One of Thiel’s controversial initiatives is his 20 under 20 program. The broad outline of the program is as follows: any person under the age of 20 can apply, and those who have great ideas in science, technology, or entrepreneurship are selected and are given $100,000 over two years as well as special mentoring and access to resources to pursue their ideas. These people are required to “stop” out of college for the duration of two years, though they can go back to college once the time period is over.

Thiel first announced this in a TechCrunch interview. Here’s what Thiel said at the end of the interview:

Well, we’ve been, you know we’ve been brainstorming a lot on, on what we’re gonna, you know, what we can do to try to break the sort of relative stasis we’ve been in as a society and you know as a BC Fund we’ve been investing in these breakthrough technology companies. One of the things that we think is very important is to encourage potentially young entrepreneurs to actually get involved in a science technology and in creating all this great value for the next generation. So one of the initiatives that we’re gonna be starting over the next few weeks is a program for offering grants of up to $100,000 to up to 20 people under age 20 for starting something new. [...] One of the big problems with the education system in both, it’s undergrad and grad school levels which a lot worse when I went to school 20 years ago is that it just cost so much more and so you end up with people burden with incredible amounts of debts and well, they maybe able to track towards certain safe jobs is actually a lot harder for people to take the risks you need and we think you’re gonna have to take a lot of risks to build the next generation of companies. So the, we’re basically are gonna be taking applications through the end of this year for 2011. The basic thing is you can apply individually or in a team of up to four people. It is a two year program up to $100,000 and we will, we will encourage people to do something from California to get involve in the, a breakthrough technology, anywhere in the world. [...] Twenty under 20.

This led to some negative responses, such as this one from Jacob Weisberg at Newsweek, which Bryan Caplan at EconLog described as “Accurate summary plus impotent fuming” aka “priceless publicity” for Thiel’s cause.

Recently, the Thiel Foundation announced that it had decided the list of Thiel Foundation fellows for 2011. 24 fellows have been selected, instead of the originally planned 20. It seems likely that this program will be continued for future years, with the next round of applications due at the end of this year, but there has been no official announcement of a next round so far.

The announcement received coverage in many media outlets, including the Chronicle of Higher Education. There was also some critical discussion in the Washington Post, Fortune, as well as on Bijan Sabet’s blog.

In a subsequent blog post some time from now, I’ll take a look at the 20 under 20 program, the current list of Thiel Fellows, and what I think of the promise of this program and what it reflects about the merits and demerits of staying in college versus “stopping” out if you want to pursue great ideas in science and technology.

While the Internet remains in flux, it is likely that it has matured in many important respects, and the major growth will now be one of increasing volume and penetration rather than fundamental changes in the nature of the Internet. In a TechCrunch interview, PayPal founder and venture capital funder and philanthropist Peter Thiel says:

It’s relatively under valued. So, it be, you know, if you had a choice between Google and Facebook, you should be on Facebook. But, it’s, it’s, I do think, I do think this doesn’t necessarily mean the start up investing is at a good point. So, it’s quite possible that the internet is at in more mature stage than people realize.

And so, there are a lot of people talk about cellphones and mobile applications and we’ve been very nervous about investing in these areas because we think so much the value on these things will be captured by the larger existing companies so that, you know, the yelp of cellphones will be yelp. The Google of cellphones will be Google and so it maybe like the car industry in the 1950′s where it became w as much more important culturally and socially than it was on the 1920′s, it b ecame a lot bigger. But the car companies themselves were actually started in 20′s even though they grew a lot in the 50′s and they transformed the US in the 50′s. If you wanna work in the car industry in 1950 you should join one of the big companies, and if you we’re a technology investor in 1950 you should not have funded new car companies and so.

So what aspect of science and technology will see the most explosive growth in the near future. There could be growth in privately funded space exploration (SpaceX being an example), nanotechnology (which has seen significant strides), or robotics/artificial intelligence. But my personal best guess would be that the coming few years will be the years of biology. Specifically, we’ll see great progress in these areas: personal genomic services, medical innovation spurred by genetic data, and radical life extension.

Personal genomic services

By 2020 (and probably much earlier!) a person will be able to get a full genome mapping for about $100 if living in a developed country (and probably not more than twice as much in a developing country). This full genome mapping will allow the person to track risk factors for diseases — big and small. These services will routinely be used for infants so that their parents know about their health risks from day one. Instead of exchanging horoscopes, people interested in marrying or dating each other will be swapping genomes.

Ronald Bailey has a nice article with a combination of facts and future speculation. Currently, genetic testing (not full genome mapping, but the identification of key gene sequences and SNPs that affect risk factors for important diseases) is available for less than $500 (and if you sign up before 25 December, then you can get a test done by 23andMe for just $99 + shipping and other costs, totaling to less than $200 — offer valid only in some countries). He also predicts that people will get used to sharing their genetic data publicly or semi-publicly.

Medical innovation spurred by genetic data

When lots of people use Google’s search services, not only do they get the search results they were looking for, but Google also gets information about what users are looking for. This information can be seen, for instance, at Google Trends. Google engineers came up with the idea of using Google search traffic to report Flu Trends quickly, and similar real-time information can be gleaned through social media such as Twitter and Facebook.

Similarly, as more and more people rush to have their genomes mapped, more and more reliable aggregate data will be available, that will allow for correlations between genes and specific medical conditions to be tested for more reliably, without having to spend money separately on expensive large-scale studies. Just recently, 23andMe (one of the personal genomic services companies) received NIH funding for a study to validating this approach for pharmacogenomic research. It’s a win-win: as more individuals have their genomes mapped, the aggregate data gets better and more reliable, which in turn allows the genetic testing companies to offer individuals better assessment of their risk factors and tendencies.

Currently, the use of genetic information is mainly in predicting allergies or possible non-working of some drugs: see here. The growth of personal genomics would make this process much smoother and more integrated, allowing patients to find a drug regime with greater likelihood of success. Universal personalized medicine may be as unremarkable a decade from now as universal personalized search is today. Much of the expertise needed to do this personalization will be incorporated into computers, saving money for patients and consumers and freeing the time of doctors for more tricky pursuits. Ten years from now, you may be able to swipe a card at any pharmacy worldwide, choose your ailment, and have a computer automatically predict the best medicine for you based on your ailment and genome (and other biological factors), and “sell” these medicines to you.

Radical life extension

The twentieth century has been one of the greatest centuries in terms of adding to the life expectancy of people was concerned: life expectancy at the beginning of the century was less than fifty years almost everywhere. Today, it tops 60 years in most countries and exceeds 75 years in most developed countries. In the first half of the twentieth century, the main source of improvement was in better nutrition, better sanitation, and vaccinations. In many developing countries, these approaches have still not been exhausted. But for developed countries, much of the increase in the last fifty years has been in a number of other factors: declining accident rates and declining mortality rates from accidents due to better accident response (including road accidents, home accidents, and fires), declining rates of homicide and war (though this isn’t universal), better post-disaster relief, and, of course, the better medical understanding of prevention, diagnosis, and treatment of conditions such as cancer and heart disease.

One of the most promising possibilities for all those who prefer life to death is the possibility of “biological immmortality” (Wikipedia entry) — or the elimination of aging. This does not mean “immortality” in the sense that one cannot die, but rather, that there is no progressive deterioration of body function that makes one more susceptible to death. Probabilistically, this means that every additional day that you survive does not brin you a day closer to death. Evolutionary biologist Michael Rose thinks he already has one possible route to biological immortality.

In practice, what is needed is not biological immortality as much as “escape velocity” — the slowing down of aging to a sufficient degree that it gets postponed to a later time, by which more technological progress can slow down aging yet again. The scientist Aubrey de Grey, who heads the SENS foundation, has identified possible strategies that can be used against all the most common aging mechanisms, and believes that,with sufficient funding, there is a 50% chance of reaching escape velocity by around 2040. He has also started the Methuselah Foundation which uses a prize model to encourage innovation in radical life extension strategies.

Ronald Bailey discusses the Methuselah Manifesto here and discusses the ethical/Malthusian objections here.

Open notebook science is a form of scientific practice where the scientist or researcher notes down each step of the discovery process and experiments, including the failed experiments or the insights/steps that did not lead to anything substantive. This helps tackle problems such as: the file-drawer effect (where researchers decide not to send certain results for publication because the results aren’t interesting, contradict established results, or just support established results without adding anything new), publication bias (where publishers are likely to accept research that produces certain kinds of conclusions as opposed to other research), and the absence of specific details and raw data in published papers that makes it hard for others to attempt to reproduce the results.

The UsefulChem project in the Bradley lab at Drexel University, headed by Jean-Claude Bradley, is one example. They have a lab notebook hosted by Wikispaces, which gives details of their experiments. In addition, the same research group coordinates an Open Notebook Solubility Challenge, whose goal is to get accurate and verifiable information on solubilities, backed with details of experiments that were done to compute these solubilities. The corresponding lab notebook, again hosted by Wikispaces, is here. UsefulChem also have a blog, that they use for more general observations. The ONS wiki also has links to similar open notebook science efforts by others. In addition to using a wiki, they also make their solubility and other data available through Google Spreadsheets (that can be downloaded, viewed online, or queried using a web-based API) and in archival formats, and periodically release print versions of the solubility data as books via Lulu.

In February 2010, Science Commons and Microsoft Research teamed up to hold a conference on open data sharing in the sciences — a summary blog post with links can be found here. The first session included presentations by Jean-Claude Bradley of the UsefulChem project mentioned above, as well as one by Cameron Neylon (blog), also an open notebook scientist. Video of the first session is available here. Bradley’s presentation gives some examples of how open sharing of data and experiments helped resolve controversies, such as the NaH oxidation.

Open notebook science proponents have proposed the Panton Principles for sharing raw data of experiments. These principles include the release of all raw data under a public domain-type license that allows unrestricted use of any kind. The two licenses they suggest are the PDDL (Public Domain and Distribution License) and CCZero (Creative Commons Zero Waiver).

There are very few examples of open notebook science principles being applied outside experimental fields. In mathematics, there have been a bunch of experiments in collaborative solution of specific mathematics problems, called the Polymath projects. These have had mixed success, and offer promise for the future, but are very different from the “open notebook science” approach of carrying out all one’s thinking in the open. One researcher maintains a lab notebook in differential geometry (aptly titled Deferential Geometry) but the trend doesn’t appear to have caught on. Also, a bunch of researchers, mostly in higher category theory, started a n-Category Lab which serves as a hybrid between a lab notebook and a reference resource for their topic.

The story

A son of Bangladeshi/Indian immigrants to the United States, Salman Khan did his undergraduate studies at MIT, where he got degrees in mathematics and EECS, following which he received an MS in EECS (again from MIT) and an MBA for Harvard Business School. Khan worked at a hedge fund for a few years, and started preparing online videos in an attempt to tutor some of his relatives who had trouble with mathematics. As he found that more and more people were using his videos, Khan shifted more and more of his time to creating videos. Eventually, his wife allowed him to quit his hedge fund job and devote himself full time to creating videos.

Khan describes the story in more detail in an interview for Mixergy.com.

In recent times, Khan’s videos were praised by some time programmer and now philanthropist Bill Gates — Gates also got a chance to meet up with Khan and posted his notes here (if you don’t have Silverlight, you can view a YouTube version here). A complete list of press coverage of the Khan Academy can be found here.

Khan’s thesis

Khan lays out his grand mission, vision, and social return here. He thinks of the open collection of educational videos as only one part of a much larger vision: an online school where every student can learn at his or her own pace, and where instructors and peers can help students in a focused manner rather than teaching the same material to lots of students. Toward this end, Khan, and some hirees and volunteers, are currently working on exercise apps for much of the mathematical content, whereby students can watch videos, try lots of exercises, and keep trying the exercises till they get ten in a row right. If, as the student tries an exercise, a specific deficiency is discovered, the student can go back and watch the relevant portion of Khan’s own video. In an article for the Chronicle of Higher Education, Khan elaborates on the themes of what he considers problematic with most school and university education: (i) Long lectures delivered to huge audiences assuming a single pace of instruction for all, (ii) Everybody is expected to move on to the next topic even if they haven’t understood the previous topic thoroughly enough, (iii) Very little actual feedback to instructors and students on specifics of where they’re going wrong and what changes need to be made.

How good a teacher is Khan? And how useful is he?

I’ve watched Sal’s (Salman Khan lies to call himself Sal) videos on a range of topics including: mathematics and physics (not for actual learning, just to see his style and know if I can recommend the videos to others), biology and finance (both for actual learning and to see his style), and history (just out of curiosity and to see his style, though I ended up learning some stuff too).

First, I think that Sal Khan is not the best teacher. Without intending to praise myself too much, I could probably do a better job, at least on the mathematics and physics videos than he did. For mathematics, there are a lot of somewhat better teachers available — one example is Patrick’s Just Math Tutorials; another (much smaller) is Better Explained.

And certainly, Salman Khan’s videos are nowhere near as good as those of Thinkwell. Thinkwell’s full video courses cost about $150 per course, but you can see many of their sample videos at their blog and snippets of most of their lectures at MindBites. The contrast between Sal’s uninformed but intelligent speculation on a lot of subject matter and the informed commentary of Thinkwell’s lecturers is all too evident (to me).

Despite these drawbacks, Sal Khan has, in my view, created a very important niche. Thinkwell’s videos are an awesome production job, but they’re costly. In contrast with Thinkwell, Sal Khan can create his videos at a much lower cost — and hence offer them effectively for free. He can also create many more videos in numerical terms, and hence can cover many more areas. With many videos at a price of zero, Khan has reached out to a much larger number of viewers.

Moreover, the subtle differences between a Thinkwell and a Sal Khan may matter a lot at the higher echelons of education, but for large numbers of people struggling with basic stuff, the subtle distinctions in quality are not as much of a priority as having something fairly good that can be played and replayed for free. Sal Khan may be only at the 75th percentile among people teaching college-level mathematics material, but that still makes him better than 75% of such people, and add on to that the advantages of playing, replaying, and exercise apps, and his usefulness goes up tremendously.

That said, I find some of the estimates of social utility made by Khan in his video unusually high. For instance, Khan comes up with a total potential figure of $250 million in social utility generated per year, but it relies on a number of dubious assumptions. The most dubious assumption of these is the assumption that a single video view generates about $1 in social utility. While this is certainly true for some people viewing the videos — particularly those using Sal as their private tutor, it probably will not be true for the majority of people viewing the video. My own calculations would indicate a (eventual) social utility generated per year (by video views) of something in the range of $10-15 million. That is still a
huge social utility generated and I commend Sal Khan for his visionary effort in this direction.

Even if many people find Khan’s videos non-optimal to learn from, competition from people like Khan may encourage companies like Thinkwell to lower their prices, offer a larger fraction of their videos for free, and further distinguish the quality of their products. It might encourage others to enter the arena offering free videos, perhaps of a better quality than Khan, and perhaps with a different style and emphasis.

First, a bit of terminology. As is customary in many such discussions, I’ll use the acronym STEM for “Science, Technology, Engineering and Mathematics” — a broad array of related fields that are roughly characterised by the use of the scientific method and a mathematically based language of discourse. When I talk of people in STEM fields, I am including jobs both in academia and industry, and jobs that include both research and more mundane application of prior research (such as a job as an engineer in a construction company). Also, I won’t draw a fine distinction between declared gender and biological sex, since, for the vast majority of people, the two are the same.

The main factual claim is that, across the world, to varying degrees, the proportion of females in STEM fields is less than their proportion in the population or the relevant demographic segment of the population. The proportion of women in a large national population at any given age is close to 50% (about 51% of naturally occurring births are male, but females have higher survival rates through childhood in the absence of significant differences in access to nutrition or health care). So, as a reasonable rule of thumb, if the proportion of females in a given STEM field is much less than half, women would be called “under-represented” in that field.

Under-representation: why is it a problem?

Women are under-represented in STEM fields across the world. Why, though, is this under-representation a problem? Before you protest that this is a silly question, note that some people don’t see it as a problem, and even the people who do see it as a problem often have many different reasons for seeing it as a problem. What are these? Here are some:

• Individual tragedy: A particular woman who is capable of entering a STEM field and enjoying/benefiting from it more than the alternative she chooses, does not do so. This “individual tragedy” may be due to discrimination, due to stereotypes, because she never even knew she could do it, etc. Note that the individual tragedy scenario could occur even without discrimination by educational institutions, though discrimination of various forms of course leads to individual tragedies. Note also that the simple fact of a woman capable of a STEM field not choosing it is not in itself an individual tragedy — may be she is choosing something that works even better.

• Collective loss: If large numbers of females who can make great contributions to, say, physical chemistry, do not enter this field, then the field suffers, and there is less of the technological progress that would arise from breakthroughs in the field.

• Skewed perspective: This is based on the idea that each group brings its own perspective on the subject to the table, and a lower representation of one group leads to an under-representation of a particular perspective and hence derails the progress of the subject. For instance, a journalist who comes from a poor rural third-world country may be able to offer more insight when writing articles or filing reports about events in poor rural third-word countries, thanks to his personal experiences. Similarly, a journalist who spent her school years high on drugs and sex may be able to offer more personal insight when reporting on drug abuse by school-going teenagers. While journalism offers an obvious example where perspective could matter, some argue that groups with different experiences have different approaches to subjects such as mathematics or physics and that each approach has some unique merits.

Women elsewhere

To put the discussion of women in STEM fields in context, here are some facts. In the United States, more than half of undergraduate degrees are awarded to females. I’ve got a collection of links plus speculation on the meaning of this. The finding is not limited to the United States — it holds in many developed countries, and even in some Middle Eastern Islamic countries. The latest Council of Graduate Schools report (with data from 2009) also indicate that females outnumber males in graduate study and doctoral degrees in the United States — see here, here, and here, for instance. While the female majority in doctoral degrees is slim (50.4%) and 2009 is the first year this has happened, the overall female majority in graduate programs (58.9%) is pretty robust.

Of the 11 fields of graduate study reported in the CGS report, women outnumber men is 7 of the 11 fields. What are the remaining four? Business (female 45.7% — less than half, but hardly a significant skew) and the STEM fields: engineering (22.3% female), math and computer science (29% female) and physical sciences (37.3% female). In other words, when we are looking at data from the United States, the only fields with a significant under-representation of females are the STEM fields.

Why worry?

With this context, we can ask: why the obsession with STEM fields? Why is there so much concern about male majorities in STEM fields, rather than female domination of the field of education and the health sciences, at both the undergraduate and graduate and professional levels? If we go back to why under-representation could pose a problem, reasons appear thin. If we are concerned about individual tragedies, is there any reason to believe that there are fewer males who are missing out on the pursuit of sociology or political science or education research than there are females who are missing out on mathematics and computer science? Under the assumption that both males and females are equally suited on average for every job, the statistics don’t bear such a conclusion.

Is it the case that there is greater collective loss to society if a talented person capable of mathematics or engineering stays away, and less of such a loss if the same happens with sociology or history? This seems plausibly true on average — which is one reason why the most talented people are generally more strongly encouraged, through both reputational and financial incentives, to enter STEM fields than the social sciences or humanities. But it is unclear if this is true at the margin, i.e., whether more people should switch into STEM fields given their current levels. We should keep in mind that most of the females who could switch into STEM fields would be drawn, not from the lowest rungs of academic talent, but from other high-end disciplines that currently enjoy female domination. An influx of female engineers would come, not from females who had been planning on becoming sales clerks, but from females who had been planning on becoming doctors or social scientists.

The third point, which is about skewed perspectives, could be of some concern in STEM fields, but considerably less than in most other fields, since, for the most part, STEM fields offer a number of checks and balances to ensure rigour and verifiability. These fields are more constrained by empirical evidence and mathematical reasoning. More importantly, though, I am not aware of any specific assertions about particular female advantages in perspective that are of importance to STEM fields. (This is not to say that there are no ability advantages — slightly better average female verbal ability may be a slight advantage in understanding some things in the subject, but that I have seen no hypothesis on a difference of perspective that would affect the nature of the new ideas that are developed).

A man’s game

An alternative explanation for why female under-representation in STEM fields seems more worrisome than male under-representation in other fields is provided by Robin Hanson in this blog post:

So why do both men and women prefer sports that emphasize male hunting type physical skills, over female gathering type skills? Looking for parallels, I notice that women are said to look good in male-style clothes (e.g., suits), far more than men are said to look good in female-style clothes (e.g., dresses). Women also earn more respect succeeding at male-dominated professions than men earn by succeeding at female-dominated professions.

The general pattern in all three cases is that we seem to respect women doing well at what mostly men do far more than we respect men doing well at what mostly women do. For better or worse, male abilities seem to more define which abilities count most for high status. Doesn’t seem fair to women, but there it is.

The causes of female under-representation

Setting aside the question of why it should matter, we may still be curious about understanding the causes for female under-representation in STEM fields. Jessica’s post suggests that there are two competing theories: the sociological theories and the biological theories. Biological theories claim that there are genetically influenced differences in the population distributions of a number of traits for males and females. These traits may include various aspects of cognitive abilities (such as verbal skills, visuo-spatial memory, visuo-spatial manipulation skills, short-term memory), personality/character traits (perseverance/conscientiousness, openness to experience, extraversion, neuroticism), desires/goals/ambitions in life, etc. Differences in the distribution of these is a factor in the different degrees to which males and females take up different kinds of jobs.

First, it is worth noting that just because a trait is genetically influenced, and because the distribution of that trait differs between males and females, does not imply that the difference in distribution between males and females can itself be attributed to genetic/biological factors. However, a nonzero heritability does open up the possibility of biologically influenced sex differences. For almost all the traits under consideration, heritabilities are significantly greater than zero. Heritability estimates for cognitive abilities range between 0.5 and 0.8, with the most reliable kinds of studies such as the Minnesota Study of Twins Reared Apart yielding estimates near 0.75 for cognitive ability and up to 0.5 for various personality traits. [Caveats: (i) These are broad-sense heritabilities and including emergenic phenomena. Narrow-sense heritabilities, which are used to predict correlations between the arithmetic mean of parents' value and the child's value, are closer to the 0.5-0.6 range for cognitive ability and probably lower for most other traits; (ii) these heritability estimates are obtained within a relatively narrow band of socio-economic status and wealth in the United States. When comparing people across highly diverse socio-economic conditions, such as Bill Gates versus a starving rural Indian, heritability estimates would be a lot lower.] This, of course, proves nothing about sex differences per se, but it means that the naive counter-argument which says that intelligence is purely environmentally determined simply doesn’t hold water.

One common argument made by those who posit a 100% social origin of the under-representation of women in STEM fields is that the degree of female representation differs radically between countries. This would be a knock-down argument against 100% biological determinism, but it certainly does not indicate that the biological equilibrium is a 50-50 split. There is no reason to believe that, in the realm of the abilities and character traits needed for STEM fields, males and females would have identical distributions. (Note: The abilities and character traits needed themselves keep changing as the nature of the fields changes, which further complicates the issue but does not affect the underlying point). For almost all physical and physiological traits (height, weight, BMI), and many acknowledged character traits, the distributions of males and females differ. Even in cases where they are nearly equal, there are hidden differences. As mentioned earlier, about 51-52% of naturally occurring births are male. In fact, about 60-65% of conceptions are male, but males have a much lower chance of making it in the womb during those crucial nine months. Again, social factors can have an impact — high status families produce more male offspring, and females under higher stress produce more female offspring. Despite this, the sex of one’s offspring is not considered socially determined.

As Jessica pointed out, males do outperform females at the upper end of the SAT score distribution in the United States, and the precise reasons are unknown. On the other hand, females outperform males in high school grades, and averaging out high school grades and SAT scores typically provides a tool with equal predictive validity for both sexes as far as college grades are concerned. Interestingly, the situation with SAT scores is reversed in some segments of the US population, such as Black Americans, as well as in some other countries, such as Iceland. As also pointed out, there are profile differences in the score distributions of male and female abilities in verbal and visuo-spatial skills on IQ tests — and many of these are fairly subtle differences that simply cannot be explained by stereotypes (because the stereotypes are too crude to predict these very specific patterns). For instance, in an EDGE debate between Steven Pinker and Elizabeth Spelke, Pinker summarises some of the differences revealed by research:

Indeed, in cases where there are differences, there are as many instances in which women do slightly better than men as ones in which men do slightly better than women. For example, men are better at throwing, but women are more dexterous. Men are better at mentally rotating shapes; women are better at visual memory. Men are better at mathematical problem-solving; women are better at mathematical calculation. And so on.

Personally, I am not convinced that better male performance at the upper end of the SAT score distribution is a significant reason for male domination in STEM fields, but it is certainly a possibility that has not been ruled out. Other possibilities (which may be part-biological and part-socially influenced), as documented by Steven Pinker in the same debate, include: males are more likely to chase status, males are generally more risk-taking (partly a consequence of testosterone — females with higher than usual levels of testosterone are greater risk takers too), males are more likely to enjoy abstract rule systems (even assuming an equal level of ability to handle them), males are somewhat better (at the tails) at three-dimensional visuo-spatial manipulation (as opposed to visual memory), and males are somewhat more variable.

If something should be done, what is it?

Assuming that you see female under-representation in STEM fields as a significant problem, what can or should be done about it?

First, a minimisation of individual tragedies, in the sense that females (or for that matter, males) who would enjoy a STEM career more than the best alternative career, get adequate exposure to, and accurate information about, the STEM career. The key point here is accurate information. More on this later.

Second, some aspects of the bureaucracy of STEM fields, both in academia and industry, may create unnecessary inconveniences for both sexes, but ones that particularly hinder the choice of such a career by women who want to have children. For instance, the “publish or perish” tenure track in academia may be one such example. This would definitely be something to think about.

Third, an emphasis on people identifying their individual strengths and desires and not being unduly influenced by or bothered about knowledge of group averages. Jessica, and many others, have referred to “stereotype threat” theories that claim that females, when reminded of stereotypes that females cannot do math, do worse on a test. While I am sceptical of such theories having sweeping power outside of their narrow experimental context, they do point to one uncontroversial area where there is scope for improvement: reducing females’ identification with and emotional concern about broad-based statement about gender averages (whether such statements are true or false). Personally, I find it ridiculous that somebody should spend their emotional energies worrying about how statements about a large heterogeneous involuntarily chosen group such as “women” (or for that matter “children” or “Africans”) might reflect on them or predict anything about them personally. [Things are a bit different for voluntarily chosen group identifications such as "Marxist" or "Muslim" or "Wikipedian"].

Fourth, it is unhelpful, and probably counter-productive, to distort science beyond recognition so as to make it appear sexy to women, often based on (possibly true and possibly false, but decidedly crude) stereotypes about what will attract women. While such works may be useful to help women with mathematical difficulties grasp school-level mathematics (for instance, this kind of book) it is not at all the kind of approach that should be used to attract people into professions that demand serious mathematics.

For instance, around the late 1980s and early 1990s, there was a craze for “reform calculus” at the freshman college level in the United States. The problem was that large numbers of American freshmen were failing their introductory college calculus courses, and even those who passed didn’t seem to have learned or appreciated much of it. Further, people who did poorly in introductory college calculus had many mathematically intensive options, particularly STEM fields, closed to them. “Women and minorities” were somewhat worse hit. Enter calculus reform. The idea was to reduce the algebraic skills needed in the calculus courses, increase calculator use, introduce more “group work”, reduce rote memorisation of formulas, and have more real-world projects and applications. The claim was that original formula-based rote memorisation and the competitive plug-n-chug environment was not suited to modern needs, and further, that it disadvantaged women who were more cooperative and preferred to work in groups.

I’m not sure whether these claims were true, but regardless of whether they are true, they miss the point — which is that an introductory course in a subject should provide an honest and accurate picture of how the subject is done, rather than pander to what people might find sexy and appealing. (For more on my take on this subject, see here. For articles on reform calculus, see this, this, and this (PDF).

This brings to my mind long email threads that I got into four years ago on the ever-so-volatile issue of reservations (and a pretty terrible blog post I wrote on the subject — I have learned a few things about writing style in the last four years). It is always interesting to me how much passion this excites on both sides of the debate. This may well be one of the topics that draws a lot of heat and little light.

I have made some passing observations about affirmative action and reservations/quota policies in the past here (see also a somewhat related post on privileges versus incentives) and would also recommend Thomas Sowell’s book and Thomas Weisskopf’s book on the subject for those who are interested. For this blog post, though, I will avoid the subject of actual policies (the prescriptive aspect) and instead critique the descriptive claims that Ruchira makes in her blog post.

Merit sherit

Ruchira writes:

Of course the nature of this oppression has changed. Earlier it was untouchability, being denied education and being looked upon as unclean as a consequence of the caste division of labour. Now it is merit. Oppression works in insidious ways. It prevents inter caste marriages, denies education to one’s forefathers and then declares that all are equal and one must compete on the basis of merit. What is this merit, this talent, this intelligence?

The sentiments expressed here are widely shared. A Google search for “so called merit” reservations returns about 12,000 results, such as this, this, and this. Since “merit” is the common buzzword in arguments against quota policies, this is unsurprising. But what does all this really mean? And are people just using the wrong English word?

I distinguish between three somewhat different notions. The first is the notion of desert, which is the noun form of deserve. Somehow, the term isn’t used very commonly in India. Just deserts refers to what we think people deserve based on our notions of justice and fairness. Since different people have different conceptions of what is fair and what is unfair, notions of desert vary. But here’s one example where there would be reasonable agreement among large classes of people — somebody who has been through a lot of humiliation and beating deserves special care and treatment to help him/her recover from that oppression. Of course, there are further questions of who owes this person that special care and treatment — particularly if it is not possible to coerce that person’s past oppressors into providing that special care and treatment. But I think it’s a fair statement to say that many would feel that, practical considerations notwithstanding, such desert is important. Another application of the concept of desert may be where we say that a person who shows exemplary qualities of character such as being hardworking, conscientious, kind, compassionate, and responsible, deserves to live a life of dignity with at least a basic minimum standard of living. Again, there may be the question of whether this intuitive desert translates to actual obligations on other people to provide this person with that basic standard of living — questions that I’ll set aside for now.

There is then the concept of productivity (primer on productivity at EconLib). In the context of work, productivity refers to the output per unit time (e.g., how many widgets do you produce per hour?) or per month worked — which would give a somewhat different measure since some people work more hours than others. Clearly, different people have different levels of productivity in different occupations, depending on their personality, their experience, and their abilities. And when comparing people across different occupations, productivity comparisons would invoke the value (monetary or other) placed on the various types of activity — and these comparisons can change depending on what kinds of goods other people are interested in using or buying.

What then is merit? It means different things to different people, but for now I’ll treat it as meaning potential productivity. In admissions to colleges, for instance, a merit criterion could be how productive you will be on average in your college years at the main task of college — learning. Another related criterion might be your productivity in tasks related to the discipline you’re learning about at the end of college. Yet another variant would measure the gain in productivity you would get through the college education relative to your current level of productivity.

The first question then is: does merit so defined make sense? The second question is: do college entrance tests measure this merit with reasonable accuracy? I think the answer to both questions is roughly yes. People who do poorly on a test such as the IIT-JEE are generally not prepared for studying engineering or other related disciplines at the IIT. Although I haven’t myself studied at the IIT, I did take the JEE and I know that ranks at the IIT-JEE correlate reasonably well with other rough measures of cognitive ability that I could use. In particular, many of the brightest people I knew (based on independent personal assessments) did well in the JEE, and many of these same people are now in cognitively more challenging and higher end jobs than those who scraped through at the lower end of the JEE. Exceptions abound, and I don’t expect the correlation to be strong within a restricted sample, but it’s there. (I don’t know too many quantitative studies done in India. But similar studies regarding the SAT in the United States have yielded similar results — see this for instance. There’s a huge literature that you can start getting a peek of with this Google Scholar search on the SAT’s predictive validity for college grades).

It is still possible to justify reservations on merit grounds. It is possible to argue that although entrance tests generally do a reasonable job of assessing merit, they under-predict the merit of people from certain backgrounds. This under-prediction could take the form of lower predictive validity (i.e., the test score of a person from the under-privileged group reveals less about his/her “merit” than for a person from a privileged group) or a difference in intercept (i.e., a uniform under-prediction of the performance of one group, which can be corrected for by a suitable monotonic transformation on the scores). Roughly, if either story is true, then a student from an under-privileged background who scores the same as a student from a privileged background in the entrance test should have higher merit (because achieving that same score required a more uphill struggle), and hence, should perform better (and definitely not worse) by the end of college. (To take an analogy, suppose a person was severely ill and unable to concentrate on the day that she wrote the JEE, and she got the same score as another person who was feeling fit. Then, you might predict that the person who was severely ill has more inner talent and hence should do better through college, unless the illness will debilitate her throughout the rest of her college days.)

While I’m not sure if either an intercept difference or a difference in predictive validity have been observed in India, the story in the United States in white-black comparisons suggests that blacks with equivalent SAT scores do somewhat poorer than whites — the opposite of what the story would be if the entrance test were the main point/source of discrimination. There are again a whole host of studies in this regard, yielding somewhat different conclusions, but the general consensus seems to be that if the SAT is biased against blacks, it is less biased than other methods of evaluation, including college grades. See here for a start on exploring the studies. Again, things could be different in India, because test design in India is probably not such a well developed field.

So, the “merit” argument — as I use the term — could in principle be used to justify reservations, but this justification is empirically testable, and it seems to me (though I don’t have area specific evidence and am open to being convinced to the contrary) that the empirical tests would largely refute this merit argument.

Other arguments continue to exist — namely the “just deserts” argument. This argument would say that, hey, even if the person from the under-privileged background does not do as well as the person from the privileged background whom she displaces, i.e., the productivity acquired at the end of college is lower, it is still the right thing to admit this person, because the person has worked harder and suffered more discrimination and oppression. This matches with some intuitions of fairness, where people who work hard and are denied the opportunity to acquire certain skills through no fault of their own, deserve an extra leg up. These arguments can again be subject to empirical tests — are the people who benefit from reservations generally more oppressed and discriminated against than the ones who are left out? And, does reservation help them more than hurt them? Here, the answers seem to me to be (relatively) more in the affirmative, and hence, they provide a stronger rationale for reservations. However, as promised, I will not go into the detailed pros and cons.

My point here is simply to delineate the merit argument clearly and point out that this argument is perfectly valid and important, regardless of the way the evidence on it turns out, and regardless of how much other concerns (such as “just deserts”) matter. Moreover, the two arguments are distinct (though related) and hence one argument cannot be refuted with the other.

Head held high

Repeating this sentence from Ruchira:

Today, I can hold my head up and say that I am intelligent and my merit can take me anywhere.

Although she can certainly say that, is it true? Can her “merit” take her anywhere? Can she, if she so chose, become an outstanding person in any field of endeavour, or even in her chosen field of endeavour?

Sowing and reaping

Ruchira further writes:

I can speak well, write well and I can handle basic math. This is not because I have worked harder on these things than anyone else but because my parents have post graduate degrees. My mother’s father was also a post graduate and had in fact, topped both, BSc as well as MSc. My father’s father was a BA and he couldn’t have gone that far if his uncle hadn’t been in the academic profession. Both my grandfathers worked and struggled hard against phenomenal odds but though they weren’t Brahmin, they did not belong to backward castes. Of course, there are first generation literates who do get into higher education and learn to speak and write well but they are more the exception than the norm. Most people who get to do their MAs, leave alone their MPhils and PhDs, are people like me –children of educated parents. Don’t believe me? Check out the econometric models set by Amartya Sen and Jean Dreze in India: Development and Participation. The number of years of formal education of the mother have a positive, significant effect on that of the child. This is not just an empirical result but an intuitive argument. Having educated parents means that education means something in your family. No one will expect you to drop out of school and college to work. Also, educated parents can help you with homework, sympathise and work out ameliorative measures when you fail an exam and support and advise you on how to get ahead- something economists tend to ignore as the emotional side of the story.

While nothing here is wrong per se, it is a severely limited perspective. Psychologists, sociologists, and economists have for the past several decades been trying to figure out the extent of similarity between parental and child outcomes, and the causes behind this similarity. Just a few days ago, I came across yet another paper (PDF) (linked to from this blog post by Matthew Yglesias) that considered four possible sources of the correlation between parent and child outcomes. The paper, based on analysis of data in the United States, says:

We find positive correlations between specific characteristics of parents and children. But we also find that few parental characteristics predict characteristics of children other than the same one that is measured in parents. Four mechanisms might explain such correlations — socioeconomic resources, parenting practices, genetic inheritance, and role modeling. These four mechanisms make varying predictions about which parental traits will be correlated with which child traits; whether the traits of fathers or mothers should be more important to sons or daughters; and to what extent parental socioeconomic characteristics [SES], parenting behaviors, and children’s identification with their parents account for the observed correlations. Our evidence provides little support for the SES and parenting explanations, but more substantial support that role modeling may account for some of the intergenerational correlations, and genetic factors may account for others.

Since data differs from country to country and place to place, the authors’ conclusions may well be disputed in contexts different from those they used for their data. Each of the four explanations (SES, parenting, genetics, and role model/identification) are “obvious” and “intuitive” and empirical data can be gathered to support it.

If you just concentrate on one explanation, it is usually not hard to “explain” everything using that one explanation. If I were adopting a strictly genetic perspective, I could argue, for instance, that the fact that Ruchira’s personal educational outcomes matched those of her relatives were due to genetic similarities: in fact, even a correlation of 0.7 with parental outcomes could be consistent with a 100% genetic explanation because children share only about 50% of their segregating genes with their parents (modulo some caveats; I will not go into the technical details here). This of course is not to say that a genetic explanation is correct, but rather, that the fact of a correlation between parents and children does not say much either way about the causes of that correlation (duh!) — and some causal mechanisms that are important in some places and times may be relatively less important in other places and times. The tricky thing (which these authors and many others attempt to do) is devise approaches (such as controlling for various parameters) that allow them to judge the relative strength of each of the proposed causal mechanisms. Doing this in the social sciences is a tricky job — see this article by Jim Manzi — but that doesn’t make blanket declarations any more trustworthy.

Ruchira then writes:

Thus, this ‘merit argument’ is eyewash. A system has denied education and what Nivedita Menon calls ‘cultural capital’ to a family over generations.

This is an inaccurate caricature since the “merit argument” does not deny the existence or importance of cultural capital — it simply argues from the perspective of merit, not desert. By the merit argument, a person from an under-privileged background who has done poorly on an entrance test but can “overcome” the cultural under-privilege once that person has joined the college or organisation, should be given admission. The question then (from the perspective of the merit argument) is whether the cultural capital handicap is overcome, or narrowed significantly, once the person is admitted. The broader question may be whether “merit” should really be the criterion rather than “just desert” — but whatever Ruchira’s personal opinions on the relative weighting of merit and desert, she’s wrong to say that the merit argument is hogwash.

She further writes:

Going by the prime assumption within the merit argument itself –that there are no social differences between people –why are there so many Dalit construction workers, shoemakers, crematorium workers, cobblers and washermen and such few Dalit teachers, professors, experts or corporate sector employees? Is it that the Dalits do not have merit? Is that not a casteist conclusion?

Here, Ruchira uses a remarkably bizarre debating tactic. First, she sets up an argument that could potentially undermine her position. Then, she says that if you agree with that argument, then you are a “casteist” — and since nobody wants to be called casteist, you must oppose that argument, and hence support her position.

Ruchira writes:

Talking to people on reservations is generally frustrating. For one thing, they give you arguments like –“But you know, you wouldn’t want to be in a college or filling a post through a quota. You’d like to do it yourself. A quota is an insult. It implies you can’t do it on your own. Quotas are casteist.” This argument usually frustrates me so much that I am usually left agape, striving hard to pick up my scattered wits.

I’m surprised Ruchira had a hard time knocking this (weak) argument down. Some people hate the idea of filling a quota position. Others don’t mind it that much. Some people don’t mind filling a quota position, but don’t like it when others take note of the fact. This is all natural. Of course, if everybody greatly hated the idea of filling quota positions, people would not eagerly be re-classifying themselves into castes and tribes that were eligible for quotas. So, on balance, at least for some individuals, their desire for college positions, jobs, and the other perks that reservations entitle them to overcomes whatever self-image they may be sacrificing.

The more correct version of this is that quotas generate over-resentment (because more people think they’ve missed out on an admission because of quotas than those who actually have), as many people have argued, and the stigma of being a quota beneficiary also extends to individuals from the oppressed castes who did not benefit from quotas, or who would have done equally well without quotas.

Living examples

She then writes:

A cogent argument that often proves to be hard to refute is that reservations should not be given at the level of higher education –that a Dalit graduate is akin to any other graduate. But no one quite understands how very important it is to have more Dalits in teaching posts. It can break the Brahmin hegemony in education! It can even break the all powerful notion that Dalits are not meritorious. More Dalits in professors’ posts will go to show that levelling the playing field can put a Dalit where a Brahmin considers it her natural right to be. More Dalits in influential posts can help lobby against ‘merit’-based arguments and ensure reservations elsewhere aren’t scuttled. They can also make policy recommendations to ensure social justice more effectively -redistribution of land, reservations in primary schools and can politically engage with and beat the merit argument. It is also psychological. For all the Dalits who are afraid to chase their dreams, they can be told, “Looks at XYZ. He’s a Dalit like us.” As a woman, I feel that way about our few women professors. Whether they are feminists or not, seeing them there gives me hope.

These are all plausible arguments, but the problem is that opposing arguments are equally, and sometimes more, plausible. Ruchira says that more Dalits in teaching posts can break the all powerful notion that Dalits are not meritorious. It could, if the Dalits in teaching posts are just as good teachers as non-Dalits in corresponding teaching posts. (In fact, in so far as existing prejudice biases people against Dalits, the Dalits in teaching posts would need to be better than corresponding non-Dalits to break even in public perceptions). But selecting Dalits to fill quotas if they don’t have the requisite qualifications and skills could undermine this very purpose. People who formerly only had a hearsay prejudice that Dalits are incompetent teachers now get living confirmation of the fact. Of course, it’s possible that in the type of jobs that Ruchira is referring to, selection for teaching posts is anyway so far from meritorious that reservations will not cause any significant decline. I’m just pointing out that the line of argument I’m making is at least as plausible as Ruchira’s. If public perceptions are key, it may make sense to be even more selective when hiring Dalits so that students see only the best Dalits and hence form even more positive impressions of them.

The rest of her arguments involve the idea that larger Dalit presence would lead to greater endorsement of specific policy positions that Ruchira supports (and this is a good thing in her view). I’m not exactly sure why Ruchira thinks that people from Dalit backgrounds would share her particular policy positions, but since I don’t think that teaching posts are or should be a place for political activism, I’ll refrain from commenting on this. Perhaps she thinks that the self-interest of Dalits will lead them to support positions that she (as a non-Dalit) supports for selfless ideological reasons? While identity politics is certainly strong and influential, this by no means implies that most or even all Dalits who reach high positions would or should be supportive of reservations policies. Again, taking an analogy with Blacks in the United States, people ranging from Thomas Sowell to John McWhorter to Walter Williams have written out in varying degrees against affirmative action for blacks.

The anti-climax

Lastly, reservations are a right. No one has the right to look down on someone who has got a post or a seat via reservation. She is only exercising her democratic right.

I’m not sure in what sense Ruchira is using the term “right.” Let’s assume that reservations are a right (at least for the SC/ST groups for which it was originally intended), on the grounds that the Indian constitution makes an explicit provision for it. But a person having a right to reservations does not deprive aonther person of the right to look down on that person for having got a seat for reservation. My having a “right” to express views that Ruchira considers obnoxious, or to beg on the street, or to praise the virtues of patriarchy or free love or fundamentalist Islam, does not deprive Ruchira of the “right” to disapprove of these activities, or look down upon me for performing them.

• Better pay and material conditions and better general environment

• More rewarding and productive environment for research, teaching, and exchange of ideas

• Less ideological and bureaucratic frustration

Many of these same considerations probably apply to high-end programming and information technology jobs as well as research and development jobs in other industries, though I will stick to research in my discussion and example.

In this post, I will not get into the question of the pros and cons of this brain drain, in so far as it exists.

Pay and material conditions

Average PPP-adjusted per capita incomes in the United States are about 16 times those in India. The pays of researchers are similarly substantially more than in India, though the proportional difference may not be as much (see a discussion here). Not only the pay, but also the availability of a number of resources is an inducement to move to the United States or United Kingdom (or Canada, Australia, or continental Europe) if one can manage it. The surrounding environment is cleaner (generally) and more advanced medical facilities are available.

I don’t have a lot to say about pay differentials, except that it should not be especially important for research. Pay differentials are high in research, but the construction worker salary in the United States has an even more skewed proportion. Also, pay differentials are more a consequence of India’s general poverty (and possibly of the government running of universities) and the main way out of them seems to be general economic growth (and perhaps a liberalisation that allows more private universities including foreign universities to set up in India).

Better research environment

Workers in the same job can complement each other as well as substitute for each other. In the complementary story, each new worker increases the productivity and value of existing workers. In the substitution story, each new worker reduces the marginal value of existing workers by making the existing worker’s skills and abilities less unique and less in demand. In other words, in some areas of work, workers impose positive externalities on each other (the complementary story) while in other areas of work, workers impose negative externalities on each other. Worker complementarity, as well as other kinds of complementarities, are cited as reasons, why a lot of industries in the same area set shop close to each other, rather than being distributed across larger geographic regions — Silicon Valley (software, Internet enterprises), Hollywood (US movie industry), Bollywood (Indian Hindi movie industry in Mumbai), are just some examples of geographical concentration of an industry. (For more technical background, see this).

With research (and more generally, with a lot of knowledge work), I suspect that the complementarity story strongly outweighs the substitution story. The presence of other researchers in an area greatly increases the productivity of a researcher. This is because an individual researcher in a small sub-discipline of a discipline can be saved from the burden of rediscovering related material in neighbouring sub-disciplines and concentrate on adding something genuinely new. More people in a research area can also lead to greater and more effective critiquing and checking of existing research ideas, reducing the half-life of wrong ideas. “Lone genius” myths notwithstanding, research is fundamentally an enterprise relying heavily on collaboration, hence phrases such as standing on the shoulders of giants and admonitions to avoid reinventing the wheel.

In so far as there are very few Indian researchers in an area, and there is very little productive engagement between these researchers, a new up-and-coming researcher has all the more incentive to leave India. Thus, the complementary nature of research leads to a self-reinforcing and self-perpetuating cycle, at least in this simple model.

The coming of the Internet has allowed researchers to collaborate across huge geographic distances. For instance, the mathematics Q&A site Math Overflow, intended for people to ask and answer research level mathematics questions, allows for communication across geographical lines (thus permitting a theoretical physics student in TIFR, India to enjoy a fruitful interaction with a mathematics undergraduate at MIT). The greater availability of preprints and online journals, some free and some with subscription fees, again allows people to benefit from the positive externalities while staying in India. Examples include ArXiV (for mathematics/physics/computer science) and PubMedCentral (for medical/biology stuff). The proliferation of online course materials such as MIT Open CourseWare — see also the OCW Consortium — allows people to learn stuff from some of the best teachers across the world.

However, it is worth remembering that the Internet helps everybody, and does not necessarily close the differential between being in India and being in a good research environment in the United States or United Kingdom. A contrary effect might also be operational — the Internet might increase people’s desire and ability to get into better research environments by serving as an advertisement for these better environments.

Ideological and bureaucratic frustration

This is least likely to occur in a subject such as mathematics, where truth is determined more objectively, despite what some caricatures might suggest. A distinction can be drawn between mathematics research (which rarely gets enmeshed in broader political disputes) and mathematics education, where interest groups ranging from Marxist social justice advocates to Vedic mathematics champions try to sneak their ideas into school curricula. But actual research in mathematics, since it has very little direct effect on hot button issues, is unlikely to succumb to ideological pressures. In addition, coming up with fake proofs of mathematical results for ideological purposes is generally harder than proving stuff for real.

However, ideological pressures could and do operate in the social sciences, particularly economics, history, and political science. These subjects claim to have something to say about public policy and the manner that social and economic interactions are organised — hence, the scope for ideological and vested interests to pay attention to these subjects is greater.

The story of trade economist and ardent free trader Jagdish Bhagwati (homepage, Wikipedia entry) is illustrative. Gurcharan Das describes what happened in his book India Unbound (pp. 162-163):

I sometimes wonder how we could have gone so wrong [on economic policy]when we had such brilliant economists. The Delhi School of Economics had acquired a formidable and well-deserved international reputation as a center of excellence. It had Jagdish Bhagwati doing seminal work on trade; A. L. Nagar was engaged in pathbreaking work in econometrics; [...] However, Professor Jagdish Bhagwati was the only Indian economist of note who tried to rouse Indians to the danger of Nehruvian socialism. His book (with [wife] Padma Desai) Indian Planning and Industrialisation was the first comprehensive study of the variety of ailments caused by our [India's] left turns of the 1950s and 1960s. As the intellectual climate worsened, rather than bang against the wall of intellectual intolerance erected by Indira Gandhi, Bhagwati left to teach first at MIT and then at Columbia University in New York.

In other words, Bhagwati’s views on free trade and market solutions to poverty did not sit well with the popular notions in the 1960s, particularly at a time when Indira Gandhi was in the ascendancy and shortly before her popular Garibi Hatao slogan was coined. Since he left India, Bhagwati has tirelessly advocated for free trade. He has written books such as In Defense of Globalization and given interviews such as this one.

It is unclear whether Bhagwati left India because his positions made life difficult for him personally or because of the frustration of no policy maker listening to those positions. In any event, there is an impression among many that Marxist perspectives enjoy a privileged position in the social sciences and people with free market liberal perspectives, or other perspectives that challenge Marxism, face an unfair and unpleasant battle. Scholars who do not conform to the Marxist mould are expected to fall in the “Hindutva” camp — the glorification of India’s Hindu heritage as a basis for what India should be as a nation today. Classical liberal perspectives (Wikipedia primer on classical liberalism) or their variants generally find a hard time surviving, and people holding such perspectives encounter overt and covert censorship — or perceive such. See, for instance, How Free Are We? by Jakob de Roover and The Absent Liberal by Ramachandra Guha in Outlook India. Here’s a review of Arun Shourie’s polemic against what he considers the rewriting of India’s history of Marxist historians and their poor intellectual credentials.

Unfortunately, brain drain is more harmful in the social sciences than in the sciences, so to the extent that the above ideological story plays out in reality, it hurts the future of social science knowledge. The reason is that social science knowledge, unlike science knowledge, is not so easily or readily universal — while the list of possible mechanisms underlying social phenomena may be universal, their relative proportions differ from place to place and time to time and culture to culture. Social science research relevant to India requires actual field work in India, whereas chemistry or mathematics research in France can be directly imported to India. This is not to say that the study of other places is irrelevant, but that it can go only so far. (See this on the difficulty of doing social science research).

Conclusion

In conclusion, all the three factors are probably operational, though ideological factors play a bigger role in the social sciences (and possibly humanities). In later posts, I will look at the pros and cons of research brain drain from different perspectives and further actions that may be suggested on account of this analysis.