More about Intelligence

Do genes matter? You betcha! See geneticist Gregory Cochran’s “Everything Is Different but the Same” and “Missing Heritability — Found?” (Useful Wikipedia articles for explanations of terms used by Cochran: “Genome-wide association study,” “Genetic load,” and “Allele.”) Snippets:

Another new paper finds that the GWAS hits for IQ – largely determined in Europeans – don’t work in people of African descent.

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There is an interesting new paper out on genetics and IQ. The claim is that they have found the missing heritability – in rare variants, generally different in each family.

Cochran, in typical fashion, ends the second item with a bombastic put-down of the purported dysgenic trend, about which I’ve written here.

Psychologist James Thompson seems to put stock in the dysgenic trend. See, for example, his post “The Woodley Effect“:

[W]e could say that the Flynn Effect is about adding fertilizer to the soil, whereas the Woodley Effect is about noting the genetic quality of the plants. In my last post I described the current situation thus: The Flynn Effect co-exists with the Woodley Effect. Since roughly 1870 the Flynn Effect has been stronger, at an apparent 3 points per decade. The Woodley effect is weaker, at very roughly 1 point per decade. Think of Flynn as the soil fertilizer effect and Woodley as the plant genetics effect. The fertilizer effect seems to be fading away in rich countries, while continuing in poor countries, though not as fast as one would desire. The genetic effect seems to show a persistent gradual fall in underlying ability.

But Thompson joins Cochran in his willingness to accept what the data show, namely, that there are strong linkages between race and intelligence. See, for example, “County IQs and Their Consequences” (and my related post). Thompson writes:

[I]n social interaction it is not always either possible or desirable to make intelligence estimates. More relevant is to look at technical innovation rates, patents, science publications and the like…. If there were no differences [in such] measures, then the associations between mental ability and social outcomes would be weakened, and eventually disconfirmed. However, the general link between national IQs and economic outcomes holds up pretty well….

… Smart fraction research suggests that the impact of the brightest persons in a national economy has a disproportionately positive effect on GDP. Rindermann and I have argued, following others, that the brightest 5% of every country make the greatest contribution by far, though of course many others of lower ability are required to implement the discoveries and strategies of the brightest.

Though Thompson doesn’t directly address race and intelligence in “10 Replicants in Search of Fame,” he leaves no doubt about dominance of genes over environment in the determination of traits; for example:

[A] review of the world’s literature on intelligence that included 10,000 pairs of twins showed identical twins to be significantly more similar than fraternal twins (twin correlations of about .85 and .60, respectively), with corroborating results from family and adoption studies, implying significant genetic influence….

Some traits, such as individual differences in height, yield heritability as high as 90%. Behavioural traits are less reliably measured than physical traits such as height, and error of measurement contributes to nonheritable variance….

[A] review of 23 twin studies and 12 family studies confirmed that anxiety and depression are correlated entirely for genetic reasons. In other words, the same genes affect both disorders, meaning that from a genetic perspective they are the same disorder. [I have personally witnessed this effect: TEA.]…

The heritability of intelligence increases throughout development. This is a strange and counter-intuitive finding: one would expect the effects of learning to accumulate with experience, increasing the strength of the environmental factor, but the opposite is true….

[M]easures of the environment widely used in psychological science—such as parenting, social support, and life events—can be treated as dependent measures in genetic analyses….

In sum, environments are partly genetically-influenced niches….

People to some extent make their own environments….

[F]or most behavioral dimensions and disorders, it is genetics that accounts for similarity among siblings.

In several of the snippets quoted above, Thompson is referring to a phenomenon known as genetic confounding, which is to say that genetic effects are often mistaken for environmental effects. Brian Boutwell and JC Barnes address an aspect of genetic confounding in “Is Crime Genetic? Scientists Don’t Know Because They’re Afraid to Ask.” A small sample:

The effects of genetic differences make some people more impulsive and shortsighted than others, some people more healthy or infirm than others, and, despite how uncomfortable it might be to admit, genes also make some folks more likely to break the law than others.

John Ray addresses another aspect of genetic confounding in “Blacks, Whites, Genes, and Disease,” where he comments about a recent article in the Journal of the American Medical Association:

It says things that the Left do not want to hear. But it says those things in verbose academic language that hides the point. So let me translate into plain English:

* The poor get more illness and die younger
* Blacks get more illness than whites and die younger
* Part of that difference is traceable to genetic differences between blacks and whites.
* But environmental differences — such as education — explain more than genetic differences do
* Researchers often ignore genetics for ideological reasons
* You don’t fully understand what is going on in an illness unless you know about any genetic factors that may be at work.
* Genetics research should pay more attention to blacks

Most of those things I have been saying for years — with one exception:

They find that environmental factors have greater effect than genetics. But they do that by making one huge and false assumption. They assume that education is an environmental factor. It is not. Educational success is hugely correlated with IQ, which is about two thirds genetic. High IQ people stay in the educational system for longer because they are better at it, whereas low IQ people (many of whom are blacks) just can’t do it at all. So if we treated education as a genetic factor, environmental differences would fade way as causes of disease. As Hans Eysenck once said to me in a casual comment: “It’s ALL genetic”. That’s not wholly true but it comes close

So the recommendation of the study — that we work on improving environmental factors that affect disease — is unlikely to achieve much. They are aiming their gun towards where the rabbit is not. If it were an actual rabbit, it would probably say: “What’s up Doc?”

Some problems are unfixable but knowing which problems they are can help us to avoid wasting resources on them. The black/white gap probably has no medical solution.

I return to James Thompson for a pair of less incendiary items. “The Secret in Your Eyes” points to a link between intelligence and pupil size. In “Group IQ Doesn’t Exist,” Thompson points out the fatuousness of the belief that a group is somehow more intelligent that the smartest member of the group. As Thompson puts it:

So, if you want a problem solved, don’t form a team. Find the brightest person and let [him] work on it. Placing [him] in a team will, on average, reduce [his] productivity. My advice would be: never form a team if there is one person who can sort out the problem.

Forcing the brightest person to act as a member of a team often results in the suppression of that person’s ideas by the (usually) more extroverted and therefore less-intelligent members of the team.

Added 04/05/17: James Thompson issues a challenge to IQ-deniers in “IQ Does Not Exist (Lead Poisoning Aside)“:

[T]his study shows how a neuro-toxin can have an effect on intelligence, of similar magnitude to low birth weight….

[I]f someone tells you they do not believe in intelligence reply that you wish them well, but that if they have children they should keep them well away from neuro-toxins because, among other things, they reduce social mobility.

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Related posts:
Race and Reason: The Victims of Affirmative Action
Race and Reason: The Achievement Gap — Causes and Implications
“Conversing” about Race
Evolution and Race
“Wading” into Race, Culture, and IQ
Round Up the Usual Suspects
Evolution, Culture, and “Diversity”
The Harmful Myth of Inherent Equality
Let’s Have That “Conversation” about Race
Affirmative Action Comes Home to Roost
The IQ of Nations
Race and Social Engineering

Brilliant Crazies

Gregory Cochran avers that “smart people are susceptible to all kinds of ideological craziness.” Cochran’s case in point is Neil Turok, a theoretical physicist from South Africa, currently head of the Perimeter Institute for Theoretical Physics in Canada. Back to Cochran:

I mentioned that [Turok] was a smart guy. He’s also crazy. He thinks that sub-Saharan Africans today are analogous to Ashkenazi Jews in 1850 or so – ready to explode into the intellectual world and tear it a new asshole.

Wanna bet? With African math scores at the 5th percentile? With their IQ scores two standard deviations below those of Europeans, three below the Askenazim? That low average tremendously suppresses the fraction above a high threshold. With every event in life its own self consistent with those statistics – not just in Africa, but everywhere in the African diaspora?

And he has no excuse [other than his commie family history]. He grew up in South Africa: there are plenty of things he would have seen if this picture of the world were true, and he’s never seen any of them. Did black kids out-argue him, beat him at chess, win the math competitions even though their parents were poor as synagogue mice? No sirree.

A very smart person like Neil Turok is probably eligible for the Triple-Nine Society (as I was before old age set in). That is, his IQ probably places him at or above the 99.9th percentile of the population: the top 0.1 percent. You’re unlikely to run into one of the 0.1 percenters unless you hang around a university, a research lab, a think tank, or a big professional-services company. They cluster in such places like birds on telephone wires.

Such persons usually do well for themselves. If they aren’t in the top one-percent of the income distribution, it’s because they don’t have the kind of personality (or athletic ability or photogenic qualities) it takes to get there. Wheeling and dealing isn’t for introverts, who are more likely than extroverts to be very smart. But very smart people have the wherewithal to make a good living, especially when the kinds of things they are good at and enjoy (e.g., teaching, writing, conducting research, and crafting legal arguments) are subsidized by taxpayers and bankrolled by wealthy clients and foundations.

Thus very smart persons usually have the luxury of thinking impossible things and dreaming impossible dreams. And when they do, they detach themselves from reality; that is, they become crazy. Like Turok, they often make a good living at it. They’re paid and encouraged to be crazy — to treat reality as an option.

Albert Einstein, for example, held a sinecure at the Institute for Advanced Study of Princeton University for the final 22 years of his life. During that time he added essentially nothing to his monumental work on special relativity, general relativity, and early quantum theory. His career played out in a Quixotic fashion: dreaming the dream (perhaps an impossible one) of a unified field theory and trying in vain to discredit quantum theory as it had developed after his early contributions. But Einstein wasn’t entirely harmless in his dotage. He was a socialist and advocate of world government, and should be dishonored for lending his prestige to those abominable causes.

Cochran is right: High intelligence doesn’t immunize a person from ideological craziness. Nor from nastiness. There’s nothing nastier than an intellectual in attack mode. As a denizen of a Ph.D.-laden think-tank for 30 years, I saw a lot of intellectual savagery at first hand. It was ugly, and I’m ashamed to say that I committed some of it.

High intelligence is highly overrated as a virtue. But if you have it you probably wouldn’t trade it for a million dollars. Well, maybe less than that. I’m open to offers.

Not Just for Baseball Fans

I have substantially revised “Bigger, Stronger, and Faster — But Not Quicker?” I set out to test Dr. Michael Woodley’s hypothesis that reaction times have slowed since the Victorian era:

It seems to me that if Woodley’s hypothesis has merit, it ought to be confirmed by the course of major-league batting averages over the decades. Other things being equal, quicker reaction times ought to produce higher batting averages. Of course, there’s a lot to hold equal, given the many changes in equipment, playing conditions, player conditioning, “style” of the game (e.g., greater emphasis on home runs), and other key variables over the course of more than a century.

I conclude that my analysis

says nothing definitive about reaction times, even though it sheds a lot of light on the relative hitting prowess of American League batters over the past 116 years. (I’ll have more to say about that in a future post.)

It’s been great fun but it was just one of those things.

Sandwiched between those statements you’ll find much statistical meat (about baseball) to chew on.

Bigger, Stronger, and Faster — but Not Quicker?


There’s some controversial IQ research which suggests that reaction times have slowed and people are getting dumber, not smarter. Here’s Dr. James Thompson’s summary of the hypothesis:

We keep hearing that people are getting brighter, at least as measured by IQ tests. This improvement, called the Flynn Effect, suggests that each generation is brighter than the previous one. This might be due to improved living standards as reflected in better food, better health services, better schools and perhaps, according to some, because of the influence of the internet and computer games. In fact, these improvements in intelligence seem to have been going on for almost a century, and even extend to babies not in school. If this apparent improvement in intelligence is real we should all be much, much brighter than the Victorians.

Although IQ tests are good at picking out the brightest, they are not so good at providing a benchmark of performance. They can show you how you perform relative to people of your age, but because of cultural changes relating to the sorts of problems we have to solve, they are not designed to compare you across different decades with say, your grandparents.

Is there no way to measure changes in intelligence over time on some absolute scale using an instrument that does not change its properties? In the Special Issue on the Flynn Effect of the journal Intelligence Drs Michael Woodley (UK), Jan te Nijenhuis (the Netherlands) and Raegan Murphy (Ireland) have taken a novel approach in answering this question. It has long been known that simple reaction time is faster in brighter people. Reaction times are a reasonable predictor of general intelligence. These researchers have looked back at average reaction times since 1889 and their findings, based on a meta-analysis of 14 studies, are very sobering.

It seems that, far from speeding up, we are slowing down. We now take longer to solve this very simple reaction time “problem”.  This straightforward benchmark suggests that we are getting duller, not brighter. The loss is equivalent to about 14 IQ points since Victorian times.

So, we are duller than the Victorians on this unchanging measure of intelligence. Although our living standards have improved, our minds apparently have not. What has gone wrong? [“The Victorians Were Cleverer Than Us!” Psychological Comments, April 29, 2013]

Thompson discusses this and other relevant research in many posts, which you can find by searching his blog for Victorians and Woodley. I’m not going to venture my unqualified opinion of Woodley’s hypothesis, but I am going to offer some (perhaps) relevant analysis based on — you guessed it — baseball statistics.

It seems to me that if Woodley’s hypothesis has merit, it ought to be confirmed by the course of major-league batting averages over the decades. Other things being equal, quicker reaction times ought to produce higher batting averages. Of course, there’s a lot to hold equal, given the many changes in equipment, playing conditions, player conditioning, “style” of the game (e.g., greater emphasis on home runs), and other key variables over the course of more than a century.

Undaunted, I used the Play Index search tool at to obtain single-season batting statistics for “regular” American League (AL) players from 1901 through 2016. My definition of a regular player is one who had at least 3 plate appearances (PAs) per scheduled game in a season. That’s a minimum of 420 PAs in a season from 1901 through 1903, when the AL played a 140-game schedule; 462 PAs in the 154-game seasons from 1904 through 1960; and 486 PAs in the 162-game seasons from 1961 through 2016. I found 6,603 qualifying player-seasons, and a long string of batting statistics for each of them: the batter’s age, his batting average, his number of at-bats, his number of PAs, etc.

The raw record of batting averages looks like this, fitted with a 6th-order polynomial to trace the shifts over time:


Everything else being the same, the best fit would be a straight line that rises gradually, falls gradually, or has no slope. The undulation reflects the fact that everything hasn’t stayed the same. Major-league baseball wasn’t integrated until 1947, and integration was only token for a couple of decades after that. For example: night games weren’t played until 1935, and didn’t become common until after World War II; a lot of regular players went to war, and those who replaced them were (obviously) of inferior quality — and hitting suffered more than pitching; the “deadball” era ended after the 1919 season and averages soared in the 1920s and 1930s; fielders’ gloves became larger and larger.

The list goes on, but you get the drift. Playing conditions and the talent pool have changed so much over the decades that it’s hard to pin down just what caused batting averages to undulate rather than move in a relatively straight line. It’s unlikely that batters became a lot better, only to get worse, then better again, and then worse again, and so on.

Something else has been going on — a lot of somethings, in fact. And the 6th-order polynomial captures them in an undifferentiated way. What remains to be explained are the differences between official BA and the estimates yielded by the 6th-order polynomial. Those differences are the stage 1 residuals displayed in this graph:


There’s a lot of variability in the residuals, despite the straight, horizontal regression line through them. That line, by the way, represents a 6th-order polynomial fit, not a linear one. So the application of the equation shown in figure 1 does an excellent job of de-trending the data.

The variability of the stage 1 residuals has two causes: (1) general changes in the game and (2) the performance of individual players, given those changes. If the effects of the general changes can be estimated, the remaining, unexplained variability should represent the “true” performance of individual batters.

In stage 2, I considered 16 variables in an effort to isolate the general changes. I began by finding the correlations between each of the 16 candidate variables and the stage 1 residuals. I then estimated a regression equation with stage 1 residuals as the dependent variable and the most highly correlated variable as the explanatory variable. I next found the correlations between the residuals of that regression equation and the remaining 15 variables. I introduced the most highly correlated variable into a new regression equation, as a second explanatory variable. I continued this process until I had a regression equation with 16 explanatory variables. I chose to use the 13th equation, which was the last one to introduce a variable with a highly significant p-value (less than 0.01). Along the way, because of collinearity among the variables, the p-values of a few others became high, but I kept them in the equation because they contributed to its overall explanatory power.

Here’s the 13-variable equation (REV13), with each coefficient given to 3 significant figures:

R1 = 1.22 – 0.0185WW – 0.0270DB – 1.26FA + 0.00500DR + 0.00106PT + 0.00197Pa + 0.00191LT – 0.000122Ba – 0.00000765TR + 0.000816DH – 0.206IP + 0.0153BL – 0.000215CG


R1 = stage 1 residuals

WW = World War II years (1 for 1942-1945, 0 for all other years)

DB = “deadball” era (1 for 1901-1919, 0 thereafter)

FA = league fielding average for the season

DR = prevalence of performance-enhancing drugs (1 for 1994-2007, 0 for all other seasons)

PT = number of pitchers per team

Pa = average age of league’s pitchers for the season

LT = fraction of teams with stadiums equipped with lights for night baseball

Ba = batter’s age for the season (not a general condition but one that can explain the variability of a batter’s performance)

TR = maximum distance traveled between cities for the season

DH = designated-hitter rule in effect (0 for 1901-1972, 1 for 1973-2016)

IP = average number of innings pitched per pitcher per game (counting all pitchers in the league during a season)

BL = fraction of teams with at least 1 black player

CG = average number of complete games pitched by each team during the season

The r-squared for the equation is 0.035, which seems rather low, but isn’t surprising given the apparent randomness of the dependent variable. Moreover, with 6,603 observations, the equation has an extremely significant f-value of 1.99E-43.

A positive coefficient means that the variable increases the value of the stage 1 residuals. That is, it causes batting averages to rise, other things being equal. A negative coefficient means the opposite, of course. Do the signs of the coefficients seem intuitively right, and if not, why are they the opposite of what might be expected? I’ll take them one at a time:

World War II (WW)

A lot of the game’s best batters were in uniform in 1942-1945. That left regular positions open to older, weaker batters, some of whom wouldn’t otherwise have been regulars or even in the major leagues. The negative coefficient on this variable captures the war’s effect on hitting, which suffered despite the fact that a lot of the game’s best pitchers also went to war.

Deadball era (DB)

The so-called deadball era lasted from the beginning of major-league baseball in 1871 through 1919 (roughly). It was called the deadball era because the ball stayed in play for a long time (often for an entire game), so that it lost much of its resilience and became hard to see because it accumulated dirt and scuffs. Those difficulties (for batters) were compounded by the spitball, the use of which was officially curtailed beginning with the 1920 season. (See this and this.) As figure 1 indicates, batting averages rose markedly after 1919, so the negative coefficient on DB is unsurprising.

Performance-enhancing drugs (DR)

Their rampant use seems to have begun in the early 1990s and trailed off in the late 2000s. I assigned a dummy variable of 1 to all seasons from 1994 through 2007 in an effort to capture the effect of PEDs. The coefficient suggests that the effect was (on balance) positive.

Number of pitchers per AL team (PT)

This variable, surprisingly, has a positive coefficient. One would expect the use of more pitchers to cause BA to drop. PT may be a complementary variable, one that’s meaningless without the inclusion of related variable(s). (See IP.)

Average age of AL pitchers (Pa)

The stage 1 residuals rise with respect to Pa rise until Pa = 27.4 , then they begin to drop. This variable represents the difference between 27.4 and the average age of AL pitchers during a particular season. The coefficient is multiplied by 27.4 minus average age; that is, by a positive number for ages lower than 27.4, by zero for age 27.4, and by a negative number for ages above 27.4. The positive coefficient suggests that, other things being equal, pitchers younger than 27.4 give up hits at a lower rate than pitchers older than 27.4. I’m agnostic on the issue.

Night baseball, that is, baseball played under lights (LT)

It has long been thought that batting is more difficult under artificial lighting than in sunlight. This variable measures the fraction of AL teams equipped with lights, but it doesn’t measure the rise in night games as a fraction of all games. I know from observation that that fraction continued to rise even after all AL stadiums were equipped with lights. The positive coefficient on LT suggests that it’s a complementary variable. It’s very highly correlated with BL, for example.

Batter’s age (Ba)

The stage 1 residuals don’t vary with Ba until Ba = 37 , whereupon the residuals begin to drop. The coefficient is multiplied by 37 minus the batter’s age; that is, by a positive number for ages lower than 37, by zero for age 37, and by a negative number for ages above 37. The very small negative coefficient probably picks up the effects of batters who were good enough to have long careers and hit for high averages at relatively advanced ages (e.g., Ty Cobb and Ted Williams). Their longevity causes them to be “over represented” in the sample.

Maximum distance traveled by AL teams (TR)

Does travel affect play? Probably, but the mode and speed of travel (airplane vs. train) probably also affects it. The tiny negative coefficient on this variable — which is highly correlated with several others — is meaningless, except insofar as it combines with all the other variables to account for the stage 1 residuals. TR is highly correlated with the number of teams (expansion), which suggests that expansion has had little effect on hitting.

Designated-hitter rule (DH)

The small positive coefficient on this variable suggests that the DH is a slightly better hitter, on average, than other regular position players.

Innings pitched per AL pitcher per game (IP)

This variable reflects the long-term trend toward the use of more pitchers in a game, which means that batters more often face rested pitchers who come at them with a different delivery and repertoire of pitches than their predecessors. IP has dropped steadily over the decades, presumably exerting a negative effect on BA. This is reflected in the rather large, negative coefficient on the variable, which means that it’s prudent to treat this variable as a complement to PT (discussed above) and CG (discussed below), both of which have counterintuitive signs.

Integration (BL)

I chose this variable to approximate the effect of the influx of black players (including non-white Hispanics) since 1947. BL measures only the fraction of AL teams that had at least one black player for each full season. It begins at 0.25 in 1948 (the Indians and Browns signed Larry Doby and Hank Thompson during the 1947 season) and rises to 1 in 1960, following the signing of Pumpsie Green by the Red Sox during the 1959 season. The positive coefficient on this variable is consistent with the hypothesis that segregation had prevented the use of players superior to many of the whites who occupied roster slots because of their color.

Complete games per AL team (CG)

A higher rate of complete games should mean that starting pitchers stay in games longer, on average, and therefore give up more hits, on average. The negative coefficient seems to contradict that hypothesis. But there are other, related variables (PT and CG), so this one should be thought of as a complementary variable.

Despite all of that fancy footwork, the equation accounts for only a small portion of the stage 1 residuals:


What’s left over — the stage 2 residuals — is (or should be) a good measure of comparative hitting ability, everything else being more or less the same. One thing that’s not the same, and not yet accounted for is the long-term trend in home-park advantage, which has slightly (and gradually) weakened. Here’s a graph of the inverse of the trend, normalized to the overall mean value of home-park advantage:


To get a true picture of a player’s single-season batting average, it’s just a matter of adding the stage 2 residual for that player’s season to the baseline batting average for the entire sample of 6,603 single-season performances. The resulting value is then multiplied by the equation given in figure 4. The baseline is .280, which is the overall average  for 1901-2016, from which individual performances diverge. Thus, for example, the stage 2 residual for Jimmy Williams’s 1901 season, adjusted for the long-term trend shown in figure 4, is .022. Adding that residual to .280 results in an adjusted (true) BA of .302, which is 15 points (.015) lower than Williams’s official BA of .317 in 1901.

Here are the changes from official to adjusted averages, by year:


Unsurprisingly, the pattern is roughly a mirror image of the 6th-order polynomial regression line in figure 1.

Here’s how the adjusted batting averages (vertical axis) correlate with the official batting averages (horizontal axis):


The red line represents the correlation between official and adjusted BA. The dotted gray line represents a perfect correlation. The actual correlation is very high (r = 0.93), and has a slightly lower slope than a perfect correlation. High averages tend to be adjusted downward and low averages tend to be adjusted upward. The gap separates the highly inflated averages of the 1920s and 1930s (lower right) from the less-inflated averages of most other decades (upper left).

Here’s a time-series view of the official and adjusted averages:


The wavy, bold line is the 6th-order polynomial fit from figure 1. The lighter, almost-flat line is a 6th-order polynomial fit to the adjusted values. The flatness is a good sign that most of the general changes in game conditions have been accounted for, and that what’s left (the gray plot points) is a good approximation of “real” batting averages.

What about reaction times? Have they improved or deteriorated since 1901? The results are inconclusive. Year (YR) doesn’t enter the stage 2 analysis until step 15, and it’s statistically insignificant (p-value = 0.65). Moreover, with the introduction of another variable in step 16, the sign of the coefficient on YR flips from slightly positive to slightly negative.

In sum, this analysis says nothing definitive about reaction times, even though it sheds a lot of light on the relative hitting prowess of American League batters over the past 116 years. (I’ll have more to say about that in a future post.)

It’s been great fun but it was just one of those things.

IQ, Political Correctness, and America’s Present Condition

This is a wandering post, in which I use a recent controversy about IQ to make some observations about political correctness, which leads to a tale of leftist subversion and America’s descent into statism.

Since my last post about IQ, more than a year ago, the biggest kerfuffle on the IQ front arose when Jason Richwine was chased from his job at Heritage Foundation. The proximate cause of Richwine’s departure from Heritage was the usual kind of witch hunt that accompanies the discovery of anything coming from a conservative source that might offend political correctness. Richwine was “guilty” of having penned a dissertation that contains unremarkable statements about ethnic differences in average IQ, including the IQ difference between Hispanics and non-Hispanic whites.

These are excerpts of John Derbyshire’s narration of l’affaire Richwine as it unfolded:

… Following the release of a report by the Heritage Foundation arguing that the Rubio-Schumer immigration bill will cost the nation $6.3 trillion, the Slave Power set their dwarf miners to digging.

They soon found gold. One of the co-authors of the study is twentysomething Jason Richwine, a Heritage analyst. Not just an analyst, but a quantitative analyst: “Heritage’s senior policy analyst in empirical studies.” …

After a few days’ digging the Nibelungs turned up Richwine’s Ph.D. thesis from Harvard University, title: “IQ and Immigration Policy.” The mother lode! (You can download it from here.)

The Washington Post ran a gleeful story on the find under the headline “Heritage study co-author opposed letting in immigrants with low IQs.” [By Dylan Matthews, May 8, 2013]. They note that:

Richwine’s dissertation asserts that there are deep-set differentials in intelligence between races.

Eek! A witch! …

Post columnist Jennifer Rubin, on secondment from Conservatism, Inc. to offer some pretense of “balance” at the Post, hastened to join the lynch mob. “It undermines the cause of all immigration opponents to have their prized work authored by such a character,” she wrote, reading Richwine out of respectable society….

She then brings in Jennifer S. Korn for a quote. Ms. Korn was Secretary for Hispandering in the George W. Bush White House….

What does Ms. Korn have to tell us?

Richwine’s comments are bigoted and ignorant. America is a nation of immigrants; to impugn the intelligence of immigrants is to offend each and every American and the foundation of our country….

Even if you take Ms. Korn’s usage of “impugn” to mean Richwine has stated that immigrants have lower mean IQ than natives, she is wrong. Table 2.2 in the thesis (p. 30) gives an average estimated mean IQ of 105.5 for immigrants from Northeast Asia….

And so another “anti-racist” witch hunt commences….

The forces of orthodoxy have identified a heretic. They’re marching on his hut with pitchforks and flaming brands. The cry echoes around the internet: “Burn the witch!” … (“‘Burn the Witch’: Heritage Foundation Scuttles Away from Jason Richwine–and the Cold, Hard Facts,”, May 9, 2013)

The impetus for politically correct witch-hunting comes from the left, of course. This is unsurprising because leftists, on average, are dumber than conservatives and libertarians. (See this and this, for example.) Which would explain their haste to take offense when the subject of IQ is raised.

But facts are facts, and Richwine summarizes them neatly in a recent (post-Heritage) essay; for example:

The American Psychological Association (APA) tried to set the record straight in 1996 with a report written by a committee of experts. Among the specific conclusions drawn by the APA were that IQ tests reliably measure a real human trait, that ethnic differences in average IQ exist, that good tests of IQ are not culturally biased against minority groups, and that IQ is a product of both genetic inheritance and early childhood environment. Another report signed by 52 experts, entitled “Mainstream Science on Intelligence,” stated similar facts and was printed in the Wall Street Journal. (“Why Can’t We Talk about IQ?,” Politico, August 9, 2013)

Richwine continues:

[W]hen Larry Summers, then the president of Harvard University, speculated in 2005 that women might be naturally less gifted in math and science, the intense backlash contributed to his ouster.Two years later, when famed scientist James Watson noted the low average IQ scores of sub-Saharan Africans, he was forced to resign from his lab, taking his Nobel Prize with him.

When a Harvard law student was discovered in 2010 to have suggested in a private email that the black-white IQ gap might have a genetic component, the dean publicly condemned her amid a campus-wide outcry. Only profuse apologies seem to have saved her career.

In none of these cases did an appeal to science tamp down the controversy or help to prevent future ones. My own time in the media crosshairs would be no different.

So what did I write that created such a fuss? In brief, my dissertation shows that recent immigrants score lower than U.S.-born whites on a variety of cognitive tests. Using statistical analysis, it suggests that the test-score differential is due primarily to a real cognitive deficit rather than to culture or language bias. It analyzes how that deficit could affect socioeconomic assimilation, and concludes by exploring how IQ selection might be incorporated, as one factor among many, into immigration policy.

Because a large number of recent immigrants are from Latin America, I reviewed the literature showing that Hispanic IQ scores fall between white and black scores in the United States. This fact isn’t controversial among experts, but citing it seems to have fueled much of the media backlash.

Derbyshire follows up:

Jason, who can hardly be more than thirty, has not yet grasped an important thing about humanity at large: that most of our thinking is magical, superstitious, religious, social, and egotistical. Very little of it is empirical. I myself am as stone-cold an empiricist as you’ll meet in a month of Sundays; yet every day when I walk my dog there is a certain tree I have to pat as we pass it. (It’s on the wrong side of the road. The family joke is that I shall one day be hit by a truck while crossing the road to pat my lucky tree.)

Hence Jason’s puzzlement that 25 years after Snyderman and Rothman, 19 years after The Bell Curve and the follow-up “Mainstream Science on Intelligence” declaration, the public discourse even in quality outlets is dominated by innumerate journo-school graduates parroting half-remembered half-truths from Stephen Jay Gould’s The Mismeasure of Man, the greatest work of Cultural Marxist propaganda yet produced.

That’s how we are. That’s the shape of human nature. Alan Cromer explained it in his 1993 book Uncommon Sense: The Heretical Nature of Science. Not many people can think empirically much of the time. At the aggregate level, where the lowest common denominator takes over and social acceptance is at the front of everyone’s mind, empiricism doesn’t stand a chance unless it delivers some useful technology.

Nor is it quite the case that “emotion trumps reason.” What mostly trumps reason is the yearning for respectability, leading us to conform to ambient dogmas—in the present-day West, the dogmas of Cultural Marxism, which waft around us like a noxious vapor….

This is how we are: jumbles of superstition, emotion, self-deception, and social conformism, with reason and science trotting along behind trying to keep up.

Science insists that there is an external world beyond our emotions and wish-fulfillment fantasies. It claims that we can find out true facts about that world, including facts with no immediate technological application. The human sciences insist even more audaciously that we ourselves are part of that world and can be described as dispassionately as stars, rocks, and microbes. Perhaps one day it will be socially acceptable to believe this. (“Why We Can’t Talk about IQ,” Taki’s Magazine, August 15, 2013)

Much has been made of the “bland” 1950s and the supposed pressure to conform to the Ozzie and Harriett way of life. Though i was never clear about the preferred alternative. On the evidence of the past 50 years, it seems to have been a potent mix of blue language, promiscuous sex, sodomy, broken families, drugs, violence, and ear-blasting “music.”

The true forces of conformity had begun their work many years before Ricky Nelson was a gleam in his father’s eye. There was, of course, the Progressive Era of the late 1800s and early 1900s, from which America was beginning to recover by the late 1920s.. But then came the Great Depression, the New Deal, and the establishment in America of a fifth column dedicated to the suppression of liberty:

As recounted in [KGB: The Inside Story by KGB Colonel Oleg Gordievsky and Cambridge intelligence expert Christopher Andrew]  … Harry Hopkins — FDR’s confidant, advisor, and policy czar, who actually resided in the White House during World War II — was the Big Enchilada among American agents of influence working for the USSR. Gordievsky recounts attending a lecture early in his career by Iskhak Akhmerov, the KGB’s top “illegal” spy in the U.S. during the 1940s (In espionage parlance, “illegals” do not have legal cover if caught). According to Gordievsky, Akhmerov spoke for a long period about Hopkins, calling him the top Soviet asset in the US. Yet, Gordievsky and Andrew tiptoe around this allegation by representing that Hopkins was a naïve devotee who only courted Stalin to ensure victory over Hitler’s Germany.

Although I know Andrew well, and have met Gordievsky twice, I now doubt their characterization of Hopkins…. It does not ring true that Hopkins was an innocent dupe dedicated solely to defeating the Nazis. Hopkins comes over in history as crafty, secretive and no one’s fool, hardly the personality traits of a naïve fellow traveler. And his fingerprints are on the large majority of pro-Soviet policies implemented by the Roosevelt administration. [Diana] West [author of American Betrayal: Secret Assault on Our Nation’s Character] deserves respect for cutting through the dross that obscures the evidence about Hopkins, and for screaming from the rooftops that the U.S. was the victim of a successful Soviet intelligence operation….

West mines Venona, the testimony of “Red spy queen” Elizabeth Bentley — who confessed her work for the communist underground to the FBI in 1945 — and the book Blacklisted by History by M. Stanton Evans, a re-examination of the McCarthy era using Venona and hundreds of other recently declassified documents from the FBI, CIA, and other agencies. And West lambastes the Truman administration for not revealing data from Venona that would have exonerated McCarthy and informed the nation that Soviet agents had indeed infiltrated key departments of the FDR administration….

The Rosenbergs, Alger Hiss, Harry Dexter White, Laurence Duggan, and 397 more American agents have been confirmed and verified as Soviet agents. West claims Harry Hopkins has been outed too in Venona, but Radosh and other scholars say this identification is bogus. But the Soviets also ran important agents of influence with great attention to the security of their identities. In essence, whether or not Hopkins is ever identified in Venona, he remains, as the cops say, a person of interest. (Bernie Reeves, “Reds under the Beds: Diana West Can’t Sleep,” American Thinker, August 10, 2013)

Influence flows downhill. What happened in Washington was repeated in many a city and State because the New Deal had made leftism respectable. By the end of World War II, which made nationalization the norm, the “mainstream” had shifted far to the left of where it had flowed before the Great Depression.

Influence also flows laterally. The growing respectability of leftism emboldened and empowered those institutions that naturally lean left: the media, academia, and the arts and letters. And so they went forth into the wilderness, amplifying the gospel according to Marx.

The most insidious influence has been the indoctrination of students — from pre-Kindergarten to graduate school — in the language and ideals of leftism: world government (i.e., anit-Americanism); redistributionism (as long as it hits only the “rich,” of course); favoritism for “minorities” (i.e., everyone but straight, white males); cultural diversity (any kind of crap in the arts, music, and literature, as long as it wasn’t produced by dead, white mailes); moral relativism (e.g., anti-feminism is bad, unless it’s practiced by Muslims). All of that, and much more, is the stuff of political correctness, which is an especially corrosive manifestation of social conformism, as Jason Richwine learned the hard way.

And then came the “pod people.” These are the masses of “ordinary people” who may have been deaf or impervious to indoctrination by teachers and professors, but who in vast numbers were (and continue to be) seduced by into collaboration with the left by years and decades of post-educational exposure to leftist cant. Seduced by slanted opinionators — usually disguised as reporters. Seduced by novelists, screenwriters, playwrights, and other denizens of the world of arts and letters. Seduced by politicians (even “conservative” ones) trading “free lunches” and “local jobs” for votes.

It is more than a small wonder that there is such a sizable remnant of true conservatives and non-leftish libertarians (unlike this leftish one). But we are vastly outnumbered by staunch leftists, wishy-washy “moderates,” and “conservatives” whose first instinct is to defend sacred cows (Social Security and Medicare, for example) instead of defending liberty.

I will have more to say, in future posts, about the subversion of “Old America.” For now, I end with this observation from an earlier post:
If America was ever close to being a nation united and free, it has drifted far from that condition — arguably, almost as far as it  had by 1861. And America’s condition will only worsen unless leaders emerge who will set the nation (or a large, independent portion of it) back on course. Barring the emergence of such leaders, America will continue to slide into baseness, divisiveness, and servitude.

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Related posts:
Affirmative Action: Two Views from the Academy
Affirmative Action, One More Time
A Contrarian View of Segregation
After the Bell Curve
A Footnote . . .
Schelling and Segregation
Affirmative Action: Two Views from the Academy, Revisited
“Family Values,” Liberty, and the State
Is There Such a Thing as Society
Intellectuals and Capitalism
A New, New Constitution
Secession Redux
A New Cold War or Secession?
The Real Constitution and Civil Disobedience
A Declaration of Independence
First Principles
The Shape of Things to Come
The Near-Victory of Communism
The Constitution: Original Meaning, Corruption, and Restoration
“Intellectuals and Society”: A Review
Intelligence, Personality, Politics, and Happiness
The Left’s Agenda
The Left and Its Delusions
Intelligence as a Dirty Word
Crimes against Humanity
Abortion and Logic
The Myth That Same-Sex “Marriage” Causes No Harm
The Spoiled Children of Capitalism
Politics, Sophistry, and the Academy
Subsidizing the Enemies of Liberty
Are You in the Bubble?
Abortion, Doublethink, and Left-Wing Blather
Reclaiming Liberty throughout the Land
Race and Reason: The Victims of Affirmative Action
Abortion, “Gay Rights,” and Liberty
Race and Reason: The Achievement Gap–Causes and Implications
Dan Quayle Was (Almost) Right
Tolerance on the Left
The Eclipse of “Old America”
Genetic Kinship and Society
Government in Macroeconomic Perspective
Keynesianism: Upside-Down Economics in the Collectivist Cause
Secession for All Seasons
Liberty and Society
Liberty as a Social Construct: Moral Relativism?
A Contrarian View of Universal Suffrage
Well-Founded Pessimism
America: Past, Present, and Future
Defending Liberty against (Pseudo) Libertarians
“Conversing” about Race
The Fallacy of Human Progress
Political Correctness vs. Civility

“Intelligence” As a Dirty Word

I came across a blog post (in Chinese, I think) that links to my most popular post, “Intelligence, Personality, Politics, and Happiness.” The same post includes links to a couple of other posts on the subject of intelligence. One of those posts, “The Nature of Intelligence,” appears at a blog named MBTI Truths. Here is the entire text of the post:

A commonly held misconception within the MBTI community is that iNtuitives are smarter than Sensors. They are thought to have higher intelligence, but this belief is misguided. In an assessment of famous people with high IQs, the vast majority of them are iNtuitive. However, IQ tests measure only two types of intelligences: linguistic and logical-mathematical. In addition to these, there are six other types of intelligence: spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal, and naturalistic. Sensors would probably outscore iNtuitives in several of these areas. Perhaps MBTI users should come to see iNtuitives, who make up 25 percent of the population, as having a unique type of intelligence instead of superior intelligence.

The use of “intelligence” with respect to traits other than brain-power is miguided. “Intelligence” has a clear and unambiguous meaning in everyday language; for example:

1. a. The capacity to acquire and apply knowledge.

That is the way in which I use “intelligence” in “Intelligence, Personality, Politics, and Happiness,” and it is the way in which the word is commonly understood. The application of “intelligence” to other kinds of ability — musical, interpersonal, etc. — is a fairly recent development that smacks of anti-elitism. It is a way of saying that highly intelligent individuals (where “intelligence” carries its traditional meaning) are not necessarily superior in all respects. No kidding!

As to the merits of the post at MBTI Truths, it is mere hand-waving to say that “Sensors would probably outscore iNtuitives in several of these” other types of ability. And what is naturalistic intelligence, anyway?

Returning to a key point of my post, “Intelligence, Personality, Politics, and Happiness,” the claim that iNtuitives are generally smarter than Sensors is nothing but a claim about the relative capacity of iNtuitives to acquire and apply knowledge. It is quite correct to say that iNtuitives are not necessarily better than Sensors at, say, sports, music, glad-handing, and so one. It is also quite correct to say that iNtuitives generally are more intelligent than Sensors, in the standard meaning of “intelligence.”

Other so-called types of intelligence are not types of intelligence, at all. They are simply other types of ability, each of them is (perhaps) valuable in its own way. But calling them types of intelligence is a transparent effort to denigrate the importance of real intelligence, which is an important determinant of significant life outcomes: learning, job performance, income, health, and criminality (in the negative).

It is a sign of the times that an important human trait is played down in an effort to inflate the egos of persons who are not well endowed with respect to that trait. The attempt to redefine or minimize intelligence is of a piece with the use of genteelisms, which Wilson Follett defines as

soft-spoken expressions that are either unnecessary or too regularly used. The modern world is much given to making up euphemisms that turn into genteelisms. Thus newspapers and politicians shirk speaking of the poor and the crippled. These persons become, respectively, the underprivileged (or disadvantaged) and the handicapped [and now -challenged and -abled: ED]. (Modern American Usage (1966), p. 169)


Genteelisms may be of … the old-fashioned sort that will not name common things outright, such as the absurd plural bosoms for breasts, and phrases that try to conceal accidental associations of ideas, such as back of for behind. The advertiser’s genteelisms are too numerous to count. They range from the false comparative (e.g., the better hotels) to the soapy phrase (e.g., gracious living), which is supposed to poeticize and perfume the proffer of bodily comforts. (Ibid., p. 170)

And so it is that such traits as athleticism, musical virtuosity, and garrulousness become kinds of intelligence. Why? Because it is somehow inegalitarian — and therefore unmentionable — that some persons are smarter than others. It would be doubly inegalitarian — but likely true — that smarter persons also have genetic tendencies to greater health and physical attractiveness.

Life just isn’t fair, so get over it.

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Related posts:
Intelligence, Personality, Politics, and Happiness
Intelligence and Intuition

Intelligence, Personality, Politics, and Happiness

This post can now be found here. Don’t worry, it’s a safe link. I’m not leading you to a malicious site.

The link leads you to the home page of the site. You should easily find “Intelligence, Personality, Politics, and Happiness” when you’ve arrived at the site. Consider it an IQ test.