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Monday, February 27, 2012

Class, brains and income

Class (SES; author uses "SEB" = Socio-Economic Background) primarily affects starting salaries, whereas intelligence affects salary growth over time. Don't tell the sociologist down the hall -- he or she might lash out at you. (Click for larger figure.)

Note, in the figure the curves for above or below average SEB are computed at fixed (mean) IQ and vice versa. (So, e.g., one curve describes people with average IQ but above average SEB; another describes people with average SEB but above average IQ, etc.) It is only a coincidence (see last sentence in excerpt below) that the effect on age 19 income of being above or below average in SEB is approximately the same as for IQ. Were it not for this numerical coincidence the two high curves and the two low curves would not coincide at the age 19 intercept.

IIRC (from some earlier work), controlling for IQ (AFQT score) in this dataset (NLSY) eliminates almost all the earnings differential between blacks and whites.



A dynamic analysis of the effects of intelligence and socioeconomic background on job-market success

Yoav Ganzach, Faculty of Management, Tel Aviv University, Tel Aviv, Israel

Intelligence 39 (2011) 120–129


... The data were taken from the National Longitudinal Survey of Youth (NLSY), conducted with a probability sample of 12,686 Americans (with an oversampling of Afro-Americans, Hispanics and economically disadvantaged whites) born between 1957 and 1964.

... The measure of intelligence in the NLSY is derived from participants' test scores in the Armed Forces Qualifying Test (AFQT).

... four variables as indicators of SEB: education of the two parents, parent's family income, and occupational status of the parent holding the higher occupation.

... Fig. 2 provides a graphical representation of trajectories of participants high (one standard deviation above the mean) and low (one standard deviation below the mean) on intelligence and SEB, keeping the other time- invariant variables constant at their means. It is clear from the figure that while the trajectories of high and low intelligence diverge, the trajectories of high and low SEB do not. This suggests that intelligence, but not SEB, affected the slope of wage trajectories. It also suggests that SEB affected entry wages, and that its effect was similar to the effect of intelligence.

The effects of the two characteristics on the intercept, or entry level pay, are rather similar. On the basis of the multi-level parameters, one standard deviation increase in SEB [intelligence] leads to a 4.6% [5.4%] increase in entry pay.

Sunday, February 26, 2012

Test preparation and SAT scores

How easy is it to raise SAT scores via test prep? The figures below come from The Effect of Admissions Test Preparation: Evidence from NELS:88, by D.C. Briggs.

Black dots are students who participated in test preparation (e.g., an expensive commercial course, or even a private tutor; see below) between taking the PSAT and taking the SAT. White dots are students who did not. Click for larger versions.

But who needs data like this when one can simply assert that it is obvious that test scores reflect SES or preparation, rather than actual ability? Even a casual investigation into this topic reveals that, at least on average, SAT scores are not easily improved, even through extensive effort. (Insert IQ or g score for SAT score if desired.)





Biggs: ... When researchers have estimated the effect of commercial test preparation programs on the SAT while taking the above factors into account, the effect of commercial test preparation has appeared relatively small. A comprehensive 1999 study by Don Powers and Don Rock published in the Journal of Educational Measurement estimated a coaching effect on the math section somewhere between 13 and 18 points, and an effect on the verbal section between 6 and 12 points. Powers and Rock concluded that the combined effect of coaching on the SAT I is between 21 and 34 points. Similarly, extensive metanalyses conducted by Betsy Jane Becker in 1990 and by Nan Laird in 1983 found that the typical effect of commercial preparatory courses on the SAT was in the range of 9-25 points on the verbal section, and 15-25 points on the math section.

One of the most remarkable aspects of this line of research has been the lack of impact it has had on the public consciousness. ...

... By far the largest effect sizes belong to the those preparation activities involving either a commercial course or private tutor [NEVERTHELESS THE SCORE CHANGES ARE NOT LARGE], and the effects differ for each section of the SAT. On average students with private tutors improve their math scores by 19 points more than those students without private tutors. The effect is less on the verbal section, where having a private tutor only improves scores on average by seven points. Taking a commercial course has a similarly large effect on math scores, improving them on average by 17 points, and has the largest effect on verbal scores, improving them on average by 13 points. With the exception of studying with a book, no other activity analyzed in this manner has an effect on test score changes that is statistically different from zero at a .05 significance level.

... Does test preparation help improve student performance on the SAT and ACT? For students that have taken the test before and would like to boost their scores, coaching seems to help, but by a rather small amount. After controlling for group differences, the average coaching boost on the math section of the SAT is 14 to 15 points. The boost is smaller on the verbal section of the test, just 6 to 8 points. The combined effect of coaching on the SAT for the NELS sample is about 20 points.

Turing and wavefunction collapse

Some interesting discussion by Turing biographer and mathematical physicist Andrew Hodges of Turing's early thoughts about the brain as a quantum computer and the possible connection to quantum measurement. I doubt the brain makes use of quantum coherence (i.e., it can probably be efficiently simulated by a Turing machine), but nevertheless these thoughts led Turing to the fundamental problems of quantum mechanics. He came close to noticing that a quantum computer might be outside the class of machines that a Universal Turing Machine could efficiently simulate.

Hodges' Enigma (biography of Turing) is an incredible triumph. Turing's life was tragic, but at least he was granted a biographer worthy of his contributions to mankind.

A shorter precis of Turing's life and thought, also by Hodges, can be found here.

Hodges: ... Turing described the universal machine property, applying it to the brain, but said that its applicability required that the machine whose behaviour is to be imitated

…should be of the sort whose behaviour is in principle predictable by calculation. We certainly do not know how any such calculation should be done, and it was even argued by Sir Arthur Eddington that on account of the indeterminacy principle in quantum mechanics no such prediction is even theoretically possible.

... Turing here is discussing the possibility that, when seen as as a quantum-mechanical machine rather than a classical machine, the Turing machine model is inadequate. The correct connection to draw is not with Turing's 1938 work on ordinal logics, but with his knowledge of quantum mechanics from Eddington and von Neumann in his youth. Indeed, in an early speculation, influenced by Eddington, Turing had suggested that quantum mechanical physics could yield the basis of free-will (Hodges 1983, p. 63). Von Neumann's axioms of quantum mechanics involve two processes: unitary evolution of the wave function, which is predictable, and the measurement or reduction operation, which introduces unpredictability. Turing's reference to unpredictability must therefore refer to the reduction process. The essential difficulty is that still to this day there is no agreed or compelling theory of when or how reduction actually occurs. (It should be noted that ‘quantum computing,’ in the standard modern sense, is based on the predictability of the unitary evolution, and does not, as yet, go into the question of how reduction occurs.) It seems that this single sentence indicates the beginning of a new field of investigation for Turing, this time into the foundations of quantum mechanics. In 1953 Turing wrote to his friend and student Robin Gandy that he was ‘trying to invent a new Quantum Mechanics but it won't really work.’

[ Advances in the theory of decoherence and in experimental abilities to precisely control quantum systems have led to a much better understanding of quantum measurement. The unanswered question is, of course, whether wavefunctions actually collapse or whether they merely appear to do so. ]

At Turing's death in June 1954, Gandy reported in a letter to Newman on what he knew of Turing's current work (Gandy 1954). He wrote of Turing having discussed a problem in understanding the reduction process, in the form of

…‘the Turing Paradox’; it is easy to show using standard theory that if a system start in an eigenstate of some observable, and measurements are made of that observable N times a second, then, even if the state is not a stationary one, the probability that the system will be in the same state after, say, 1 second, tends to one as N tends to infinity; i.e. that continual observation will prevent motion. Alan and I tackled one or two theoretical physicists with this, and they rather pooh-poohed it by saying that continual observation is not possible. But there is nothing in the standard books (e.g., Dirac's) to this effect, so that at least the paradox shows up an inadequacy of Quantum Theory as usually presented. ...

[ This is sometimes referred to as the Quantum Zeno Effect. A modern understanding of measurement incorporating decoherence shows that this is not really a paradox. ]


Turing as polymath:

In a similar way Turing found a home in Cambridge mathematical culture, yet did not belong entirely to it. The division between 'pure' and 'applied' mathematics was at Cambridge then as now very strong, but Turing ignored it, and he never showed mathematical parochialism. If anything, it was the attitude of a Russell that he acquired, assuming that mastery of so difficult a subject granted the right to invade others. Turing showed little intellectual diffidence once in his stride: in March 1933 he acquired Russell's Introduction to Mathematical Philosophy, and on 1 December 1933, the philosopher R. B. Braithwaite minuted in the Moral Science Club records: 'A. M. Turing read a paper on 'Mathematics and logic.' He suggested that a purely logistic view of mathematics was inadequate; and that mathematical propositions possessed a variety of interpretations, of which the logistic was merely one.' At the same time he was studying von Neumann's 1932 Grundlagen den Quantenmechanik. Thus, it may be that Eddington's claims for quantum mechanics had encouraged the shift of Turing's interest towards logical foundations. And it was logic that made Alan Turing's name.

Thursday, February 23, 2012

Wednesday, February 22, 2012

Beyond Race in Affirmative Action

The NYTimes asked me to comment on Fisher v. Texas, the case involving affirmative action in higher education that the Supreme Court has agreed to hear.

Needless to say I anticipate being viciously attacked. I invite you to visit the Times comment section and engage my detractors ;-)

Rely on Merit, Not Race

In considering Fisher v. University of Texas, let’s acknowledge a key factual point about affirmative action: We have good tools for predicting college success, and those tools work about equally well across all ethnic groups and even for rich legacy candidates.

... Race-based preference produces a population of students whose average intellectual strength varies strongly according to race. Surely this is opposite to the meritocratic ideal and highly corrosive to the atmosphere on campus. Furthermore, the evidence is strong that students of weaker ability who are admitted via preference do not close the gap during college. For these reasons, the Supreme Court would be wise to end the practice of race-based preference in college admissions.

Tuesday, February 21, 2012

Linsanity on SNL



Pseudo-Chinese gibberish -- didn't Shaq do the same to Yao Ming when he first came in the league? I think Yao Ming played it off: "Chinese is hard!" or something like that.

Wikipedia: ... Lin has regularly heard bigoted jeers at games such as "Wonton soup", "Sweet and sour pork", "Open your eyes!", "Go back to China", "Orchestra is on the other side of campus", or pseudo-Chinese gibberish.[7][155][157] Lin says this occurred at most if not all Ivy League gyms. He does not react to it. "I expect it, I'm used to it, it is what it is," says Lin.[155] The heckling came mostly from opposing fans and not as much from players.[160] According to Harvard teammate Oliver McNally, a fellow Ivy League player once called Lin the ethnic slur chink.

Luke and Maddie revisited

Econtalk interviews Adam Davidson about his recent Atlantic article on US manufacturing. Adam says he "is good at math and computers" and is a "certified Mac (Apple) technician" but is sure he "couldn't do Luke's job". (Luke is a skilled machinist who has to test tolerances, program a half million dollar cutting machine, visualize spatial relationships, use calculus!, etc. For this he is paid a bit over $50k per year.) What fraction of Maddies (unskilled workers) could, given access to training?

(An interesting remark could be made about M,V,S cognitive profiles, but I will refrain.)

If you listen carefully, you'll notice that jobs for Maddies are as threatened by robots as by foreign competition.


Jack and Diane (John Mellencamp, 1982)

Little ditty 'bout Jack and Diane
Two american kids growin up in the heartland
Jackies gonna be a football star
Diane debutante backseat of Jackies car

...

Little ditty, 'bout Jack and Diane --
Two american kids doin', best they can

Saturday, February 18, 2012

Intergenerational mobility: Bowles and Gintis and Clark

I went back to Bowles and Gintis to compare their results to those of Greg Clark that I posted about recently. The largest correlation reported by Bowles and Gintis for intergenerational earnings is 0.65, obtained when fathers' and sons' earnings are averaged over multiyear periods, whereas Clark finds a (roughly) 0.7 -- 0.8 correlation between parental and children's social and economic status. Clark was studying the past 200 years, using rare surnames, whereas Bowles and Gintis concentrated on the modern era. Even the lower value of 0.42 (more typical of results cited by Bowles and Gintis) implies some persistent stratification, as shown in the figure below.





Bowles and Gintis: ... The relevant facts on which most researchers now agree include the following: brothers’ incomes are much more similar than those of randomly chosen males of the same race and similar age differences; the incomes of identical twins are much more similar than fraternal twins or non-twin brothers; the children of well-off parents obtain more and higher quality schooling; and wealth inheritance makes an important contribution to the wealth owned by the offspring of the very rich. On the basis of these and other empirical regularities, it seems safe to conclude that the intergenerational transmission of economic status is accounted for by a heterogeneous collection of mechanisms, including the genetic and cultural transmission of cognitive skills and non-cognitive personality traits in demand by employers, the inheritance of wealth and income enhancing group memberships such as race, and the superior education and health status enjoyed by the children of higher status families. ...

Here are Bowles and Gintis on IQ and earnings:

We have located 65 estimates of the normalized regression coefficient of a test score in an earnings equation in 24 different studies of U.S. data over a period of three decades. Our meta-analysis of these studies is presented in Bowles, Gintis, and Osborne (2002a). The mean of these estimates is 0.15, indicating that a standard deviation change in the cognitive score, holding constant the remaining variables (including schooling), changes the natural logarithm of earnings by about one-seventh of a standard deviation. By contrast, the mean value of the normalized regression coefficient of years of schooling in the same equation predicting the natural log of earnings in these studies is 0.22, suggesting a somewhat larger independent effect of schooling. We checked to see if these results were dependent on the weight of overrepresented authors, the type of cognitive test used, at what age the test was taken and other differences among the studies and found no significant effects. An estimate of the causal impact of childhood IQ on years of schooling (also normalized) is 0.53 (Winship and Korenman 1999). A rough estimate of the direct and indirect effect of IQ on earnings, call it b, is then b = 0.15+(0.53)(0.22) = 0.266.

... Using the values estimated above, we see that the contribution of genetic inheritance of IQ to the intergenerational transmission of income is (h2 (1+m)/2)(0.266)^2 = .035(1 + m) h2. If the heritability [h2] of IQ were 0.5 and the degree of assortation, m, were 0.2 (both reasonable, if only ball park estimates) and the genetic inheritance of IQ were the only mechanism accounting for intergenerational income transmission,then the intergenerational correlation would be 0.01, or roughly two percent the observed intergenerational correlation. Note the conclusion that the contribution of genetic inheritance of IQ is negligible is not the result of any assumptions concerning assortative mating or the heritability of IQ: the IQ genotype of parents could be perfectly correlated and the heritability of IQ 100 per cent without appreciably changing the qualitative conclusions. The estimate results from the fact that IQ is just not an important enough determinant of economic success.

[THESE RESULTS ARE SURPRISING SMALL. I'D GUESS THAT AVG IQ VARIES FROM ABOUT -.5 to -.75 SD AT 10TH PERCENTILE INCOME TO +.5 to +.75 SD (COLLEGE GRADUATES) AT 90TH PERCENTILE INCOME, SO CORRELATION OF PERHAPS 0.3 -- 0.5. I GUESS THE LITERATURE SUPPORTS 0.3.

BUT NOTE THAT AVERAGING INCOMES OVER EXTENDED PERIODS (LIKE 10 YEARS) INCREASES THE INTERGENERATIONAL CORRELATION SIGNIFICANTLY (I.E., FROM 0.4 TO .65, PRESUMABLY BY ELIMINATING SOME OF THE NOISE IN THE INCOME MEASUREMENT), SO ONE MIGHT EXPECT THE SAME FOR THE IQ-INCOME CORRELATION. (THE INTERGENERATIONAL STUFF IS COMING FROM SOMEWHERE!) SINCE THE IQ-INCOME CORRELATION ENTERS SQUARED, INCREASING IT HAS A BIG EFFECT.

IF WE TAKE THE INCOME-IQ CORRELATION AS 0.5 AND h2 = 0.5, THE CONTRIBUTION TO INTERGENERATIONAL CORRELATION IS (1/2)^3 = 0.125 WHICH IS A FOURTH OR FIFTH OF THE TOTAL.]

In the Terman study of gifted individuals, additional IQ above 135 or so had a relatively small effect on lifetime earnings: an increase of 15-20% for an additional SD of IQ. In contrast, personality factors such as Conscientiousness and Extraversion were strongly correlated with increased earnings (see figures at link above): an increase of about 50% from 10th to 90th percentile was observed. Note that personality factors are heritable, with h2 roughly 0.5 or so. These findings, which come from a high IQ sample and are thus affected by restriction of range, are nevertheless suggestive of effect sizes in the overall population.

While it doesn't appear that IQ alone can account for most of intergenerational earnings transmission, a combination of IQ, Conscientiousness, Extraversion, ambition, and traits related to social intelligence (even, physical attractiveness) likely play an important and heritable role. (Note the strong similarity in earnings of identical twins.) Perhaps this "success bundle" of traits is what elite (Ivy) holistic admissions is searching for? :-)


For more discussion of Bowles and Gintis, see GNXP 2011 and GNXP 2008. A numerical error in B&G is noted, but it doesn't change the qualitative conclusions.

Solvay 1961


This photo takes up an entire wall of the UC Davis physics building, next to the elevators on the theorists' fourth floor. Click for larger version.

Thursday, February 16, 2012

Greg Clark: Are there ruling classes?

While at UC Davis to give a colloquium earlier this week, I had the pleasure of meeting economic historian Greg Clark in person. Here's a sample of his latest work, which suggests that convergence of social classes has been surprisingly slow: averaged parent-child correlations of variables such as wealth, education and occupation are in the 0.7 -- 0.8 range over the last 200 years, the same as found in India, with its caste system! IIRC, Greg said he got the idea of using rare surnames from Nicholas Wade during an interview :-)

Are there Ruling Classes? Surnames and Social Mobility in England, 1800-2011

Using rare surnames we track the socio-economic status of descendants of a sample of English rich and poor in 1800, until 2011. We measure social status through wealth, education, occupation, and age at death. Our method allows unbiased estimates of mobility rates. Paradoxically, we find two things. Mobility rates are lower than conventionally estimated. There is considerable persistence of status, even after 200 years. But there is convergence with each generation. The 1800 underclass has already attained mediocrity. And the 1800 upper class will eventually dissolve into the mass of society, though perhaps not for another 300 years, or longer.




More discussion, including recent US data, here.

Sunday, February 12, 2012

History is impossible

... and economic history is even harder.

Andy Lo (MIT) explains that economists have yet to agree on the causes and consequences of and remedies for the recent financial crisis. This is a must read. I hope to provide further comments when I have more time.

Although I covered the housing bubble (which I called a bubble as early as 2004) and ensuing financial crisis in great detail on this blog, I've spent very little time discussing books about the crisis. That's because many (most?) of the authors (who, as Lo points out, tend to disagree strongly with each other) are rehearsing their own priors rather than seeking truth. My talk on the financial crisis.

Reading About the Financial Crisis: A 21-Book Review

Andrew W. Lo

Abstract
The recent financial crisis has generated many distinct perspectives from various quarters. In this article, I review a diverse set of 21 books on the crisis, 11 written by academics, and 10 written by journalists and one former Treasury Secretary. No single narrative emerges from this broad and often contradictory collection of interpretations, but the sheer variety of conclusions is informative, and underscores the desperate need for the economics profession to establish a single set of facts from which more accurate inferences and narratives can be constructed.

From the introduction:

... Six decades later, Kurosawa’s message of multiple truths couldn’t be more relevant as we sift through the wreckage of the worst financial crisis since the Great Depression. Even the Financial Crisis Inquiry Commission—a prestigious bipartisan committee of 10 experts with subpoena power who deliberated for 18 months, interviewed over 700 witnesses, and held 19 days of public hearings—presented three different conclusions in its final report. Apparently, it’s complicated.

To illustrate just how complicated it can get, consider the following “facts” that have become part of the folk wisdom of the crisis:

1. The devotion to the Efficient Markets Hypothesis led investors astray [CERTAINLY TRUE], causing them to ignore the possibility that securitized debt was mispriced and that the real-estate bubble could burst. [TOO STRONG]

2. Wall Street compensation contracts were too focused on short-term trading profits rather than longer-term incentives. Also, there was excessive risk-taking because these CEOs were betting with other people’s money, not their own. [CEOS DID NOT KNOW WHAT WAS GOING ON -- HAVE TO LOOK AT INCENTIVES OF LOWER LEVEL PEOPLE]

3. Investment banks greatly increased their leverage in the years leading up to the crisis, thanks to a rule change by the U.S. Securities and Exchange Commission (SEC). [REPORTEDLY TRUE... BUT SEE THE PAPER FOR INTERESTING DETAILS]

While each of these claims seems perfectly plausible, especially in light of the events of 2007–2009, the empirical evidence isn’t as clear. ...

From the conclusions:

There are several observations to be made from the number and variety of narratives that the authors in this review have proffered. The most obvious is that there is still significant disagreement as to what the underlying causes of the crisis were, and even less agreement as to what to do about it. But what may be more disconcerting for most economists is the fact that we can’t even agree on all the facts. Did CEOs take too much risk, or were they acting as they were incentivized to act? [NOT CEOS, LOWER LEVEL TRADERS; YES] Was there too much leverage in the system? [YES] Did regulators do their jobs [NO] or was forbearance a significant factor? [REGULATORS DID NOT UNDERSTAND CDOS OR CDS] Was the Fed’s low interest-rate policy responsible for the housing bubble [PARTIALLY, BUT GSES LIKE FANNIE DESERVE MUCH MORE BLAME], or did other factors cause housing prices to skyrocket? [ANIMAL SPIRITS; IRRATIONAL EXUBERANCE; BOUNDED COGNITION] Was liquidity the issue with respect to the run on the repo market, or was it more of a solvency issue among a handful of “problem” banks? [IT WAS FEAR AND COMPLEXITY]

[THERE WAS REGULATORY CAPTURE TO GET THE CASINO GAMES GOING IN THE FIRST PLACE, BUT NO "TOO BIG TO FAIL" MORAL HAZARD. ONLY ACADEMICS AND JOURNALISTS COULD THINK SO. DURING THE CRISIS REAL FINANCIERS WERE SCARED OUT OF THEIR MINDS AND HAD NO FAITH IN A GOVT BAILOUT. (I WAS ON THE PHONE WITH LOTS OF THEM.) MANY PEOPLE, FROM CEOS DOWN TO MD LEVEL AND BELOW TRADERS, LOST MUCH OR MOST OF THEIR NET WORTH IN THE COLLAPSE. DOES THAT SOUND LIKE MORAL HAZARD? ACADEMIC ECONOMISTS HAVE A CUTE THEORY (OR IDEOLOGY) AND WANT TO CONFIRM IT. STUPIDITY EXPLAINS A LOT MORE THAN CONSPIRACY.]

For financial economists—who are used to dealing with precise concepts such as no-arbitrage conditions, portfolio optimization, linear risk/reward trade-offs, and dynamic hedging strategies—this is a terribly frustrating state of affairs. Many of us like to think of financial economics as a science [ONLY IN THE MOST LIMITED SENSE], but complex events like the financial crisis suggest that this conceit may be more wishful thinking than reality. John Maynard Keynes had even greater ambitions for economics when he wrote, “If economists could manage to get themselves thought of as humble, competent people on a level with dentists, that would be splendid”. Instead, we’re now more likely to be thought of as astrologers, making pronouncements and predictions without any basis in fact or empirical evidence.

To make this contrast more stark, compare the authoritative and conclusive accident reports of the National Transportation Safety Board—which investigates and documents the who-what-when-where-and-why of every single plane crash—with the 21 separate and sometimes inconsistent accounts of the financial crisis we’ve just reviewed (and more books are surely forthcoming). Why is there such a difference? The answer is simple: complexity and human behavior. ...

See also Physics Envy by Lo and Mueller.

Noted added: I think the movie Margin Call knows more than the worst 10 of these 21 books ;-)

Jeremy Lin in historical perspective

This is an interesting statistical analysis of the significance of Jeremy Lin's performance in his first 4 games. As Magic said during the Lakers-Knicks half-time show: "Jeremy Lin is FOR REAL"!

His 20 points and 8 assists (but below 50% shooting percentage) last night against the Timberwolves may have elevated him to roughly the 5 streak level. The other factor that needs to be considered is that these were his first 5 games as a starter. As far as that goes he's number one in recent NBA history, beating out Allen Iverson.

It's kind of shocking to think that a phenom like Kenny Anderson turned out to be an "average" NBA player. (Video -- "Greatest guard ever in the history of NYC HS basketball.") But that's what happens in a field that selects effectively from a large talent pool.





Does this look like a future NBA All-Star? (Age 15)


Friday, February 10, 2012

Class and Race

This recent paper (NYTimes coverage) notes that achievement gaps between 10th and 90th percentile income families are now larger than corresponding black-white gaps.

Click for larger figures. The shaded shapes indicate 10-90 gaps, whereas unshaded figures indicate B-W gaps.




UC Davis colloquium

I'll be giving a colloquium at UC Davis on Monday. Please come if you can!

slides

Title: Genetics, intelligence and other quantitative traits

Abstract: How do genes affect cognitive ability or other quantitative traits such as height? I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a "general factor" or IQ score. The main results concern the stability, validity (predictive power), and heritability of adult IQ. Next, I discuss ongoing Genome Wide Association Studies which investigate the genetic basis of intelligence. Due mainly to the rapidly decreasing cost of sequencing, it is likely that within the next 5-10 years we will identify genes which account for a significant fraction of total IQ variation.

Monday, Feb 13 4:10pm Room 55 Roessler

Thursday, February 09, 2012

Wharton MBA compensation by industry

A correspondent supplied this interesting data from a 2010 survey of Wharton alumni. As expected, financiers out-earned everyone else, with hedgies at the front of the pack. The average hedgie who graduated in 2000-3 made almost 10x what his counterpart in Consumer Goods or Manufacturing or even Technology and Media made, and about 7x what those in Consulting and Professional Services made. Who are the suckers?

Total compensation seems to peak about 10-20 years after graduation.

There's more data than the two pages below, but they're enough to get the general idea.






Related posts:

US income inequality caused by financiers and tech entrepreneurs (2006)

The illusion of skill

Top 1 percent by profession

Banker pay

Financier pay: it's crazy, there's no 2nd or 3rd (2007)

A New Class War (2006)

The Haves against the Have Mores. Pity the poor doctors, lawyers and management consultants. Even the I-bankers, now that hedge fund management has become the ne plus ultra of capitalism. The only guys that the hedgies envy are the super-lucky entrepreneurs who can make their centi-million all in one pop!

As far as doctors and lawyers, I once asked a friend of mine in finance, who lives in a 3000+ sq ft apartment on the upper east side, who else lived in his building. After counting all the money guys, he let slip -- "Oh, I guess there are some doctors and lawyers as well. I don't know how they can afford it." You're so money, and you don't even know it!

Tuesday, February 07, 2012

Trouble ahead



A correspondent sent this depressing figure. I suppose this distribution of majors was OK when Americans could make their livings by selling houses to each other, but we're in a tougher world now.

See also this post from 2005: History repeats

More data.

Sunday, February 05, 2012

Paper Promises: Money, Debt and the new World Order

This is a great lecture, informed by Coggan's background as a historian. If you already know something about the subject I suggest starting at 36 minutes in. The talk is also available as a podcast via iTunes (look for LSE public lectures).

LSE public lecture: ... as Philip Coggan shows in this new book, Paper Promises: Money, Debt and the new World Order which he will talk about in this lecture, the crisis is part of an age-old battle between creditors and borrowers. And that battle has been fought over the nature of money. Creditors always want sound money to ensure that they are paid back in full; borrowers want easy money to reduce the burden of repaying their debts. Money was once linked to gold, a commodity in limited supply; now central banks can create it with the click of a computer mouse.

Time and again, this cycle has resulted in financial and economic crises. In the 1930s, countries abandoned the gold standard in the face of the Great Depression. In the 1970s, they abandoned the system of fixed exchange rates and ushered in a period of paper money. The results have been a long series of asset bubbles, from dotcom stocks to housing, and the elevation of the financial sector to economic dominance.

The current crisis not only pits creditors against debtors, but taxpayers against public sector workers, young against old and the western world against Asia. As in the 1930s and 1970s, a new monetary system will emerge; the rules for which will likely be set by the world's rising economic power, China.

Philip Coggan was a Financial Times journalist for over twenty years, including spells as a Lex columnist, personal finance editor and investment editor, and is now the Buttonwood columnist of The Economist.

Jeremy Lin represents




Jeremy Lin, in da house! A Jason Kidd-like performance. I thought he was going to wash out of the league. Lin was not heavily recruited out of HS and played his college ball at Harvard.


Saturday, February 04, 2012

Personnel Selection: horsepower matters

Personnel Selection, whether by sports teams, militaries, universities or corporations, is all about identifying statistical predictors of future performance. How good are these predictors?



Let's take college football as an example. Talent evaluation is difficult, but scouts definitely know something. A five star high school football prospect is almost four times more likely to become an NCAA All-American than a four star prospect. (Graphs from this article; NFL draft order related to HS ranking here.)



Oregon, which finished last season ranked #4 in the country (Rose Bowl and PAC-12 champs), and played in BCS bowls each of the last three seasons, landed only one five star recruit this year. Schools like Alabama (3), Texas (3), USC (3) and Michigan (2) landed significantly more.

What about other kinds of talent? Below is an example from psychometrics applied to 13 year olds.



Horsepower matters: Can psychometrics separate the top .1 percent from the top 1 percent in ability? Yes: SAT-M quartile within top 1 percent predicts future scientific success, even when the testing is done at age 13. The top quartile clearly outperforms the lower quartiles. These results strongly refute the "IQ above 120 doesn't matter" claim, at least in fields like science and engineering; everyone in this sample is above 120 and the top quartile are at the 1 in 10,000 level. The data comes from the Study of Mathematically Precocious Youth (SMPY), a planned 50-year longitudinal study of intellectual talent. ...


Another example: this graph displays upper bounds on probability of graduating with a physics GPA greater than 3.5 (about .5 SD above the average) at Oregon as a function of SAT-M. Note the blue markers are conservative (95 percent confidence level) upper bounds; the central value for the probability at SAT-M > 750 is around 50 percent. The upper bounds were computed to show that the probability for SAT-M below about 600 is close to zero. The red line is the probability of earning an A in calculus-based introductory physics.

Thursday, February 02, 2012

Transparency in college admissions

Daniel Golden (Bloomberg) reports that the Department of Education is now investigating both Princeton and Harvard regarding discrimination against Asian-American applicants.

Below is an excerpt from an op-ed Bloomberg asked me to write on this topic.

What Harvard Owes Its Top Asian-American Applicants: Stephen Hsu

It’s a common belief among Asian- American families that their children are held to higher academic standards than college applicants from other ethnic groups. Such practices were openly acknowledged after investigations at universities like Berkeley and Stanford in the 1980s and 1990s.

Have they been corrected?

The U.S. Education Department is investigating complaints that Harvard University and Princeton University discriminated against Asian-Americans in undergraduate admissions.

Statistics seem to support the claim of bias across most of elite higher education. For example, in comprehensive data compiled as part of Duke University’s Campus Life and Learning project (as reported in a recent analysis by Duke economist Peter Arcidiacono and collaborators), Asian-Americans who enrolled at the school in 2001 averaged 1457 out of 1600 on the math and reading part of the SAT, compared with 1416 for whites, 1347 for Hispanics and 1275 for blacks.

Holistic Admissions

There is every reason to believe that a similar pattern holds at nearly all elite universities in the U.S., with notable exceptions such as the California Institute of Technology. In fact, Duke may be one of the mildest offenders when it comes to Asian-American admissions: With the goal of increasing its overall student quality, Duke has reportedly been more friendly recently to Asian-American applicants than traditional powers such as Harvard and Princeton.

Schools like Harvard and Princeton brag that each year they reject numerous applicants such as Jian Li (who filed a complaint against Princeton) who score a perfect 2400 on the SAT. How would we feel if it were revealed that almost all of these rejected top scorers, year after year, were Asian- Americans? I challenge Harvard and Princeton to refute this possibility.

To be fair, most elite universities practice what is known as holistic admissions: Each candidate is evaluated on a variety of measures, including athletic and leadership activities in addition to academic performance. It is possible that the gap in academic average between Asian-American and white admitted students is compensated by gaps in the opposite direction on these other variables. Looking again at internal evaluations by Duke’s admissions office, we find Asian-Americans had higher averages than whites in the following categories: achievement, curriculum (each about one-third of a standard deviation) and letters of recommendation, while trailing very slightly (less than one-tenth of a standard deviation) in personal qualities.

Lacking data on factors such as legacy and recruited athlete status, we can’t make a complete determination of the fairness of the process, and in fact the appropriate weight of the various factors in a holistic admissions process will be subject to vigorous debate. ...

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