinks. The examiner can also notice when an explanation has been misunderstood and when attention is wandering, and can stop the test and continue after a break or on another occasion. The results are presented together with a written evaluation of how the person approached the individual tests, identifying strengths and weaknesses, and often suggesting areas where subsequent testing might show higher scores.

Wechsler put together the decathlon of tests on the pragmatic basis of having examined how particular tests functioned, and paid attention to the verbal versus non-verbal dichotomy, as well as complex and simple, speed versus untimed, thinking on the hoof versus testing for acquired mental skills. It does a pretty good job. More pragmatically, having 10 tests (can be up to 15 if subsidiary tests are included) gives both examiner and person something to ponder about. Originally the 5 verbal tests were added together to give a Verbal IQ, and the other 5 a Performance IQ estimate. Later that moved to 4 factors based on 2 or sometimes 3 tests each, which was less reliable, but allowed more discussion about different skills, and the supposed discrepancies between those skills. I think it is over-factored at the moment, and attempted new subtests often get dropped at the next revision.

Given that there is a lot of debate about the appropriateness of intelligence testing of Africans, it is particularly interesting to look at Wechsler results to see if their finer detail about different skills can cast light on the general pattern of African mental abilities.

A cross-cultural comparison between South African and British students on the Wechsler Adult Intelligence Scales Third Edition (WAIS-III). Kate Cockcroft, Tracy Alloway, Evan Copello and Robyn Milligan. Front. Psychol., 13 March 2015 | https://doi.org/10.3389/fpsyg.2015.00297

https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00297/full

There is debate regarding the appropriate use of Western cognitive measures with individuals from very diverse backgrounds to that of the norm population. Given the dated research in this area and the considerable socio-economic changes that South Africa has witnessed over the past 20 years, this paper reports on the use of the Wechsler Adult Intelligence Scale Third Edition (WAIS-III), the most commonly used measure of intelligence, with an English second language, multilingual, low socio-economic group of black, South African university students. Their performance on the WAIS-III was compared to that of a predominantly white, British, monolingual, higher socio-economic group. A multi-group confirmatory factor analysis showed that the WAIS-III lacks measurement invariance between the two groups, suggesting that it may be tapping different constructs in each group. The UK group significantly outperformed the SA group on the knowledge-based verbal, and some non-verbal subtests, while the SA group performed significantly better on measures of Processing Speed (PS). The groups did not differ significantly on the Matrix Reasoning subtest and on those working memory subtests with minimal reliance on language, which appear to be the least culturally biased. Group differences were investigated further in a set of principal components analyses, which revealed that the WAIS-III scores loaded differently for the UK and SA groups. While the SA group appeared to treat the Processing Speed subtests differently to those measuring perceptual organization and non-verbal reasoning, the UK group seemed to approach all of these subtests similarly. These results have important implications for the cognitive assessment of individuals from culturally, linguistically, and socio-economically diverse circumstances.

The first thing to note is that the authors found that the factor structure of the WAIS results were different in the black South Africans compared to the white British. The caution is that the whites were not only white, but rich; and the black South Africans not only black, but poor. The sample sizes are rather small for factor analytic studies, but in the very strict interpretation of measurement invariance these two genetic groups should be seen by the authors as having an underlyingly different structure of intelligence. I think that the “measurement invariance” requirement is too harsh for all but large groups of subjects, and if we really apply it universally we end up being unable to discuss any group differences at all.

Also of importance is that there were few South Africans in the sample, only 107 as opposed to 349 for the British sample. Against that, far more data have been obtained on each person than is the case in group tests. There should be some bonus points for that, and for collecting Wechsler results in Africa, which are in short supply. Indeed, the authors gave 13 subtests, which is very good. However, factor studies on 107 people are not very likely to produce stable results.

The Africans were not rich:

All of the SA participants came from low socio-economic circumstances. The majority (82%) resided in rural areas, in a basic brick house with running water and electricity. Hardly any (98%) had washing machines, microwave ovens, or tumble-dryers. Less than 1% of families owned a motor vehicle or personal computer.

However, those conditions were similar to British life in the 1950s, at which time intelligence test scores were roughly IQ 100. On the other hand, educational provision then was probably much better than current day South Africa.

There is a 0.44 effect size on Performance IQ and a massive 1.53 effect size on Verbal IQ. These are big differences, given that both samples are university students. Another approach is to look at the results and list the South African subtest scores from strong to weak:

Matrix Reasoning 10.68
Information 10.19
Digit Symbol Coding 9.75
Symbol Search 9.71
Digit Span 9.35
Letter-Number Sequencing 9.17
Comprehension 8.99
Vocabulary 8.92
Block Design 8.67
Arithmetic 8.66
Similarities 8.12
Picture Completion 8.05

This is an interesting hierarchy, in that the very culture-loaded “Information” subtest (composed of very general, general knowledge questions) is a strength, not a weakness. Amusingly, in the US context it used to be considered too culture-loaded to be included in measures of group differences.

Continuing with the discussion about the results the authors say:

There was no evidence of cultural biases in the Matrix Reasoning subtest or in the WM subtests that had minimal reliance on language. (2) All of the verbal and most non-verbal subtests, as well as the PS subtests, showed evidence of cross-cultural differences. (3) The SA and UK samples’ scores revealed different factor structures.

 
• Category: Science • Tags: Africans, IQ, South Africa 
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It takes a certain courage to title a paper: Genetic “General Intelligence,” Objectively Determined and Measured.

Javier de la Fuente, Gail Davies, Andrew D. Grotzinger, Elliot M. Tucker-Drob, Ian J. Deary

doi: https://doi.org/10.1101/766600

Objectively? Is such language permissible in contemporary science? Should we not instead be cautiously shuffling towards seven types of ambiguity, hedged in with eight layers of limitations? Who are these wild types willing to risk all in search of glory? In fact, a look at the names shows that this group have established an excellent track record, so they have in all probability chosen their words carefully, with the facts on their side.

First, a digression. Since 1969 there have been factions eager to denigrate intelligence, saying that its measures are based on arbitrary mental tasks, gathered together in the statistical artefact of a make-believe common factor, which is based on precisely nothing. How can one possible counter this all-encompassing dismissal of the psychometric project?

One approach would be to link a common factor to a genetic substrate, and anchor it in the genome.

It has been known for 115 years that, in humans, diverse cognitive traits are positively intercorrelated; this forms the basis for the general factor of intelligence (g). We directly test for a genetic basis for g using data from seven different cognitive tests (N = 11,263 to N = 331,679) and genome-wide autosomal single nucleotide polymorphisms. A genetic g factor accounts for 58.4% (SE = 4.8%) of the genetic variance in the cognitive traits, with trait-specific genetic factors accounting for the remaining 41.6%. We distill genetic loci broadly relevant for many cognitive traits (g) from loci associated with only individual cognitive traits. These results elucidate the etiological basis for a long-known yet poorly-understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits.

The authors go on to explain that tests vary in the amount of general or specific factors required for their successful completion. Some variance in each test is shared with all other tests (g) and some is specific to each test (s). Hundreds of studies show that the g factor replicates, and accounts for 40% of test variance. Twin studies show that general intelligence is strongly heritable, suggesting an overlapping genetic architecture. However, the GWAS approach does not distinguish between “g” and “s”. The authors try to search for “g” directly, using a multivariate molecular genetics approach to the hierarchy of intelligence, g at the top, cognitive domains second, and individual tests at the bottom.

They used UK Biobank, blessed be its name, and seven cognitive tests:

Reaction Time (n = 330,024; perceptual motor speed), Matrix Pattern Recognition (n = 11,356; nonverbal reasoning), Verbal Numerical Reasoning (VNR; n = 171,304; verbal and numeric problem solving; the test is called ‘Fluid intelligence’ in UK Biobank), Symbol Digit Substitution (n = 87,741; information processing speed), Pairs Matching Test (n = 331,679; episodic memory), Tower Rearranging (n = 11,263; executive functioning), and Trail Making Test – B (Trails-B; n = 78,547; executive functioning). A positive manifold of phenotypic correlations was observed across the seven cognitive traits.

The authors then investigate the genetic contribution of g to variation in each of the cognitive tests. Genetic correlation is simply the correlation between the genetic contributors to each of the measured abilities. It is correlation at the level of genes, not test scores. If the brain is made up of modules, then one would expect such genetic correlations to be low. On the other hand, a brain largely based on general ability would have strong correlations. In fact, the genetic correlations range from .14 to .87, with a mean of .53 and the first principal component accounted for a total of 62.17% of the genetic variance. The genetics of intelligence is largely g based, it would seem.

Further work identifies the tests that are most g loaded:

Trails-B (95.30% genetic g; 4.70% genetic s), Tower (72.80% genetic g; 27.20% genetic s), Symbol Digit (69.10% genetic g; 30.90% genetic s), and Matrices (68.20% genetic g; 31.80% genetic s). Verbal Numerical Reasoning (51.40% genetic g; 48.60% genetic s) and Memory (42.40% genetic g; 57.60% genetic s) are more evenly split. Reaction Time has the majority of its genetic influence from a genetic s (9.50% genetic g; 90.50% genetic s). We emphasize one important implication of these results, i.e. that genetic analyses of some of these individual traits will largely reveal results relevant to g rather than to the specific abilities thought to be required to perform the test.

Reaction time is somewhat of an outlier from the genetic point of view, as might be expected by the very simple, knee-jerk nature of the task.

Anyway, which locations on the genome are contributors to g? Getting an answer is important, since a GWAS hit could be generalizable to a broad universe of cognitive traits, or specific to a particular task, and knowing which makes a difference. In the explanation below, Q is a measure of heterogeneity, opposite to g.

Miami plot of unique, independent hits for genetic g (top) and Q (bottom). Q is a heterogeneity statistic that indexes whether a SNP evinces patterns of associations with the cognitive traits that departs from the pattern that would be expected if it were to act on the traits via genetic g. The solid grey horizontal lines are the genome-wide significance threshold (p < 5×10−8) and the dotted grey horizontal lines are the suggestive threshold (p < 1 ×10−5). The following genome wide significant loci are highlighted: Red triangles : g loci unique of univariate loci. Blue triangles : g loci in common with univariate loci. Green circles : univariate loci not in common with g loci. Yellow triangles : g loci in common with Q loci. Yellow diamonds : Q loci unique of g loci.

Overall, we identified 30 genome-wide significant (p < 5×10−8) loci for genetic g, 23 of which were common with the univariate GWAS of the individual cognitive traits that served as the basis for our multivariate analysis. We identified, in total, 24 genome-wide significant loci for Q, 3 of which were significantly associated with genetic g (and therefore likely to be relevant to more specific cognitive traits, and false discoveries on g) and 15 of which were significantly associated with at least one individual cognitive trait in the test-specific GWASs.

Although it was not intended to be part of the study, seven new locations for memory were found, and some of those locations have been associated with Schizophrenia, anti-saccade response, linguistic errors, hand grip strength and bone mineral density.

 
• Category: Science • Tags: Behavior Genetics, General Intelligence, IQ 
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It cannot have been easy to be the first reporter on the tragic scene at Aberfan. A vast slagheap of colliery spoil above a small Welsh mining village gave way in 1966, roaring down the valley and flattening the school and killing 116 children, and also destroying houses, killing 28 adults in all. When John Humphrys arrived the miners, having heard the dreadful news, had come up from the pits to dig frantically in the ruins, in search of their children. The slagheap was a known danger which had been left unresolved, and it was natural that for ever afterwards John had no time for evasive, self-excusing authority figures. Far from being disordered, he became a post-traumatic interrogator, clearly standing up for those whose fears and pleas had been ignored.

This morning he finished his 32 years as the chief interviewer in a morning radio program. Just think of that: the best show in town is on “steam radio”. What was the attraction? In British life, important people watch TV sparingly, and not at all in the mornings. Radio is the acceptable morning companion for them, because they can have breakfast, read the papers and even be driven into work while the program is on. The Today program is compulsory listening and a treasured mouthpiece for the movers and shakers: Prime Ministers, Government Ministers, Members of Parliament, Archbishops, Rabbis, Government Chief scientists, Nobel Laureates, novelists, playwrights, and assorted talkative worthies. They even, on occasion, invited in the odd psychologist.

Why talk about the program to a world-wide audience many of whom know nothing about him? Because truth matters. Many journalists have neither the energy nor the talent to hunt for the story behind the official story. Others claim to have that very thing, but don’t have enough evidence to back it up. What Today achieved was a forensic examination of the high and mighty, who were often revealed as low and crafty, their smarm disarmed, their schemes exposed. If there was blood on the floor, it was in a good cause. The best interviews kept people in their kitchens, the morning commute postponed, or stuck in their cars outside the office, waiting for the final explosion as a great edifice of conceit toppled.

John developed a reputation as a fearsome, adversarial and relentless questioner, quick to pounce on chinks in the well-practiced political operator’s armour so as to land the killer question which impaled them forever: unable to respond truthfully to a fundamental enquiry. Politicians wanted to avoid him, and at the same time wanted most of all to get the better of him, which they rarely did.
His trick was basically simple: with the help of the all night Today team he came in at 4 am to read all the day’s newspapers, many of the relevant government papers and statements, and to collect the clippings of the previous promises, clarifications, evasions and protestations uttered by the great and good so as to be able to skewer any of them who tried to re-write the past.

Sometimes he overdid it, and became an interrupter rather than a questioner, but that was mostly excusable because he had heard the evasive excuses thousands of times before. The Today program became his platform for investigative journalism, and he moulded it to his character. It became his program. He was as big as it. It became the premier program in Britain, and could pull in the top leaders and thinkers from all over the world. To be invited was mostly a blessing. To be evaluated beforehand was sometimes more demanding than the interview itself. The researcher, inevitably a woman, would begin her telephone call in a warm and mildly seductive manner, making you feel important and worth listening to. Then, under the guise of getting a few notes for John, she would then switch in to a “are you worth speaking to” interview. The cull was brutal: you had to be quick on your feet, able to speak more than coherently, and had to expose your preferred positions, your quips, quotes and treasured answers in the hope of getting on the short list. Sometimes they would come back with a gentle excuse: the story had moved on. In disappointment one might find the next morning that the story had indeed moved on, but to another interviewee.

Waiting in the green room was almost as much fun as being interviewed. I began conversations with notable figures I would otherwise never have had access to, and continued the conversations after we had each been grilled. Fun to find that an ex-Chancellor had the same opinions on quantitative easing, or that a scientist was able to quickly settle a number of issues first hand.

I considered John Humphrys to be a mate of mine, on the very slender basis that on my something like 12-18 Today program interviews he was often the interviewer, and since my appearances were usually after 8.30 am I would stay on to talk with him. We had both lost a big chunk of our pensions when the Equitable Life Assurance Society (the oldest in the world) went bust, so we discussed the various bits of advice we had received as to whether we should cash in the slender remains or hand over what was left to some other company. At other times I would jokingly brief him on what the PR agents of famous actors had advised them to say, as they waited in the green room before seeing him. He hated PR agents above all else. I met actors and actresses, ex Chancellors of the Exchequer and the more oratorical Members of Parliament, such as Tony Benn.

The program showed the power of the spoken word, with the advantage of being able to focus on the content and tone of what was being said, without the distractions of the speaker’s appearance or facial expression. It required concentration, but repaid attention.

Of course, the BBC has a house style, and some preferences. Try as it might to be fair, that is always in question. The Today program is the ultimate meeting place of the chattering classes, and sometimes no more than studiously correct with the whispering classes, who are prone to lamentable populism and blunt opinions. There are rules about these things.

I was almost always called in on non-political stories, often associated with trauma and its treatment, and plays and books on those themes, and occasionally on broader issues. Little controversy there. Nonetheless, I did get interviewed on the Rotherham grooming gangs a few years ago. That side of life, in which those without a voice were cruelly mistreated, was one which John investigated with compassion. He was able to get the painful testimony of people who did not have PR agents, and little facility with public speaking. He managed to help them speak their private thoughts.

In my view was wary and critical of the Movers and Shakers, and more on the side of the moved and shaken.

 
• Category: Culture/Society • Tags: Britain 
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There is a popular genre of commentary which wishes to show that bright people make as many errors as less bright people, perhaps as a consequence of divine retribution. “Einstein made an error in maths which was spotted by a bus conductor” lifts the hearts of some readers. Of course, bright people make errors. Do they do so at a higher, lower, or the same rate as everyone else?

Can we test this in a way that favours the average citizen? How about ignoring high finance and stock picking and just concentrating on basic aspects of household financial decision-making, the nitty gritty of so many of our lives? Can we also avoid the charge that some have levelled against the correlation of intelligence and earnings and wealth, that some people just don’t want to be rich? Let us restrict the focus to seeing whether people, whatever their income, can avoid costly financial mistakes. I assume that even those who don’t wish to pursue inordinate wealth, but instead want to live happily on an average income, still wish to handle their meagre emoluments in a sensible fashion, avoiding hare-brained incompetence and fiscal irresponsibility.

Agarwal and Mazumder think this matter is worth exploring.

Sumit Agarwal and Bhashkar Mazumder. Cognitive Abilities and Household Financial Decision Making. American Economic Journal: Applied Economics 2013, 5(1): 193–207http://dx.doi.org/10.1257/app.5.1.193

https://drive.google.com/file/d/1tYKD-6nZnIJd9H2Pt2-bXWFCcIkEthWQ/view?usp=sharing

We analyze the effects of cognitive abilities on two examples of consumer financial decisions where suboptimal behavior is well defined. The first example features the optimal use of credit cards for convenience transactions after a balance transfer and the second involves a financial mistake on a home equity loan application. We find that consumers with higher overall test scores, and specifically those with higher math scores, are substantially less likely to make a financial mistake. These mistakes are generally not associated with non-math test scores.

A 1 standard deviation increase in the composite AFQT score is associated with a 24 percentage point increase in the probability that a consumer will discover the optimal balance transfer strategy and an 11 percentage point decrease in the likelihood of making a rate-changing mistake in the home loan application process. Interestingly, we find that verbal scores are not at all associated with balance transfer mistakes and are much less strongly associated with rate-changing mistakes.

This was based on military personnel on whom there were results available on the Armed Forces Qualifying Test, then linked to data from a credit card finance company, so these are real data, able to pick up sub-optimal choices, more commonly known as mistakes.

The “balance-transfer mistake” is to be fooled by a “teaser” low APR rate on a new card into using the new card rather than the old one during the transfer period. This is because while the old purchase balance is transferred at the new low rate, new purchases on the new card are at a new much higher rate. Sneaky. Some will learn from their mistake as the monthly bills come in with surprisingly high interest rates, and have a “eureka” moment. Others will take more time to learn their mistake.

Lower ability people get fooled more often, brighter people learn about their mistake more often, and the brightest (those above 70th percentile) never make the mistake in the first place, so there is a hierarchy of “eureka” moments.

The next issue the authors consider is the mistake of picking the wrong rate when requesting a loan based on a home valuation. This is another opportunity for the lender to crank up the interest rate, but in this case the rates are visible before the loan is taken out, so it is possible to turn down a poor offer, and try another lender.

Again, the mistake (which increases the cost of the loan by 2.7%) is made at lower ability levels, and not in those above the 70th percentile.

Interestingly, given that the 2008 financial crisis was sometimes portrayed as being particularly unfair to black borrowers, the race difference in this study is in the opposite direction, so long as one controls for intelligence.

Interestingly, we find that the effect on being black is actually positive conditional on AFQT scores and education. This finding is interesting in light of the theoretical model developed by Lang and Manove (2011) who argue that blacks of similar ability to whites may need to signal their productivity to employers by acquiring more education. They cite studies suggesting that blacks are not rewarded the same as whites in the labor market for equivalent AFQT scores. It is possible that the increased likelihood of discovering the optimal balance transfer strategy among blacks who have the same measured ability as whites, reflects their greater investments along other dimensions of human capital.

Being intelligent implies an ability to learn quickly, so it is interesting to plot out how long it takes people to recognize their financial errors, and move to an optimal strategy. High scorers avoid any errors (they can see the error in advance, and avoid it) while those of lower ability take 5 months to correct their mistakes.

To illustrate the effects of AFQT scores on the speed at which individuals learn, we plot in Figure 3 the unadjusted mean AFQT scores for borrowers based on how many months it took them to discover the optimal strategy. The chart shows that AFQT is monotonically decreasing in the number of months it takes borrowers to learn. We estimate that a 1 standard deviation increase in AFQT scores is associated with a 1.5 month reduction in the time it takes to achieve optimal behavior speed. This analysis suggests that cognitive skills also affect the “intensive” margin of optimal financial decision-making behavior.

These two real-life financial errors show the importance of cognitive ability, and may explain a wider range of bad choices regarding finance.

In any case, we think that our analysis likely only touches the tip of the iceberg in terms of the effects of poor financial decision making, due to low cognitive ability, on individual and social welfare. It is highly plausible that similar types of financial mistakes have played a role in explaining loan default, foreclosures, and bankruptcies. In a highly complementary paper to ours, Gerardi, Goette, and Meier (2010) find a strong association between numerical ability and mortgage delinquency and default during the recent financial crisis. Future research may shed more light on the quantitative importance of cognitive ability.

In summary, I think that there are plentiful studies showing that intelligence tests produce scores which are predictive of a wide variety of important real-life measures: earnings, savings and managing loans.

 
• Category: Science • Tags: IQ 
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“All happy families are alike” declaimed Tolstoy, so as to then add the equally unsubstantiated coda: “each unhappy family is unhappy in its own way”.

Readers may say: “So true, so very true”, but that would be in the literary sense, in that if it sounds profound it is judged to be so. Like all novelists, Tolstoy was not upon oath. It was enough that his observations be thought profound for them to be valued as such. Empirical support was not required. The truth about families may be different: unhappy families might be made alike by their troubles, while happy families might be free to divert themselves and become unalike in their own disparate individual ways.

Sociologists often regard families as powerhouses of social privilege, able to provide children unmerited advantages in the form of money, experiences, tuition and social connections. In this theory rich families are like powerful artillery guns, shooting their children further forward than the families of equally meritorious poor children, giving them fame, fortune and a headstart in the race for social advancement.

What emerges if we take an empirical approach to family success? Charles Murray (1998) looked at the NLSY79 data set, seeing to what extent intelligence test results explained later earnings levels.

https://analyseeconomique.wordpress.com/2012/11/10/income-inequality-and-iq-by-charles-murray/

The most recent calendar year with income data is 1993. All dollar figures are stated in 1993 dollars. The measure of IQ is the Armed Forces Qualification Test, 1989 scoring version, normalized for each year’s birth cohort to an IQ metric with a mean of 100 and a standard deviation of 15 (NLSY subjects were born from 1957 through 1964)
.
Children were put into 5 groups for analytical purposes. In the IQ metric, this means break points at scores of approximately 80, 90, 110, and 120.

The Very Bright start slowly, most probably because they are at college gaining degrees which will help them get higher incomes in the long-term, as shown by the after 1982. Everyone gets age-related salary increases, but the two lowest groups reach a plateau very quickly.

This is the pattern for total family income, which includes welfare payments and spouse’s earnings.

The effect of including welfare payments and spouse’s income (the two most common types of income added to total family income) is to narrow the proportional gaps among cognitive classes while tending to widen the raw dollar gaps. The regularity of the statistical relationship is similar for both measures. The bivariate correlation of IQ to income in this population of adults in their late twenties to mid-thirties was .37 for earned income and .38 for total family income.

Those who take a largely sociological perspective might still want to argue that social forces determine both earnings and intelligence, such that social class is the hidden but fundamental factor. In fact, putting socio-economic status in the regression equation (Beta .10) does not make it more powerful than the effects of IQ (Beta .31).

An extra IQ point is associated with an extra $462 in wages independently of parental SES. However, it is still possible to argue that there are some unmeasured aspects of growing up in that particular family that ensure that family life (social class) is the main driver, and that the socio-economic status measures do not capture those unspecified factors.

Charles Murray took a look at this by using the simple technique of comparing one sibling in a family with another sibling. That is, he compares siblings who had grown up in the same home, with the same parents, but who had different IQs. If families are the engines of privilege that sociologists assume, each sibling will have an equal chance of being propelled forwards into further privilege and higher earnings.

Murray’s method was to pick a sibling in the average range (the “normals” IQs 90-109), and then find the IQ results for another sibling in that same family. By the way, these are biological siblings living with both biological parents. “Families”, they used to be called.

What does this method reveal? If families really are the engines siblings will be pretty much alike in their achievements, intelligence scores (which some aver are no more than measures of social class); in their educational achievements (which some aver are heavily manipulated by the social class of parents), and higher degrees (which some aver are very heavily manipulated by the social class and wealth of parents). All of these translate into the ability to command higher wages.

To start with intelligence, just look at the wide range of intellectual levels to be found in normal families. Yes, most of the siblings are in the normal range of intelligence, but there is evidently considerable regression to the mean. 199 out of 2148 (9.3%) of these much loved, pampered children, despite being read to every evening, and exposed to the uplifting parental level of discourse, are in the very dull range. Another 421 of these children (19.6%) are below average, something which never happens in Lake Wogebon. On the brighter side, 15.2% are brighter, and 6% are very bright.

In summary, the family is not a very efficient engine of social manipulation as regards intelligence. These average children have drifted down somewhat, and on this reading it could be because of measurement error or a genetic regression effect, but they have not all been propelled forwards by social advantage. Try as they might, parents cannot pass on all of their normalcy to all their children. Something has caused these siblings to vary, and it is unlikely to be something which is being manipulated within the family.

Will years of education show a strong family effect?

Not really. The picture is very much like that for intelligence. There are more siblings (475) with below average years of education than with above average years of education (375).

Murray observes:

Same household, same parents, different IQs – and markedly different educational careers. The typical Normal had 1.6 years more education than his Very Dull sibling and 1.9 years less education than his Very Bright sibling. These differences in mean years of education translate into wide differences in the probability of getting a college degree.

Murray looks at the effects of degrees, of occupational privilege and eventually looks at what intelligence differences mean for earned income, the subject of our current interests.

In a very telling passage, Murray reflects on these results:

 
• Category: Science • Tags: IQ, Iq and Wealth, Wealth inequality 
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Do bright people earn more than others? If not, it would strengthen the view that intelligence tests are no more than meaningless scores on paper and pencil tests composed of arbitrary items which have no relevance to real life. So, it is with trepidation that I responded to a suggestion by a reader that I look at a paper appearing to show that there is no relationship between intellect and wealth, and furthermore that clever people are just as likely to experience financial difficulties as any other citizen.

Do you have to be smart to be rich? The impact of IQ on wealth, income and financial distress
Jay L. Zagorsky. Intelligence 35 (2007) 489–501

The abstract apparently bears out that interpretation:

How important is intelligence to financial success? Using the NLSY79, which tracks a large group of young U.S. baby boomers, this research shows that each point increase in IQ test scores raises income by between $234 and $616 per year after holding a variety of factors constant. Regression results suggest no statistically distinguishable relationship between IQ scores and wealth. Financial distress, such as problems paying bills, going bankrupt or reaching credit card limits, is related to IQ scores not linearly but instead in a quadratic relationship. This means higher IQ scores sometimes increase the probability of being in financial difficulty.

Happily, another reader, “res” had a closer look at the paper, and found that all was not as it seems in the abstract. That is to say, the results in the paper actually show a relationship between intelligence, earnings and wealth, as shown in the histogram below, which I have drawn based on the results from that very paper:

I think this shows that income gradually increases with intelligence, and wealth increases more strongly with intelligence, in a roughly linear trend. It is relevant to know that income at the lower levels of ability include welfare payments and that incomes at the very highest level have been capped for reasons of confidentiality which both reduce the real relationship between intelligence and earning power. However, the general picture is clear, and somewhat different from what the abstract suggests.

Introduction. How important is intelligence to success in life? Success is a multi-dimensional concept but one key component for many individuals is how well they do financially. It is still not well understood why some people are rich and others are poor. Previous research, discussed below, has investigated the relationship between intelligence and income and found individuals with higher IQ test scores have higher income. Income alone is not a complete measure of financial success. This research completes the picture by investigating if there is a relationship between IQ scores and wealth and if there is a relationship between IQ scores and financial difficulty. What are the differences between these three financial measures? Income is the amount of money earned each time period, such as a weekly pay check. It is the stream of money off which people live. Wealth is the difference between a person’s assets and liabilities. It is the reserve or cushion that people fall back upon to meet large expenditures, unexpected emergencies and periods when income is expected to be low. Financial difficulty is getting into a situation where a respondent’s credit is adversely impacted such as not paying bills or charging a credit card to the maximum limit. These situations prevent or reduce the ability of individuals to borrow money in the future. Financial success for most people is a combination of all three measures; having a steady income stream, a stock of wealth to buffer life’s storms, and not worrying about being close to or beyond their financial limits. Previous research has focused on intelligence’s impact on income and found a positive relationship.

The authors have given an interpretation in the abstract which does not fairly reflect what they actually found. The data plot above, taken from that paper, shows a strong relationship between intelligence, income and wealth.

Commentator “res” shows that the 2007 figures probably under-estimate the effects of intelligence. First, incomes at the lower level of intelligence are inflated by welfare payments, and those are not shown separately. That is a real problem, since it hides the lower wages actually paid to low ability workers. Second, the wages of the brightest 2% of workers have been truncated, apparently so as to protect their anonymity. Not sure how their anonymity was threatened, but it is a nuisance, since in the early years top income were artificially capped at $75,000 then later at $100,000 and finally shown as the average of that category. This is a real pity, because it obscures the incomes of the smart fraction. Though the sample size is perfectly adequate for most purposes, eminent minds are 1 in 10,000 so the sample will contain only 1 or 2 of them. It would be good to know how much they earn, and how much wealth they have.

As luck would have it, I had commented on the same dataset in 2015 and, despite a few cautionary statements, came to the conclusion that intelligence was strongly related to income, and to savings. I also established a rule-of-thumb, that when young adults are doing well financially, their savings equal 4 years of their normal annual earnings.

I should explain that the 2007 paper being referred to above was an early look at the data, and the later work a more up-to-date set of findings. Both give substantially the same pattern of results, though the actual numbers have changed somewhat, as one would expect as the subjects get older, earn more, and have more time in which to save.

http://www.unz.com/jthompson/the-wages-of-intellect/

Looking back, there is a lot to ponder in these posts by Steve Hsu. For example, it seems that for both white and black children in the NLSY dataset, progress is identical when children are considered in terms of their IQ.

 

Steve dryly notes:

This last figure is very problematic for the “Social Status/Wealth causes IQ” position. It seems to be the other way around: the kids escaping bottom quintile childhoods all experienced poverty, but the ones with higher cognitive ability were more likely to move up. (Recall that adopted children tend to resemble their biological parents much more than their adoptive ones; family environment has a limited effect on IQ, which is highly heritable.)

 
• Category: Science • Tags: Income, IQ 
Cognitive power leads to monetary accumulation.
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It is just a coincidence, but the initials NNT are best known to me as Numbers Needed to Treat. This is a measure of the numbers of patients you need to give a drug to in order to get one cure. For example, an NNT of 5 means that you have to treat five patients in order to get one patient cured. Even very useful drugs do not help everybody, so several people need to take the drug before one of them benefits.

I do not know how many posts I need to write to convince one person that Nassim Nicholas Taleb is an unreliable guide to intelligence research. It might take 30 posts, but I still think it would be worthwhile.

One of NNT’s complaints was that intelligence test scores did not predict important real-world achievements, such as being a good investor. A very quick search immediately came up with a paper which showed that brighter people had more successful investment histories. Those authors found that by making better stock selections and achieving lower transaction costs high IQ subjects did 4.9% per year better than low IQ subjects. Given that real returns average 7%, this is a massive difference which will accumulate over time and result in far high net personal worth for brighter investors. By the way, intelligence was measured at conscription age, long before there is much investment history, so it is more likely to be causal.

https://www.sciencedirect.com/science/article/pii/S0304405X1100211X

In this post I will describe another paper which shows that an intelligence test predicts savings 40 years later. This is real-world skin-in-the-game of which NNT should approve.

Furnham, A., & Cheng, H. (2019). Factors influencing adult savings and investment: Findings from a nationally representative sample. Personality and Individual Differences, 151, 109510. doi:10.1016/j.paid.2019.109510

https://drive.google.com/file/d/10TFhHwSEUhrSIOCBNdZnNUhQxNstPb6-/view?usp=sharing

The authors review the cognitive and personality measures which affect savings rates: neuroticism and conscientiousness affect outcomes. Both these measures involve paper and pencil questionnaires designed by psychologists, yet are associated with real life outcomes.

The National Child Development Study 1958 is a large-scale longitudinal study of the 17,415 individuals who were born in Great Britain in a week in March 1958 (Ferri, Bynner, & Wadsworth, 2003). They were a representative sample of the country at the time. 14,134 children at age 11 completed tests of cognitive ability (response =87%).

At 33 years, 11,141 participants provided information on their educational qualifications obtained (response =72%). At age 50 years, 8210 participants provided information on their current occupational levels (response = 67%); 9790 participants completed a questionnaire on personality (response = 79%); 9762 participants provided information on their self-assessed financial situation (response= 79%), 9729 participants provided information on their savings and investment (response =57%). The analytic sample comprises 5766 cohort members (50% females) for whom complete data were collected at birth, at ages 11years, and the outcome measure at age 50years. Bias due to attrition of the sample during childhood has been shown to be minimal (Davie, Butler, & Goldstein; 1972).

3.2. Measures

1. Family Social Background at Birth Family social background includes information on parental social class and parental education. Parental social class at birth was measured by the Registrar General’s measure of social class (RGSC). Parental education is measured by the age parents had left their full-time education.

2. Childhood Intelligence Childhood intelligence was assessed at age 11 in school using a general ability test (Douglas, 1964) consisting of 40 verbal and 40 non-verbal items. For the verbal items, children were presented with an example set of four words that were linked either logically, semantically, or phonologically. For the non-verbal tasks, shapes or symbols were used. The children were then given another set of three words or shapes or symbols with a blank. Participants were required to select the missing item from a list of five alternatives. Scores from these two set of tests correlate strongly with scores on an IQ-type test used for secondary school selection (r =0.93, Douglas, 1964) suggesting a high degree of validity.

3. Educational Qualifications At age 33, participants were asked about their highest academic or vocational qualifications. Responses are coded to the six-point scale of National Vocational Qualifications levels (NVQ) ranging from ‘none’ to ‘higher degree level’: 0= no qualifications; 1=some qualifications [Certificate of Secondary Education Grades 2 to 5]; 2= O level [equivalent to qualifications taken at the end of compulsory schooling]; 3= A level [equivalent to university entrance level qualifications]; 4=postsecondary degree/diploma and equivalent; and 5=higher post-graduate degrees and equivalent.

4. Personality Traits Personality traits were assessed at age 50, by the 50 questions from the International Personality Item Pool (IPIP)(Goldberg, 1999). Responses (5-point, from “Strongly Agree” to “Strongly Disagree”) are summed to provide scores on the so called ‘Big-5’ personality traits: Extraversion, Emotionality/neuroticism, Conscientiousness, Agreeableness and Openness. Scores on each trait range between 5 and 50 with higher scores equating to higher levels of each trait, of which 10 items for each trait. A preliminary test showed that the associations between traits Extraversion and Agreeableness were not significantly associated with adult savings and investment, thus these two traits were excluded from the following analyses. Preliminary analysis which included these two traits in the SEM model demonstrated that they were not moderator variables. Alpha was 0.88 for emotionality/neuroticism, 0.77 for conscientiousness, and 0.79 for intellect/openness.

5. Occupational Prestige Data on current or last occupation held by cohort members at age 50 are coded according to the RGSC described above, using a 6-point classification.

6. Financial Assessment was assessed at age 50. Participants were asked to assess their personal financial situation on a 5-point measure (1 =Finding it very difficult, 2= Finding it quite difficult, 3=Just about getting by, 4=Doing all right, 5= Living comfortably).

7. Adult Savings and Investment. At age 50, participants provided information on the amount of savings and investment they had, which were logged in the following analyses. In addition, participants also mentioned the specific types of their savings and investment, of which bank or building society =70.2%, ISA=51.8%, premium bonds= 35.0%, stocks and/or other shares =32.9%.

All this is fine, though self-report on wealth could be a problem. Asking people about their wealth can be a tricky business in the UK. If anything, people might be tempted to downplay it to avoid any tax enquiries. One simple control would have been to study the postcodes of participants, from which it is easy to get wealth estimates. Worth doing as a further measure of the accuracy of self-reports.

The verbal and non-verbal scores have been added for an overall ability latent variable, which has more predictive power.

The strongest association was between personal financial assessment and adult savings and investment, followed by education and occupation. This is a well-established finding. However, what was particularly interesting was the correlation between IQ measured as age 11 and savings measured 39 years later.

 
• Category: Science • Tags: Finance, Intelligence, Nassim Nicholas Taleb, Wealth 
When dull meet bright, tough love rules
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What happens when above average and below average ability people have to deal with each other?

Specifically, how will they interact when potentially both are able to gain from the exchange? It seems obvious that they should cooperate, and extract the greatest amount of mutual gain, but does this really happen in situations where there are also gains to be made from not cooperating?

How do bright and dull cope with each other now, in ordinary life, particularly when they cannot all meet face to face, but have to deal with the consequences of each other’s behaviours. Do these two groups understand each other, or are they always at loggerheads, doomed to perpetual conflict? Why can’t we all get along with each other?

I never say of any book that it has changed my life. People and events have changed my life, but books have only changed my mind. Robert Axelrod’s 1990 “The evolution of co-operation” was one such book. He wondered how co-operation could emerge in a world of self-seeking egoists – whether superpowers, businesses, or individuals – when there was no central authority to police their actions. I enjoyed his analysis of Prisoner’s Dilemma competitions, and the simplicity of “Tit for Tat”, which turned out to be the winning strategy. Start by cooperating, then do unto others as they do unto you.

The Prisoner’s Dilemma is a conceptual game in which two people accused of a crime are held separately, and each is told that if they implicate the other they will be set free. Clearly, if both keep quiet they both will be released for lack of evidence, but the sting is that the person who cooperates with the Police gets sets free and the other denounced person serves a long time behind bars. If there is solidarity among criminals then both will keep quiet and each will be released. If either one breaks, then the other is heavily punished. If prisoners doubt each other, both may denounce each other, to their profound mutual disadvantage.

It is a long time since I looked at the literature on this game, but decades ago I think no-one bothered to research whether intelligence made a difference. Experimentalists rarely considered this possibility. Now a team have looked at this, with very interesting results, which may have wide application. They studied how intelligence and personality affected the outcomes of games, focusing on repeated interactions that provide the opportunity for profitable cooperation.

Intelligence, personality and gains from cooperation in repeated interactions.
EUGENIO PROTO, ALDO RUSTICHINI, and ANDIS SOFIANOS.
Journal of Political Economy (2019).

https://drive.google.com/file/d/1glPuvZAJ8taVTWdzgYuTX41mQNAgGMbP/view?usp=sharing

This is a very interesting and complex paper, and I have left out any consideration of the other games they have tested, and the further neuro-scanning measures they took of participants while playing games, which reveal that intelligent people showed more brain activity, presumably as they worked on the different strategies required for optimal cooperation. I will mention their personality measures in passing, because the intelligence differences were the most significant.

The method was as follows:

Our design involves a two-part experiment administered over two different days separated by one day in between. Participants are allocated into two groups according to some individual characteristic that is measured during the first part, and they are asked to return to a specific session to play several repetitions of a repeated game. Each repeated game is played with a new partner. The individual characteristics that we consider are: intelligence, Agreeableness and Conscientiousness, across different treatments that we will define as IQ-split, A-split and C-split, respectively.

In one treatment, participants are not separated according to any characteristic, but rather allocated to ensure similar groups across characteristics; we define this the combined treatment.

There were 792 subjects in all, university students on a wide range of courses, and on average they earned £20 each, of which £4 was paid for showing up. Motivating but not life-changing. Only 1 or 2 per 100 mentioned intelligence as the possible difference between groups, which strongly suggests that other explanations came to mind more readily. Instructive that we assume complicated motives rather than simple lack of understanding. It may be yet another example that bright people assume that others can think like them.

First, the authors show that brighter people do better at cooperation games than duller people. They cooperate more, and thus end up with higher final scores. Since the scores convert to money, they end up richer. They avoid immediate selfish gains in order to obtain higher long-term cooperative returns. Smart strategy. As an analogy for how they operate in real life, they are likely to reap the benefits of maximal cooperation, leading to increasing wealth.

The researchers then deliberately paired up an above average intelligence player with one who was below average to see what happened. The overall return to the participants fell, because lower ability players tended to defect so as to obtain an immediate advantage, at great cost to the other player. How should the bright player respond? Simply continuing to try to cooperate does not work, because the duller player is then rewarded for his lack of cooperation. Instead, the “tit for tat” punishment strategy is required. Start by cooperating, and on the next round do whatever the other person did: if they cooperated, you cooperate; if they defected, you defect. The researchers call this “tough love”.

Four applications of retaliation were, on average, required to teach the lesson that lack of cooperation would be punished with reciprocal lack of cooperation. Eventually cooperation is established between bright and dull, but at an initial cost. Lower intelligence players learn to cooperate, because higher intelligence players punish them if they don’t. In societies where cooperation is already low, lenient and forgiving strategies become less frequent. There is very probably a level at which trust can be assumed, but below that punishment will be the norm. Where is the social tipping point below which cooperation is too costly a strategy? At what point do civil societies collapse and turn into uncivil bands?

The outcome of games with a trade-off between short-run gain and continuation value loss was strikingly different when played by subjects with higher or lower levels of intelligence. Higher intelligence resulted in significantly higher levels of cooperation and earnings. The failure of individuals with lower intelligence to appropriately estimate the future consequences of current actions accounts for these differences in outcomes. Personality also affects behavior, but in smaller measure, and with low persistence. These results have potentially important implications for policy. For example, while the complex effects of early childhood intervention on the development of intelligence are still currently being evaluated (e.g. Heckman, 2006), our results suggest that any such effect would potentially enhance not only the economic success of the individual, but the level of cooperation in society (at least when interactions are repeated).

 
• Category: Science • Tags: Game Theory, IQ 
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As every conference attendee knows, a few minutes with a researcher is worth many hours of reading their work. What researchers say in person will be up to date, generally unvarnished and to the point. Compared to writing, conversation is speedy, interactive, and tends towards confession: the spoken word accompanied by the revealed emotion, a multi-level signal, rich in content. Ambiguities can be probed with short queries about meaning and anything contentious subjected to rapid forensic examination, in a two-way process which homes in on core issues. All this would take weeks by email, and in 5 exchanges would lead to boiling rage on Twitter.

Minneapolis is a fine city, with an excellent gallery. Cranach knew a thing or two about the human condition.

ISIR2019 was a conference at which one was spoilt for choice, since within speaking distance over coffee one could chat with Charles Murray, Steve Hsu, James Lee, Greg Cochran, Greg Clark, Razib Khan, Bruce Lahn and Neven Sesardic and many others. At breakfast with Tim Bates I met an amiable couple and, assuming they were wild-variant humans who happened to be staying at the hotel, launched immediately into a general enquiry about life in Minneapolis. They were a sparky and fun couple, and later in the day I realized I had been giving car buying advice to Prof Tom Bouchard, a legendary figure in twin research.

Even better, all of the prominent researchers were excited to see so many younger researchers, whom they quizzed enthusiastically. There is an excellent crop of young scientists already making their mark, and they were the de facto stars of the event, because established participants are all too aware that a decade ago such new talent was rare: it was a conference for older researchers. (ISIR offers special inducements for researchers at the start of their careers). http://www.isironline.org/category/awards-2/student/

The first day of the conference had a Symposium on Science and Ethics of genetic engineering, with Greg Cochran, Steve Hsu, Razib Khan, Bruce Lahn and Neven Sesardic. Sesardic argued that John Rawls’s work was a far from perfect guide to ethics in this field. Impossible to cover each contribution, but a general theme was that “designer babies” were unlikely, mostly because of doubts about unintended consequences. Crispr techniques are accurate for point deletions and small sequence insertions, but not so accurate when dealing with longer stretches of DNA. The panel as a whole was cautious about any gene editing procedures at this stage, though Razib Khan said that some in the genetics world, while condemning He Jiankui for his work on twin babies susceptible to HIV, were also grudgingly impressed with what he had done.

In answer to a question, Bruce Lahn said that genetic engineering in mouse was accurate, and came up with very few unintended effects, of the order of 1%. There was a common agreement among the panel that the appropriate ethical standards would prevent such experimentation in the West, but uncertainty about whether this would be the case in China. This raised a possibility that whichever nation relaxed ethical standards to allow experimentation might gain a considerable advantage over other, merely by the deletion of intelligence-lowering mutations. The panel also noted that screening for Down’s syndrome was now routine. In-vitro fertizilation was now running at over a million births a year, and these children has been previously stigmatized as “test tube babies”. Attitudes change if people are give the ability to choose new techniques.

This is a very brief summary, but here is the sequence as I see it, from those likely to happen soon to those much further downstream and happening later, if at all:

1) In countries where pregnancies can be terminated, more pre-natal screening, not only for Down’s syndrome, but for other forms of severe mental handicap and, when possible, for some genetic disorders like cystic fibrosis and Huntington’s chorea.
2) In the case of in-vitro fertilization, far more screening based on polygenic risk scores for a wider variety of disorders, concentrating initially on those with the very highest scores which put embryos most at risk. This depends on having viable foetuses to select from. No changes are made to the foetus, but choice is guided by polygenic risk scores.
3) Limited use of Crispr on foetuses to remove mutations directly linked to serious genetic disorders.
4) Crispr being used more generally to remove SNPs which increase vulnerability to a broader range of genetic disorders.
5) Crispr being used even more generally to remove SNPs which increase vulnerability to psychiatric disorders and low intelligence.
6) Crispr or other techniques being used to create “disease resistant” embryos.

Incidentally, one prominent researcher said that he and his colleagues were perplexed as to exactly what had been said at the London Conference which had caused so much trouble. I replied that I too was perplexed, but thought that it was because one of the 59 papers given at UCL was about eugenics, arguing it would only be contemplated in the setting of Malthusian over-population, and that it would not select for intelligence, but for a propensity to be happy. “Really” he replied “but that is far, far less than we have discussed here today”.

Strange are the ways of humankind.

 
• Category: Science • Tags: Genetic Engineering 
James Thompson
About James Thompson

James Thompson has lectured in Psychology at the University of London all his working life. His first publication and conference presentation was a critique of Jensen’s 1969 paper, with Arthur Jensen in the audience. He also taught Arthur how to use an English public telephone. Many topics have taken up his attention since then, but mostly he comments on intelligence research.