Author Archives: editor

French for Bread

14 Feb
Posted on Author editor

Pain is an unpleasant sensation. It is generally assumed that pain has two purposes: 1. to stop us from engaging in a behavior that is causing the pain, say, continuing to dip the hand in boiling water, and 2. to train the body to not engage in such behavior in the future. Given the purpose, pain seems to be poorly implemented.

Think of a system that is coded to send a message to the controller to alert it to the damage and to ask it to reconsider engaging in an activity that is causing the damage (or independently take some hard-coded action). One envisions that the message is sent in a manner that “makes” the controller pay attention, if such attention is warranted, and efficiently conveys a summary of what is going wrong and to what degree, and what particular action that the user is taking that is causing that to happen. One also imagines an “acknowledge” button that the controller presses to assume the responsibility for further action. Then using this information, the controller, depending on the circumstance, takes some action and updates the memory and circuiting, if warranted, to create an appropriate aversion for certain activities.

Such signaling is implemented very differently in our body. Firstly it is implemented as “pain.” Next, pain is not proportional to the extent of the injury. This sometimes creates “irrational” aversion. More bizarrely, some harmful things are pleasant, while some good things are painful. Thirdly, there is no direct way for the brain to acknowledge the signal, assume responsibility for action, and shut off the pain. Next, and worryingly, depending on the extent to which our brain is distracted (say, watching television), pain’s intensity varies (This last point has been exploited to build “treatments” for pain). Lastly, our brains can’t temporarily order the signals shut.

It is ‘possible’ that people may warm up to the science of warming

25 Jan
Posted on Author editor

In science we believe

Belief in science is likely partly based on scientists’ ability to predict.
As M.S. notes, climate scientists accurately predicted that temperatures were going to rise in the future in late 1980s. Hence, for people who are aware of that (like himself), belief in climate science is greater.

Similarly, unpredictability in weather (as opposed to climate), e.g., snowstorms, which are typically widely covered in media, etc., may lower people’s belief in climate science.

Possibility of showers in the afternoon
Over conversations with lay and not to say lay people, I have observed that sometimes people conflate probability and possibility. In particular, they typically over-weight the probability of a possible event and then use that inflated weight to form the judgment. When I ask them to assign a probability to the event they identify as a possibility, they almost always assign very low probabilities, and their opinion comes to better reflect this realization.

Think of it like this: a possibility for people, once raised (by them or others) is very real. Only consciously thinking about the probability of that possibility allows them to get out of funky thinking.

Something else to note. Politicians use ‘possibility’ as a persuasion tool, e.g., ‘there is a possibility of a terror attack’ etc. This is something I have dealt with before but I leave the task of where to people motivated to pursue the topic.

Institutional Distrust

25 Jan
Posted on Author editor

It is sometimes assumed that high levels of institutional distrust in America are peculiar to it. So much so that a variety of theories have been offered to ‘explain’ this peculiarity including, but not limited to, elite polarization, income inequality, polarized media, etc. Empirical support for the ‘American exceptionalism’ however is somewhat less clear – across some major Western democracies (outside of the perennially ‘sunny’ Danes; one may talk about Scandinavian Exceptionalism perhaps), percent who ‘tend not to trust’ [pay attention to the y-axis] national government, national parliament, and political parties is alarmingly high. These high levels raise concerns about the legitimacy of the system.

help(R): matrix indexing

19 Jan
Posted on Author editor

In R, some want to treat matrix like a data frame. It is a bad idea. And no – one cannot use the dollar sign to pick out a column of a matrix. The underlying data structure for a matrix in R is a vector, whereas data.frame object in R is of type list (see using typeof(object)) and it carries within it vectors, reachable using the dollar sign followed by name of the vector (a way to only access elements within lists). 


> a <- data.frame(ab=1:2, bb=1:2)
> b <- matrix(ncol=2, nrow=2)
> colnames(b) <- c("ab", "bb")

> a$ab
[1] 1 2

> b$bb
Error in b$bb : $ operator is invalid for atomic vectors

# here's why
> dput(a)
structure(list(ab = 1:2, bb = 1:2), .Names = c("ab", "bb"), row.names = c(NA, 
-2L), class = "data.frame")

> dput(b)
structure(c(NA, NA, NA, NA), .Dim = c(2L, 2L), .Dimnames = list(NULL, c("ab", "bb")))

# list 
> c <- list(ab=c(1,2,3), bb=c(1,2,3))

> dput(c)
structure(list(ab = c(1, 2, 3), bb = c(1, 2, 3)), .Names = c("ab", 
"bb"))

# so you can do 
c$ab
[1] 1 2 3

# for matrices, data frames, etc. one can however use 
a[,"ab"]
b[,"ab"]

# or
b[,2]

# for people wishing to do so, here's why treating matrices as data frames is a bad idea -
> a <- matrix(ncol=2, nrow=2)
> a[,1] <- 1:2
> a[,2] <- c("abc", "bbc")
> str(a[,1])
chr [1:2] "1" "2"
> a[,1] <- as.numeric(a[,1])
> str(a[,1])
 chr [1:2] "1" "2"

# Notes on coercion
a <- matrix(ncol=2, nrow=3)
> a$b <- 1:2
Warning message:
In a$b <- 1:2 : Coercing LHS to a list
> typeof(a)
[1] "list"

Affectively Polarized? – Partisan Polarization Among the Masses

9 Jan
Posted on Author editor

The shooting of Representative Giffords has reignited the debate about the extent to which the public is polarized. Some political scientists have answered the question by evaluating data on people’s policy positions over the years. And the data are clear on the question—no, not really.

However, a lack of ‘real’ differences hasn’t always meant a lack of perceived differences. Nor has it meant lack of negative affect. Affective dislike, conditional on similarity, is unsurprising and typical, as the history of racial and ethnic hatred will attest to.

So, we tested to see whether partisans dislike each other. We asked a representative sample of Americans whether certain traits described Republicans and Democrats. From evaluations on 18 traits (selfish, generous, close-minded, honest, etc.), we created a latent measure of partisan affect (ICC here). Here’s a plot of latent partisan affect by partisan self-identification.

GSS and ANES: Alike Yet Different

1 Jan
Posted on Author editor

The General Social Survey (GSS), run out of National Opinion Research Center at University of Chicago, and American National Election Studies (ANES), which until recently ran out of University of Michigan’s Institute for Social Research, are two preeminent surveys tracking over-time trends in social and political attitudes, beliefs and behavior of the US adult population.

Outside of their shared Midwestern roots, GSS and ANES also share sampling design—both use a stratified random sample, with the selection of PSUs affected by necessities of in-person interviewing, and during the 1980s and 1990s, sampling frame. However, in spite of this relative close coordination in sampling, common mode of interview, responses to few questions asked identically in the two surveys diverge systematically.

In 1996, 2000, 2004, and 2008, GSS and ANES included exact same questions on racial trait ratings. Limiting the sample to just White respondents, mean difference in trait ratings of Whites and Blacks was always greater in ANES – ratings of hardwork and intelligence, almost always statistically significantly so.

Separately, difference in proportion of self-identified Republicans estimated by ANES and GSS is declining over time.

This unexplained directional variance poses a considerable threat to inference. The problem takes additional gravity given that the surveys are the bedrock of important empirical research in social science.

Another Coding Issue in the ANES Cumulative File

29 Dec
Posted on Author editor

Technology has made it easy to analyze data. However, we have paid inadequate attention to developing automation in data analysis software that pays more attention to potential problems with the data itself. For example, I was recently exploring how interviewer rated political knowledge varied by respondent’s level of education within each year over time using ANES cumulative file. It was only when I plotted the confidence bounds (not earlier) that I found that in 2004 7-category education variable (VCF0140a) had fewer than 7 levels—a highly unlikely scenario. To verify, I checked the number of unique levels of education in 2004 and indeed there were only 5.


unique(nes$vcf0140a[nes$vcf0004=="2004"])
[1] 6 5 2 3 1

The variable from which the 7-category variable is ostensibly constructed (V043254) in 2004 has 8 levels. Since the plot looks reasonable for 2004, the problem was likely due to the case of (unwarranted) collapsing of adjacent categories than switching order more irresponsibly. Tallying raw counts revealed that categories 6 and 7, 0 and 1, and 4 and 5 had been collapsed.

On to the point about developing software that automatically flags potential problems. It would be nice if the software flagged differing number of levels of the same variable by year. However, this suggestion is piecemeal and more careful thinking ought to be brought to bear to design issues.

Delay Shooting the Help

25 Dec
Posted on Author editor

Procrastination

Procrastination, delaying without reason doing something that one has to do, makes little sense. If the voluntary delay also causes anxiety, which in most cases it does, it may be particularly pointless. Yet a lot of people procrastinate at least some of the times. Why?

One can make a case for postponing unpleasant things that are avoidable—in fact why bother doing those things at all—but not things that are unavoidable, or things that a person intends to do.

The desirability of a task influences the decision to procrastinate or not. Rationally, it should not matter but the fact that it does provides a possible toehold into why we procrastinate:

  1. Sometimes problems don’t appear to have a good solution right away and one hopes that a solution would appear over time even though one may not have good reasons to think that such good fortune would strike.
  2. For example, one hopes that the ‘unavoidable’ task would become avoidable? Or we wait till the point ‘it is clear’ that the problem cannot be avoided?

Procrastination is understood exclusively as a problem about ordering and assumes that the net amount of time expended on task remains constant, irrespective of order. Perhaps that is a problematic assumption. Starting things later may just mean that we spend less time on the task than we otherwise would. However, one can easily reframe the issue as one about when to spend the reduced time, than one where we must delay achieving the aim of spending less time on the task.

Shooting the help

At times when people are worried, and when well-intentioned people try to help them, they simply become annoyed, or even mildly angry at them. Why is it that we refuse help, or more puzzlingly become angry or annoyed with people who are trying in good faith to be of help.

When people offer advice, they often use munitions from similar events and incidents they have encountered. This can be a bit galling as it undermines the ‘uniqueness’ of our problems. This ‘feeling’ is further compounded by the fact that many a time people are also over-eager and often too quick to offer solutions without a more patient listening to the individuating data. And then arguably many people while eager to help don’t do much thinking (either through incapacity, lack of motivation, or on the assumption that no thought is needed) about the problem itself, and offer comments that are not particularly insightful. And then many a time all people want is a sympathetic ear or a pat on the back. In other words, sometimes public self-pity is all we want. This is typically so when either the solutions are obvious or non-existent.

Outside of this, it is also the case that high achievers are less likely to seek help, and bristle when offered help, for seeking help forces them to face their own vulnerability.

The Unscientific Republican

16 Dec
Posted on Author editor

Only 6% of scientists in a random sample of American Association for the Advancement of Science (AAAS) identify themselves as Republicans, according to a Pew 2009 Study. Assuming AAAS is not tremendously unrepresentative of scientists as a whole, what explains under-representation of Republicans in science?

While Republicans are under-represented among people with advanced degrees more generally, I here limit myself to offering some possible hypotheses for explaining under-representation of Republicans in science, refraining from any attempt to analyze their validity.

Science education or practice causes liberalism:

  1. Brainwashing: Science is taught by liberals, who brainwash their students into believing their preferred ideology.
  2. Diversity: Upper echelon of science today is racially and culturally? diverse. Interacting with a diverse set of people causes people to become liberal.
  3. Cynical: Science is funded by the government, and people in the field, like elsewhere, love the hand that feeds them.
  4. People practicing science come to see the value of policy guided by science. On some major issues – like climate change and evolution, Republican Party has taken ‘anti-science’ stances.
  5. Science causes people to question religious dogma, become atheists or even anti-theists, etc. causing them to choose a party less aligned with religion.

Confounders:

  1. Intelligence: Intelligence causes liberalism (Kanazawa). Intelligence is one of the things that affect the choice of the discipline and the number of years a person chooses to study. Intelligence is the confounding variable that predicts both.

Liberals select into science, and Republicans select out of it.

  1. Conservatives “have a need for cognitive closure” which is not amenable to open questions and scientific inquiry (Jost).
  2. Concept of “conservatism” is one of maintaining the status quo.
  3. Social dominance orientation: Republicans prefer going into status quo enhancing occupations like economics, business, police; Democrats less so (Sidanius).
  4. Religious people don’t go into science, which they see as anti-religion.

Structural Inequality

27 Nov
Posted on Author editor

Nick Clegg, leader of the Liberal Democrats, recently spoke about social mobility. He said,

My particular focus is on inter-generational social mobility – the extent to which a person’s income or social class is influenced by the income or social class of their parents. Social mobility is a measure of the degree to which the patterns of advantage and disadvantage in one generation are passed on to the next. How far, if you like, the sins of the father are visited on the son.

There is, of course, plenty of argument within the social science community about precise measures, international comparisons, and preferred metrics. But I think intergenerational social mobility speaks to most people’s definition of fairness.

Fairness means everyone having the chance to do well, irrespective of their beginnings. Fairness means that no one is held back by the circumstances of their birth. Fairness demands that what counts is not the school you went to or the jobs your parents did, but your ability and your ambition.

In other words, fairness means social mobility.

Social mobility is only half-imagined — as movement from lower rungs to upper rungs, not vice versa. Society, currently constructed, offers a relatively fixed (likely declining) number of upper shelf jobs, and it thus reasons that for every n transitioning to the upper echelon, a similar ought to transition to the lower rungs. Now a politician wouldn’t sell his idea — that he wants a certain number of rich people to make way for the poor and in turn, take their place — but then we all expect such diplomacy from politicians.

Fairness as a level playing field or a fair lottery is widely accepted as an ideal. Wide acceptance is no insurance against fundamental problems. To help illustrate the problems, here’s an example.

Imagine a fair marriage in which at the beginning husband and wife flip a coin — heads the wife does all chores for the entire tenure of the marriage, tails the wife never has to do chores. Of course, marriages based on this fair coin toss don’t seem fair to us — we would ideally want all couples to share the unpleasant chores equally or by some such equitable arrangement arrived at by mutual agreement.

Carrying over the analogy to society — we would want everyone to take part in unpleasant chores and everyone to take part in more pleasant activities equally. Of course, such a lack of specialization makes for a very inefficient system. So perhaps one can prorate the wage to the unpleasantness of work, with people stuck doing unpleasant work being provided wages at higher rates, greater leisure time, etc. exactly opposite to the system we have in place now.

In summary, the current society is unfair not only because not everyone has a similar chance of success but also because there are only a few good opportunities — mandating that there be a large set of losers and a small set of winners.

p.s. I discuss the relationship between education and economic equality here.

On Love

25 Nov
Posted on Author editor

Mothers spend a great deal of their time and energy on their kids, especially newborns. They spend far more time thinking, and working to ensure welfare of another human being (their kid), than most humans will spend on any other human being, say their romantic partner or sibling. Add to this that till the child reaches adulthood, often till much later, the relationship is overwhelmingly one-way – with children thinking little about their parent’s welfare. Still, most mothers find the experience, and the work that goes with it, greatly rewarding.

Joy despite this sizable disparity has been explained by cynics, but only poorly. While parents proclaim that children bring joy to their lives, it doesn’t cause parents to invest time and money for often similar joy can be had at much lower rates of work. And financial investments, investments of time, the toll on the mother’s body, and inconveniences like lack of sleep, problems traveling, etc. likely outweigh potential financial benefits, which are likely either way away from most parents’ minds.

In this puzzle there are perhaps a couple of lessons about love – one is that relationships that are overwhelmingly one-way, say in resources like time and money, can still be basis for mutual joy; second perhaps is that spending more time on people we love can make those relationships more fulfilling, and us happier.

Measuring Love

Love and to love are vague conceptually and in minds of people who use these terms. The concepts have evolved such that attempts to define or deliberate these concepts rile sensibilities – offend the notion that love is really the domain of emotion, not thought. Such thoughtlessness has meant that nearly everyone can get away with claiming love, even when the overall impact on the quality of life they have on their loved one is a deeply negative one.

One way to think about how much we love each other is to take into account how much time we spend actively thinking about the welfare of those we love. Such exercise when done without deliberation elicits an emotionally biased line up of instances where we were thoughtful. To move beyond such selective counting, for what matters are averages, with a penalty for egregious negatives, one ought to think carefully and honestly, and perhaps base assessments on the testimony of the loved one. Intentionality ought to play a part in calculations but not as a blanket defense – I truly want your welfare but there is nothing in the record that backs up the claim – but to down-weight some instances of sub-optimal decision making under pressure, and limited resources.

Idealog: ‘Automatically’ Summarize Articles and Judicial Opinions

16 Nov
Posted on Author editor

While many of the articles and judicial opinions are never cited, a system can be established to build summaries of those that are. The idea is straightforward- harvest sentences just before the citation in academic articles citing the article of interest. One can learn from the summaries of the articles citing the articles, or from the summaries of other co-cited articles. The framework can be applied to judicial opinions, and to tally positions by politicians/institutions over a topic, etc.

This form of “summarization” utilizes intellectual work done by others, and allows for tracking major rebuttals and weaknesses identified by others. On the downside, summarization is necessarily limited to points made by others. Either way, the method is likely to produce superior results than methods that harvest ‘topic sentences’ etc.

Update: In 2015, I released the first draft of autosum.

The Perils of Balancing Scales

15 Nov
Posted on Author editor

Randomization of scale order (balancing) across respondents is common practice. It is done to ‘cancel’ errors generated by ‘satisficers’ who presumably pick the first option on a scale without regard to content. The practice is assumed to have no impact on the propensity of satisficers to pick the first option, or on other respondents, both somewhat unlikely assumptions.

A far more reasonable hypothesis is that reversing scale order does have an impact on respondents, on both non-satisficers and satisficers. Empirically, people take longer to fill out reverse ordered scales, and it is conceivable that they pay more attention to filling out the responses — reducing satisficing and perhaps boosting the quality of responses, either way not simply ‘canceling’ errors among a subset, as hypothesized.

Within satisficers, without randomization, correlated bias may produce artificial correlations across variables where none existed. For example, satisficers (say uneducated) love candy (love candy to hate candy scale). Such a calamity ought to be avoided. However, in a minority of cases where satisficers true preferences are those expressed in the first choice, randomization will artificially produce null results. Randomization may be more sub-optimal still if there indeed are effects on rest of the respondents.

Within survey experiments, where balancing randomization is “orthogonal” (typically just separate) to the main randomization, it has to be further assumed that manipulation has equal impact on “satisficers” in either reverse or regularly ordered scale, again a somewhat tenuous assumption.

The entire exercise of randomization is devoted not to find out the true preferences of the satisficers, a more honorable purpose, but to eliminate them from the sample. There are better ways to catch ‘satisficers’ than randomizing across the entire sample. One possibility is to randomize within a smaller set of likely satisficers. On knowledge questions, ability estimated over multiple questions can be used to inform propensity the first option (if correct and if chosen) was not a guess. Response latency can be used as well to inform judgments. For attitude questions, follow up questions measuring the strength of attitude etc. can be used to weight responses on attitude questions.

If we are interested in getting true attitudes from ‘satisficers,’ we may want to motivate respondents either by interspersed exhortations that their responses matter, or by providing financial incentives.

Lastly, it is important to note that combining two kinds of systematic error doesn’t make it a ‘random’ error. And no variance in data can be a conservative attribute of data (with hardworking social scientists around).

Merging Data with Dirty Keys

8 Oct
Posted on Author editor
  1. Remove stuff irrelevant to matching, e.g., font style, spaces, articles (the, a, an), etc.
  2. Standardize different versions of the same word. One common problem is abbreviations, e.g., Township and Twp., Saint and St., etc. Common misspellings can also be handled similarly. But do all this with care. For example, St. may stand for state and saint.
  3. Remove duplicates if many-to-one (or one-to-many or many-to-many) matches are not allowed. Note that our ability to detect duplicates will be limited by how dirty the data are within each dataset.
  4. Often, a few common problems are at the heart of most issues. For instance, say that you are matching on country, state, and city. Noise in how the countries are spelled across datasets may explain missed matches. Fix those.

  5. To match the rest, the options are:

    • Do a fuzzy outer join with a large enough distance to get most matches. (There is generally a left table and if there is one, do a fuzzy left join.) Create a column that stores the quality of the match.

      • Manually review cases where the matches.
      • If manually reviewing all the rows isn’t an option, code a small random sample and figure out the optimal distance that gives reasonable FP/FN.
      • Many a time keeping only the matches with the smallest distance is sufficient. For instance, say the joined data is as follows:
        id, key1, key2, dist
        1, ram, rama, 1
        2, ram, bama, 1
        3, ram, drama, 2

        Only keep rows 1 and 2 and drop 3. You can validate the assumption with a random sample.

  6. If you are building software for manual review:

    1. Arrange matches intelligently – for example, string + number, followed by string, etc. When there are more than 5 matches, arranging alphabetically works well as a default.
    2. If you have lots of data, consider Mechanical Turk or Captcha.

Learning about Delhi: A Second Look at The Delhi Walla

3 Sep
Posted on Author editor

In Delhi, one is surrounded by history, but oblivious of it.

Worn stone and brick arches with peeling MCD plaster, whitewashed either white or the color of basic clay pots, soot-blackened and carrying crudely carved avocations of love, appear suddenly across noisy traffic-filled roads but don’t jolt one into a reverie about the world that once was. Overgrown crumbling fragments of monuments in roundabouts seem disconnected and undistinguished.

Part of the obliviousness to culture stems from the fact that Delhi is a city of immigrants, as William Dalrymple states somewhere. Immigrants not only lack that generational connection that people have to a city’s monuments, but they are often also committed to establishing their own culture than embracing what was there before. And then many of those who came in after partition, living in tents for years, found themselves too busy with necessities of life and subsequently establishing and reinforcing their own (financial) roots. A countless number of poor laborers, middle-class professionals, alike, who have immigrated to Delhi since then, share the same drive, and the same blindness to history around them.

The city is a backdrop of the daily hustle to carve out a living. Monuments are either to be ignored or to be forcefully enjoyed, loud praise barely enough at hiding disappointment. These forlorn palaces, forts, and landmarks, eviscerated remorselessly of their jewels and glory by the British and Indians alike, uncared for by poorly funded and corrupt ASI, need a somberness of mood and mind, a willingness to step back and fill in the melancholy silences evoked by neglect, and commonness. These monuments need time and care.

The Delhi Walla attempts to capture these moments, more than the monuments. He often partially succeeds. His achievement, however, is greater than the sum of its parts. In a slow progression of hasty glimpses, he seems to be instilling in us the ability to look, and to look past the deficiencies, into the past, and into ourselves.

From worn monuments to worn out people, The Delhi Walla redeems. In a society so split by class, “Mission Delhi,” perhaps gives many a new capacity to look at each other as human beings.

Given the achievement, it is all the more galling that many a time the writing on The Delhi Walla is forgettable – words are ill-chosen, thoughts left marooned in a grammarless sea, and glimpses of wit and style never nourished to full health. This isn’t calumny but fact. And it is as much our loss as his that the writer doesn’t have the time. Good writing above all needs time; time allows for reflection about what we want to convey and how.

While The Delhi Walla wouldn’t have come about without its author, some of the interest in Delhi’s history and culture is a result of sociological forces gathering pace in a post-liberalization India. The Delhi Walla has found an audience in the expatriates, some of the “youth” who finally have the time and money and nouveau middle-class energies and sensibilities bred partly on Western media, to seek higher culture than saas-bahu dramas, and more generally a people beginning to create and form identities, less based on family than on cultural consumption, which are so essential to modern capitalism. Culture is being commoditized finally, by Indians. This in itself is a welcome relief from genuine doubts about whether we had a culture to speak of.

Complimentary Spam

5 Aug
Posted on Author editor

Those who don’t read this blog are its biggest fans. No surprise there. Here below is a selection of comments left by spam-bots. Spelling and grammatical ‘errors’, likely deliberately put in by spammers, have been left untouched. The word ‘errors’ has bunny ears because you can’t really call a deliberate strategy an error. These ‘errors’ also bring up the question that in the era of computers, perhaps errors will be the one indication that the work is produced by a human (directly or indirectly).

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Idealog: Monetizing Websites using Mechanical Turk

14 Jul
Posted on Author editor

Mechanical Turk, a system for crowd-sourcing complex Human Intelligence Tasks (HITs) through parceling a large task and putting the parcels on a marketplace, has seen a recent resurgence, partly on the back of the success of easier to work with clones like crowdflower.com.

Given that on the Internet people find it easier to part with labor than with money, one way to monetize websites may be to request users to help pay for the site by doing a task on Mechanical Turk, made easily available as part of the website. Websites can ask users to fill out the kind of tasks they would like to work on as part of their profiles.

For example, a news website may also provide its users a choice between watching ads and doing a small number of tasks for every X number of articles that they read. Similarly, a news website may ask its users to proofread a sentence or two of its own articles, thereby reducing costs of its production.

Education of a senator

30 Jun
Posted on Author editor

While top colleges have been known to buck their admission criteria for the wrong reasons, they, on average, admit smarter students and the academic training they provide is above average. So it may be instructive to know what percentage of Senators, the most elite of the political brass, have graced the hallowed corridors of top academic institutions, and if the proportion attending top colleges varies by party.

While 5 of the 42 Republican senators have attended top colleges (in the top 20), 22 of the 57 Democratic senators have done the same (data are posted here). The skew in numbers may be because New England is home to both top schools and many Democratic senators. But the privilege accorded by the accident of geography is no less consequential than one afforded by some more equitable regime.

Discounting elite education, one may want to look at the extent of education. There one finds that whereas 73% of Democratic senators have an advanced degree, only 64% of Republican senators have the same. While again it is likely that going to top schools increases admission to good advanced degree programs, thereby making getting advanced degrees more attractive perhaps, one can conjecture that whatever the reason, some people, due to mere privilege perhaps, have better skills than others.

Personal Milestone Campaigners

24 May
Posted on Author editor

We have all read about beautiful women undressing to raise awareness about cruelty to animals (less handsome women just don’t care about the animals), men and women strutting on runways wearing fashionable clothes to raise awareness about global warming, intrepid souls sailing across vast oceans to raise awareness about plastic waste in the sea, people climbing mountains to raise awareness about cancer and global warming, and swarms of marathoners running many times a year to raise awareness about breast cancer and of late, St. Jude’s Children Research Hospital. To this pantheon of heroes, add the ‘human polar bear’, who recently swam in a glacial lake in the Himalayas to raise awareness about global warming [BBC].

I often wonder how many things we would have remained in the dark about had it not been for these heroic men and women, and the camera crews in tow. And then I mull over the contributions they have made to the world. It is work by such people and cameras following them that has led 76% of the people to believe that breast cancer is the single most deadly disease afflicting women. (It is the fourth largest.)

Not only do these men and women raise awareness, but they also raise money. The ‘acts’ they perform are now sponsored by corporations; the boosters have their boosters. And all of these incredible personal achievements, which only incidentally flood trophy cases and Facebook walls, have been accumulated selflessly in service of some worthy cause (applause). It also reasons that the money and hours that these selfless volunteers spend on training, recovering from training, registering for races, and organizing events, pales in comparison to the money they raise.