Analysing the BBMP Elections

The Bruhat Bengaluru Mahanagara Palike (BBMP) went to polls on Saturday, and votes were counted today. The BJP has retained its majority in the house, winning 100 out of 198 available seats. While this is a downer compared to the 113 seats they had won in the previous elections in 2010, the fact that they have won despite being in opposition in the state is a significant achievement.

Based on data put out by Citizen Matters, here is some rudimentary analysis. The first is a choropleth of where each party has performed. Note that this is likely to be misleading since constituencies with large areas are over-represented, but this can give you a good picture.

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Red: BJP Green: Congress Cyan: JD(S) Blue: Independents Black: Others

Notice that there are a few “bands” where the BJP has performed really well. There is the south-western part of the city that it has literally swept, and it has done well in the south-eastern and northern suburbs, too. And the party hasn’t done too well in the north-west, the traditional “cantonment” area.

We can get a better picture of this by looking at the choropleths by assembly constituency. These shapes might be familiar to regular readers of the blog since I’d done one post on gerrymandering.

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This tells you where each party has done well. As was evident from the first figure, the BJP has done rather well in Basavanagudi, Jayanagar and Padmanabhanagar in “traditional South Bangalore” and blanked out in Pulakeshi Nagar in the cantonment. In fact, if you try to correlate these results with that of the last Assembly elections, the correlation is rather strong. Most constituencies have gone the way of the assembly segments they are part of.

Then there is the issue of reservation – there were a lot of murmurs that the Congress party which is in power in the state changed the reservations to suit itself. Yet, there are a few interesting factoids that indicate that the new set of reservations were rather logical.

The next two graphs show the distribution of SC and ST populations respectively as a function of the reservations of the constituencies (population data from http://openbangalore.org).

Rplot03 Rplot02Notice that the constituencies reserved for SCs and STs are among those that have the highest SC and ST populations respectively. The trick in gerrymandering was in terms of distributions between general and OBC constituencies, and among women.

I could put the performance of different parties by reservation categories, and on whether the reservation of a constituency has changed has any effect on results, but (un)fortunately, there aren’t any trends, and consequently there is little information content. Hence I won’t bother putting them in.

Nevertheless, these have been extremely interesting elections. All the postponement, all the drama and court case, and finally the underdog (based on previous trends) winning. Yet, given the structure of the corporation, there is little hope that much good will come of the city in the coming years. And there is nothing in the election results that can alter this.

 

Uppi2 Review

Uppi2 is easily the best movie I’ve watched in recent times. Across languages. And I’m not joking. It’s a bloody good movie, and breaks all kind of stereotypes. Having watched the movie, I have half a mind to log on to IMDB and add one more perfect 10 rating.

I have an unusual way of rating movies – essentially, any movie that manages to hold my attention through its length is a great movie by my definition. This is a consequence of my extreme attention deficit, but in general I find it hard to sit through movies. The story needs to be tight right from the beginning, else I’m extremely likely to switch off. The number of half-watched movies on my Tata Sky recording box is not funny, for example.

From this perspective, the best compliment that I can pay to Uppi2 is that never once did I start wondering when the movie would end. There have been several otherwise great movies which have dragged a bit as it has gone along and I’ve found myself checking my watch to see how much longer it might go on for. Not with Uppi2. The movie keeps you fully engaged right till the end and doesn’t drag one bit. And I wouldn’t write more here since that would be giving too much away.

There is only one jarring thing about the movie, and that is the songs. All songs are little better than “extra fittings” and none adds to the story. Most songs are fairly atrocious, but there is one truly outstanding song. And you might be surprised that I’m actually recommending a hip-hop song. Enjoy off:

The story, as you might expect from a Upendra directed movie, is rather complex and has lots of twists and turns. There is a fair bit of self-reference, and you might do well to keep in mind the concept of recursion while you watch it. There are references to several other movies, including Upendra’s earlier movies.

If you think you’re a “buddhivanta” (intellectual) this movie will surely tingle your intellectual nerves. There are a lot of stupid jokes also, like the play on the lead character’s name “Neenu” (“you”), and the pun in the title of the movie itself. And stuff like one guy scolding another guy (whose name is Bala) as “LK Bala”. So even if you aren’t a buddhivanta, there is much for you in the movie.

As this excellent review by Jogi in Udayavani describes it, go expecting to see an Upendra movie, or to eat “Uppit”, and you’ll not come back disappointed. If you go expecting to eat “Obbatt”, on the other hand, you’ll surely be disappointed!

I thought Upendra had done a wonderful job with his last offering Super, but I must confess he has outdone himself with Uppi2. Please go and watch! And contribute to the movie’s rating on IMDB.

On Sub-nationalism

Pramit Bhattacharya has a nice piece in MintOnSunday about the positives of sub-nationalism, which fosters provision of public and common goods. He cites academic research to contrast Kerala and Uttar Pradesh, which were similar in the mid-19th century, but now have significantly differing levels of public goods.

The research Bhattacharya cites argues that the linguistic sub-nationalism that was formed in Kerala in the mid 19th century was responsible for the state’s high levels of public goods and development. The absence of such sub-nationalism has resulted in weak institutions and weak development in UP, he says.

He ends the piece saying that sub-nationalism is not always a good thing and can lead to secessionist tendencies. He cites the example of Assam, where sub-nationalism has actually hampered development rather than fostering it.

This discussion reminds me about last year’s “unofficial” referendum in Catalunya about whether to secede from Spain. The vote was unofficial since the Spanish Parliament didn’t authorise it, but there were strong signs of Catalan nationalism when I visited Barcelona last October. The Yellow, Red and Blue flag of Catalan nationalism hung from several windows. There was a clock in one of the main squares counting down to the referendum (which finally didn’t matter).

And while there were several emotional reasons for the demand for secessions, including repression at the hands of the “Castilians”, one of the main reasons was economic – the share of national spending on Catalunya was far less than the proportion of Catalunya’s contribution to the Spanish National Budget. The feeling of “why should we subsidise the rest of the country?” was rampant.

This little story illustrates both the positive and negative aspects of sub-nationalism. The negative is easy to see from the above – strong sub-nationalism leads to a strong “us and them” sentiment towards the rest of the country, and the region begins to resent the rest of the country, especially if the latter gets a larger share of the national pie. And this can lead to secessionist tendencies as is evident in Catalunya.

The positive thing about sub-nationalism, on the other hand, is that it subsumes groupism at smaller levels. A strong sub-nationalist feeling means that people think of themselves as members of that sub-nationalist group, and solidarity to any “lower level” groups weakens.

The problem with high solidarity among small groups is that it may lead to provision of private goods at the expense of public goods. When a place is strongly divided by caste, for example, each caste group wants to maximise the interest of the particular caste, and thus invests in a way that the caste gets a bigger share of the seemingly fixed pie.

When the solidarity is at the level of a state or region, on the other hand, the best way to develop the region or state is to provide for public goods or welfare schemes that span the entire state or region, and this leads to an expansion of the pie and the overall development of the region. In other words, when the “us” is a largish geographical area, it is more likely that investments happen in terms of public goods for the area rather than private goods.

Coming back to the example of Kerala, the strong Malayali subnationalism of the mid 19th century had the effect of pushing down casteism. Consequently, the groupism happened at a level (“Malayalis”) that was larger and more diverse than the caste-level groupism that happened elsewhere (like in UP) where there was no strong sub-nationlist movement. The lack of sub-nationalism in a place like UP has meant that casteist divisions in the region have remained strong, and solidarity at that level doesn’t lead to public goods or development.

Think of the nation as a hierarchy, of sub-nations and sub-sub-nations and so forth. And each person’s loyalty is divided in different extents up and down the person’s “chain”. And among these different layers, it is a zero sum game. Thus, strong loyalties at a particular level are resented both by levels higher and lower, and justifiably so. But the higher the level at which the loyalty remains, the better it is for the provision of public goods and development. Chew on it.

Tinder and Arranged Scissors

For those of you who have been following my blog for a while know, I was in the arranged marriage market for a brief period in 2009, before Priyanka magically materialised (from the comments section of this blog) and bailed me out. I may not have covered this in any of the Arranged Scissors posts that I wrote back then (ok I alluded to this but not really), but I had what I can now call a “Tinder moment” during the course of my time in the market.

So on this fine day in Bangalore, I was taken to this Marriage Exchange called Aseema. The name of the exchange is quite apt, since based on two data points (my own and one acquaintance’s), if you go there your search for a spouse is literally endless.

My uncle, who took me there and who was acting as my broker-dealer for that brief period, told me that they literally had binders full of women (note that this was three years before Romney), and that I could search leisurely if I accompanied him there on Saturday morning.

My uncle didn’t lie. This place did have several binders full of women (and men – I too ended up in one such binder after I signed up) and four binders that said “Smartha (my subcaste) Girls” were pulled out and handed over to me. My uncle probably expected me to spend a few hours ruminating through the binders and coming up with a shortlist.

It was nothing like it. Each profile in the binder followed a standard format. There was this 4 by 6 full-length photo. You knew where to look for educational qualifications. And professional summary. It was like LinkedIn meets Facebook profile picture. And that was it.

I remember having some criteria, which I don’t remember now. But once I had gone through the first few pages, it became mechanical. I knew exactly where to look in a particular profile page. And quickly come to a judgment if I should express interest.

Thinking back, I might have just been swiping (mostly left – I came up with a grand shortlist of one after the exercise) on Tinder. The amount of time I spent on each profile wasn’t much more than what the average user spends on Tinder. Except that rather than looking only at the photo, I was also looking at a few profile parameters (though of course whether I would want to sleep with her was one of the axes on which I evaluated the profiles). But it was just the same – leafing through a large number of profiles in a short amount of time and either swiping left or right instantly. Talking to a few other friends (some of it at the now legendary Benjarong conference) about this, my experience seemed representative (note that I’m still in anecdata territory).

Maybe there is a lesson in this for all those people who are designing apps for arranged marriage (including the venerable Shaadi.com and BharatMatrimony.com). That even though the stated intent is a long-term relationship, the initial process through which people shortlist is no different from what people follow on Tinder. Maybe there surely is a market for a Tinder-like arranged marriage application!

Elasticity and Discounted Pricing

The common trend among startups nowadays is to give away their product for a low price (or no price), and often below what it costs them to make it. The reasoning is that this helps them build traction, and marketshare, quickly. And that once the market has taken to the product, and the product has become a significant part of the customer’s life, prices can be raised and money can be made.

The problem with this approach is the beast known as elasticity. Elasticity means that when you increase your price, quantity demanded falls. Some products are highly elastic – a small increase in price can result in a large drop in quantity. Others are less so. Yet, it is extremely rare to find a product whose elasticity is zero, that is, whose quantity demanded does not vary with price. And even if such products exist, it is extremely unlikely that a product produced by a startup will fall in that category.

A good example of elasticity hitting is the shutting down of this American company called HomeJoy. As this piece in Forbes explains, the chief reason for HomeJoy shutting down is that it couldn’t hold on to its customers when it started charging market rates:

Not only did that kind of discounting make Homejoy lose significant money, it also brought in the wrong kind of customer. Many never booked again because they weren’t willing or able to pay the full price, which ranged from $25 to $35 an hour. Homejoy changed its pricing last year to make recurring cleanings cheaper and encourage repeat business. In response, some customers simply booked at the cheaper price and cancelled future appointments.

Based on the above explanation, it seems like subsidising customers to gain traction is a bad idea, and that a business should not be willing to make losses in the initial days in order to gain market. Yet, that would be like throwing out the baby with the bathwater, for subsidising at the “right level” can help ramp up significantly without elasticity hitting later. The question is what the right level is.

A feature of many businesses, and especially marketplace kind of businesses that startups nowadays are getting into, is economies of scale. This means that as the number of units “sold” increases, the cost per unit falls drastically. In other words, such businesses work well when they have built up sufficient scale, but collapse at lower levels. For such businesses, the thinking goes, it is impossible to bootstrap, and the solution is to subsidise customers until the requisite scale can be built up, at which point in time you can start making money.

The question is regarding the “sweet spot” of subsidy that should be given to the customer in order to build up the business. If you subsidise the customer too much in the initial days, there is the risk of elasticity hitting you at steady state, and things rapidly unravelling. If you subsidise too little, you may never build the scale.

The answer is rather straightforward, and possibly intuitive – start out by charging the price to the customer at which the business will be profitable and sustainable in the steady state. This will imply losses in the initial days, since your unit costs will be significantly higher (due to lack of scale). Yet, as you ramp up and hit steady state, you don’t have the problem of raising the price which might result in elasticity hitting your business.

What if, on the other hand, the subsidy you are giving out is not enough, and you are not willing to build traction? That is answered with the “Queen of Hearts” paradigm. The paradigm says that if the only way you can make your contract is if West holds the Queen of Hearts (talking about contract bridge here), you simply assume that West holds the card and play on. If he held the card, you would win. If not, you would have never won anyway!

Similarly, the only way your business might be long-run-sustainable is if you can generate sufficient traction at your long-run-sustainable price. If you need to drop the price below this in order to gain initial traction, it means that you will have the risk of losing customers when you eventually raise the price to the long-run-sustainable-price, which means that your business is perhaps not long-run-sustainable, and it is best for you to cut your losses and move on.

 

Now think of all the heavily-discounted startups out there and tabulate who are the ones who are charging what you think is a long-run-sustainable price, and who runs the risk of getting hit by elasticity.

On Uppi2’s top rating

So it appears that my former neighbour Upendra’s new magnum opus Uppi2 is currently the top rated movie on IMDB, with a rating of 9.7/10.0. The Times of India is so surprised that it has done an entire story about it, which I’ve screenshot here: Screen Shot 2015-08-17 at 8.50.33 pm

The story also mentions that another Kannada movie RangiTaranga (which I’ve reviewed here) is in third spot, with a rating of 9.4 out of 10. This might lead you to wonder why Kannada movies have suddenly turned out to be so good. The answer, however, lies in simple logic.

The first is that both are relatively new movies and hence their ratings suffer from “small sample bias”. Of course, the sample isn’t that small – Uppi2 has received 1900 votes, which is 3 times as much as its 1999 prequel Upendra. Yet, it being a new movie, only a subset of the small set of people who have watched it so far would have reviewed it.

The second is selection bias. The people who see a movie in its first week are usually the hardcore fans, and in this case it is hardcore fans of Upendra’s movies. And hardcore fans usually find it hard to have their belief shaken (a version of what I’ve written about online opinions for Mint here), and hence they all give the movie a high rating.

As time goes by, and people who are not as hardcore fans of Upendra start watching and reviewing the movie, the ratings are likely to rationalise. Finally, ratings are easy to rig, especially when samples are small. For example, an Upendra fan club might have decided to play up the movie online by voting en masse on IMDB, and pushing up its ratings. This might explain both why the movie already has 1900 ratings in four days, and most of them are extremely positive.

The solution for this is for the rating system (IMDB in this case) to pay more weightage for “verified ratings” (by people who have rated more movies in the past, for instance), or remove highly correlated ratings. Right now, the rating algorithm seems pretty naive.

Coming back to Uppi2, from what I’ve heard from people, the movie is supposed to be really good, though perhaps not 9.7 good. I plan to watch the movie in the next few days and will write a review once I do so.

Meanwhile, read this absolutely brilliant review (in Kannada) written by this guy called “Jogi”

One Rank One Pension – some thoughts

There has been a lot of debate of late on whether veterans should be moved to a “one rank one pension” system. I won’t bother explaining the whole deal here, I’ll let you read this brilliant post by Ajay Shah about the numbers behind the move. Now that the quant has been outsourced, I can put forth my “qualitative” arguments.

I’m not a fan of this One Rank One Pension (OROP) move. I’m not against paying our soldiers, or veterans, well – I think it must definitely pay above market rates for the skills required for the job. Yet, I think OROP is a “one delta” solution to the problem (previous post here about government’s one delta thinking on agriculture), and can lead to massive unfunded liabilities.

The problem with any kind of pension scheme is that you create liabilities today that need to be funded later on. And at a later date these liabilities might become unserviceable. From this perspective, it is important to try and fund any future liabilities today, or at least have a handle on the precise magnitude of liabilities required. OROP, being “inflation indexed” (that’s Ajay Shah’s nice model to look at it), doesn’t allow for proper budgeting and long-term planning.

It is precisely due to this budgeting issue that the government moved most of its incoming employees to the New Pension Scheme (NPS) in 2004. NPS, unlike previous pension schemes, is a “defined contribution” scheme, where your pension is paid out of a corpus you create by your own saving. From an accounting perspective, it moves liabilities from tomorrow (pensions) to today (higher salary to fund the contributions), and is an excellent move. And there is no reason for it not to apply to the armed forces.

Most of the arguments being made in favour of OROP are emotional (“how can you deny our veterans money” etc.), and not well backed up by logical or economic reasoning. One of those is that lower-level military persons retire when they are 35, and hence need a “one rank one pension” (which I absolutely fail to understand). While I understand that the rigours of the role imply early retirement, I don’t see why defined contribution doesn’t solve the problem. It will have to be matched with higher salaries (to fund the contribution required for a long lifetime of retirement), but that implies liabilities are funded today, which is superior to pushing liabilities under the carpet for future  generations.

The thing with NPS is that it cannot be pushed retrospectively, and hence can apply at best to all forthcoming hires. We still need a solution for the existing employees and veterans, who are already on a defined benefit scheme. Yet, the important thing to consider is that the beneficiaries should be divided into three categories – current veterans, current servicemen and future servicemen, and we should find separate solutions for the three.

It might be argued that without defined benefit pensions, it might be hard to attract talent for a high-risk job like the military, and that is why we might need OROP. This is where the “derivative thinking” comes in. The thing about a job in the military is that there is a higher-than-civilian risk of losing life or limb. The solution to that is not blanket higher compensation – it is risk management.

What we need is generous death and disability insurance for our military, and this too should be purchased by the military from a professional Life Insurance firm. A generous insurance package can help mitigate the risks to life borne by military personnel, and should be sufficient to attract necessary talent. The purchase of such policies from professional insurers is important, for you don’t want the military to be doing an actuary’s job. More importantly, such a purchase will push liabilities to today rather than to tomorrow, and the last thing an army will want during the time of war is increased expenses on account of insurance.

The current debate about OROP has opened the door for a complete overhaul of military compensation. The government should jump at this, rather than simply get bullied by veterans’ groups. As Nitin Pai argues in this editorial in the Business Standard, compensation is an economic decision and should be made based on economic (and financial) reasoning, not based on emotion.