All these results have been obtained by typing the first word or words in Google search and noting down what Google suggested as the first suggestion.

Tried some cities. Pune university. Bombay high court. Delhi metro. Chennai rain. Trivandrum airport. So, apparently, people in Pune are studious, in Bombay they are litigative, and in Trivandrum, they’re just trying to get out.

Politicians were more interesting:

• “Sonia Gandhi biography”. Really? That’s what people find most interesting about her? I was vaguely dissatisfied, so I looked at the remain suggestions, and was further appalled: “Sonia Gandhi hot”, “Sonia Gandhi photos”. Yikes! The internet sure has some weirdos. And the next was “Sonia Gandhi address.” I hope CBI is taking note.
• Rahul Gandhi girlfrield. Yep! Even after so many years, people are primarily concerned about that. No wonder he fared so badly in Bihar.
• Atal Bihari Vajpayee poems. Poor guy. After all he’d done, he’s going to be remembered as the PM who used to quote poems in parliament.
• Suresh Kalmadi Jokes. Not surprised!
• Sharad Pawar international school. Also Sharad Pawar college of pharmacy. Followed by Sharad Pawar cancer.
• Raj Thackeray ringtones. Yup, really, that’s the second suggestion. Scary!
• Shashi Tharoor weds Sunanda Puskhar.

I decided to move on to media:

• Arnab Goswami is a moron. Ok I cheated a little. The first result is “Arnab Goswami twitter.” But I swear the 4th result is “Arnab Goswami is a moron.”
• Rajdeep Sardesai salary. (Actually the first three results were “blog”, “twitter”, and “email”, but it’s interesting that so many people are interested in his salary.)
• Udayan Mukherjee wife. Poor guy. How prominent does a guy have to be before people start taking an interest in him and not his wife?
• Barkha Dutt Husband. After all the #barkhagate and #radiatapes affairs, this is the top search on Google?!

I was sure Bollywood would generate some interesting ones. So here they are:

• Deepika Padukone in bikini.
• Gul Panag hot videos. All her intelligent conversations on twitter haven’t helped.
• Kajol baby boy. Ouch!
• For most actors (Aamir Khan, Salman Khan, Shahrukh Khan, Ranbir Kapoor the first three results were “twitter”, or “biography”, or “blog”.)
• Rakhi Sawant is a man. Obviously this wasn’t the first suggestion, but really, there are people searching for this phrase?!
• Raju Hirani next. Wow. That, If a majority of the people on the internet are wondering what is your next project, that is a major achievement

You have any interesting ones to add to this list?

(Random FYI: “Navin Kabra” gets “old songs” as a suggested completion.)

World Covers Ears, Chants Loudly As EPA Releases Ozone-Depletion Statistics
450,000 Unsold Earth Day Issues Of Time Trucked To Landfill
Small, Dedicated Group Of Concerned Citizens Fails To Change World
Report: 98 Percent Of U.S. Commuters Favor Public Transportation For Others

And this one is not really about the environment, but…
Middle East Conflict Intensifies As Blah Blah Blah, Etc. Etc.

The issue in which the Onion reported on the 9/11 terrorist attacks in the US remains one of my favorites. The Onion did a much better job of capturing things than the mainstream media.

And, Our Dumb Century is a masterpiece that is a must read for anyone intererested in a cynical, satirical, and very funny look at the events of the 20th Century.

Image by M@rg via Flickr

The Telegraph has this bittersweet article about a Alzheimer’s nursing home which had to come up with a creative way to prevent the patients from trying to take a bus home:

the centre was forced to rely on police to retrieve patients who wanted to return to their homes and families but had forgotten that in many cases neither existed any longer.

When asked to suggest a solution to this problem, I’m sure most people will think of compound walls and locked gates. They have come up with a much sweeter solution. Just outside the home, they created a fake bus stop. It looks like a regular bus stop with all the appropriate signs. With the small difference that no bus actually comes there.

“It sounds funny,” said Old Lions Chairman Franz-Josef Goebel, “but it helps. Our members are 84 years-old on average. Their short-term memory hardly works at all, but the long-term memory is still active. They know the green and yellow bus sign and remember that waiting there means they will go home.” The result is that errant patients now wait for their trip home at the bus stop, before quickly forgetting why they were there in the first place.

“We will approach them and say that the bus is coming later today and invite them in to the home for a coffee,” said Mr Neureither. “Five minutes later they have completely forgotten they wanted to leave.” The idea has proved so successful that it has now been adopted by several other homes across Germany.

See full article (found here)

• Human = eat + sleep + work + enjoy
• Donkey = eat + sleep
• Therefore, Human = Donkey + work + enjoy
• And, Human – enjoy = Donkey + work

So, are you enjoying your work?

If they lock him up, then his belief was true, and he wasn’t really deluded, was he? So they shouldn’t be able to lock up him. But then his belief would turn out to be false. And they can lock him up after all. Somewhere, Alfred Tarski is getting uncomfortable in his grave.

See full article (via boingboing.

Tidying some pencils (dull, March 16)
Some pencils were scattered around on my desk. I picked them up one by one. I placed the pencils in the drawer which I use to store pencils.

Opening a cupboard door (dull, October 17)
There was a cupboard in the corner of the room. I reached out my hand and gripped the door handle. I pulled the door towards me, thereby opening the cupboard.

Found: here. See full article.

The purpose of this invention is to provide an improved method of proposing marriage to an individual. The method of proposing to an individual generally comprising the steps of meeting the individual; exchanging names with the individual; dating the individual (not necessary); drafting a government document having a proposal to marry the individual incorporated therein; and showing the government document to the individual. The government document may be a patent application. The patent application may claim the method by which the proposor will make a marriage proposal to the individual. The proposor could then use the method claimed in the patent application to propose to the individual. The patent application could be the actual marriage proposal.

You really should see the patent application. Has interesting stuff like:

[0015] One of the principal objects of the invention is to provide a method by which a proposor’s clever proposal coaxes an individual into marrying the proposor.

[0016] Yet another object of the invention is to provide an entirely unique method for a proposor to propose marriage to an individual.

[0017] Still another object of the invention is to provide a method for proposing marriage to an individual where the proposor can righteously tout the uniqueness of the proposal.

[0018] Yet another object of the invention is to provide a method of proposing marriage to an individual where the method of proposing is at least partially related to the proposor’s future profession.

[0019] Still another object of the invention is to provide a method of proposing to an individual where the method is recorded in a government document for all the world to see.

[0020] These and other objects will be obvious to those skilled in the art.

This was the original:

If you look closely (by clicking on the photo to get the full size version), you’ll notice that I’ve applied lipstick, foundation, blush, blue contact lenses, mascara, and of course, changed the hairstyle. For each one of those, there are many different options to choose from. Obviously, the “doctoring” of the photo is very visible, but I did not spend too much time trying to get it just right. I have other things to do.

But, try Taaz.com yourself. You’ll have lots of fun.

This data comes from a paper by Esther Duflo, a professor at MIT. The actual teacher program incentive was run by Seva Mandir an NGO. Duflo and her colleagues analyzed the results and reached some interesting conclusions.

What exactly is the question that we are trying answer?

We ask three main questions: If teachers are given incentives to attend school, will they actually attend school more? If they attend school more, will they teach more? Finally, if teacher absenteeism is reduced, will children learn more?

Consider the first question. It is not obvious that increased incentives to teachers will result increased attendance:

[...] the incentives could fail to improve attendance for a variety of reasons. First, teachers may be unable to take advantage of the incentives if they must participate in village meetings, training sessions, election or census duty. These pressures may be particularly high on para-teachers, who are often among the few literate individuals in the village. Second, the incentive schemes may crowd out the teacher’s intrinsic motivation to attend school (Benabou and Tirole, 2006). Finally, some teachers, who previously believed that they were required to work every day, may decide to stop working once they have reached their target income for the month (Fehr and Gotte, 2002).

The last two sentences of that paragraph point towards some interesting research showing how well-meaning schemes can actually be counter productive. (I am lazy so I haven’t included the full references here. If you are interested, go to the original paper and find the references at the end of the paper.)

Anyway, even if we manage to get teachers into the classrooms, that might not mean anything:

Even if incentives increase teacher attendance, it is unclear whether child learning levels will actually increase. Teachers may multitask (Holmstrom and Milgrom, 1991), reducing their efforts along other dimensions. Such schemes may also demoralize teachers, resulting in less effort (Fehr and Schmidt, 2004), or may harm teachers’ intrinsic motivation to teach (Kreps, 1997). On the other hand, incentives can improve learning levels if the main cost of working is the opportunity cost of attending school and, once in school, the marginal cost of teaching is low. In this case, an incentive system that directly rewards presence would stand a good chance of increasing child learning. Thus, whether or not the incentives can improve school quality is ultimately an empirical question.

Again, things are not as simple as one would initially have though. Will all that background out of the way, we get on to the real research:

We study a teacher incentive program run by the NGO Seva Mandir. Seva Mandir runs single teacher NFEs in the rural villages of Rajasthan, India. As in many rural areas, teacher absenteeism is high, despite the threat of dismissal for repeated absence. In our baseline study (August 2003), the absence rate was 44 percent. Faced with such high absenteeism, Seva Mandir implemented an innovative monitoring and incentive program in September 2003. In 57 randomly selected program schools, Seva Mandir gave teachers a camera, along with instructions to have one of the students take a picture of the teacher and the other students at the start and close of each school day. The cameras had tamper-proof date and time functions, allowing for the collection of precise data on teacher attendance that could be used to calculate teachers’ salaries.

Yup, the attendance records were used to determine the salaries of the teachers. So there was a financial (dis)incentive scheme at work:

Each teacher was then paid according to a non-linear function of the number of valid school days for which they were actually present, where a “valid” day was defined as one for which the opening and closing photographs were separated by at least five hours and both photographs showed at least eight children. Specifically, they received Rs 500 if they attended fewer than 10 days in a given month, and Rs 50 for any additional day (up to a maximum of 25 or 26 days depending on the month). In the 56 comparison schools, teachers were paid a fixed rate for the month, and were told (as usual) that they could be dismissed for repeated, unexcused absences.

Well, that is the experiment. Now on to the results:

The program resulted in an immediate and long lasting improvement in teacher attendance rates in treatment schools, as measured through monthly unannounced visits in both treatment and comparison schools. Over the 30 months in which attendance was tracked, teachers at program schools had an absence rate of 21 percent, compared to 44 percent baseline and the 42 percent in the comparison schools. Absence rates stayed low after the end of the proper evaluation phase (the first fourteen months of the program), suggesting that teachers did not change their behavior simply for the evaluation.

Absenteeism halved! And stayed that way.

Now on to another interesting question. How much of the improvement was because of cameras (the monitoring) and how much of the effect was because of the financial incentive scheme? What if the financial incentives were different, or not present at all? That is where the MIT economists broke out their complex math formulae to figure this out:

To answer these questions, we exploit the non-linear nature of the incentive scheme to estimate a dynamic labor supply model using the daily attendance data in the treatment schools. The identification exploits the fact that the incentive for a teacher to attend school on a single day changes as a function of the number of days they attend school in the month, and the number of days left in the month. This is because they have to attend at least 10 days in a month to begin to receive the incentive (by working in the beginning of the month, the teacher builds up the option to work for Rs 50 per day at the end of the month). Indeed, regression discontinuity design estimates show that teachers work significantly more at the beginning of the month than at the end of the previous month, when they had not accumulated at least 10 days of work in that month.

We use this fact to estimate the teachers’ marginal utility of money. We allow serial correlation in the opportunity cost of attending school and heterogeneity in teachers’ outside option, and we use the method of simulated moments to estimate the parameters. Allowing for serial correlation and heterogeneity considerably complicates the estimation procedure, but we show that these features are very important in this application.

And according to their calculations:

We find that teachers are very responsive to the financial incentives: our preferred estimates suggest that the elasticity of labor supply with respect to the level of the financial bonus is 0.306. Furthermore, decreasing the number of days that workers must work until they are eligible for the incentive by a single day increases the expected number of days worked by about 1.29 percent. An unusual feature of this application is the ability to carry out convincing out-of-sample tests based on the randomized evaluation (as in Todd and Wolpin (2007)). When allowing for serial correlation and heterogeneity, we find that our model accurately predicts the difference in attendance in the treatment and the control group, as well as the number of days worked under a new incentive system initiated by Seva Mandir after the experiment.

I don’t think I really understand the meaning of the numbers in that paragraph, but basically, the financial incentives were very important. i.e. You can’t implement this scheme with just cameras alone. Simple threats “that they could be dismissed for repeated, unexcused absences” are not good enough. You need to be able to control their salaries.

But at the end of all this, the fruits of labor are sweet:

Student attendance when the school was open was similar in both groups, so student in treatment group received more days of instruction. A year into the program, test scores in the treatment schools were 0.17 standard deviations higher than in the comparison schools. Two and a half years into the program, children from the treatment schools were also 10 percentage points (or 62 percent) more likely to transfer to formal primary schools, which requires passing a competency test.

See full paper. More interesting than watching Twenty20 these days…