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: Department of Economics, Helmholtz Centre for Environmental Research UFZ, Permoserstraße 15/ 04318, Leipzig, Germany, 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-floodinsurance demand in the rural floodplains of Bangladesh. In a large-scale household survey we asked 3,000 residents in six different districts of Bangladesh about their willingness to participate and pay for a hypothetical flood-insurance programme. Combining factors put forward in risk theory and economics, we employed the triple-hurdle approach to model the three stage sequential decision problem. Our results show that post-disaster microcredit has a positive relationship with insurance participation decision and a negative relationship with willingness to pay premium for flood-insurance. One possible explanation for these findings is that, on average and other things remaining the same, households who accessed post-disaster microcredit to cope with natural disaster induced losses in the past were willing to invest in flood-insurance as an alternative disaster coping measure so long as they considered the proposed insurance premium cheaper than the cost of accessing post-disaster microcredit. Key Words: natural hazard, microinsurance, microcredit, 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 Reimund Schwarze, Robert D. Cairns, Sonia Laszlo and Roy Brouwer for their valuable inputs. Dr. Ricardo Fuentes Nieva s contribution as discussant of this paper is also greatly appreciated. 3

4 1. INTRODUCTION Microinsurance is often referred to as an important and effective natural hazard risk coping mechanism (Botzen and van den Bergh, 2008). Accordingly, natural hazard risk insurance programmes have been introduced in many developing countries in order to help the poor cope with increased climatic disaster risks (Mechler et al., 2006). However, the available evidence indicates that the voluntary participation to these insurance programs has been much lower than anticipated by their proponents. For example, Gine et al. (2008) showed that fewer than five percent of the eligible farmers in a drought prone region of India bought inexpensive rainfall insurance. The offered insurance scheme failed to attract the target group of farmers as the insurance was purchased by those who needed it least. Low take-up rate for weather related microinsurance products is often referred to as a puzzle in need of an explanation in the microinsurance literature (Cole et al. 2009, Karlan and Morduch 2009). According to standard microeconomic theory individual with an initial wealth of W 0 who suffers from damage L with probability π, chooses to buy a coverage of amount C paying premium of amount P= pc. Assuming that the individual household has a von Neumann-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: (1) πu(w0 pc L + C) + (1 π)u(w0 pc) > πu(w0 L) + (1 π)u(w0 ) Therefore, household decision of purchasing insurance is expected to be influenced by the probability of a hazardous incidence (π ), level of wealth (W 0 ), expected amount of damage (L) and rate of premium (p). However, this standard textbook theory fails to adequately explain 1 U(W) is continuous and twice differentiable; that is, marginal utility U (W)>0 and U (W)<0. 4

5 actual decision making in real situations in case of weather related events as people use ad hoc rules to assess the underlying risk and uncertainty associated with the occurrence of the event as well as the credibility of risk transfer instruments in question (Camerer and Kunreuther, 1989). Gine et al. (2008) found that insurance demand was constrained by consumers lack of experience with the insurance product. Availability and access to pre disaster microcredit was also found to be an important stimulator of microinsurance take-up rate (Gine et al., 2008; Cole et al., 2009). Such relationship is substantiated on the basis of the 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 well-established in the microinsurance literature, the role of post-disaster microcredit in determining microinsurance demand is not clearly understood. Post-disaster microcredit is often referred to as an implicit insurance against natural hazards in the social vulnerability literature (Brouwer et al., 2007). Therefore, post-disaster microcredit and microinsurance are expected to be substitute goods given that they independently serve as disaster loss mitigation instruments. As a result, it is reasonable to expect 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 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 the low take-up rate puzzle of microinsurance in developing countries. The main objective of 5

6 this study is to understand the nexus between post-disaster microcredit and microinsurance in the natural hazard risk management domain. To the best of our knowledge, no empirical study has investigated this issue before. As part of a commercial feasibility study of a potential microflood-insurance program, a large scale household survey was conducted in Bangladesh in Three-thousand riverine and coastal floodplain residents were asked for their preferences for a hypothetical flood-insurance scheme. A hypothetical insurance market was constructed because a real flood-insurance market currently does not exist in Bangladesh. We applied a triple-hurdle approach to model the three stage sequential decision problem. In addition to the factors predicted by microeconomics and environmental risk theory, we investigated the role postdisaster microcredit access in determining microinsurance participation and willingness to pay (WTP) premium for the insurance contract. The rest of the paper is organized as follows: the next section presents a description of the case study followed by a description of the questionnaire and survey in Section 3. We then describe the development of our empirical model in Section 4. Then the paper gives the statistical analysis results in Section 5, and offers a conclusion and policy recommendation in Section CASE STUDY Bangladesh is primarily an agrarian economy, with close to 65 percent of the total workforce being involved in agriculture, either directly or as day laborers, and generating about 22 percent of the country s GDP (BBS, 2005). Weather related risk is a major source of income fluctuations for rural households of Bangladesh. Riverine floods and coastal cyclones cause asset loss, crop damage, unemployment and diseases once in every five to ten years. At the national level, 6

7 natural hazard risk management has traditionally focused on infrastructural measures such as building embankments and polders. The non-structural measures include distribution of postdisaster relief and microcredit. Although the widely held belief suggests that majority of the post-disaster microcredit is accessed through formal sources (e.g. non-government organization, microfinance institutions, rural agricultural banks, co-operatives), empirical evidence shows that the informal credit market plays a dominant role (relatives, neighbors, family friends, richer, well-off families in a village) by providing hazard stricken households loan and other assistance (Brouwer et al., 2007). Following the overwhelming success of microcredit in Bangladesh, there is a growing optimism in microinsurance solutions to protect rural 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 a climate change risk adaptation strategy. Following the NAPA recommendation, a commercial feasibility study of micro-flood-insurance was undertaken in 2006 (see Akter et al. 2009; Akter et al., 2011). The study involved a large-scale stated preference survey in the riverine and coastal 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 contingent valuation (CV) method and choice experiment belong to the stated preference class of non-market valuation techniques. These methods employ public surveys to ask the relevant group of population about their WTP by constructing a hypothetical market or referendum. 7

8 3. QUESTIONNAIRE AND SURVEY DESCRIPTION The questionnaire used in our survey was developed and finalized based on focus group discussions and pre-tests in each of the study areas. Around 3,000 household heads were interviewed during the final survey from the third week of August until the first week of October 2006 by 20 trained interviewers. The questionnaire used for the final survey consisted of around 50 questions and was divided into three main sections: a) socio-demographic profile (e.g. age, occupation, education, family size, sources of income, assets) b) flood damage information (e.g. 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 a hypothetical flood insurance program. The valuation question was posed in three subsequent steps. First, respondents were asked a payment principle question which is fairly standard in CV surveys (see for example Spash et al. 2009). In a payment principle question, respondents are asked if they are willing to pay for the non-market good in principle. The main objective of this question is to separate those respondents who posses a zero value for the good from those who posses some positive value (Spash, 2008). In the second step, respondents who replied positively to the payment principle question were asked to choose an insurance scheme among four available options (house property, crop, health, unemployment). In the final step of the valuation question, respondents were asked for a weekly premium for their most preferred insurance scheme ranging between BDT 5 (USD 0.07) and BDT 50 (USD 0.71). These weekly premiums were chosen from a 8

9 previous CV survey (see Brouwer et al. 2009). We employed a double-bounded (DB) dichotomous-choice (DC) elicitation method where respondents were asked two WTP questions: do you accept a start bid and do you accept a follow-up bid. For each of the six starting bids, a high and 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. Survey sites were selected based on the information received from a series of key informant interviews with the Director of Flood Forecasting and Warning Center at the Bangladesh Water Development Board, officials at Climate Change Cell in the Department of Environment, the Government of Bangladesh and policy planners in the Water Resource Planning Organization. The geographical locations of the study areas are presented in Figure 1. 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 largest 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 were constructed to reduce the frequency and intensity of flooding, they have historically contributed to water logging inside the embanked area due to their poor design, construction, and maintenance standards (Nahar et al., 2010). 9

10 INSERT FIGURE 1 HERE From the six main districts we selected seven sub-districts in total. Lower administrative units such as district unions and ultimately individual villages were chosen from these sub-districts following a random sampling procedure. In total around 500 household heads were interviewed in each district. The selection of households in each of the villages followed a systematic random sampling approach where every fifth household located along the village road was interviewed. 4. SAMPLE CHARACTERISTICS, MAGNITUDE OF THE NATURAL HAZARD DAMAGE AND COPING STRATEGIES Table 1 compares the general demographic and socio-economic characteristics of the 3,000 sampled households with the national population statistics. Almost 99 percent household heads interviewed in the survey were men. The average age of the respondents was 44 years, ranging between 30 and 75 years. About half of the respondents included in the survey was unable to read and write. Just over a quarter finished primary school and only 14 percent finished high school. Each household consisted, on average, of six family members. Around half (47%) of the sample households were involved in agricultural farming as their main source of livelihood, while approximately 14 percent of the sample population worked as agricultural day laborers. The remainder of the sample was employed in trade (15%), transportation (taxi, ferry) (4.5%), the service sector (administrator) (6.5%) and in construction (3.2%). INSERT TABLE 1 HERE 10

11 Average annual household income (related to the past 12 months) was about US$ 960, while half of the sample population earned US$683 per year. Dividing the median yearly income by the average household size and 12 months, average per capita income equaled US$12.4 per month, which is slightly less than the national average rural per capita income (US$ 14) (BBS, 2005). Average household damage costs due to natural hazards were US$342 per household per hazardous event. This is equivalent to about three to seven percent of average yearly household income (based on the assumption that a natural hazard takes place once in every five to 10 years). The most important damage categories were crop damage (67.2%) and damage to house property (51.7%). Other damage categories include income losses due to unemployment (32.5%) and fish pond damage (11.5%). Average damage cost inside the embanked area was significantly higher than the damage cost in the unprotected riverine and coastal districts (Kruskal Wallis χ 2 = 34; p<0.001). Although this finding appears to be counterfactual, it is consistent with existing empirical evidence which shows that households living in an embanked area are more vulnerable to damage than those outside the embankment (Rasid and Mallik, 1993). This is because farmers inside the embanked area follow different cropping patterns and use larger proportion 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). 11

12 Over 95 percent respondents mentioned that they take no ex-ante measures to protect themselves from natural disaster risk and 99 percent respondents did not have insurance against flood damage. Households relied on a mixture of ex-post mechanisms to cope with natural disaster induced damage. About half (48%) of the respondents used their savings while 82 percent relied on either formal, informal or both form of microcredit. However, reliance on informal sources (58%) was significantly (Z=21, p<0.001) higher than formal sources (25%). This finding is consistent with the results reported by Brouwer et al. (2007) where they concluded that informal credit arrangement is a dominant post-disaster coping strategy in rural Bangladesh. Seven percent of the respondents indicated that they had no means to cope with damages. As expected, this group is significantly poorer (Z=-5, p<0.001) than those who had some coping capacity. About 20 percent of the sampled households received disaster relief. These households are relatively poorer and they experienced relatively lower amount of absolute damage. 5. DEVELOPMENT OF AN EMPIRICAL MODEL As illustrated in Figure 2, household decision to accept or reject the offered bid level involved three tires. First, they decided whether or not to be a part of the insurance program. Second, they decided which insurance scheme to purchase and finally, they decided whether or not they would like to pay the offered insurance premium. A triple hurdle approach is most appropriate to model this decision problem as it helps to maintain the randomness of the main sample. The hurdle model, originally proposed by Cragg (1971), is one of the most widely used econometric approaches to estimate participation models in social science research. The model assumes that household decisions to purchasing an item passes through at least two hurdles. First, based on 12

13 impediments to acquisition, households decide whether or not to purchase the good, and second, according to the intensity of the desire for the good, households decide how much to purchase. The hurdle approach models this decision problem in a sequential manner through separate processes for participation and quantities. It allows incorporating same or different sets of explanatory variables in each process. Let us begin with the first stage where households decide whether they want to participate in the insurance program. Following the standard probit method, we assume: (2) Pr (D = 1 x,β) = Φ(xβ) (3) Pr (D = 0 x,β) = 1 Φ(xβ) Where D stands for the decision to participate in the insurance program. D=1 means that household is willing to participate and D=0 means household is unwilling to participate. Φ is the standard Normal cumulative distribution function, x is a vector of independent variables influencing participation decisions and β is a vector of parameters to be estimated. The full distribution of D is: (4) f (D x) = [1 Φ(xβ)] [D=0] [Φ(xβ)] [D=1] Now focusing on the second stage, we define C as the decision to purchase crop insurance. Again, a standard probit function can be used to model this decision: (5) Pr (C = 1 y,α) = Φ(yα) (6) Pr (C = 0 y,α) = 1 Φ(yα) 13

14 C is one for households who chose to purchase crop insurance scheme and zero otherwise. y is a vector of independent variable influencing the choice of crop insurance and α is the vector of parameters to be estimated in the second stage. The full distribution of C is: (7) f (C x) = [1 Φ(yα)] [C=0] [Φ(yα)] [C=1] Finally, in the third stage, the following ordered probit model can be used to understand farmers WTP for crop insurance: (8) Pr (WTP = 1 z,γ, δ) = Pr (p* δ 1 z) = Φ(δ 1 zγ) (9) Pr (WTP = 2 z,γ, δ) = Pr (δ 1 <p*<δ 2 z) = Φ(δ 2 zγ) Φ(δ 1 zγ) (10) Pr (WTP = 3 z,γ, δ) = Pr (δ 2 <p*<δ 3 z) = Φ(δ 3 zγ) Φ(δ 2 zγ) (11) Pr (WTP = 4 z,γ, δ) = Pr (p*> δ 4 z) = 1 Φ(δ 4 zγ) In Equations 8 to 11, WTP=1 if respondents reject both the start bid and follow-up bid, WTP=2 if they reject the start bid and accepts the follow-up bid, WTP=3 if they accept the start bid and reject the follow-up bid, WTP=4 if they accept both the start bid and follow-up bid. z is the vector of independent variables influencing the WTP values, γ is the vector of parameters to be estimated, p * represents the latent risk premium farmers would like to pay to purchase crop insurance and the δs (δ 1 < δ 2 < δ 3 < δ 4 ) are the unknown threshold parameters. Thus, the full distribution of WTP is: (12) f (WTP z) = [Φ(δ 1 zγ)] [WTP=1] [Φ(δ 2 zγ) Φ(δ 1 zγ)] [WTP=2] [Φ(δ 3 zγ) Φ(δ 2 zγ)] [WTP=3] [1 Φ(δ 4 zγ)] [WTP=4] 14

15 6. EMPIRICAL RESULTS 6.1. Participation Decision Around half of all the sampled households said No to the payment principle question (n=1473). These respondents were asked to state their reasons for not participating in the proposed insurance program. Most respondents referred to limited financial income (40%) and dislike of the terms and conditions of the proposed insurance scheme (35%) as the two main reasons. The most unpopular feature of the proposed insurance scheme was that the insured would not be given any monetary return in case of no disaster (mentioned by 65% of the respondents who stated dislike of terms and conditions as their main reason for nonparticipation). Respondents, who said Yes to the payment principle question, were given the opportunity to choose their most preferred insurance product. Over a third of the respondents (42%) chose crop insurance while around a quarter (27%) wanted to buy house property insurance followed by around 20 percent respondents who preferred unemployment insurance and 12 percent chose health insurance. We only focus on crop insurance demand function in this paper. This is because the number of valid observations obtained for other insurance schemes were not sufficient to estimate individual demand model Mean WTP for Crop Insurance We now present the mean WTP estimate for crop insurance scheme. Table 2 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 15

16 denote the follow-up higher bid (in the case where they accepted the start bid), 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. INSERT TABLE 2 HERE Over half of the farmers (59%) who wanted to buy crop insurance 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. In a follow-up question respondents were asked to state their reason for rejecting the bid level. Ninety-two percent respondents said that the offered bid was higher than their expectation. Five respondents (1.5% of those who rejected the bid) said they did not like the conditions of the insurance to pay this much money, seven respondents (2.3% of those who rejected the bid) said that they were not able to assess if it was worth paying this amount and one respondent mentioned about his lack of trust as reason for declining to pay. 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 two percent of the average weekly income of the farm households who wanted to purchase crop insurance and 30 to 60 percent of their annual crop damage cost. Krinsky and Robb confidence interval of the mean WTP value was generated by applying Monte Carlo 16

17 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 Participation and WTP for Crop Insurance The following paragraphs discuss the triple-hurdle regression results. The summary statistics of the explanatory variables used in the regression models are presented in Table 3. Table 4 presents the results. Model 1 in Table 4 presents the results of the binary probit model from the first stage (Equation 4) which estimates the determinants of probability of participating in the insurance program (n=3003). Model 2 shows the results of a binary probit model from the second stage (Equation 7) which estimates the determinants of probability for choosing crop insurance versus other insurance schemes. The sample for this equation is limited to only those respondents who wanted to participate in the insurance program (n=1530). Model 3 shows the results of an ordered probit estimation results for crop insurance demand (Equation 12). The sample for this equation is limited to those respondents who wanted to purchase crop insurance only. We have used 566 valid observations (excluding missing observations) for this sub-sample. It should be noted here that, there are no restriction regarding the explanatory variables to be used in each model. They can be the same or different explanatory variables in each stage. Also note that the coefficients presented in Table 4 are not marginal effects. The marginal effect of any given explanatory variable can only be understood when all parameter estimates are considered simultaneously. However, these results help us understand the direction and statistical significance of effects of the explanatory variables on their corresponding dependent variables. 17

18 INSERT TABLE 3 HERE INSERT TABLE 4 HERE The results presented in Model 1 suggest that household wealth was not a significant determinant of insurance participation decision. However, the magnitude of damage incurred during the last disaster incident, as expected, had a significant positive impact on respondents participation decision. The variables Return Period (the return period of disaster events) and River Distance (distance of a household dwelling from the river in km) were included in the regression model as indicators of environmental risk exposure level. Return Period, as expected, had a significant negative impact on the insurance participation decision implying that the higher the number of years it took for a natural disaster to occur, the lower the likelihood of participation, all other factors being constant. The insurance participation decision furthermore had a significant negative relationship with River Distance suggesting that the further away households were located from the river, the less likely that they would participate in the proposed insurance program. Significant heterogeneity in participation decision was also observed across broader geographical regions. Households located in the riverine and embanked districts were more likely to participate than those in the coastal district. The variable Income Sources (the number of non-nature dependent income sources) was included in the regression model to control for informal insurance scheme. The coefficient of this variable was significant at the one percent level with the coefficients showing a negative sign. This implies that households who had a large number of non-nature dependent income sources 18

19 (informal insurance scheme) were less likely to participate in the formal insurance program. The variable Credit (a dummy variable) refers to those households who accessed formal, informal or both credit sources to cope with damage in the past. The coefficient of this variable was positive suggesting that these households were more likely to participate in the insurance program than those who did not access post-disaster microcredit. The participation decision was furthermore influenced by household familiarity insurance contracts. This result corresponds to the findings reported by Gine et al. (2008). They found that households were less likely to purchase insurance as a result of the uncertainty about the risk mitigation instrument that arises from their lack of experience with it. Education and sex also influenced insurance participation decision. Respondents who completed at least secondary school were more likely to participate than those who were illiterate or completed primary school only. Male respondents were more likely to participate than female respondents. This result can be explained in light of the evidence in the economics literature which consistently found women being more risk averse than men (see Eckel and Grossman 2008 for a review). Given that insurance is a fairly unfamiliar concept and the proposed insurance scheme is new program, it is not surprising that women were more likely to disregard its potential benefit than men. Finally, heterogeneity in participation was observed across respondents primary occupation. In particular, farmers were significantly more likely to participate than respondents belonging to other occupational categories. Model 2 presents the results of the second stage decision making where respondents decided between crop insurance and other insurance schemes. The probit regression results presented in 19

20 Model 2 suggest that households, who relied primarily on crop cultivation for their livelihood, owned the farm land where they cultivate their crop and experienced relatively higher crop damage in the past were more likely to choose crop insurance. We again observed significant heterogeneity in the choice of insurance schemes across geographical regions. While households living in an embanked area were more likely to choose crop insurance as opposed to the households living in coastal district, households living in the revierine districts were significantly less likely to choose crop insurance. This finding is consistent with the significantly high crop damage cost in the embanked district compared to the coastal district (Z=8, p<0.001). Insurance Familiarity had a significant positive influence on respondents choices of insurance scheme implying that those respondents who were familiar with insurance were more likely to select crop insurance than those who were not familiar with it. Respondent education and access to post-disaster microcredit had no statistically significant influence on their decisions to buy crop insurance versus other insurance schemes. Model 2 include a unique hazard rate or inverse Mills ratio (Heckman, 1979). This rate or ratio is designed to measure the unobserved heterogeneity which also controls (and tests) for selection bias. The hazard ratio coefficient is statistically significant at the one percent level. This suggests that the hypothesis of no selection bias can be rejected at 99 percent level of confidence. Model 3 presents results of the ordered probit regression model. This is the final stage of decision making where those who were willing to purchase crop insurance decided whether or not they want to pay the offered crop insurance premium. As predicted by microeconomic theory, this model shows that household wealth is a significant positive determinant of crop 20

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