The role of microcredit and microinsurance in coping with natural hazard risks

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1 The role of microcredit and microinsurance in coping with natural hazard risks Sonia Akter 1 and Naureen Fatema 2 1 Corresponding author: Crawford School of Economics and Government, The Australian National University, Canberra, ACT 2601, Australia, Tel: , Fax: Department of Economics, McGill University, Canada. 1

2 ABSTRACT The study investigates the role of post disaster credit market access in determining micro-cropinsurance demand in the rural floodplains of Bangladesh. In a double bounded contingent valuation study, over 500 flood stricken farmers were asked for their preferences to pay premium to protect themselves against crop damage risks. Our results show a negative relationship between farmers access to post disaster microcredit and their willingness to pay premium for a crop insurance contract. This finding was consistent across institutional characteristics of the rural credit market. This result has a number of policy implications. The most important of all is that the recent and growing trend of offering compulsory bundled insurance scheme is likely to curb the demand for microfinance products that are linked with weather related income generation activities in developing countries. Key Words: Flood, crop insurance, microcredit, Asia, Bangladesh 2

3 Acknowledgements The work presented in this paper is part of the Poverty Reduction and Environmental Management (PREM) program in Bangladesh funded by the Dutch Ministry of Foreign Affairs. We gratefully acknowledge the cooperation of the following organizations at various stages of this research: Bangladesh Water Development Board (BWDB), Climate Change Cell (CCC) at Department of Environment (DOE), Flood Forecasting and Warning Center in Bangladesh (FFWC), Water Resource Planning Organization (WARPO) and Geographic Information System (GIS) cell in Local Government Engineering Department. We, furthermore, thank Professors Robert D. Cairns, Sonia Laszlo and Roy Brouwer for their valuable inputs. 3

4 1. INTRODUCTION Microcredit and microinsurance are often referred to as important and effective ex post natural hazard risk coping mechanisms (Brouwer et al., 2007, Khandker, 2007, Botzen and van den Bergh, 2008; Brouwer and Akter, 2010). Accordingly, natural hazard risk insurance programmes have been introduced alongside the existing microcredit programs in many developing countries in order to help the poor cope with increased climatic disaster risks (Mechler et al., 2006; Akter et al., 2009). In majority of the instances, such insurance products are offered by microfinance institutions that traditionally and predominantly focus on the provision of microcredit (ProVention/IIASA, 2006). In some cases, providers offer microinsurance products bundled with microcredit loans. Such schemes require the uptake of insurance as a condition for extending loans or savings arrangements to the microfinance clients. For example, a variant of index-based insurance was implemented in Malawi that offers microlending together with mandatory crop insurance contract (ProVention/IIASA, 2006). Bundled insurance schemes have three key supply side advantages. First, the system enables the insurer to diversify risks by adding other risks to the portfolio that are uncorrelated across clients. Second, adverse selection is reduced if clients are obliged to purchase the insurance, including those facing low risk of natural hazard. Third, if the insurance is offered jointly with other products, transaction costs are lower than if they were sold separately. Despite these advantages from the provider s viewpoint, there is a real risk that bundled insurance may affect the take-up rate of weather insurances by reducing its popularity among insurance clients. Although both microcredit and microinsurance helps risk coping and microcredit is often referred to as implicit insurance against natural hazards (Brouwer et al., 2007), there are important differences in the 4

5 way these instruments function. Microcredit provides households access to financial resources after natural hazard strikes and thus helps cope with natural disaster induced losses or damage incurred to any asset owned by the household. A microinsurance scheme, on the other hand, requires payment on a regular basis before the hazardous event takes place. It generally covers damage or losses incurred to the product(s) against which the insurance was purchased, for example crop, livestock or house property. Finally, the amount of compensation offered by an insurance contract is often uncertain as it is subject to post disaster damage assessment by the insurance provider. Given the differences in the way microcredit and microinsurance operates, the nature of their interactions in the natural hazard risk coping domain is not clearly understood. A case study by Gine et al. (2008) examined the relationship between farmers access to pre disaster credit facility and demand for rainfall-index insurance in a drought prone region in India. They found that credit constrained farmers were less likely to purchase rainfall-index insurance implying that pre disaster microcredit and microinsurance are complimentary goods. Such relationship is substantiated on the basis of the so-called affordability argument, i.e. access to pre disaster microcredit increases insurance affordability by enhancing household income or by relaxing liquidity constraints. While the complementary nature of pre disaster microcredit and weather related microinsurance is being increasingly taken into accounts now-a-days to package microfinance products in developing countries by offering bundled insurance schemes, the substitutability between them as disaster risk coping instruments are being vastly ignored. According to microeconomic theory, 5

6 post disaster microcredit and microinsurance are expected to be substitute goods given that they independently serve as natural disaster loss mitigation instruments. This implies that access to post disaster credit facility and insurance demand are expected to be negatively related. It is reasonable to presume that household decisions to purchase an insurance contract ex ante will, to some extent, be influenced by the availability and access to microcredit ex post. If the direction and magnitude of this influence are not clearly understood and not accounted for in the design of microfinance products, it is likely that these newly innovated bundled microfinance products will fail to accomplish their goals of eradicating poverty by reducing weather induced vulnerability in developing countries. Against this background, we carried out an in-depth empirical examination to shed further light on this issue. More specifically, the main objective of this study is to understand the nexus between ex post credit and microinsurance in the natural hazard risk management domain. To the best of our knowledge, no empirical study has addressed this issue before. Building upon the growing empirical evidence regarding natural hazard insurance demand, we conducted a double bounded (DB) contingent valuation (CV) study where flood stricken farmers in the rural floodplains of Bangladesh were asked for their preferences to pay premium to protect themselves against crop damage risks due to natural hazards. A hypothetical insurance market was constructed because a real crop insurance market currently does not exist in Bangladesh. We estimated a series of econometric models and tested the relationship between farmers access to post disaster microcredit and their willingness to pay (WTP) premium for the hypothetical crop insurance contract. The institutional characteristics of the rural credit market were taken into account by controlling for farmers access to formal and informal credit institutions. We 6

7 observed a statistically significant negative relationship between access to microcredit and crop insurance demand. More specifically, respondents WTP for flood-crop-insurance was found to be curbed by their access to both formal and informal credit institutions and their degree of access to the credit sources. This finding conforms to the proposition of conventional microeconomic theory which suggests post disaster microcredit and microinsurance, to some extent, substitute each other. The rest of the paper is organized as follows: the next section provides a description of the case study and survey design. We then describe the development of our empirical model. Then the paper gives the statistical analysis results, and offers a conclusion and policy recommendation. 2. STUDY SITE AND SURVEY DESCRIPTION Bangladesh is primarily an agrarian economy, with close to 65 percent of the total workforce being involved in agriculture, either directly or as day labourers, and generating about 22 percent of the country s GDP (BBS, 2005). The high sensitivity of agriculture to weather shocks combined with the geophysical characteristics of the country (riverine delta and low land elevation) make the country s agricultural sector vulnerable to flooding. Typical instruments to cope with flood risks at the individual farm level include product diversification, cultivation of local, deep-water and shorter maturity period crop varieties, changing the cropping cycle or skipping a cycle altogether, future markets and vertical integration. At the national level, flood risk management has traditionally focused on infrastructural measures such as building embankments, and ex post flood relief measures, such as distribution of free seed, fertilizer and increased access to post-flood credit facilities. 7

8 Following the overwhelming success of microcredit in Bangladesh, there is a growing optimism in microinsurance solutions to protect rural farm households from income shocks resulting from weather related events. The National Adaptation Program of Action (NAPA), prepared by the Ministry of Environment and Forests (2005), suggests exploring options of a flood insurance market as an important climate change risk adaptation strategy. Following the NAPA recommendation, a commercial feasibility study of microinsuarnce was undertaken in 2006 (see Akter et al. 2009; Akter et al., forthcoming). The study involved a large-scale stated preference survey in the riverine floodplains of Bangladesh. The stated preference techniques estimate monetary values of non-market environmental goods and services by analyzing individuals stated behavior in hypothetical settings. The CV method and choice experiment belong to the stated preference class of non-market valuation techniques. These methods employ public surveys to ask the affected (or relevant) group of population about their WTP by constructing a hypothetical market or referendum. The questionnaire used for the survey gathered information on three broad categories: a) sociodemographic profile such as age, occupation, education, family size, sources of income, assets, etc; b) flood damage information such as the type and extent of damage, duration of floods, inundation level, level of preparedness, type of ex ante and/or ex post disaster loss mitigation measures adopted, access to formal and informal credit institutions and c) the valuation section where the households were asked about their WTP for different hypothetical insurance schemes such as crop, house property, health and unemployment insurance. 566 respondents expressed preferences solely for a crop insurance scheme. The current study used these 566 observations. 8

9 The survey used a DB DC elicitation method where respondents were asked two WTP questions: do you accept a start bid and do you accept a follow up bid. In recent years, the DB DC method has gained popularity over the single bounded approach based on the finding that this method generates more efficient estimates than those based on a single bounded CV (Hanemann et al., 1991; Cameron and Quiggin, 1994; Alberini, 1995). After respondents were explained the proposed insurance scheme, they were asked for a weekly premium ranging between BDT 5 (USD 0.07) and BDT 50 (USD 0.71). These weekly premiums were chosen from a previous CV survey (see Brouwer et al. 2009). A total of six different start bids were used. For each of the six starting bids, a high bid level and a low bid level were pre-assigned, e.g. for a start bid of 30, if respondents rejected this bid, they were asked if 20 was acceptable and if they accepted the start bid of 30, they were asked if 40 was acceptable. The bid levels were assigned randomly across respondents to avoid starting point bias. Four un-embanked riverine districts located near the two major rivers (Meghna and Jamuna) were selected on the basis of damage intensity levels monitored during the 2004 flood. One district located inside the Ganges-Kobadak project (one of the oldest and biggest Flood Control and Irrigation Projects in the country) and one coastal district (surrounded by the Bay of Bengal and lower Meghna) were selected. An embanked area was included as one of the study sites because of the high failure rate of flood protection embankments in Bangladesh. Although flood protection embankments help reduce the frequency and intensity of riverine flooding, they have historically contributed to water logging inside the embanked area due to poor design, construction, and maintenance standards (Rashid and Mallik, 1993; Hossain and Sakai, 2008; Nahar et al., 2010). The geographical locations of the study areas are presented in Figure 1. The 9

10 selection of households in each of the villages followed a random sampling approach where every fifth household located along the village roads was interviewed. Only the heads of households were interviewed in this survey. INSERT FIGURE 1 HERE 3. SAMPLE CHARACTERISTICS, MAGNITUDE OF THE NATURAL HAZARD DAMAGE AND CREDIT MARKET ACCESS Table 1 compares the demographic and socio-economic characteristics of the 566 farmers included in the sample with the national population statistics. All household heads interviewed in the survey were men. The average age of the respondents was 46 years, ranging between 30 and 75 years. About half (45%) of the respondents included in the survey was unable to read and write. Just about a quarter (24%) finished primary school and only 13 percent finished high school. Each household consisted, on average, of six family members. Agricultural farming was the primary occupation of about three quarters (74%) of the sampled households. Approximately 18 percent of the sample population was involved in agricultural farming as a secondary source of livelihood. Almost all sampled farmers (97%) owned the farmland where they cultivate their crops. The average size of the farm land was one hectare. Average yearly crop income accounted for 70 percent of yearly household income. INSERT TABLE 1 HERE Average annual household income (related to the past 12 months) was about US$1750, while half of the sample population earned US$1176 per year. Dividing the median yearly income by the average household size and 12 months, average per capita income equaled US$16 per month, 10

11 which is slightly higher than the national average rural per capita income (US$14) (BBS, 2005). Average household crop damage costs due to natural hazards were US$509 per household per flood event. Trimming off five percent high and low values, the average household crop damage cost equals to US$397 which is equivalent to about three to six percent of average yearly household income (based on the assumption that a natural hazard takes place once in every five to 10 years). Average crop damage cost inside the embanked area was significantly higher than the damage cost in the unprotected riverine area (Kruskal Wallis χ 2 = 24; p<0.001). Although this finding appears to be counterfactual, it is consistent with existing empirical evidence which shows that farmers within an embanked area are more vulnerable to crop damage than those outside the embankment (Rashid and Mallik, 1993). This is because farmers inside the embanked area follow different cropping patterns and use larger proportion of farmland for crop growing including low-lying lands. Therefore, a poorly functioning protection causes a larger amount of damage compared to the farmlands in the unprotected area. However, the frequency of natural hazard inside and outside the embankment was found to be significantly different. Households living within an embanked area experienced relatively lower frequency of water logging (once every six years) than those living outside or without the embankment (once every five years). When respondents were asked how they coped with losses induced by the flood event of 2004, over 80 percent of them said that they used their savings and 70 percent stated that they borrowed money from the rural credit market. Significant heterogeneity was observed across respondents in terms of the nature (formal and informal) and degree of accessibility (number of 11

12 credit sources) to the ex-post disaster credit facilities. About 44 percent of the sampled farm households did not access to any form of post disaster credit sources while the rest accessed to at least one source with a maximum of three. About a quarter (26%) of those who borrowed money to cope with flood damage accessed to the formal credit market (bank or microcredit institutions) and 87 percent accessed to informal credit sources (e.g., relatives, friends, or village leader). On average, relatively wealthier households who owned larger farmland and earned higher yearly average agricultural income relied entirely on savings to cope with the losses (land size: Mann-Whitney Z=-6, p<0.001; agricultural income: Mann-Whitney Z=-8, p<0.001). Size of farmland and agricultural income were found to be inversely related to the degree of credit market access (land size: Chi square=72, p<0.001; agricultural income: Chi square=69, p<0.001). In other words, farmers who owned smaller farmland and earned relatively low agricultural income, on average and other things remaining the same, were found to have sought loans from multiple sources. Finally, on average, relatively low income farmers with smaller number of livelihood sources borrowed money from formal credit institutions more than medium or high income farmers and farmers with larger number of livelihood sources (agricultural income: Mann-Whitney Z=-4, p<0.001; number of income sources: Mann-Whitney Z=-2, p<0.001). 4. DEVELOPMENT OF AN EMPIRICAL MODEL According to standard microeconomic theory individual with an initial wealth of W 0 who suffers from damage D with risk exposure level π, chooses to buy a coverage of amount C paying premium of amount P=pC. Assuming that the individual household has a von Neumann- 12

13 Morgenstern utility function U(W) 1, insurance will be purchased if and only if household s expected utility from risk transfer exceeds expected utility from an uninsured state, that is: π U ( W 0 pc D + C ) + (1 π ) U ( W 0 pc ) > π U ( W 0 D ) + (1 π ) U ( W 0 ) Therefore, households decision of purchasing insurance is expected to be influenced by the level of risk exposure (π ), level of wealth (W 0 ), expected amount of damage and rate of premium (p). Household wealth, in part, reflects households ability to pay premium and damage refers to the consequence of a hazardous incident. The higher the wealth and damage, other things remaining the same, the more should be the farmer s WTP for crop insurance. The demand for crop insurance is expected to be positively related to the level of environmental risk (π ), i.e. the higher the exposure level to environmental risk, higher the likelihood of purchasing crop insurance. Exposure to environmental risk can be divided into two components, namely, exogenous and endogenous risks (Shogren and Crocker, 1991; Smith, 1992). Exogenous risk refers to the aspect of environmental risk on which households have no control. The exposure to exogenous risk can be measured by either subjective or objective probability of natural hazards (e.g. flood return period). Subjective probability of flood return period is reflected by households perceptions of flood occurrence in future while objective probability of flooding can be measured through the frequency of flooding in the past. Endogenous risk refers to the aspect of environmental risk that, to some extent, can be mitigated through protective actions. The social vulnerability literature refers to the distance people live from the source of hazard as an indicator of endogenous risk exposure level (e.g. the closer to the 1 U(W) is continuous and twice differentiable; that is, marginal utility U (W)>0 and U (W)<0. 13

14 river, the higher the probability of getting affected by flooding) (Brouwer et al., 2009). Geographical location is considered an endogenous factor because farmers have the choice to reduce the risk exposure level by moving further away from the river (Brouwer et al., 2010). Flood protection embankment can also be viewed as another indicator of endogenous risk exposure level. An embankment supposedly protects the embanked area from flooding. Hence, theoretically, households without a flood protection embankment are more exposed to the risk of flooding than the households living within an embankment. According to microeconomic theory, demand of a commodity is determined by the availability of its substitute and complement products. The demand for crop insurance, an ex post hazard coping strategy, is expected to depend upon the availability of alternative ex ante and ex post hazard risk coping instruments. Diversification of income sources is a well documented ex ante risk transfer strategy in rural areas (e.g. Rosenzweig and Stark, 1989; Brouwer et al. 2007). Brouwer et al. (2007) showed that households with higher number of non-nature dependent income sources were better able to cope with flood damage. Therefore, it can be expected that farmers with larger number of non-nature dependent income sources would be less likely to purchase crop insurance. Access to post disaster credit facility, ex post disaster relief 2 and savings are common ex post hazard coping strategies in developing economies. The relationship between ex post hazard relief and hazard insurance is fairly straightforward and well documented in the natural hazard literature, i.e. the provision of ex post disaster relief reduces incentives to buy private insurance 2 Ex-post hazard relief refers to distribution of food, drinking water, clothing and medical assistance by government and non-government organizations during and after the natural hazard. 14

15 contracts against natural disasters (Kunreuther, 2006; Raschky, 2007). Savings and insurance demand are expected to be positively related simply because households with savings are less cash constrained and hence are able to pay insurance premium. The relationship between access to ex post credit facilities and hazard insurance has not been investigated before. On one hand, access to post disaster credit facilities is considered an ex post disaster coping mechanism and an implicit insurance scheme (Brouwer et al., 2007). Hence, the relationship between post disaster credit and flood-crop-insurance is expected to be negative implying that the existence of one reduces the desirability for the other. On the other hand, there is widespread evidence that suggests microcredit in general enhances households income generation activities (Khadaker et al., 1998; Khadaker, 2005) and thus increases their affordability to purchase insurance. This implies that access to credit facilities may in general induce the demand for microinsurance. The statistical model through which we aim to test the hypotheses takes the following form: (1) WTP (Crop Insurance) = β 5 i + β Embankment+ β Relief β Wealth i 1 + β 7 i Credit + β Crop Damage + β Flood Frequency + β River Distance i 2 8 i i 3 + β IncomeSources + β Demographic Characteristics The summary statistics of the explanatory variables used in the statistical model are presented in Table 2. INSERT TABLE 2 HERE 5.1. WTP for Crop Insurance 5. EMPIRICAL RESULTS 9 i 4 i + e i Table 3 summarizes farmers responses to the DB WTP questions. For each respondent, let Bid 0 denote the start bid, Bid l denote the follow-up lower bid (in the case where they rejected the start bid) and Bid 2 denote the follow-up higher bid (in the case where they accepted the start bid), 15

16 where Bid l < Bid 0 < Bid 2 for each individual. The interval for the WTP for each respondent was generated as shown in Table 3 WTP L and WTP H denote the lower and upper bounds of the WTP respectively and WTP* is the latent WTP. INSERT TABLE 3 HERE Over half of the sampled farmers (59%) said Yes to both bid levels while about a quarter (24%) of the farmers rejected the first bid but accepted the lower bid level. Eight percent of the sample farmers said No to both bid level and the rest accepted the first bid but rejected the higher bid. The referendum CV program (GAUSS) written by Cooper (1999) was used to estimate mean WTP for crop insurance and its 95 percent confidence interval. The mean WTP for crop insurance was estimated at BDT 42 (US$0.6) per household per week. This amounts to approximately 15 percent of the average weekly income of the sample farm households. Krinsky and Robb confidence interval of the mean WTP value was generated by applying Monte Carlo simulation technique as adapted by Park et al (1991). The 95 percent confidence interval of mean WTP for crop insurance is BDT 40- BDT Determinants of WTP Next we estimated a series of regression models to identify the determinants of farmers WTP for crop insurance. The interval (or grouped data) regression approach, similar to an ordered probit model, was applied to analyze the data. The interval regression approach is applicable when the dependent variable is limited to a certain number of categories, but the ranges of the 16

17 underlying variable to which each category refers to are known (Wooldridge, 2007). Theoretically, an interval regression approach is more efficient than an ordered probit approach to model this later type of variable since the estimation procedure utilizes information provided by the thresholds values to produce an estimate of the standard deviation rather than requiring that this be normalized to one (Horowitz, 1994). Note that an alternate estimation could be to use the Ordinary Least Squares (OLS) regression with the average of the lower and upper bounds of the WTP as the dependent variable. However, this analysis would neither reflect the uncertainty concerning the nature of the exact values within each interval, nor would it deal adequately with the left- and right-censoring issues in the tails. Table 4 presents the results from five different model specifications. The models differed because four different independent variables to control for credit market characteristics were used 3. All of the estimated models turned out to be significant at the one percent level as measured through the likelihood ratio test, which implies that the estimated parameters in each model were significantly different from zero (i.e. the model with a constant term only). INSERT TABLE 4 HERE In all regression models presented in Table 4, wealth and crop damage are statistically significant determinants of WTP at the five and 10 percent level respectively. Note that the results shown report the marginal effects of the explanatory variables on the dependent variable evaluated the means of the independent variables. Therefore, the estimated coefficient for wealth varying between 0.05 and 0.06 in Models 1 and 5 can be interpreted as: an yearly increase in an 3 Due to high positive correlation among the variables constructed to control for credit market characteristics, it was not possible to use them simultaneously in one model. 17

18 individual s wealth in thousand BDT leads to an average increase in the WTP for crop insurance by BDT 0.05 to BDT 0.06 per week from its mean value of BDT 42 per week, holding all other explanatory variables constant at their mean values. Likewise, the estimated coefficient of (ln of) crop damage varies from 1.2 to 1.5 in Models 1 and 5 implying that a one percent increase in crop damage per hazardous incident leads to an average increase in the WTP for crop insurance by BDT 1.2 to BDT 1.5 per week. Flood frequency, measured through households perceptions of the frequency of flooding in future, an indicator of exogenous risk exposure level, as expected, has significant negative impact on the WTP for crop insurance 4 in Models 1 and 5. This implies that the higher the number of years it took for a natural disaster to recur, other things remaining the same, the lower the WTP for crop insurance. If flood was expected to recur an additional year later, the mean WTP fell by BDT 1.32 per week. The mean coefficient of River Distance, an indicator of endogenous risk exposure level, is negative and statistically significant at less than 10 and five percent levels in all estimated regression models presented in Table 4. This suggests that the further away the household lived from the main river, other things remaining the same, the lower the WTP for crop insurance. More specifically, for an increment of one kilometer distance to the household from the main river, mean WTP for crop insurance decreased by BDT 0.54 to BDT 0.60 per week. The mean value of the coefficient of embankment, another indicator of exogenous risk exposure level, is positive and statistically significant at less than one percent level in Models 1 to 5. The 4 Note that the objective probability of flooding, measured through the frequency of flooding in the past, were used as an alternative indicator of exogenous risk exposure level. No statistically significant influence of this variable was determined on farmers WTP for crop insurance. 18

19 magnitude of the coefficient varied between 9 to 10 implying that respondents, who lived inside a protected area, on average and other things remaining the same, were willing to pay additional BDT 9 to BDT 10 per week to purchase crop insurance. This finding is consistent with the higher crop damage costs reported by the farmers located within the flood protection embankment. Of particular interest to this study is the relationship between access to post disaster credit facility and crop insurance demand. The influence of access to credit on farmers WTP for crop insurance was tested by controlling for the institutional characteristics of the rural credit market and the degree of accessibility to the credit sources. In Model 1, the mean coefficients of Credit, reflecting farmers access to formal or informal or both sources of credit to mitigate flood damage costs incurred during the 2004 flood year, was negative and statistically significant at less than one percent level. This implies that, on average and other things remaining the same, farmers who accessed post disaster microcredit (either only formal, only informal or both formal and informal) were willing to pay BDT 6 to BDT 8 less to purchase crop insurance than farmers who did not access post flood credit facilities. In Model 2, the variable Formal Credit refers to farmers access to formal credit institutions only. The mean coefficient of the variable is negative and statistically significant at less than ten percent level. Likewise, the variable Informal Credit, reflecting farmers access to the informal sources of microcredit, in Model 3 is negative and statistically significant at less than ten percent level. These results imply that the institutional characteristics of the rural credit market do not play any role in determining the relationship between post disaster credit and farmers WTP for flood-crop-insurance. Finally, the variable Credit Sources in Model 4 measures the number of 19

20 credit sources farmers accessed during the 2004 flood event. The mean coefficient of the variable is negative and statistically significant at the one percent level implying that the higher the number of sources farmers accessed, the lower the WTP for crop insurance. The mean coefficient of Savings is positive and statistically significant at the five percent level implying farmers who used their savings to cope with the flood damage of 2004 were willing to pay more to purchase crop insurance. The coefficient of the variable Income Sources reflecting the number of non-nature dependent income sources per farm household is negative in Models 1 to 4 but statistically significant at the ten percent level only in Model 2. This variable was used as an indicator of farmers ex ante preparedness to cope with natural disaster losses. The negative sign of the variable was expected. This suggests that farmers with higher number of non-nature dependent income sources were willing to pay less for crop insurance. The variable Relief measures farmers access to ex post disaster relief assistance. This variable was used as an indicator of alternative ex post flood damage coping mechanism. Although the mean values of the coefficient have the expected negative sign in Models 1 to 5 implying that farmers who received post disaster relief assistance were willing to pay less for crop insurance, the coefficient is not statistically significant at the ten percent level. Note that only eight percent of the farmers claimed to have received any form of flood relief, which could a reason for the effect of variable not being statistically significant. Farmers familiarity with the concept of insurance or their education and other demographic variables for example age, family size were not significant determinants of WTP for crop insurance. 20

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