Insuring against Climatic Shocks: Evidence on Farm Households’ Willingness to Pay

for Rainfall Insurance Product in Rural India

1: Introduction

In the absence of formal risk sharing/diffusing mechanisms, farmers relay on traditional

modes and methods to deal with production risk in agriculture (Ruthenberg 1976; Collison

1972; Norman 1974; Haswell 1973; Navarro 1977). Coping strategies adopted by the

farmers are their short-term response to secure or manage their livelihood when they deal

with a natural disaster (Okonya et al, 2013). Farmers adopt different responses to cope with

Climate Induced Natural Disasters (CINDs). According to International Strategy for

Disaster Reduction (ISDR), coping is defined the ways people using their available

resources and their abilities to face and manage adverse circumstance (ISDR, as cited in

Yasmin and Ahmad 2013). Different coping mechanisms identified all over the globe

depending upon people‘s cultural and socio-economic context rather than the vulnerability

severity or risks. It also varies within the same geographical boundaries. Every community

practices their own knowledge that has developed over the years (Yasmin and Ahmad,

2013). Most of the responses are reactive i.e. triggered on the face of a disaster. It also varies

within the same geographical boundaries. Farmers make various adjustments in their

production, consumption and livelihood practices and adopt conservative measures to

reduce the negative impact of drought or flood both when they anticipate or receive warning

of a disaster and after the advent of a disaster to reduce the loss. Coping strategies can be

classified into ex ante and ex post, according to whether they help reduce risk or reduce the

impact of risk after the production shortfall has occurred. Ex ante and ex post risk coping

strategies can be literally defined as measures taken before and after experiencing shocks

respectively. Ex ante strategies such as diversification of income sources (e.g. ICRISAT

1979; Newbery and Stiglitz 1981); tenancy contracts such as sharecropping (e.g. Ghatak and

Pandey 2000) are the practices adopted by farmers before disaster. Diversification is simply

captured in the principle of ‘not putting all one’s eggs in one basket’ (Pandey and Bhandari,

2009). Covariate risks such as drought, flood and Cyclone strikes agricultural sector in

general and Farmers in particular. Even when disaster ends it impact lingers. So, the farmers

find it essential to practice the ex-post coping strategies. Ex-post coping strategies such as

cutting down on household consumption, dissaving, asset depletion, credit transactions and

labour supply adjustments have also been observed among rural households (Deaton 1991;

Townsend 1995; Rosenzweig and Wolpin 1993; Rose 2001 ) are the practices followed by

the farmers after the disaster recedes to deal with the losses caused by the disaster. The ex-

ante measures mostly aim at smoothing income, whereas the expost measures are mostly

consumption smoothing (Morduch, 1995). Most of the Indian farmers are having small size

of land holding are usually risk averse and in the face of production risk and absence of

insurance, they manage their farms so as to minimize loss rather than maximizing profit

(Mahendradev, 2012). In the absence of any dependable formal credit and insurance facility,

farmers borrow from informal moneylenders at exorbitant interest rates; sell livestock, land

and other durable assets and fall into the poverty trap (Barnett et al. 2008). Crop failure

affects the borrowers, creditors and also a vast cross-section of the population. Hence, crop

insurance can be viewed as an institution of security (Ahsan 1985). Turvey (1991) argues

that agricultural insurance is one method by which farmers can protect and stabilize farm

income and investment from disastrous effect of crop losses due to natural hazards or low

market prices. Since 1999, National Agricultural Insurance Scheme (NAIS) has been

implemented, but the performance of NAIS is not satisfactory due to its low coverage, poor

financial performance and delay in indemnity payment (Raju and Chand, 2008 as cited in

Swain, 2014). Then Weather Based Crop Insurance Schemes (WBCIS) is considered as an

improvement over NAIS due to its less administrative cost, more transparency and quicker

payment of compensation (Gine et al. 2008) has been launched in India since 2007(Swain,

2014). The major drawback of WBCIS is the basis risk i.e. the mismatch between the actual

crop loss suffered by the insurance buyer and the indemnity received on the basis of the

weather index (Binswanger-Mkhize, 2012 as cited in Swain, 2014). There are studies

(Sherrick et al., 2004; Simon and Fiorentino, 2014; Jin et al., 2016; Lyu and Barre, 2017)

that highlighted the factors that might influence farmers’ decisions on purchasing an

insurance product. The standard theoretical model of behavior under risk assumes that

farmers’ risk preferences play an important role in their decisions under uncertainty (Just et

al. ,1983; Lusk and Coble, 2005; Liu and Huang, 2013; Qui et al., 2014; Jin et al., 2016; Lyu

and Barre, 2017). However, studies that explore the potential impact of farmers’ risk

preferences on the decision to purchase rainfall insurance in India have been few. Because

of different climatic, economic, political, and institutional conditions, farmers’ participation

decision on insurance product and their determinants may be different in different countries

and regions (Hisali et al., 2011). Therefore more location or country specific empirical

studies are needed (Uy et al., 2011). Therefore, considering the overwhelming impact of

unmitigated weather risk on crop output in general, and its disastrous consequences on the

farmers in particular, there is a justifiable need for stabilizing farm incomes in a setting

where formal insurance mechanisms are lacking or incomplete.

2: Objectives of the Study

The objective of this study is

1) To calculate the willingness to pay for rainfall insurance of households living in

Coastal and rain-fed areas prone to risky weather.

2) To identify the socio-economic factors that influences the purchase of a risk

insurance program in rural Odisha.

3) To provide scientific evidence for the Government in designing and

implementing its upcoming weather insurance plan.

3: Hypotheses and Methodology

The following are some hypotheses that the study seeks to test:

H-1: Current low insurance take-up is partly attributable to the lack of awareness by

rural households about rainfall insurance as a risk-coping strategy.

H-2: Poor households in areas prone to severe weather conditions have a higher

demand for rainfall insurance. But their willingness to pay for insurance product is

much lower than the willingness to pay for the same by richer households.

H-3: The more risk coping strategy a household has, the less they will buy such

insurance.

4: Theoretical set up

Empirical literature suggested three ways of estimating farmers’ willingness to pay for an

insurance product. The first method is contingent valuation method; where the farmer is

directly ask what he or she would be willing to pay for an insurance scheme. The second

approach is the revealed preference theory where inferences with regards to farmers’

willingness to pay are made from the analysis of the pattern of production and other

behavior of the farmers. The third approach combines the use of theory along with micro

economic household variables and market variables to estimate indirectly the appropriate

market premium. The main implication of this indirect approach is to estimate farmers’

willingness to pay by comparing their level of utility with and without insurance and

determine what they would be willing to pay to be indifferent in moving from a world

without to a world with insurance (Xiu et al., 2012; Long et al., 2013; Abbeam et al., 2014;

and Okoffo et al., 2016). Among the stated preference approach, the contingent valuation

method have been highly recommended in instances where there is no or little market

information and has been widely used by researchers (Sarris et al., 2006; Liu and Zhang,

2011; Nakanyike, 2014; Long et al., 2013; Okoffo et al., 2016). This method is helpful in a

market situation where the sample farmers’ have the opportunity to accept or reject the

insurance product. The contingent Valuation method makes use of surveys that are

particularly intended for measuring preferences and willingness to pay (Taneja et al., 2014).

The actual market data in Odisha, India show that the market rainfall insurance is currently

very small, both on the demand and supply sides. Therefore, whatever market “prices”

exist may not truly reflect what farmers are willing to pay for rainfall insurance. In this

case, “contingent valuation” (CV) survey methods enable farmers to “reveal” their

willingness to pay. In general, the goal of CV is to measure “willingness to pay” (WTP) or

“willingness to accept” (WTA) for a good in question. WTP is the appropriate measure

when a person is acquiring the good, while WTA is appropriate if the person is losing the

good (Long et al., 2013). Following Long et al., 2013, Contingent valuation methods will

be used to test the above hypotheses. The problem at hand is to elicit from the

households covered by a CV survey their responses on their WTP in acquiring some type

of insurance. Consider a case where a person is deciding on his WTP. Suppose he enjoys

an initial level of welfare yielded by the indirect utility function V (y, p0, q0; Z); where y

is income, p0 is the price vector for the goods vector q0 without insurance, and Z is a vector

of individual characteristics. If the same person were asked if he would be willing to pay to

obtain q1 with insurance, his answer would be “Yes” if the following condition held:

Pr (Yes) = Pr (V (y – WTP, p0, q1; Z) ≥ V (y, p0, q0; Z) ) ………………………………..(1)

From equation-1, it is clear that V (y – WTP, p0, q1; Z) ≥ V (y, p0, q0; Z) ……………….(2)

The vector q1 contains one more good than the vector q0 and that good is nothing but the

“insurance policy or contract”. Because this is paid for by WTP, the price vector p0 remains

the same. The situation in equation 2 pertains to a “rational” person who pays WTP for an

insurance amount I for a calamity with potential losses L, but the calamity does not

happen. As such, he does not receive benefits I, but neither does he incur losses L.

However, (2) implies that the person feels they are better off with insurance than without,

possibly because it gives him “peace of mind” or a feeling of security. For this person the

feeling of security is worth at least WTP. However, if a calamity happens, the person’s

disposable income changes from y - WTP to y - WTP + I – L. Rationally, the change in

income should restore his original level of utility. That is,

V (y – WTP + I –L, p0, q1; Z) = V (y, p0, q0; Z )………………………………………… (3)

It follows from equation-2 and 3 that,

V (y – WTP, p0, q1; Z) ≥ V (y – WTP + I –L, p0, q1; Z) ………………………………… (4)

Invoking the property of V (.) that it is non-decreasing in income, equation implies that:

y – WTP ≥ y – WTP + I – L or L ≥ I …………………………………………………….. (5)

That is, a rational person will buy an amount of insurance not exceeding potential losses.

Interestingly, this result is independent of y and WTP since they cancel out in equation 5.

Therefore in monetary terms, I may or may not fully compensate for L so that he could

incur a net loss since (I - L) ≤ 0. However, this person could be restored to his original

utility, because equation 2 shows that having insurance makes him feel just as good as

when he had no insurance. Equation 5 also explains why farmers will not buy insurance

even when they continually incur losses from periodic natural disasters in Odisha.

Moreover, assuming the existence of risk aversion, equation 5 is consistent with the

observation that farmers over time develop informal risk-coping arrangements or strategies

that discourage them from buying insurance.

5: Hypothetical rainfall insurance program

To explore the demand for rainfall insurance following Long et al., 2013, we have also

designed an insurance scheme as follows: Those participating in the program will

receive 50-90 percent of the loss due to disasters (e.g., flood, storm, drought or rain).

Because every household in a community experiences the same type of disaster, the rate

will be determined collectively by the community and the insurance providers. To elicit the

amount that each household would be willing to pay for an insurance product, we can

adopt the following contingent valuation procedure: There are six cards indicating bid

prices ranging from INR 500 to INR 1000. We can ask respondents’ to randomly pick a

card. They are then can be asked whether they like to buy an insurance based on the

amount printed on their card for 1000 sq m per annum. If the initial response is “no,” the

respondent will be presented with a new lower bid price. If the initial response is “yes,” the

respondent will be offered a new higher bid price. The new bid price is either upwards of

INR 100 or less than INR 100. The cycle continues until the respondent answers “yes” if

the initial response is “no” and “no” if the initial response is “yes.” In case the respondent

still answers “no” or “yes” for the lowest or highest bid value, respectively, he or she will

be asked to state the likely value. Because the gap from the last “no” answer to the “yes”

answer in case of the initial response of “no” (or vice versa) is INR 100, the amount the

respondent is willing to pay lies somewhere between the last “no” value and “yes” value, or

the last “yes” value to “no” value. For example, if at the price bid of INR 200, the

respondent still declines to buy but agrees to buy at INR 100, the amount he is willing to

pay is INR 150 (equivalent to half the total of INR 100 and INR 200).

There are several studies that highlighted the determinants of willingness to pay for an

insurance product have used either the double-hurdle model or the Heckman’s sample

selection model (Cragg ,1971; Norris and Batie ,1987; Gabre-Madhin et al., 2003; Sindi,

2008; Yu and Abler, 2010; Musah, 2013; Long et al, 2013; Danso-Abbeam etal., 2014;

Okoffo et al., 2016). We will adopt the double-hurdle model based on its advantage over

Heckman’s sample selection model because the latter assumes that no zero response will be

present in the second hurdle of the analysis once the first hurdle is passed while the former

recognizes the possibility of zero observations in the second stage (Wodjao, 2008). The

possibility of zero response arises due to the fact that sample farmer may refuse to answer

due to lack of knowledge or complexity of questions (Okoffo et al., 2016). It is further

possible that farmers cannot give a number representing their willingness to pay but may

recognize the fact that they have a positive WTP (Yu and Abler, 2010; Okoffo et al; 2016).

Following Cragg (1971) and Abbeam et al (2014), the study can use the independent

double-hurdle model with the assumption that farm households’ interest to take up rainfall

insurance and the actual insurance premium they are willing to pay are two distinct or

independent decisions. The double-hurdle model proposed by Cragg (1971), assumes that

farm household make two sequential decisions with regards to their interest in insuring

their crop and actual insurance premium they are willing to pay for such insurance

contracts. In this double-hurdle model, a different latent variable is used to model each

decision making process, with a binary choice probit model determining farm households’

interest in the insurance policy and a censored truncated regression model determining the

actual insurance premium amount farmers are willing to pay.

Following Cragg (1971), farmers’ decision to adopt insurance policy and the minimum

price they are willing to pay can be modeled as:

A*1i = X'1i ɑ1 + ui ; Ui ≈ N (0, 1) …………………………………………………………..(6)

A1 = 1 , if A*1i > 0, and is 0 if A*

1i ≤ 0 (Crop Insurance taking decision)

A*2i = X'2i ɑ2 + vi ; Vi ≈ N (0, σ2) ………………………………………………………….(7)

A2i = A*2i if A1i = 1 and A*

2i > 0, and is 0 if A1i ≤ 1 and A*2i ≤ 0 (Minimum price to pay)

A*1i is a discrete latent variable describing farm households interest in taking up insurance

policy, A*2i is the latent minimum price farmers are willing to pay for insurance contract,

X'1i represents vector of independent variables hypothesized to influence farmers decision

to take up insurance policies, X'2i represents vector of independent variables hypothesized

to influence the minimum price farmers are willing to pay for insurance policy, u i and vi are

respective error terms assumed to be independent and normally distributed.

The independent error term of the double-hurdle model can be estimated by the help of

following log-likelihood equation:

Log L = ∑0

ln [1-ɑ (X'1i β1) Ø ( X ' 2 i β 2σ

)] + ∑ ln [ɑ (X'1i β1) 1σ Ø (

A 2−X ' 1i β 2σ )]

……………….(8)

The first term in the equation 8 corresponds to the contribution of all the observations with

an observed zero values (McDowell, 2003). This indicates that zero observations are not

coming from only farmers who express their interest in insurance policy contract but also

from the amount they are willing to pay as insurance premium. The second term in the

equation 8 accounts for all the observations with non-zero interest in the insurance policy

contract (Abbeam et al., 2014). Under the assumption of the above log-likelihood function

of the double-hurdle is equivalent to the sum of the truncated regression model and a

univariate probit model (Aristei and Pieroni, 2007; McDowell, 2003). Therefore, the log-

likelihood functions of the double hurdle model allows for maximization without loss of

information by the separate maximization of the two components such as the probit model

followed by a truncated regression model on the non-zero observations (Jones, 1989;

McDowell, 2003 ; and Abbeam et al., 2014).

6. Study area

The study villages were selected from six blocks of two districts, Cuttack and Bolangir.

From each block two villages were chosen for primary survey. Six study villages such as

Kalapada, Routrapur ,Manikunda, Uradha, Pasanga, and Olansa were selected from Cuttack

Sadar, Kantapada, and Niali block of Cuttack district. The main reason for choosing these

blocks of Cuttack district for the present study is the higher developmental facilities like

irrigation for agriculture as compared to the South-western and Eastern part of the district.

Agricultural machineries and irrigation facilities are better in North-western part. All

villages are well connected with canal irrigation and located on the bank of river Kathajori,

and Kandala which is a rivulet of river Mahanadi and Debi. Thus, lift irrigation is also well

prevalent at low cost in the areas where canal does not reach. The villages under study are

20-30 kms away from Cuttack, the main town. The climate of these villages is warm and

temperature usually reaches the highest in April-May and the lowest in January.

Similarly, the six study villages such as Magurbeda, Bramhanidungri , Kharjura,

Jharbalangir, Hardatal, and Khuntpali were selected from Luisinga, Puintala, and Bolangir

block of Bolangir district. All villages are having a purely rainfed ecosystem for cultivation.

There is no assured irrigation in the form of canal, pond or lift irrigation for these villages.

The farming practices get started only after receiving Southwest monsoon. But, in rest of the

year there is no water for cultivation. All villages are clustered together and 15-20 kms away

from the Bolangir town. These villages share similar soil quality. The climatic condition of

these villages is warm and temperature usually reaches highest in April-May and lowest in

January.

People living along coastal areas have higher probability of facing extreme weather events

like flood and cyclone. We have selected 3 blocks of Cuttack district namely Cuttack Sadar,

Kantapada and Niali respectively. Out of these 3 blocks, 6 villages were selected looking at

the severity of crisis like flood and cyclone. These 3 blocks of Cuttack district are mostly

affected by flood due to the presence of river Mahanadi in the north and river Kathajodi in

the south. People living along rain-fed areas viz. Bolangir district have higher probability of

facing drought like situation. The barren hills and hillocks and the exposed landmass further

intensify the problem. Heat wave and acute drinking water shortage are the common

features of the summer months (DDMA, 2014-15). We have selected 3 blocks of Bolangir

district namely Bolangir, Loisingha, and Puintala respectively. These 3 blocks are selected

from Bolangir sub-division because it is affected by extreme weather events and climate

variation. Out of these 3 blocks, 6 villages were selected looking at the severity of crisis like

drought.

6.1: Demographic Profile and Household Characteristics

6.1.1: Household Size and its distribution

Table 1 reveals the distribution of household size of farmers surveyed in the study villages

along with the total size of farmers surveyed in two districts. We categorized the household

size into three groups like small size having 5 members or less than 5, medium family size

having 6-9 members and big family having 10 members or more than that. In Kalapada, we

surveyed 32 farmers and observed that 17 farmers are having small household size and 14

farmers are having medium household size and only 1 farmer is having big families.

Similarly, in second village of irrigated study region, i.e. in Routrapur, we surveyed 34

farmers and observed that 28 farmers are having small household size, 6 farmers are having

medium household size and none of the farmers belong to big family. In the third village,

i.e. in Manikunda, we interviewed 33 farmers and observed that 22 farmers are having small

household size, 11 farmers are having medium household size, and none of the farmers

belong to big family. In Uradha, we surveyed 35 farmers and observed that 20 farmers are

having small household size and 10 farmers are having medium household size and 5

farmers are having big families. In Pasanga, we surveyed 35 farmers and observed that 19

farmers are having small household size and 15 farmers are having medium household size

and only 1 farmer is having big families. Finally, in Olansa, we surveyed 31 farmers and

observed that 18 farmers are having small household size and 11 farmers are having medium

household size and only 2 farmers are having big families.

Table 1: Distribution of Household Sizes in Sample Villages

Village Household Size Total Household

≤ 5 6 to 9 ≥10Kalapada 17 14 1 32Routrapur 28 06 0 34Manikunda 22 11 0 33Uradha 20 10 5 35Pasanga 19 15 1 35Olansa 18 11 2 31Irrigated Region 124 67 9 200Magurbeda 18 16 4 38Bramhanidungri 21 11 5 37Kharjura 21 12 3 36Jhar Bolangir 23 12 4 39Hardatal 17 08 2 27Khuntpali 08 13 2 23Rainfed Region 108 72 20 200Total Sample 232 139 29 400

Source: Author’s Field Survey, 2016

However, in rainfed region, we observed that in all villages majority of farmers are having

small household size except in Khuntpali village where the majority of farmers are having

medium household size. Overall comparison shows that both region follow the same pattern

in the sense that majority of farmers are having small household size, followed by medium

family size. However, more number of farmers are having family size of more than 10

members in rainfed region.

6.1.2: Age Structure and Educational Level of Farmers

The age structure of farmers along with their educational level is represented in Table 2.We

observed that there are almost same number of young farmers in both regions. Most of the

farmers belong to the age group of more than 51 years and 40 -50 in both regions. It means

most of the farmers are experienced in farming activities. However, there is little difference

between number of farmers in the age group 31-39 in both regions indicating that a sizable

number of farmers are little young in Cuttack district as compared to Bolangir district. As

far as the educational level is concerned, the picture is different in irrigated region in

comparison to rainfed region. In irrigated region, many farmers are illiterate and majority of

them belong to either primary education level or higher secondary level of education.

Farmer belongs to secondary level of education is reported least in our survey. But, in

rainfed region, majority of farmers belong to secondary level of education. The second

highest numbers of farmers are illiterate in this region. 33 farmers from the entire sample in

this region are highly educated; whereas 23 farmers belong to primary level of education in

this region.

Table 2: Age Structure and Educational Level of Farmers in Sample Villages

Village

Age Structure of FarmersTotal HH

Education Level of Farmers

≤ 3031

to 3940

to 50 ≥ 51 Illiterate Primary SecondaryHigher

SecondaryKalapada 1 7 13 11 32 13 10 6 3Routrapur 3 11 12 8 34 15 9 4 6Manikunda 2 4 13 14 33 16 6 9 2Uradha 1 3 13 18 35 11 4 1 19Pasanga 3 4 15 13 35 13 5 6 11Olansa 5 3 10 13 31 9 8 8 6Irrigated Region 15 32 76 77 200 77 42 34 47Magurbeda 4 5 13 16 38 13 4 14 7Bramhanidungri 3 5 10 19 37 12 4 16 5Kharjura 2 4 16 14 36 11 4 17 4Jhar Bolangir 3 2 17 17 39 10 5 15 9Hardatal 2 4 13 8 27 6 4 9 8Khuntpali 2 3 10 8 23 10 2 11 0Rainfed Region 16 23 79 82 200 62 23 82 33Total Sample 31 55 155 159 400 139 65 116 80% to Total Sample 7.75 13.75 38.75 39.75 100 34.75 16.25 29 20

Source: Author’s Field Survey, 2016.Note: Farmer is generally the head of the household. Majority of farmers are high school educated, followed by illiterate and farmers having

higher secondary education. The comparison shows that more number of farmers are

educated in rainfed region as compared to irrigated region.

6.1.3: Occupation and Income Distribution

In this section, we have shown the income distribution through various occupations of

sample families surveyed in different villages of two regions. Out of various sources, it has

been observed that people dependent on agriculture is much higher in all villages of both the

regions. Agriculture includes both own cultivation as well as leased cultivation.

Table 3: Total Annual Income and its Various Sources

Village

Income Sources (Rs Lakh)  

Income(Rs Lakh)Agriculture LivestockWaged labour

Govt/Pvt jobs

Other income

Kalapada 12.31 1.27 1.40 3.28 0.43 18.69

Routrapur 14.53 2.04 1.46 2.90 0.63 21.56

Manikunda 13.00 2.37 0.88 3.58 0.55 20.38

Uradha 15.95 2.03 1.57 2.64 0.31 22.5

Pasanga 13.85 1.67 0.70 2.88 0.27 19.37

Olansa 11.54 1.20 0.26 2.88 0.08 15.96

Irrigated Region 81.18 10.58 6.27 18.16 2.27 118.46% of total income 68.53 8.93 5.29 15.33 1.92 100.00

Magurbeda 7.97 1.76 3.81 3.02 0.42 16.98

Bramhanidungri 14.56 1.07 4.17 3.63 0.47 23.9

Kharjura 17.23 0.64 3.36 1.20 0.40 22.83

Jhar Bolangir 17.81 0.42 2.57 1.49 0.54 22.83

Hardatal 13.17 0.22 1.23 1.55 0.34 16.51

Khuntpali 8.43 0.21 1.53 0.70 0.15 11.02

Rainfed Region 79.17 4.32 16.67 11.59 2.32 114.07% of total income 69.04 3.79 14.61 10.16 2.03 100.00

Total Sample 160.35 14.9 22.94 29.75 4.59 232.53Source: Author’s Field Survey during May-August, 2016

Table 3 shows the total annual income from different sources. In six villages of irrigated

region, we observed that the contributions to total annual income of the sample households

are largely made by agriculture, followed by govt. or private job holders. Livestock

constitutes an integral part of the total annual earning of the household. Waged labour is also

another source of total earning of the household. Income of other sources constitutes a

meager part of the total earning of the household. This is the general tendency in all the

villages of irrigated region. In irrigated region, agriculture contributes 68.53 percent while

govt. and private service contribute 15.33 percent of total annual earning. Similarly,

livestock and waged labour contributes 8.93 and 5.29 percent respectively. Income from

other sources contribute only 2.27 percent of total annual earning of sample household in

Cuttack district

On the other hand, in six villages of rainfed region, we observed that the

contributions to total annual income of the sample households are largely made by

agriculture, followed by waged labour and govt or private service holders. Livestock and

income from other sources constitutes a meager part of the total annual earning of the

household. This is the general tendency in all the villages of irrigated region. In rain-fed

region, agriculture contributes 69.04 percent while waged labour and govt. or private service

holders contribute 14.61 and 10.16 percent of total annual earnings. Whereas, livestock and

income from other sources contribute only 3.79 and 2.03 percent respectively of total annual

earnings of sample household in Bolangir district

6.1.4 Operational Land Holding and Average Land Holding

This section shows the distribution of land holding among the various groups of farmers and

their corresponding average size of holding. The classification of farmers here follows the

National Sample Survey (NSS) classification with little modifications where the large

farmers group consists of the range of land greater than 5 ha that comes after being

converted into acres as 12.35 acres. But the NSS classification shows as the amount of land

Irrigated Region (Area in acres)

Rainfed Region (Area in acres)

Total Sample (Area in acres)

Farmers Type

Total Operational

Holding

Average Operational

Holding

Total Operational

Holding

Average Operational

Holding

Total Operational

Holding

Average Operational

Holding Marginal 74.7 0.79

24.00 1.00

98.7 0.84 Small 75.4 1.98

66.00 2.00

141.4 1.99

Medium 124 3.65

337.00 4.43

461.00 4.19 Large 313 9.78

547 8.63

860.00 9.01

as greater than 8 ha which comes as 19.77 acres. Following the earlier literature and NSS we

modified our classification as follows:

Marginal Farmers: Less than 1 ha (Less than 2.47 acres)

Small Farmers: 1-2 ha (2.47-4.94 acres)

Medium Farmers: 2-5 ha (4.94 -12.35 acres)

Large farmers: Greater than 5 ha (Greater than 12.35 acres)

Accordingly we observed the distribution of size of holding. The size of land holding is

largely dominated by marginal farmers (118) in terms of number of holder and the amount

of land holding, followed by the medium farmers (110) in terms of both indicators.

According to our classification we got 97 large farmers and 75 small farmers in both the

study regions.

Table 4: Distribution of Operational Land Holding and Average Holding

Source: Author’s Field Survey, 2016Note: Operational holding includes both owned land as well as leased land.

Table 4 shows the distribution of operational holding in both regions as well as in the entire

study region. It is observed that the total operational holding and average operational

holding in rainfed region is less in comparison to irrigated region in case of small and

marginal farmers. Whereas the total operational holding and average operational holding in

rainfed region is more in comparison to irrigated region in case of medium farmers. In case

of large farmers, the total operational holding is more in rainfed region but the average

operational holding is less as compared to irrigated regions. The overall sample figures

shows that the total holding is 98.7 acres in case of marginal farmers rendering 0.84 acres as

average holding and the total holding of small farmers in entire study region is 141.4 acres

leading the average holding as 1.99 acres. Similarly the total holding of medium farmers is

461 acres and average holding is 4.19 acres and for large farmers it is 9.01 acres as average

holding.

7: Empirical framework of the model

Farm households’ willingness to take up minimum rainfall insurance can be specified as:

WTI = ɑ + ∑j=1

N

βj Xj + εi ……………………………………………………………….. (9)

Where WTI is a dichotomous dependent variable representing farm households’

willingness to take up an insurance product.

WTI = 1 (Rainfall Insurance Takers)

WTI = 0 (Non-rainfall Insurance Takers)

Xj ………..XN represents socio-economic factors and ε is the random variable for

unobserved factors, ɑ and β are parameters to be estimated.

The empirical model for farm households’ interest in insurance policy can be written as:

WTIi = ɑ + β1age+β2age2+ β3 gender + β4marital status + β5education+β6household size+

β7farm experience+ β8 farm group membership+ β9 farm size + β10 ownership+ β11average

age of the land+ β12average age of the land2+ β13 income+ β14 awareness + ε1………… (10)

Where WTIi is the farm households’ willingness to take up rainfall insurance program, ɑ ,

β1…. β14 are the parameters to be estimated and ε1 is the random error term

The truncated regression model can be used to estimate the insurance premium amount

farm households are willing to pay can be written as:

Yi = ɑ + δ1age+δ2age2+ δ3 gender + δ4marital status + δ5education+δ6household size+

δ7farm experience+ δ8 farm group membership+ δ9 farm size + δ10 ownership+ δ11average

age of the land+ δ12average age of the land2+ δ13 income+ δ14 awareness + ε2 …………(11)

Where Yi is the last bid value offered to farm households in the study region, ɑ , δ 1……. δ14

are the parameters to be estimated, ε2 is the random error term.

Table 5 shows construction of variables for the above econometric model.

Table 5: Construction of variables

Regressors Units of

measuremen

t

A priori expectation of the variables

Age Years Age can negatively influence farmers’ WTI his firm. Older

farmers are less likely to adopt insurance than younger ones.

Since older farmers tend to gather experience from farming

and stick to primitive ways of production and do not easily

adopt newly introduced technology (Baidu-Forson 1999;

Langyintuo and Mulugetta 2005; Danso-Abbeam et al.,

2014, Okoffo et al.,2016)

Gender 1 if male, 0

otherwise

It is hypothesized that male farmers have higher probability

of adopting insurance as well as paying higher premium

amounts than female farmers because former are well

endowed with resources such as land than latter (Okoffo et

al., 2016).

Marital status 1 if married, 0

otherwise

It is hypothesized that married farmers will consider the

survival of their family should any uncertainty strikes

(Danso-Abbeam et al., 2014) influences their decision to

adopt the insurance policy and also can pay higher premium.

Education Years It is hypothesized that a farmer who has gained formal

education can critically analyse and make own decisions

between technologies (Enete and Igbokwe 2009; Caleb and

Ramatu 2013) influences their decision greatly to adopt

rainfall insurance and can also pay higher amounts of

premium.

Household

size

Numbers It is hypothesized that household size can positively or

negatively influence a farmer to adopt rainfall insurance

because a farmer may not want to spend in any other

activities who has large family size but use it to cater for his

family and on the other hand, a farmer also would not like to

take risk of losing his farm at the expense of his family in

case of a disaster would like to adopt insurance as well as

pay higher amounts of premium (Okoffo et al., 2016).

Farm size Acres It is hypothesized to be positively influencing insurance

adoption because larger the cropped area, the more likely the

farmer would adopt insurance (Danso-Abbeam et al., 2014).

On the other hand, the premium a farmer will pay would

increase as the size of cropped area increases (Okoffo et al.,

2016).

Farmer group

membership

1 if farmer

belongs to

any

organization,

0 otherwise

It is hypothesized that there exists a positive association

between farmers group membership with their insurance

adoption decision (Danso-Abbeam et al., 2014).

Land

ownership

1 if farmer

owns the farm

land, 0

otherwise

It is hypothesized that land ownership can positively or

negatively influence a farmer to adopt insurance because

Farmers with their own land to farm on and not go into share

cropping or have inherited from family are more likely to

show interest in insurance policy (Danso-Abbeam et al.,

2014). On the other hand, famers who have their own land

stick to primitive ways of production and are less likely to

show interest in insurance policy (Kwadzo et al., 2013;

Sherrick et al., 2004).

Farm age Years It is hypothesized that there exists a positive correlation

between age of farms and insurance take-up decision

(Danso-Abbeam et al., 2014).

Farming

experience

Years It is hypothesized that farmers with greater number of years

in farming might understand the impact of farm perils on

their economic life better than their colleagues with less

experience and are more likely to be interested in insurance

policy (Kouame & Koumenan, 2012; Danso-Abbeam et al.,

2014)

Income INR It is hypothesized to be positive because farmers who obtain

higher income from their farms have a higher probability of

insuring their farms and willing to pay higher premium than

their colleagues with less farm income (Danso-Abbeam et

al., 2014)

Awareness 1 if farmer is

aware of crop

insurance, 0

otherwise

It is hypothesized that farmers with fair knowledge of

insurance policy are more likely to show interest in the

insurance policy than their counterparts with little or no

knowledge in insurance policy (Danso-Abbeam et al., 2014).

Off-farm

income source

1 if farmer

earns other

It is hypothesized that income from other sources can

increase the likelihood of a farmer purchasing an insurance

than crop

production, 0

otherwise

scheme and therefore paying higher amount as premium

(Okoffo et al., 2016).

7: Results and Discussion

Table 6: Descriptive information about sample villages in Bolangir district

Category Variables Unit Mean Sd Min Max

Dependent

variables

1. Dummy: WTI in 2016

RIP

2. WTP in 2016 RIP

Yes= 1, No=0

INR/1000 Sq

m

0.75

489

0.43

241.6

5

0

100

1

1000

Regressors Age Years 48.94 12.10 24 76

Gender Male =1,

female =0

0.93 0.255 0 1

Education Years 6.29 4.43 0 15

Marital status Married =1,

unmarried =0

0.955 0.21 0 1

Farm experience Years 25.43 11.99 4 50

Farm size Acres 4.99 3.23 1 17

Off-farm income source Yes=1, No=0 0.5 0.50 0 1

Farm group membership Yes =1, No=0 0.25 0.03 0 1

Household size Number 6.145 2.87 2 17

Income from Rice INR 55105 2523

8.84

2500

0

1200

00

Land ownership Yes=1, No=0 0.89 0.313 0 1

Farm age Years 33.715 8.35 16 70

Awareness Yes =1, No

=0

0.55 0.49 0 1

Note: Authors’ own calculation based on field survey during May to August, 2016.

Table 6 presents descriptive statistics on the farmers that participated in the experiment in

Bolangir district. About 75% of the farmers are willing to take up rainfall insurance policy

in 2016. The average insurance premium amount per 1000 sq m the participants in the study

area are willing to pay is INR 489. The maximum amount is INR 1000 whereas the

minimum amount is INR 100 .The average age of participants is 48.94 years ranging from

24 to 76 years of age. About 93% of the participants are male and rests are female members’

reveals complete male dominance in the agriculture sector of Bolangir districts. The sample

respondent has obtained more than 6 years of education. About 95.5 % of the farmers are

married. The average farming experience of the sample households are 25.43 years ranging

from 4 to 50 years of experience. The average farm size is almost 5 acres ranging from 1

acre to 17 acres. About 50% of the participants having off-farm income i.e. income other

than agriculture. About 25% of the participants are Member of a farmer group organization.

The average household size is 6.145 ranging from 2 to 17 in number. The average total

annual income from rice is INR 55105 ranging from INR 25000 to Rs 120000. About 89%

of the participants have their own land to farm on. The average farm age in the study region

is nearly 38 years of age. About 55% of the farmer in the study region are aware about the

crop insurance program.

Table 7: Double Hurdle Model on factors determining farm households’ WTI and

WTP in Bolangir district

Willingness to Insure

(Probit estimates)

Willingness to

Pay

(Tobit

estimates)

Regressors Coefficient standard error Coefficien

t

standard

error

Age 0.6222831 0.5545382 -10.76681 13.40657

Gender -0.9101205* 0.5557584 -59.03876 69.33233

Education 0.2967437**

*

0.1041005 8.007523*

*

3.981141

Marital status 0.345494 0.5922817 175.3036* 112.1221

Farm experience -0.4894296* 0.2790267 -0.750654 2.149553

Farm size -0.003177 0.0482613 -

15.86276*

*

8.684083

Off-farm income

source

-0.0143131 0.2085969 48.9985* 30.77487

Farm group

membership

0.2461156 0.2433386 23.45065 40.29966

Household size -0.0.0314546 0.0513205 14.87539* 8.960958

Income from Rice 0.3363478* 0.2311122 0.000286 0.0006952

Land ownership 0.8275344**

*

0.3512803 103.9056* 61.75919

Farm age 0.1277936 0.4429544 20.25783* 12.27742

Awareness -0.0565366 0.2063595 -

58.71617*

35.36743

Constant -4.332653 3.541026 69.3349 416.5763

LR chi2 23.02** LR chi2 19.39

Note: Authors field survey during May to August, 2015-16. * p<0.1, ** p<0.05, *** p<0.01

Table 7 presented the results of the double-hurdle model which provides the estimates of

factors influencing farmers’ willingness to insure and willingness to pay the insurance

amount for rainfall insurance program. From the table, one can observe that variables like

age and farmer group membership have no significant influence on both willingness to

insure and willingness to pay for rainfall insurance program. However age has positive

association with adoption but has negative relation with willingness to pay for rainfall

insurance. This is quite understandable that older farmers in the study region always wants

to insure their crop but don’t want to pay the insurance premium amount and always

demand for full govt. subsidy on premium amount. The latter part of the result however

contradicted findings by Baidu-Forson (1999), Langyintuo and Mulugetta (2005), Aidoo et

al. (2014), and Okoffo et al. 2016 which stated that as age of the farmer increases, the

premium amount he/she is willing to pay also increases. Even though farmer group

membership has no significant relationship with willingness to insure and willingness to

pay for insurance but has positive association with both WTI and WTP which is in line

with findings of Danso-Abbeam et al., 2014. The remaining 11 variables have significant

influence on either farmers’ WTI or WTP for rainfall insurance program in the study

region. Gender has significant negative relationship with insurance adoption but has no

significant influence on WTP for insurance policy. Therefore it did not confirm to the a-

priori expectation which is positive. This may be due to the fact that even though male

farmers are well endowed with resource but they are risk loving compared to female

farmers, hence may not find it useful to insure their farms and also pay for insurance

premium. There are certain studies which found women are more risk-averse than men

(Palson 1996; Donkers et al., 2001; Hartog et al. 2002; Cohen and Einav 2007; Dohem et

al. 2011; and Okoffo et al. 2016). The level of education of farm household positively

influenced their WTI and WTP for rainfall insurance program and also was found highly

statistically significant. Thus, it can be said that the higher the educational level of a farmer,

the more likely he/she would be willing to insure and hence would like to pay more

insurance premium. This result confirmed to our a priori expectation and is in line with

findings of Piyasiri and Ariya-wardana (2002), Falola et al. (2013), Aidoo et al. (2014),

Danso-Abbeam et al. (2014), and Okoffo et al. (2016). Marital status also positively

influenced farmers’ WTI and WTP for insurance program but was found only significant

variable influencing farmers’ insurance adoption. This result confirmed again our a priori

prediction and is in line with findings of Danso-Abbeam et al. (2014) ,and Okoffo et al.

(2016). This is due to the fact that married farmers always consider the survival of their

family again any disaster. Farming experience was found to be significant variable

influencing negatively to the farmers’ insurance adoption but has no significant association

with WTP for insurance program. This may due to the fact that as farmers work greater

number of years in a particular crop firm, his/her adaptive capacity to climate change

increases greatly therefore he/she has shown no/very low interest in taking up the insurance

program. They have the ability to manage their firms very well and are exposed to various

risk management practices and therefore less likely to engage in insurance (Black and

Dorfman, 2000). This findings contradicts the findings of Kouame and Koumenan (2012)

and Danso-Abbeam et al. (2014) who estimated a positive coefficient for farmers

experience in both insurance adoption and hence their WTP for the insurance premium

amount. Size of rice area has negative influence on farmers’ WTI and WTP for insurance

program. This shows that the bigger the cropped area the less likely a farmer would be WTI

his/her farm. The result also revealed that farm size has significant and negative association

with farmers’ WTP for the insurance program. This means that as size of rice area increases

by 1 acre, the insurance premium amount farmers are WTP for insurance program reduces

by INR 15.86. This result contradicts the findings of Danso-Abbeam et al. (2014) and

Okoffo et al, (2016) and is in line with findings of Kumar et al. (2011) which reported that

size of cropped area negatively influence WTP an amount for rainfall insurance. Income

from other than agriculture was not significant and negatively influenced farmers’

willingness to insure their crop but it is positive and significant factor influencing farmers’

WTP for the insurance program. This result contradicts the findings of Okoffo et al. (2016)

which reported that income from other sources was not significant and negatively

influenced both farmers’ WTI and WTP for insurance program. Household size was

negatively influenced farmers’ willingness to adopt rainfall insurance because a farmer may

not want to spend in any other activities who has large family size but use it to cater for his

family which is in line with findings of Falola et al. (2013) and Danso-Abbeam et al. (2014)

but on the other hand, a farmer also would not like to take risk of losing his farm at the

expense of his family in case of a disaster would like to pay higher amounts of premium.

We found positive and significant association between household size and farmers’ WTP

for an insurance program which is in line with findings of Okoffo et al., (2016). Income

generated from rice crop was a significant factor influencing farmers’ willingness to insure

his/her crop. It also had a positive effect and hence confirms to the a-priori expectation and

in line with findings of Danso-Abbeam et al. (2014). On the other hand, it also positively

influence even though not significant a farmers’ willingness to pay for insurance program

which is again in line with findings of Danso-Abbeam et al. 2014 but contradicts the

findings of Okoffo et al. 2016 who reported that crop income has negative influence on

farmers’ WTP for rainfall insurance program. The ownership of land for rice farming

variable was highly significant and consistent with expected positive influence on both

farmers’ willingness to insure and willingness to pay for insurance program. This means as

farmers have their own land to farm on and not go into share cropping or have land

inherited from family are more likely to join insurance program and hence pay more

premium which is in line with findings of Danso-Abbeam et al. (2014) but contradicts the

findings of Kwadzo et al (2013) and Sherick et al. (2000) who found a negative coefficient

for land tenure suggesting that farmers who have their own land are less likely to

participate and pay for insurance program. Farm age has significant positive influence on

farmers’ WTP and again positive effect on farmers’ WTI even though not significant is in

line with findings of Danso- Abbeam et al. (2014) and hence confirms our a priori

expectation. This means that as farm becomes old, farmers’ willingness to insure and

willingness to pay increases due to the fear of less production and problems associated with

soil fertility. Awareness variable was found to be significant and negatively signed. This

implies farmers with fair knowledge of rainfall insurance policy are less likely to show

interest in the policy than their counterparts with little or no knowledge in insurance policy

which contradicts the findings of Danso-Abbeam eta l. (2014) and also does not confirm

our a priori expectation in the study region. This may due to the fact that in the rain-fed

region, farmers’ experience in insurance purchase is a significant determinant of WTP but

20% of farmers who purchased insurance in 2015 did not renew their contracts in 2016 due

to low coverage, delays in payment of compensation; and specifically the compensation

amount that is paid to them does not cover losses (discussed in last chapter).

Table 8: Descriptive information about sample villages in Cuttack district

Category Variables Unit Mean Sd Min Max

Dependent

variables

1. Dummy: WTI in 2016

CIP

2. WTP in 2016 CIP

Yes= 1, No=0

INR/1000 Sq

m

0.815

422

0.39

180.84

0

100

1

900

Regressors Age Years 48.64 12.73 20 85

Gender Male =1,

female =0

0.915 0.279 0 1

Education Years 5.74 5.14 0 15

Marital status Married =1,

unmarried =0

0.95 0.22 0 1

Farm experience Years 22.99 11.29 3 55

Farm size Acres 2.96 3.35 0.4 15

Off-farm income source Yes=1, No=0 0.3 0.46 0 1

Farm group membership Yes =1, No=0 0.27 0.44 0 1

Household size Number 5.185 2.04 2 13

Income from Rice INR 51745 24109.

67

10000 1500

00

Land ownership Yes=1, No=0 0.82 0.385 0 1

Farm age Years 34.38 8.97 9 60

Awareness Yes =1, No

=0

0.74 0.44 0 1

Note: Authors’ own calculation based on field survey during May to August, 2016.

Table 8 presents descriptive statistics on the farmers that participated in the experiment in

Cuttack district. About 81.5% of the farmers are willing to take up rainfall insurance policy

in 2016. The average insurance premium amount per 1000 sq m the participants in the study

area are willing to pay is INR 422. The maximum amount is INR 900 whereas the minimum

amount is INR 100 .The average age of participants is 48.64 years ranging from 20 to 85

years of age. About 91.5% of the participants are male and rests are female members’

reveals complete male dominance in the agriculture sector of Cuttack districts. The sample

respondent has obtained more than 5 years of education. About 95 % of the farmers in the

study region are married. The average farming experience of the sample households are

nearly 23 years ranging from 3 to 55 years of experience. The average farm size is almost 3

acres ranging from 0.4 acre to 15 acres. About 30% of the participants having off-farm

income i.e. income other than agriculture. About 27% of the participants are Member of a

farmer group organization. The average household size is 5.185 ranging from 2 to 13 in

number. The average total annual income from rice is INR 51745 ranging from INR 10000

to Rs 150000. About 82% of the participants have their own land to farm on. The average

farm age in the study region is 34 years of age. About 74% of the farmer in the study region

are aware about the insurance program.

Table 9: Double Hurdle Model on factors determining farm households’ WTI and

WTP in Cuttack district

Willingness to Insure

(Probit estimates)

Willinness to

Pay

(Tobit

estimates)

Regressors Coefficient standard error Coefficien

t

standard

error

Age -0.5882973 1.29131 -5.054479 8.38942

Gender 0.0322796 1.284306 -26.55553 51.20326

Education -0.1737307 0.2260312 -

0.2149638

2.736073

Marital status -0.2865105 1.805295 177.7191*

*

87.16173

Farm experience 0.4673827 0.7312022 -1.183163 1.763836

Farm size 0.1945991**

*

0.0724668 0.2223054 4.340322

Off-farm income

source

2.801599 476.0264 -

153.4664*

80.91161

*

Farm group

membership

-4.603736 476.0261 136.2277* 83.71679

Household size 0.783164 0.1209333 -

18.9916**

*

6.975908

Income from Rice 0.7329096* 0.1052806 -

0.0005395

0.0005877

Land ownership 0.8744613 0.7456516 76.16152*

*

37.422

Farm age 0.4065365 0.8770204 -

0.6946938

7.535107

Awareness 5.480486*** 1.028391 368.073**

*

32.18987

Constant 6.600966 6.856109 309.7802 234.2187

LR chi2 197.18*** LR chi2 113.66***

Note: Authors field survey during May to August, 2015-16. * p<0.1, ** p<0.05, *** p<0.01

Table 9 presented the results of the double-hurdle model which provides the estimates of

factors influencing farmers’ willingness to insure and willingness to pay the insurance

amount for insurance program. From the table, one can observe that variables like age,

gender, education, farm experience and farm age have no significant influence on both

willingness to insure and willingness to pay for insurance program. However age has

negative association with both willingness to insure and willingness to pay for rainfall

insurance. This result is in line with findings by Baidu-Forson (1999), Langyintuo and

Mulugetta (2005), Aidoo et al. (2014), and Okoffo et al. 2016 which stated that older

farmers are less likely to adopt insurance than younger ones and as age of the farmer

increases, the premium amount he/she is willing to pay reduces. Gender has no significant

influence on farmers’ insurance adoption and WTP for insurance policy. This result is

consistent with the findings of Curak et al. (2013) and Okoffo et al. (2016) who found that

gender has no significant relationship with insurance policy. But however it confirms to the

a-priori expectation that male farmers are higher probability of adopting insurance than

their female counterparts but it did not confirm the a-priori expectation with respect to its

relationship with farmers’ WTP. Here we found negative association with gender and WTP.

This may be due to the fact that even though male farmers are well endowed with resource

but they are risk loving compared to female farmers, hence may not find it useful to pay for

insurance premium. There are certain studies which found women are more risk-averse

than men (Palson 1996; Donkers et al., 2001; Hartog et al. 2002; Cohen and Einav 2007;

Dohem et al. 2011; and Okoffo et al. 2016). The level of education of farm household

negatively influenced their WTI and WTP for insurance program even though not found

statistically significant. Thus, it can be said that the higher the educational level of a farmer,

the less likely he/she would be willing to insure and hence would like to pay less insurance

premium. As the farmer becomes more educated he/she exposed to various other risk

management strategies than rainfall insurance. This result did not confirm our a priori

expectation and contradicted the findings of Piyasiri and Ariya-wardana (2002), Falola et

al. (2013), Aidoo et al. (2014), Danso-Abbeam et al. (2014), and Okoffo et al. (2016).

Farming experience has no significant influence on farmers’ insurance adoption but has

positive association with WTI for insurance program. This may due to the fact that as

farmers with greater number of years in a particular crop firm might understand the impact

of farm perils on their economic life better than their colleagues with less experience and

therefore more likely to be interested in insurance policy which is in line with findings by

Danso-Abbeam et al. (2014). On the other hand, as they have the ability to manage their

firms very well and are exposed to various risk management practices and therefore less

likely to engage and pay premium in insurance program (Black and Dorfman, 2000). This

findings contradicts the findings of Kouame and Koumenan (2012) and Danso-Abbeam et

al. (2014) who estimated a positive coefficient for farmers experience in both insurance

adoption and hence their WTP for the insurance premium amount. Farm age has positive

even though not significant influence on farmers’ WTI is in line with findings of Danso-

Abbeam et al. (2014) and hence confirms our a priori expectation. This means that as farm

becomes old, farmers’ willingness to insure increases due to the fear of less production and

problems associated with soil fertility but as farm becomes old; farmers’ willingness to pay

higher premium declines contradicted the findings by Danso-Abbeam et al. (2014). The

remaining 8 variables have significant influence on either farmers’ WTI or WTP for

insurance program in the study region. Marital status has mixed effect in the study region.

It is negatively influenced farmers’ WTI but positively influenced WTP for insurance

program and also found to be significant variable influencing farmers’ willingness to pay

for rainfall insurance program. This result partly confirms and partly rejects our a priori

prediction and partly supported the findings of Danso-Abbeam et al. (2014), and Okoffo et

al. (2016). This is due to the fact that married farmers always consider the survival of their

family against any disaster. Therefore their WTP for rainfall insurance against covariate

risks is always higher. Size of rice area has positive influence on farmers’ WTI and WTP

for insurance program. This shows that the bigger the cropped area the more likely a farmer

would be WTI his/her farm. The result also revealed that farm size has significant and

positive association with farmers’ WTI for the insurance program. This means that as size

of rice area increases by 1 acre, the probability that a farmer would insure his /her farm

increases by 0.194. This result is in line with findings of Danso-Abbeam et al. (2014) and

Okoffo et al, (2016) and contradicted the findings of Kumar et al. (2011) which reported

that size of cropped area negatively influence WTP an amount for insurance. Income from

other than agriculture was not significant but positively influenced farmers’ willingness to

insure but it is negative and significant factor influencing farmers’ WTP for the insurance

program. This is due to the fact that as farmer diversifies his/her income sources, he/she

feels secured about any disaster and therefore would not be willing to pay large premium

for rainfall insurance. This result partly supports the findings of Okoffo et al. (2016) which

reported that income from other sources was not significant and negatively influenced both

farmers’ WTI and WTP for rainfall insurance program. Farmer group membership has no

significant relationship with willingness to insure but it has positive and significant

influence on farmers’ WTP for rainfall insurance program which partly supports the

findings of Danso-Abbeam et al., 2014. On the other hand, it has negative association with

willingness to insure because as the farmer used to participate with some kinds of

discussion it always makes them unsure about a particular insurance product even though

that strongly influences their WTP due to the neighborhood effect. Household size was

positively influenced farmers’ willingness to adopt rainfall insurance because a farmer

would not like to take risk of losing his farm at the expense of his family in case of a

disaster hence would like to insure his/her farm against it. On the other hand, a farmer may

not want to spend in any other activities who has large family size but use it to cater for his

family which is in line with findings of Falola et al. (2013) and Danso-Abbeam et al. (2014)

but contradicted the findings of Okoffo et al., (2016) who has reported that a farmer is

willing to pay more as his/her household increase by one person. Income generated from

rice crop was a significant factor influencing farmers’ willingness to insure his/her farm. It

also had a positive effect and hence confirms to the a-priori expectation and in line with

findings of Danso-Abbeam et al. (2014). On the other hand, it negatively influence even

though not significant a farmers’ willingness to pay for insurance program which

contradicted the findings of Danso-Abbeam et al. 2014 but is in line with the findings of

Okoffo et al. 2016 who reported that crop income has negative influence on farmers’ WTP

for rainfall insurance program. The ownership of land for rice farming variable was highly

significant and consistent with expected positive influence on both farmers’ willingness to

insure and willingness to pay for insurance program. This means as farmers have their own

land to farm on and not go into share cropping or have land inherited from family are more

likely to join insurance program and hence pay more premium which is in line with

findings of Danso-Abbeam et al. (2014) but contradicts the findings of Kwadzo et al (2013)

and Sherick et al. (2000) who found a negative coefficient for land tenure suggesting that

farmers who have their own land are less likely to participate and pay for rainfall insurance

program. Awareness variable was found to be significant and positively signed. This

implies that farmers with fair knowledge of rainfall insurance policy are more likely to

show interest in the policy than their counterparts with little or no knowledge in insurance

policy which is in line with the findings of Danso-Abbeam eta l. (2014) and also confirms

our a priori expectation in the study region. This may due to the fact that in the coastal

region, farmers’ experience in insurance purchase is a significant determinant of WTP and

hence all the farmers who purchased insurance in 2015 renewed their contracts in 2016. So

therefore, awareness plays an important role in insurance purchase decision as well as

willingness to pay a certain premium amount in coastal region.

8: Summary and Conclusion

The results of the double-hurdle model observed that variables like age and farmer group

membership have no significant influence on both willingness to insure and willingness to

pay for rainfall insurance program where as variables like gender, education, farm

experience, income from rice, and land ownership have significant influence on WTI for

insurance program and on the other hand, variables like education, marital status, farm size ,

off-farm income source, household size, land ownership, farm age, and awareness found to

be significant factor influencing farmers’ WTP for insurance program in Bolangir district.

So education is the only variable which is significant in both the models. In Cuttack region,

variables like age, gender, education, farm experience and farm age have no significant

influence on both willingness to insure and willingness to pay for an insurance program

where as variables like marital status, off-firm income source, farmer group membership,

household size, land ownership and awareness have significant influence on farmers’ WTP

for rainfall insurance and on the other hand, farm size, income from rice and awareness

found to be significant factor influencing WTI for insurance program in Cuttack district. So,

awareness is the only variable which is significant in both these models. The study therefore

recommends that the design to implement rainfall insurance scheme should take into

consideration the education and awareness level of sample household in these regions.

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