What Do Farmers Want from Crop Insurance Schemes: A Stated Preference Approach
Dennis Opiyo Olila*, Rose Adhiambo Nyikal, David Jakinda Otieno University of Nairobi Department of Agricultural Economics Corresponding author:
[email protected] Abstract Climate change and weather variability are perhaps some of the major challenges facing the world today. In the phase of these challenges, various climate mitigation strategies including financial, production, as well as marketing aspects have played a significant role in cushioning farmers against adverse effects. In Kenya, agricultural insurance is still at a pilot stage after the unsustainable effort in the 1970’s. Despite the noble intention to revive the crop insurance industry, limited empirical information exists on farmers’ preferences for crop insurance. The study employed Choice Experiment (CE) to elicit farmers’ preferences for crop insurance design features among 300 farmers. The analysis employed a random parameter logit (RPL) model. The results show that farmers are willing to pay for various features of crop insurance. These findings are important in informing ex-ante design and improvement of crop insurance programmes in Kenya and the rest of the world. Key words: Crop insurance, Choice experiment, Random parameter logit, Kenya JEL CODE: C90
1.0 Introduction 1.1 Background information Like many developing economies, agriculture is the key driver of economic growth and development in Kenya. Various government publications attest to this fact; for example, the Medium Term Expenditure Framework (MTPF) 2014/2015 – 2016/2017 reports that agriculture contributes 24.5 percent to the Gross Domestic Product (GDP). Moreover, the sector contributes on average 27 percent to the GDP through linkages with manufacturing, distribution and other service related sectors. Further, it accounts for 65 percent of exports, 18 percent, and 60 percent of the formal and informal employment respectively. Having taken into cognisance the important role played by the agricultural sector in the economy, the government through Vision 2030 has identified agriculture as one of the six key sectors envisaged to deliver a 10 percent annual economic growth in the next 15 years. Some scholars such as Odhiambo et al. (2004) argue that agricultural sector performance directly mirrors that of the overall economy. This implies that there is a direct nexus of agriculture transformation and economic development. According to Odhiambo et al. (2004), the sector is intricately linked to the rest of the economy. As a result, its performance is a prerequisite to the advancement of other sectors of the economy. Even though the sector remains the most vital in the Kenyan economy, declining growth has been noted as a major threat to economic prosperity. One principal determinant of the aforementioned decline is the inevitable climate change and weather variability. The Ministry of Water and Irrigation (MOWI, 2005) posits that only 16 percent of Kenyan landmass is classified as an area of high agricultural potential while the rest fall under Arid and Semi- Arid areas (ASALs). In recent times, statistics indicate that Kenya experiences episodes of adverse weather conditions every five years and severe drought every decade (Nyamwange, 1995). Empirical evidence such as that of Olila and Pambo (2014) reveal that a majority of farmers in Kenya depend on rain-fed agriculture. According to Hardaker (1997) agricultural risk and uncertainty results in diminishing output.
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The impact is manifested in the deteriorating economic welfare of the financially constrained farmers who depend on agriculture as their main source of livelihood. Amidst these challenges, over the years, farmers have employed a myriad of climate mitigation strategies with the sole aim of improving productivity at the farm level. Generally, the risk mitigation strategies available to farmers fall under three categories, focusing on financial, production, and marketing aspects. According to Smithers (1998), production risk management involves enterprise diversification and appropriate farm management while the marketing aspect involves forward contracting, hedging and future markets. On the other hand, financial facet of risk management involves off-farm employment and crop insurance. Besides these, public assistance in form of safety nets and other programmes cushion the vulnerable farmers from agricultural risk and uncertainty. One of the feasible strategies to deal with uncertainty in agriculture is crop insurance. The rationale underlying crop insurance is to compensate against yield losses by providing payment of an indemnity. This enables farmers to easily manage deviations in revenue posed by both yield and price volatility. According to Shields (2012), crop insurance as a risk mitigation strategy dates back tom 1938 in the United States of America (USA) with the establishment of the Federal Crop Insurance Corporation (FCIC). The program began on an experimental basis by offering Multiple Peril Crop Insurance (MPCI) particularly on major crops. Empirical evidence on the growth of USA crop insurance sector indicates that during the initial stages, adoption rates were generally low. However, by the year 2008, farmer participation rate reached 80 percent (Goodwin and Smith (2009). Like many other developing countries, Kenya has had a long history in agricultural risk management. For example, the colonial government introduced a program known as Guaranteed Minimum Returns (GMR) in early 1930s. The program’s core objective was to guarantee farmers a minimum price for their produce besides insuring their produce against unavoidable crop failure Kerer (2013). Further, according to Makau (1984), the program was designed to benefit both small and large scale farmers who experienced loss in output because of weather vagaries. The loan was taken from the Land and Agriculture Bank (later became the Agricultural Finance Corporation (AFC). The loan was payable once farmers sold their produce.
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Through agricultural extension officers, the government would get a feedback on the performance of the program. However, despite the noble intention of the GMR program, opportunistic behavior among the implementing officials and farmers created a major disincentive to on the part of the government. Farmers who were powerful colluded with implementing officials and had their crop declared a failure and in return had their loans written off. Moreover, a lot of rent seeking took place amongst extension agents and AFC officers who deliberately failed to hold the farmers accountable. Another contributing factor to the inefficiency of the program was lack of in-country capacity to deliver besides failing to engage farmers who were the key stakeholders. The inefficiency in the GMR led to its discontinuation in 1978. A recent study by Mahul and Stutley (2010) reveal that regulatory frameworks governing insurance markets in many low and middle-income countries tend to be less developed. Many decades after the discontinuation of the GMR, crop insurance as a risk mitigation strategy was virtually unavailable in Kenya. The second phase of crop insurance in Kenya launched in the year 2009 as a pilot study had the objective of reviving the agricultural insurance industry. Empirical literature show that in developing countries, agricultural insurance has been in operation for only 5 to 10 years (Mahul and Stutley, 2010; Korir et al., 2011). Crop insurance as a risk mitigation strategy plays a significant role in insulating farmers against weather related risks. Despite the recent noble interventions to revive the crop insurance industry, an empirical gap in knowledge exists on farmers’ preferences for crop insurance schemes. The previous initiatives were based on a top-down approach, thus lacking local stakeholder, particularly farmers involvement in the programme design processes. The main challenge is failure to engage farmers in the design of programmes they pay for. As such, their priorities, needs, and constraints facing them on the ground are not considered. Some of the main consequences of stakeholder omission are low levels of programme acceptance by the target group and reduced chances of success for such development programmes (Feder et al.,1981).
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Unlike the previous emprical studies that have tackled crop insurance from an ex-post perspective, the current study evaluates demand for crop insurance from and ex-ante approach. This approach is grounded in empirical studies like that of Bekele (2004) who reported that prior identification of stakeholder preferences could help design development interventions that are more acceptable and cost effective besides aligning the interventions with the needs of the different regions and categories of farmers. The main objective of the study was to evaluate maize farmers’ preferences for crop insurance features. Specifically, the study aimed at analyzing farmers’ willingness to pay for crop insurance features. We hypthesize that maize farmers in Trans-Nzoia County are not willing to pay any amount of money for crop insurance features. 1.2 Problem statement Maize is the main staple food in Kenya. According to GoK (2011) the availability and accessibility of this commodity is a useful indicator of food security in the country. Over the years, the production of this important crop is dependent on rain-fed agriculture. The main challenge in the current mode of production is the inevitable climate change and weather uncertainty. As a result, failed rains and market price volatility makes farmers incur losses. According to MOWI (2005), only 16 percent of Kenyan landmass is considered an area of high agricultural potential while the rest falls under ASALs. Other empirical studies such as Nyamwange (1995) reveals that Kenya experiences episodes of adverse weather conditions and severe droughts every five and ten years respectively. In order to mitigate the effects of climate change and weather variability, various humble reactions to the risk element, which include diversification, have not been impressive, and crop insurance approach has emerged over the past decade or so, and not without challenges. Although various attempts to address crop insurance challenges in Kenya have been made, previous initiatives were based on a top-down approach, thus lacking local stakeholder, particularly farmer’s involvement in the programme design processes. The main challenge is that farmers fail to be engaged in the design of programmes they pay for. As such, their priorities, needs, and constraints facing them on the ground are not considered.
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Some of the main consequences of stakeholder omission are low levels of programme acceptance by the target group and reduced chances of success for such development programmes (Feder et al., 1981). Prior identification of farmers’ preferences can help design development interventions that are more acceptable and cost effective. Moreover, prior knowledge of farmers’ priority, problems and predisposition with respect to the usefulness of a development intervention can also help align the interventions with the needs of the different regions and categories of farmers (Bekele, 2004). 1.3 Outline of the paper The rest of this paper is organized as follows. Section two presents CE design and its applications in policy formulation. Section three presents the methodology of the study while the fourth section outlines the study findings and discussions. Finally, summary, conclusions, and policy recommendations are given in section five while section six illustarates suggestions for further research. 2.0 Choice Experiment approach Theoretically, CE is grounded in the Lancasterian characteristic theory of value (Lancaster, 1966). This theory posits that consumers derive utility from the various attributes of the good rather than the good per se. Further, the econometric basis of CE approach rests in the models of random utility (Thurstone, 1927; McFadden, 1974; Manski, 1977). The random utility theory describes discrete choices in a utility maximizing framework (Ben-Akiva and Lerman, 1985). The assumption is that individuals usually make choices with the objective of maximizing utility; a concept known as random utility maximization hypothesis. Following the aforementioned approach, farmers’ preferences for crop insurance are measured by assessing their willingness to pay for the various attributes (features) of the crop insurance programme. Since participation in crop insurance scheme is voluntary, it is imperative to involve stakeholders in the design of an intervention programme (Wilson, 1996). The CE study begins with the identification of policy relevant attributes and their respective levels. Otieno et al. (2010) argue that the selection of attributes included in the CE design requires extensive review of literature.
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According to Adamowicz et al. (1994), the combination of the attribute levels applies a statistical theory that combines them into profiles. These profiles are then presented to the respondents to determine their preferences. The design of choice sets may be based on either orthogonal designs or efficient designs. The criteria for designing CE are controversial. This is attributed to the fact that proponents of orthogonality such as Khulfeld et al. (1994) argue that as opposed to efficient deigns, orthogonal designs easily allow the estimation of a single attribute contribution to the dependent variable. Further, orthogonal designs are easy to construct. In terms of efficient design, proponents argue that it minimizes the sample size thus reducing the costs associated with the survey besides the ability to generate accurate information (Huber and Zwerina, 1996; Rose and Bliemer, 2009). In the current study, we conceptualize that farmer participation in a crop insurance scheme is driven by the utility derived from the various attributes of the programme. Thus, these attributes offer incentive among farmers to purchase crop insurance programmes. According to Street et al. (2005), CE can be used to estimate the effect of attributes on the “attractiveness” of the product under consideration. Moreover, Ruto and Garrod (2009) posit that the attributes chosen should be policy relevant besides being important in choice decisions. Six voluntary attributes were then validated in the focus group discussions (FGD) held prior to the pre-test. Among the crop insurance attributes selected were level of coverage, compensation, content design, risk cover, nature of coverage, and a monetary attribute denoted by price (premium). These were optional attributes chosen to enter into the CE design. Moreover, compulsory attributes were included. According to Otieno et al. (2010), compulsory features are those that must be adhered to by all participating farmers in the crop insurance programme. The reason for their inclusion is to ensure the programme operates within a regulatory framework that enhances public confidence (Olila et al., 2014). The compulsory features used in the study were legal registration by the government, premium to be paid by farmers in order to belong to the programme, compensation to be done within a period of one week after assessment by provider, continuous auditing of the provider firm and disclosure of full information to farmers who belong to the programme. It is envisaged that these have the capacity of enhancing compliance with Kenya’s new constitution where stakeholders have a right to information to programmes they pay for. Table 1 presents crop insurance attributes and levels used in the CE design. 7
A statistical design theory was used to combine the level of attributes into smaller alternatives. According to Scarpa and Rose (2008), experimental designs represent a systematic arrangement in the matrices of the values that researchers use to describe the attributes representing the alternative policy options of the hypothetical choice sets. The pre-test study was formed the basis of coming up with an efficient design. A conventional orthogonal design applied in a preliminary survey of 36 farmers to obtain prior coefficients required for efficient design. The analysis of data from the pre-test study produced the multinomial logit (MNL) model coefficients needed subsequently to generate an efficient design. The final efficient design has the advantage of estimating main and interaction effects. Usually, good experimental designs must exhibit two efficiency measures.
These are D-
optimality i.e. experimental design should posses a small determinant of the variance covariance matrix. The second efficiency measure considered is the B-statistic i.e., measurement of a degree of utility balance of alternatives in the design (Kessels et al., 2004). Usually, acceptable efficient designs should have B-efficiency measures between 70 to 90 percent (ChoiceMetrics, 2009). The final efficient design had a D-efficiency measure of 83 percent and B-estimate of 82 percent. A statistical software known as NGENE was used in the generation of the design (ChoiceMetrics, 2009). The final experimental design resulted in an efficient design with 24 choice sets. Since the 24 choice situations were still too large to be given to a single respondent, it was necessary to block them randomly into 6 sets with four scenarios. Each scenario contained three alternatives namely crop insurance scheme A, crop insurance scheme B and a baseline alternative, corresponding to the status quo or ‘do nothing’, in each choice set. It is important to note that the status quo takes care of the respondents current feasible choice set with an aim of facilitating the interpretation of results in the standard welfare economic terms (Hanley et al., 2001). A total 300 respondents treated each scenario independently. An example of a choice set presented to farmers is as shown in table 2.
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3. Sampling, Data collection and Model specification 3.1 Sampling and data collection The survey was carried out in the three districts of Trans-Nzoia County namely Trans-Nzoia west, East and Kwanza during the months of April and May 2013. A multistage sampling procedure was employed as it facilitates sequential sampling across two or more hierarchical levels (Cochran, 1977). According to Horpila and Peltonen (1992), multistage sampling is convenience besides being efficient relative to other sampling methods. The aim of the sampling approach was to narrow down into smaller administrative units. In order to arrive at a specific respondent, systematic random sampling technique where every third, sixth and ninth respondent were interviewed on a face-to-face basis respectively. For purposes of ensuring that the respondents selected were not biased, the study used a random route procedure where enumerators first interviewed farmers on one side of the road (left) before moving to the other side (right). A final sample size of 300 maize farmers was justified by the budget constraint and past similar studies. Examples of such studies are those done by Otieno et al. (2011) with a sample size of 303; Espinosa-Godded et al. (2009) with a sample size of 300 respondents and Hanley et al. (2001) with a sample size of 267 among others. 3.2 Model specification The CE approach is consistent with the Lancasterian theory of consumer choice (Lancaster, 1966), which postulates that consumers derive utility from the various features of the good as opposed to the good as a whole. The econometric basis of the approach rests on the behavioral framework of random utility theory (McFadden, 1974). The discrete choices follows utility maximization framework. The MNL model is the most commonly used discrete choice model for the analysis of results from the CE data. Even though it is advantageous due to its relative simplicity, it suffers some important drawbacks that limit its application in the current study: first is the assumption of constant variance that results in the independence of irrelevant alternatives (IIA). This property postulate that the ratio of choice probabilities between two alternatives in a choice set is unaffected by changes in that choice set.
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Second, it assumes homogeneity in tastes across all respondents. This assumption fails to put into consideration the fact that preferences are unobservable to the researcher and that they vary among respondents with identical socio-demographics. Finally, MNL model violates consumer axioms of transitivity and stability of choices. It does this by imposing independence of unobserved factors over time in repeated choices. In many cases, one would expect that the unobserved factors that affect the choice in one period would persist, at least somewhat, into the next period, inducing dependence among choices over time (Otieno, 2011). Ideally, choice tasks are a learning process with experience carried over across situations. Therefore, the assumption of MNL that the unobserved part,
ε
ni
is independently and identically distributed (iid) extreme
value for all i makes it inapplicable in a CE study. Following these limitations of MNL, the RPL model is preferred because it accounts for preference heterogeneity by allowing utility parameters to vary randomly and continuously over individuals. This enables computation of unbiased estimates of individual preferences. In addition, accounting for preference heterogeneity; through interaction of the RPL model with farmer socio-demographic characteristics provides a broader picture of the distributional consequences and other impacts of policy options and provides better insights of policy outcomes. RPL is a highly flexible model that can approximate any random utility model (McFaden and Train, 2000). Each respondent was represented with a series of M = 4 choices. In each choice set, a respondent faces a choice between J = 2 alternatives plus a status quo. In each scenario (choice set), farmers were asked to choose between two alternatives allowing for a status quo (neither choose option). The status quo represents the respondent’s current feasible choice set. This is important in interpreting the results in standard welfare economic terms (Hanley et al., 2001). Therefore, the attribute of alternative i in choice situation t faced by individual n are collectively labelled as a vector X int . As such, a decision maker faces a choice among j alternatives. Revelt and Train (1998) give the specification of the utility derived by person n from alternative j as follows:
U
nj
=β
' n
X
nj
+
ε
nj ............................................................................................................
(1) 10
X
Where
nj
are the observed variables that relate to the alternative and the decision maker, β , a n
vector of coefficients of these variables for person n representing that person’s tastes and
ε
nj
is
a random term that is iid extreme value. The coefficients vary over decision makers in the population with density f (β / θ ). This density is a function of parameters θ that represent the n mean and covariance of the The value of
β
n
and
ε
nj
'
β s in the population. are only known by the decision maker for all j alternatives and
chooses alternative i if and only if U ni 〉 U nj not
β
n
∀
≠ i . Now, the researcher observes
X The probability that individual n chooses alternative i conditional on β
or ε nj ’s.
j
nj
n
but , is
given by the standard MNL as follows:
β
L
ni
e (β )= ∑ eβ
n X ni
n
n X nj
..................................................................................................... (2)
j
Let i(n) denote the alternative chosen by individual n in choice situation t. The probability of individual n’s observed sequence of choices (conditional on β ) is the product of the MNL with n
the assumption that the individual tastes, β , do not vary over choice situations in repeated n
choice tasks (although are assumed heterogeneous over individuals):
G (β n
n
)= ∏ L (β )................................................................................................................. (3) ni
n
However, since we do not know β , we cannot condition on β . Thus, the unconditional choice n
probability is therefore the integral of
n
L (β n ) over all possible values is expressed as: ni
' β'X ni e f (β / θ ) dβ = ' P ni ∫ .......................................................................................... (4) β'X nj ∑ e j
11
'
β 'X
Where
ni L (β ) = e β ' ...................................................................................................... (5) ∑ j e X nj '
ni
Thus, the choice probability follows the expression:
P = ∫ L (β ni
ni
n
) f (β
n
)
/ θ dβ ...................................................................................................... (6)
The expression in equation (8) above has two sets of parameters. The
β
is a vector of
n
parameters that are specific to individual n (representing individual tastes, which vary between respondents) and θ are parameters that describe the distribution of the individual specific estimates (such as the mean covariance of β ). The main objective of RPL is to specify the n
function f
(β
n
/θ
) and estimate the
parameter θ . The estimation of the parameter θ is done
through simulation of the choice probability. This attributed to the fact that the integral equation (6) above cannot be computed analytically due to its mathematical closed form (Train, 2003). The log-likelihood function is specified as: LL (θ ) = ∑ Ln n
The
P (θ ) ................................................................................................................... (7) n
P (θ ) is approximated by a summation over randomly chosen values of β n
value of parameter θ , a value of
β
n
is drawn from its distribution and
G (β n
n
n
. For a selected
) representing the
product of the standard MNL is computed. Repeated calculations are done for several draws and the average of
G (β n
n
) is considered as the approximate choice probability. Three major steps
can be used to estimate the probabilities: First, we draw a value of β from f
(β
n
)
/ θ and label it
β r with the subscript r =1 referring to the first draw. Secondly, the logit formula, L ni ( β
r
) is
n
calculated with the draw got from step one. Steps 1 and 2 repeated severally before averaging the results. The average is the simulated probability given by: Λ
P ni=
1 R
R
∑ L β n ni
r =1
r
............................................................................................................... (8)
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Λ
Λ
Where R is the number of draws and P ni is unbiased estimator of Pni by construction. The P ni is twice differentiable in the parameter θ and variable x , which facilitates numerical search for the maximum likelihood function and the calculation of elasticities. Then, the simulated probabilities are inserted into the log-likelihood function to give a simulated log-likelihood (SLL) function given as: J
N
∑d P
SLL = ∑
d
nj
................................................................................................................. (9)
j −1
n =1
Where
ν
nj
nj
=1 if n chooses j and zero otherwise. Thus, the maximum simulated likelihood
estimator (MSLE) is the value of θ that maximizes SLL . This procedure maintains independence over decision makers of simulated probabilities that enter SLL . The main goal of the current study was to analyze farmers’ willingness to pay for a crop insurance scheme. In order to achieve this, the ratio of an attribute coefficient and the price coefficient representing the implicit price (WTP or part-worth) was estimated. This represents the trade-offs between crop insurance attributes and money, which is the marginal WTP. The Computation of WTP is as follows: β WTP = − 1 * k ………………………………............................…………………………. (10) β p
Where
β
k
is the estimated coefficient for an attribute level in the choice set and
β
p
is the
marginal utility of income given by the coefficient of the price attribute (Hannemann, 1984). The part-worth (implicit price) for the discrete change in an attribute or attribute level provides a measure of the relative importance that respondents attach to attribute within the crop insurance design. This means that willingness to pay represents the marginal rate of substitution or trade-off between insurance attributes and money. Analysis of the data was performed in LIMDEP version 9/NLOGIT version 4.0 software (Green, 2007).
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In order to inform policy on the most appropriate design of crop insurance from an ex-ante perspective, we estimated the overall willingness to pay i.e., compensating surplus (CS) for both small and large scale farmers. The overall CS is estimated as (Hanemann, 1984):
CS =
−1
β
(V −V ) ………………………………………………………………………….. (11) 1
0
p
Where V1 represents the value of the indirect utility associated with the attributes of the crop insurance scenario whereas V0 is the indirect utility of the baseline scenario of no crop insurance. Therefore the CS is the difference between the value of indirect utility before the change and the value of the same after the change converted into monetary units using the coefficient on the cost attribute,
β
. Thus, CS measure provides useful ex-ante information on the potential P
acceptability of the new crop insurance policy. 4. Results and Discussions 4.1 Farmer characteristics
This section presents summary of farmers’ characteristics (Table 3). The results of the descriptive statistics indicate that large scale farmers had higher incomes as compared to their small scale counterparts within the farmer category. Moreover, in terms of accessibility to loans, it is evident that the loan recipients are majorly large scale farmers. Higher incomes among the large scale farmers are attributed to the ability to access loans. Empirical evidence shows that generally, small scale farmers face financial constraints besides being vulnerable to climate related risks. This diminishes their credit worthiness to various financial institutions. In terms of age, the findings revealed that large scale farmers were older as compared to small scale farmers. However, small scale farmers accounted for 64 percent of the sampled population. Further, in terms of membership to development groups such as Mary-go-round, farmers’ savings and credit cooperative organization (SACCO) among others, the findings reveal that within the farmer category, a majority of large scale farmers (53 percent) were members of a development group as opposed to small scale farmers (46 percent).
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Finally, in terms of farmers’ awareness about crop insurance, only 32 percent and 43 percent of small scale and large scale farmers within the farmer category respectively had knowledge about the existence of crop insurance a risk mitigation strategy. In the pooled sample, only 36 percent of both small and large scale farmers were aware of crop insurance. 4.2 Farmers preferences for crop insurance 4.2.1 Description of variables used in the choice set
Table 4 presents the variables used in the choice set. Among the nine variables, price was important in the calculation of the marginal willingness to pay for various crop insurance attributes. It is important to note that while all other attributes were treated as random parameters assuming normal distribution, the cost (price) attribute had a fixed value. 4.2.2 The econometric results
Table 5 presents RPL estimates on preference for crop insurance. The RPL model results indicate that all the mean coefficients of the six attributes investigated are statistically significant. Furthermore, the estimated model has a good explanatory power (McFadden Pseudo-R2 = 0.496). This offers an estimate of how much variation the model accounts in the CE data. The econometric model results show that farmers prefer higher levels of coverage. This is depicted by the positive coefficient implying that with an increment in the level of coverage, willingness to pay shifts upwards. The finding corroborates that of Nganje et al. (2004) that the probability of farmers participating in a crop insurance scheme is directly proportional to the level of coverage. In terms of farmer compensation, it was revealed that there exists incentive to purchase crop insurance policy that offer good indemnification. This is shown by the positive and statistically significant coefficient implying that as the compensation level increases, so is the probability of participation. The finding is consistent with the economic axiom of non-satiation (never enough). Arguably, given two market baskets, A and B, the consumer will always prefer the basket that has more of at least one item or no less of the other items (Sproule and Valsan, 2009). In separate empirical study by Espinosa-Goded et al. (2009), it was revealed that farmers were willing to participate for lower compensation in Agri-Environment schemes. However, the study recommended for a higher compensation to enhance participation. 15
Further, the coefficient of the attributes content design and nature of coverage (crop and market and crop and medical) are both positive and statistically significant at one percent. The willingness to pay for content design (stakeholder) consultation implies that currently farmers lack satisfaction with development intervention programmes that fail to consider their needs and constraints on the ground. It is therefore imperative to engage farmers in the design of any development intervention programme to encourage ownership and implementation. In respect to risk, cover i.e., whether farmers prefer a single risk of multiple risk cover, findings indicate that generally, a crop insurance that indemnifies them against several risks is given preference. Finally, the cost attribute denoted by price was negative and statistically significant at one percent. The negative sign in theory enables the computation of trade-offs of each attribute and money thus giving WTP values (Ruto and Garrod, 2009). In the context of the current study, higher premiums inclusive of both administrative and overhead costs act as a disincentive to purchase crop insurance especially among the low income small scale farmers. Studies on demand for crop insurance indicate that Ceteris paribus, an increment in premium rate lowers the level of coverage purchased (Knight and Coble, 1997; Goodwin and Smith, 2003). Table 6 presents the results of the marginal WTP for crop insurance attributes. This is achieved by dividing the ratio of an attribute coefficient and the price cost. This gives the mean WTP (Part-worth). The results of WTP confirm that farmers have heterogeneous preferences for all the crop insurance attributes. The overall results indicate that farmers are WTP for the various crop insurance attributes. In summary, farmers are WTP on average, Kshs 86 for low level of coverage and Kshs 158 for medium level of coverage. In terms of compensation, farmers were Kshs 166 and Kshs 230 in order to receive compensation equivalent to the percent of the value of a 90 Kg bag of maize; Kshs 57 for stakeholder consultation during the design. Moreover, farmers are WTP Kshs 216 for a multiple peril crop cover; Kshs 291 for a crop insurance inclusive of market volatility risks and Kshs 443 for a crop insurance inclusive of medical cover. It is interesting to note that the willingness to pay is relatively high for a crop insurance scheme that covers multiple risks in agriculture, crop, and market risks as well as medical. High preference for a multiple risk cover is attributed feature is justified by the theory of human bounded rationality. 16
This is a concept in the economics of imperfect information. It posits that in human decision making, rationality of individuals are limited by the information they have, cognitive failure of the mind and the finite amount of time needed to make a decision (Akerlof, 1970). Furthermore, the WTP for the crop and medical insurance was significantly high. We attribute this to the rising cost of healthcare among farmers who are vulnerable to health related risks in agriculture such as agricultural chemicals and machinery. The impact is visible in the increased healthcare premium. The current medical insurance offered by the government under the National Hospital Insurance Fund (NHIF) costs Ksh 320 per month for people with incomes above Ksh 15,000, but the majority of peasant farmers are unable to afford. The study envisages that holistic crop insurance inclusive of medical aspect has a huge potential of enticing farmers to participate in crop insurance scheme. Finally, it is important to note that the willingness to pay values are not absolute but relative values only realized in the event that the programme is implemented. 4.2.3 Compensating surplus
In order to illustrate how both small and large scale farmers would respond to different attribute combinations, the CS (overall willingness to pay) was estimated using the RPL model (see equation 11). Usually, CS estimates represents policy scenarios for the design of a crop insurance programme suitable for the duality aspect of farmers. The study developed two scenarios for the crop insurance programme i.e., scenario 1 and 2 representing small and large scale farmers respectively (see table 7). Since all the CS scenarios were positive, we conclude that maize farmers are generally willing to move from the base-line of no crop insurance to the current proposed crop insurance scheme. It is evident that the CS for small scale farmers is higher as compared to that of large scale farmers. This is attributed to the fact that small scale farmers are more vulnerable to climate related risks in agriculture. Despite the absolute values of both the CS being almost the same, they are significantly different from each other. The high CS estimates among small scale farmers is justified by high preference for MPCI and health insurance. The CS estimates are higher where the scenario has an element of multiple risk cover and crop and health insurance. Empirical findings show that vulnerability to risk is considerably higher among small scale farmers as opposed to their large scale counterparts. According to Hazell and Norton (1986), agricultural risks put a huge burden among small scale farmers in developing countries. 17
Furthermore, small scale farmers exhibited high preference for crop and medical insurance (health insurance coverage). According to Nganje et al. (2004), small scale farmers are generally selfemployed and thus disadvantaged when purchasing health insurance. Therefore, a policy intervention programme that offers both crop and medical insurance would offer incentive among farmers to adopt crop insurance as risk mitigation strategy. Finally, we present a graphical representation of farmers’ preferences for the various crop insurance features as a percentage of the sampled population (see fig.1). Generally, over 90 percent of the farmers had a positive preference for each of the attributes included in the CE design. A majority of the farmers clearly preferred the crop insurance features included in the CE, suggesting that collectively these features fully captured farmers’ range of crop insurance. 5. Summary, conclusions and policy recommendations
The key purpose of the study was evaluate farmers’ preferences for crop insurance features in Kenya using the CE method. The overall finding is that farmers are WTP for various crop insurance features as postulated in the Lancaster characteristic theory of value. This implies that collectively, the features used in the CE design fully captured farmers’ preference range for crop insurance. Of interest was the finding that farmers are willing to pay more for a crop insurance scheme that indemnifies them against production risk as well as taking care of their medical cover expenses. In order to take risk management to the next level and instill confidence among farmers, we recommend a full implementation of the two policy scenarios suiting the duality aspect of farmers in Kenya. 6. Suggestions for further research
Even though the findings of this study give useful insights of farmers’ preferences for crop insurance, little is known on the costs and benefits of implementing the proposed crop insurance scheme, and as such provides an opportunity for further research. It is believed that such information may be important to any investor who is willing to implement the proposed crop insurance scheme.
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Table 1: Crop insurance attributes and levels used in the CE design Attribute
Description
Level of coverage
How
Level
much
coverage 50%, 65% , 70%
purchased as a percentage of the total acreage Compensation
Farmers
compensated 50%, 60% , 70%
based on the market price of a 90 kg bag of maize. Content design
Whether
stakeholder Joint or provider only
involvement in design is preferred or not Risk cover
Whether
single
or Single peril, Multiple peril
multiple peril
Nature of coverage
What
the
insurance Crop only, crop and market,
should cover
or crop social issues such as medical
Cost (Ksh/acre)
Insurance cost per acre
110, 170, 280
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Table 2: An example of a crop insurance choice set Insurance scheme A
Insurance scheme B
Level of coverage
70%
50%
Compensation
60%
50%
Content design
Provider only
Joint
Risk cover
Single peril
Multiple peril
Nature of coverage
Crop only
Crop and medical
110
280
Cost (Kshs)
per
Statusquo
acre
Which one would you prefer?
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Table 3: Summary of farmers’ characteristics Small scale farmers Large scale farmers Characteristics
(N = 191)
(N = 109) 11.39
Pooled (N = 300)
Education (mean years)
10.36
10.73
Awareness (%)
31.90
Development group (%)
46.10
53.20
48.70
Monthly income (mean)
16,174.00
81,860.00
40,040.00
Access to credit (%)
22.50
44.00
30.30
Mean age (years)
42.71
48.94
44.98
Average farm size (acres)
2.53
22.19
9.67
43,10
36.00
Average maize yield (90 bags/acre)
22.60
Gender (%):
Male
43.30
Female
56.70
None
3.30
Primary
37.70
Secondary
33.70
College
20.30
University
3.30
Masters
1.70
Education (%):
Household income (Ksh/month) < 10,000
27.67
10,000 - 25,000
39.67
25,001 - 35,000
6.67
35,001 - 45,000
7.67
More than > 45,000
18.33
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Table 4: Description of variables used in the choice set Variable
Description
LEVCOVME
Medium level of coverage [60%]
LEVCOVHI
High level of coverage [70%]
Medium compensation of the current price of a 90 kg bag of maize COMPENME
[60%]
COMPENHI
High compensation of the current price of 90 kg bag of maize [70%]
Content design involving stakeholder [1 = joint, 0 = CONTJOIN
provider only]
Multiple risk cover [1 = Multiple risk MULTPRSK
cover, 0 = otherwise]
Crop and market insurance coverage [1 = crop and market, CROPMKT
0 = otherwise]
Crop and medical coverage [1 = crop and medical, 0 = CROPMED
otherwise]
PRICE
Annual insurance cost (Ksh/acre) [110, 170, 280]
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Table 5: RPL estimates on Preferences for crop insurance Variable
Mean coefficient
Standard error
P-values
LEVCOVME
1.757
0.619
0.005***
LEVCOVHI
3.249
0.925
0.000***
COMPENME
3.401
1.119
0.002***
COMPENHI
4.726
1.266
0.000***
CONTJOIN
1.190
0.456
0.011***
MULTPRSK
4.420
1.201
0.000***
CROPMKT
5.965
1.712
0.001***
CROPMED
9.068
2.629
0.001***
PRICE
-0.021
0.006
0.001***
Standard deviations of parameter distributions NSLEVCOVME
2.214
0.823
0.007***
NSLEVCOVHI
2.210
0.823
0.007***
NSCOMPENME
2.164
0.798
0.007***
NSCOMPENHI
2.164
0.798
0.007***
NSCONTJOIN
1.946
0.824
0.018**
NSMULTRSK
3.141
1.007
0.002***
NSCROPMKT
1.701
0.652
0.009***
NSCROPMED
4.640
0.652
0.003***
Log-likelihood
-664.556
2
Pseudo-R
0.496
N respondents
300.000
N choices
1200.000
Notes: Statistical significance levels: ***1%, **5% and *10% respectively
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Table 6: Marginal WTP estimates for Crop Insurance attributes (Kshs) Marginal Variable LEVCOVME
WTP
(95%
interval) 85.689
H0:
confidence P-value
testing
0.00370 H0 rejected
(27.892 to 143.487) LEVCOVHI
158.482
0.00000 H0 rejected
(117.312 to 199.652) COMPENME
165.897
0.00000 H0 rejected
(101.143 to 230.653) COMPENHI
230.484
0.00000 H0 rejected
(180.237 to 280.732) CONTJOIN
56.527
0.00000 H0 rejected
(29.505 to 83.548) MULPRSK
215.584
0.00000 H0 rejected
(174.761 to 256.408) CROPMKT
290.916
0.00000 H0 rejected
(243.689 to 338.144) CROPMED
442.272
0.00000 H0 rejected
(364.474 to 520.071) Notes: Statistical significance levels: ***1%, **5% and *10% respectively.
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Table 7: Attribute levels and Compensating Surplus for Crop Insurance Policy Scenarios (Kshs).
Provider Scenario
Low High 70%
1 2
Joint alone
Single
Multiple
Crop
peril
peril
market
Notes: Indicates that the attribute is present in a scenario at the non-zero level.
P-value
Standard error
and Crop and medical
Surplus
Compensating
Nature of coverage
Risk cover
Content design
Compensation
Level of coverage
Attributes
16,791.70
1,836.10 0.0000
16,639.80
1,825.00 0.0000
Percentage preference
Farmers' preferences for various attributes 100 80 60 40 Negative
20
Positive
0
Crop insurance attributes
Figure 1: Farmer preferences for crop insurance features. Source: Authors’ survey
Acknowledgement The authors express their gratitude for the financial support offered by the African Economic Research Consortium (AERC) and the Government of Kenya (GoK) while collecting primary data used in this study.
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