Indian Society for Probability and Statistics
Letter from the President to all members and Friends of ISPS
(August 2006)
Dear Colleagues,
I propose to write to members periodically about matters of concern to all of us. I
feel that communication between office bearers and members should go well beyond the
single annual conference. My plan is to put before you some ideas, some proposals and
action plans. These will concern all three prongs of our professional activities, namely
teaching, research and consulting. These mails will be in the form of loud thinking and I
hope that members would take active interest and participate in the brainstorming.
This note is inspired by an article in The Indian Express (Monday March 20th,
2006). Author of the article is Ms. Sucheta Dalal. (e-mail addresssuchetadalal@yahoo.com) She is a regular columnist and writes about
economic/financial matters. Her emphasis is on good governance and social well-being.
This year the Government of India recognized her yeoman’s service to the profession of
journalism with an award of ‘Padmashri’. I strongly recommend her column (along with
a weekly column in The Times of India titled ‘Swaminomics’ by Swaminathan
Ankalesaria Aiyer) to all students and teachers of statistics.
Statistics has played an important role in development of agriculture in postindependence India. Perhaps foremost use is in estimation of area under different crops
and estimation/forecasting of yield. This is very important for policy making, though it is
of limited interest to individual farmers.
The second use is in designing experiments for selecting the best agronomic
practices. The third field of application is plant/animal genetics and selection of improved
varieties/breeds.
A crucial aspect of agriculture is weather. It can make or break a farmer’s fortune.
If rains fail, crops fail. That is obvious. If rains come at wrong times, then too the result is
the same namely crop failure. Weather can help or hinder growth of insect pests or fungal
disease. Hence a college of agriculture always has a department of meteorology. Its job is
to teach how to use weather forecast for better crop management. The advice is
sometimes rather simple-minded. If rain is expected, don’t irrigate. If a frost is expected,
give some warmth to your orchard. If cloudy weather is expected, harvest your caulyflower quickly or else it will become loose and will fetch a poor price in the market. All
this is useful but not enough.
Of late one reads about farmers’ suicides in different parts of our country.
Sometimes the cause may be market fluctuation. Thus for example, some areca nut
farmers in coastal Karnataka had to face a virtual meltdown when very cheap supplies
from Southeast Asian countries reached our markets after the import barriers were lifted.
But otherwise, the most common cause of suicide is crop failures.
How do we, you and I, prepare to face disasters? By purchasing an insurance
policy! So, we can ask (like Professor Higgins in ‘My fair lady’) why can’t the farmers in
India be like us? Perhaps they do not know about availability of insurance or it may
indeed not be available. But that is not true. We hear about crop insurance all the time. It
does not seem to help. I have some inkling of the difficulties in running a crop insurance
operation. Estimating crop loss due to an unexpected weather event must be very
difficult. In fact estimation of potential yield and actual yield are both difficult (either
scientifically or administratively or both).
It seems that an excellent alternative exists. That is weather insurance. You pay a
premium (similar to car accident insurance) that the company keeps if weather remains
normal. If weather turns bad, insurance company pays you an agreed multiple of the
premium amount. This eliminates the need to estimate crop loss. Hence the method
seems much more practicable.
In order for such an insurance policy to be fair to both sides, we need good risk
assessment. This is where statisticians can play a useful role. Let us think about a
hailstorm, the kind that wiped out citrus fruit crop in eastern Maharashtra this March. If
the risk of a hailstorm were 1%, what would be a reasonable trade off? A farmer should
be paid 100 times the premium if the disaster occurs. [Of course there has to be some
provision to cover the administrative expenses and profit of the insurance company.] So
statisticians need to study risks faced by farmers. Such studies can become the basis for
designing a fair insurance policy.
How big is this task? Here is my off-the-cuff estimate. There are about 30 districts
per state (i.e. about 750 districts in the country). A typical district may have two agro
climatic zones. My own district of Pune has an eastern half that is a low rainfall zone
while the western half is a high rainfall zone. Thus there may be about 1500 agro climatic
zones. Each zone may have say two crops of interest. In eastern part of Pune we can
consider grapes and figs. Thus there will be 3000 separate entities to be studied. (My
sneaking suspicion is that I may have underestimated the size of the task by one order of
magnitude since each crop has many varieties and each variety may have distinct weather
requirement.)
It seems to me that every college in India offering a degree in statistics can take
up a distinct study that may be of interest to the local economy. There can be an “All
India Coordinated Network” or AICON specially developed for this purpose. Perhaps
insurance companies interested in expanding activity in this sector may sponsor such
work. At present Agricultural Insurance Company of India offers insurance of the type
we have in mind. (Please do visit their website for more information). Perhaps
ICICILombard insurance company also has a policy for this purpose.
Under the program I visualize, a typical project will involve the following steps:
1.Select a local crop (say sorghum and mango)
2. For each crop select one (or more) major risk(s). As an example, in case of
Jowar (sorghum) if it rains just before harvest, grains turn dark and fetch a lower price. In
case of mango, if it rains during the flowering season in spring, the yield goes down. [For
identifying these aspects, liaison with botanists, agricultural colleges and practicing
farmers is necessary].
3. Collect weather data relevant to your problem (from India Meteorology
Department or local agricultural meteorologist etc.). Analyze the data and estimate the
risks of events of interest.
4. Design a model insurance policy. [Liaison with Commerce College or Business
Management College will be useful.]
This is my proposal. It can be implemented as a research project with funding
from the government or sponsorship from a commercial organization or a co-curricular
activity. Kindly send me your reactions. If the community of statistics teachers is, on the
whole, supportive, we can launch an action plan. I look forward to hearing from you. I
am quite excited about the possibility of our playing an active role in helping our
distressed rural brethren.
With warm regards,
Yours truly,
Anil P Gore
Professor
Department of Statistics, University of Pune
President, ISPS.
PS: I have postal addresses of members. The list is over 600 individuals. I would really
like to avoid snail mail and use only e-mail. Unfortunately I have only about 100 email
addresses. Please do me a favor and send me e-mail addresses of your friends who are
members of ISPS.
Varsha Bima
I wrote the above note about weather insurance and showed it to some
colleagues. The reaction was very positive. Hence I dwelt some more on the theme. I
realized that the note lacked adequate concrete suggestions for a program to be taken up.
So, I approached the designer of the insurance policy, Dr Rajas Parachure
(rajasparchure@niapune.com) with a request to give a seminar. He delivered it on April
29th 2006. What follows is a summary of his talk.
There are about 100 million farmers in India. They seem to work the hardest and
yet go through a life of hardship. Among other things, they seem to have to face very
high levels of risk in their enterprise. Any government that is committed to welfare of
farmers has to think about reducing the risk in farming. Crop insurance is one possible
way of doing that. In India, a pioneering attempt at crop insurance was made in 1870 in
the erstwhile Mysore state but the idea did not spread. In the post independent India,
there were many committees that deliberated on the feasibility of crop insurance. But one
man who promoted the idea very strongly and successfully was late Prof. V M Dandekar.
Under his intellectual leadership, the Government of India launched a pilot crop
insurance scheme in 1979. It was administered by the General Insurance Corporation and
limited to some areas of Maharashtra and Gujarat. In this pilot phase a modest premium
amount of Rs. 1.65 crores was collected and claims were paid to the extent of Rs. 1.35
crores. A more comprehensive scheme covered about 1.5 crore farmers. This is targeted
to increase to 5 crore farmers.
Nature of the scheme: In the simplest terms the scheme collects insurance premia
and compensates farmers for loss of yield. How is the loss assessed? For that we have to
know expected yield and actual yield. Expected yield is a 3 to 5 year (prior to year of
interest) moving average of actual yield. Actual yield is assessed by crop cutting
experiments. Estimates are obtained, not on any individual farmer’s field, but for a large
‘homogeneous area’ such as a Taluqa. If estimated yield was below expected yield some
indemnity was paid. It was calculated as {[expected yield-actual yield]/expected yield}
multiplied by sum assured. All this was done through the banking system. Sum assured
was always the crop loan taken by the farmer. The bank paid premium and indemnity was
paid into the loan account. It took about a year for payment. Farmers really did not know
what was going on.
New scheme: The new scheme called ‘Varsha Bima’ operates differently. It is
motivated by the fact that over half the variability in yield is due to rainfall variation and
that over two thirds of all farm yield is in kharif season. Hence the scheme tries to
evaluate elasticity of yield in response to rainfall.[Statistician associated with this work is
Dr Ashwini Kulkarni (ashwini@tropmet.res.in)]. It is calculated using the regression
equation
Ln(yt)=a + bt +c{ln[actual rain/normal rain]}
‘yt ’ is yield. C is the elasticity parameter. ‘t’ is time. This equation is used to compute
expected yield. Estimated reduction in yield multiplied by the minimum support price of
the crop gives the amount of indemnity. Thus only data on rain is enough to compute the
amount payable if any (assuming that the model is ready).
Two more policies have been drawn up. One is called a ‘sowing failure policy’. If
rains fail during pre-specified sowing season, costs of sowing are reimbursed. The other
policy recognizes that total rainfall is not adequate to predict yield. It distribution during
the season matters a lot. So, rain requirements in different growth phases are considered
and a composite rainfall distribution index is constructed. Indemnity is based on this
index. This particular product has received a cool response from the market. No one
wants to buy this policy, perhaps because it is complicated and evaluation of the index is
not as transparent etc.
There are some more schemes in pipeline. One is excess rainfall insurance. The
other is drought proofing for district collectors. If there is a drought, the collector will be
paid a sum to cover relief operations. It is proposed to float ‘varying interest bonds’ to be
sold in the capital market with interest rate tied to rainfall. If rain is good, interest rate is
high and if rains fail, the interest rate is low.
What can students and teachers of statistics do?
I asked Dr Parchure to suggest concrete ways in which a statistics department in a
college or university can play a useful role in this field. He has a few suggestions.
1. Good insurance policies will be based on reliable data; about rain and about yield.
There are about 500 rain gauge stations while there should be ten times as many.
A simple and yet very useful activity will be to measure rain every day in one’s
locality. You can get a measuring cylinder with suitable markings and place it in a
suitable place and keep record. This has to be done for a long period of time
(many years). Data collection is a good project in the first term and its analysis
can become a project for the second term. In fact, schools can do this data
collection as well.
2. Modeling. If we try to measure actual yield, work becomes very involved and
there is endless delay. So, as a substitute we need a good model. For this a tie up
with nearest agricultural college is essential. Once a good data source is
identified, good amount of effort should be devoted to building a reliable model.
3. Note that a policy to cover ‘sowing failure risk’ is attractive while one based on a
complex index is not. So, everyone should apply their minds to identification of
suitable risks. The event should be easy to observe and should have a significant
impact on the farmer. (My two suggestions are- risk of rain just before harvest
and risk of rain that adversely affects flowering in mango).