Drivers in India

One of the recent data sets in data.gov.in is the number of driving licenses issued in different states of India until March 2012. Based on that, it is interesting to see which states have more drivers. The first chart here shows the proportion of population of each state that has a driving license (states for which data is unavailable have been left out). Note that this proportion is an overestimate since the number of licenses given includes people who have subsequently died, and thus not been counted in the state’s population as of 2011. Nevertheless, as a relative measure, this is useful:

dl

 

Notice that the highest numbers are in Goa, Tamil Nadu and Maharashtra, all of them among the more prosperous states. States at the bottom include Assam, Uttarakhand and Jammu and Kashmir. The latter two are extremely hilly, thus discouraging driving. Nevertheless it would be an interesting correlation between proportion of drivers in a state and its per capita GSDP. Which is what we do next:

dlgsdp

 

We see that apart from Delhi (where presumably a large portion of the population gets its licenses from other states?) and Sikkim (a hilly area where not too many are expected to drive), there is a strong correlation between the proportion of drivers and the per capita GSDP!

Finally, what proportion of drivers in each state are women? The following graph shows that:

dlwomen

 

Manipur, where over 30% of licenses have been handed out to women, stands way ahead of other states. The other state that stands out is Andhra Pradesh, where a measly 1.5% of driving licenses belong to women. Contrast this with neighbouring states such as Odisha (12%), Karnataka (15%) and Tamil Nadu (8%)!

Visas to India

Between January 2012 and October 2013, India issued over 34000 employment visas. Where did these 34000 foreign workers come from?

empvisas

 

Notice that the number of people with employment visas from Japan and Germany outstrip all other countries, by a long way! The United States is not even in the top 12! The other notable exception? Bangladesh!

What about tourists? Where do India’s tourists come from? Between January 2012 and October 2013, India issued about 4.5 million tourist visas. And where did these tourists come from? This graph here shows the percentages:

tourvisas

 

And which country contributes the maximum number of tourists to India? Bangladesh! 800,000 or almost 18% of India’s tourists in these 22 months came from Bangladesh! And they didn’t come here for medical treatment – that has been taken care of in another category of visas!

Go figure.

Pricing railway safety

Yet another railway accident has happened. As someone on twitter pointed out,

The problem with the Indian Railways is that there is no real measure of safety. How do we know how much safer the trains and tracks are compared to last year? Given the way the Railway finances are put out currently, there is no way to figure this out. Without the railways putting out more disclosures, is there a way to put a number on how safe the Indian Railways are? In other words, is there a way to “price” railway safety?

As you are well aware, and as the above tweet points out, it is standard practice in Indian Railway accidents for the Railway Minister to announce an ex-gratia payment to the families of the dead and the injured in case of any accident. I’m not sure if there is a formula to this but one cannot rule out the arbitrariness of this amount. As I had pointed out in an earlier post on RQ, accident compensation needs to be predictable and automatic. Can we use this to price railway safety?

First of all, we need to point out that the railways follows a cash accounting system, and thus doesn’t need to account for any contingent liabilities such as ex-gratia payment (last weekend I sat through an awesome lecture by Prof. Mukul Asher (councillor to Takshashila) on public finances, and he pointed this out). Hence, it would be prudent on behalf of the Indian Railways to hedge out this contingent liability.

How do you hedge a contingent liability? By buying insurance! What the Indian Railways needs to do is to buy group accident insurance – all the ex-gratia payments will then by paid out by the insurance company, and the railways will only pay a premium to these companies, thus hedging out the risk! And this process will help put a price on railway safety!

How is that? Let us say that given the railways’ bad record in safety, and its continued promises that safety will be improved each year, the railways decides to take up group accident insurance on an annual basis. Let us say that there is a competitive bidding process among general insurers in India (both public and private sector) to provide this insurance (railways is a large organization, and insuring them will be a matter of prestige, so companies will bid for it). The premium as determined by this competitive bidding process is the price of railway safety!

We can do better – instead of buying one overall policy, the Railways can think of insuring different routes separately, or perhaps zones. This will help put a price on the safety of each route or zone! There will be some transaction cost, of course, but price discovery will happen, and we will be able to put a price on risk!

But then, this is all wishful thinking. It is unlikely this will happen because:

1. Given the cash accounting system followed by the railways, there is no incentive to hedge contingent liabilities
2. Buying insurance means increasing scrutiny. The railways will not want to be scrutinized too hard. It is currently an opaque organization and it will want to be that way.
3. Given the railways are wholly government owned and there are government owned general insurers, there might be some collusion which might  result in underpricing the risk.
And so forth…

Nevertheless, the point of this post is that it is possible to put a price on safety!

Commute Distance and Prosperity

There is an interesting report on The Hindu Blogs about commute distance and prosperity. Referring to a World Bank report in 2005, the blog post talks about richer people commuting longer distances to work. Rukmini S, who has written the piece, also finds from the latest NSSO data that richer states in India have a higher proportion of people commuting more than 5 km to work.

I didn’t like the visualization (or the lack of it) in Rukmini’s article, and hence this post. I thought the point about long commutes to work and richer states would be better made in a scatter plot, and that is what I produce here:

commutegsdp

 

On the X axis is the proportion of the Urban population in each state that commutes over 5 km to work each way. The data is from the latest NSSO Survey (page 28-29). On the Y axis I have a measure of the level of economic activity in a state – the per capita Gross State Domestic Product. The advantage of this measure is that it takes out from the equation the size of the state itself, and instead focuses on the level of economic activity per person. The figures are from 2011-12 and the numbers are based on 2004-05 prices. The data is from the RBI website.

The correlation is clear – barring a few small states, the above plot clearly shoes that more the proportion of people that commute long distances to work, the greater the economic activity in that state. The question, however, is whether there is a causal effect and if so, in which direction – does people traveling longer distances cause greater economic activity or does greater economic activity lead to people commuting longer distances?

The world bank paper proposes that the more well to do commute longer distances only because the cost of local transport in Mumbai is high and the poor cannot afford that. This is a view that Rukmini endorses in her piece in the Hindu. The argument doesn’t particularly make sense, though. Do the world bank researchers intend to say that transport costs outstrip housing costs in prime areas in Mumbai? If so, it is extremely hard to believe.

At the state level, one possible reason why people in richer states travel more is because greater economic activity happens in bigger urban agglomerations. The economic activity of a town or village is a super-linear function of the number of people living there. And when you have larger urban agglomerations, people tend to live farther from their workplaces, and thus commute more.

Again – this is a chicken and egg problem – a level of economic activity in a town or village leads to increase in population, which results in greater commutes. Increase in population leads to even greater economic activity, and this sets off a virtuous cycle. The 20-fold increase in Bangalore’s population in the last 70 years can be attested to this cycle, and it is hard to put a direction of causation to it.

The above explanation, however, doesn’t explain the following graph. This graph is identical to the one above except that here we look at the proportion of rural residents who commute over 5 km to work. And this is again positively correlated with economic activity!

commutegsdprural

 

What can possibly explain this? One way to explain this is that when people stay close to a town or city with high economic activity, they might prefer to participate in that rather than working in the village itself, and thus they might be commuting longer distances. States with high economic activity are likely to have a larger number of villages close to urban/semi-urban centres of high economic activity, and thus people are likely to travel longer distances.

When more people are willing to travel longer distances for work, it leads to people coming together to work at a higher rate than it normally happens in a village, and this leads to higher economci activity! Again, it is hard to put a directionality to the causation!

Analyzing Bangalore’s Growth

Banglore’s population has grown 20-fold and area 10-fold since 1941, going by this chart (via Gautam John on Facebook, photo taken at MG Road Metro station).

Bangalore Population and Area
Bangalore Population and Area

What would be interesting to see is when the spurt in Bangalore population actually happened. Checking that is quite simple. Using the population figures from the census, we can derive the CAGR (compounded annual growth rate) of the population in each decade. This is presented in the chart below:

bangalorepop

Conventional wisdom is that Bangalore was a sleepy little city until the “IT revolution” happened around the turn of the millennium after which the city exploded. The chart above calls that wisdom into question. While the annual growth rate of Bangalore’s population has been higher in the noughties compared to the earlier two decades, this is by no means the period of Bangalore’s fastest growth.

Bangalore grew fastest in the 1940s, perhaps because it was made capital of Mysore State after independence, thus leading to the arrival of a large number of government servants in the city. Interestingly, the next period of high growth in the city was in the 1970s, which was even before the seeds of the IT revolution had been sold (the setting up of the Texas Instruments office in Bangalore in the early 80s is regarded as the beginning of Bangalore as an IT hub).

What might have led to the perception of Bangalore’s growth being fastest in the noughties is that the strain on a city’s infrastructure is a superlinear function of the city’s population. And with a lot of the city’s infrastructure having been stagnant over the years, the strain started getting really noticed in this decade.

Teaching marketing

Recently, the Alumni Association of IIM Bangalore had invited alumni to give interviewing practice to second year students at the institute. This was in an attempt to help them prepare for their “final” placements that are coming up in March. With a view towards brushing up my interviewing skills (haven’t interviewed anyone for close to three years now) and also to check out the kind of people that go to IIM nowadays I decided to volunteer. And ended up interviewing some five or six people.

I had told the organizers that I’d be interviewing for a hypothetical job in my firm and that they should preferably send students with an inclination for “quant” and for consulting. Perhaps there was a mismatch in communication, and perhaps I sent my “requirements” too late, but it so happened that at least half the people who came to were “majoring” in marketing (nowadays they’ve introduced the “major-minor” system at IIMB. If you do five electives from one “area” you “major” in that. Three electives from an “area” gets you a minor. There is no compulsion to either major or minor, though).

Given that the questions that I’d prepared were inclined towards interviewing for a quant/consulting/analytics kind of role (basically whatever I currently do in my “job”), I decided to not veer too far from that while teaching these people. To each of them I put forth a “case”, where the central problem was marketing-related but needed numbers to “solve”. In fact, I made up the case on the spot after one of these students told me he had interned at an e-commerce firm.

So I told them that they are the marketing manager of an e-commerce firm and the firm has launched a few advertising campaigns and now needs to test the effectiveness of such campaigns. I asked them how they would measure this.

Given that they might have just about started off practicing for their placements, I realized they didn’t have much expertise doing “case interviews”, and so tried to help them navigate the case. So for each of them I started by asking them what kind of metric they would use for measuring the effectiveness of the campaigns. And this is the stage that each of their “interviews” came unstuck.

Incredibly, each of them independently started off with “we will first understand what segment this campaign is targeted at”. And then their process of measurement involved identifying a sample of customers of this segment and then “measuring if they had got the intended message of the campaign”. When I told each of them that they weren’t allowed to do a survey, and added for good measure that a focus group discussion is also out of question, they all seemed absolutely lost. I couldn’t really proceed with their cases.

I find it incredible that the three of them (granted – small sample) who are second year students in one of India’s better business schools (at least I hope so) completely failed to imagine that the effectiveness of an advertising campaign can be measured in terms of “sales” or “website hits” or “click through rate” (depending upon the “intended message” of the campaign, one of these becomes the appropriate metric). It seemed to me that their management education had clouded their ability to think intuitively.

In my limited experience in interacting with marketers, I’ve found that a large number of them are fairly resistant to using numbers in their business, and speak in terms not fathomable to the common man (I once made the mistake of applying to a marketing analytics firm, and was promptly sent some questions about measurement of “brand feeling” and such like. I withdrew my application). The impression I get from my small sample is that marketers’ way of thinking is completely divorced from that of other people in business, and have always wondered about why this is. I had assumed it might be a function of getting ingrained into certain marketing jobs, but now it seems like this way of thinking is more deep-rooted.

I was taught core marketing by a somnolent professor who was renowned to be a “great marketer”. He clearly didn’t market marketing too well, for I didn’t take any marketing electives after that. However, I think I “get” related fields such as game theory and behavioural economics, and try to understand marketing using those frameworks. Usually it doesn’t take you too far in a conversation with marketers, though.

Based on my interactions with the three marketing major students I interviewed, it seems to me that something is wrong with marketing teaching, especially at IIMB. It seems to me that marketing is taught as a rule-based discipline, rather than based on first principles. Perhaps that is how recruiters of marketing majors want it to be like, but it seems like this kind of “education” is only going to create poor quality marketers.

PS: I admit to small sample bias, extrapolation and such like.

Provisioning for Non Performing Assets at Banks

K C Chakrabarty, a Deputy Governor at the Reserve Bank of India recently made a presentation on the credit quality at Indian banks (HT: Deepak Shenoy). In this presentation Dr. Chakrabarty talks about the deteriorating quality of credit in Indian banks, especially public sector banks.

What caught my eye as I went through the presentation, however, was this graph that he presented on “Gross” and “Net” NPAs (Non-Performing Assets). Now, every bank is required to “provision” for NPAs. If I’ve lent out Rs. 100 and I estimate that I can recover Rs. 98 out of this, I need to “provision” for the other Rs. 2 which I expect to become “bad assets”. Essentially even before there is the default of Rs. 2, you account for it in your books, so that when the default does occur, it won’t be a surprise to either you or your investors.

Now, NPAs are measured in two ways – gross and net. Gross NPAs is just the total assets that you’ve lent out that you cannot recover. Net NPAs are gross NPAs less provisioning – for example, if you expected that this year Rs. 2 out of Rs. 100 will not come back, and indeed you manage to collect Rs. 98, then your Net NPA is zero, since you’ve “provisioned” for the Rs. 2 of assets that went bad. If on the other hand, you’ve expected and provisioned for Rs. 2 out of Rs. 100 to be “bad”, and you manage to collect only Rs. 97, your “Net NPA” is Re. 1, since you now have Gross NPA of Rs. 3 of which only Rs. 2 had been provisioned for.

This graph is from Dr. Chakarabarty’s presentation, indicating the movement of total NPAs (across banks, gross and net) over the years:

Source: Presentation by K C Chakrabarty, RBI Dy. Gov. , via Capital Mind

What should strike you is that the net NPA number has always been strictly positive. What this means is that our banks, collectively, have never provisioned enough to offset the total quantity of loans that went bad. I’m not saying that they are not forecasting accurately enough – loan defaults are mighty hard to forecast and it is hard for the banks to get it right down to the last rupee. What I’m saying is that there seems to be a consistent bias in the forecast – banks are consistently under-forecasting the proportion of their assets that go bad, and are not provisioning enough for it. This has been a consistent trend over the years.

This fundamentally indicates a failure of regulation, on the part of both the bank regulator (RBI) and the stock market regulator (SEBI). That the banks are not provisioning enough means that they are misleading their investors by telling them that they are going to have lesser bad assets than actually are there (SEBI). That the banks are not provisioning enough also means that they are exposing themselves to a higher chance (small, but positive) of defaulting on their deposit holders (RBI).

How would this graph look like if the banks were provisioning properly?

The Gross NPA line would have remained where it is, for it doesn’t depend on provisioning. However, if the banks were provisioning adequately, the Net NPA line should have been hovering around zero, going both positive and negative, but mean-reverting to zero! This is because banks would periodically over and under-forecast their bad assets and provision accordingly, and then dynamically change the model. And so forth..

Read the full post by Deepak to understand more about our bank assets.

Eroding Trust in the Indian National Government

The latest issue of The Economist carries an article which talks about the “eroding trust in national governments”. This article is based on a poll conducted by Gallup in 2007 and again in 2012 with one simple question “do you trust your national government?”. World over, the proportion of people answering “yes” to this question has dipped significantly between the two years.

Source: The Economist, Nov 16th 2013

 

Now, this graph has been sorted by the orange dots (2012 data) so India is lost somewhere in the middle. What if, however, this graph were sorted by the 2007 numbers (white dots)? Notice that the white dot for India is very close to the 90% mark – the highest ever achieved among all countries surveyed in this poll!

This just goes to show the kind of confidence the Indian National Government (UPA-1) enjoyed back in 2007, perhaps a result of the populist schemes it had launched such as the NREGA. This was before any of the scams hit, and this goodwill might have resulted in the government getting voted back into power in 2009. The interesting thing, though, is that the number for India is still higher than that of a large number of OECD countries.

PS: I would have drawn this graph differently. Rather than using a scatter-plot like this, I would have rather used a slope-graph, which would have shown the relative standings in both years and also the way the ratings have moved.

 

Wheat and rice production revisited

Towards the end of last month we had looked at states in India with the maximum land under wheat and rice cultivation (both on an absolute and a relative basis). We revisit that topic of rice and wheat cultivation here, except that now we look at production (in KG) and productivity (KG per hectare). The data at data.gov.in spans from 1998 to 2010, but data for all states is not available for 2009 and 2010, so assuming that production patterns don’t change drastically, I’ve used data from 2008 to look at the biggest producers of these commodities.

Four figures offered without further comment.

1. Top wheat growing states in India (as of 2008)

wheat1

2. Productivity growth in major wheat growing states of India

wheat2

3. Top rice growing states of India (as of 2008)

rice3

4. Productivity growth in major rice growing states of India

rice2