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: oliladennis@gmail.com

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

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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.

7

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.

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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)

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Where X njare the observed variables that relate to the alternative and the decision maker, β

n, a

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 ( )θβ /n

f . This density is a function of parameters θ that represent the

mean and covariance of the β's in the population.

The value of βn

and ε nj are only known by the decision maker for all j alternatives and

chooses alternative i if and only ifU ni⟩ U nj ∀j

≠ i . Now, the researcher observes X nj but

not βn orε nj

’s. The probability that individual n chooses alternative i conditional on βn, is

given by the standard MNL as follows:

Lni( )β

n= ∑ j e

en X nj

niXn

β

β

..................................................................................................... (2)

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 βn) is the product of the MNL with

the assumption that the individual tastes, βn, do not vary over choice situations in repeated

choice tasks (although are assumed heterogeneous over individuals):

Gn( )β

n=∏Lni

( )βn

................................................................................................................. (3)

However, since we do not know βn, we cannot condition on β

n. Thus, the unconditional choice

probability is therefore the integral of Lni( )β

n over all possible values is expressed as:

Pni= ∫

∑

j e

e

X nj

X ni

'

'

'

'

β

β

( )θβ /f βd .......................................................................................... (4)

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Where Lni( )β =

∑ j e

e

X nj

X ni

'

'

'

'

β

β

...................................................................................................... (5)

Thus, the choice probability follows the expression:

Pni= ∫Lni

( )βn

( )θβ /n

f βd ...................................................................................................... (6)

The expression in equation (8) above has two sets of parameters. The βn

is a vector of

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 βn). The main objective of RPL is to specify the

function ( )θβ /n

f 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:

( )θLL =∑n

Ln Pn( )θ ................................................................................................................... (7)

The Pn( )θ is approximated by a summation over randomly chosen values of β

n. For a selected

value of parameter θ , a value of βnis drawn from its distribution and Gn

( )βn

representing the

product of the standard MNL is computed. Repeated calculations are done for several draws and

the average of Gn( )β

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 ( )θβ /n

f and label it

β r with the subscript r =1 referring to the first draw. Secondly, the logit formula, L ni( β

n

r) is

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=

R

1∑

=

R

rniL

1

β n

r

............................................................................................................... (8)

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Where R is the number of draws and Λ

P ni is unbiased estimator of Pni by construction. The Λ

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:

SLL =∑=

N

n 1

∑−

J

jnjd

1Pnj

ν................................................................................................................. (9)

Where d 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:

=WTP *1−

ββ

p

k ………………………………............................…………………………. (10)

Where βk

is the estimated coefficient for an attribute level in the choice set and βp

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):

=CSβ

p

1− ( )VV 01− ………………………………………………………………………….. (11)

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, βP

. Thus, CS measure provides useful ex-ante information on the potential

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.

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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.

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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.

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 self-

employed 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.

19

References

Adamowicz, W. L., Luviere, J., Williams, M., 1994. Combining Revealed and Stated Preference

Methods for Valueing Environmental Amenities. Journal of Environmental Ecconomics and

Managemnt, Volume 26, pp. 271-292.

Akerlof, G.A., 1970. The Market of "Lemmons": Quality, Uncertainty and Market Mechanism. The

Qurterly Journal of Economics, 84(3), pp. 488-500.

Bekele, W., 2004. Analysis of Farmers' Preferences for Development Intervention Programs, A Case

Study of Subsistence Farmers from Eastern Ethiopian Highlands. African Development and Poverty

Reduction: The Macro-Micro Linkage Forum Paper.

Ben-Akiva, M., Lerman,S.R.., 1985. Discrete Choice Analysis, Theory and Application to Travel

Demand. Cambridge, MA: MIT Press.

ChoiceMetrics, 2009. [Online]

Available at: http://www.choice-metrics.com/

[Accessed 1 August 2013].

Cochran, W., 1977. [Online]

Available at: http://www.gobooke.net/cochran-sampling-techniques/

Espinosa-Godded, M., Barreiro.-Hurle, J., Ruto, E., 2010. What Do Farmers' Want from Agri-

Environmental Scheme Design: A Choice Experiment Approach. Journal of Agricultural

Economics.doi:10.1111/j.1477-9552.2010.00244.x

Feder, G. R., Just, R., Sibeman. D., 1981. Adoption of Agricultural Innovations in Developing

Countries, Washington D.C, U.S.A: World Bank: Staff Working Paper, NO. 444.

GoK, 2011. National Food and Nutrition Policy, Nairobi: Government Printers.

Goodwin, B.K., Smith, V.H., 2003. An Ex-post evaluation of the Conservation Reserve, Federal Crop

Insurnace, and other Government Programs: Program participation and Soil Erosion. Journal of

Agricultural and Resource Economics, 28(2), pp. 201-216.

20

Goodwin, B.K., Smioth, V.H., 2009. Private and Public Roles in Providing Agricultural Insurance in

the United States. Public and Private Roles in Insurance,AEI Press., Washington D.C.

Green, W.H., 2007. LIMDEP Version 9.0/NLOGIT vesion 4.0 Econometric Modelling Guide, New

York: Plainview, NY: Econometric software.

Hanley, N., Mourato. S., Write. R.E., 2001. Choice Modelling Approaches: A Superior Alternative to

Environmental Evaluation?. Journal of Economics Surveys, 15(3), pp. 436-462.

Hannemann, W.M., 1984. Welafare Evaluations in Contingent valuation experiments with Discrete

responses. American Journal of Agricultural Economics, 66(3), pp. 332-341.

Hardaker, J.B., Huirne, R.B.M., Anderson,. J.R., 1997. Coping with Risk in Agriculture, cab

International, Wallingford, United Kingdom.

Hazell, P. b.r., Norton. R.D., 1986. Mathematical Programming for Economic Analysis in Agriculture ,

New York: Macmillan.

Horppila, J., Peltonem,. H., 1992. Optimizing Sampling from trawl catches: Contemporaneous

multistage sampling for age and length structure. Canadian Journal of Fisheries and Aquatic Science,

Issue 49, pp. 1555-1559.

Huber, J., Zwerina. K., 1996. The Importance of Utility Balance in Efficient Choice Designs. Journal

of Market Research, 33(3), pp. 307-317.

Kerer, J., 2013. Adaptation to Climate Change and Insurance. Available at:

http://www.acci.co.ke/accio/wp/wp-content/uploads/2013/06/ACCI_Insurance-Background-Kenya_6-

2013.pdf [Last accessed: 13th, December 2013].. [Online]

Available at: Adaptation to http://www.acci.co.ke/accio/wp/wp-

content/uploads/2013/06/ACCI_Insurance-Background-Kenya_6-2013.pdf

Kessels, R., Goose,. P., Vandebroek, M., 2004. Comparing algorithms and criteria for Designing

Bayesian Conjoint Choice Experiments, Leuven.

Khulfeld, W. F., Tobias,. R. D., Gurratt, M., 1994. Efficient Experimental Design wth marketing

research applications. Journal of Marketing Research, Volume 31, pp. 545-557.

21

Knight, F. H., Coble, K.H., 1997. A survey of Literature on U.S. Multiple Peril Crop Insurance since

1980. Review of Agricultural Economics, 19(1), pp. 128-156.

Korir, L.K., Lagat, J. K., Njehia. B.K., 2011. Risk Management Among Agricultural households and

the Role of off- Farm investments in Uasin-Gishu County, Kenya. A thesis Submitted to the graduate

school in partial fulfilment for the Requirements of MSc Degree in Agricultural and Apllied & Applied

Economics. Egerton University, Kenya.

Lancaster, K., 1966. A New Approach to consumer Theory. Journal of Political Economy, 74(2), pp.

132-157.

Mahul, O., Stutley, C., 2010. Government Support to Agricyultural Insurance: Challenges and Options

for Developing Countries. s.l.:World Bank Publications.

Makau, B.K., 1984. ‘Measurement of Economic Returns to Research and Development: The Case of

Wheat Research in Kenya’, A Thesis Submitted to the Graduate School in Partial fulfillment for the

Requirements of the Master of Arts Degree, Deapartment of Economics, University of Nairobi.

Manski, C., 1977. The Structure of Random Utility Models. Theory of Decisions, 8(1), pp. 229-254.

McFadden, D., 1974. Conditional Logit Analysis of Qualitative Choice Behaviour. s.l.:P. Zarembka

(Ed.), Frontiers of Econometerics, Academic Press.

McFaden, D., Train. K., 2000. Mixed MNL models for Discrete response. Journal of Applied

Econometrics, 15(1), pp. 447-470.

Ministry of Water and Irrigation (MOWI)., 2005. Overview of Irrigation Development in Kenya and

Water Sector Reforms: Achievements and challenges, Government printers, Nairobi,Kenya.

Nganje, W. H.R., Orth, M., Gustafson, C., 2004. Using Choice Experiment to elicit Framers'

Preferences for Crop and Helath Insurance. Denver, Colorado.

Nyamwange, M., 1995. Famine mitigation in Kenya: Some practices, impact, and lessons. Middle

States Geographer’, 28(2):37-44.

Odhiambo, W., Nyangito, H.O., Nzuma,. M.J., 2004. Sources and Determinants of Agricultural

Growth and Productivity in Kenya. KIPPRA Discussion Paper No. 34 March 2004.

22

Olila, D.O., Pambo,. K..O., 2014. Determinants of Farmers’ Awareness about Crop Insurance:

Evidence from Trans-Nzoia County, Kenya. Selected paper prepared for oral presentation at the 8th

Annual Egerton University International conference: 26th – 28th March, 2014.

Olila, D.O., Nyikal, R.A., Otieno, D.J., 2014. An Assessment Of Maize Farmers’ Preferences For Crop

Insurance Features in Trans-Nzoia County, Kenya, Nairobi. A thesis submitted in partial fulfilment of

Master of Science Degreee in Agricultural and Applied Economics, University of Nairobi, Kenya.

Otieno, D.J., Ruto, E., Hubbard, L., 2010. Cattle Framers' Preferences for Disease Free-Zones: A

Choice Experiment Analysis in Kenya. Conference paper presented at the 84th

Annual conference of

the Agricultural Economics Society, Edinburgh, s.n., pp. 29-31.

Revelt, D., Train, K., 1998. Mixed Logit with repeated Choices:Household Choices of Appliance

Efficiency Level. The Review of Economics and Statistics, 80(4), pp. 587-617.

Rose, J.M., Bliemer.M.C.J., 2009. Constructing Efficient Stated Choice Experimental Designs.

Transport Reviews , 29(5), pp. 587-617.

Ruto, E, Garrod, G., 2009. Investigating Farmers' Preferences for Design of Agri-Environment

schemes: A Choice Experiment Approach. Journal Environmental Planning and Management, 52(5),

pp. 631-647.

Scarpa, R., Rose. J.M., 2008. Design Efficiency of Non-Market Valuation with Choice Modelling:

how to measure it, what to report and why. The Australian Journal of Agricultural and Resource

Economics, 52(3), pp. 253-282.

Shields, D.A, 2012. Federal Crop Insurance: Background, CRS Report for Congress, prepared for

members and communities of Congress.

Smithers, C., 1998. Crop Insurance and Farm Management of Weather Related Risks, A Thesis

presented to the Faculty of Graduate Studies of the University of Guleph.

Sproule, R.S., Valsan, C., 2009. A simple test for the Violation of Non-stiation axiom under

uncertainty: The theory. Economics Bulletin, 29(4), pp. 2656-2664.

23

Street, D. J., Burgess, L., Luviere, J.J., 2005. Quick and Easy Choice Sets: Constructing Optimal and

Nearly Optimal Stated Choice Experiments. International Journal of Research in Marketing, Volume

22, pp. 459-470.

Thurstone, L., 1927. A law of Comparative Judgement. Psychological Review , 34(2), pp. 273-286.

Train, K., 2003. Discrete Choice Models with Simulation, s.l.: New York Cambridge University Press.

Wilson, G.A., 1996. Farmer Environmental Attitudes and ESA Participation. Geoforum, Volume 27,

pp. 115-131.

Table 1: Crop insurance attributes and levels used in the CE design

Attribute Description Level

Level of coverage How much coverage

purchased as a percentage

of the total acreage

50%, 65% , 70%

Compensation Farmers compensated

based on the market price

of a 90 kg bag of maize.

50%, 60% , 70%

Content design Whether stakeholder

involvement in design is

preferred or not

Joint or provider only

Risk cover Whether single or

multiple peril

Single peril, Multiple peril

Nature of coverage What the insurance

should cover

Crop only, crop and market,

or crop social issues such as

medical

Cost (Ksh/acre) Insurance cost per acre 110, 170, 280

24

Table 2: An example of a crop insurance choice set

Insurance scheme

A Insurance scheme B Statusquo

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

Cost per acre

(Kshs) 110 280

Which one would you prefer?

25

Table 3: Summary of farmers’ characteristics

Small scale farmers Large scale farmers Pooled

Characteristics (N = 191) (N = 109) (N = 300)

Education (mean years) 10.36 11.39 10.73

Awareness (%) 31.90 43,10 36.00

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

Average maize yield (90 bags/acre) 22.60

Gender (%): Male 43.30

Female 56.70

Education (%): None 3.30

Primary 37.70

Secondary 33.70

College 20.30

University 3.30

Masters 1.70

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

26

Table 4: Description of variables used in the choice set

Variable Description

LEVCOVME Medium level of coverage [60%]

LEVCOVHI High level of coverage [70%]

COMPENME

Medium compensation of the current price of a 90 kg bag of maize

[60%]

COMPENHI High compensation of the current price of 90 kg bag of maize [70%]

CONTJOIN

Content design involving stakeholder [1 = joint, 0 =

provider only]

MULTPRSK

Multiple risk cover [1 = Multiple risk

cover, 0 = otherwise]

CROPMKT

Crop and market insurance coverage [1 = crop and market,

0 = otherwise]

CROPMED

Crop and medical coverage [1 = crop and medical, 0 =

otherwise]

PRICE Annual insurance cost (Ksh/acre) [110, 170, 280]

27

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

Pseudo-R2 0.496

N respondents 300.000

N choices 1200.000

Notes: Statistical significance levels: ***1%, **5% and *10% respectively

28

Table 6: Marginal WTP estimates for Crop Insurance attributes (Kshs)

Variable

Marginal WTP (95% confidence

interval) P-value

H0:

testing

LEVCOVME 85.689 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.

Table 7: Attribute levels and Compensating Surplus for Crop Insurance Policy Scenarios (Kshs).

Attributes

Lev

el o

f co

ver

age

Com

pen

sati

on

Con

ten

t d

esig

n

Ris

k c

over

Nat

ure

of

cov

erag

e

Com

pen

sati

ng

Su

rplu

s

Sta

nd

ard

err

or

P-v

alu

e

Scenario Low High 70% Joint

Provider

alone

Single

peril

Multiple

peril

Crop and

market

Crop and

medical

1 � � � � 16,791.70 1,836.10 0.0000

2 � � � � � 16,639.80 1,825.00 0.0000

Notes: �Indicates that the attribute is present in a scenario at the non-zero level.

30

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.

0

20

40

60

80

100

Per

cen

tage

pre

fere

nce

Crop insurance attributes

Farmers' preferences for various attributes

Negative

Positive