Yahoo! Inc. has put up online the data of the number of data requests it received from various sovereign governments. As expected, the maximum requests came from the United States. Interestingly, Germany is in second place, followed by Italy, Taiwan and France. India also has a prominent presence, with about 1500 data requests. Full report here.
So how did Yahoo! deal with the 1500 requests from India? You can find the details here. The following pie chart summarizes the responses:
How Yahoo! dealt with data requests from India
Note that the data given is restricted to those countries where Yahoo! operates a legal entity and is hence bound by local laws to disclose data.
At the outset, I must admit the idea for this post is not my own. In fact, even the idea to do a scatter plot between these two variables is not mine. It’s from IESE Business School Professor Pankaj Ghemawat‘s book “World 3.0”. The original plot can be seen here:
Source: Pankaj Ghemawat’s World 3.0
While the graph above might be appropriate for a book (given its size, etc.) it’s not particularly well drawn. For example, circles are drawn around some countries (represents their population), which makes it harder to read. Then, there are the arrows which are not self-sufficient (without any accompanying text, which is probably there in the book, there is no way you can make sense of the arrows). Then you have some countries with a perceived need for cultural protection at over 100%.
So when Prof Ghemawat shared the data source (on twitter), I thought it might make sense to re-draw the graph. As you can see below (click for a larger version of the graph) the new graph is also far from perfect. Since I’ve used full names of countries (easier without accompanying text than three-letter codes), there is significant overlap. Yet, I think it does better in conveying the information. In place of Ghemawat’s arrows, I have a regression line!
Source: Pew Survey on Global Attitudes 2007
The insight is clear (was clear in the earlier graph also) – the greater the citizens of a country believe in the superiority of their own culture, the greater is their perceived need for “cultural protection”.
PS: Note that the data is from 2007. I’ll try to get more recent data and re-plot presently.
<update>This post has nothing to do with the recent debate on fake encounters in Gujarat. The time period of the Gujarat encounters currently in focus predates the time period of the data available here. This post only covers encounters from 2009 onwards </update>
The Ministry of Home Affairs, in response to an unstarred question in the Lok Sabha, has put out statistics on the number of fake encounters per state in the last four years (HT: @offstumped). Let us look at which states have had the most encounters relative to their population.
Manipur is far far ahead of every other state, with 22 encounters in the last 4 years per million population (total of 62 encounters in the last 4 years). Andaman and Nicobar Islands follow, but that is an aberration since the islands have had one fake encounter, and that has shown up as significant thanks to their small population. Interestingly, Maharashtra, Karnataka, Andhra Pradesh and Gujarat fall towards the bottom of the table, as does Bihar.
To get a better picture of the other states, let us remove Manipur and see the same graph.
I leave it to you to form your own conclusions, but encourage you to share your conclusions in the comments section below.
One common argument by experts who claim that there is nothing to be worried about the decline and fall of the Indian Rupee in recent times is that the Indian Rupee is not the only currency that is falling, and that other emerging market currencies are also falling equally badly. In order to test this out, we will look at the movement of the rupee as a function of other so-called “emerging market” currencies.
I’m just going to offer the graphs here (of movements of various currencies against the rupee) without any comment. All graphs are of the form “how many units of foreign currency does it take to buy a rupee”. So the higher this graph, the higher the level of the rupee compared to this particular foreign currency. And it is on purpose that I’ve drawn all charts starting from Jan 1st 2008, so that the US financial crisis is also captured.
About a week back, I had sent a long email to my client espousing the virtues of the power law distribution, and how it was relevant to the project that I was working on. To be honest, I didn’t really expect a reply to that mail. It was fairly long, and parts of it were technical, while most of my clients are primarily business people. In the best case, I decided after sending the mail, it would be ignored, and in worse cases, the client would think of me as a freak and start wondering why I’ve been engaged.
The next morning I got a rather long reply from my client. It appeared that he had taken great pains to understand my model and its relevance to his problem, and he responded with the model that he had built for similar analysis. While the model he built was simplistic (during the course of his email he repeatedly pointed out his non-statistical background), I found it to be amazingly accurate, and that it modeled the behaviour we were looking to model rather accurately. The only drawback of the model was that it wasn’t a particularly scalable model, and it wasn’t easy to modify.
Given my experience at a rather large and successful investment bank that used to work for (documented here), this was indeed a pleasant surprise. The modeling department of that bank had been virtually taken over by people skilled in continuous math and linear algebra, and the people there had little patience for any model that didn’t follow some variation of Brownian motion (of course, most of that firm’s systems were set up assuming one such distribution). Despite having been through the lows of the 2008 Financial Crisis, the modelers there steadfastly refused to let go of their supposedly time-tested models, and any comments of distributions not being normal were seen with suspicion.
Over a year and half back, when I decided that I should move out of the investment banking industry (where I found quant having saturated to some extent) and into other sectors where there wasn’t much utilization of quantitative techniques, one of my apprehensions had been that I would frequently run into business people who simply wouldn’t touch modeling with a barge pole. I had been apprehensive of working with people with absolutely no quantitative background.
After my recent experience, though, I’m happy to have made the transition. I do hope that other clients are also as receptive to new ideas in quantitative techniques.
Earlier today, I was going through some “Exposure drafts” that the IRDA has put out proposing some changes to the way Insurance and Pensions are regulated in India. The part that has got some people excited is that now insurance companies are going to be allowed to invest in corporate bonds that are rated AA (hitherto, they were only allowed to invest in AAA rated securities). An article in Monday’s Business Standard makes the usual noises about “gambling with people’s hard earned savings”.
Reading through the draft today, I realized that even this investment in AA rated securities is limited to 15% of the insurer’s assets, of which a whopping 50% needs to be invested in government and other “approved” securities. The question I ask, therefore is, “why only AA?”
As a class of investors, insurance and pension funds are unique in terms of the horizon over which they need to invest. They have cash inflows over the short term, but need to invest for large potential cash outflows over the long term. In that sense, they are among the most stable investors since they are able to tide over short-term blips due to the far-sightedness of their investments. Additionally, their massive corpuses also gives opportunity for significant diversification, and that allows them to make the odd risky investment.
The reason that corporate bond trading is next to dead in India is because insurers have hitherto only been able to invest in bonds rated AAA. With the regulator now permitting investments in AA bonds, there is a chance for that market to develop in a tiny fashion (though the 15% cap that the regulator has imposed, and this includes AAA securities, means it will only lead to minor development). However, unless insurance and pension funds are allowed to invest in bonds of lower grades, there is absolutely no chance of those markets developing, and that only means greater strain on the banking sector. Let me explain.
In another world where corporate bonds were widely traded, it would be extremely unlikely that Kingfisher Airlines would have raised as much bank debt as it has. It would have instead gone to the corporate debt market, and sold paper to the public. And the investors who had invested in Kingfisher’s debt would, as the company steadily declined, have been able to cut their losses and trade out of the company, and be replaced by a different set of investors with a greater risk appetite (you can notice that this has happened in the widely traded and liquid equity market. On the debt side, though, SBI and a few other banks are stuck with tonnes of bad loans).
The systemic risk in banks owning too much risk is that it encourages good money to go after bad. Knowing that the money it has lent to KFA is as good as gone should the airline go down, there is a perverse incentive for SBI to continue lending to KFA in the hope that it would somehow be kept afloat. Had it invested, instead, through the bond market, the bank could have steadily traded out of KFA’s debt and there wouldn’t be one player with perverse incentives saddled with the entire debt.
So where do insurance companies come in? As I explained earlier, they have a long-term horizon in investing and they are huge and hence diversified. Hence, they are the ideal catalysts for building a domestic corporate bond market. Due to their investment horizon, they won’t suddenly desert bonds as a class when the equity market jumps up. Due to their size, if one of the companies they have lent to (via the bond market) goes down, it is likely to be absorbed by some of their other investments that would have done well. World over, it has been seen that insurance and pension funds (and endowment funds of large universities) have played a critical role in creation of liquid markets for new asset classes (including Michael Milken’s junk bond market in the 1980s).
So what should be done? Insurance companies should be allowed to invest in whatever the hell they want to invest in, as long as they keep a certain risk-weighted score under control. The current mandate is for 50% to be in government and other “approved” securities. Instead, the risk-weight for these securities can simply be made to zero. AAA securities can have a higher risk weight, AA even higher and so forth with junk bonds having the highest risk weight. And the regulation can be of the form of an insurer’s weighted average risk being bounded from above by a certain number (this is similar to the Basel II norms for banks – something I’ve ranted about before, but I’ll save that for another day). What risk-weighted regulation does is to allow investors to choose their own investment profiles – for example, one investor might choose to invest all his money in AAA bonds, while another might invest 90% in government securities and the rest in junk.
Now what of the “playing with people’s life savings” argument? The answer is that people don’t keep all their life savings in insurance or pension schemes. They keep a significant proportion of their savings in banks also, and what the provisions I’ve proposed does is to reduce the stress from the banking system. If KFA goes down tomorrow, depositors in SBI and other banks (notwithstanding SBI’s de facto sovereign guarantee) stand to lose. The regulation I propose will simply diversify this risk between depositors of SBI and investors in SBI Life – thus taking one dimension out of the headache people might face in their investment decisions; and at the same time reduce the overall risk in the financial sector.
The problem with current financial regulation in India is that it is siloed. We have one regulator for banks, another for capital markets and yet another for insurance and (maybe) another for pension funds (PFRDA). All this ensures is that each regulator tries to decrease the risk within his sector, without regard to risk to the financial sector as a whole. The sooner we recognize this, the safer the life’s savings of our people will be.
Krish Ashok and Puram Politics have been collecting data from various government sources and converting them to excel. This data contains a wealth of information on social indicators in India. You can expect the next few issues of RQ to be based on this dataset. Data is drawn from various government sources including the Ministry of Statistics and Program Implementation (MOSPI).
Today we will look at the workforce participation of women across the states of India. First, let us look at rural women. Notice that the all India average participation is close to 60%. Himachal Pradesh ranks the highest with over 83% while (perhaps surprisingly, developed states such as ) Delhi, Kerala and Punjab bring up the rear.
Next we will look at the workforce participation of urban women. Note now that the all India average drops to an abysmal 20%! While migration to urban areas is generally associated with increased standard of living, it is interesting to note that more and more women don’t work in urban areas. It is perhaps a reflection of the kind of jobs that are available in urban India.
Notice that once again, Himachal Pradesh is top and Punjab and Delhi bring up the rear. Actually there seems to be a correlation between workforce participation of rural and urban women across states. Let us explore that with a scatter plot.
Notice that there is a strong positive correlation. Interestingly, Himachal Pradesh and Tamil Nadu (states associated with excellent education levels) display superior participation of urban women in the workforce relative to the participation of their rural women. Karnataka, Andhra Pradesh and Rajasthan are also found to be above the regression line. Interestingly, it is hard to draw a pattern from this data in terms of which state is more developed.
During my talk at the Takshashila Chennai Shala in 2011 (related Pragati article here), I had argued that the underlying reason for market failure in Chennai autorickshaws was regulatory failure. Despite costs for auto rickshaws going up significantly, I had argued that the regulated fare was a lowly Rs. 7 per kilometer, because of which no auto rickshaw in Chennai traveled by meter.
In the same talk I had argued about the benefits of having a regulated fare (no time wasted in haggling, etc.) so this new move by the Tamil Nadu government to regulate auto rickshaw fares is welcome. Note at the end of the article that someone from the Auto Rickshaw Drivers union has welcomed the new fares. This, and the fact that the fare has been set rather high (compared to other Indian cities) should hopefully lead to wide uptake in the use of meters among auto rickshaws in Chennai.
This stabilization in price, I argue, will lead to greater use of auto rickshaws by the general public (since there is no uncertainty now) and should also contribute to greater revenues for the drivers, thus creating a strong ecosystem.
The graph below compares the per kilometer auto rickshaw fares in different cities in India. Note here that Chennai is the most expensive. My argument, however, is that given the unregulated market that is in place now, this higher fare is a reasonable price to pay for good regulation and fair fares.
Earlier today on Twitter, RahulRG pointed out a research report by Credit Suisse analysts Neelkanth Mishra and Ravi Shankar which talks about India’s massive informal economy. The report says that by nature the informal economy cannot be measured, because of which our estimates of GDP may not be accurate. The analysts point out that every time we move to a new series of GDP (we last did so in 2004, and are likely to do so again shortly), there is an upward revision in the GDP for the preceding series, which they attribute to underestimation of the contribution of the informal sector.
While these numbers are likely to get fixed when we move to a new series, what I’m concerned about is what this uncertainty in GDP estimation means with respect to the GDP growth rate, since that is the one number that analysts of all hues track when trying to understand how the country is doing. For example, if you google around you will see analysts arguing about whether India’s GDP growth in the next quarter will be 4.7% or 4.8%. Before we settle to argue on such minutae, I argue, we first need to understand the possible uncertainty in GDP estimates.
In order to estimate the impact of uncertainty of the GDP calculation on uncertainty in GDP growth, I did what I know best – a simulation. For different levels of accuracy, I calculated the range that the actual GDP growth can take. The results are presented in the following table. The first column in the table refers to the accuracy of the GDP estimate at the 95% confidence level. That is, if the first column shows 1%, it means that if the GDP is estimated to be 100, the “true” value of the GDP will be between 99 and 101 95% of the time.
Error
True GDP Growth Rate
5%
6%
7%
8%
0.05%
4.93-5.07
5.93-6.07
6.92-7.08
7.92-8.08
0.1%
4.85-5.15
5.85-6.15
6.85-7.15
7.85-8.15
0.2%
4.7-5.3
5.7-6.3
6.7-7.3
7.69-8.31
0.5%
4.26-5.74
5.26-6.75
6.25-7.76
7.24-8.77
1%
3.54-6.49
4.51-7.52
5.5-8.52
6.48-9.53
2%
2.09-8.03
3.03-9.02
4-10.05
4.98-11.13
Notice that even if the measurement of the actual GDP is accurate up to 0.05% (or 5 basis points), we can estimate the growth in GDP only up to an accuracy of 15 basis points! So arguing whether the GDP growth will be 4.7% or 4.8% is, in my opinion, moot! Unless our statisticians can say that the accuracy in measurement of the GDP is within 5 basis points that is!
Recently, my colleague Pavan Srinath put out a post on testing whether someone “defers to scientific reason above and beyond ideologies”. In his post, he made three statements, which he said are all strongly backed by scientific evidence:
The core argument in climate change is that the earth’s surface warmed significantly in the 20th century due to human-linked emissions of greenhouse gases.
The argument with nuclear safety is that health risks from nuclear power generation, both chronic and acute, have been grossly exaggerated and that due to an obsession with nuclear safety for the past 6 decades, nuclear power is now safer than most other sources of energy.
The argument with genetically modified crops is that they are just as safe as other crops, both for growing and for consumption. Additionally, crop modification through targeted molecular biology techniques is in fact less genetically invasive than conventional hybridisation techniques.
All three arguments have overwhelming scientific evidence on their side, and the nature of the scientific debate is very different from the public and political discussions regarding the same.
If you were not ideologically biased and if you were scientifically aware, he said, you would be extremely likely to agree with all the three above statements. If, however, you were biased to the “left” you were likely to agree with the first statement but not with the last two. If biased to the “right”, you were likely to agree with the latter two and not with the first.
We decided to test these beliefs by putting out a survey. The survey had exactly three questions – the above three statements that Pavan mentioned in his blog, and the respondent was supposed to agree or disagree with these statements on a five-point Likert scale. The “sample” on which the survey was administered was biased – most respondents we believe were connected on Facebook or Twitter (the two avenues we used to publicize the survey) to someone in the Takshashila community. It is very likely that most of the respondents were educated urban upper-middle-class Indians (this is a guess; we didn’t ask for these data points in the survey itself).
142 people responded to the survey. Most of these responses came within a day of our putting out the survey. Here are the results of the survey:
Firstly, we will look at the individual responses to each of the three questions:
Source: Takshashila Survey
This shows that opinion in favour of global warming is fairly strong.
Source: Takshashila Survey
While a majority of the people believe that health risks from nuclear power have been exaggerated, the opinion is not as overwhelming as it is on the global warming front. There still exist a significant number of doubters of safety of nuclear energy.
Source: Takshashila Survey
When it comes to GM crops, however, public opinion is largely divided. As many people agree that GM crops are safe, as do people who believe they are unsafe.
Next, we will look at interactions. The next three graphs here show bilateral “heatmaps” of responses to the three questions. The greater the redness of a particular cell in this map, the greater the number of respondents who fall in that cell.
There seems to be a positive correlation between these two beliefs that are towards different ends of the political spectrum. Among people who agree that global warming exists, more people believe that nuclear power risks are exaggerated than otherwise.
An interesting thing here is that extreme views on one issue are correlated with extreme views on another. Note that people who strongly agree on global warming are more likely to strongly agree or disagree with GM Food safety, while those who merely “agree” with global warming are more likely to simply “agree” or “disagree” with GM Food safety. Also notice the large mass of people who strongly agree with global warming but are neutral about the safety of GM Foods. This indicates that there isn’t as much debate and discourse on the safety of GM foods as there should be.
These are the two “right wing issues”. Notice that the top left and bottom right areas are almost empty. People who agree with one of these are more likely to agree with the other.
So how many of our respondents can be classified as being “scientifically aware” based on Pavan’s metrics? Given that Pavan states that someone who is scientifically aware should agree with all three statements, we will consider someone who has “agreed” or “strongly agreed” with all three statements as being “scientifically aware”. This number comes out to 27 out of 142 respondents or about 19%.
How many of our respondents are scientifically unaware? For this we will look at people who either disagree or strongly disagree with each of the above statements. There are only 3 people among those we surveyed who can be thus classified (and one of them has given his/her name as “Troll” so we may not take that seriously).
Then, again going back to Pavan’s definitions, how many left-wingers do we have? For this we will consider people who agree with the statement on global warming and disagree with the other two. There are 17 such people. What about right wingers? These are people who disagree with the statement on global warming but agree with the other two. There are 7 of them. There are 63 respondents who have said that they are neutral on at least one of the three questions.
There is so much more one can do with these responses. I have anonymized the responses and put up the data here for your benefit. You are free to analyze it and draw your own conclusions. However, I would encourage you to share your conclusions with the larger community by leaving a comment on this post.
