Fast times in Jamaica

Ever wonder how Jamaica, a country of 3 million people, can compete with the US and totally dominate all of Europe and Asia when it comes to the sprints? China has spent billions on a Soviet-style sports program that selects promising athletes at a young age and sends them to special sports schools. When Liu Xiang won the 110 hurdles at the last Olympics, Chinese officials referred to his gold as the "heaviest" of all medals won by Chinese in Athens. There is no lack of Chinese desire to win sprint gold -- Liu Xiang is the biggest sports star in China after Yao Ming! Similarly, the US and Europe have far more money than Jamaica for training facilities, coaches, scholarships, stipends, etc. World class athletes in Jamaica train on a grass track and in weight rooms with rusty barbells. Most US high schools have superior facilities. (See video here.)

The times below are phenomenal -- they rival the times put up this weekend in Eugene at the US Olympic trials, and totally surpass the performance of any European or Asian nation.

World record-holder Usain Bolt beat former record-holder Asafa Powell in the 100-meter final in Jamaica's Olympic trials, finishing in 9.85 seconds in Kingston.

Powell was second in 9.97. Last month in New York, Bolt ran a 9.72 to break Powell's world record of 9.74.

Kerron Stewart won the women's 100 in 10.80, the second-fastest time by a Jamaican woman ever. Shelly-Ann Fraster was second in 10.85, Sherone Simpson followed in 10.87 and world champion Veronica Campbell-Brown was fourth in 10.87.

Higher education and human capital II

I thought I'd also link to some interesting data from a paper by UT Austin economist Daniel Hamermesh, discussed here on the NYTimes Freakonomics blog.

Their survey covered UT Austin alumni between the ages of 23 and 43, revealing enormous variations in average earnings between different majors. Not surprisingly, the business majors and engineers tend to be high earners. However, the highest earners of all are the Plan II (honors college) alumni, who have by far the highest average SAT scores (1364). Note the huge variance within each major (SD = number in parentheses below the mean value), in particular for business, natural sciences and Plan II. For these majors the standard deviation is larger than the mean, suggesting, perhaps, that a few millionaires (startup founders, entrepreneurs?) are skewing the results.

The effect of college curriculum on earnings: ...Clearly, there are large differences across major in average earnings, with the highest-earning majors (Honors Plan II, and "Hard" Business) having averages almost three times that of the lowest (Education). Much of the differences across majors must be due to differences in what the students bring to and do at the University. Students in the higher-earning majors generally have higher SAT totals upon entry, and the fractions of students taking upper-division math and science courses and doing well in them are greater too. The differences are also consistent with the results of differential effort in the labor market and male-female differences in earnings. Thus respondents in the higher-earning majors tend to state that they work longer hours than those in lower-earning majors; and except for the Honors Plan II major, the fraction of women in the higher-earning majors is lower. On the other hand, advanced degrees are more prevalent among those graduates who have majored in subjects that eventually generate lower earnings. Family incomes in the areas where the students attended high school do not differ across majors ...

...Even within major, taking more upper-division science or math courses and doing better in them raise eventual earnings. While the effects are not highly significant statistically, the t-statistics generally exceed 1.28. A student who takes 15 credits of upper-division science and math courses and obtains a B average in them will earn about 10 percent more than an otherwise identical student in the same major ... who takes no upper-division classes in these areas. There is clearly a return to taking these difficult courses. This holds true even after we have adjusted for differences in mathematical ability by using the total SAT score ... . The importance of access to this information should not be underestimated. Estimated earnings differences across majors are substantially higher (e.g., the premium for "hard" business rises to 64 log points, that for engineering to 50 log points) when the information on science and math courses is excluded from the equation in Column (1).

Click on the figure below for a larger, more readable version.

Higher education and human capital

What good is higher education? The conventional view is that, in addition to producing a well-informed citizenry, it builds important human capital and raises national productivity. But what is the evidence for these assertions? In policy debates we are typically presented with faulty logic: workers in desirable, high value-added jobs (e.g., at Google or Biogen) tend to have lots of education. Therefore, if we want Americans to have such jobs we had better expand access to higher education. The counter argument, that returns to society as a whole from education diminish as access increases beyond the cognitive elite, is given below by a well-known curmudgeon and psychometric realist:

Brutal, just brutal: ...There is no magic point at which a genuine college-level education becomes an option, but anything below an IQ of 110 is problematic. If you want to do well, you should have an IQ of 115 or higher. Put another way, it makes sense for only about 15% of the population, 25% if one stretches it, to get a college education. And yet more than 45% of recent high school graduates enroll in four-year colleges. Adjust that percentage to account for high-school dropouts, and more than 40% of all persons in their late teens are trying to go to a four-year college--enough people to absorb everyone down through an IQ of 104.

Note the claim is not that benefits from higher education are zero for the average student, but merely that they diminish significantly as we expand access. At some point we need to consider whether the marginal cost exceeds the marginal benefit. No amount of schooling will turn an average student into a materials engineer, tax lawyer or derivatives trader.

I'm afraid these kinds of thoughts lurk in the minds of most professors these days -- I've heard them discussed many times. Why can't my students write? Why can't my students do simple math? Does the bottom half of the class really absorb anything from my lectures? Is science just too difficult for some people? If I showed you some of the emails I receive from students in my physics 101 course, you would cry at the lack of mastery of grammar and spelling, let alone physics.

Below I excerpt some depressing results from researchers at Stanford and Yale, which support the sorting and signalling model of higher ed, rather than the human capital building model.

Education and Verbal Ability over Time: Evidence from Three Multi-Time Sources

Nie, Golde and Butler

Abstract: During the 20th century, there was an unprecedented expansion in the level of educational attainment in America. Using three separate measures, this paper investigates whether there was a concurrent increase in verbal ability and skills. Changes in verbal ability in the general population as well as changes in the verbal ability of graduates of different levels of education are investigated. An additional investigation of how changes in the differences between males' and females' educational attainment are associated with changes in differences between their respective verbal abilities follows. The main finding is that there is little evidence that the large increase in educational attainment has resulted in an increase in any of the measures of verbal abilities and skills.

From the paper:

The results from using these three different measures of verbal ability and skills all show the same striking patterns: (1) there is no increase in scores in the overall population over time; (2) as the number of people obtaining a certain level of education increased, the verbal ability of those terminating with that degree has decreased. ...

Comment for the psychometric cognoscenti: where is the Flynn effect here? I see no overall increase in verbal IQ.






See also this less technical exposition:

Nie and Golde: ...Our initial hypothesis was that if amount of schooling causally affects any outcome, it would be verbal ability. The vast expansion of the American education system over the course of the 20th century served as our test bed. We expected that the huge increase in educational attainment in the U.S. across the decades would be accompanied by a substantial improvement in verbal abilities. To our initial amazement, we found no evidence for such improvement.

We started our investigation by showing that there is, indeed, a strong correlation between education and verbal ability. The data on which our analyses are based came from the General Social Survey, a program of in-person interviews that has been conducted regularly since 1972 by the National Opinion Research Center at the University of Chicago. While the samples were nationally representative, to avoid complications caused by changing demographics and questions about the validity of such tests with minority and immigrant populations, we included only the native-born, white American population 30 to 65 years of age, using information collected over the last 35 years of parallel surveys. (We used only those 30 years or older to ensure that we were dealing only with people who had completed their education; we stopped at age 65, lest we contaminate the analysis by differential mortality rates.)

Education levels and scores on a vocabulary test given to subjects are indeed correlated (see Figure 1). Over the three-plus decades studied, those with more education got better vocabulary scores, and vice versa.

Those results, however, do not necessarily imply that education causes increased verbal ability. If education did increase verbal ability, we would expect increasing levels of education over time to bring about measurably higher levels of verbal ability. During the 20th century, there was an unprecedented expansion in the levels of educational attainment in the U.S. The average American born between 1910 and 1914 received a bit more than 10 years of education. The average American born between 1970 and 1974 received 14 years of education. In 60 years, the "average American" went from being a high school dropout to having two years of college — a remarkable increase. The increase in education is across the board. A person born between 1910 and 1914 who obtained some postgraduate education was in the top 6 percent of his or her cohort in terms of education. By the 1970s, nearly 16 percent of the birth cohort had some postgraduate education. The percentage of college graduates or beyond has almost quadrupled over the same period, from just over 10 percent to almost 40 percent.

But, as Figure 2 shows, even though education has increased considerably through the decades, and even though education is correlated with verbal ability, verbal ability has stayed practically constant over time. The lack of change in the average vocabulary score of Americans, despite the large increase in the population's average years of schooling, is an intriguing finding. ...

Women in the classroom

Interesting comments from Judith Warner of the Times, as she reports on a brain science workshop for journalists, held at MIT. The audience was more engaged (less intimidated?) by female lecturers. I find as well that women are less likely to try to get by with "proof by intimidation" than men, and that their presence tends to improve the quality of scientific discussion, assuming an equal level of competence.

Anyone familiar with Feynman idolatry knows that "man crushes" are just as real as what is described below for women.

At M.I.T., we were mostly spoken to by men, various kinds of men, of different ages and with different speaking styles, and we interacted with them with typical reportorial formality. Some were more popular with us than others; some were more engaged with us than others. Some spoke right over our heads; some reached even me with perfect clarity.

Something very different happened, however, on the two occasions when we were spoken to by women. The atmosphere in the room changed. We all became more familiar. We asked more questions. We interrupted more. We made sounds of assent or dissent; we questioned methods, concepts, base assumptions. It was as though, with the women, the boundaries dissolved. We were all immediately drawn into relationships.

How much of this had to do with the fact that the women tended to speak more relationally (“I think,” “I feel”), I don’t know. I don’t know if it was created by the fact that the women — to varying degrees — turned the story of their work into personal narratives.

I know that there was no conscious desire on anyone’s part to talk back to them or treat them with less respect. But one woman in particular, Rebecca Saxe, a young, dynamic professor of neurobiology at M.I.T. who gave a riveting presentation on social cognition — “how we reason about the desires and intentions that motivate others’ actions” — was interrupted so much by her super-engaged audience that she didn’t have time to get through essential portions of her talk.

I did not ask questions of this amazing young woman. I was struck, once again, with one of my crippling bouts of shyness, and besides that, I was too busy writing down her every word and wondering why on earth I had never taken science and whether my daughters might attend M.I.T.

Maybe I could send them to do summer study, I thought. (Once they’d both learned their multiplication tables, of course.) Maybe I should sport little wire glasses and wear my hair in a long braid. Or buy Birkenstocks.

“What did you think?” I breathed to a fellow female fellow, as we filed out of the classroom for lunch.

“I have a crush on her,” she said. The women around us made approving noises.

“It was her passion and energy and approach that was infectious,” she later explained in an e-mail. “I really had an emotional reaction to her, and found myself day dreaming about being her friend.”

What is this thing we so often do, when confronted with an impressive woman? Why do they, in particular, set off such a Pavlovian rush of emotion? Why, for women in particular, do they set off this me/not me engagement, this game of my friend/not my friend, this eternal, sometimes infernal play of positive or negative mirroring?

Men do a version of this with women, too — though I think it plays out more in terms of validates me/doesn’t validate me, which may amount, in slightly altered form, to much the same thing. I don’t see them doing it with other men. I don’t hear of men getting “crushes” on other men because they’re impressed with them. They don’t seem to get so flooded with the desire to be them, to try on their skins; they don’t appear to be constantly testing their identities against another man’s example, calling into question, at the drop of a hat, their clothing style or hair or general sense of being in the world.

What's your social status?

Calculate your social status using this tool from the NY Times. (Click on the components of class tab.) The inputs are occupation, education level, income and wealth. The tool was created to run with a week long series of articles on class in America, back in 2005.

Amazingly, my result is 95 (averaged over the 4 inputs), despite being pulled down by the occupational prestige score 82 of astronomers and physicists :-) We're ranked number 5 overall out of several hundred occupations listed, with doctors and lawyers 1-2, and, strangely, database and system administrators 3-4. CEOs are way down at 46!

While social status can be crudely modeled by the average of the percentile results from the four inputs (higher average = higher social status), the figure doesn't actually represent the percentage of the population with lower status, since the four inputs are correlated and the score assigned to occupation isn't really a population percentile.

Brain drain: engineers shun defense industry

Guess where they're headed instead:

NYTimes: At M.I.T., a 2007 survey showed that 28.7 percent of undergraduates were headed for work in finance, 13.7 in management consulting and just 7.5 percent in aerospace and defense. The top 10 employers included McKinsey, Google, Morgan Stanley, Lehman Brothers, Bain, JPMorgan and Oracle — but not a single military contractor or government office.

OK, could be worse. At Harvard the reported fraction heading into finance is 50%! Pity the public university kids who don't know what proprietary trading is and don't have Goldman et al. recruiting aggressively on campus.

Here is what old school investors like Paul Steinhardt and Charlie Munger have to say about it.

This is what I heard coming out of the gym last time I was at Caltech:

Undergrad 1: Yeah, they emailed me the test. I guess it's honor code.

Undergrad 2: But it's a hedge fund, right? Is there an honor code?

Undergrad 1: Hmmm.... good point.

Darwin's savages

Somehow I don't think he and Jared Diamond share the same view of indigenous peoples. From The Descent of Man:

The main conclusion arrived at in this work, namely that man is descended from some lowly organised form, will, I regret to think, be highly distasteful to many. But there can hardly be a doubt that we are descended from barbarians. The astonishment which I felt on first seeing a party of Fuegians on a wild and broken shore will never be forgotten by me, for the reflection at once rushed into my mind - such were our ancestors. These men were absolutely naked and bedaubed with paint, their long hair was tangled, their mouths frothed with excitement, and their expression was wild, startled, and distrustful. They possessed hardly any arts, and like wild animals lived on what they could catch; they had no government, and were merciless to every one not of their own small tribe. He who has seen a savage in his native land will not feel much shame, if forced to acknowledge that the blood of some more humble creature flows in his veins. For my own part I would as soon be descended from that heroic little monkey, who braved his dreaded enemy in order to save the life of his keeper, or from that old baboon, who descending from the mountains, carried away in triumph his young comrade from a crowd of astonished dogs - as from a savage who delights to torture his enemies, offers up bloody sacrifices, practises infanticide without remorse, treats his wives like slaves, knows no decency, and is haunted by the grossest superstitions.

Also, from Charles Darwin, A Naturalist’s Voyage Round the World, in which he describes Fuegians as

‘the most abject and miserable creatures I anywhere beheld’ and as existing ‘in a lower state of improvement than in any part of the world.’ … ‘These poor wretches were stunted in their growth, their hideous faces bedaubed with white paint, their skins filthy and greasy, their hair entangled, their voices discordant, and their gestures violent. Viewing such men, one can hardly make oneself believe that they are fellow creatures and inhabitants of the same world. It is a common subject of conjecture what pleasure in life some of the lower animals can enjoy; how much more reasonably the same question may be asked with respect to these barbarians. At night, five or six human beings, naked and scarcely protected from the wind and rain of this tempestuous climate, sleep on the wet ground coiled up like animals.’

Parallel universes

This BBC documentary follows Mark Everett, the rock singer son of Hugh Everett III (the discoverer of many worlds quantum mechanics) as he seeks to understand his father's legacy. Unfortunately I can only find the snippet linked to above -- does anyone have a copy? Apparently Hugh Everett and his son were not close. Mark Everett explains that the first time he held his father was on the day he discovered him dead. Hugh was 51, Mark 19.

Another related (and possibly copyright violating) request: anyone have a copy of this Believer article by novelist Rivka Galchen on many worlds quantum mechanics?

BBC News:

...Mark Oliver Everett's own career path couldn't have been more different from that of his father.

Mark Everett is the creative force behind the successful American cult rock band Eels. He is the first to admit that he can barely add up a restaurant tip and knows virtually nothing about quantum physics.

The splitting universe

But the main reason Mark decided to participate in the documentary was that he has always felt estranged from his father, and this would be an opportunity to understand his father better.

Along the way, Mark meets many of his father's old colleagues and also younger physicists who have been inspired by Hugh Everett's work. ...

Asian-White IQ variance from PISA results

The vexing question of average differences between groups of humans has been the subject of scrutiny for a very long time. Differences in variance or standard deviation (SD) are less well understood, but have important implications as well. This point was emphasized during the Larry Summers debacle, in which he posited that the variance in male intelligence might be larger than for women, even though the averages are similar (more very dumb and very bright men than women). Summers argued that this effect might explain the preponderance of males in science and engineering, even for a very small difference in SD.

Summers NBER speech:
...If one supposes, as I think is reasonable, that if one is talking about physicists at a top twenty-five research university, one is not talking about people who are two standard deviations above the mean. And perhaps it's not even talking about somebody who is three standard deviations above the mean. But it's talking about people who are three and a half, four standard deviations above the mean in the one in 5,000, one in 10,000 class. Even small differences in the standard deviation will translate into very large differences in the available pool substantially out [on the tail].

I did a very crude calculation, which I'm sure was wrong and certainly was unsubtle, twenty different ways. I looked at the Xie and Shauman paper-looked at the book, rather-looked at the evidence on the sex ratios in the top 5% of twelfth graders. If you look at those-they're all over the map, depends on which test, whether it's math, or science, and so forth-but 50% women, one woman for every two men, would be a high-end estimate from their estimates. From that, you can back out a difference in the implied standard deviations that works out to be about 20%. And from that, you can work out the difference out several standard deviations. If you do that calculation-and I have no reason to think that it couldn't be refined in a hundred ways-you get five to one, at the high end.

I've occasionally heard a variant of the Summers argument applied to Europeans vs Asians (specifically, NE Asians such as Japanese, Koreans and Chinese): although NE Asians exhibit higher averages than whites in psychometric tests (SAT, IQ, etc.), some suspect a smaller variance, leading to fewer "geniuses" per capita, despite the higher mean. See, e.g., this article in National Review:

...The two populations also differ in the variability of their scores. A representative sample of Americans or Europeans will show more variability than will an East Asian sample. In the familiar bell-shaped distribution curve, the bell is much narrower for the Japanese--which is what you would expect from such a homogeneous population.

This difference is a major matter, and it is worth focusing hard on the data. Just about all Western populations report a standard deviation of 15 IQ points. (The SD, a basic measure of variability, quantifies the extent to which a series of figures deviates from its mean.) But the SD for the Japanese and other East Asian populations appears to be a shade under 13 IQ points. That difference does not sound like a big deal, and, in fact, it does not change things much in the center of the distribution. ...

...but it does make a big difference at the high end, and it affects estimates of elite human capital availability in different countries.

I've never seen any data to support the smaller NE Asian SD claim. Looking at SAT data shows a larger variance for the Asian-Pacific Islander category, but that is not surprising since it's a catch-all category that includes S. Asians, SE Asians, NE Asians and Pacific Islanders. I've found very little analysis specific to NE Asians, so I decided to produce some myself. I took the 2006 PISA (OECD Program for International Student Assessment) data, which is painstakingly assembled every 3 years by a huge team of psychologists and educators (400k students from 57 countries tested). The samples are supposed to be statistically representative of the various countries, and the tests are carefully translated into different languages. Most studies of national IQ are quite crude, and subject to numerous methodological uncertainties, although the overall results tend to correlate with PISA results.

Below is what I obtained from the 2006 PISA mathematics exam data (overall rankings by average score here). To get the data, scroll down this page and download the chapter 6 data in .xls spreadsheet format. Level 6 is the highest achievement category listed in the data. For most OECD countries, e.g., France, Germany, UK, only a few percent of students attained this level of performance. In NE Asian countries as many as 11% of students performed at this level. Using these percentages and the country averages, one can extract the SD. (Level 6 = raw score 669, or +1.88SD for OECD, +1.28SD for NE Asians.)

OECD AVG=500 SD=90

NE Asia (HK, Korea, Taiwan) AVG=548 SD=95

The NE Asians performed about .5 SD better on average (consistent with IQ test results), and exhibited similar (somewhat higher) variance. (After doing my calculations I realized that there is actually a table of means and SDs in the spreadsheet, that more or less agree with my results. The standard error for the given SDs is only 1-2 points, so I guess a gap of 5 or 10 points is statistically significant.)

Interestingly, the Finns performed quite well on the exam, posting a very high average, but their SD is smaller. The usual arguments about a (slightly) "narrow bell curve" might apply to the Finns, but apparently not to the NE Asians.

Finland AVG=548 SD=80

Returning to Summers' calculation, and boldly extrapolating the normal distribution to the far tail (not necessarily reliable, but let's follow Larry a bit further), the fraction of NE Asians at +4SD (relative to the OECD avg) is about 1 in 4k, whereas the fraction of Europeans at +4SD is 1 in 33k. So the relative representation is about 8 to 1. (This assumed the same SD=90 for both populations. The Finnish numbers might be similar, although it depends crucially on whether you use the smaller SD=80.) Are these results plausible? Have a look at the pictures here of the last dozen or so US Mathematical Olympiad teams (the US Asian population percentage is about 3 percent; the most recent team seems to be about half Asians). The IMO results from 2007 are here. Of the top 15 countries, half are East Asian (including tiny Hong Kong, which outperformed Germany, India and the UK).

Incidentally, again assuming a normal distribution, there are only about 10k people in the US who perform at +4SD (and a similar number in Europe), so this is quite a select population (roughly, the top few hundred high school seniors each year in the US). If you extrapolate the NE Asian numbers to the 1.3 billion population of China you get something like 300k individuals at this level, which is pretty overwhelming.

As for verbal abilities, we have the following 2006 PISA results: OECD reading avg is about 490; France 488, Germany 495, UK 495, Italy 469, Spain 461. NE Asian scores: Japan 498 Korea 556 HK 536 Taiwan 496. Again, slightly higher scores for NE Asians. Some interesting US data here shows that on 1995 SATs, low-income Asians have lower verbal scores than whites, but by family income of $60k have caught up and Asians with family income of >$70k outscore white families of similar affluence. This strikes me as an immigrant / bilingual family effect. Children raised in immigrant families, where the parents do not speak English at home, tend to score lower on the verbal part of the SAT.


Although it's all there in the data set, I didn't have time to examine the male-female variances in mathematical ability (and don't want to deal with the abuse that might be heaped on me based on what I might find), but I encourage any interested readers to have a look. The authors of the PISA report wisely only reported that male-female averages are similar ;-)

Note: as often happens with this kind of topic, a related discussion has broken out at GNXP.

Neal Stephenson on wiring the world

I'd thought I'd share a link to this somewhat obscure WIRED article written in 1996 by science fiction author Neal Stephenson, about the laying of transcontinental fiber. (Warning: the article is very, very long.) Ever wonder how, exactly, your packets get to that web server in Japan or India? I found it quite inspiring at the time.

Mother Earth Mother Board

The hacker tourist ventures forth across the wide and wondrous meatspace of three continents, chronicling the laying of the longest wire on Earth.

By Neal Stephenson

Many of the themes from the WIRED article also appear in my favorite Stephenson novel, Cryptonomicon (Google Books version). I have a particularly soft spot for the novel, since its plot parallels some spooky, crypto aspects of my own startup experience.

Equities vs real estate

Which is the better long term investment, equities or real estate? The conventional (but not necessarily correct!) wisdom for a long time has been real estate, although this may be changing with the current housing bust. Note here I mean property as an investment, not as a primary residence, which has different considerations such as saved rent, etc.

Something that complicates the discussion is that typical investors are much more familiar with the use of leverage (e.g., 10% down) in buying a house than in buying stocks. The discussion below does a good job of clarifying. I'm not sure you can buy a long-dated 5 year index call at 80% strike for 28%, but that's probably the right ball park. Note the writer is in the UK.

Related posts here (see first figure below), here (second figure below) and here.

Re: the property v equities argument

Property has naturally outperformed in the last 7 years as its a much easier asset class for an individual to leverage. So its no surprise that through the final phases of the credit bubble its done much better as much broader class of people can borrow against it.

However, over a much longer time period the relative returns havet been much different. Each has had their relative booms and busts (.com bubble, property bubble etc)

The question now is which will do better as we go from a decade of excessive leveraging to a long period of de-leveraging.

Its hard to argue for property in that context, especially as its "earnings yield" is maybe 4.5% at best after all costs, while that of the average equity is more like 8% now

You can leverage an equity investment just as much as property without the hassle of finding a tenant, the stamp costs, the illiquidity etc etc

Assume a 5 year investment horizon. Imagine you had 100k of equity that you wanted to leverage 5 times into a LT investment

A) Buy a property. Borrowing 80% of purchase price, Investing 20% of your own equity and 8% for stamp. Equating to 28% cash investment upfront all-in

OR

B) Buy a 5year call option to buy the Eurostoxx @ 80% of where it is now. This will cost 28% as well

Economically you have the exact same exposure to leverage...if the value of either asset is 50% higher in 5 years you come out with 5.35x your original outlay (ie 150% / 28%)

Cool...


Advantage of the property investment;

1- You dont see the value of the investment every day, you just optimistically assume its going higher!

Advantage of the equity Option;

1- Your maximum loss is the 28%. [If the property falls > 28% and u sell u lose > 28%]
2- Its more liquid, you can sell anytime of the day Mon-Fri
3- You dont have to find a tenant or risk cashflow issues if it becomes hard to rent or int rates suddenly spike.

The first figure below, which covers 1980-2005 (i.e.,extending almost to the peak of the real estate bubble; see second figure below), shows that equity returns have exceeded real estate returns even in the hottest markets.

On Crick and Watson

Eminent biologist Erwin Chargaff was extremely bitter about not receiving a Nobel for his important work on DNA, which contributed to Crick and Watson's discovery of the double helix. I've been paging through his strange, but occasionally brilliant, memoir Heraclitean Fire: Sketches of a Life before Nature.

On meeting Crick and Watson at the Cavendish lab. Crick, 35, had already had a career in physics interrupted by the war and despaired of making his great contribution to science. Watson was a callow 23, fresh from Indiana.

It was clear to me that I was faced with a novelty: enormous ambition and aggressiveness, coupled with an almost complete ignorance of, and a contempt for, chemistry, that most real of exact sciences - a contempt that was later to have a nefarious influence on the development of "molecular biology." Thinking of the many sweaty years of making preparations of nucleic acids and of the innumerable hours spent on analyzing them, I could not help being baffled. I am sure that, had I had more contact with, for instance, theoretical physicists, my astonishment would have been less great. In any event, there they were, speculating, pondering, angling for information. ...

Thanks for digging around down there -- what did you find, again? Great! I've got more horsepower, so I'll just connect the dots for you now... :-) From Wikipedia on Crick:

Crick had to adjust from the "elegance and deep simplicity" of physics to the "elaborate chemical mechanisms that natural selection had evolved over billions of years." He described this transition as, "almost as if one had to be born again." According to Crick, the experience of learning physics had taught him something important—hubris—and the conviction that since physics was already a success, great advances should also be possible in other sciences such as biology. Crick felt that this attitude encouraged him to be more daring than typical biologists who tended to concern themselves with the daunting problems of biology and not the past successes of physics.

More (morons?) on the Singularity

Very, very middle brow discussion of the Singularity here at the NYTimes. Yes, I mean by Kurzweil as well as others:

Kurzweil: For example, I point out that the complexity of the design of the brain is at least 100 million times simpler than it appears because the design is in the genome. Even including the genetic machinery that implements the genome, the compressed genome is only about 50 million bytes (which I analyze in the book), and that is a level of complexity we can handle.

Yes, Ray, and how much computing power will it take to explore a reasonable fraction of 2^(8*50,000,000) possibilities? Maybe by reverse-engineering what nature did, but how else? Previous discussion here.

Go read What is Thought? instead of wasting time with the Times on science... (Don't even think of looking at the comments there -- it will cost you 10 points of IQ.)

Confessions of a car dealer

Ever wonder what's behind the horrible experience of buying a car at a dealership? What the negotiation looks like from the other side?

There's a great podcast up on Econtalk, in which Russ Roberts (econ prof at George Mason) interviews a sales manager at a Honda dealership. Did you know that everyone working on the sales side, including the manager, is on 100% commission-based compensation? The average amount earned by the salesman on the sale of a new car is about $350. Check out the comments as well.

The sales manager adopts a stubbornly behavioral position for why prices of cars are not set in a more transparent manner -- that we are culturally conditioned to haggle over car prices. In the comments several more traditional economic (game or information theoretic) arguments are proposed. How does it work in other countries? Are Japanese car salesmen just like their American counterparts?

To have, and have not

This NYTimes article starkly illustrates the gap between rich and poor in India. It depicts a gated high-rise community with its own water, power, security and health systems, surrounded by slums from which are drawn the 2.2 servants per affluent occupant family. The high-rise is visible in the background of the picture above (slideshow).

Many of the relatively few Indians who enjoy first-world living standards, including the family in the article, do so as a consequence of globalization.

...“Things have gotten better for the lucky class,” Mrs. Chand, 36, said one day, as she fixed lunch in full view of Chakkarpur, the shantytown where one of her two maids, Shefali Das, lives. “Otherwise, it is still a fight.”

When the power goes out, the lights of Hamilton Court bathe Chakkarpur in a dusky glow. Under the open sky, across the street from the tower, Mrs. Das’s sons take cold bucket baths each day. The slum is as much a product of the new India as Hamilton Court, the opportunities of this new city drawing hundreds of thousands from the hungry hinterlands.

...For those with the right skills, the good times have been very good. Mr. Chand, 34, a business school graduate who runs the regional operations for an American manufacturing firm, has seen his salary grow eightfold in the last five years, which is not unusual for upper class Indians like him.

The Chands are typical of Hamilton Court residents: Well-traveled young professionals, some returnees to India after years abroad, grateful for the conveniences. Some of them are also the first in their families to live so comfortably.

...Mrs. Chand, a doctor who decided to stay home to raise her children, trained in a government hospital. Her other maid told her recently that her own daughter had given birth at home, down there in the slum.

Sometimes, Mrs. Chand said, she thinks of opening a clinic there. But she also said she understood that there was little that she, or anyone, could do. “Two worlds,” she observed, “just across the street.”

The following Bloomberg article describes a recent World Bank study. The results are amazingly consistent with an estimate I mentioned in a previous post, that the "effective" first-world population of India in human capital terms might be around 100 million (4 high performers for every 10 in the US).

The study links the results of a test given to 6,000 teenagers in two states -- Rajasthan in western India and Orissa in the east -- to students' performance on the same exam in 51 other countries.

The researchers conclude that mathematical abilities of India's 14-year-olds vary widely between the worst and the best students.

If other states are similar to the ones studied then it would mean that 17 million Indian students don't meet the lowest international benchmark of ``some basic mathematical knowledge.'' That's 22 times the corresponding figure for the U.S.

At the same time, the depth of India's math talent -- those whose test scores are considered to be of an advanced level -- is also significant. ``For every 10 top performers in the U.S., there are four in India,'' the World Bank economists say. That's 100,000 students, or more than any European country.

This latter group is supplying the bulk of India's scientific, technical, managerial and entrepreneurial talent and is responsible for the country's growing clout in the global knowledge economy.

MacKenzie on the credit crisis

Edinburgh sociology professor Donald MacKenzie wrote what I feel is the best history (so far) of modern finance and derivatives. In this article in the London Review of Books, he tackles the current credit crisis. Highly recommended.

On Gaussian copula (cognitive limitations restrict attention to an obviously oversimplified model; big brains were worried from the start):

Correlation is by far the trickiest issue in valuing a CDO. Indeed, it is difficult to be precise about what correlation actually means: in practice, its determination is a task of mathematical modelling. Over the past ten years, a model known as the ‘single-factor Gaussian copula’ has become standard. ‘Single-factor’ means that the degree of correlation is assumed to reflect the varying extent to which fortunes of each debt-issuer depend on a single underlying variable, which one can interpret as the health of the economy. ‘Copula’ indicates that the mathematical issue being addressed is the connectedness of default risks, and ‘Gaussian’ refers to the use of a multi-dimensional variant of the statistician’s standard bell-shaped curve to model this connectedness.

The single-factor Gaussian copula is far from perfect: even before the crisis hit, I wasn’t able to get a single insider to express complete confidence in it. Nevertheless, it became a market Esperanto, allowing people in different institutions to discuss CDO valuation in a mutually intelligible way. But having a standard model is only part of the task of understanding correlation. Historical data are much less useful here. Defaults are rare events, and producing a plausible statistical estimate of the extent of the correlation between, say, the risk of default by Ford and by General Motors is difficult or impossible. So as CDOs gained popularity in the late 1990s and early years of this decade, often the best one could do was simply to employ a uniform, standard figure such as 30 per cent correlation, or use the correlation between two corporations’ stock prices as a proxy for their default correlations.

Ratings, indices and implied correlation:

However imperfect the modelling of CDOs was, the results were regarded by the rating agencies as facts solid enough to allow them to grade CDO tranches. Indeed, the agencies made the models they used public knowledge in the credit markets: Standard & Poor’s, for example, was prepared to supply participants with copies of its ‘CDO Evaluator’ software package. A bank or hedge fund setting up a standard CDO could therefore be confident of the ratings it would achieve. Creators of CDOs liked that it was then possible to offer attractive returns to investors – which are normally banks, hedge funds, insurance companies, pension funds and the like, not private individuals – while retaining enough of the cash-flow from the asset pool to make the effort worthwhile. As markets recovered from the bursting of the dotcom and telecom bubble in 2000-2, the returns from traditional assets – including the premium for holding risky assets – fell sharply. (The effectiveness of CDOs and other credit derivatives in allowing banks to shed credit risk meant that they generally survived the end of the bubble without significant financial distress.) By early 2007, market conditions had been benign for nearly five years, and central bankers were beginning to talk of the ‘Great Stability’. In it, CDOs flourished.

Ratings aside, however, the world of CDOs remained primarily one of private facts. Each CDO is normally different from every other, and the prices at which tranches are sold to investors are not usually publicly known. So credible market prices did not exist. The problem was compounded by one of the repercussions of the Enron scandal. A trader who has done a derivatives deal wants to be able to ‘book’ the profits immediately, in other words have them recognised straightaway in his employer’s accounts and thus in the bonus that he is awarded that year. Enron and its traders had been doing this on the basis of questionable assumptions, and accounting regulators and auditors – the latter mindful of the way in which the giant auditing firm Arthur Andersen collapsed having been prosecuted for its role in the Enron episode – began to clamp down, insisting on the use of facts (observable market values) rather than mere assumptions in ‘booking’ derivatives. That credit correlation was not observable thus became much more of a problem.

From 2003 to 2004, however, the leading dealers in the credit-derivatives market set up fact-generating mechanisms that alleviated these difficulties: credit indices. These resemble CDOs, but do not involve the purchase of assets and, crucially, are standard in their construction. For example, the European and the North American investment-grade indices (the iTraxx and CDX IG) cover set lists of 125 investment-grade corporations. In the terminology of the market, you can ‘buy protection’ or ‘sell protection’ on either an index as a whole or on standard tranches of it. A protection seller receives fees from the buyer, but has to pay out if one or more defaults hit the index or tranche in question.

The fluctuating price of protection on an index as a whole, which is publicly known, provides a snapshot of market perceptions of credit conditions, while the trading of index tranches made correlation into something apparently observable and even tradeable. The Gaussian copula or a similar model can be applied ‘backwards’ to work out the level of correlation implied by the cost of protection on a tranche, which again is publicly known. That helped to satisfy auditors and to facilitate the booking of profits. A new breed of ‘correlation traders’ emerged, who trade index tranches as a way of taking a position on shifts in credit correlation.

Indices and other tranches quickly became a huge-volume, liquid market. They facilitated the creation not just of standard CDOs but of bespoke products such as CDO-like structures that consist only of mezzanine tranches (which offer combinations of returns and ratings that many investors found especially attractive). Products of this kind leave their creators heavily exposed to changes in credit-market conditions, but the index market permitted them to hedge (that is, offset) this exposure.

Quants and massive computational power (one wonders whether the mathematics and computers did nothing more than lend a spurious air of technicality to untrustworthy basic assumptions):

With problems such as the non-observability of correlation apparently adequately solved by the development of indices, the credit-derivatives market, which emerged little more than a decade ago, had grown by June 2007 to an aggregate total of outstanding contracts of $51 trillion, the equivalent of $7,700 for every person on the planet. It is perhaps the most sophisticated sector of the global financial markets, and a fertile source of employment for mathematicians, whose skills are needed to develop models better than the single-factor Gaussian copula.

The credit market is also one of the most computationally intensive activities in the modern world. An investment bank with a big presence in the market will have thousands of positions in credit default swaps, CDOs, indices and similar products. The calculations needed to understand and hedge the exposure of this portfolio to market movements are run, often overnight, on grids of several hundred interconnected computers. The banks’ modellers would love to add as many extra computers as possible to the grids, but often they can’t do so because of the limits imposed by the capacity of air-conditioning systems to remove heat from computer rooms. In the City, the strain put on electricity-supply networks can also be a problem. Those who sell computer hardware to investment banks are now sharply aware that ‘performance per watt’ is part of what they have to deliver.

Collapse of rating agency credibility:

The rating agencies are businesses, and the issuers of debt instruments pay the agencies to rate them. The potential conflict of interest has always been there, even in the days when the agencies mainly graded bonds, which generally they did quite sensibly. However, the way in which the crisis has thrust the conflict into the public eye has further threatened the credibility of ratings. ‘In today’s market, you really can’t trust any ratings,’ one money-market fund manager told Bloomberg Markets in October 2007. She was far from alone in that verdict, and the result was cognitive contagion. Most investors’ ‘knowledge’ of the properties of CDOs and other structured products had been based chiefly on ratings, and the loss of confidence in them affected all such products, not just those based on sub-prime mortgages. Since last summer, it has been just about impossible to set up a new CDO.

Illiquid assets, difficulty of mark to market:

Over recent months, banks have frequently been accused of hiding their credit losses. The truth is scarier: such losses are extremely hard to measure credibly. Marking-to-market requires that there be plausible market prices to use in valuing a portfolio. But the issuing of CDOs has effectively stopped, liquidity has dried up in large sectors of the credit default swap market, and the credibility of the cost of protection in the index market has been damaged by processes of the kind I’ve been discussing.

How, for example, can one value a portfolio of mortgage-backed securities when trading in those securities has ceased? It has become common to use a set of credit indices, the ABX-HE (Asset Backed, Home Equity), as a proxy for the underlying mortgage market, which is now too illiquid for prices in it to be credible. However, the ABX-HE is itself affected by the processes that have undermined the robustness of the apparent facts produced by other sectors of the index market; in particular, the large demand for protection and reduced supply of it may mean the indices have often painted too uniformly dire a picture of the prospects for mortgage-backed securities. One trader told the Financial Times in April that the liquidity of the indices had become very poor: ‘Trading is mostly happening on interdealer screens between eight or ten guys, and this means that prices can move wildly on very light volume.’ Yet because the level of the ABX-HE indices is used by banks’ accountants and auditors to value their multi-billion dollar portfolios of mortgage-backed securities, this esoteric market has considerable effects, since low valuations weaken banks’ balance sheets, curtailing their capacity to lend and thus damaging the wider economy.

Josef Ackermann, the head of Deutsche Bank, has caused a stir by admitting ‘I no longer believe in the market’s self-healing power.’ ...

Obamanomics

If this article by John Cassidy in the New York Review of Books is any guide, Obama's grasp of economics may be orders of magnitude deeper than that of John McCain. A long exposure to the market-obsessed Chicago School has probably made him at least familiar with the arguments favoring market outcomes over government intervention. The remainder of the article is mainly about behavioral economics, rather than Obama, but well worth reading.

...Should Obama win the nomination, political considerations may well force upon him a more interventionist position, but his first inclination is to seek a path between big government and laissez-faire, a trait that reflects his age—he was born in 1961—and the intellectual milieu he emerged from. Before entering the Illinois state Senate, he spent ten years teaching constitutional law at the University of Chicago, where respect for the free market is a cherished tradition. His senior economic adviser, Austan Goolsbee, is a former colleague of his at Chicago and an expert on the economics of high-tech industries. Goolsbee is not a member of the "Chicago School" of Milton Friedman and Gary Becker, but he is not well known as a critic of American capitalism either. As recently as March 2007, he published an article in The New York Times pointing out the virtues of subprime mortgages. "The three decades from 1970 to 2000 witnessed an incredible flowering of new types of home loans," Goolsbee wrote. "These innovations mainly served to give people power to make their own decisions about housing, and they ended up being quite sensible with their newfound access to capital."

When I spoke to Goolsbee earlier this year, he said that one of the things that distinguished Obama from Clinton was his skepticism about standard Keynesian prescriptions, such as relying on tax policy to stimulate investment and saving. In a recent posting on HuffingtonPost.com, Cass Sunstein, who for ten years was a colleague of Obama's at the University of Chicago Law School—and has said he is "an informal, occasional adviser to him"—made a similar point regarding government oversight of the financial markets: "With respect to the mortgage crisis, credit cards and the broader debate over credit markets," Sunstein wrote, "Obama rejects heavy-handed regulation and insists above all on disclosure, so that consumers will know exactly what they are getting."

If Obama isn't an old-school Keynesian, what is he? One answer is that he is a behavioralist—the term economists use to describe those who subscribe to the tenets of behavioral economics, an increasingly popular discipline that seeks to marry the insights of psychology to the rigor of economics. Although its intellectual roots go back more than thirty years, to the pioneering work of two Israeli psychologists, Amos Tversky and Daniel Kahneman, behavioral economics took off only about ten years ago, and many of its leading lights, among them David Laibson and Andrei Shleifer, of Harvard; Matt Rabin, of Berkeley; and Colin Camerer, of Caltech, are still in their thirties or forties. One of the reasons this approach has proved so popular is that it appears to provide a center ground between the Friedmanites and the Keynesians, whose intellectual jousting dominated economics for most of the twentieth century.

The Singularity, AI and IEEE

An entire special issue of IEEE Spectrum has been devoted to the Singularity, with contributions from people like Vernor Vinge, Rodney Brooks, Gordon Moore and Douglas Hofstader. I'm confident it won't happen in my lifetime. I don't even think a machine will pass a strong version of the Turing test while I am around.

My favorite book on AI is Eric Baum's What is Thought? (Google books version). Baum (former theoretical physicist retooled as computer scientist) notes that evolution has compressed a huge amount of information in the structure of our brains (and genes), a process that AI would have to somehow replicate. A very crude estimate of the amount of computational power used by nature in this process leads to a pessimistic prognosis for AI even if one is willing to extrapolate Moore's Law well into the future. Most naive analyses of AI and computational power only ask what is required to simulate a human brain, but do not ask what is required to evolve one. I would guess that our best hope is to cheat by using what nature has already given us -- emulating the human brain as much as possible.

This perspective seems quite obvious now that I have kids -- their rate of learning about the world is clearly enhanced by pre-evolved capabilities. They're not generalized learning engines -- they're optimized to do things like recognize patterns (e.g., faces), use specific concepts (e.g., integers), communicate using language, etc.

What is Thought?

In What Is Thought? Eric Baum proposes a computational explanation of thought. Just as Erwin Schrodinger in his classic 1944 work What Is Life? argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation. Baum argues that the complexity of mind is the outcome of evolution, which has built thought processes that act unlike the standard algorithms of computer science and that to understand the mind we need to understand these thought processes and the evolutionary process that produced them in computational terms.

Baum proposes that underlying mind is a complex but compact program that exploits the underlying structure of the world. He argues further that the mind is essentially programmed by DNA. We learn more rapidly than computer scientists have so far been able to explain because the DNA code has programmed the mind to deal only with meaningful possibilities. Thus the mind understands by exploiting semantics, or meaning, for the purposes of computation; constraints are built in so that although there are myriad possibilities, only a few make sense. Evolution discovered corresponding subroutines or shortcuts to speed up its processes and to construct creatures whose survival depends on making the right choice quickly. Baum argues that the structure and nature of thought, meaning, sensation, and consciousness therefore arise naturally from the evolution of programs that exploit the compact structure of the world.

IIT uber alles?

I recently came across this interesting web site maintained by Kamal Sinha, an IIT (Indian Institute of Technology) Bombay alum who has worked at Mitsubishi in Japan and in Silicon Valley.

It has been widely claimed (e.g., CBS Sixty Minutes) that IITs are the most selective universities in the world -- each year about 300k applicants compete for about 4000 spots. To enter the most competitive (e.g., EECS) departments, applicants must score amongst the top few hundred! I know several theoretical physicists in the US who were "toppers" on the IIT-JEE (Joint Entrance Exam), including one who placed first in all of India his year ("first ranker")! Perhaps ironically, the first ranker didn't attend IIT -- he chose Caltech instead.

Despite the hype (see below) Sinha seems to think IIT is roughly comparable to other elite national universities like University of Tokyo, Seoul National University or Taiwan National University. Note he estimates the effective population base (the number of people who have access to first world educational resources in K-12) of India as only comparable to that of Japan (about 125 million; see here for a similar estimate by a well-known physicist). The estimates that lead to the conclusion that IIT is the most competitive in the world usually normalize to the entire Indian population of nearly 1 billion. I would say that China's effective population (in this sense) is around 200-300 million people (and growing rapidly), so perhaps Beida (Beijing University) and Tsinghua are the most competitive universities in the world.

I'd be interested in the opinions of other IIT graduates! Here is some detailed discussion of the JEE exam by an IIT-Kanpur professor (link provided by a commenter). The professor suggests that the test is too hard: beyond the first few hundred or thousand rankers, noise dominates signal (i.e., even many admitted students have very low absolute scores, in which luck may have played a role).

Hype:

"This is IIT Bombay. Put Harvard, MIT and Princeton together, and you begin to get an idea of the status of this school in India." (Lesley Stahl, co-anchor on CBS 60 Minutes)

"And it's hard to think of anything like IIT anywhere in the world. It is a very unique institution." (Bill Gates, Microsoft)

"Per capita, IIT has produced more millionaires than any other undergraduate institution." (Salon Magazine)


Sinha on IIT acceptance rate:

Admission to IITs is extremely difficult. Only the top 2 percent of the applicants are admitted and to get into a decent department, about half a percent is a reasonable corresponding figure. Here I will explore whether IITs are the hardest school to get into and later I will check if high selectivity results in higher quality. "Hard" facts will be supplied when they become available.

Extremely low Acceptance Rate?

Having results of a single entrance examination determine whether one would be accepted or not is a common feature among the educational institutions in East Asian countries. I worked in Japan for six years and therefore being somewhat familiar with them will compare Japanese figures with that of IITs. All figures ae based on certain assumptions.

Selective Admissions in Japan

While it might not make the CBS news, Tokyo University, or Todai, an abbreviated form of Tokyo Daigaku is the place Japanese moms start thinking of to send their children for undergraduate studies before they are even born. There are 8 national universities like the Tokyo university, Todai being the most coveted one, and a few prestigious private schools like the Keio and Waseda, and these are the schools where almost every graduating school senior hopes to get into. Among technical schools Tokyo Institute of Technology (part of those 8 national universities) leads the pack. Each year news of a few students committing suicide on failing to secure admission into one of these schools is not uncommon.

Tokyo University admits fewer than 1500. My guess is that all the top private universities and the eight national universities combined admit fewer than 15000 applicants. How many students applied for these seats? About one and a half million which is about the total number of graduating seniors. Means about one percent!

Applicant Pool Size

'Wait a minute' I can hear you saying. Unlike in Japan where almost everyone takes the test to get into an an university, in India not everybody applies to IITs. Most of the applicants who take the JEE are quite good, there being a self-selection process. In response I will point out that if we compare the potential applicant pools, the following factors stand out:

IIT JEE is taken mostly by middle class applicants from urban areas with total population about 125 million. Japan has about same population with lower percentage of test-takers compared to that of India because of lower birth rates. There almost all eligible seniors take the test to get into these prestigious universities, meaning the potential applicant pools are almost equal.

Engineering schools like Tokyo Institute of Technlogy and various engineering departments in other universities are more selective than the average department. Assume those to be twice as selective.

IITs accept about 3500 of applicants. Given the above assumptions it is about (15000/2)/3500 = two times more selective than the average engineering department in these Japanese universities.

Take Tokyo University for comparision. An overwhelming majority of the grduating seniors choose Todai as their first choice. This means that Tokyo University is about twice (3500/1500) as selective as the IITs and more likely at least four times as selective than the IITs when engineering departments are compared.

Quality of Potential Applicant Pools

Japanese seniors in schools perform near the top in international tests in sciences and mathematics. (Seniors from Korea, Hong Kong and Singapore perform equally well.) Indians are not included in most comparison studies but there seems to be some evidence that the average Indian students would have performed near the average, probably somewhat below it. Moreover, after graduation many Japanese students take time off to study for the entrance exam and their dedication has to be seen to be believed. It leads me to believe that their potential applicant pool of of higher quality.

Other Asian Countries like Korea, Singapore, Hongkong, China?

It may be assumed that the student quality and the selection rates are similar to that in Japan, if not better. Means it appears that IITs, however difficult they are to get into, could be overshadowed by institutions in neighboring countries with more difficult admission standards.

There is a difference though. While graduates of universities like Tokyo are quietly working hard to bring their countries up to top and compete with the West, India with its population of a billion or so, through its IIT and other engineering college graduates, seems destined to become a country where the developed world can chooose its low-cost subcontractors to do the jobs they don't want to do or have a shortage of workers.

Admissions in the USA

While it seems true that admission rate at IITs is less than even the most selective US school like the CalTech, it does not mean IIT recruits students of higher caliber. In a country like the USA, educational resources were well developed and the enrollment capacity for engineering majors is kept about the same as the number of seniors intending to enter those programs, if not more. It means less desparation. Moreover, there are lot of top-notch schools schools of about equal caliber which decreases their selectivity figures. My guess is that the top 50 engineering schools in the USA exceed IITs in almost all respect and another 100 or so other schools are not far behind.