GM Crops, Kiran Shaw, NN Taleb and Pascal’s Wager

I was a witness to a small discussion on twitter today. It started with Biocon MD Kiran Mazumdar Shaw tweeting this article in the Economist which talks about a massive recent study that indicates that genetically modified crops have “widespread benefits” (disclosure: I’m yet to read that article).

Next, an anonymous tweeter called Sleuth Stock drew the attention of Nassim Nicholas Taleb, former banker, risk scientist and author of Fooled by randomness, The black swan and Antifragile, and a prominent opponent of GM crops to the tweet. This indicated a link to a paper by Taleb that indicates that GM crops have an embedded systemic risk that has not been exposed yet.

Taleb himself then responded to the tweet, with a snapshot of a paper that he is in the process of writing regarding why GM crops are dangerous. The paper is basically a lot of math, with some Greeks, and beyond the reach of this Resident Quant. It was this tweet, by Taleb, mentioning Kiran Shaw, Shekhar Gupta, Devinder Sharma and S Gurumurthy that drew my attention to this discussion.

Then Sleuth Stock responded, saying that people had ignored Taleb when he had talked about systemic risk in banking, and then people started worshipping him when Lehman happened. And now that he is talking about systemic risk in GM crops, we should listen. Bizarrely, Taleb retweeted this tweet (but then some people have the habit of retweeting any faint praise they get on twitter) .

First of all, by Taleb’s own explanations in his first “popular” book Fooled by randomness, that you’ve got a forecast right once in one domain has no bearing on your getting your forecasts right in another situation in another domain. While Taleb had very good reasons to call out the systemic risk in international finance, that the risks he cautioned against were actually borne out has an element of randomness to it. Which is why Taleb’s retweet of Sleuth Stock’s tweet is rather bizarre.

Now that that is out of the way, let us get to GM. Cutting through and ignoring all the math in Taleb’s paper, this is my perception of what the matter is about. The basic idea is genetically modified crops (modified as they now are, rather than by means such as grafting, cross-breeding etc. which has been practiced for millennia) carry an element of risk – we do not know what the probability that they are going to be harmful is, but it is yet to be (and never likely to be, according to RQ) proved to be zero. So there is a non-zero probability (which is known to be small, but whose quantum is unknown) that GM crops might be harmful.

Related to this, it is not known what the potential damage caused by GM crops could be if the risk embedded in them bears out. Given the experiments and trials so far, scientists have not been able to quantify the extent of the damage (however small the probability) that might be caused by GM crops in case of the risks embedded in them being borne out.

The expected value of the “trouble” caused by GM crops can be defined as the probability of trouble multiplied by the extent of the damage caused by such trouble. The trouble (pun not intended) is that neither of these two numbers are well defined. We know that the probability of trouble is small, but only have a very loose upper bound on it, and we will never be able to narrow down the precise probability. We know that the damage caused by the trouble can be potentially large, but we don’t know how large, and we don’t know with what probability the damage can be that large.

When you multiply a small unknown number with a large unknown number, where the order of magnitude of either number is itself unknown, the range that the product of these two numbers can take is rather large. This means that there is a small (and again unknown, but not zero) probability that the costs of introducing GM Crops might be higher than the perceived benefits (as measured by the studies referred to by The Economist). Taleb’s argument is that since such a possibility exists, and the extent of expected costs of GM crops is not known, we should not embrace GM crops.

While it is true that there is a small chance the costs might embrace the benefits, the simple truth is that we don’t know, and we never will know. The probability of the “trouble” is so small, and the possible damage so large that even with a large number of studies and spending long years on it, it is going to be impossible for us to get a handle on either of these two numbers, and consequently the expected value. So the expected value of the “trouble” from GM crops is a classic “unknown unknown”.

Given that this unknown unknown will never even become a known unknown, adoption of GM crops is going to necessarily have, albeit however small, a leap of faith. The question is if we should take this leap. If we are extremely conservative, as Taleb normally is, we would be wary of taking even this small a leap of faith. Given the amount of scientific testing we have gone through already, and the lack of information value in any further testing, though, and the facts of growing world population and the need for food and commodity supply to keep up,  it would be well worth it to make the leap of faith. We should also keep in mind that such a leap of faith when it comes to GM crops is not so much longer than the leaps of faith we have taken while adopting many other scientific advancements. So we should go ahead with GM crops, but know that there is an unknown unknown in the “tail risk”.

Finally where does Pascal’s wager come in? The basic philosophy there is that even if the probability of God’s existence is infinitesimal, His power is so great that if you were not a believer, you shall be damned. The greatness of God’s power, according to the wager, implies that even if the probability of existence of God is infinitesimal, the expected value of being a believer is positive, and hence you better believe!

It is similar to the arguments of the likes of Taleb in the GM case in that potential harmful effects of GM can be so large (and yet unmeasured so far) that even if there is an infinitesimal probability that GM crops might be harmful we should not be adopting them.

Health and fitness not a rural concern?

It is now well accepted among nutritionists that excessive consumption of cereals is actually harmful to to health and can lead to problems such as high cholesterol, triglycerides, diabetes and fatness. In response to this, we have a number of new-fad diets such as the paleo and the keto and the Atkins which restrict intake of cereals. Even though the number of people practicing such diets might be low, in general there seems to be a trend away from cereal consumption.

Anyway, yesterday Mint put out a set of charts on malnutrition in India and the relative success of the Public Distribution System (in terms of prices for the end-consumer and nutrition only – not in terms of efficiency). What caught my attention was the last chart – the one on per capita cereal consumption in rural and urban areas.

I wasn’t comfortable with the dynamic chart on the Mint website (they have a slightly better multiple-column chart in the paper this morning), so I redrew it using lines. I’m still not sure if drawing it using lines (since the X-axis is deciles which is ordered but strictly not numerical) is the most appropriate but haven’t been able to find a better way to draw it so here goes.


The Mint piece talks only about the ratio of consumption of top and bottom deciles in rural and urban areas and stop by saying that in urban areas the poorest consume more cereal than the richest. The “trends” in the above two lines tells me a different story, though.

As you see, as we go towards the right (i.e. richer people), consumption of cereal in urban areas (the red line) actually drops! I would put this down to greater health-consciousness among the richer people of urban areas who are cutting down on cereals (either voluntarily or following the discovery of a lifestyle disease such as diabetes or cholesterol).

The blue rural line doesn’t show the same effect though – in fact, the richer you get the more cereal you consume if you are in a rural area. It either means that rural people are immune to lifestyle diseases (unlikely), or their lifestyles means that they aren’t as affected by lifestyle diseases as urban people (rather more likely) or that their lifestyle diseases go undiagnosed (perhaps even more likely) or that they have no choice but to eat cereals (unlikely again) or non-cereal sources of nutrition are too expensive for even the rich in the rural areas because of which they just consume more cereal.

Nevertheless, the trend shown in this graph is extremely interesting, and definitely shows among other things the power of aggregation when it comes to analysing data!

27% and building narratives using numbers

Some numbers scare you. Some numbers look so unreasonably large that it seems daunting to you, infeasible even. Other numbers, when wrapped in the right kind of narrative, seem so unreasonably small that they sway you (the Rs. 32 per person per day poverty line comes to mind). Thus, when you are dealing with numbers that intuitively look very large or very small, it is important that you build the right narrative around them. Wrap them well so that it doesn’t scare or haunt people. As the old Mirinda Lime ad used to say, “zor ka jhatka.. dheere se lage..”.

So the number in the headline of this blog post is the proposed rate of the Goods and Service Tax. While it is the revenue-neutral amount that needs to be charge should excise and sales and other taxes go, the number looks stupendously large. The way this number was reported on the front pages of business newspapers this morning, it looks so large and out of whack that people might decide that it is better to not have a GST at all.

I’m not blaming the papers for this – they have reported what they’ve been told. It is a question of building narratives by the government. The government, and the GST sub-panel, has done a lousy job of communicating this number, and guiding how it needs to be reported in the media. It is almost as if the way the number was reported is an attempt to further delay the implementation of the GST.

The GST is too important a piece of legislation to be derailed by bad narratives. The government must make every attempt to build a narrative that shows the GST as being conducive to people and to businesses, to show how the transaction costs it reduces will result in better prices for both consumers and businesses, and why it makes lives better. Reporting numbers that look really large doesn’t help matters.

Also, the quant in me is disappointed to see one precise number being put out as the “revenue neutral rate”. Since different goods and services which are now being taxed at differential rates are going to be brought into this one umbrella rate, the real revenue neutral rate is actually a function of the mix of the contribution of each of these goods and services to the GDP. Given that in a dynamic economy these rates are constantly changing, reporting one revenue neutral rate simply doesn’t make sense. A range would be a better way of going about it.

Related to this, given that the revenue neutral rate is a function of mix of goods and services, and this mix will change over time, the assumptions and forecasts that need to be taken into account in the process of fixing the rate are important. The GST panel would do well to take into account the risk of product-and-service mix changing that can make all calculations go awry!

PS: If only they were to hire me as a consultant to this panel 😛


Black Money: Stocks and flows

Any physical quantity can be measured in two ways – as a “stock” and as a “flow”. Stock refers to the total amount of the quantity at a particular location at a particular time. Think, for example, of water in a reservoir. “The stock of water at XXX reservoir is 1000 cubic metres” is a “stock measurement” –  it tells us how much water was there at a particular point in time.

The other way of measurement is as a “flow”. This refers to the quantity of the quantity that “changes” or “passes through” between two fixed points in time. For example, we can say that “the flow of the water in my bathroom tap is 20 litres per minute” or “in the last one hour 100 litres of water flowed down my drainage pipe”. Dimensionally speaking, flow has a “quantity per time” dimension while stock has a “quantity” dimension. Flow, unless expressed as a rate of change, also has two points in time mentioned while stock is measured at a single point in time.

Moving from physical quantities (such as water) to financial matters, a company’s balance sheet is the measurement of stock, since it measures the assets and liabilities at a particular point in time (“as of 31st March 2014”). The profit and loss statement, on the other hand, is a measurement of flow, since it tells us about the revenues, costs and profits of the firm in a particular time period (“between 1st April 2013 and 31st March 2014”, for example).

Taking matters more global, any measurement of wealth is a stock, since it is measured at a particular point in time. Measurement of economic activity, such as GDP, on the other hand, is a flow, since it measures the quantity of activity over a period of time. A common fallacy is to compare stocks to flows (this piece in ET is a stellar example of this fallacy), though it must be mentioned that it’s a common practice in financial accounting, analysis and valuation (but there the dimensionality is maintained and recognised. It is common, for example, to divide inventory (a stock) by sales (a flow) to determine “how many days of inventory” a company has on hand). More worryingly, there is sometimes a worrying lack of understanding between stocks and flows when it comes to policy recommendations.

One of the features of the BJP’s campaign over the years, and especially in its run up to this year’s General Elections, is to “bring back black money stored abroad”.

Pop Quiz: Is black money stored abroad a stock or a flow?

During the duration of this “bring back black money” campaign, there have been many fanciful estimates of the amount of black money stashed away abroad and what that can do to India’s deficits if it can be “brought back”. Some of the WhastApp and email forwards estimate, extremely fancifully, what the bringing back of the black money can do to the USD/INR exchange rate even!

The problem with this whole idea of “bringing back black money abroad” is that it fundamentally attacks a particular stock of black money and not the flow (there you have the answer to the pop quiz). Black money stashed away abroad is “harmless” in that it just sits there without being used for any transaction. In that sense the value of that money is lower since it is not being constantly exchanged for something that is more valuable.

What should be of more of a concern to us is the “flow” of black money rather than the stock. Every time a transaction is financed by black money (i.e. without a paper trail or a receipt, and money changing hands in hard cash), the government loses out on the taxes that would have otherwise have to be paid on the transaction. The more the number and value of transactions that can be conduced “in black”, the more the incentive for people to do other transactions “in black” and keep their money in cash, and not accounted for. This has a multiplier effect in terms of number and value of transactions that are unaccounted, and thus help evade taxes.

Rather than one-time efforts to “bring back black money” which attack only the stock of black money, our policy should be geared towards cutting down the flow of black money. A regime of high stamp duties and low property taxes, for example, lead to the perverse incentive for under-invoicing the price of real estate and paying the difference in cash – the “saving” is such that even otherwise law-abiding citizens have an incentive to play along in the black money game and trade in cash. Enabling easier peer-to-peer mobile payments, for example, can have the effect of dramatically increasing the number of transactions that can be conducted without cash, and will be an important step in chipping away at flows of black money. Value Added Tax, with its “input credits” was designed as a system to check under-invoicing within a supply chain, but what if the entire supply chain of a particular commodity runs on cash without written contracts (social capital within business communities allows this, for example)?

Periodic attacks on the stocks of black money (stored either abroad or domestically) can bring in large amounts of money into government coffers and this might make such attacks glamorous. But they are tedious to implement and government resources are much better off employed in cutting off the flows of black money.

Marrying out of caste – 2

We stay with our analysis of the National Family Health Survey data on marrying out of caste. In this edition we look at factors that lead to higher rate of inter-caste marriage.

Based on the data given, age definitely has an impact on the rate of inter-caste marriage. To put it in another way, considering the entire survey was conducted at a particular point in time, what this means is that the incidence of inter-caste marriages in India has been steadily increasing, though at a glacial pace.

Women aged 45-49 at the time of survey were only 8.5% likely to marry someone outside their caste, while women aged 15-19 at the time of survey were 11.75% likely to marry outside their caste. This shows there is definitely a shift in rate of inter-caste marriage, but it is a very slow shift.

The other factors that have been examined, however, seem to give fairly contradictory results (compared to “conventional wisdom”, that is), and that forms an important factor in the survey. For example, only 11.6% of rural women surveyed married out of caste while only 10.6% of urban women surveyed did so (while the difference looks small, the sample size makes it statistically significant), turning on its head the conventional wisdom that urbanisation might lead to higher incidence of inter-caste marriages.

Education shows a bizarre pattern, though. Women educated at the school (primary or secondary) level are more likely to marry out of caste than uneducated women (the difference is small, though), but this trend “regresses” as the women get further educated – women with higher education are less likely to marry out of caste than even uneducated women! The pattern is the same with respect to the woman’s husband’s education level also.


There are more sources of contradiction – working women are as likely to marry within caste compared to non-working women (while there is a small difference it is hardly statistically significant, despite the large sample sizes). Standard of living also has no impact on likelihood of marrying within caste (difference too small to be statistically significant).

The most surprising result, though is regarding “exposure to media” (I don’t know how they have measured it). The likelihood of marrying out of caste is highest among women who have declared their access to mass media as “partial exposure”, followed by women whose declared access to mass media is “no exposure”. Women with highest access to mass media (“full exposure”) have, quite counterintuitively, the smallest chance of marrying outside of caste (this is a statistically significant result with high degree of confidence)!

A reasonably dominant discourse nowadays in certain circles is that exposure to media, urbanisation and women going out to work are responsible for destroying “our culture”. If we take likelihood of marrying within caste as a proxy for “culture” (the one that must be preserved as per these worthies), it turns out that urbanisation, access to higher education and exposure to mass media actually help preserve this “culture”, while access to employment for women and nuclear families do little to “destroy” this culture!

Now if we assume the above correlation to imply causation, and incidence of same-caste marriages as preservation of “culture”, what we need to do to preserve our culture is to increase exposure to mass media, access to higher education for women and urbanisation. While we are at it, we can promote nuclear families and access to employment for women also!

If only the Khaps and other self-proclaimed preservers of culture were to look at hard data before making their pronouncements!


Marrying out of caste – 1

This is the first in what is going to hopefully be a long series of posts on inter-caste marriages. As you might have figured out, I’ve stumbled upon a nice data set with lots of data on this topic (Hat tip: Nitin Pai and Rohit Pradhan), and there are some beautiful insights in the data.

The data is based on a National Family Health Survey which was conducted in 2005-06. The sample size of the survey itself was massive – close to a lakh respondents for the entire survey, and about 43,000 women who were surveyed on the inter-caste marriage question alone. So the survey, which was carried out in all states in India, asked “ever-married” women whether they were married to someone from the same caste, or to someone from a higher caste, or to someone from a lower caste. There was also some demographic data collected which leads to some interesting cross-tabs we can explore in either this post or one other of the series.

If there is one single piece of information that can summarise the survey, it is that the national average for the percentage of women who are married to someone of their own caste is 89%, and this number doesn’t vary by much across demographics or region or any other socio-economic indicators.

Of course, there are differences, and some regional differences are vast. For example, 97% of women surveyed in Tamil Nadu were married to someone from the same caste, while the corresponding figure in Punjab is only 80%. Figure 1 here shows the distribution across states of the percentage of women married to men of the same caste.



Different colours here represent different regions of India, and considering that the data in the above graph has been sorted by the value, the reasonably random distribution of colour in this graph (anyone notice a pattern anywhere?) shows that there is no real regional trend. But the inter-state differences represented in this graph are stark (80% to 97%). It raises the question regarding the homogeneity of castes and possibly differing definitions of castes in different states.

For example, some people might define caste as their “varna”, while some might go deeper into the family’s traditional occupation. Others might go further deeper – there is no end to the level you can reach in the caste hierarchy. Might it be possible that the stark regional differences can be explained by the varying definitions of caste?

Another interesting piece of data given is the percentage of women in each state married to either men of a higher or a lower caste. Now, in the interest of natural balance and matching, these two numbers ought to be equal (the paper notices a surprise that these two numbers are equal in most states – but there is no reason to be surprised). Actually we can create an “imbalance index” for each state – the difference between the percentage of women married to men of a higher caste and the percentage of women married to men of a lower caste.

A positive index indicates that women in the state prefer to “marry up” (men of higher caste) than “marry down”. It also indicates that in the absence of inter-state “trade” of marital partners, there will be large numbers of unmarried men of the lowest caste and women of the highest caste in that state! A negative index implies an excess of single men of the highest caste and single women of the lowest caste (both these calculations assume, of course, that the sex ratio is the same across castes). The second figure here plots this index across states. The  colouring scheme is the same.


This shows that there are states with massive imbalances – Maharashtra, for example will end up having a large number of single men of the lowest caste and single women of the highest caste unless they get “cleared” in “trade” with other states. Kerala has the opposite problem. It is interesting to notice that Punjab, which has the highest percentage of inter-caste marriages, also has a reasonably balanced market.

So should we explore if there exists a relationship between the proportion of women married to men of the same caste and how balanced the marriage market is in the state with respect to caste? The hypothesis, based on the example of Punjab in the above two graphs, is that the greater the incidence of inter-caste marriage in a state, the smaller the imbalance in terms of caste in the market. Let’s do a scatter plot which includes the above two bar plots and see for ourselves:


On the X axis we have the percentage of women married to men of the same caste. On the Y axis, we have the absolute value of the imbalance index (in other words, we don’t care which way it is imbalanced, we only want to know how imbalanced the caste dynamics in marriage is in each state). The blue line is the line of best fit. Notice that it slopes downward. In other words, the greater the number of same caste marriages, the smaller is the imbalance between women marrying above and below their own caste, which is interesting. Notice that Punjab sits all alone as an outlier at the bottom left of the above graph! Kerala is an outlier at the top left corner!

Now you might posit that if fewer people are available for inter-caste marriage, the difference between those “marrying up” and those “marrying down” is bound to be lower, since the sum is lower. However, if we normalise the index for each state by the proportion of inter-caste marriages in that state, the above graph will still look pretty much the same!

Caste and marriage are more complicated than we think!

The problem with precedence

One common bureaucratic practice across bureaucracies and across countries is that of “precedence”. If a certain action has “precedence” and the results of that preceding actions have been broadly good, that action immediately becomes kosher. However, from the point of view of logical consistency, there are several problems with this procedure.

The first issue is that of small samples – if there is a small number of times a certain action has been tried in the past, the degree of randomness associated with the result of that action is significant. Thus, relying on the result of a handful of instances of prior action is not likely to be reliable.

The second, and related, issue is that of correlation and causation. That the particular action in the past was associated with a particular result doesn’t necessarily mean that the result, whether good or bad, was a consequence of the action. The question we need to ask in this case is whether the result was because of or in spite of the action. It is well possible that a lousy policy in the past led to good results thanks to a favourable market environment. It is also equally possible that a fantastic policy led to lousy results because of a lousy environment.

Thus, when we evaluate precedence, we should evaluate the process and methodology, rather than result. We should accept that the action alone can never fully explain the result of the action, and thus evaluate the action in light of the prevailing conditions, etc. rather than just by the result.

It is going to take significant bureaucratic rethinking to accept this, but unless this happens it is unlikely that a bureaucracy can function effectively.

Comparing Airline Pricing across countries

The WSJ reports, based on a survey, that airline prices are cheapest in India (HT: Nitin Pai). They evaluate the cost of flying in terms of cost per 100 km. The usual ridiculous comparisons that go with any such article are present in full here – they compare the per kilometer cost of flying to train and bus fares, and conclude that flying is cheapest (this reminds me of an equally ridiculous report in the Times of India which showed that the cost of India’s Mars mission was less than that of taking a bus in Mumbai).

A few thoughts on this report by the WSJ:

  • Per km is a wrong way at looking at air fares. In most markets (from my experience pricing air tickets and cargo), fares are set based on competition and to fill capacity. Notice that marginal cost of a passenger is really really low, so once a flight is in place airlines will do what they can to maximize their revenues from that.
  • Taking this forward air fares depend on the competition in a particular sector (btw, the way airlines price it, Bangalore-Barcelona is one sector, and the price of that doesn’t depend on the Bangalore-Frankfurt and Frankfurt-Barcelona prices. These are three independent markets and triangle inequality doesn’t necessarily hold. Just FYI). So going by the report, India has a lot more competition compared to other countries in most sectors.
  • Now think of other large countries (you need big area for flights to make sense) and think of their income levels compared to India. Only developed countries and other BRICS come to mind. All of them have a higher willingness to pay than India.
  • Airline prices are thus a function of simple (elastic) demand and (inelastic – flight schedules are announced by “season”) supply. So once in a season we have a lot of flights scheduled, competitive forces push prices down
  • Given that it’s demand and supply that determines airline prices and not costs, in my opinion the airline industry goes through cycles. You have lots of competing airlines. Prices are low and they lose money. In the course of time one or two go out of business or scale down, and that leads to increased prices. Airlines make money for a while, and then looking at the supernormal profits you have new entrants and so on. India right now is going through the phase where you aer getting more investors (Air Asia, Air Costa, Tata-SIA, etc.). That depresses prices. In a year or so I would think someone like SpiceJet will go out of business and that might push fares up for a while.
  • There’s also the seasonality factor – based on regular travel to Bombay over the last two years I’ve found that fares in the monsoon months are half of the fares at any other point in the year. It’s a function of demand, again (Indian seasons don’t exactly tally with international seasons according to which schedules are made, so this results in flawed matching)! Given the timing of the piece it is possible that Indian fares in the monsoon months have been sampled.


Marginal and effective tax rates

In a recent blog post, corporate finance and valuation guru Aswath Damodaran (of New York University) has put out data of marginal and effective tax rates in different countries. The point of the post is about the “insanity of the US tax system” and the reason Damodaran presents this data is to show that the US has one of the largest differences between marginal and effective tax rates, and the company it keeps in terms of other countries that have  similar differences is not very worthy.

In this post we analyze the same data, but broadly from an Indian perspective. Where does India stand in terms of its marginal and effective tax rates? Figure 1 has a scatter plot of the marginal and effective tax rates. A few prominent countries have been marked.


A few pertinent observations:

  • 25% and 30% seem to be the most popular choices of marginal tax rate across countries. Other round figures such as 10, 15 and 20 also see significant representation
  • The highest theoretical marginal tax rate is in the US, followed by Japan. The lowest marginal tax rates (10%) are seen in three countries – Bulgaria, Gibraltar and Qatar (not marked on plot)
  • The countries with lowest effective tax collection are Kazakhstan (at a paltry 2% – compare that to its official marginal tax rate of 20%), Qatar (2.5%) and Cambodia (4%)
  • The countries with most effective tax collection are Norway (51% – compared to marginal 28%), Argentina, Papua New Guinea and Bangladesh! You can draw your own conclusions
  • India is remarkably close to Brazil and Pakistan in terms of its marginal and effective tax rates
  • India’s marginal income tax rate is on the higher side, but effective rate is much lower.
  • The best tax systems need to be effective and efficient – the closer a country is to the red line, the better its tax administration is IMHO

The entire data set is here. You can play around and draw your own conclusions.

India’s messed up agricultural markets

Indicat seems like a good website for data on Indian commodity prices. I came across this website when someone tweeted about tomato prices. What struck me was the wide variation in tomato prices both within markets and across markets.

Now, in terms of price variations within market, some of it can be explained by way of commodities of different grades – for example if I’ve brought bright firm and big tomatoes, I’ll expect a better price than what your small squishy tomatoes might fetch. Agricultural markets usually solve this problem by means of grading (unfortunately data of grading is not available, though the website gives data on different kinds of tomato).

The bigger reason why there is such wide price disparity within a market has to do with the auctioning process itself. For this I’ll dwell upon my limited experience of one data point in terms of a day that I spent in the potato market of the APMC yard in Yeshwantpur, Bangalore.

The issue is that the auction happens in lots, and quantities are not aggregated. Let’s say I bring in a couple of sacks of potatoes. The market makers inspect and grade them, and then proceed on an open outcry auction to auction my potatoes. The auction (increasing price English auction) quickly done, the market maker moves on to the next lot which someone else has brought. And so forth.

What this means is that potatoes that a trader can purchase is limited to the extent of the potatoes that were auctioned when he was present at the market. This process of continuous auctioning thus leads to large investments in time for the traders.

More importantly, having several small auctions rather than one big auction (where goods of similar grade are aggregated) leads to fewer buyers and sellers, and that leads to inferior price discovery. A significant portion of the bid-ask spread and uncertainty in the mandies can be eliminated by just aggregating demand and supply and having only one or two auctions for a particular commodity in a day.

In this mechanism, when I bring goods into the market, they will get graded and I will be given a receipt indicating quantity and grade. At the end of the day, there will be auctions for each “micro-commodity” (commodity of a very specific type) and based on the clearing price of the auction I an exchange my receipt for the appropriate amount. This receipt can be made negotiable – in that if I choose to not wait until the auction happens or want to lock in a price, I can simply sell the receipt outside of the market!

There is no rocket science to this mechanism. The reason it is not being implemented is because incumbent operators of the APMC (agricultural produce marketing committee) yards have a monopoly on trading commodities within their respective “areas” and thus have no incentive to improve their mechanisms. On the other hand, the current mechanisms are actually beneficial to increasing the returns to the market makers, with the producers and consumers bearing the price.

Then there is the issue of price variation across markets. While local markets might provide convenience to farmers and prevent them from traveling afar to sell their produce, it once again leads to fragmentation. For example, there is no reason that there need to be 15 tomato markets in Himachal Pradesh or 75 markets in Punjab! If we have a mandi receipt system, the problem of farmers traveling can be solved by aggregators, who can issue grade-quantity receipts to the farmers and collect and aggregate produce, and then take it to the mandi. While it is important that there is competition among mandis (so that the mandi with lowest bid-ask spread gets most of the business – which is how it happens in financial markets), we should not encourage fragmentation. And with increased competition, fragmentation will simply go away.

There has been some indication that the current union government is willing to reform the APMC Act (the first step towards it was taken by the previous NDA dispensation in 2003, but went into cold storage during 10 years of UPA rule). It is perhaps the one step that can have maximum impact on controlling inflation while at the same time giving good returns to farmers and horticulturists.

Figure 1 here shows the number of markets and quantity transacted in various states:



Figure 2 shows that the more the quantity transacted per mandi (that is, better the aggregation), the lower is the variation in price:


There is massive scope for beating down food inflation by simply increasing efficiencies and cutting down bid-ask spreads in agricultural markets. Unfortunately so far policy in this direction has been hostage to monopolists who dominate such markets. It is imperative that India opens up its agricultural markets in order to aid better price discovery of agricultural goods and consequently provide better prices for both producers and consumers.