Insuring against climatic shocks: Evidence on farm households’ willingness to pay for rainfall insurance product in rural India

International Journal of Disaster Risk Reduction xxx (xxxx) xxx

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International Journal of Disaster Risk Reduction journal homepage: http://www.elsevier.com/locate/ijdrr

Insuring against climatic shocks: Evidence on farm households’ willingness to pay for rainfall insurance product in rural India Asis Kumar Senapati P. G. Department of Economics, Ravenshaw University, Cuttack, 753003, Odisha, India

1. Introduction Developing countries are worst hit by climate change as their major source of income like agriculture, fishery, tourism, etc. depends on the climatic factors. The climate spawned natural disasters like floods, droughts, and cyclone are the major sources of risk and uncertainty leading to the wide fluctuations in agricultural output. Farm households are susceptible not only to ‘idiosyncratic shocks’ such as illness or yield loss due to poor soil quality, but also ‘covariate shocks’ such as flood, drought or natural disasters. Climatic shocks such as excess rainfall, deficit rainfall, delayed onset of the monsoon, weather induced pest incidence are a frequent threat to crop yields. Apart from these shocks, households also have to routinely deal with uncertain input prices, output price volatility, abrupt policy changes and a rapidly changing technologies and markets. Given the considerable risks and the likeli­ hood of informal risk management strategies failing in the face of co­ variate shocks, it is natural to ask, what options do the households have to manage these risks? This question becomes even more relevant in the context of rural households in a developing country context since the avenues for formal risk management are limited because the financial markets – markets for credit and insurance are either absent or under­ developed. Under such limiting circumstances, a failure to adjust and insure against the considerable risks and uncertainties could have serious welfare consequences. The vulnerability of farmers on account of uninsured downside production risks is due to the following reasons: the traditional risk management strategies, that is, the set of informal risk management mechanisms such as ‘income smoothing’ and ‘consumption smoothing’ mechanisms [1] which have evolved over generations are costly in terms of the higher income opportunities foregone by farmers who are mostly assumed to be ‘risk averse’ [2–7] and which might be effective against idiosyncratic shocks and low magnitude losses, might well be ineffective against covariate shocks such as floods, droughts, prolonged dry spells and disastrous extreme events [8–12]. In the absence of formal risk

1

sharing/diffusing mechanisms, farmers relay on traditional modes and methods to deal with production risk in agriculture [13,14]. Coping strategies adopted by the farmers are their short-term response to secure or manage their livelihood when they deal with a natural disaster [15]. The standard theoretical model of behavior under risk assumes that farmers’ risk preferences play an important role in their decisions under uncertainty [16–21]. Because of different climatic, economic, political, and institutional conditions, farmers’ participation decision on insur­ ance product and their determinants may be different in different countries and regions [22]. Therefore more location or country specific empirical studies are needed [23]. Moreover, the area based multi-peril yield insurance in India is primarily involuntary in nature. There are several issues with the crop insurance design and implementation that influence perceptions of insurance among farmers. Since February 2016, Pradhan Mantri Fasal Bima Yojana (PMFBY) has been announced by the government of India. This scheme especially available to loanee farmers under any type of seasonal agricultural loans but optional for the non-loanee farmer’s aims at crop insurance penetration in India. How­ ever, the implementation has been happening at a snail’s pace. Rainfall insurance which was relatively unknown in India few decades ago initially developed by ICICI Lombard piloted in 2003 with BASIX1 is now available with other regions of the country through Agricultural Insur­ ance Company of India [24]. There exist tremendous benefits from its adoption because rainfall insurance protects against climatic shocks. Despite its potential benefits, available literature suggests that its adoption rate is relatively slow. Several studies also noted various possible reasons for its better adoption. Gaurav et al. [25] evaluated the effect of the financial literacy training and marketing experiment in insurance adoption using a randomized controlled trial in Gujarat, India. They found that financial education has a positive and significant effect on rainfall insurance adoption. Gaurav and Singh [26] also empirically tested the financial literacy and cognitive ability of farmers in Gujarat, India and found that farmers’ education and financial experience has been correlated with their achievements in test for ability in

E-mail address: [email protected]. Basix is a Hyderabad based Micro Finance Institution, India.

https://doi.org/10.1016/j.ijdrr.2019.101351 Received 15 May 2019; Received in revised form 20 September 2019; Accepted 1 October 2019 Available online 24 October 2019 2212-4209/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Asis Kumar Senapati, International Journal of Disaster Risk Reduction, https://doi.org/10.1016/j.ijdrr.2019.101351

A.K. Senapati

International Journal of Disaster Risk Reduction xxx (xxxx) xxx

mathematics and probability. So from the available literature, it is evident that awareness, cognitive ability of farmers, farm experience, and spatial location based research is imperative for better insurance adoption. The present study reports on an experiment conducted in both irrigated and rainfed regions of Odisha, India designed to test the will­ ingness to pay for rainfall insurance product and identify various socio-economic factors that influences the purchase of a risk insurance program. The present study has several implications further than the useful lessons it highlights about promoting rainfall insurance adoption. First, it demonstrates how large scale field survey in two geographically different located regions, can also be used to test theories of consumer demand with respect to rainfall insurance adoption. Second, it presents how different socio-economic factors can influences financial decision making which may form the basis for developing a theory. The study seeks to test that 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. Current low in­ surance take-up is partly attributable to the lack of awareness by rural households about rainfall insurance as a risk-coping strategy. The paper is organized in this manner: after the introduction and the statement of the objectives in the first section, a simple theoretical model is developed in the second section. The third section deals with study region, data collection and variable construction. Empirical results and subsequent discussions take place in the fourth section. Finally, the paper concludes with some policy implications.

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 in­ surance 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 x - WTP to x - WTP þ I – L. Rationally, the change in income should restore his original level of utility. That is, � � U x–WTP þ I–L; p0 ; q1 ; Y ¼ U x; p0 ; q0 ; Y (3) It follows from equations (2) and (3) that, � � U x–WTP; p0 ; q1 ; Y � U x–WTP þ I–L; p0 ; q1 ; Y

Invoking the property of U (.) that it is non-decreasing in income, equation implies that: x–WTP � x–WTP þ I–L or L � I

Empirical literature suggested various ways of obtaining farmers’ willingness to pay for rainfall insurance product. One can use contingent valuation method asking directly the farmer what would be his/her willingness to pay for an insurance program. Another way of knowing their WTP is to analyse the pattern of production and behavioural response of other farmers using revealed preference approach. Similarly one can also use both theory taking both economic and certain market determined variables so as to calculate indirectly their WTP for an in­ surance product. The main implication of this approach is to obtain appropriate premium amount by analyzing farm household expected utility both with insurance and without insurance [27,28]. The entire bidding process is based on Becker et al. [29] method. Following Karni and Safra [30]; and [31]; here the study has followed the expected utility hypothesis where the Becker–DeGroot–Marschak (BDM) method is incentive compatible. Here, “contingent valuation” (CV) survey methods enable farmers to “reveal” their willingness to pay. In general, the main goal of CV is to measure both willingness to pay (WTP) and willingness to accept (WTA) for a product 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 [28]. Following Long et al. [28]; 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 U (x, p0, q0; Y); where x is income, p0 is the price vector for the goods vector q0 without insurance, and Y is a vector of individual characteristics. If we will ask the same person what if he would be willing to pay to obtain q1 with insurance, he would say yes, if and only if � �� PðYesÞ ¼ P U x–WTP; p0 ; q1 ; Y � U x; p0 ; q0 ; Y (1)

1

(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.

2. Theoretical set up

From equation (1), it is clear that � � U x–WTP; p0 ; q1 ; Y � U y; p0 ; q0 ; Y

(4)

2.1. Hypothetical rainfall insurance program To explore the demand for rainfall insurance following Long et al. [28]; I have also designed an insurance scheme as follows: The insurance scheme covers crops against rainfall deficiency during Kharif season from June to October. Even though June 1 is considered as the starting date of south west monsoon in Kerala, India, this insurance policy starts on July 1 because in Odisha, monsoon hits during this period in most of the regions. The total policy duration is therefore for 120 days. Insur­ ance scheme also covers all the three agricultural phases i.e. sowing (30 days), vegetative growth (30 days) and harvest (45 days). In the sowing and vegetative growth stage, payouts are linked to deficit in rainfall and during the harvest stage; it is based on excess in rainfall. Those participating in the program will receive 50–90% of the loss due to di­ sasters (e.g., flood, storm, drought or rain). Rural households are sus­ ceptible to both ‘idiosyncratic shocks’ such as injuries, illness or yield loss, fire or theft particularly felt by an individual household but also ‘covariate shocks’ which are felt across all the households in a given area such as flood or drought or natural disaster. 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 farm household would be interested 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 a hectare. If his initial response is “no,” we can show the respondent the immediate lower bid price. If his initial response is “yes,” the respondent will be offered the immediate 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

(2) 0

The vector q contains one more good than the vector q and that 2

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answers “yes” if his initial response was “no” and “no” if his initial response was “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 be­ tween the last “no” value and “yes” value, or the last “yes” value to “no” value. The ‘payment card’ with Rs. 600 as the starting bid is randomly selected and if the respondent says ‘YES’ to it, then the next bid is increased by Rs. 100, i. e to Rs. 700. If the respondent says NO to Rs. 600, then the bid is reduced by Rs. 100, i.e. to Rs. 500. For example, if at the price bid of INR 600, the respondent still declines to buy but agrees to buy at INR 500, the respondents’ WTP value falls between Rs. 500 and Rs. 600. It can be Rs. 501 or Rs. 599, but I have taken the average value of the bid i.e. the amount he is willing to pay is INR 550 (equivalent to half the total of INR 500 and INR 600). There are several studies that analysed the determinants of farmers’ WTP for an insurance product have basically used Heckman’s model and Double-Hurdle model [28,32–35]. In this study following Cragg [36] and Danso-Abbeam et al. [34] I have adopted the independent double-hurdle model to determine farm households both willingness to insure and then their interest to pay the required premium amount. The double-hurdle model according to Cragg [36]; takes into account farm household decisions in insuring their crop and actual premium they are interested to pay for such insurance programs in the insurance contract. The two different decisions of farm household can be thus measured empirically using a probit model determining the former and a Tobit model determining the latter. Farmers’ decision to purchase an insurance policy and the minimum price farmers’ interested to pay for it can be shown empirically as:

Cuttack district is an irrigated district while Bolangir is rainfed district on the basis of the argument that if one region is having more than 60% of its total cultivable land irrigated by canal or any other sources except rainfall then it is called as the irrigated region, otherwise it is called rainfed region. In case of Cuttack district, more than 90% of cropped area is irrigated while in case of Bolangir district it is about 41%.2 Thus, by the definition, I have selected two districts belonging to two different ecosystems and it enables us to study the differential aspects afore­ mentioned issues related to agriculture sector of both regions. The study villages, keeping the objectives strictly in mind, are selected on the basis of following reasons: 1. In the irrigated region, the study villages are connected with canal irrigation with two times cultivation facilities. The assured canal irrigation enables the farmers to go for both Kharif and Rabi culti­ vation. However, in rainfed region they only cultivate after receiving southwest monsoon and cultivate once in a year in Kharif season. 2. The climatic risks that farmers face in both types of agriculture are not the same in their character and magnitude. In the irrigated region of study villages, mostly floods/submergence are major threat and in rainfed region drought type of situation occurs at any stage of crop growth. Data from 400 households are collected, out of which 200 are from irrigated region of Cuttack and 200 from rain-fed region of Bolangir district. Distance of each village from the main city Bolangir is 15–20 km in case of rainfed region and a maximum of 30 km in case of villages of irrigated region from Cuttack, the major city. I have collected data for Kharif season only since in the rain-fed region, the farmers completely depend on monsoon and cultivate only in Kharif season. So for main­ taining uniformity, we have collected data for one common season. To select farmers, I have used a multistage sampling where the sampling units at the final stage were selected at random based on the sampling lists. In the first step, I purposively selected the above two districts. In the second step, I randomly selected villages. In the third step, I randomly chose farmers at village level. The farmers were then requested to participate in a household survey and an experiment. Participants were mostly either the household head or their spouse because they are those most likely to be faced with risky choices and important economic decisions. (see Figs. 1 and 2)

(6)

R*1i ¼ X1i ɑ1 þ εi where εi eNð0; 1Þ 0

R1 ¼ 1, if R*1i > 0, and is 0 if R*1i � 0 (Rainfall Insurance taking decision) � 0 (7) R*2i ¼ X2i ɑ2 þ ui where ui eN 0; σ2 R2i ¼ R*2i if R1i ¼ 1 and R*2i > 0, and is 0 if R1i � 1 and R*2i � 0 (Minimum price to pay). R*1i is a latent variable indicating farm households willingness to insure, R*2i is then latent minimum price farm households are willing to pay for rainfall insurance contract, X0 1i represents vector of independent variables influencing farm households decision to take up an insurance policies, similarly, X0 2i represents vector of independent variables influencing the minimum price they are willing to pay for purchasing an insurance policy, εi and ui are error terms respectively and both are iid. The independent error term of the double-hurdle model can be computed by the help of log-likelihood equations such as: � �� X � � X ’ 2iβ2 0 Log L ¼ ln 1 ɑ X1i β1 Ø þ

The land utilization patterns of selected districts are presented in Table A1. The total geographical area of Cuttack district is 393 thousand ha, out of which the forest area covers around 20% or 79 thousand ha, miscellaneous trees and groves account for 11 thousand ha or 2.8%. Again 2.8% of total land is permanent pasture and 2.5% of land is a culturable waste. A major chunk of land is being diverted towards nonagricultural use, 83 thousand ha or 21.1% of total land which is more than forest area. Barren and unculturable land accounts for 2.5% while current fallow account for 7.88%. Other fallow land accounts for only 0.25% of total land. The net sown area accounts for 39.94% of the total geographical area. Whereas, the geographical land of the Bolangir dis­ trict is 657 thousand ha, out of which the forest area covers around 23.44% or 154 thousand ha, miscellaneous trees and groves account for 4 thousand ha or 0.61%. Again 7% of total land is permanent pasture and 2.74% of land is a culturable waste. Similarly, land for nonagricultural use accounts for 53 thousand ha or 8.07%. Barren and unculturable land accounts for 3.5% while current fallow account for 8.22%. Other fallow land accounts for 1.98% of total land. The net sown area accounts for 44.44% of the total geographical area.

σ

0

X

3.1. Land utilization pattern in sample districts

� � 1 A2 ln ɑðX’1i β1 Þ Ø

σ

0

X 1i β2

��

σ

(8)

The log-likelihood functions of the double hurdle model allow for maximization of two important components such as the probit model followed by a Tobit model [34]. 3. Study region, data collection and variable construction The selection of districts as well as study villages is done on the basis of the objective of the study to have a comparative analysis of produc­ tion decision in both irrigated and rainfed region of the state. Production loss due to various natural hazards such as flood, drought, and cyclone and farmers vulnerability to extreme climate change in both the regions is another rationale behind choosing the two particular districts. For our study, I have selected two districts such as Cuttack and Bolangir. The

2

3

Odisha Agriculture Statistics, 2013–14.

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International Journal of Disaster Risk Reduction xxx (xxxx) xxx

Fig. 1. Map of Cuttack district. Source: https://www.mapsofindia.com/maps/orissa/districts/cuttack.htm

3.2. Cropping pattern of sample districts

Bolangir district, it occupied more than 65% of the land.

The cropping pattern of both districts shown in Table A2 is skewed in the sense that rice is the dominant crop in Kharif season, occupying around 90% of land in Cuttack district where as it is 56.6% in case of Bolangir district. Out of total area devoted to food grains, total cereals account for a major chunk of land while pulses only occupy 1% of total cropped area in Cuttack district where as it occupies more than 20% in case of Bolangir district. Total oilseeds, total fibres and total spices ac­ count for less than 2% of land in Cuttack district where as in case of total fibres, it occupied more than 11% area in Bolangir district. However, total vegetables account for 2% in Cuttack district where as it is 6% in case of Bolangir district. In Rabi season, there is just reverse of that in the sense that total pulses and total foodgrains occupy more than 70% while cereals only account 2.13% in Cuttack district; where as in case of

3.3. 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 distribution of operational holding of both districts is given in Table A3. In Cuttack district, the marginal farmers having an average land holding of 0.56 ha constitute 80%. The small farmers having 1.71 ha of average land constitute 16.12% of total operational holding. Semi medium farmers constitute 3.25% having an average landholding 3.05ha and medium farmers constitute only 0.37% and own on an average 5.82 ha of land. Large farmers constitute only 0.06% but hold on an average 35.51 ha of land.

Fig. 2. Map of Bolangir district. Source: http://kbk.nic.in/maps/balangir.gif 4

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International Journal of Disaster Risk Reduction xxx (xxxx) xxx

Similarly, in Bolangir district, the marginal farmers are 71.27% holding an average land of 0.62 ha, followed by small farmers who constitute 20.43% having 1.66 ha. Semi-medium farmers constitute 6.58% and hold on an average 3.04 ha, medium farmers constitute only 1.56 having 6.3 ha. The large farmers only constitute 0.16% but contain 18.71 ha as the average operational size. A further comparison between the two districts as far as distribution of operational holding is con­ cerned; we observed that operational holding in Bolangir district is greater than Cuttack district except for a number of marginal farmers. However, size of holding is comparatively more in case of Cuttack dis­ trict. The marginal farmers are more in case of Cuttack district than Bolangir district. The classification of farmers here follows the National Sample Sur­ vey (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 as greater than 8 ha which comes as 19.77 acres. Following the earlier literature and NSS we modified our classi­ fication 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 1 shows the distribution of operational holding in both regions as well as in the entire study region. Here we merge semi-medium and medium farmers into medium category. 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 39.94 ha in case of marginal farmers rendering 0.34 ha as average holding and the total holding of small farmers in entire study region is 57.22 ha leading the average holding as 0.81 ha. Similarly the total holding of medium farmers is 186.56 ha and average holding is 1.69 ha and for large farmers it is 3.35 ha as average holding (see Table 2).

Table 2 Construction of variables for awareness model.

3.3.1. Farmers’ general perception and awareness towards rainfall insurance program Farmers ‘awareness towards rainfall insurance program is one of the important variables in insurance purchase decision. It is imperative to know in detail how farmers were aware about insurance program in coastal as well as rain-fed region of Odisha. The probit model was employed following Kumar et al. [37] to study awareness about rainfall

Regressors

Units of measurement

A priori expectation of the variables

Age

Years

Gender

1 if male, 0 otherwise

Education

Years

Farmer group membership

1 if farmer belongs to any organization, 0 otherwise

Farm age

Years

Farming experience

Years

Land ownership

1 if a farmer owns the farm land, 0 otherwise

Credit

1 if farmer avails credit, 0 otherwise

Extension contact

Contact with extension officers (1 for yes, 0 for No)

Information access

reading habits of farmer (1 for yes, 0 for No)

Past disaster experience

1 for disaster experience in 2015, 0 otherwise

Age can negatively influence farmers’ awareness level. Older farmers have less chance to be aware of insurance than younger ones. It is hypothesized that male farmers have a higher probability of knowing insurance schemes than female farmers because former is well endowed with resources such as land than latter [35]. It is hypothesized that a farmer with formal education has better knowledge about the rainfall insurance program. It is hypothesized that there exists a positive association between farmers’ group membership with their insurance awareness [37]. It is hypothesized that there exists a positive relationship between age of farms and rainfall insurance awareness level. As the age of the firm becomes old, farmer always worried about several problems related to land viz, soil fertility, choice of weather resistant crop variety, etc. It is hypothesized that farmers with greater work experience in farming are more likely to be aware of rainfall insurance policy [34,37,38] Land ownership has positive association with rainfall insurance awareness [34]. It is hypothesized that availability of credit from formal institutions has a positive association with farmers’ awareness level. This is due to the fact that most of the insurance schemes operated earlier in form of crop-credit insurance. It is hypothesized that as farmer contacts with extension officer of their region, the probability of knowing various risk mitigation strategies increases. It is hypothesized that regular newspaper reading habits have a positive association with insurance awareness level. It is hypothesized that past disaster experience has a positive association with insurance awareness level.

Source: Authors’ own classification from various sources.

Table 1 Distribution of operational land holding and average holding. Farmers Type Marginal Small Medium Large

Irrigated Region (Area in hectares)

Rainfed Region (Area in hectares)

Total Sample (Area in hectares)

Total operational Holding

Average Operational Holding

Total operational Holding

Average Operational Holding

Total operational Holding

Average Operational Holding

30.23 30.51 50.18 126.67

0.32 0.8 1.48 3.95

9.71 26.7 136.38 221.36

0.4 0.81 1.79 3.49

39.94 57.22 186.56 348.02

0.34 0.81 1.69 3.65

Note: Operational holding includes both owned land as well as leased land. Source: Field Survey during May 2016. 5

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International Journal of Disaster Risk Reduction xxx (xxxx) xxx

insurance schemes in Odisha.

N X

WTI ¼ ɑ þ

3.3.2. The probit model specification In Probit model, it is assumed that the awareness of the ith farmer about rainfall insurance depends on an unobservable index Ii (a latent variable) is determined by a vector of variables i.e. Xi in such a way that the larger the value of the index Ii, the greater the probability of a farmer aware about insurance program. The unobservable index Ii can be expressed as: Ii ¼ β 1 þ β 2 X i

βj Xj þ εi

(11)

j¼1

Where WTI represents a dichotomous dependent variable representing farm households’ willingness to insure rainfall insurance. WTI ¼ 1 (Rainfall Insurance Takers) WTI ¼ 0 (Non-rainfall Insurance Takers) Xj … … …. .XN represents various socio-economic factors and ε is the unobserved factors, ɑ and β are unknown parameters to be estimated from the model.

(9)

Where Xi represents the vector of independent variables. But here question arises how is the unobservable index Ii related to the awareness on rainfall insurance program? Let Yi ¼ 1 if the farmers have adequate knowledge of rainfall insur­

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

WTIi ¼ ɑ þ β1 age þ β2 age2 þ β3 gender þ β4 marital status þ β5 education þ β6 household ​ sizeþ β7 farm ​ exp erience þ β8 farm ​ group ​ membership þ β9 farm ​ size þ β10 ownership þ β11 average age ​ of ​ the ​ land þ β12 average ​ age ​ of ​ the ​ land2 þ β13 income þ β14 awareness þ ε1

(12)

Where WTIi is the farm households’ willingness to take up rainfall in­ surance program, ɑ, β1 …. β14 are unknown parameters and ε1 is the stochastic random term. We have used the Tobit model to obtain the minimum insurance premium amount farm households are willing to pay can be written as:

ance program, and 0, otherwise. Now, it is reasonable to assume that there is an Ii* known as the threshold level such that. If Ii > Ii*, then the farmer is aware of an insurance product, otherwise not. The threshold level Ii* like Ii is again unobservable, but if it is normally distributed with same mean and variance, it is then possible

Yi ¼ ɑ þ δ1 age þ δ2 age2 þ δ3 gender þ δ4 marital ​ status þ δ5 education þ δ6 household ​ sizeþ δ7 farm ​ exp erience þ δ8 farm ​ group ​ membership þ δ9 farm ​ size þ δ10 ownership þ δ11 average age of the land þ δ12 average age of the land2 þ δ13 income þ δ14 awareness þ ε2

not only to estimate the parameters of the index but also to know some information about the unobservable index itself. Rice production plays a very significant role in our study regions than those in other regions in Odisha. Since the governments of India have already launched several weather-based insurance schemes recently, realizing the importance of rainfall insurance as an important tool so as to manage various Covariate risks such as drought and flood, the present study has verified the farm household awareness level about rainfall insurance program and also highlights several constraints to farmers’ participation in insurance. The Probit model explained above is used to study the awareness level about insurance program in the study region.

(13)

Where Yi represents the last bid price offered to farm households in the study region, ɑ, δ1 … …. δ14 are unknown parameters and ε2 is the stochastic random term. 4. Results and discussion This section highlights the descriptive statistics of several indicators selected in the study region and explores various determinants of rainfall insurance purchase decision. About 75% of the farmers in Bolangir region are interested to insure their crop in 2016. The average insurance premium amount per hectare

Yi ¼ ɑ0 þ β1 age þ β2 gender þ β3 education þ β4 farm exp þ β5 extncontact þ β6 credit þ β7 fgm þ β8 infoaccess ​ þβ9 landownership þ β10 farmage þ β11 pastdisaster exp þ Ui

(10)

(10000 sq m) the participants in Bolangir region are willing to pay is INR 489. The maximum amount is INR 1000 whereas the minimum amount is INR 100. The average age of the participants is 48.94 years. About 93% of them 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 is 25.43 years ranging from 4 to 50 years of expe­ rience. The average farm size is almost 5 acres. About 50% of the

Dependent Variable Yi is a dummy variable represents awareness about rainfall insurance program where Yi ¼ 1 if farmers have adequate knowledge about the insurance product and 0 otherwise. 3.3.3. Empirical framework of the double hurdle model Farm households’ willingness to insure rainfall insurance can be further specified as follows:

6

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International Journal of Disaster Risk Reduction xxx (xxxx) xxx

participants are having off-farm income, i.e., income other than agri­ culture. 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 of the crop insurance program. About 81.5% of farm households in Cuttack region are willing to take up rainfall insurance policy in 2016 as shown in the above table. The average insurance premium amount per hectare (10000 sq m) the par­ ticipants are willing to pay is INR 422. The maximum amount is INR 900 whereas the minimum amount is INR 100. The mean age of participants is 48.64 years. About 91.5% of them 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 are married. The average farming experience of the sample households is nearly 23 years ranging from 3 to 55 years of experience. The average farm size is almost 3 acres ranging from 0.4 acres to 15 acres. About 30% of the participants are 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 farmers in the study region are aware of the insurance program. From Table 6, it is clear that availability of credit facilities and in­ formation access found to have significant influence on the farmers’ overall awareness about rainfall insurance. When the farmers of the state access formal institution for agricultural credit, the probability of aware is found to be very high. Similarly, the information access through reading the newspaper, watching television, and listening radio regu­ larly significantly influences the probability of awareness about rainfall insurance among sample farmers. All other explanatory variables are not significantly influencing the awareness level in the study region despite their theoretical relevance. It could be understood from the result that encouraging the crop credit insurance will enhance the awareness of the sample farmers. Similarly, mass media and print media are essential to improve the overall awareness of rainfall insurance scheme in Bolangir district (see Table 3–5). Table 7 shows that gender, level of education, the age of the farm, availability of credit facilities, and past disaster experience found to have significant influence on the farmers’ overall awareness about rainfall insurance schemes in Cuttack region. Surprisingly gender is positively associated with awareness level. Similarly, the education level of farmers significantly influences the probability of awareness about rainfall insurance among sample farmers. As the age of the firm becomes old, farmer always worried about several problems related to land viz, soil fertility, choice of weather resistant crop variety, etc. therefore tried to collect information about various risk mitigation strategies viz, in­ surance. When the farmers of the state access formal institution for agricultural credit, the probability of aware is found to be very high. Similarly, the information access through reading the newspaper, watching television, and listening radio regularly significantly in­ fluences the probability of awareness about insurance among sample farmers. Most importantly, farmers who have experienced a disaster in the last 5 years are more likely to be aware of various insurance pro­ grams introduced by the government and other financial institutions (see Tables 8 and 9). All other explanatory variables are not significantly influencing the awareness level in the study region despite their theoretical relevance. It could be understood from the result that encouraging the crop credit insurance will raise the awareness of the farmers in the study region. Similarly, Education found to be an important tool so as to improve the

Table 3 Construction of variables for the Double Hurdle model. Regressors

Units of measurement

A priori expectation of the variables

Age

Years

Gender

1 if male, 0 otherwise

Marital status

1 if married, 0 otherwise

Education

Years

Family size

Numbers

Farm size

Hectares

Farmer group membership

1 if farmer belongs to any organization, 0 otherwise

Land ownership

1 if a farmer owns the farm land, 0 otherwise

Farm age

Years

Farming experience

Years

Income

INR

Awareness

1 if a farmer is aware of rainfall insurance, 0 otherwise

Off-farm income source

1 if farmer earns other than crop production, 0 otherwise

Age can negatively influence farmers’ WTI his firm. Older farmers have less chance to adopt an insurance policy than younger ones [34,35] It is hypothesized that male farmers have a 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 [35]. It is hypothesized that married farmers can adopt the insurance policy and also can pay a higher premium [34] It is hypothesized that a farmer who has obtained formal education has higher chance of adopting rainfall insurance and can also pay higher amounts of premium [39,40] It is hypothesized that family size can both have positive and negative chance of adopting as well as interested to pay for rainfall insurance program [35]. It is hypothesized to be positively influencing insurance adoption because farmers having large size of holding more likely to adopt an insurance policy [34]. On the other hand, the premium amount increases as the size of area under crop cultivation increases [35]. It is hypothesized that there exists a positive association between farmers group membership with their insurance adoption decision [34]. Farmers having their own land are more likely to take interest in the insurance policy [34]. It is hypothesized that as age of the farm increases, there is high chance of considering an insurance policy [34]. It is hypothesized that farmers having greater experience have more chance of adopting an insurance policy. It is hypothesized to be positive because those who receives high farm revenue from their land willing to take up as well as can pay higher premium amount [34] It is hypothesized that those who have already aware about the possible benefits of an insurance policy always willing to take up as well as can pay higher premium amount [34]. It is hypothesized that off-farm income source has a positive association with both WTI and WTP for an insurance policy [35].

Source: Authors’ own classification from various sources.

7

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International Journal of Disaster Risk Reduction xxx (xxxx) xxx

Table 4 Descriptive information about sample villages in Bolangir district (Double-hurdle model). Category

Variables

Unit

Mean

Dependent variables

1. Dummy: WTI in 2016 RIP 2. WTP in 2016 RIP Age Gender Education Marital status Farm experience Farm size other income source Farm group membership Household size Income from Rice Land ownership Farm age Awareness

Yes ¼ 1, No ¼ 0 INR/per hectare (10000 Sq m) Years Male ¼ 1, female ¼ 0 Years Married ¼ 1, unmarried ¼ 0 Years Acres Yes ¼ 1, No ¼ 0 Yes ¼ 1, No ¼ 0 Number INR Yes ¼ 1, No ¼ 0 Years Yes ¼ 1, No ¼ 0

0.75 489 48.94 0.93 6.29 0.955 25.43 4.99 0.5 0.25 6.145 55105 0.89 33.715 0.55

Regressors

Sd

Min

Max

12.10

24

76

4.43

0

15

11.99 3.23

4 1

50 17

2.87 25238.84

2 25000

17 120000

8.35

16

70

Sd

Min

Max

12.73

20

85

5.14

0

15

11.29 3.35

3 0.4

55 15

2.04 24109.67

2 10000

13 150000

8.97

9

60

Note: own calculation based on field survey from May to August 2016. RIP: Rainfall Insurance Program. Table 5 Descriptive information about sample villages in Cuttack district (Double-hurdle model). Category

Variables

Unit

Mean

Dependent variables

1. Dummy: WTI in 2016 RIP 2. WTP in 2016 RIP Age Gender Education Marital status Farm experience Farm size other income source Farm group membership Household size Income from Rice Land ownership Farm age Awareness

Yes ¼ 1, No ¼ 0 INR/per hectare (10000 sq m) Years Male ¼ 1, female ¼ 0 Years Married ¼ 1, unmarried ¼ 0 Years Acres Yes ¼ 1, No ¼ 0 Yes ¼ 1, No ¼ 0 Number INR Yes ¼ 1, No ¼ 0 Years Yes ¼ 1, No ¼ 0

0.815 422 48.64 0.915 5.74 0.95 22.99 2.96 0.3 0.27 5.185 51745 0.82 34.38 0.74

Regressors

Note: own calculation based on field survey from May to August 2016. RIP: Rainfall Insurance Program. Table 6 Farmers’ awareness about rainfall insurance program in Bolangir district.

Table 7 Farmers’ awareness about rainfall insurance program in Cuttack district.

Variables

Coefficient

Standard error

Variables

Coefficient

Standard error

Age Gender Education Farmer group membership Farm age Farm experience Land ownership Credit Extension contact Information access Disaster experience Constant LR chi2

0.39 0.024 0.007 0.005 0.21 0.22 0.02 0.24* 0.25 0.37** 0.49 0.27 8.59

0.49 0.36 0.094 0.22 0.43 0.23 0.301 0.09 0.27 0.19 0.52 2.39

age gender Education Farmer group membership Farm age Farm experience Land ownership Credit Extension contact Information access Disaster experience Constant LR chi2

0.002 0.71* 0.06*** 0.302 0.03*** 0.003 0.22 0.73*** 0.35 0.13 0.73* 1.47* 19.35*

0.11 0.43 0.02 0.24 0.01 0.01 0.26 0.31 0.28 0.24 0.54 1.009

Source and note: field survey from May to August 2016. *, **, *** represent statistically significance level at 10%, 5%, and 1% level of significance, respectively.

Source and note: field survey from May to August 2016. *, **, *** represent statistically significance level at 10%, 5%, and 1% level of significance, respectively.

awareness of insurance scheme in Cuttack district. It is also clear that male farmers have better awareness level about rainfall insurance schemes than their female counterparts. From the above table, one can observe that variables like age and farmer group membership have no significant influence both on will­ ingness to take up an insurance policy as well as willingness to pay for rainfall insurance program. The mean VIF is 1.53 with explanatory variables having a VIF ranging from 1.04 to 2.68. The VIF for the in­ dependent variables are less than five implying there is no

multicollinearity. However age has positive association with adoption but has negative relation with WTP for rainfall insurance. It 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 our result however contradicted the earlier findings by Ref. [35]. Farmer group membership has no significant relationship with WTI and WTP for an insurance policy but has positive association with both WTI and WTP. This result is in conformity with the earlier study by 8

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International Journal of Disaster Risk Reduction xxx (xxxx) xxx

farmer always considers the endurance of his family from any type of 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 is because as farmers’ work more in a particular crop firm, his/her adaptive ca­ pacity to climate change increases greatly therefore he/she has shown no/very low interest in taking up the insurance program. Size of rice area has negative influence on farmers’ WTI and WTP for insurance program. This shows that the larger the area under crop production there is less chance that the farmer would WTI his land. This result also revealed that farm size has significant and negative association with farmers’ WTP for the insurance program. This implies as rice area in­ creases by 1 ha, the premium amount reduces by INR 15.86. Income from other sources is not significant but has a negative effect on farmers’ WTI but it is positive and significant factor influencing farmers’ WTP for the 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 but on the other hand, risk of losing his farm at the expense of his family in case of any types of disaster would like to pay higher amounts of premium. The study found positive and significant association between household size and farmers’ WTP for an insurance program. Income from rice was a significant factor influencing farmers’ willingness to take up an insurance plan. On the other hand, it also positively influence even though not significant a farmers’ willingness to pay for insurance pro­ gram. Land ownership was highly significant and has positive influence on both these household decisions. Farm age has significant positive influence on farmers’ WTP and again positive effect on farmers’ WTI even though not significant and confirms our a priori expectation. This means that as farm becomes old, it always have the fear of less pro­ duction and problems associated with soil fertility. Awareness variable was found to be significant and negatively signed. This implies that farmers with adequate knowledge of rainfall insurance policy have less chance of taking up one policy. This is because in the rain-fed region, farmers’ experience in insurance purchase is a significant determinant of WTP but 20% of farmers who bought insurance in 2015 did not show their interests in the insurance 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. From the above table, one can observe that variables like age, gender, education, farm experience and farm age have no significant influence on both WTI and WTP for an insurance program. The mean VIF is 1.31 with explanatory variables having a VIF ranging from 1.05 to 2.25. The VIF for the independent variables are less than five implying there is no multicollinearity. However, age has a negative association with both these household decisions for rainfall insurance. This implies that older farmers have very less chance to adopt insurance than younger ones and as age of the farmer increases, the premium amount he/she is willing to pay reduces. The variable Gender has absolutely no significant influence on farmers’ insurance adoption and WTP for an 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 a negative association with gender and WTP. The level of education of farm household negatively influenced their WTI and WTP for insurance program even though not found statistically significant. As the farmer becomes more educated he/she exposed to various other risk manage­ ment strategies than rainfall insurance. This result did not confirm our a priori expectation. Farming experience has no significant influence on farmers’ insurance adoption but has a positive association with WTI for the insurance program. This is because farmers with greater work experience in a particular crop firm are more likely to be interested in insurance policy. Farm age has positive even though not significant in­ fluence on farmers’ WTI and hence confirms our a priori expectation.

Table 8 Double Hurdle model on factors determining farm households’ WTI and WTP in Bolangir district. Regressors

Age Gender Education Marital status Farm experience Farm size Off-farm income source Farm group membership Household size Income from Rice Land ownership Farm age Awareness Constant LR chi2 Mean VIF

WTI (First Hurdle)

WTP (Second Hurdle)

Coefficient

standard error

Coefficient

standard error

0.62 0.91* 0.29*** 0.34 0.48* 0.003 0.01

0.55 0.56 0.104 0.59 0.28 0.05 0.21

10.76 59.04 8.007** 175.303* 0.75 15.86** 48.99*

13.41 69.33 3.98 112.12 2.15 8.68 30.77

0.25

0.24

23.45

40.29

0.03 0.34* 0.83*** 0.13 0.05 4.33 23.02** 1.53

0.05 0.23 0.35 0.44 0.21 3.54 LR chi2

14.87* 0.0002 103.91* 20.26* 58.72* 69.33 19.39

8.96 0.0007 61.76 12.27 35.36 416.57

Note: field survey from May to August 2016. *, **, and *** represent statistically significant at 10%, 5%, and 1% level, respectively. Table 9 Double Hurdle model determining farm households’ WTI and WTP in Cuttack district. Regressors

Age Gender Education Marital status Farm experience Farm size Off-farm income source Farm group membership Household size Income from Rice Land ownership Farm age Awareness Constant LR chi2 Mean VIF

WTI (First Hurdle)

WTP (Second Hurdle)

Coefficient

standard error

Coefficient

standard error

0.58 0.03 0.17 0.29 0.47 0.19*** 2.801

1.29 1.28 0.23 1.81 0.73 0.07 476.03

5.05 26.55 0.21 177.72** 1.18 0.22 153.46**

8.39 51.203 2.74 87.16 1.76 4.34 80.91

4.603

476.02

136.23*

83.72

0.78 0.73* 0.87 0.41 5.48*** 6.601 197.18*** 1.31

0.12 0.105 0.74 0.87 1.03 6.85 LR chi2

18.99*** 0.0005 76.16** 0.69 368.07*** 309.78 113.66***

6.97 0.0006 37.42 7.53 32.19 234.22

Note: field survey from May to August 2015–16. *, **, *** represent statistically significance level at 10%, 5%, and 1% level of significance, respectively.

Ref. [34] in the Ghanaian cocoa industry. The remaining 11 variables have significant influence on either farmers’ WTI or WTP for rainfall insurance program in the study region. Gender has a negative and sig­ nificant relationship with rainfall insurance adoption but has no sig­ nificant influence on WTP for insurance policy. Therefore it did not confirm to the a-priori expectation which is positive. The level of edu­ cation of farm household positively influenced their WTI and WTP for rainfall insurance program and also was found highly statistically sig­ nificant. Thus, it can be said that the higher the farmer attains formal education, the greater chance that he/she would be interested to take up an insurance policy and also pay more insurance premium. This result confirmed to our a priori expectation. 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. This is because a married 9

A.K. Senapati

International Journal of Disaster Risk Reduction xxx (xxxx) xxx

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 Ref. [34]. The remaining 8 variables have a significant influence on either farmers’ WTI or WTP for an 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’ interest to pay for rainfall in­ surance program. This result partly confirms and partly rejects our a priori prediction. This is because a married farmer always considers the endurance of his family against any types of 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 insur­ ance program. This shows that the larger the area under crop production there is higher chance that the farmer would WTI his land. This result also revealed that farm size has significant and positive association with farmers’ WTP for the insurance program. This implies as rice area in­ creases by 1 ha, the premium amount increases by 0.194. Income from other than agriculture found to be not significant but positively influ­ enced farmers’ willingness to insure but it is negative and significant factor influencing farmers’ WTP for the insurance program. This is because farm household diversify their income, and felt secured about any types of disaster and therefore less interested to pay large premium for rainfall insurance. 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. 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 insur­ ance because a farmer at the expense of his family would always like to insure his/her farm against any types of disaster. On the other hand, a farmer may not want to spend in any other activities that have large family size but use it to cater for his family. Income from rice found to be a significant factor influencing farmers’ interest to insure his/her land. On the other hand, it negatively influence even though not significant a farmers’ WTP for an insurance policy. Land ownership found to be highly significant and has a positive effect on both household decisions. This implies households who have their own land very likely to partic­ ipate in the insurance policy and hence can pay more premium amount. Awareness variable was found to be positive and significant. This im­ plies that farmers having adequate knowledge of rainfall insurance policy more likely to show their interest in an insurance policy and also confirms our a priori expectation in the study region. This is because in the coastal region, farmers’ experience in insurance purchase is a sig­ nificant 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.

significant influence on WTI for insurance program; where as 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. Education is the only variable which is significant in both the models. This suggests that the government should focus on providing more formal education to the farmers in order for them to accept and pay more for insurance. In Cuttack region, variables like marital status, off-firm income source, farmer group membership, household size, land ownership and awareness have significant influ­ ence 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 rainfall insurance scheme should always take into consideration the education and awareness level of sample household in these regions. Land ownership is a significant factor influencing the willingness to pay for insurance against climatic shocks. Sample respondents shared several reasons of their non-participation in insurance programs. First, they said they are too poor living with immense hardship; therefore do not have enough money/resources to afford insurance schemes. Second, they also perceived that insurance schemes are not reliable because it is very complicated to get indemnity when there is a disaster due to huge crop losses. Third, since crop pro­ duction is a very high risk venture, they are planning in near future to move into other types of activities and may migrate to cities for better job opportunities. Fourth, some respondents also perceived that since their crop field/area is too small so insurance is not worth buying for them because income from paddy production is too small to manage their day to day family needs. From the awareness model, it is evident that availability of credit facilities and information access found to have significant influence on the farmers’ overall awareness about rainfall insurance schemes implemented by the government and financial institutions in Bolangir district where as gender, level of education, age of the farm, availability of credit facilities, and past disaster experience found to have significant influence on the farmers’ overall awareness about rainfall insurance schemes implemented by the government and financial institutions in Cuttack district. It could be understood from the result that encouraging the crop credit insurance will boost the awareness of the farmers. Similarly, mass media and print media are important tools to improve the awareness of insurance scheme in Bolangir district, on the other hand, education is considered as a significant tool so as to raise the awareness of insurance scheme in Cuttack district. This study has several implications further than the useful lessons it highlights about promoting rainfall insurance adoption. First, it dem­ onstrates how large scale field survey in two geographically different located regions, can also be used to test theories of consumer demand with respect to rainfall insurance adoption. Second, it presents how different socio-economic factors can influences financial decision mak­ ing which may form the basis for developing a theory. Nevertheless, the results of this study do need to be interpreted with some caution. This present study suffers from many limitations which need to be noted. First, the sample size is not enough to represent the total population. The author has interviewed 400 sample farmers, 200 sample farmers from the six villages of the irrigated region of Cuttack and 200 farmers from six villages of the rainfed region of Bolangir district. However, taking a sample of 400 farmers in total is not enough to study the insurance adaptation diversity of farmers of an agrarian economy of a particular state. Second, the theoretical model used in this study is based on the ‘rationality’ assumption of the mainstream economics but in recent years, the behavioural economists found that the individuals are boun­ ded rational. Third, the author has not included non-agricultural households or non-farm households in the analysis and only focused on landowning cultivator households to study their production and adaptation decisions under the situation of risk and uncertainty. An

5. Summary and conclusion The present study identifies various determinants of insurance adaptation options and willingness to pay for rainfall insurance product so as to assess climatic variability and climate change based on cross –sectional survey data collected during 2016–17 kharif season in the drought and flood-prone villages in eastern India. Double Hurdle model was employed to explore the determinants of insurance adaptation di­ versity, and the following salient points emerge from the analysis. To sum up, the implications of the above findings are that variables like age and farmer group membership have negligible impact on household’s decision for rainfall insurance program where as variables like gender, education, farm experience, income from rice, and land ownership have 10

A.K. Senapati

International Journal of Disaster Risk Reduction xxx (xxxx) xxx

Prof. Phanindra Goyari, School of Economics, university of Hyderabad, India for his encouragement and support during early phase of research. A special thank goes to a number of people who helped me in collecting primary data during my field survey. I express my sincere thanks to all my respondents who spared some of their valuable time and provided me necessary information for my research. I would also like to thank all the officials especially Revenue Inspector of both Cuttack and Bolangir districts, Chief manager of ADB, Bolangir, Regional manager of AICL, Bhubaneswar for their useful information in order to carry out a smooth survey. The author is grateful to Dr. Pranta Pratik Patnaik and Dr. Bis­ wajit Mohanty for necessary grammatical correction on the earlier draft of this paper. However, usual disclaimer applies.

analytical consequence of excluding non-farm households is that the author unable to address questions of general equilibrium analysis. Yet, the findings which emerge from this study have their relevance in guiding policies and interventions specific to the intensity of damage caused by covariate shocks such as droughts and floods which are increasingly anticipated with the changing behavior of climate across districts of Odisha. Acknowledgments The author would like to thank the editor and anonymous referees for their useful and constructive comments. The author is grateful to

Appendix Table A1 Land utilization pattern in selected districts Land Utilization Pattern

Area (’000 ha)

Sl. No. Forest area Misc. tree & groves Permanent Pasture Culturable Waste Land put to non-agricultural use Barren & uncultivable land Current fallow Other fallow Net sown area Geographical land

Cuttack 79 (20%) 11 (2.8%) 11 (2.8%) 10 (2.5%) 83 (21.1%) 10 (2.5%) 31 (7.88%) 1 (0.25%) 157 (39.94%) 393 (100%)

Bolangir 154 (23.44%) 4 (0.61%) 46 (7%) 18 (2.74%) 53 (8.07%) 23 (3.5%) 54 (8.22%) 13 (1.98%) 292 (44.44%) 657 (100%)

Note: Figures within brackets indicate the percentage of the total. Source: Odisha Agricultural Statistics 2013–14, Directorate of Agriculture & Food Production, Odisha.

Table A2 Season-wise cropping pattern of selected districts Name of the crop

Rice Other cereals Total cereals Total pulses Total foodgrains Total oilseeds Total vegetables Total fibres Total Spices Sugarcane Total cropped area

Cuttack District

Bolangir District

Kharif season

Rabi season

Kharif season

Rabi season

Area (in ‘000 ha)

% of total cropped area

Area (in ‘000 ha)

% of total cropped area

Area (in ‘000 ha)

% of total cropped area

Area (in ‘000 ha)

% of total cropped area

118.21 1.62 119.83 1.4 121.23 1.47 2.67 1.68 2.87 – 129.92

90.98 1.25 92.23 1.08 93.31 1.13 2.05 1.29 2.21 – 100.00

2.89 0.51 3.40 112.94 116.34 14.73 22.3 – 4.17 2.27 159.81

1.81 0.32 2.13 70.67 72.79 9.21 13.95 – 2.61 1.42 100.00

200.30 7.31 207.61 73.77 281.38 8.78 21.43 40.84 1.45 – 353.88

56.6 2.06 58.66 20.84 79.51 2.48 6.05 11.54 0.41 – 100.00

3.86 1.18 5.04 76.89 81.93 12.04 19.63 – 1.94 2.36 117.90

3.27 1.00 4.27 65.21 69.49 10.21 16.64 – 1.64 2.00 100.00

Source: Odisha Agricultural Statistics 2013–14, Directorate of Agriculture & Food Production, Odisha. Table A3 Operational holdings by major size groups in selected districts Size groups

Marginal (<1.0 ha) Small (1.0–2.0 ha) Semi-medium (2.0–4.0 ha)

Cuttack District

Bolangir District

No. of operational holdings

Area operated (in ha)

Average size of holding (in ha)

No. of operational holdings

Area operated (in ha)

Average size of holding (in ha)

132308 (80.2%) 26604 (16.12) 5363 (3.25)

74089 45353 16384

0.56 1.7 3.05

177877 (71.27) 50994 (20.43) 16432 (6.58)

110475 84681 50071

0.62 1.66 3.04 (continued on next page)

11

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A.K. Senapati

Table A3 (continued ) Size groups

Medium (4.0–10.0 ha) Large (>10.0 ha) All sizes

Cuttack District

Bolangir District

No. of operational holdings

Area operated (in ha)

Average size of holding (in ha)

No. of operational holdings

Area operated (in ha)

Average size of holding (in ha)

617 (0.37) 91 (0.06) 164983 (100)

3591 3231 142648

5.82 35.51 0.86

3902 (1.56) 400 (0.16) 249605 (100)

24579 7483 277289

6.3 18.71 1.11

Note: Figures within brackets indicate percentage to the total for each size group. Source: Odisha Agricultural Statistics, 2013–14, Directorate of Economics & Statistics, Odisha, Bhubaneswar.

References

[23] N. Uy, Y. Takeuchi, R. Shaw, Local adaptation for livelihood resilience in Albay, Philippines, Environ. Hazards 10 (2) (2011) 139–153. [24] K. Byjesh, U. Deb, C. Bantilan, Rainfall insurance in India: does it deal with risks in dryland farming?, in: 8th Conference of the Asian Society of Agricultural Economists (ASAE), 15-17 October 2014 BRAC Centre for Development Management (BRAC-CDM), Savar, Dhaka, Bangladesh, 2014. [25] S. Gaurav, S. Cole, J. Tobacman, Marketing complex financial products in emerging markets: evidence from rainfall insurance in India, J. Mark. Res. 48 (SPL) (2011) S150–S162. [26] S. Gaurav, A. Singh, An inquiry into the financial literacy and cognitive ability of farmers: evidence from rural India, Oxf. Dev. Stud. 40 (3) (2012) 358–380. [27] F.L. Xiu, F.G. Xiu, S. Bauer, Farmers’ willingness to pay for cow insurance in Shaanxi Province, China, Procedia Econ. Financ. 1 (2012) 431–440. [28] T.Q. Long, T.B. Minh, N.C. Manh, V.T. Thanh, Farm Households’ Willingness to Pay for Crop (Micro) Insurance in Rural Vietnam: an Investigation Using Contingent Valuation Method. East Asian Development Network (EADN ) Working, 2013. Paper No. 64. [29] G.M. Becker, M.H. DeGroot, J. Marschak, Measuring utility by a single-response sequential method, Behav. Sci. 9 (3) (1964) 226–232. [30] E. Karni, Z. Safra, “Preference reversal” and the observability of preferences by experimental methods, Econometrica 55 (3) (1987) 675–685. [31] J.K. Horowitz, The Becker-DeGroot-Marschak mechanism is not necessarily incentive compatible, even for non-random goods, Econ. Lett. 93 (1) (2006) 6–11. [32] E.Z. Gabre-Madhin, C.B. Barrett, P. Dorosh, in: Technological Change and Price Effects in Agriculture: Conceptual and Comparative Perspectives, Markets Trade and Institutions Division Discussion Paper, vol. 62, International Food Policy Research Institute, Washington, D.C, 2003. [33] X. Yu, D. Abler, Incorporating zero and missing responses into CVM with openended bidding: willingness to pay for Blue Skies in Beijing, Environ. Dev. Econ. 15 (5) (2010) 535–556. [34] G. Danso-Abbeam, K.N. Addai, D. Ehiakpor, Willingness to pay for farm insurance by Small holder Cocoa farmers in Ghana, J. Soc. Sci. Policy Implic. 2 (1) (2014) 163–183. [35] E.V. Okoffo, E.K. Denkyirah, D.T. Adu, B.Y. Fosu-Mensah, A double-hurdle model estimation of Cocoa farmers’ willingness to pay for crop insurance in Ghana 5 (1) (2016) 1–19. Springer Open. [36] J.G. Cragg, Some statistical models for limited dependent variables with 496 applications to the demand for durable goods, Econometrica 39 (5) (1971) 829–844. [37] D. Kumar, B.C. Barah, C.R. Ranganathan, R. Venkatram, S. Gurunathan, S. Thirumoorthy, An analysis of farmers’ perception and awareness towards crop insurance as a tool for risk management in Tamil Nadu, Agric. Econ. Res. Rev. 24 (2011) 37–46. [38] E.B.H. Kouame, N. Koumenan, in: Risk Preference and Demand for Insurance under Price Uncertainty: an Experimental Approach for Cocoa Farmers in Coted’Ivoire. Micro-insurance Innovation Facility, 2012. Research paper No. 13. [39] A.A. Enete, E.M. Igbokwe, Cassava market participation decisions of producing households in Africa, Tropicultura 27 (3) (2009) 129–136. [40] D. Caleb, A. Ramatu, Factors influencing participation in rice development projects: the case of small holder rice farmers in Northern Ghana, Int. J. Dev. Econ. Sustain. 1 (2) (2013) 13–27.

[1] J. Morduch, Income smoothing and consumption smoothing, J. Econ. Perspect. 9 (3) (1995) 103–114. [2] J.R. Anderson, J.L. Dillon, J.B. Hardaker, Agricultural Decision Analysis, The Iowa State University, Ames, 1977. [3] ICRISAT, Socioeconomic Constraints to Development of Semi-arid Tropical Agriculture, ICRISAT, Hyderabad, 1979. [4] J. Antle, Econometric estimation of producers’ risk attitudes, Am. J. Agric. Econ. 69 (3) (1987) 509–522. [5] J.R. Anderson, J.L. Dillon, Risk analysis in dryland farming systems, in: Farming Systems Management Series, vol. 2, Food and Agriculture Organization, Rome, 1992. [6] H.P. Binswanger, D.A. Sillers, Risk aversion and credit constraints in farmers’ decision making: a reinterpretation, J. Dev. Stud. 20 (1) (1984) 5–21. [7] K.P.C. Rao, Changes in dry land agriculture in the semi-arid tropics of India: 19752004, Eur. J. Dev. Res. 20 (4) (2008) 562–578. [8] A. Alderman, C. Paxson, Do the Poor Insure? A Synthesis of Literature on Risk and Consumption in Developing Countries. Policy Research Working Paper No.1008, The World Bank, Washington D.C, 1992. [9] M. Fafchamps, Cash crop production, food price volatility and rural market integration in the third world, Am. J. Agric. Econ. 74 (1) (1992) 90–99. [10] M. Fafchamps, Sequential labor decisions under uncertainty: an estimable household model of West-African farmers, Econometrica 61 (5) (1993) 1173–1197. [11] M. Fafchamps, Rural Poverty, Risk and Development, Edward Elgar, Cheltenham, 2003. [12] H. Jacoby, E. Skoufias, Testing theories of consumption behaviour using information on aggregate shocks: income seasonality and rainfall in rural India, Am. J. Agric. Econ. 80 (1) (1998) 1–14. [13] M.P. Collison, Farm Management in Peasant Agriculture, Praeger, London, 1972. [14] C.S. Bahinipati, L. Venkatachalam, What drives farmers to adopt farm-level adaptation practices to climate extremes: empirical Evidence from Odisha, India, Int. J. Disaster Risk Reduct. 14 (1) (2015) 347–356. [15] S.J. Okonya, K. Syndikush, J. Kroschel, Farmers’ Perception of and Coping strategies to climate change : evidence from six Agro-ecological zones of Uganda, J. Agric. Sci. 5 (8) (2013) 252–263. [16] R.E. Just, D. Zilberman, E. Hochman, Estimation of multi crop production functions, Am. J. Agric. Econ. 65 (4) (1983) 770–780. [17] J.L. Lusk, K.H. Coble, Risk perceptions, risk preference, and acceptance of risky food, Am. J. Agric. Econ. 87 (2) (2005) 393–405. [18] E.M. Liu, J. Huang, Risk preferences and pesticide use by cotton farmers in China, J. Dev. Econ. 103 (C) (2013) 202–215. [19] Y. Qui, G. Colson, C. Grebitus, Risk preferences and purchase of energy-efficient technologies in the residential sector, Ecol. Econ. 107 (2014) 216–229. [20] J. Jin, W. Wang, X. Wang, Farmers’ risk preferences and agricultural weather index insurance uptake in rural China, Int. J. Disaster Risk Sci. 7 (4) (2016) 366–373. [21] K. Lyu, T.J. Barre, Risk aversion in crop insurance program purchase decisions: evidence from maize production areas in China, China Agric. Econ. Rev. 9 (1) (2017) 62–80. [22] E. Hisali, P. Birungi, F. Buyinza, Adaptation to climate change in Uganda: evidence from micro level data, Glob. Environ. Change 21 (4) (2011) 1245–1261.

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