OSIPP Discussion Paper: DP-2017-E-002

Temperature and Rainfall Index Insurance in India§
April 19, 2017
Ayako Matsuda*
Research Fellow, Japan Society for the Promotion of Science
Osaka School of International Public Policy (OSIPP)

Takashi Kurosaki**Professor, Institute of Economic Research, Hitotsubashi University【キーワード】Weather Insurance, Temperature Insurance, Demand for Insurance.
【JEL Classification】O13, O16, G22【要約】Weather index insurance has been attracting much attention from academics and policy makers. This
paper investigates the demand for temperature and rainfall index insurance in India using the data fromrandomized subsidy experiments. We find that price, income and asset levels influence the demand for bothtemperature and rainfall insurance. We also show that richer farmers are less price-sensitive and farmers’
response to the discount becomes less price-sensitive as the amount of discount increases. Non-price factors
such as age and education level of a respondent are important correlates. Purchase decisions are also
influenced by individual prior experience and society experience of insurance.
*1-31 Machikaneyamacho Toyonaka, Osaka, 560-0043, Japan. Email: a-matsuda@osipp.osaka-u.ac.jp** 2-1 Naka, Kunitachi, Tokyo, 186-8601, Japan. E-mail: kurosaki@ier.hit-u.ac.jp.
§

We are grateful to Pranab Bardhan, Kei Kajisa, Ethan Ligon, Yasuyuki Sawada, Brian Wright, and audiences

at UC Berkeley, Hitotsubashi University, and the Japanese Economic Association 2015 Autumn Meeting forhelpful comments. Funding for this project was provided by JSPS Grant-in-Aid for Scientific Research-S(22223003), JSPS Program for Next Generation World-Leading Researchers, the Nakajima Foundation, andthe Institute of Business and Economic Research at University of California, Berkeley. The authors are alsoindebted to IFFCO-Tokio General Insurance Co. Ltd. and Pranam Welfare Society for the survey
implementation. We would like to acknowledge especially Shubham Jain for his administrative and researchsupport in this regard.

1.

Introduction

Farmers around the world face a variety of risks during agricultural production. In particular,
uninsured weather risk has been a signi…cant barrier for farmers engaging in ex ante risk
management and in ex post risk coping. Irrigation is often not available in developing countries,
thus agricultural pro…ts largely depend on seasonal and temporal weather variations, leaving
the risk of inclement weather as a signi…cant cause of production ine¢ ciency and income
variability.1 We examine data collected in India, where dependence on monsoons is known tobe very high. Nearly 70% of India’s cultivable land is rain-fed. Thus hedging weather risk isan essential way to improve household welfare by enabling greater production stability.2
Historical experience suggests that traditional crop insurance has not been …nancially viable,both in developed and developing countries. The claim payouts of crop insurance are determinedon the basis of realized harvests, and hence, an insurance agent has to assess the farmers’yields(either at the individual or regional level). However, the costs of obtaining accurate informationon the yield loss and of monitoring farmer behavior are prohibitively high, raising the problems
of moral hazard and adverse selection (Besley, 1995). In addition, systemic weather e¤ects
induce high correlations among farm-level yield, defeating insurer e¤orts to pool risk across
farms (Miranda and Glauber, 1997). Consequently, indemnity payouts and administrative costs
are far more than collected premiums, leading the insurance system to become insolvent.3
As an alternative formal insurance mechanism, weather index insurance has been attracting
much attention from academics, policy makers, and NGOs (Patrick, 1988; Hazell, 2003; Skees
et al., 2005; Morduch, 2006; Barnett and Mahul, 2007; Barnett et al., 2007; Chantarat etal., 2007; Alderman and Haque, 2007; Mobarak and Rosenzweig, 2013; Karlan et al. 2014).4
1

Rosenzweig and Binswanger (1993) …nd that a one-standard-deviation decrease in weather risk would raise

the average pro…ts by 35% among the poorest quartile in India.
2
Since the 1990s, there has been remarkable progress in theoretical and empirical studies on risk and insurance in developing countries. Self-insurance mechanisms as a means of precautionary savings, such as storing
crops and holding livestock, are often suboptimal; there are other more productive resources that farmers could
invest in. Kurosaki and Fafchamps (2002) …nd that farmers under-invest in more productive but risky crops
when they face severe weather shocks. Informal insurance, such as mutual help and rotating savings and credit
associations (ROSCAs), plays an important role when access to credit markets is limited (Morduch, 1994, 1995;
Dercon et al. 2008). Townsend (1994), Udry (1994), Ligon et al. (2002) and Ligon (2008) show how informal
insurance mechanisms have been working against various kinds of idiosyncratic shocks. However, informal
insurance might not be able to play a large role against weather risk, such as drought, ‡oods, and typhoons,
which are highly covariate in a village.
3
See, for examples, Hazell,(1992), Goodwin (1993), Wright and Hewitt (1994), Besley (1995), Miranda and
Glauber (1997) and Mahul and Wright (2003).
4
Weather index insurance products are available in many developing countries, including Bangladesh, Benin,
Burkina Faso, Cameroon, the Caribbean Islands, China, Ethiopia, Ghana, Guatemala, India, Kenya, Malawi,
Mali, Mexico, Mongolia, Morocco, Nicaragua, Peru, Senegal, South Africa, Tanzania, Thailand, Vietnam,
Ukraine, and Zambia. They are provided either as pilot projects or on a larger scale. Barnett, et al. (2007)2

In weather index insurance, payouts are usually based on weather parameters (e.g. rainfall,temperature, air moisture, and satellite-measured vegetation level) observed at a particularweather station. For example, typical rainfall insurance starts with a contract by which an
insurer indemni…es a farmer for his income loss if the amount of precipitation in a given time
period is below the pre-determined cuto¤.
The primary advantage of this insurance is that claim payments are made only on the basisof observable and veri…able indices, not on individual losses, so farmers cannot manipulate theindices conditional on that weather stations are securely locked. Second, the contract signi…cantly mitigates adverse selection problems because the claim payments are independent of thecharacteristics of insured farmers. Finally, in practice, there is no need to estimate the actual
loss experienced by the policyholder (Barnett and Mahul, 2007). Thus, the implementation costis less than that of indemnity-based insurance in that the claim rate is invariant across farmers
in a village, ensuring prompt payments with minimum costs. Providing payouts as quickly as
possible is especially important when farm households face credit constraints. The argument
in favor of index insurance mentioned above is mostly theoretical and our understanding of
weather index insurance and farmers’demand for it is still limited.
In this study, we conduct an empirical analysis of the demand for weather index insurance
products in low-income developing country. In collaboration with an insurance company, we
collected primary data of farmers through questionnaires and …eld experiments in which thefarmers randomly receive di¤erent levels of price subsidies. Using the dataset, we describe thedemand patterns for rainfall and temperature insurance. Main characteristics of our study
are as follows. First, our sample farmers do not face liquidity constraints as a confounding
factor because all of them have bank accounts to receive agricultural credits and the insurance
premium was deducted from their bank accounts. Second, we o¤er randomized insurance
premium rates to farmers. Third, farmers’basis risk.is considered by randomly choosing theones who are located di¤erently to their nearest weather station. Our study highlights how
the demand for weather index insurance is correlated with the above characteristics. The main
contribution of this study to the literature is that we cleanly identify price elasticities using theexogenous variation in o¤ered prices. Furthermore, the price response of insurance demand is
not contaminated by credit constraints. These aspects distinguish the estimation results of ourstudy from those in the literature on the price response of insurance demand.
This paper presents details of surveys implemented under the project conducted by theauthors and describes the key variables collected. In our regression analysis, we compare how
price, income and asset levels in‡uence the demand both for temperature and rainfall insurance
policies. The results show that farmers respond less to the price of rainfall insurance than to
the temperature insurance. We also …nd that richer farmers are less price-sensitive and farmers’
response to the discount becomes less price-sensitive as the amount of discount increases. In
provide a summary of ongoing programs in middle- and low-income countries.

3

addition, we show that non-price factors such as age and education level of a respondent andeducation are important correlates. Previous experience with an insurer, which is a proxy
for trust to an insurer, also matters. Finally, purchase decisions are also in‡uenced both byindividual prior experience and by society experience of insurance. The latter suggests spillovere¤ects among farmers in the same society.
The rest of the paper is organized as follows. Section 2 discusses the related literature on
weather index insurance. Section 3 describes the details of the insurance contracts analyzed inthis paper. Section 4 discusses the study sites and the sampling strategy. Section 5 provides thecharacteristics of the sample, and Section 6 and 7 investigate the correlates of the demand byregression analysis. Section 8 provides discussion and Section 9 contains concluding remarks.

2.

Literature on weather index insurance

In the last decade, important progress has been made in the empirical literature on weather
index insurance. The existing studies assume that there is a substantial demand and attempt
to identify the factors that a¤ect take-up. First, price is certainly a major determinant. Many
existing studies include (either hypothetical or actual) subsidies for the premium to increase
take-up (Giné and Yang, 2009; Miura and Sakurai, 2012; Cole et al., 2013; Mobarak andRosenzweig, 2012, 2013; Cai et al., 2015; Karlan et al., 2014; Takahashi et al. 2016). Cole etal. (2013) estimate the slope of the demand curve by randomly varying the price of insurance,and …nd signi…cant price sensitivity: a 10% price decrease leads to a 10:4%

11:6% take-up

increase.5
Second, liquidity constraint is another important factor. Households purchase insurance atthe beginning of the planting season when there are many other expenses to manage, such as
payments toward labor for land preparation, seeds, and/or fertilizer. Thus, households withless land and less wealth are less likely to buy insurance (Giné et al., 2008; Chen et al. 2012;
Cole et al. 2013; Matul et al., 2013).6
Third, Giné et al. (2008), Miura and Sakurai (2012) and Cole et al. (2013) …nd thatmeasured household risk aversion is negatively correlated with insurance demand. This result isinconsistent with what standard microeconomic theory predicts. Giné et al. (2008) conjecturethat uninformed risk-averse households are unwilling to experiment with the new …nancial
5

Cole et al. (2013) estimate that the price elasticity of their insurance product isand Rosenzweig (2012) discusses that the price elasticity for their product is

0:66 to0:88. Mobarak

0:44. Hill et al (2016) …nd theprice elasticity of their product is 0:58.
6
Cole et al. (2013) randomly give households high cash rewards as compensation for taking part in theirstudy and found that having enough cash increases insurance take-up. Chen et al. (2012) allow a deferral in thepremium payments by providing credit vouchers to farmers and …nd an increase in take-up by 11 percentage
points.

4

product, given their limited experience with it. These existing papers use risk aversion measures
elicited by a framed …eld experiment as de…ned by Harrison and List (2004).7 Their labexperimental games follow the approach developed by Binswanger (1980) by asking respondents
to choose among cash lotteries varying in risks and expected returns. While the implementationof the lab games is relatively convenient, it is very di¢ cult to realistically frame the games in thecontext of a particular insurance contract. Also, the stakes delineated in the lab-experimental
games are generally much lower than the real stakes farmers face. In addition, Rabin (2000)
proves that risk preferences elicited by small-stake games cannot accurately reveal large-stake
real-world risk preferences.
Fourth, existing literature has suggested that basis risk is one of the most important reasons
for weather index insurance not being attractive to potential clients. A theoretical study by
Clarke (2016) showed that a farmer might be worse o¤ with the insurance than without it. The
existence of basis risk means it is possible that a farmer pays the premiums, experiences a loss,
but then, does not receive an insurance payout. Clarke (2016) also showed that the demandfor index insurance of risk-averse agents would be low when the basis risk is high. Existing
empirical studies have also addressed the signi…cance of basis risk. In order to measure thebasis risk, Gine et al. (2008) used: i) a dummy variable, which takes a value one if a farmer
uses accumulated rainfall to decide when to sow and ii) the percentage of land used for Kharif
crops. Mobarak and Rosenzweig (2012a, 2012b) used perceived distance reported by the study
participants, which was converted to zero if the weather stations were situated in the village.
There are other important factors which appear to curb demand. One factor is subjective
uncertainty.8 Households’pessimism about the weather is positively correlated with take-up(Giné et al., 2008). Gallagher (2014) …nd that ‡ood insurance take-up increases by 9% soonafter experiencing a ‡ood. This recency bias is also discussed in Section 7.4. of the current
paper. Galarza and Carter (2010) …nd the opposite, with farmers tending to believe thata ‡ood is less likely to happen during the subsequent season. A lack of understanding andlimited attention are also possible factors that discourage purchasing, given that people have
only limited …nancial literacy and are not always able to evaluate the insurance (Cole et al.,2013; Cai et al., 2015).
Some studies contend that trust is another key factor impacting demand. Giné et al. (2008),
Cole et al. (2013) and Karlan et al. (2014) …nd that take-up decisions are strongly correlated
7

Harrison and List (2004) propose the following taxonomy of …eld experiments: i) an artefactual …eld

experiment is the same as a conventional lab experiment but with a nonstandard subject pool; ii) a framed …eld
experiment is the same as an artefactual …eld experiment but with a …eld context in either the commodity,task, or information set that the subjects can use; iii) a natural …eld experiment is the same as a framed …eld
experiment but where the environment is one where the subjects naturally undertake these tasks and where
the subjects do not know that they are in an experiment.
8
Delavande et al. (2011a, 2011b) discuss di¤erent methods for eliciting subjective expectations in developing
countries.

5with measures of familiarity with, and the reputation of the insurer. Salience and framing
are also important. Giné et al. (2008) …nd that the use of negative framing language on a‡yer and conducting household visits have a positive e¤ect on take-up. Networks appear tobe another in‡uential factor. Galarza and Carter (2010) show that having peers who su¤eredfrom a disaster increases the likelihood of take-up. Cai et al. (2015) also …nds that having afriend who has received …nancial education raises take-up by almost half as much as directly
obtaining …nancial education. Existing risk-sharing mechanisms are also related to low take-up.
Sakurai and Reardon (1997) …nd that demand varies according to individuals’self-insurance
strategies. Dercon et al. (2014) …nd that index insurance is a complement to informal risk
sharing and that demand is higher among groups of individuals that can share risk. In contrast,
Mobarak and Rosenzweig (2013, 2014) show that the availability of caste-based informal risk
sharing arrangements lower the take-up of their product, as the caste network could cover both
idiosyncratic and aggregate risk. Takahashi et al. (2016) …nd that consumer education throughthe provision of learning kits improves knowledge of the index insurance, but that improved
knowledge per se does not cause greater uptake.
Certain studies focus on supply-side issues. Carter et al. (2011) prove that take-up is higher
when index insurance is interlinked with credit contracts, while Giné and Yang (2009) showthat take-up among farmers that were o¤ered a bundled loan was actually 13% lower thanfor the control group o¤ered a standard loan.9 De Janvry et al. (2013) show that demandfor insurance can increase if the policy is sold to groups with common interests rather than toindividuals when free-riding and coordination failure are serious problems.
Other studies analyze household behavior. Simulations by Ragoubi et al. (2013) and deNicola (2014) show that the provision of weather insurance induces investment in riskier but
more productive crop varieties. Karlan et al. (2014) …nd an increase in agricultural investment
among households which are randomly given index insurance. Fuchs and Wol¤ (2011) …nd anincrease in yield among the insured maize farmers. Janzen and Carter (2013) show that insured
households are reported to be less likely to anticipate drawing down assets and reducing meals.
Among these factors a¤ecting the insurance demand, we focus on the e¤ect of price (premium) of the insurance product. Our study explores variation in prices that is orthogonal toother factors. In particular, we o¤ered potential customers four di¤erent price levels, and oneof them is randomly selected. We also consider heterogeneity in demand due to basis risk ininterpreting the demand patterns. A more detailed discussion of our survey and experimental
design is provided in Section 4. For the insurance contract studied in this paper, cash was not
required for the payment and the income levels of the sample households were far above thesubsistence level. Therefore, liquidity constraints does not have a key role. Also, our target
population had been able to borrow su¢ cient credit from the bank. Therefore, our demand9

The authors interpret the result as indicating that since farmers were already implicitly insured via limited

liability in the standard loan, they did not value the insurance.

6

estimates are not contaminated by credit constraints. Trust issues are addressed in this paperusing the variation in farmers’ previous exposure to similar products or companies o¤ering
the insurance product. A more detailed discussion of the insurance contract and its history isprovided in Section 3.

3.

Contract details
The weather index insurance market in India is the world’s largest, covering more than

9 million farmers.10 In this paper, we study two insurance products, rainfall index insurance
and temperature index insurance, sold by IFFCO-Tokio General Insurance Co. Ltd. (ITGI),
which is one of the major insurance companies in India.11 The rainfall insurance product iscalled Barish Bima Yojna (BBY), “rainfall insurance scheme”and temperature index insurance
product is called Mausam Bima Yojna (MBY) “temperature insurance scheme”.12 There are
two main crop seasons in India, Kharif and Rabi: a Kharif crop is a monsoon or autumn crop,with sowing usually occurring in June–July and harvests in September–November. Rabi crops
are usually sowed in November-December and harvested in March-April. For this reason, an
agricultural year in India is de…ned by combining the Kharif and Rabi crops in this order,
and we denote it as, for example, 2011/12 (that is, Kharif 2011 plus Rabi 2011/12). BBY is
indexed to the precipitation during the Kharif season, while MBY is indexed to temperaturesduring the Rabi season. ITGI started selling BBY insurance in 2004, and has since expanded
its market to most of the country.
The current study focuses only on the state of Madhya Pradesh (Figure 1), which is oneof the biggest markets with more than 110,000 farmers. Once ITGI approaches, the insurance
product is o¤ered to all farmers, regardless of the type of crops they cultivate, but they have tobe eligible to borrow from the District Central Cooperative Bank (DCCB). The DCCB is anagricultural bank a¢ liated with a cooperative society, which is an agricultural unit in which
each farmer holds a share13 .
1011

For an overview of weather insurance products sold in India, see Clarke et al. (2013).
ITGI is a subsidiary of a former public fertilizer company, Indian Farmers Fertiliser Cooperative Limited(IFFCO).
12
Mausam Bima Yojna literally means “weather insurance scheme.”In this paper, we call MBY as “temperature index insurance” considering its contract terms.
13
The DCCB’s branches are usually located at (or next to) a cooperative society’s buildings. Most of thelandowner farmers borrow money from the DCCB once or twice a year. Prior to the beginning of Kharif orRabi, a farmer visits his society manager to ask for a new loan. The society manager approves and sets the loan
limit. The loan limit is usually determined by the landholdings, repayment status, and crop portfolio. Thenthe society accountant …lls in the farmer’s passbook with a certi…cate and the farmer brings his passbook to thebank branch of the society to receive the loan. The gross interest rate for a short-term (one-year) loan is 12%(9% is subsidized by the local government) when the current study was conducted. There are other …nancial

7

[Figure 1]

3.1

BBY 2007-11

BBY (rainfall index insurance) is sold in May-June, prior to the beginning of the Kharif
season. The details of the contract vary across di¤erent districts with di¤erent seasons andyears covered. In this subsection, we describe the contract details that were in force during thecurrent study. We specify the year to denote the agricultural year that the BBY covered. For
example, BBY 2011 corresponds to the rainfall index insurance for Kharif 2011. The insurance
terms were changed between BBY 2011 and BBY 2012, which is explained in further detail in
Section 3.3.
The premium rates are listed in Tables 1. In the past, the government o¤ered a subsidy forthe insurance premium, but this was not available for BBY 2011. The premium rate increased
from 4:5% in 2007 to 8% in 2011 (Table 1).14 This is both because the subsidy from thegovernment suspended and the area became “riskier” from the insurer’s viewpoint as moreclaim payouts were made in 2007 and 2008. The premium of each insurance product is higher
than the actuarial fair level with a mark-up of around 25% in 2007, and increased to 75% in
2011.15[Table 1]
Clients chose the amount of the sum insured (SI), which is the maximum amount a farmer
can be paid in the event of loss. The actual premium payment is calculated as (the premiumrate) (SI).
BBY 2007

11 was indexed to the total rainfall from June to September. The monsoon

rainfall in Madhya Pradesh is the heaviest during these months, and thus the cumulative
precipitation over this period is a good measure of the monsoon conditions. The trigger level
was 768:8 mm, which was calculated and speci…ed by ITGI. The weather station whose records
are used for BBY is situated in the center of the district, in the district hall.16 The insurer pays
claims if the amount of total precipitation over the four months is below the predeterminedcuto¤. The payment schedule is provided in Table 2. The claim rate is de…ned as a percentage
of SI.
institutions, including informal moneylenders, from which farmers can borrow, but interest rates are generally
higher than those o¤ered by the DCCB.
14
In our study site, BBY was not sold during 2009-2010 because of capacity constraints experienced by the
supplier. In 2010, BBY was available in other districts of Madhya Pradesh.
15
The markup was calculated by the authors. The data used to calculate the actuarially fair premium wastaken from the National Climate Data Center, Climate Data Online from the National Oceanic and Atmospheric
Administration: http://www7.ncdc.noaa.gov/CDO/cdo.
16
Appendix shows pictures of the weather station and the rain gauge.

8

[Table 2]
Suppose a farmer is eligible to borrow Rs 20; 000 from the DCCB, and the loan is distributed
prior to the beginning of Kharif. In May, an insurance agent approaches the farmer about
possibly taking up insurance. If the farmer agrees, he decides the amount of SI. Suppose thefarmer’s SI is Rs 10; 000. If the premium rate of the area is 8:0%, his premium will be Rs 800.
This will be deducted from his bank account. Cash is not required for the premium payment,
and hence liquidity constraints are not a factor discouraging take-up of the insurance. Afterthe coverage period, the insurer declares the amount of the claim on the basis of the weather
data reported by the Indian Meteorological Department. If there are positive claim payouts,
an insurance agent visits the village to distribute checks to the individual clients before thebeginning of the next season. A claim payout is calculated according to the de…ciency rate.
The de…ciency rate (%) is de…ned as 1[(total observed rainfall)=(trigger level)]. As shown in

Table ??, if the de…ciency rate is 30%, the insurer will make an insurance payment of 10% of
the SI.
During the current study, the per-acre price of the insurance was presented to elicit thefarmers’ individual demand, i.e. the number of acres they want to insure at that price. As
discussed earlier, for 2007-2011, farmers were asked to choose the amount of the Sum Insured.
In contrast, during the subsidy experiments of the current study, farmers were asked to choose
the number of acres, not SI.17 Then the interviewers o¤ered four di¤erent levels of subsidy(0%, 25%, 50%, and 75%) if a farmer chose to participate in the study and buy insurance. The
details of this subsidy experiment are provided in Section 4:2:
As is clear from the insurance contract details, there is little potential for adverse selection
because the claim payments are independent of the characteristics of insured farmers. Also,
there is little reason to believe that moral hazard is an impediment since the policyholder
cannot in‡uence the realization of the underlying weather index (Barnett and Mahul, 2007).

3.2.

MBY 2012

ITGI’s temperature index insurance is called Mausam Bima Yojna (MBY: temperature
insurance scheme). MBY is a unique index insurance product that covers damage to crops
attributable to extreme heat during the growing and ‡owering periods for Rabi crops.18 For
instance, wheat, which is the main Rabi crop, is highly vulnerable to high temperatures during
January–February. If a high temperature hits the wheat crop during these months, the harvest
is substantially reduced.
17

This change was made because the insurance company found asking about acreage coverage was easier for

farmers to answer.
18
Appendix shows a picture of a thermometer used in the current district.

9

The details of the contract vary across di¤erent districts during di¤erent dates and years
covered. Although MBY has been available in other places, it was introduced for the …rst time
to our studied region for Rabi 2011/12. Since the insurance is indexed to the temperatures inearly 2012, we refer to it as MBY 2012, and describe it in this section.
MBY is sold in October–November, prior to the beginning of the Rabi season. The contract
details of MBY 2012 are summarized in Table 3. The contract divides the season into two phases
and six periods. Each period is two weeks long. Trigger levels and strikes are di¤erent for eachphase. The indices are two-week averages of the daily maximum temperature and two-week
averages of the daily average temperature. The per acre premium of MBY 2012 is Rs 576. For
simplicity, the insurer speci…ed the SI of MBY 2012 to be Rs 7; 000.
To calculate the claim payout of Phase I, suppose that the actual temperatures observed
during Period 1 and Period 2 were X1 ( C) and X2 ( C). Let the triggers, which are the averageof the daily average temperature and the average of the daily maximum temperature for each
two-week period, be T1 ( C) and T2 ( C), respectively. The strike is de…ned as S( C). Then,the claim payout (Rs) is calculated as follows:
Per Acre Claim Payout = 350[max f(X1T1 ) + (X2T2 )S; 0g]The notional is Rs 350 and the maximum payout is Rs 3; 500: Suppose there is a farmer whopurchased this product for one acre. He paid Rs 576 for the premium. Suppose then that the
average observed maximum temperature of Period 1 (X1 ) was 28 C. As shown in Table 4,
this is greater than the trigger level (T1 = 27) of Period 1 in Phase I by one degree. Similarly,
suppose that the average maximum temperature of Period 2 (X2 ) was 36 C. This is greaterthan the trigger level (T2 = 30) of Period 2 in Phase I by six degrees. Therefore, the total
number of degrees exceeded throughout Phase I was seven degrees. This is greater than the
strike (S = 4) by three degrees. Therefore, the farmer will be paid 350

3 = Rs 1; 050. In

MBY 2012, the actual claim payout was Rs 157. This was paid to clients in May–June 2012.
The claim payout for Phase II is calculated in a similar way.
[Table 3]

3.3.

BBY 2012

Prior to Kharif 2012, the insurer changed the terms of the BBY, in e¤ect extending thecoverage to include excess rain and consecutive dryness, in addition to the drought conditions
already covered under BBY 2007

11. The premium, de…ned in terms of insured land, was Rs

750 per acre. Again, farmers were asked to state the number of acre(s) they want to insure.
The insurer speci…ed the SI of BBY 2012 to be Rs 5; 000 for the cumulative rainfall de…cit, Rs
2; 500 for the excess rainfall, and Rs 1; 500 for the consecutive dry days. The details of thismodi…ed BBY are summarized below.
10First, we explain the cumulative rainfall de…cit insurance. There are four phases, and strikes
and notionals are di¤erent for each phase (Table 3). In order to calculate the claim payout
on rainfall de…ciency of Phase I, suppose that the actual precipitation is denoted by R (mm).
There are two ranges of strikes. Denote the upper and the lower bounds of the two strike
ranges as U1 ; L1 ; U2 ; L2 (U1 > L1 = U2 > L2 ). Notionals for each range of the strike are N1
and N2 , respectively. Then, the claim payout (Rs) is calculated as follows:
8>0
if U1 < R>>>
< N
(U1 R1 )if L1 < R
1
Per Acre Claim Payout =>N1 (U1 L1 ) + N2 (U2 R)
if L2 < R
>
>
>
:
1250
if R L2

U1
U2

As Table 1 shows, for Phase 1, fU1 ; L1 ; U2 ; L2 ; N1 ; N2 g = f60; 30; 30; 10; 10; 47:5g. The maxi-

mum total payout is Rs 5; 000: If the rainfall of Phase 1 is 40 mm, the claim amount will be
10(6040) = Rs 200. If the cumulative rainfall of the phase is 11 mm, the claim amountwill be 10(6030) + 47:5

(3011) = Rs 1202:5.

[Table 4]
The second component of BBY 2012 is the excess rainfall insurance. A claim for excess
rainfall is paid when the cumulative rainfall of any two consecutive days in a phase is greaterthan the strike (mm). The strikes, notionals, and maximum payouts for each phase are listed
in Table 4. The claim is calculated as:()(Cumulative rainfall of any two consecutive days - Strike)
Per Acre Claim Payout = max
Notional, 0[Table 5]
Third, BBY 2012 insures the event of consecutive dry days (CCD). The Consecutive Dry
Days’(CCD) index is applied from July 5 to September 15 (Table 6). A claim is paid when
the total number of consecutive days with a daily rainfall of less than 2.5 mm exceeds a strike.
The maximum payout for CCD coverage Rs 1,500. The total claim payout of BBY 2012 is thesum of the payouts for all three covers.19[Table 6]
19

The possible maximum total payout is Rs 6; 500. This is the case when there was little rain throughout

Kharif season and maximum claims for the cumulative rainfall de…cit and consecutive dry days are paid.

11

4. Study sites and data
4.1.

Study sites, sampling strategy, and primary surveys

In Madhya Pradesh, we chose the Burhanpur District in the East Nimar region for ourstudy, located in the southern part of the state, bordering the state of Maharashtra (Figure 1).
Before explaining the details of our surveys, we describe our studied region as the backgroundinformation.
The Burhanpur District is known for its rain-fed agriculture. Tubewells are available only
in a few areas. The major crops of the district are cotton, bananas, soybeans, sugarcane,
wheat and vegetables. Table 7 shows the proportions of land in the district cultivated for these
speci…c major crops, in comparison with the total land cultivated in the state. Cotton is themost important cash crop, occupying the largest share of the gross cultivated area (23:3%). It
is a Kharif crop, although its harvest may extend into the early months of the Rabi season, as it
usually takes 6 to 8 months to complete one crop cycle. The main cereals are jowar (sorghum)in Kharif and wheat in Rabi, both of which are suitable for rain-fed agriculture. These crops
are mostly grown for subsistence purposes. As a whole, cereals account for only 14:8% of thegross cultivated area. Other important cash crops are soybeans and bananas.20 Soybeans aremostly grown as a Kharif crop, although it is also harvested in Rabi. It is a fairly new cropin Indian agriculture, and its production spread throughout Madhya Pradesh during the 1990s
as a cash crop from which vegetable oil is extracted. Banana cultivation takes on average twoyears to harvest. Therefore it is not classi…ed as either a Kharif or Rabi crop. A timeline
showing key events covered in the study is presented in Table 8.
In the Burhanpur District, formal insurance is not new at all; governmental crop insurance,
motor, property, life, and health insurance have been available for years in some parts of the
district. Government crop insurance, introduced in 1985 and originally called the Comprehensive Crop Insurance Scheme (CCIS), now called the National Agricultural Insurance Scheme(NAIS), is provided by the Agriculture Insurance Company of India Limited (AICI). Under
NAIS, insurance for food crops, oilseeds and selected commercial crops is mandatory for all
farmers that borrow from …nancial institutions such as DCCB.21
Our surveys and experiments were conducted as follows. As BBY is sold through cooperative societies, this is the …rst tier from which we drew the sample. Farmers often visit thecooperative society to purchase inputs such as seeds and fertilizer, and to gather for meetings.
Its o¢ ce building is usually located within 5-10 minutes from each house on foot or by motorcycle. Our strategy was to draw a random sample of farmers belonging to each cooperative
20
21

Bananas are classi…ed as “Fruits” in Table ??.
In reality, almost no farmer in the current sample is aware of NAIS. No farmer actually has received any

claim. Clarke et al. (2012) point out that delays in claim settlement of NAIS has been a serious concern.

12society, with a substantial variation in the geographical distances between the weather stationand the farmers’landholdings.22 This is because the physical distance from the weather station
provided us with the proxy for the basis risk (see Section 4.3. for the details). To obtain precise
information on studied geographical locations and distances, we collected GPS information on
all the farmers’houses. The number of samples collected at each society is proportionate to
the size of the society.
[Table 7][Table 8]
Following the above methodology, the sample size of the current study consists of 433
farmers from 8 villages. The sampled farmers were active account holders of the DCCB, andwere all landowners.23 As discussed in Section 2, previous literature suggests one reason for
low take-up of index insurance is a liquidity constraint. However, a unique characteristic of
this paper’s sampling strategy was to target the population that had been able to borrowsu¢ cient credit from DCCB. The insurance premium is paid after the credit is disbursed, but
before the starting of the next agricultural season. Therefore, the targeted population is notcredit constrained at least at the time of the insurance purchase. There were six cooperative
societies: Bambada, Chapora, Dedtalai, Loni, Phopnar and Shahpur. BBY was available in
Shahpur, Bambada, Chapora, and Phopnar since 2007, but not in Loni and Dedtalai becauseof the insurer’s supply constraints. MBY was introduced to all six societies for the …rst timein Rabi 2011/12. As summarized in Table 9, claims were paid in 2007, 2008, and 2012.
[Table 9]
The current dataset consists of the information collected in two rounds of surveys. The…rst survey (denoted as “Survey 2011”) was conducted in October-November 2011, when MBY
2012 was being sold. The actual claim payout was Rs 157 per acre insured. This was paid toclients in May-June 2012. The second survey (denoted as “Survey 2012”) was conducted in
May-June 2012, when BBY 2012 was being sold. For both surveys, sample farmers were invited
to the buildings of their cooperative societies to be interviewed on the basis of a structured
questionnaire. Information on past take-up of BBYs was collected in a retrospective way fromeach farmer, and validated by crosschecking it with the administrative data maintained by the
insurer.24
22

In the survey sites, almost all farmers belong to cooperative societies. Section 5.3 discusses the heterogeneity

in socio-economic characteristics of the sample across cooperative societies.
23
The sample represents potential demanders. ITGI’s potential clients are farmers who own the DCCB
accounts for seasonal loans or have a positive balance in their saving accounts. Landless farmers are not eligible
to borrow agricultural loans from the DCCB.
24
Questionnaires used in Surveys 2011 and 2012 are available upon request.

13

4.2.

Insurance price discount experiments

To elicit the price sensitivity of farmers’individual demand, we implemented experiments ofrandom discounts in both Survey 2011 and Survey 2012. Once a farmer chose to participate in
the study, the interviewers o¤ered four di¤erent levels of subsidy (0%, 25%, 50%, and 75%).25
A 0% subsidy is equivalent to o¤ering the product at the original price, whereas a 50% subsidy
is equivalent to o¤ering the product at a half of the original price. The 75% discount is below
the actuarially fair level. Next, the subject was asked to specify their demand in terms ofacres at each subsidized price level (the insurance products were sold in 0:5-acre increments)26 .
Farmers agreed that they would purchase the quantity they speci…ed but the actual prices
would be determined randomly. By using this method, we elicited four price-quantity pairs per
farmer. We used the same procedure in both surveys.
After all four price-quantity pairs were recorded, the subjects rolled an eight-sided dice.
Each face value of the dice corresponded to the four options (1 and 2 to receive a 0% subsidy,
3 and 4 to receive a 25% subsidy, 5 and 6 for 50%, and 7 and 8 for 75%). Suppose a farmer
answered 0:5 acres for 0%, 0:5 acres for 25%, 1 acre for 50%, and 2 acres for 75% in Survey 2011
for MBY 2012. The per acre premium was Rs 576. If the number on the dice he rolled was 7,then the actual amount payable by him would be Rs 576

225%= Rs 288. The di¤erence

between the subsidized price and the market price was the amount paid to the subject by theauthors.

4.3.

Measurement of the basis risk

Ideally, to estimate the basis risk, we need detailed precipitation data measured on bothindividual plots and weather stations. However, for this study, installing rain gauges on theplots of 433 farmers (who often also have multiple plots) was not practically possible, due
to …nancial and logistical constraints.27 Giné et al. (2008) used two variables: i) a dummy
variable, which takes the value one if a farmer uses accumulated rainfall to decide when to sow,
and ii) the percentage of land used for Kharif crops. These variables are indirect measuresof basis risk, as Giné et al. (2008) state that an “alternative variable for measuring basis
risk would be the distance to the rain gauge or some other direct measure of the di¤erence inweather between the farm and the weather station.” Mobarak and Rosenzweig (2013, 2014)
used a perceived distance which was reported by the study participants. This distance wasconverted to zero if the weather stations were situated in the village.28
25

All farmers agreed to participate in the experiment. This was likely to be because the experiment was

followed by the socio-economic survey.
26
Every participant was asked to answer the demand at the same order (from 0% subsidy to 75% subsidy).
27
Miura and Sakurai (2012) collected rainfall data on individual plots for 48 households in Zambia.
28
The mean of the reported distance was 4 kilometers, with a standard deviation of 5.9 kilometers.

14

Instead, we used the physical distance between farmers’ houses and the weather station
as a proxy variable for the basis risk. While the physical distance is not a perfect proxy,
meteorologists and agronomists claim that the basis risk is large when the insured’s plot is far
away from the meteorological station (Fisher et al. 2013). This measure is also intuitive in that
farmers are likely to estimate their basis risk subjectively on the basis of the di¤erence between
the weather near their house and that at the nearest weather station.29 Therefore, we collected
geocodes (latitude, longitude, and altitude) for the weather station and for each respondent’s
house. Given that farmers have multiple plots, they may not subjectively calculate the amountof the basis risk for each plot.30 Therefore having the distance from their houses to the weather
station is a reasonable proxy.
We show how these geocodes varied across societies and sampled farmers in Table 10. The
mean distance to the weather station is 11:6km. The variance in the distance to the weather
station is fairly large with the coe¢ cient of variation at 111% (12:87 divided by 11:60). As
shown in the table, most of this variation is between societies. However, it is important topoint out that within-society variation is not negligible, especially in Chapora and Bambada.
The mean altitude (above sea level) is 267:6m. The variance in altitude is very small, which
implies that the area is ‡at.
[Table 10]

5.

Characteristics of sample farmers
In both Surveys 2011 and 2012; we collected detailed information on the sample farmers’socio-economic characteristics, such as family roster, assets, income, agricultural activities,
insurance take-up and claim receipts in previous seasons, consumption, and demand for the
insurance that was sold during the surveys. In this section, we describe the summary statisticsof those variables.

5.1.

Basic characteristics

Table 11 summarizes the sample’s socio-economic characteristics. The average age of the
farmers is 51:4 years old, and the average household size is 5:48 persons: The farmers havereceived an average of 6:42 years of education, and have a literacy rate of 52:9%. This levelof literacy is comparable to the state-wide level of 57:8% (rural areas only):31 12:6% of the29

Basis risk can also be caused by other topographical di¤erences in ground contours, slope, etc. These

factors are important, but beyond the scope of this paper.
30This was con…rmed from casual conversation with respondents during interviews.
31
Census 2011.

15

sample belong to the Scheduled Castes (SC) or Scheduled Tribes (ST),32 and the majority ofthe sample (81:6%) belongs to the Other Backward Classes (OBC). Though 62:8% of them haveaccess to wells, they still don’t have enough water supply for agricultural uses: they pay anaverage of Rs 15; 100 (= USD 316) to rent motor pumps from their neighbors during Kharif.33
Indeed, 88:4% of them answered that weather risk is the biggest risk that they face.34
Table 11 also lists summary statistics on assets and farming activities. As already described,
all sample farmers own land. The average landholding size is 4:7 acres. This is slightly larger
than the average landholding size of all farmers in Madhya Pradesh (3:68 acres in 2003),35
indicating that our sample does not disproportionately represent wealthy farmers, but contains
a number of small and medium farmers. Of the 4:7 acres of average landholdings, 4:03 acres
are irrigated.36 The average value of a house is Rs 335; 000 (that is, USD 7002).
Table 11 also reports the total agricultural income for each season. The total agricultural
income during Kharif 2011 is Rs 135; 700 (= USD 2836). The average value of the loans the
sample farmers for Kharif 2011 was Rs 85; 600 (= USD 1789). The total agricultural income
during Rabi 2011/12 was Rs 111; 200 (= USD 2324), while the average loan provided at the
beginning of this period was Rs 63; 800 (= USD 1329).
Areas under each crop are also listed in Table 11. Consistent with state-wide statistics, area
under cotton is the largest in Kharif seasons. The area under bananas is much higher thanthe district average (Table10), suggesting that our sample comprises farmers with a stronger
commercial orientation than the district average. After cotton and banana, jowar accounts forabout 3:6% in Kharif, and wheat for 10:5%. These crops are highly susceptible to extreme
weather. The numbers for Kharif 2012 were planned ones because Survey 2012 was conducted
right before Kharif 2012. Disbursement of the Kharif crop loan was also in process.
In the sample, 97% of the farmers were clients of IFFCO, the fertilizer company. As wediscuss later, this familiarity with the parent fertilizer company played an important role inenabling the insurer to sell the insurance products, by reducing potential farmer mistrustof the product and/or provider. The questionnaire also included eight arithmetic questions
to determine the farmers’ numeracy. The average number of correct answers was 3:1 (Thestandard deviation was 3:4).
[Table 11]32

29:4% of the entire population of the state belong to the SC/ST (Census 2011).
The average exchange rate of the …nancial year 2011/12 is Rs 100 = 2:09 USD (Government of India, 2012).
34
Farmers were asked to rank di¤erent kinds of risks (weather risk, price risk, lack of money, lack of family
33

labor, lack of land, and lack of infrastructure) from the most to least serious. This question is taken from Coleet al. (2013) where 89% of the sampled households cite the weather risk as the biggest risk.
35
This …gure excludes data on landless households and is taken from Statement 4 (state-wise average size ofhousehold ownership holdings), NSSO (2006), p.15.
36
The change in landholding between the two surveys was very small. Although many farmers have accessto some sort of irrigation, available water supply is usually not su¢ cient.

16

5.2.

The take-up of weather insurance

The incidence and depth with respect to the take-up of MBY and BBY are summarizedin Table 12. First, we describe the take-up in previous years. The table shows that 24% of
the farmers had purchased BBY prior to the current project. This low level of penetration isconsistent with existing studies.37
Second, we move to the results of our price discount experiments. In both Survey 2011 and2012, we collected four price-quantity pairs per subject. To our surprise (we will discuss thisbelow), those who demanded zero when discount was 0% continued to demand none even when
positive discounts were provided. Therefore, although we collected the information about theuptake at the level of farmer-price pairs, we did not obtain any variation for those farmers
regarding the take-up dummy. As a result, our measure of take-up becomes a dummy for those
farmers. It takes 1 if the farmer demanded a positive amount of insurance and 0 if the farmer
demanded no insurance.
As shown in the middle of panel of Table 12, in 2011, 72% of the respondents purchased
MBY 2012. This take-up rate is extremely high when compared to existing studies. It might
not be surprising, however, as our sample di¤ers substantially from that of the existing studies
in that the income level is higher, credit constraints are less likely to be binding, and index
insurance is not at all new. In Survey 2012, BBY 2012 was purchased by 41% of the respondents(see the lower panel of Table 12). The decrease in the take-up rate of BBY 2012 might bebecause people were disappointed by the small (yet positive) claim amount (Rs 157) of MBY
2012. Also, there is a substantial heterogeneity in the take-up across cooperative societies.
Detailed discussion on this is provided in Section 5:3:
[Table 12]
The results of the subsidy experiments are also summarized in Table 12. As expected,
insurance demand is a decreasing function of insurance price for both MBY and BBY. Thisshows that subjects all well understood the rules of the experiments. It is striking that,conditional on the take-up, demand function of MBY is more price-sensitive. This may be
because people are more likely to experiment the brand-new temperature insurance if thepremium is discounted. Section 6 and 7 examine this point in details.
To our surprise, however, 64% (MBY) and 91% (BBY) of the 433 farmers demanded thesame insurance coverage regardless of the price discounts. Figures 2 and 3 show the distributionof average demand for inelastic and elastic groups, across prices. The inelastic groups are those
who did not change their demands at all when di¤erent levels of discounts were given, whereas the elastic groups did change their demands.
37

As mentioned before, the take-up rates of brand-new index insurance products implemented by Giné et al.

(2008), Cai et al. (2014) and Cole et al. (2013) were 4:6%; 20% and 23%; respectively.

17

The inelastic groups are divided in two types: those who didn’t demand anything regardlessof the discount levels, and those whose demands were invariant across discount levels. From
our …eld observations, the reason for the absence of price response for the …rst type of farmers
would most likely to be general disinterest in or distrust against the insurance program. To
them, price discounts were not e¤ective to change their demand.
Among those whose demand for MBY was positive but inelastic, 13 farmers demanded
0:5 acres, 123 farmers demanded 1 acre, and 12 farmers demanded 2 acres, regardless of theprice. For BBY, 52 farmers demanded 0:5 acres, 68 farmers demanded 1 acre, and 14 farmers
demanded 2 acres, regardless of the price.38 As shown in those …gures, one farmer demanded
10 acres for all the four prices for both products.39

5.3.

Di¤erences across cooperative societies

Tables 13 and 14 show that there is a signi…cant heterogeneity in the baseline characteristics,
farming activities, and insurance-related characteristics across cooperative societies. Amongthe six societies, Phopnar and Loni are the richest. Though the average amount of land owned
per household (8:4 acres) is the second largest in Dedtalai, the share of the irrigated land (79%)
is the lowest, and this society has the second lowest average income. Also the literacy rate offarmers is the lowest (31%), and the ratio of SC/ST is very high (56%) in Dedtalai.
Almost all of the farmers in the six societies are clients of the fertilizer company. Cotton
occupies the largest share in the cropping pattern of all the societies, except Shahpur. Bananas
are also very important in all of the societies except Dedtalai, where its cultivation is almost
impossible, due to the area’s lack of water. The area of land used for banana cultivation
was slightly reduced in Kharif 2012, probably because the banana production cycle ended in
the previous season. However, given that these numbers are only planned …gures, the actual
cropping pattern might not be that di¤erent to that of Kharif 2011.
Demand for BBY 2012 was lower in Loni and Dedtalai than the overall average. This might
be attributable to the non-exposure to weather index insurance until Kharif 2011; with BBY
introduced for the …rst time to those societies in that year. In the case of Dedtalai, the sizeof the basis risk may have been responsible, since this society is the farthest from the weather
station (Table 10). Among the four societies with previous exposure, the demand for BBY
2012 was the highest in Chapora and lowest in Phopnar.

38

This observation is consistent with Cole et al. (2013). In their sample, households almost universallypurchase only one policy unit when they ever do purchase insurance.
39
The landholding of this farmer was 60 acres. Therefore, the demand in terms of the share of landholding
is 0:6; which is an average amount in the current sample.

18

6.

Regression analysis

6.1.

Model

Using the data described above, we estimate multiple regression models for the insurance
demand. The motivation of this analysis is descriptive. We are interested in expected values ofan insurance demand conditional on speci…c combinations of prices and farmers’characteristics.
Therefore, we estimate reduced form models by OLS with insurance demand as the dependent
variable and a list of explanatory variables. See Matsuda (2014) for an analysis with structural
estimation using the same dataset.
Speci…cally, for individual i and price level j; the demand function is regressed on a vector ofvariables (Xij ). The dependent variable is the demanded acre (Demandij ). Thus the estimatedequation is
Demandij = Xij + "ij(1)

where Xij may include the discount for the insurance premium (the measure of price),socio-economic characteristics of farmer i; and the interaction terms between the two. "ij isa zero-mean error term, capturing unobservable factors such as risk preference, soil quality,
availability of informal risk-sharing arrangements, and formal risk-coping mechanisms.
By design, the price pij is exogenous and varies over both i and j. Farmers’ socioeconomic characteristics may not be exogenous and vary over i only. We will estimate theequation (1) for both temperature insurance and rainfall insurance, assuming that error terms
over the two periods are independent. Standard errors are clustered at individual level.40 As
main speci…cations, we report results using all 433 farmers, including those demanded zero acre
regardless of the discount levels.41 Empirical strategy is explained in the following subsection.

6.2.

Empirical strategy

As an empirical strategy, we …rst regress the insurance demand on price, pij , which is the exogenous variable. Next, we add individual variables which might not be completely exogenous
but highly likely to be predetermined variables, such as income, landholding, age, educationlevel, distance (km), literacy and math. Then, we add individual variables for which simultaneity would be problematic, such as previous purchase and IFFCO client. Finally we checkthe correlation of the demand and society dummies. It is important to note that individual
variables might be highly correlated with society dummies.
40

We also considered household …xed-e¤ect model. All the results are qualitatively identical to the following,as all the dependent variables except for insurance prices pij are in invariant for each farmer.
41
The results using a restricted sample of those who demanded positive acres will be discussed as robustness
checks.

19

[Figure 2][Table 13][Table 14]

7.

Results

7.1.

Results for the temperature insurance

Now we report the results of the regression of an insurance demand. Table 15 presents
estimates of the regression (1) for the sales of MBY (temperature insurance). In Column 1, we…nd that the demand signi…cantly decreases with premium (price e¤ect). 100-rupee increasein premium signi…cantly decreases the demand by 0:0696 acre. Although the magnitude isvery small, the result is robust, suggesting that price is an important predictor of demand.
Then, we add individual variables which would probably be predetermined variables, such as
income, landholding, age, education level, distance to the weather station, literacy and math. In
Columns 2-5, we show that measures of wealth are positively correlated with insurance demand.
Columns 2 and 3 show that 1% increase in income and one acre increase in landholding raise theinsurance demand by 0:195 acre and 0:0602 acre, respectively. Both estimates are statisticallysigni…cant. The results on Column 4 con…rms the robustness, but the income e¤ect is no longer
signi…cant in Column 10.
Columns 5 shows that age of the respondents decreases the insurance demand, but theestimate is not signi…cant. More educated or literate farmers, or farmers with math skills
demand more insurance (Columns 6-8). Cole et al. (2013) and Cai et al. (2015) …nd that alack of understanding and limited attention are important factors that discourage purchasing,
given that people have only limited …nancial literacy and are not always able to evaluate thevalue of an insurance product. Column 9 shows that distance is negatively correlated with theinsurance demand and the estimated coe¢ cient is signi…cant. Farmers living one kilometeraway from the weather station demand less insurance by 0:006 acre. While this may suggest
the basis risk, this might be driven by Dedtalai Society where is located very far from theweather station (Table 10). Finally, in Columns 10, we report that the price and asset e¤ects,
and distance are strongly correlated with the insurance demand. We will analyze the societye¤ects in the next tables.
We analyze the correlation of the demand and society dummies in Table 16. Being a farmerof the new society (Loni and Dedtalai) is negatively correlated with the insurance demand(Columns 1-3). The magnitude of the estimated coe¢ cient is large and signi…cant at 1%.
20

Farmers in Bambada society also demand less (Columns 1 and 3). This may re‡ect that theincome level of Bambada society is lower than other societies where the insurance had been
introduced (Tables 13 and 14). We also …nd that indicators for farmers who had previous
experience purchasing the ITGI’s insurance or IFFCO’s fertilizer is positively correlated withthe demand (Columns 4 and 5). In Column 6, the share of irrigated land is positively correlatedwith the insurance demand, but the estimate is not signi…cant.
Table 17 shows other results. In Column 1, we con…rm that price and asset e¤ects, newsociety dummy and IFFCO client dummy are signi…cant. The magnitude of the estimates
are similar to the results in Tables 15 and 16. Columns 2-4 include interaction terms. Theinteraction term, new society*price is positive in Column 3, implying that the demand of
those who belong to the new societies are less price-responsive although the magnitude is quite
small compared to the new society e¤ect (-0.777 in Column 3). This might be because thatthose farmers demand (or experiment) at least some, conditional on the farmers in the newsociety being interested in the product. The term distance*price is positive and signi…cant.
Landholding*price is negative and signi…cant, which suggests that farmers with larger land isless price-sensitive. Column 4 also shows that the interactions between IFFCO client dummy
and price is -0.000456, implying that those who have experience with IFFCO are less pricesensitive. Another interesting …nding is that the interaction between irrigation and price ispositive and signi…cant. Demand of the farmers who have access to irrigation is more pricesensitive.42[Table 15][Table 16][Table 17]

7.2.

Results for the rainfall insurance

Now we focus on the rainfall insurance. As before, we regress the demand (acre) of rainfall
insurance (BBY) on farmer characteristics, assuming that error terms for the temperature
insurance regression and rainfall insurance regression are independent. In Table 18, Columns
1-4 present the price, income and wealth e¤ects. Price per acre is exogenous. 100-rupee increasein the price decreases the demand for the rainfall insurance by 0:0116

0:0113 acre. This iscomparable to Columns 1-4 of Table 15, which …nds that 100-rupee increase in the price forthe temperature insurance raises the demand by 0:0696 acre, suggesting that farmers respondless to the price of rainfall insurance than to the temperature insurance.
42

The results discussed so far were found robust when we restricted the sample to those who demanded

positive acre(s). Results for MBY using the restricted sample are available upon request.

21

In Columns 2-4, we …nd that the income e¤ect and asset e¤ect are positive and signi…cant.
The magnitude of the income e¤ect is smaller and that of the asset e¤ect is larger than thosee¤ects for the temperature insurance. By the nature of weather insurance, receiving the claim
payout implies the weather (in this case, temperature) of the previous season was not good,which probably resulted in income loss. Hence, it is natural to think of the income e¤ect; theincome of MBY buyers might have been higher than that of non-buyers because buyers didreceive the claim payout. However, recall that the claim payout was only Rs 157 per acre. The
estimated coe¢ cient on log income in previous tables suggests that receiving the claim of only
Rs 157 was not enough to boost demand for the rainfall insurance the next season. In Column
5, we show that age is negatively but not signi…cantly correlated with the demand. One year
increase in schooling signi…cantly increases the demand by 0:025 acre (Column 6).
We analyze the correlation between the demand and several dummies in Table 19. The
corresponding table for the temperature insurance is Table 16. Consistent with the results
for the temperature insurance, farmers in the new societies still demanded less (Columns 13). Column 4 includes a dummy variable, which takes the value one if a farmer purchased
the temperature insurance in the previous period. The estimate for this dummy is positiveand signi…cant, suggesting that those who have recent experience with the insurance (andhave received payout for it) demand 0:323 acres more. It is striking that the magnitudeof the coe¢ cient for MBY purchase dummy is large compared to, for example, price andincome e¤ects. Consistent with Stein (2014), Cole et al. (2014) and Karlan et al. (2014), thisresult suggests that experience of buying insurance and receiving the claim in the preceding
season induce them to buy again.43 Takahashi et al. (2016) discuss that farmers who received
a discount for the insurance product in the previous season may have anchoring e¤ect as
the discounted price may work as a reference point. In this paper, we also estimate thecoe¢ cients for the discount of MBY (Rs) in Column 5. We …nd that the coe¢ cient is positive
but insigni…cant, suggesting that learning e¤ect might be bigger than the price anchoring e¤ect.
We also …nd that indicators for farmers who had previous experience purchasing the ITGI’s
insurance or IFFCO’s fertilizer is positively correlated with the demand (Columns 6 and 7).
Table 20 shows further results. Column 1 con…rms price, income and asset e¤ects. It
also shows the robustness of the coe¢ cient of education, distance, new society dummy andpurchased MBY. We also regress the insurance demand on a set of interaction terms of priceand farmer characteristics. Columns 3-4 …nd that the interaction between log income and priceis negative and signi…cant. This can be interpreted as richer farmers are less price-sensitive.
This is consistent with the results that landholding*price is negative and signi…cant. Similar
43

By design, farmers who purchased the temperature insurance in the previous season all received the claim.

Therefore, we cannot identify whether buying per se or buying and receiving the claim altogether increase thedemand in the subsequent period.

22

to the Column 3 of Table 17, we show that the interaction term, new society*price is positive,
but the magnitude is quite small. This suggests that those farmers are less price-responsive.
The interaction between distance and price is negative and signi…cant, implying that farmers
living far from the weather station are less price-sensitive (Columns 3 and 4). Contrast withthe result shown in Column 8 of Table 19, the coe¢ cient for irrigation is not signi…cant.
[Table 18][Table 19][Table 20]

7.3.

The di¤erential e¤ect of the discounts

As a more ‡exible estimation of the price response of insurance demand, we introduce set of
discount rate dummies. Table 21 investigates the di¤erential e¤ect of the discounts. Variablesof Discount 25%, 50% and 75% are the indicators for each level of the discount. We …nd thata marginal e¤ect of the discount decreases as the amount of the discount increases. This isconsistent with the summary statistics shown in Table 12. The corresponding mid point arc
elasticities are -0:60; 0:32 and

0:08 for the temperature insurance and0:21;

0:16; 0:04

for the rainfall insurance. Our …nding implies that even a small discount or subsidies toweather insurance can be e¤ective to boost the demand. We also con…rm that the demand for
rainfall insurance is less price-elastic than the demand for temperature insurance. The results
are robust when controlling other farmer characteristics in Columns 2 and 4. The estimatedcoe¢ cient for an indicator of purchasing MBY is positive and signi…cant in Column 4.
[Table 21]

7.4.

The dynamics of the demand

Our results above imply that the experience of purchasing the insurance and receiving theclaim payout in the previous season is an important correlate of the insurance demand for thefollowing season. Table 22 examines this e¤ect in details. The demand for MBY purchasers
is larger than that of non-purchasers by 0:323 acre in Column 1. This is consistent with
Stein (2014) and Cole et al. (2014), both of which …nd that individual experience of receiving
an insurance payout increases the probability of re-purchasing. In addition to the individual
experience, Karlan et al. (2014) …nds that network-level experiences matter. Observing others
in a society buying insurance and receiving the payout may in‡uence one’s purchase decision23

in the next season.44 Columns 2 and 5 add the total number of households in a society whopurchased MBY. In Column 2, the coe¢ cient is positive and signi…cant. If there are 10 morebuyers in a society, there is an increase in individual demand by 0:0283 acre. The coe¢ cientfor the share of the households in a society who purchased MBY is not signi…cant (Columns 3
and 5). While there are positive e¤ects on society-level experience, we …nd that individual level
experience is a stronger predictor of demand than society-level experience. Another hypothesisis that if more lands in a society are covered by the insurance, farmers (non-buyers) may be
more attentive to the existence of insurance. The total number of acres in a society coveredby MBY increases individual demand by 0:369 acre (Column 4) though the coe¢ cient is nolonger robust in Column 5.
Taken together, we …nd that individual purchase decision is in‡uenced both by an individual prior experience and by society experience of insurance. The latter result is suggestive
evidence of spillover e¤ects. Karlan et al. (2014) and Cole et al. (2014) …nd that demand
increases after either the farmer or others in his network or village receive an insurance payout,
and demand is lower if a farmer was previously insured and the weather was good, thus nopayout was made. The results shown in the Table 22 provides another suggestive evidence
that the spillover/network e¤ect is an important correlate of the demand. Controlling for theendogeneity is left for further research.
Alternative hypothesis is that the temperature shock changed farmers’ belief over theweather in the following Kharif, or, experiencing bad weather may make the tail of weather
distribution more salient. Karlan et al. (2014) and Stein (2014) discuss that insurance repurchase is driven by behavioral/psychological e¤ects such as overweighting of recent events.
Our current dataset does not allow a detailed analysis of the repurchasing decision and spillovere¤ects. Such analysis is left for future research.

8.

Discussion

Five characteristics of the current study distinguish our dataset from previous studies. First,
a quarter of our sample had experience in purchasing the insurance products prior to our
project. This is di¤erent from previous literature, which focuses on the take-up behavior of
new clients. Second, almost all the households were familiar with the fertilizer company, whichis the parent company of the current insurer. This would reduce mistrust of the insurer, which
previous literature has shown to be one of the biggest barriers to insurance take-up. Third,
there exists a wide variation among the sample households with respect to the distance to the44

While Karlan et al. (2014), Stein (2014) and Cole et al. (2014) investigate the dynamic purchasing behaviorof the same products (rainfall index insurance) sold annually, this paper analyzes the purchasing behavior ofthe di¤erent products sold in much closer timing.

24

weather station, which gives us the variation in the proxy for basis risk. Fourth, we drew oursample from farmers who had bank accounts for crop loans and whose insurance premium wasdeducted from their bank accounts. This implies that our sample farmers did not face severe
liquidity constraints. Due to the fourth characteristic, by construction, we can exclude liquidity
constraints as a reason for the low take-up of weather insurance. It should be noted that oursample covers a wide range of landholding, including small farmers. Fifth, in collaboration
with the insurance company, we analyzed the actual insurance products with real stakes, nothypothetical stakes in a lab, and experimentally changed the price for insurance to elicit theindividual demand structure. The actual premium amount was determined randomly by rollinga dice.
The descriptive statistics of the data collected in two rounds of surveys in 2011 and 2012
show a substantial variation in demand for insurance across households. The take-up rate of
the temperature insurance for the dry season was 72%, which is higher than that described inexisting literature. After about a half year, the take-up rate of the rainfall insurance for the
monsoon season declined to 39%, but the magnitude was still high. We …nd a wide variationin the demand for insurance across cooperative societies through which the insurance productwas sold.
One of the limitations of the current paper is that we do not directly analyze the causal
relationship between the insurance demand and farmer characteristics. Yet, consistent withexisting studies, our …ndings suggest that purchasing an insurance product and obtaining a
positive return will help boost the insurance participation even when the payouts are tiny.
This might be because receiving a claim payment would increase salience and trust to theinsurer and suggests that contracts should be designed to disburse claim payouts as quickly as
possible, which is not always the case in the real world. In the current project, the payouts ofthe temperature insurance was actually made only at the time of the marketing of the rainfall
insurance. Anecdotal evidence from the …eld suggests many farmers seemed to be disappointed
with receiving only a small amount of payout with a time lag. Even in this case, the regression
analysis of this paper …nds that having purchased the insurance and receiving the claim arestrong and signi…cant correlates of the insurance demand. If the payouts had been madeimmediately, farmers would have been even more salient with the relationship between theweather condition and claim payouts, enhancing the trust to the insurer. In addition, payouts
can be used to cover production loss or to fund inputs for the forthcoming season, or to repair
any agricultural devices.
Future research will attempt to evaluate the insurance by quantifying the welfare impact
with a particular focus on basis risk. Standard microeconomic theory predicts that insurance
participation increases with risk aversion, but decreases with basis risk: a trade-o¤ between
having extra protection against disaster and bearing the basis risk. Especially in areas where
the climate is highly localized, the basis risk is high. Existing studies have speculated that basis
25

risk is one of the most important reasons for why weather index insurance is not attractive topotential clients. Matsuda (2014) develops a theoretical model of demand for index insurance
in consideration of basis risk and structurally estimates the risk aversion parameters using
insurance demand data.
[Table 22]

9.

Conclusion

While formal micro-insurance products are penetrating into low income countries, their takeup levels have remained low. As an empirical research on weather index insurance in developing
countries, we conducted surveys on rainfall and temperature index insurance products in Madhya Pradesh, India. While the temperature insurance covers against excess heat during thedry season, the rainfall insurance covers drought and excess rain during the monsoon season,
This paper documented the details of surveys implemented under this project, described the
key variables collected from them and investigated the correlates of the demand.
With regression analysis, we compare how price, income and asset levels in‡uence the demand both for temperature and rainfall insurance products. The results show farmers respondless to the price of rainfall insurance than to the temperature insurance. We also …nd that farmers’response to the discount becomes less price-sensitive as the amount of discount increases.
This implies that even a small discount or subsidies to weather insurance can be e¤ective toboost the demand. Another …nding is that non-price factors such as age of respondent andeducation are important correlates. It is striking that previous experience with the insurer,
which is a proxy for the trust to the insurer, is a powerful predictor of demand. In addition
to personal experience, purchase decision is also in‡uenced by society experience of insurance,
which is suggestive evidence of spillover e¤ects. Distinguishing the impact of each of thesefactors on insurance demand and quantifying the net impact of take-up on household welfare
are left for further research.

26

2007 4.5%
2008 4.5%
2009

NA

2010

NA

2011 8.0%
Table 1: Premium rate of BBY 2007-2011

Def iciency Rate (%) Claim Rate (%)
0

0

10

0

20

0

30

10

40

15

50

25

60

35

70

45

80

75

90

90

100

100

Table 2: Claim schedule of BBY 2007-11

Phase I
Period 1
Trigger ( )Phase II

Period 2

Period 3

Period 4

1/1-1/15 1/16-1/31 2/1-2/14 2/15-2/28 3/1-3/15

3/16-3/31

27

30

Period 1

22

Period 2

24

26

Strike ( )42

Exit ( )1423

Notional (Rs)

350

166. 7

Max Payout (Rs)

3500

3500

28

Note: The index of Phase I is the tw o-w eek average of the daily maximum temperature.
The index of Phase II is the tw o-w eek average of the daily average temperature.

Table 3: MBY 201227

Phase 1

Phase 2Phase 3Phase 4

6/15-7/10 7/11-7/31

8/1-8/31

9/1-10/15

Strike 1 (mm) L 1 , U 1 30, 60 

35, 65 

50, 125 

10, 40 Strike 2 (mm) L 2 , U 2 10, 30 

10, 35 

10, 50 

0, 10 

Exit (mm)

10

10

10

0

Notional of Strike 1 N 1 

10

7. 5

7. 5

12. 5

Notional of Strike 2 N 2 

47. 5

41

17. 5

87. 5

Maximum Payout (Rs)

1250

1250

1250

1250

Maximum Total Payout (Rs)

5000

Table 4: Rainfall deficiency cover in BBY 2012

Phase 1

Phase 2Phase 3Phase 4

6/15-7/10 7/11-7/31 8/1-8/31 9/1-10/15
Strike (mm)

47. 5

60

60

62. 5

Notional (Rs)

5

6

5

5

Maximum Payout (Rs)

700

600

600

600

Table 5: Excess rainfall cover in BBY 2012

Cover Period

July 5 to September 15

Strikes (No. of CDD) L 3 , U 3  17, 22 22, 28 28, 35 35, 50 50, 60 60, 72
Claim Payout (Rs)

175

250

Maximum Payout (Rs)

375

500

1000

1500

Table 6: Consecutive dry days (CCD) cover in BBY 2012

28

1500

Burhanpur

Madhya Pradesh

1000 ha(%)

1000ha

(%)

Rice

2.3

(1.17)

1634.9(4.18)

Wheat

10.4

(5.27)

4089.3(10.45)

Maize

3.5

(1.77)

841.8(2.15)

Sorghum (Jow ar)

12.8(6.49)

534.9(1.37)

Total cereals

29.2(14.79)7671.6(19.60)Chickpea (Gram)

2.5(1.27)

2655.7(6.79)

Pigeonpea (Arhar, Tur)

3.7

(1.87)

300.5(0.77)

Total pulses

8.5(4.31)

4383.7(11.20)

Soybeans

14.3(7.25)

5187.9(13.25)

Total oilseeds

15.0(7.60)

6544.7(16.72)

Sugarcane

2.6(1.32)

77.2(0.20)

Cotton45.9(23.26)618.0(1.58)

Fruits14.4(7.31)

48.9(0.12)

Vegetables

0.7(0.36)

204.2(0.52)

Others

81.0(41.06)

19592.5(50.06)Grand total

197.4(100.00) 39140.7(100.00)

Cereals

PulsesOilseeds

Source: Compiled by the authors using the district-level databasefor Area and Production of Principal Crops in India, Ministry ofAgriculture, Government of India.

Table 7: Area under major crops in Agricultural Year 2006/07

2011/12October November December January

Rabi Crops

Planting

SurveySurvey

MBY

Sales

2012

May

Kharif Crops
Survey

BBY

Sales

March

Temperature measurement

June

April

Mid-season and harvest

July

AugustPlanting

SurveyFebruary

September OctoberMid-season and harvest

Rainfall measurement

Table 8: Timeline
29

Claim payout

NovemberKharif 2007 Kharif 2008 Kharif 2011 Rabi 2011/12 Kharif 2012
BBY 2007

BBY 2008

BBY 2011

MBY 2012

BBY 2012

Y

Y

N

Y

Y

Claim Paid?

Table 9: History of the index insuranceSociety

Obs.

Mean

Loni

54

Altitude (m) 245.18 10.61 211.23
Distance (km)Shahpur82

Chapora118

126

273.71

4.42

5.00

Altitude (m) 256.52 17.43 219.46

298.7017

9.47

Altitude (m) 262.89 10.49 226.16

294.1336

8.14

Altitude (m) 286.42 14.48 261.21

306.63

433

1.12

10.17

0.84

9.14

11.04

Altitude (m) 311.66

4.79

299.92323.09

0.74

36.01

38.68

Altitude (m) 267.58 26.67 188.67

323.09Distance (km)
Total

7.85

1.11

7.71

Distance (km)Dedtalai

7.59

0.12

6.21

Distance (km)Phopnar276.45

Altitude (m) 242.08 14.02 188.67

Distance (km)Bambada

Max

3.58

4.74

0.29

Min

1.20

Distance (km)

1.99

S.D.

Distance (km)

37.42

11.60

12.87

1.20

38.68

Note: Altitude (m) is a vertical distance from the sea level. Distance(km) is a physical length from to the reference station. Summary
statistics under “Total” are for the pooled sample. They therefore
denote the sum of w ithin– and betw een-society variations.

Table 10: Geographical attributes of the sample by society

30

Mean

S.D.

Min

Max

Age

51.4

12.9

20

88

Household Size

5.48

2.26

1

15

Education (years)

6.42

5.03

0

18

Literacy (%)

52.9

Score in arithmetic questions

3.1

3.4

0

8

SC/ST(%)

12.6

Share of OBC (%)

81.6

Access to Well (%)

62.8
22789

0

200000Characteristics of Respondent in Survey 2011

Payment for irrigation usage (Rs)

15069

Answ ered w eather risk as the biggest risk (%)

88.4

Major assets at the time of Survey 2011
Landholdings (acre)

4.70

5.05 0.25

60

Irrigated land (acre)

4.03

5.02

0

60

334728 538397

07000000

House value (Rs)

Agricultural production during Kharif 2011
Area under cotton (%)

52.1

Area under banana (%)

29.3

Area under maize (%)

7.9

Area under jow ar (%)

3.6

Area under soybeans (%)

4.4

Total income (Rs)135687 167960

01500000

Crop loan from DCCB (Rs)85584 104234

01280000

Agricultural production during Rabi 2011/12
Area under cotton (%)*

22.2

Area under banana (%)

45.2

Area under w heat (%)

10.5

Area under maize (%)

13.9

Area under jow ar (%)

3.3

Total income (Rs)111217 125821

0 100000000

Crop loan from DCCB (Rs)**

63787

64339

0

44000063372

0600000Agricultural production during Kharif 2012
Area under cotton (%)

58.2

Area under banana (%)

19.2

Area under maize (%)

10.8

Area under sorghum (%)

4.7

Area under soybean (%)

5.0

Crop loan from DCCB (Rs)43474

Note: * The area under cotton in Rabi 2011/12 show s the percentage of land occupied bycotton belonging to the Kharif 2011 crops. ** The disbursement w as in process.

Table 11: Characteristics of the sample farmers

31

Mean

S.D.

Min

Max

Fertilizer client at Survey 2011

0.97

0.16

Client of BBY ever until BBY 2011 (dummy)

0.24

0.42

Client of BBY 2011 (dummy)

0.13

0.34

Client of BBY 2008 (dummy)

0.11

0.31

Client of BBY 2007 (dummy)

0.07

0.26

Take-up (dummy)

0.72

0.45

Demand (acre) if subsidy  0%

0.70

0.76

0.0

10.0

Demand (acre) if subsidy  25%

0.83

0.80

0.0

10.0

Demand (acre) if subsidy  50%

0.95

0.86

0.0

10.0

Demand (acre) if subsidy  75%

1.00

0.90

0.0

10.0

Average demand (acre) over all subsidy levels

0.87

0.87

0.0

10.0

Demand (acre) if take-up 1 and subsidy  0%

0.92

0.75

0.5

10.0

Demand (acre) if take-up 1 and subsidy  25%

1.09

0.75

0.5

10.0

Demand (acre) if take-up 1 and subsidy  50%

1.24

0.78

0.5

10.0

Demand (acre) if take-up 1 and subsidy  75%

1.31

0.82

0.5

10.0

Average demand (acre) if take-up 1 over all subsidy levels

1.14

0.79

0.5

10.0

Average insured land under MBY (acre) if take-up  1*

1.14

0.74

0.5

10.0

Premium payment (Rs)

671.5 443.0 288 5760

Experience with previous insurance and related activities

MBY 2012 for Rabi 2011/12

BBY 2012 for Kharif 2012
Take-up (dummy)

0.41

0.49

Demand (acre) if subsidy  0%

0.39

0.74

0.0

10.0

Demand (acre) if subsidy  25%

0.41

0.76

0.0

10.0

Demand (acre) if subsidy  50%

0.44

0.78

0.0

10.0

Demand (acre) if subsidy  75%

0.45

0.79

0.0

10.0

Average demand (acre) over all subsidy levels

0.42

0.77

0.0

10.0

Demand (acre) if take-up 1 and subsidy  0%

0.92

0.90

0.5

10.0

Demand (acre) if take-up 1 and subsidy  25%

0.98

0.90

0.5

10.0

Demand (acre) if take-up 1 and subsidy  50%

1.04

0.90

0.5

10.0

Demand (acre) if take-up 1 and subsidy  75%

1.07

0.91

0.5

10.0

Average demand (acre) if take-up 1 over all subsidy levels

1.01

0.90

0.5

10.0

Average insured land under BBY (acre) if take-up  1*

1.00

0.88

0.5

10.0

Premium payment (Rs)753.8 676.1 375 7500

Note: * This is under the price given by the lottery.

Table 12: Take-up of weather insurance

32

Loni

Shahpur

ChaporaBambhada

PhopnarDedtalai

Landholding (acre)

4.80(2.96)

3.94
(3.4)

4.93
(4.66)

2.95(2.45)

11.46(13.72)

8.44(6.78)

Irrigated land (acre)

4.15(2.73)

3.21(3.28)

4.39(4.76)

2.45(2.49)

11.22(13.78)

6.68(6.83)

Literacy *

0.78

0.55

0.63

0.33

0.88

0.31

(0.42)(0.50)(0.49)(0.47)(0.33)(0.47)

SC/ST (%)
OBC (%)

18.5
88.9

9.9
77.8

12.8
84.6

5.6
92.0

5.6
77.8

55.6
36.1

Fertilizer client (%)

0.94

0.96

0.98

0.98

1.0

0.97

159240.7148993.9

134420.393548.8355352.9

11796.1(134177.9)(216155.3)(138772.1)(126835.9)(273099.6)(159539.4)

127722.2(98893.4)

88634.1(143401.5)

91931.6(94262.3)

55891.3(51773.7)

199529.4(194008.7)

44916.7(32948.8)

56.3
16.4

37.1
62.6

46.6
30.2

58.1
25.4

50.6
44.4

68.0
0.0

Survey 2011 (Baseline)

Kharif 2011
Total Income (Rs)
Crop loan from DCCB (Rs)Area under cotton (%)
Area under banana (%)

Table 13: Differences across cooperative societies

33

Loni

Shahpur

ChaporaBambhada

PhopnarDedtalai

Total Income (Rs)

146781.6(135214.9)

141720.8(103845.7)

125373.9(138132.7)

48929.73(72827.1)

162271.4(175838.6)

125757.6(159319.8)

Crop loan from DCCB (Rs)87836.7

72402.6

71295.7

38216.2

115928.645697.0(64348.8)(73207.2)(66492.8)(42434.9)(106423.3)(27000.6)

Area under cotton (%)
Area under banana (%)

39.8
12.0

30.0
56.6

41.5
22.2

67.1
13.3

41.5
25.4

52.1
0.0

MBY Take-up (dummy)

0.48

0.73

0.21

0.20

0.47

0.56

Demand MBY (acre)**0.94(0.44)

1.12(0.45)

1.27(0.39)

1.06(0.40)

1.72(0.51)

1.01(0.50)

Crop loan from DCCB (Rs)
(Disbursement was in process)45426.3(57971.3)

24451.6(43070.3)62093.5(64626.6)39269.3(68092.7)

101428.6(106688.4)

20292.9(27410.1)

Planned area under cotton (%)
Planned area under banana (%)

55.3
16.0

45.3
48.6

61.1
15.2

67.4
11.5

45.2
32.3

63.5
0.0

BBY Take-up (dummy)

0.15

0.33

0.68

0.30

0.18

0.36

Demand for BBY (acre)**

1.09

1.05

0.90

0.87

1.58

1.71

(0.37)(0.47)(0.53)(0.75)(0.72)(2.49)

Rabi 2011/12

Kharif 2012

Note: Mean is shown in the first row of each category. S.D. is shown in parenthesis. Corresponding total values are in the previous tables.
*literacy of the respondents. ** if Take-up = 1 over all subsidy levels.
Table 14: Differences across cooperative societies (Continued)

34

(1)
Price

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(10)

-0.000696*** -0.000693*** -0.000696*** -0.000693*** -0.000696*** -0.000689*** -0.000696*** -0.000696*** -0.000696*** -0.000686***(5.82e-05)

Log income(5.82e-05)(5.82e-05)(5.83e-05)(5.82e-05)(5.77e-05)(5.82e-05)(5.82e-05)(5.82e-05)(5.78e-05)

0.195***

0.0793*0.0262(0.0592)(0.0478)(0.0497)0.0602***0.0513**0.0655***(0.0198)(0.0205)(0.0235)

LandholdingAge-0.00294-0.00467(0.00263)(0.00290)

Education

0.0506**-0.0140(0.0207)(0.0196)

Literacy

0.278***

0.0994(0.0759)(0.102)Math

0.0418***0.00403(0.0121)(0.0178)

Distance

Constant(9)-0.00637**-0.0140***(0.00310)(0.00504)

1.120***-1.089*0.848***-0.0106

1.271***

0.842***

0.973***

0.991***

1.178***

0.901(0.0505)(0.645)(0.0760)(0.512)(0.154)(0.0978)(0.0453)(0.0430)(0.0605)(0.581)

Obs.

1,652

1,648

1,652

1,648

1,652

1,640

1,652

1,652

1,652

1,636

R-squared

0.018

0.067

0.112

0.118

0.020

0.035

0.045

0.047

0.022

0.147

Note: The demand for the temperature insurance (acre) is regressed on a vector of variables. The unit of the price and distance variables are per acre
and kilometer. Robust standard errors in parentheses. Standard errors are clustered at individual level. *** p0.01, ** p0.05, * p0.1.

Table 15: Determinant of MBY Demand
35

(1)
Price

Shahpur

BambadaPhopnar

Loni

(2)

(3)

(4)

(5)

(6)

-0.000696*** -0.000696*** -0.000696*** -0.000696*** -0.000696***-0.000696***(5.83e-05)(5.82e-05)(5.82e-05)(5.82e-05)-0.111-0.111(0.0929)(0.0928)-0.228*-0.228*(0.114)(0.114)0.300

0.875***(0.295)(0.292)(5.82e-05)(5.82e-05)-0.615***(0.105)

Dedtalai-0.516***(0.113)

New Society-0.369***-0.575***(0.0804)(0.0919)

Previous purchase0.297***(0.0911)IFFCO client

0.274*(0.144)Irrigation share

0.0661(0.0722)

Constant

1.326***

1.208***

1.326***

1.053***

0.853***

1.065***(0.0809)(0.0566)(0.0809)(0.0540)(0.140)(0.0650)

Observations

1,652

1,652

1,652

1,652

1,652

1,652

R-squared

0.088

0.053

0.087

0.040

0.020

0.019

Note: The demand for the temperature insurance (acre) is regressed on a vector of variables. The unit of
the price is per acre. Robust standard errors are in parentheses. Standard errors are clustered at individuallevel. *** p0.01, ** p0.05, * p0.1

Table 16: Determinant of MBY Demand (Continued)36

Price (per acre)

(1)

(2)

(3)

(4)

-0.000686***-0.000601-0.000919-0.000585(5.78e-05)(0.000816)(0.000854)(0.000866)

0.07230.07640.06470.0734(0.0468)(0.0622)(0.0617)(0.0609)8.16e-06

1.78e-05-3.03e-06

Log income

Log income * Price

Landholding(7.75e-05)(8.16e-05)(8.20e-05)

0.0644***0.0660***0.0890***0.0875***(0.0223)(0.0247)(0.0245)(0.0247)-4.07e-05-6.48e-05**-6.40e-05**(2.87e-05)(2.93e-05)(2.88e-05)

0.02330.01760.0214(0.0170)(0.0227)(0.0226)

1.26e-05

5.15e-06

Landholding * Price

Education

Education * Price

Distance (km)(2.92e-05)(2.89e-05)-0.00223-0.00672-0.00687(0.00461)(0.00586)(0.00584)

1.17e-05*

1.29e-05**(5.95e-06)(5.86e-06)-0.777***-0.724***

Distance (km)* Price

New Society-0.521***(0.117)

New Society * PriceIFFCO client(0.129)(0.133)0.000618***0.000562***(0.000132)(0.000128)

0.308*

0.472**(0.166)(0.185)IFFCO client * Price-0.000456***(0.000144)

Previous purchase

0.147

0.199*(0.0950)(0.116)

Previous purchase * Price-0.000146(0.000149)

Irrigation-0.0170-0.198*(0.0626)(0.104)Irrigation * Price

0.000502***(0.000162)

Constant-0.292-0.0435

0.134-0.328(0.568)(0.660)(0.673)(0.708)Observations

1,636

1,648

1,636

1,636

R-squared

0.209

0.119

0.205

0.215

Note: The demand for the temperature is regressed on a vector of variables. Standard.
errors are clustered at individual level. Robust standard errors are in parentheses.
*** p0.01, ** p0.05, * p0.1.

Table 17: Determinant of MBY Demand (Interaction terms)
37

Price

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

-0.000116***-0.000113***-0.000116***-0.000113***-0.000116***-0.000116***-0.000116***-0.000116***-0.000116***-0.000113***(1.99e-05)(2.00e-05)(1.99e-05)(2.00e-05)(1.99e-05)(1.99e-05)(1.99e-05)(1.99e-05)(1.99e-05)(2.01e-05)

Log income

0.143***

0.0645**0.0476(0.0494)(0.0287)(0.0350)

0.0680***0.0632**0.0600***(0.0260)(0.0256)(0.0228)

LandholdingAge-0.00236-0.00366*(0.00216)(0.00217)

Education

0.0250***-0.000239(0.00742)(0.00740)

Literacy

0.300***

0.0923(0.0711)(0.101)Math

0.0461***0.00821(0.0128)(0.0166)

Distance

Constant

0.007930.00367(0.00859)(0.00684)

0.478***-1.257**0.171*-0.594*0.599***

0.317***

0.320***

0.337***

0.406***-0.290(0.0407)(0.567)(0.0980)(0.353)(0.113)(0.0421)(0.0327)(0.0326)(0.0668)(0.487)

Obs.

1,652

1,624

1,652

1,624

1,652

1,652

1,652

1,648

1,652

1,620

R-squared

0.001

0.046

0.145

0.158

0.003

0.028

0.039

0.045

0.010

0.170

Note: The demand for the rainfall insurance (acre) is regressed on a vector of variables. The unit of the price and distance variables are per acre and kilometer.
Robust standard errors are in parentheses. Standard errors are clustered at individual level. *** p0.01, ** p0.05, * p0.1.

Table 18: Determinant of BBY Demand

38

(1)
Price

(2)

(1.99e-05)
Shahpur

BambadaPhopnar

Loni

(3)

(4)

(5)

(6)

(7)

(8)

-0.000116*** -0.000116*** -0.000116*** -0.000116*** -0.000116*** -0.000116*** -0.000116*** -0.000116***(1.99e-05)(1.99e-05)-0.162*-0.165*(0.0918)(0.0915)-0.323***-0.327***(0.0778)(0.0775)-0.249-0.253(0.240)(0.240)(1.99e-05)(1.99e-05)(1.99e-05)(1.99e-05)(1.99e-05)-0.432***(0.0815)

Dedtalai

0.00513(0.283)

New Society-0.0957-0.268**(0.124)(0.132)

Purchase MBY

0.323***(0.0609)

Discount of MBY (Rs)

0.000652(0.000690)

Previous purchase

0.225**(0.0880)

IFFCO client-0.0670(0.146)Irrigation

0.132*(0.0695)

Constant

0.667***

0.498***

0.671***

0.232***

0.377***

0.429***

0.543***

0.396***(0.0587)(0.0385)(0.0583)(0.0426)(0.0927)(0.0451)(0.141)(0.0477)

Observations

1,652

1,652

1,652

1,652

1,652

1,652

1,652

1,652

R-squared

0.046

0.004

0.030

0.033

0.006

0.016

0.001

0.008

Note: The demand for the temperature insurance (acre) is regressed on a vector of variables. Standard errors are clustered at theindividual level. Robust standard errors are in parentheses. *** p0.01, ** p0.05, * p0.1.

Table 19: Determinant of BBY Demand (Continued)
39

Price (per acre)

Log income

(1)

(2)

(3)

(4)

-0.000113***0.000133

0.00181**0.00189*(2.01e-05)(0.000201)(0.000889)(0.000980)

0.0720**0.0743**0.0950**0.0903**(0.0351)(0.0326)(0.0380)(0.0377)-2.08e-05-0.000152**-0.000144**(1.76e-05)(6.48e-05)(6.43e-05)

0.0567**0.0627**0.0595***0.0588***(0.0222)(0.0257)(0.0224)(0.0225)

9.13e-07-0.000110***-0.000109***(4.69e-06)(3.97e-05)(4.01e-05)

Log income * Price

Landholding

Landholding * Price

Education

0.0142**0.0128**0.0132**(0.00583)(0.00646)(0.00650)-2.11e-05**-2.18e-05**(1.04e-05)(1.04e-05)

Education * Price

Distance (km)0.0114*0.0129*0.0126*(0.00664)(0.00701)(0.00715)-2.09e-08*-2.05e-08*(1.21e-08)(1.24e-08)-0.449***-0.407***Distance (km) * Price

New society-0.263***(0.0808)

New society * Price

Purchased MBY

0.236***(0.0677)

Purchased MBY * PriceIFFCO client(0.0864)(0.0869)0.000660***

0.000603***(0.000135)(0.000141)-0.125***-0.124***(0.0445)(0.0437)0.00178***0.00178***(9.66e-06)(1.01e-05)-0.0612-0.0177(0.162)(0.159)IFFCO client * Price-0.000130(0.000259)

Previous Purchase

0.111

0.139

(0.0845)(0.0914)Previous Purchase * Price-0.000174(0.000148)

Irrigation-0.00299-0.0159(0.0600)(0.0681)

Irrigation * Price

6.16e-06(0.000109)

Constant-0.934*-0.709*-0.983*-0.936(0.550)(0.395)(0.518)(0.570)Observations

1,624

1,624

1,624

1,624

R-squared

0.211

0.158

0.807

0.809

Note: Full sample. Robust standard errors are in parentheses. *** p0.01, ** p0.05,* p0.1. The demand for the rainfall insurance (acre) is regressed on a vector ofvariables. Standard errors are clustered at individual level.

Table 20: Determinant of BBY Demand (Interaction terms)
40

(1)

Discount 25%

Discount 50%

Discount 75%

(3)

(4)
Rainfall

Insurance

Insurance

0.132***

0.132***

0.0254***0.0247***(0.0145)(0.0146)(0.00594)(0.00596)

0.247***

0.248***

0.0508***0.0494***(0.0208)(0.0210)(0.00906)(0.00913)

0.296***

0.294***

0.0630***0.0617***(0.0245)(0.0246)(0.0111)(0.0112)

Log income

0.04930.0609(0.0475)(0.0386)

0.0635***0.0582**(0.0235)(0.0228)--0.212***--(0.0681)Landholding

Purchased MBY(2)Temperature

Distance (km)0.0115(0.00701)

Previous take-up

New society

Constant

0.09380.136*(0.0917)(0.0787)-0.617***-0.339***(0.106)(0.0877)0.700***-0.3410.389***-0.811(0.0374)(0.590)(0.0365)(0.635)

Other controls

N

Y

N

Y

Society dummies 1

N

Y

N

Y

Observations

1,652

1,648

1,652

1,620

R-squared

0.018

0.224

0.001

0.228

Note: Robust standard errors are in parentheses *** p0.01,** p0.05,* p0.1. The demand forthe temperature insurance (Columns (1) and (2) and for the rainfall
insurance (Columns (2) and (4) is regressed on a vector of variables
Other controls include IFFCO client, Literate, Math and Share of
Irrigation.

Table 21: Determinant of insurance demand (Price Indicators)

41

(1)
Purchased MBY

(2)

(3)

(4)

(5)

0.323***

0.245***(0.0609)(0.0787)

Number of HHs in Society w ho purchased MBY

0.000647

0.00283*(0.00149)(0.00167)

Share of HHs in Society w ho purchased MBY

0.465

0.653

(0.347)
Total number of acres in Society covered by MBY(0.723)0.369*(0.188)Log income-0.188(0.392)0.0758**(0.0360)

Landholding

0.0578**(0.0227)

Price (per acre)-0.000116*** -0.000116*** -0.000116*** -0.000116***-5.91e-05(1.99e-05)(4.12e-05)(1.99e-05)(1.99e-05)(1.99e-05)

Purchased MBY * Price-7.07e-05(4.72e-05)

Distance (m)1.20e-05*(7.14e-06)

Previous purchase

0.120(0.0788)

Constant

0.232***

0.430***

0.123

0.156

-1.600**(0.0426)(0.131)(0.282)(0.181)(0.671)Other controls

N

N

N

N

Y

Observations

1,652

1,652

1,652

1,652

1,620

R-squared

0.033

0.001

0.006

0.012

0.222

Note: Full sample. Robust standard errors are in parentheses. .*** p0.01, ** p0.05, * p0.1. The demand
for the rainfall insurance (acre) is regressed on a vector of variables. Standard errors are clustered at
individual level. Other controls include IFFCO client, Literate, Math and Access to Well.

Table 22: The Dynamics of Demand42

Figure 1: Maps of State of Madhya Pradesh and Burhanpur District (Source: madhya-pradesh-tourism.com)

43

Figure 2

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48

Appendix
Pictures of weather measurement.

Weather station

Rain gauge

Thermometer