Uncategorized – Page 7 – Pertinent Observations

Poverty and distributions

No, this post is not about the distribution of poverty. This is a rather technical post about probability distributions. Just that it has something to add to the poverty debate. And like the previous post, this is a departure from the normal RQ-type posts – there will be no graphs, no tables. Just theorizing.

So in the last week or two a lot of op-ed space in India has been consumed by what is described as the “poverty debate”. A recent survey by the National Sample Survey Organization (NSSO) has revealed that poverty levels in India have declined sharply in the last couple of years. And it only accelerates a sharp decline that started after a similar survey in 2004-05. Now, you have the “growthists” and the “distributionists”. The former claim that it is high economic growth in this time period that has led to the fall in poverty. The latter think it is due to redistributionist policies such as the National Rural Employment Guarantee Act (NREGA). Both sides have their merits. However, I’m not going to step into that debate now.

I ask a more fundamental question – how well can we trust the numbers that the NSSO has put out? My concern is this – that the poverty numbers have been gleaned out of a survey. I don’t have a problem with surveying – in fact surveying is a rather well-studied science, and I’m sure people at the NSSO are well-versed with it. My concern is that in this particular survey, the results may not have been properly extrapolated.

Most surveys rely on what is known as the “law of large numbers” and the “central limit theorem” and assume that the quantity being surveyed (people’s consumption expenditure as per this survey) follows a normal distribution. Except that we know that incomes (at least at the upper side of the scale) don’t follow a normal distribution. Instead, it has been shown that they follow what is called as a Power Law distribution.

While I don’t doubt the general quality of scholarship at the NSSO, I want to ask if they have actually studied the real distribution of incomes and used the appropriate one, rather than using a normal distribution. It could be that incomes at the lower end of the scale actually do follow a normal distribution, in which case standard sampling techniques might be used. If not, however, I expect and hope that the NSSO has used a sampling and extrapolation technique appropriate to the distribution incomes actually follow.

Let me illustrate the issue with an extreme example. Let’s say that one of the names drawn as part of the NSSO’s “random sample” for Mumbai is one Mr. Mukesh D Ambani. Assume that there are 99 other persons in Mumbai who are drawn in the same sample, and each of them has an annual household income of Rs. 1 lakh. What will be the mean income of the group? Assuming Mr. Ambani earns Rs. 10 Crore a year (number pulled out of thin air), the mean income of the group of 100 will come out to be close to Rs. 11 lakh!

This is the problem with estimating incomes using surveys and standard extrapolation techniques. While the above example might have been extreme, even in smaller groups of population, there will be “local Mukesh Ambanis” – people whose incomes are much higher than their peer group. Inclusion or exclusion of such people in a standard survey can make a massive difference.

I will end with an example and a request. I remember reading that any family in India that earns over Rs. 12 lakh a year (i.e. Rs. 1 lakh a month) is in the top 1% of all families in India! My family (wife and I) earn more than Rs. 12 lakh. But do we consider ourselves rich? By no means! Why? Because people who are richer than us are much richer than us! That is the problem with quantities that follow a power law distribution.

Now for the request. Can someone instruct me on the easiest way to get the raw data out of the NSSO? Thanks.

Difficulty of Indian Education Boards

With the IITs now having a requirement that students should have scored in the top 20 percentile of their respective boards in order to qualify for admission, we have a chance to evaluate the relative difficulty of various Indian boards.

The IIT Delhi website has the cutoffs for each board. These cutoffs represent the “80th percentile scores” for each board, i.e. if you were to rank all students who took that particular board exam, these are the marks scored by students 80% from bottom. If you have written any of these board exams and got more than the corresponding 80%ile score for your board, you are eligible to join IIT (provided you score sufficiently in the JEE-main and JEE-advanced, of course).

This plot shows the cutoffs (80th %ile score) for various boards:

Source: http://jee.iitd.ac.in/percentile2013.pdf

Note that the four southern states are on top. These states are reputed to have high educational attainment. Could this be a consequence of easier board exams in these states? We don’t know.

Also, interestingly, these four states are followed by ISC and CBSE, before other state boards. Interestingly, the cutoff for ISC is higher than that for CBSE, which flies against conventional wisdom that CBSE is “easier” than ISC.

Also, if you look at the data, some states have more than one board, and the JEE council has used separate cutoffs for each of these boards. For purpose of my analysis I’ve arbitrarily chosen one board for each state – typically the one whose total is the “roundest” number.

Rupee and dollar volatility of Nifty

Note: This is not a particularly policy related post; just an interesting chart I want to present here.

Out at capitalmind, Deepak Shenoy writes that measured in US Dollars, the NSE Nifty has actually lost 8% in the last 6 years, a period in which the rupee value of the Nifty has gone up by 36%. This is on account of the depreciation of the Indian Rupee against the US Dollar.

Now, it would be interesting to see the volatility of the index as measured in the two currencies. Does the volatility in the USD/INR exchange rate add to the volatility of the Nifty or does it subtract from it? (note that when you multiply two volatile indices, the resultant can be less volatile than either of the components, if the components move in opposite directions).

As you can see from the following graph, the two volatilities actually add up, meaning the dollar volatility of the Nifty has for most part been much higher than the rupee volatility! And to add that the dollar returns have also been lower than the rupee returns. Makes you wonder why FIIs are still invested in India.

(please disregard the absolute values on the graph. In order to make the graph, I index both nifty and the dollar value of nifty to 100 on the first day of the time series I had and appropriately scaled down both series. The point to notice here is that in most parts the red line (dollar volatility) is above the blue line (rupee volatility). As earlier, I use 30-day quadratic variation as a measure of volatility )

India More Corrupt Than Pakistan?

Yes, if you go by the data put out by Transparency International. They question they asked was whether the respondent had ever paid a bribe. In India, 54% said yes, while in Pakistan only 34% did. Sierra Leone tops the list with 84% of people claiming they’ve paid a bribe.

I was planning to not put any visualization, since the one on the BBC website (linked above) is pretty good, but then I thought it makes sense to show the illustrious company India has been put in according to this report.

Interestingly, if you look at their report, their sample size is consistent across countries. While they interviewed 1025 people in India, they interviewed 1000 in tiny Fiji. This means that the results for the larger countries in the survey should be taken with more salt than that of the smaller countries.

There is a lot more in the report (linked above). Do read it if you are more interested in this.

H1B visas in 2013

It is amazing how much of the annual quota of H1B (worker) visas that the US issues goes to IT outsourcing companies. The top 20 beneficiary companies are shown in this graph.

Source: http://h1b-visas.findthecompany.com/

As you can see, Infosys is by far the biggest beneficiary of this. I wonder if it is a result of the lawsuit by an American employee last year against the company, which alleged that the company was misusing B1 (business) visa, which has led the company to play it safe by taking H1B visas instead.

Indian companies have been shaded blue, while non-Indian companies have been shaded red. The amount of blue on this plot tells you that India is the biggest beneficiary of the H1B visa system of the US.

The data also gives the mean salary paid by each of these companies to their H1B workers.

Apart from Intel, all non-Indian companies pay their H1B employees well over $90,000 per annum. None of the Indian companies even come close to that number. This might help you understand why H1B visas are such a contentious point in American domestic politics.

Minimum Support Prices

In India, we have this concept of “Minimum Support Price” for agricultural commodities. It is basically an unlimited put option written by the Government in order to protect farmers against not getting “appropriate remuneration” for their produce. In that sense it can be thought of as an implicit subsidy towards agriculture. There is merit in the argument in favour of such a measure – agriculture is a fundamentally high risk business and in the absence of such safety nets, not enough people might take the risk to sow a particular crop, leading to shortages.

On the other hand, it can be distortionary too. If the MSP is set too high, it can lead to a glut in that particular crop in that year, at the cost of other crops, leading to shortages in the latter. Hence, it is a tool that is necessary but one that should be used with care.

Now, the MSP has to be set in advance – so the MSP for the 2013-14 season has already been set. This is again a risky move but a necessary move – farmers need to know the minimum amount they can get for each crop before they make their sowing decision.

Source: Commision for Agricultural Costs and Prices — Source: Commission for Agricultural Costs and Prices

The figure on shows the Compounded Annual Growth Rate (CAGR) in the MSP of a few important agricultural commodities between 2007-08 and 2013-14. Notice that the CAGR is lowest for crops such as wheat or rice, and high for crops such as Tur Dal or Moong Dal. Under the current Public Distribution System (PDS), families below the poverty line get rice and wheat at subsidized rates, but not pulses. Note that I’m only mentioning facts and not trying to suggest any causation here.

Interestingly, the MSP for coarse grains such as Ragi and Bajra has also grown significantly faster than that of rice or wheat. Also note that prices of cotton and jute have grown rather slowly over the period of consideration.

Now, while this tells us by how much prices have changed in the last six years, it is also pertinent to see how the prices have changed – did the price rise consistently over the last 5-6 years or were there some discontinuities? The next figure tries to address this issue.

The figure on the left here charts the actual year by year growth in the Minimum Support price of the crops under consideration. To me, two things jump out from this graph – apart from sugarcane, there was a steep increase in the minimum support prices of all commodities between 2007-8 and 2008-9. You might want to be reminded that India went to polls in the summer of 2009 and Maharashtra, a prime sugarcane growing state, went to polls in the winter of the same year. Again, I don’t want to claim any causation.

Then, from 2009 to 2012, minimum support prices of these commodities remained largely constant – perhaps compensating for the large jump from 2008 to 09? And then again there was a spike from 2012 to 2013. There is no such jump from 2013 to 2014, though. Note that the nation goes to the polls in 2014.

Tur and Moong dal, however, have seen a rather secular increase in prices in the last five-six years.

How the proposed Food Security Bill will affect the MSP is left as an exercise to the reader. Comments are open.

PS: Data that I’ve used for this post is available at the website of the Commission for Agricultural Costs and Prices.

Value of skill in rural India

Earlier today I had blogged about wage rates for unskilled workers in rural India. Now, we will use the same dataset and see what premium people pay for skills. The same data gives wages for certain occupations – carpenters, masons, cobblers, blacksmiths, etc. There are also wages given for various types of farm labour, and for the purpose of this exercise I’ve used ploughing to be representative of farm labour.

The following plot shows the wage rates for different skills in different states. A note on how to read this graph. The x axis represents the state and the y axis represents the daily wage for that particular skill. The skill itself is represented in text form. So for example a carpenter in rural Kerala gets about Rs. 600 per day while a sweeper in Bihar gets about Rs. 100.

Source: Labour Bureau. Numbers for April 2013

Notice that even skilled jobs in other states don’t fetch as much as an unskilled job in Kerala. Tamil Nadu and Punjab come closest
The skills most in demand in rural areas across states are carpentry and masonry, if you go by this data
In most states, cobblers earn lower than “unskilled workers”. This is interesting because there is skill involved in making and repairing shoes. The low wages for cobblers indicates a caste bias. It is also possible that since cobblers are mostly self-employed their wage rate is inaccurate
Blacksmiths are again not too highly valued in villages
The high numbers for Kerala could be a function of the state’s lower urban-rural divide compared to the rest of India. Kerala is generally described as a semi-urban continuum with no strongly delineated urban and rural areas. Rural workers could be expensive since they are in demand for urban jobs also, unlike in other states.

The same caveats that apply to the previous post apply to this. We don’t know the sample size or the accuracy of the survey. Nevertheless, some interesting insights come out.

Wage rates in rural India

The Labour Bureau, affiliated to the Union Ministry of Labour, does a monthly survey on wages in Rural India. Wages of men and women in select occupations are polled (data is collected by the NSSO) and published on the website of the labour bureau. In this post we will look at the average daily wages of unskilled male workers (as reported by this survey) in the 20 states for which it is published (your guess is as good as mine as to why it is not published for other states).

It is interesting to note that the daily wage of the average unskilled man in Kerala is almost five times that of the average unskilled man in either Gujarat or Madhya Pradesh (states that are at the bottom of the list). Some states known to be “progressive” such as Punjab, Haryana and Tamil Nadu are also towards the top of the list while other so-called “progressive” states such as Maharashtra, Karnataka and Gujarat are close to the bottom.

Like any other data put out by the government, this should be taken with some salt. First of all, the sample sizes is not mentioned. Secondly, only the average number is reported and no measure of dispersion is given. For example, it is hypothetically possible that in Kerala they interviewed ten workers, nine of whom received Rs. 100 and the tenth received Rs. 4000 leading to an average of Rs. 490! As a thumb rule, when you put out survey data, you should always include sample size and a measure of variation (such as the standard deviation), else it is hard to conclude anything from the data.

More on USD/INR

Via email, V Anantha Nageswaran gave a simple theory on the USD/INR exchange rate. Posting it here with his permission.

Using the above chart, which charts the exchange rate over the last 20 years, he says:

The chart attached is quite clear. Except for the period between 2002-07 when actual growth and growth expectations in India shifted higher, the rupee has been on a trend depreciation.

Sustained high inflation (or, rather higher inflation relative to peers) caused by lack of fiscal discipline is the principal or predominant explanatory factor.

To bring back the experience of 2002-07, he states that we need to bring back sustainable growth rates of 7-8%.

Elsewhere, in The Hindu Business Line, S S Tarapore argues that the RBI should not intervene until the USD/INR is at 70. Quoting:

The RBI needs to accept that the rupee is still grossly overvalued despite the decline in recent days. It should not support the rupee till it reaches a rate of around $1 = Rs 70, which would be consistent with the long-term inflation rate differentials between the US and India.

My view is that this may not be enough – this view assumes that Indian businesses (specifically exporters) will be able to take advantage of the falling rupee and export more. This also assumes that domestic demand for petroleum products and gold (our two biggest imports) is elastic and will fall with the falling rupee. If these assumptions don’t come true, things are only going to get worse with a falling rupee.

Also coming back to Ananth’s point on the break in fall of USD-INR in 2002-07, I want to point out that despite our high growth rates in that period, we still didn’t run a current account surplus. It was just that our high growth attracted significant foreign investments which offset our CAD from that period to lead to a rising rupee. The consequent pulling out of those investments has hastened the fall of the rupee over the last few years.

USD/INR Volatility

The stated objective of the Reserve Bank of India when it comes to foreign exchange rates is that they want to maintain a “stable” exchange rate, and will step in only to curb volatility. There is no stated level at which the bank seeks to hold the rupee, and so it will intervene only when the rupee is volatile.

In this post, we will look at how the volatility in the USD/INR exchange rate has varied over the last seven years. For purpose of this analysis, we will use a 30-day Quadratic Variation as a measure of volatility (this is a lagging indicator, so the volatility number for today is the QV of the last 30 days).

The following graph shows both the level of USD/INR (black line, left axis) and the quadratic variation (red line, right axis).

Source: Oanda.com Volatility calculated as 30-day Quadratic variation — Source: Oanda.com
Volatility calculated as 30-day Quadratic variation

Notice that for most time periods, irrespective of the exchange rate, the RBI’s stated objectives have been met – the volatility in the exchange rate has been low for large period of time. Volatility of the exchange rate spiked once following the financial meltdown of late 2008 and again towards the end of 2011 (when Europe got into trouble).

It is interesting to note that for all the footage that the sliding rupee has received in the last month or so, the volatility of the rupee has been quite low (compared to the peaks). It will probably take a significantly higher volatility in the rate for the RBI to step in.

It is also interesting to note that in the second half of 2010, even though the rate level was fairly stable, volatility was significant!