FARMERS’ PERCEPTION AND WILLINGNESS TO PAY FOR
WEATHER INDEX BASED INSURANCE IN CENTRAL RIFT VALLEY
OF ETHIOPIA: THE CASE OF ADAMITULU JIDOKOMBOLCHA AND
BORA DISTRICTS
M.Sc. THESIS
DEREJE MERSHA
FEBRUARY 2018
HARAMAYA UNIVERSITY, HARAMAYA
ii
FARMERS’ PERCEPTION AND WILLINGNESS TO PAY FOR
WEATHER INDEX BASED INSURANCE IN CENTRAL RIFT VALLEY
OF ETHIOPIA: THE CASE OF ADAMITULU JIDOKOMBOLCHA AND
BORA DISTRICTS
A Thesis Submitted to Postgraduate Program Directorate
(School of Agricultural Economics and Agribusiness)
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirement for the Degree of
MASTER OF SCIENCE IN AGRICULTURE
(AGRICULTURAL ECONOMICS)
By
DerejeMersha
February2018
Haramaya University
iii
HARAMAYA UNIVERSITY
Postgraduate Program Directorate
We hereby certify that I have read and evaluated this Thesis entitled FARMERS’ PERCEPTION
AND WILLINGNESS TO PAY FOR WEATHER INDEX BASED INSURANCE IN
CENTRAL RIFT VALLEY OF ETHIOPIA: THE CASE OF ADAMITULU
JIDOKOMBOLCHA AND BORA DISTRICTS prepared under my guidance, by DEREJE
MERSHA. I recommend that it be submitted as fulfilling the Thesis requirement.
Major advisor signature date Co-Advisor signature date
As member of the Board of Examiners of the M. Sc. Thesis Open Defense Examination, I certify
that I have read and evaluated the Thesis prepared by DEREJE MERSHA and examined the
candidate. I recommend that the Thesis be accepted as fulfilling the Thesis requirement for the
degree of Master of Science in Agriculture (Agricultural Economics).
______________________ _____________________ ________
Chairperson Signature Date
______________________ _____________________ ________
Internal examiner Signature Date
______________________ _____________________ ________
External examiner Signature Date
iv
STATEMENT OF AUTHOR
By my signature below, I declare and affirm that this Thesis is my own work. I have followed
all ethical and technical principles of scholarship in the preparation, data collection, data
analysis and compilation of this Thesis. Any scholarly matter that is included in the Thesis has
been given recognition through citation.
This Thesis is submitted in partial fulfillment of the requirements for an M.Sc. degree at the
Haramaya University. The Thesis is deposited in the Haramaya University Library and is made
available to borrowers under the rules of the Library. I solemnly declare that this Thesis has not
been submitted to any other institution anywhere for the award of any academic degree, diploma
or certificate.
Brief quotations from this Thesis may be made without special permission provided that
accurate and complete acknowledgement of the source is made. Requests for permission for
extended quotation from or reproduction of this Thesis in whole or in part may be granted by
the Head of the School or department when in his or her judgment the proposed use of the
material is in the interest of scholarship. In all other instances, however, permission must be
obtained from the author of the Thesis.
Name: DerejeMersha Signature: ____________________
Date: February 2018
School/Department: Agricultural Economics and Agribusiness
v
ACRONYMS AND ABBREVIATIONS
ARD Agriculture and Rural Development
CSA Central Statistical Agency
ATJK Adamitulu Jidokombolcha
CVM Contingent Valuation Method
FAO Food and Agriculture Organization of the United Nation
MoWR Ministry of Water Resources
MPCI Multiple Peril Crop Insurance
NAPA National Adaptation Program of Action
PPS Probability Proportional to Size
UNDP United Nations Development Program
USAID United States Agency for International Development
WTP Willingness to Pay
vi
BIOGRAPHICAL SKETCH
The author was born in 1978atMidrekebd, Gurage Zone of Southern Nations, Nationalities, and
Peoples' Region, (SNNPR). He attended his elementary and secondary education at Meki Junior,
Meki Catholic and Nazareth Atse Gelawdewos Secondary Schools, respectively. After
completion of secondary level education he joined the then Awassa College of Agriculture (now
Hawassa University) and graduated with Diploma in Agricultural Engineering and
Mechanization in July 1998. He then joined the then Ethiopian Agricultural Research
Organization and worked at Melkassa Research Center as Technical Assistant. He then joined
Haramaya University under Summer Program in the Department of Agricultural Economics in
2004 and graduated with BSc degree in Agricultural Economics in 2008. After his graduation,
he was serving as a junior researcher at Melkassa Research Centers in the Department of
Socioeconomics, Research Extension and Farmers Linkage until he joined the School of
Graduate Studies at Haramaya University for his M.Sc. degree in Agricultural Economics in
October 2014.
vii
ACKNOWLEDGMENTS
Above all, I would like to thank the Almighty and Merciful God, for providing me all the
patience and endurance during my study. My sincere gratitude goes to my advisor Dr. Adam
Bekele for his intellectual stimulation, professional guidance and encourageme nt in undertaking
this study. My sincere gratitude also goes to my co-advisor Dr. Lemma Zemedu for his,
professional guidance and encouragement in undertaking this study. I have great respect to my
wife W/r Achamyelesh Mersha for her love and my friends for their support and encouragement
during my stay at Haramaya university and research work. I also thank friends in Melkassa
Agricultural Research Center and my families for their encouragement and valuable support
specially my sisters. In addition, I would like to express my gratitude to Ato Yitayal Abebe and
all other enumerators for their participation in the survey work. Finally, I would like to
acknowledge all individuals that directly or indirectly contributed to the successful completion
of this study.
viii
TABLE OF CONTENTS
STATEMENT OF AUTHOR iv
ACRONYMS AND ABBREVIATIONS v
BIOGRAPHICAL SKETCH vi
ACKNOWLEDGMENTS vii
TABLE OF CONTENTS viii
LIST OF TABLES x
LIST OF FIGURES xi
LIST OF TABLES IN THE APPENDIX xii
ABSTRACT xiii
1. INTRODUCTION 1
1.1. Background 1
1.2. Statement of the Problems 4
1.3. Objectives of the Study 5
1.4. Significance of the Study 6
1.5. Scope and Limitations of the Study 6
1.6. Organization of the Study 6
2. REVIEW OF LITERATURE 7
2.1. Concepts about Insurance 7
2.2. Limits of Insurance 9
2.3. Risk and Agricultural Insurance 10
2.4. Approaches to Measuring Insurance 12
2.4.1. Indemnity-based crop insurance 12
2.4.2. Index-based crop insurance 13
2.5. Estimation Methods of Willingness to Pay 15
2.5.1. Conjoint analysis 17
2.5.2. Choice experiment methods 17
2.5.3. Contingent valuation methods 17
ix
2.6. Empirical Studies on Willingness to Pay for Weather Index insurance 21
3. RESARCH METHODOLOGY 24
3.1. Description of the Study Area 24
3.2. Sampling Techniques 26
3.3. Data Type and Method of Data Collection 27
3.4. The Ordered Probit Model 27
3.5. Variable Definitions and Hypothesis 31
4. RESULTS AND DISCUSSION 35
4.1. Household Socioeconomic Characteristics 35
4.2. Sources of Risk and Management Strategies Practiced 37
4.2.1. Risk coping mechanisms 37
4.2.2. Risk management strategies 38
4.3. Perception of Farmers Towards Weather Index Based Insurance 38
4.4. Econometric Results 39
5.1. Summary and Conclusion 44
5.2. Policy Implication 46
6. REFERENCES 47
7. APPENDIX 54
x
LIST OF TABLES
Table Page
1: Comparison of area yield index and weather index insurance 14
2: Number of household and sample sizes 26
3: Variables and their measurement included in the model 34
4: Socio-demographic characteristics of sample households 36
5: Access to service (for dummy variables) 37
6: Major risk management strategies practiced by sample respondents 38
7: Perception and awareness of farmers towards weather index based insurance 39
8: Parameter estimates using ordered response models on stated willingness to pay 40
9: Marginal effects of ordered probit models on stated WTP ranges: 43
xi
LIST OF FIGURES
Figure Page
1. Location of the study area 26
2. Risk coping mechanisms practiced by sample households 38
xii
LIST OF TABLES IN THE APPENDIX
Appendix Table Page
1: Conversion Factor for Tropical Livestock Unit 54
2: Collinearity diagnostics 54
xiii
FARMERS’ PERCEPTION AND WILLINGNESS TO PAY FOR
WEATHER INDEX BASED INSURANCE IN CENTRAL RIFT VALLEY
OF ETHIOPIA: THE CASE OF ADAMITULU JIDOKOMBOLCHA AND
BORA DISTRICTS
ABSTRACT
The impact of climate change and variability on livelihoods of smallholder farmers in Ethiopia
has become severer than ever before. As a result, weather index based insurance has been
advocated as one of the recommended risk transfer mechanisms to support farmers in coping
with production risks. The objectives of this study were to assess the risk coping mechanisms,
the willingness to pay for weather index based insurance, and the factors influencing
smallholder farmers’ willingness to pay for weather index based insurance in the central Rift
Valley of Ethiopia. The data were collected from Adamitulu Jidokombolcha and Bora districts
from 147 sample household heads for the time January to December 2015. Descriptive and
ordered probit model were used to analyze the data. Results from descriptive analysis showed
that 87.1% of the respondents were willing to pay for the weather index based insurance
technology either positively or negatively. The farmers used different type of risk coping and
management strategies where sale of livestock and borrowing money were dominant for risk
coping and use of chemical and drought tolerant variety were widely used for managing risks.
The ordered probit econometric model results revealed that age of the household head, family
size, farm size, crop index, owning radio, and money saving were significant determining factors
of the WTP for weather index based insurance technology. Whereas land certification and
access to credit had significant effect only on the second category of willingness to pay. Policy
makers need to be aware that different socio-economic and institutional characteristics of
households influence the willingness to pay for weather index based insurance services
differently. In addition, they should understand that farmers risk coping and management
strategies.
Keywords: Perception, ordered probit model, WTP, and weather index based insurance.
1. INTRODUCTION
1.1. Background
Agriculture continues to be an important sector of Ethiopia’s economy. The, sector
contributes 42.9 per cent to the gross domestic product (GDP), 70 percent of the export
earnings and engages 80 percent of the workforce of Ethiopia (UNDP 2013). This sector in
turn is dominated by subsistence farming where more than 95% isa rain fed farming of which
more than 90% owned by a smallholder (mostly less than half hectare) poor farmers. These
smallholder farmers are highly exposed to the negative impact of climate change mainly
reflected in shortage of rainfall (drought) in Africa continent (Araya, 2011).
However, livelihood in agriculture is threatened by frequent crop failures and price volatility
(Boehlije and Eidman, 1994; Yesuf and Randy, 2008). The productivity of agriculture is
highly influenced by the conditions of the natural environment. In particular, changes in
climatic and weather conditions affect farmers’ yields.
In economies where agriculture is a dominant sector, rainfall and rainfall variability have
greater impact in agricultural performance and weather conditions and related effects of
climate change, such as heavy rain leading to flooding, and prolonged drought can damage
the major source of household income. Where there are no mechanisms to cope up with such
shocks to protect against large losses from extreme weather events, household income and
economic activities are likely to be depressed (USAID, 2006).
In case of Ethiopia, the impact that climate variability has on predominantly rain-fed agrarian
economies is clearly demonstrated. Current climate variability is already imposing significant
challenge to Ethiopia by affecting food security, water and energy supply, poverty reduction
and sustainable development efforts, as well as by causing natural resource degradation and
natural disasters. In response, the national adaptation program of action (NAPA) for Ethiopia
has been prepared and the basic approach to NAPA preparation was along with the
2
sustainable development goals and objective of the country where it has recognized necessity
of addressing environmental issues and natural resource management with the participat ion
of stakeholders (MoWR, 2007).
In many cases, farmers could benefit from investing in agricultural activities that require
higher initial investments but ultimately would generate higher income, if the risks affecting
these investments such as weather could be managed. Since banks or other intermedia r ies
that work with agricultural producers carry the same risks as their agricultural clients, they,
too, are hesitant to invest in agriculture due to potential defaults during or after a weather
event. Risk management strategies in which risks are shared with others include, among
others, farm financing, sharecropping, and price pooling arrangements, forward contracting
of farm products, and hedging on future markets. In addition, insurance is potentially an
important instrument to transfer part of the risks (Anderson, 2001).
Risk management instruments that would allow the transfer of risk to insurance markets
would thus allow growers and agribusinesses to protect themselves against risk, to have a
greater ability to plan for the season, and to access credit (UN, 2007). Managing weather risk
efficiently, coupled with other investment activities in the agricultural sector, could
strengthen the resilience of farmers and agribusinesses to weather shocks.(UN, 2007).
Insurance markets are growing rapidly in the developing world, as part of this growth;
innovative new products allow individual smallholder farmers to hedge against agricultura l
risks, such as drought, disease and commodity price fluctuations (World Bank, 2005). These
financial innovations hold significant promise for rural households. The demand for such
insurance particularly in developing countries has been increasing over time, because of
unpredictable weather conditions.
Ethiopia has recognized climate change as an important issue and attempts are being made
to incorporate potential response measures for reducing impact of climate change in to overall
development planning process. One important constraint that emerged as a result of
stakeholder consultative meetings is the extreme need for agricultural rainfall risk insurance.
3
It is believed that agricultural weather index based insurance is seen as one of the strategies
to minimize risk and capitalize on opportunities associated with the variable climatic
conditions.
In 2008 and 2009, 947 farmers in two cooperative unions (Lume-Adama and Yerer) across
four districts were insured for teff, wheat, lentil, haricot bean and chickpea under MPCI
(multiple peril crop insurance) contracts among total membership in these unions of 47,000
(Nyala insurance annual report, 2010). Membership to the cooperative union is a kind of
cooperating farmers together by government so that the provision of agriculture inputs comes
through this cooperative.
Nyala’s index-based drought insurance product, on the other hand, was then introduced in
2009 with the objective of protecting the livelihood of small scale farmers, who are
vulnerable to severe and catastrophic weather risks particularly drought, enhancing
smallholder farmers access to agricultural inputs, enabling the development of ex-ante market
based risk management mechanism which can be scalable in the whole country and to avoid
some of the drawback of traditional insurance mechanisms (Meherette, 2009).
An index based insurance product simply uses a measure such as rainfall, temperature once
soil moisture to insure against drought or other covariant shocks. According to Nyala
insurance, this approach reduces transaction costs, making insurance more affordable and
accessible for smallholder farmers but the conditions represented in the index may not,
however, reflect the farmers’ actual crop loss (Araya, 2011).
On the other hand, the high covariance of climatic risks, coupled with the lack of property to
be attached as collateral, makes it difficult for cooperatives, microfinance organizations, or
banks to provide financial services to smallholder farmers unless they have
insurance/reinsurance against weather risk. These conditions in turn keep farming at a
subsistence level. This research is therefore an attempt to look in to the nature and possibility
of weather insurance existence in two districts of East Shewa zone (Aamitulu Jidokombolcha
and Bora).
4
1.2. Statement of the Problems
The farming community is generally considered to have a risk aversion attitude (Anderson et
al., 1977; Dillon and Hardaker, 1993). Also, weather related agricultural production shocks
conspire to keep smallholders within the poverty trap, preventing the country from reaching
its productive potential in the agricultural field (Hess and Syroka, 2005). Production risk
comes from the unpredictable nature of the weather (Hardaker et al., 1997). Farmers in
developing countries are exposed to most types of risk, and the low-income farmers,
especially in semi-arid areas are the most exposed (Hazell 1992).
Ethiopia is among famine-prone countries in Africa, smallholder farmers’ vulnerability from
such income variability is common in the central rift valley of Ethiopia. Traditionally, farmers
have developed several coping mechanisms to mitigate the potential negative impacts of their
exposure to natural risks, namely, by investments in: crop diversification (planting mult ip le
crops with different vulnerabilities to weather events), irrigation systems (to decrease the
farmer’s dependence on precipitation), the generation of off-farm incomes, formal and
informal banking systems (either by accumulating savings or access to credit markets.
Despite the existence of these risks mitigating mechanisms, in developing countries a large
portion of weather shocks’ negative effects are still not entirely absorbed (Barnett and Mahul
2007, Alderman and Haque 2008). More generally, the lack of tools to insure sectors against
weather shocks has led to an underinvestment in the agricultural sector (Rosenzweig and
Binswanger 1993, Morduch 1995).
The National development plan of Ethiopia aims at changing the country’s subsistence or
traditional agricultural to commercial or market oriented one, which in turn will increase the
demand for goods and services and further lead to industrial development. The Government
strategy is aimed at reducing country’s dependency on food aid. To achieve the intended
goals within a short period of time, understanding smallholder farmers’ participation as well
as their willingness to pay for weather index based insurance could be vital. Reducing the
5
vulnerability of rainfall dependent communities to climate change requires building of loc al
institutions to support better adaptation practices where vulnerability is usually more clearly
expressed.
One major constraint to initialize the opportunity to operational risk covering mechanisms
like rainfall crop/input insurance in Ethiopia is limited availability and limited area of
operation of public and/or private institutions. In order to exploit the advantage associated
with good rainfall seasons, risk-financing institutions need to be encouraged to develop
operational risk insurance schemes in the marginal rainfall areas.
This study tried to identify willingness to pay for weather index based insurance by
smallholder farmers in the drought prone central rift valley of Ethiopia. Past studies did not
address the drought-prone areas adequately; constraints and opportunities of the weather
index insurance system operating in the area are not supported by local evidence. The existing
risk insuring mechanisms commonly used by smallholder farmers are not clearly known,
whether and to what extent smallholder farmers are willing to pay for weather index based
insurance. Socioeconomic and related factors that determine their willingness to pay for the
weather index based insurance are necessary to assist informed policy decision. This study
will also fill the current information gap and awareness on the subject.
1.3. Objectives of the Study
The general objective of the study was to analyses the willingness to pay for weather index
based insurance and to assess risk coping mechanisms. The specific objectives were:
1. To assess risk copping mechanism and the awareness of farmers about weather
insurance
2. To analyze the willingness to pay for weather index based insurance by small
holder farmers in the study area;
3. To identify factors influencing smallholder farmers’ willingness to pay for
weather index based insurance in the study area.
6
1.4. Significance of the Study
This research focuses on the willingness to pay for weather index based insurance contracts
that can promote more efficient program of actions in reducing problems of imperfect
information in mitigating farmers’ risks in Ethiopia. Therefore, identifying smallho lder
farmer’s willingness to pay for weather index based insurance is expected to be useful for
decision makers including the farmers, private and public institutions that are or may be
involved in the development and promotion of weather-based insurance in the study area by
providing up to date information. Also, the result can be applicable to other parts of the
country which have similar climate condition. The outcome of this study is expected to be
useful for governmental and non-governmental institutions who are involved in the weather
based insurance service.
1.5. Scope and Limitations of the Study
This study covers, analyzing willingness to pay for weather index based insurance, assessing
risk coping mechanisms and the awareness of farmers and identifying socio-economic and
institutional factors that significantly influencing the amount of money farmers are willing to
pay for weather index based insurance. The proposed research will be implemented in two
districts/woredas of the central rift valley that are experiencing erratic rainfall and Nyala
insurance is working with the farmers’ cooperatives Union located in East Shewa zone.
Furthermore, most of the data collected are for 2015 a onetime data and this might not be
enough to generate adequate information, because there could be many variables, which
could potentially be changed from one survey time to another survey time within a given
locality. However, efforts were made to minimize the limitations associated with the
methodology.
1.6. Organization of the Study
The study is organized in five chapters. Chapter oneis the introduction which deals with
background, problem statement, objectives, scope and significance of the study. Chapter, two
7
and three deal with review of theoretical and empirical literature related to weather related
insurance (rainfall) and the research methodologies, respectively. Chapter four presents
results and discussion of the study. Finally, chapter five summarizes the finding of the study
and gives recommendation.
2. REVIEW OF LITERATURE
2.1. Concepts about Insurance
8
Every business owner faces risks. A typical risk is that a business becomes unprofitable and
must close. Risk is the variation in potential economic outcomes. It is measured by the
variation between possible outcomes and the expected outcome: the greater the standard
deviation, the greater the risk (Anderson and Brown, 2005).
Risk, refers to the impact of the uncertain outcome on the quantity or value of some
economic variable. Repeated events would result different outcomes having a range of
values. Thus risk refers to the variations in value of an economic variable resulting from the
influence of an uncertain event. As such, risk (variations) may be measured in terms of
standard deviation or coefficient of variations for yield, prices and income (Singh, 2010).
Insurance can help cover the cost of unexpected events such as theft, illness or property
damage (Dunlap, 2006). Financial consumer Agency of Canada (2011) defined Insurance
as a way of reducing your potential financial loss or hardship. Insurance is sold and bought
in a market. The purchasers must perceive that the premiums and expected benefits offer
value; the sellers must see opportunity for a positive actuarial outcome, over time, and profit.
Insurance is one alternative to manage risk in yield loss by the farmers. It is the mechanism
to reduce the impact of income loss on the farmer (family and farming). Crop insurance is a
means of protecting farmers against the variations in yield resulting from uncertainty of
practically all natural factors beyond their control such as rainfall (drought or excess
rainfall), flood, hails, other weather variables (temperature, sunlight, wind), pest infestat ion,
etc. Crop insurance is a financial mechanism to minimize the impact of loss in farm income
by factoring in a large number of uncertainties which affect the crop yields. As such it is a
risk management alternative where production risk is transferred to another party at a cost
called premium (Singh, 2010).
Insurance is an agreement where, for a stipulated payment called the premium, one party
(the insurer) agrees to pay to the other (the policyholder or his designated beneficiary) a
defined amount (the claim payment or benefit) upon the occurrence of a specific loss. This
defined claim payment amount can be a fixed amount or can reimburse all or a part of the
loss that occurred. The insurer considers the losses expected for the insurance pool and the
9
potential for variation in order to charge premiums that, in total, will be sufficient to cover
all of the projected claim payments for the insurance pool. The premium charged to each of
the pool participants is that participant’s share of the total premium for the pool. Each
premium may be adjusted to reflect any special characteristics of the particular policy. The
larger the policy pool, the more predictable its results (Anderson and Brown, 2005).
Under the formal arrangement, the party agreeing to make the claim payments is the
insurance company or the insurer. The pool participant is the policyholder. The payments
that the policyholder makes to the insurer are premiums. The insurance contract is the policy.
The risk of any unanticipated losses is transferred from the policyholder to the insurer who
has the right to specify the rules and conditions for participating in the insurance pool. An
insurance contract covers a policyholder for economic loss caused by a peril named in the
policy. The policyholder pays a known premium to have the insurer guarantee payment for
the unknown loss. In this manner, the policyholder transfers the economic risk to the
insurance company.
2.2. Limits of Insurance
Ray (1998) states that, there are limits to the ability of households to insure one another,
even if the income shocks are idiosyncratic. One limit is limited information about the
outcome. A person may ask for or expect transfers from his community while providing
them with deliberately wrong or misleading information regarding his economic state: he
might lie about the size of his harvest, he might lie about illness in the family, or, at the very
least, he might be in a position to lie, in the sense that his community members do not have
information to verify that the claimed occurrences indeed occur. Also may be unlikely tha t
a farmer in one extreme of a state would be engaged in insurance with another unknown
farmer in the other extreme of the state.
So it is certainly true that this sort of information barrier kicks in and precludes insurance
over very large anonymous groups or spatial distance, even if such insurance could, in
principle, be profitable for all concerned. But it is hard to say that this sort of information
problem occur within the village despite it varies across the village. One of the strengths of
10
traditional society is that they are endowed with social capital. This social capital provides
a fund of socially available information that permits a community to interact in ways that
would not be possible in an anonymous society. Much of social capital is simply information
flow.
Another limit stated by Ray (1998) is limited information about what led to the outcome.
This is the problem of moral hazard which is the possibility of some insurable event can be
influenced by the unobservable action of the individual. Consider mutual insurance of
harvests. Everyone knows that there are idiosyncratic shocks that can affect the state of the
harvest on a particular plot. Water, fertilizer, or pesticides may have been badly applied or
there may be damage to the crop caused by events outside the farmer’s control, or (and this
is the source of the problem), the crop may be bad because the farmer deliberately skipped
on the use of these inputs.
The size of the harvest may be visible for all to see (so that the first information problem is
irrelevant) but why the harvest is what it is requires information of a different kind
altogether. The problem is that in the presence of full insurance, the incentive to deliberately
under apply inputs is higher. But if the farmer deviates in this fashion, he will presumably
be excluded from future access to insurance. There is also the question of social sanctions
and all of this adds up to a future loss if he fails to conform today. But information
considerations are also relevant here.
2.3. Risk and Agricultural Insurance
Agricultural production is an outcome of biological activity, which is highly sensitive to
changes in weather. Important weather variables such as temperature, humidity, rainfa ll,
wind etc. influence the biological process directly or indirectly. For instance, poor
precipitation during growth period results in stunted plant growth. Heavy rainfall during
early growth period causes submersion of plants. Similarly, hailstorms, wind and cyclones
damage the standing crops by lodging and uprooting especially the perennials (trees and
11
shrubs). High humidity may cause outbreak of pests and diseases. All these result in partial
loss in yield, sometimes complete crop failure, and hence reduced income to
farmers(Subhasis B.and Navin K., 2017).
In other words, deviations in the weather variables from the normal adversely affect the crop
yields and hence production and income on individual farms. As variations in weather are
more a regular phenomenon crop yields are not stable. The sword of uncertain agricultura l
prices always hangs on the farmers’ fate. Therefore, farm incomes fluctuate violently from
year to year. These variations in income are referred to as risk. The variations in income due
to changes in yield are production risk and due to changes in price are marketing risks
(Subhasis B.and Navin K., 2017).
Disasters hit hard. Adverse weather events such as drought, excessive rains, storms and
hurricanes cause heavy losses to farmers. Disasters can often not be prevented from
happening but to some extent, they canbe predicted and arrangements can be made to reduce
their impact. However, in some cases, disasters cannot be predicted and farmers will have
to cope with major losses after the event occurs. Agricultural insurance, including livestock,
fisheries and forestry, is especially geared to covering losses from adverse weather and
similar events beyond the control of farmers. It is one of the most quoted tools for managing
risks associated with farming. Many pilot programs have been developed over the years,
targeting especially small-scale farmers in developing countries, but agricultural insurance
remains primarily a business, which involves developed country farmers. Only a minor
percentage of global premiums are paid in the developing world where insurance is mainly
available only to larger and wealthier farmers. Insurance is not the universal solution to the
risk and uncertainties that farmers face. It can only address part of the losses resulting from
some perils and is not a substitute for good on-farm risk-management techniques, sound
production and farm management practices and investments in technology (FAO, 2014).
Weather related risk remains a major challenge to households in low-income economies
whose livelihoods depend on agriculture. With changes in climatic conditions, agriculture
has become an increasingly uncertain business. Well-organized insurance markets have the
12
potential to help mitigate the adverse consequences of such risks and consequently the
provision of simple and affordable insurance products to those households has received
significant attention in recent years. Recent developments in index-based weather insurance
offer new possibilities of providing insurance for smallholder farmers and, could, help
farmers adapt to and build resilience against changing weather conditions. However, basis
risk residual risk left uninsured by the index remains a key challenge to index-based weather
insurance, reducing the latter value for farmers (Berhane et.al, 2013).
2.4. Approaches to Measuring Insurance
According to ARD (2011) crop insurance products can broadly be classified into two
major groups: indemnity-based insurance and index insurance.
2.4.1. Indemnity-based crop insurance
There are two main indemnity products: Damage-based indemnity insurance (peril crop
insurance) is crop insurance in which the insurance claim is calculated by measuring the
percentage damage in the field soon after the damage occurs. The damage measured in the
field, less a deductible expressed as a percentage, is applied to the pre-agreed sum insured.
The sum insured may be based on production costs or on the expected revenue. Where
damage cannot be measured accurately immediately after the loss, the assessment may be
deferred until later in the crop season. Damage-based indemnity insurance is best known for
hail, but is also used for other named peril insurance products (Rajib et al., 2013).
Yield-based crop insurance (multiple peril crop insurance) is coverage in which an insured
yield (for example, tons/ha) is established as a percentage of the farmer’s historical average
yield. The insured yield is typically between 50 percent and 70 percent of the average yield
on the farm. If the realized yield is less than the insured yield, an indemnity is paid equal to
the difference between the actual yield and the insured yield, multiplied by a pre-agreed
value. Yield-based crop insurance typically protects against multiple perils, meaning that it
covers many different causes of yield loss (Rajib et al., 2013).
13
2.4.2. Index-based crop insurance
There are two types of index product: Area yield index insurance: Here the indemnity is
based on the realized average yield of an area such as a county or district, not the actual
yield of the insured party. The insured yield is established as a percentage of the average
yield for the area. An indemnity is paid if the realized yield for the area is less than the
insured yield regardless of the actual yield on a policyholder’s farm. This type of index
insurance requires historical area yield data (ARD, 2011).
In the case of area yield index insurance, a payment is made when the measured yield for
the region falls below a certain predetermined limit “critical threshold” or “strike-point”
(Smith & Watts, 2009). Traditional crop insurance, whose indemnities are based on
individually-assessed losses, such as named peril crop insurance and multiple peril crop
insurance, has been widely considered financially unsustainable and plagued with moral
hazard and adverse selection problems. Traditional crop insurance also suffers from high
transaction costs, notably loss adjustment ones, which hampers both the profitability of
insurers and affordability for smallholders (Binswanger-Mkhize, 2012; Carter, et. al, 2007;
Hazell, 1992).
Weather based index insurance: Here the indemnity is based on realizations of a specific
weather parameter measured over a pre specified period of time at a particular weather
station. The insurance can be structured to protect against index realizations that are either
so high or so low that they are expected to cause crop losses. For example, the insurance can
be structured to protect against either too much rainfall or too little. An indemnity is paid
whenever the realized value of the index exceeds a pre specified threshold (for example,
when protecting against too much rainfall) or when the index is less than the threshold (for
example, when protecting against too little rainfall). The indemnity is calculated based on a
pre-agreed sum insured per unit of the index (ARD, 2011).
Weather index insurance and traditional insurance are not by definition mutually exclusive.
These can co-exist and complement each other since these are really designed to target
different layers of risks and different levels of administrative capabilities. However,
14
advances in technology that lower delivery costs and loss adjustment surveys in the case of
traditional crop insurance schemes will be needed to make this type of insurance financia l ly
feasible(Ali, 2013).
There are significant advantages of index based insurance. It avoids the problems of moral
hazard and adverse selection. Because the payment of indemnity is based on the deviations
from the index and not on individual losses, no assessment of losses at the individual level
is needed. The indemnity process is quick and inexpensive to administer. Additionally, the
design of the product lessens the administrative and operational expenses. Despite these
major advantages, acceptance of this product by both insurers and insured parties is still low.
This can be explained by considering some of the constraints. From the point of view of the
insurer, it can be a costly and time-consuming task to assemble the data and construct the
appropriate indexes. Once the indexes are created, operational costs are low and this
translates into lower premiums for insured parties. The lower premiums attract small
producers who otherwise would not be able to afford insurance. The index based weather
insurance products that are properly designed can become a first step to facilitate the broader
development of robust rural financial markets that serve the needs of the poor in low-income
countries (Ali, 2013).
In the past decade, index contracts emerged and have been promoted as a powerful financ ia l
solution to address the pitfalls of traditional insurance products. Smith & Watts (2009) and
more recently Binswanger-Mhkize (2012) thoroughly reviewed the landscape for index
insurance in developing countries. The basis of this approach is the underwriting of the
contracts against specific perils or events “trigger” recorded at a local level. Examples of the
triggers are area yield index or rainfall index. Table 1 below compares these two mostly-
used index insurance products, where index insurance products are most fully developed
and applied.
Table 1:Comparison of area yield index and weather index insurance Area yield index Weather based index
Technical and practical
aspects
All peril cover (drought, excess rainfall, flood, pest infestation,
etc.)
Single (sometimes multiple) peril covers (drought, excess rainfall, low
temperature, etc.).
Easy-to-design index (estimated aggregate yields in a given area)
Technical challenges in index design (peril, crop, farming practices, agro
meteorological zone, etc.)
15
Low start-up costs High start-up costs
Slow claims settlement Faster claims settlement
Geographic
Basis Risk
Arises when the Insurance Unit
size is too large and is not homogenous in terms of
agricultural production level
Arises when a weather station is
referenced for a larger geographical area, covering areas far off from
weather station
Product Basis Risk
Yield index insurance covers risk from sowing till harvesting. As Yields are estimated at harvest
stage, losses if any suffered after harvest are not reflected in the
yield index.
Weather index covers risk arising out of deviations in parametric weather exigencies only. Risks
outside these parametric weather (like pests, diseases, hailstorm,
flooding etc.) are not covered
Product Design Basis Risk
Trigger yield used in yield index insurance is a function of moving average of past 5 years’ yield and
coverage level, which may range from 60 to 90 percent. In other
words, the shortfall between ‘normal yield’ and ‘trigger yield’ is not protected
Arises because of imperfect correlation between weather index and the production process (yield)
Source: (Mahul&Verma, 2012, p. 11; Rao, 2010, p. 200)
2.5. Estimation Methods of Willingness to Pay
Willingness to pay (WTP) is the maximum amount a person would be willing to pay or
sacrifice in exchange for a good. This term stands in contrast with willingness to accept
payment (WTA), which is the maximum amount an individual is willing to receive in order
to give up a good (Dimitri and Greene, 2002). Gunatilakeet al. (2007) explain the concept
of WTP as the economic value of a good to a person or household under given conditions.
WTP values provide crucial information for assessing economic viability of projects, setting
affordable tariffs, evaluating policy alternatives, assessing financial sustainability, as well
as designing socially equitable subsidies (Brookshire and Whittington, 1993). In explaining
16
the viability of a new product, cost of production and consumer demand for the product have
to be taken into consideration (Kimenju and De Groote, 2005; Quagrainie, 2006).
Several methods have been developed to measure consumer willingness to pay (WTP).
These methods can be differentiated as to whether they measure consumer hypothetical or
actual WTP and whether they measure WTP directly or indirectly. Direct methods of
measuring willingness to pay are often referred to as stated preference methods, while the
indirect methods are referred to as revealed preference methods which use actual revealed
behavior of consumers in the market. The stated preference methods include: Choice
experiments (conjoint analysis and choice modeling) and contingent valuation methods,
while revealed preference methods comprise of: hedonic pricing, travel cost method, dose
response approaches and averting expenditure/avoided cost approaches (Hanky et al., 1997;
Asafu, 2000).
Contingent valuation methods (CVM) have been used to evaluate farmers’ preferences in
crops and other technical innovations, particularly where revealed preference approaches
are not feasible. CVM are hence used to measure the value of non-market goods.
According to Bennet and Blamey (2001), the revealed preference methods are not very
suitable for non-market valuation, because they are limited to provision of informat ion
regarding values that have been experienced. Furthermore, revealed preference methods
may be of little interest in situations where new circumstances are expected to the proposed
change. Thirdly, there is limited number of cases where non –market values exhibit a
quantifiable relationship with a marketed good. Due to these limitations, the focus is on
stated preference methods, which deal with estimation of ‘total economic value’ of non-
market goods. Furthermore, the stated preference methods are flexible and can be applied to
a wider range of environmental goods and services. They can also be used to estimate use
values and non-use values. In addition, they are relatively straightforward for elicit ing
individuals’ valuation of non-market goods and services; and require few theoretical
assumptions compared to revealed preference approaches (Asafu, 2000). The stated
17
preference approaches (conjoint analysis, choice modeling and contingent valuation
methods) have their strengths and weaknesses.
2.5.1. Conjoint analysis
In conjoint analysis, the explicit trade-offs between attributes provide a more realistic
approach and part-utilities produced provide a common scale, facilitating direct comparison
(Murphy et al., 2000). It helps to quantify and predict the individual’s overall judgment of
a product based on its most important attributes (Monteiro et al., 2001).
Despite these strengths, it has the following shortcomings: it is difficult to make inter
personal comparisons of ranking or rating data; it is difficult for respondents to rank large
numbers of alternatives and the fact of rating tasks in particular involves a departure from
context of choice actually faced by consumers (Morrison et al., 1996). Conjoint analysis
does not provide the respondent with an opportunity to say ‘no’ to the good and considered
to be unconditional to relative measures of WTP that could be understated (Asafu-Adjaye,
2000).
2.5.2. Choice experiment methods
Choice modeling is used to value multiple alternatives and can provide conditional and
absolute measures of WTP (Asafu, 2000). It has the ability to embed a range of potential
substitute goods within the alternatives from which respondents are asked to choose (Bennet
and Blamey, 2001). However, choice modeling requires complex survey design leading to
large number of choice sets which tends to affect the outcome (Asafu, 2000).
Furthermore, there is difficulty in selection of attributes to be used to describe the choice
alternatives because of apparent contradictions between what policy makers regard as key
factors and what really matters to respondents (Bennet and Blamey, 2001).
2.5.3. Contingent valuation methods
18
The Contingent Valuation Method is a survey based technique used to examine how
consumers evaluate goods and services not found in the market place (Vankatacha lam,
2004). The contingent valuation method is the most useful technique for estimating
economic values of non-market goods and services. Contingent Valuation Methods have the
ability to estimate values which are theoretically meaningful aspects of value and useful in
hypothetical market situations (Hanley and Spash, 1993).
The Contingent Valuation Method offers respondents with one or sometimes two
alternatives to evaluate, which yields improved response rates (Asafu-Adjaye, 2000).
Hypothetical market scenarios are set up using contingent valuation methods, where
consumers are asked to value a new product (Lusk and Hudson, 2004). Potential consumers
are directly asked how much they would be willing to pay for the new product.
Contingent Valuation Method use two main approaches namely, open-ended and close-
ended questions. Under open-ended questions, no bids are set and consumers are asked to
state how much they are willing to pay for a product based on their anticipated utility. This
approach has several advantages, which include: avoiding start up bias, since no bids are set
and hence a consumer freely states a price based on his perceived utility. Secondly, a
researcher is able to generate a range of prices, which provide a wide insight about
consumers’ willingness to pay for a product, and makes it easier to determine factors
affecting consumers’ willingness to pay.
However, the problem with this method is that people find it hard to attach a price to a new
product (Hanemann and Kanninen, 1996). Arrow et al. (1993) pointed out that the open-
ended format could be problematic since respondents may not have sufficient information
to make a good judgment of the values they would attach to such products, hence may not
give realistic estimates.
Close-ended questions on the other hand are easier to respond to and are more realistic since
they correspond more to real market situations, where a consumer is presented with a price
for a product, and faces a yes/no decision (Kimenju and De Groote, 2005). Close– ended
questions take different approaches namely, single-bounded choice approach, double-
19
bounded (dichotomous choice) approach, multiple-bounded (polychotomous choice)
approach and price card approach, but the most commonly used forms are the single–
bounded and the double-bounded dichotomous choice questions (Hanemann and
Kannimem, 1996).
In the single-bounded approach, a respondent is offered only one bid (a certain product at a
certain price), to accept or reject. This method is incentive compatible because it is in the
respondent’s strategic interest to accept whether their willingness to pay is greater or equal
to the price asked and to reject otherwise (Mitchell and Carson, 1989). Utility maximiza t ion
implies that a person will then only answer “yes” to the offered bid if his maximum
willingness to pay is greater or equal to the bid. However, the method requires a large sample
size for statistical significance (Hanemanet al., 1991).
In the double – bounded dichotomous choice technique, a second bid is offered, which is
higher or lower depending on the first response. This makes the double-bounded technique
statistically more efficient than the single –bounded choice technique (Kanninen, 1993;
Hanemannet al., 1991). This method also incorporates more information about an
individual’s willingness to pay and therefore provides more efficient estimates and tighter
confidence intervals (Hanemannet al., 1991). The double-bounded approach has been used
extensively in valuing non-market goods (Mitchell and Carson, 1989). However, the
analysis requires maximum likelihood estimation and the interpretation is not always
straightforward.
The multiple-bounded (polychotomous choice) approach offersmultiple bids and hence
yields multiple responses (Alberiniet al., 2003). This method is useful when limited
information is available initially to decide which bids to include. They also allow for
multiple choices, which offer the possibility of including options for uncertainty. Multip le -
bounded approaches are, however, subject to design bias, and are influenced by the range of
bids included (Vossler et al., 2004). The underlying assumptions of the approach when
including uncertainty is still under debate (Vossler and Poe, 2005). More research currently
under way, is expected to shed more light on these issues.
20
Under the price card format, respondents are confronted with an ordered sequence of bids
to choose the maximum amount they are willing to pay. Following Welsh and Poe (1998),
the PC format is expanded beyond the traditional PC format by letting respondents value
each price and allowing them to express uncertainty. Therefore, additional thresholds and
likelihood of voting yes are included.
However, willingness to pay responses is elicited in form of intervals instead of point
valuations. Suppose TL is the maximum amount a respondent would vote ‘yes’ and TU is
defined as the lowest amount a responded would switch to ‘rather yes’. Willingness to pay
then lies somewhere in the switching interval [TL, TU]. In nonparametric and parametric
estimation approaches, the willingness to pay values simply is set at interval midpoints
which may bias results. Given the above background and the nature of this study, the
multiple-bounded approaches was adopted for this study.
While some economists have expressed skepticism about the use of direct questioning to
estimate willingness to pay, a group of world-renown economists in Arrow et al. (1993),
acknowledge that Contingent Valuation Method studies convey useful information
especially in market analysis for new and innovative products. One of the pioneers in the
field of Contingent Valuation Method surveys, Smith (1996), argues that Contingent
Valuation Method research has witnessed robust progress, enabling better understanding of
consumer preferences. On the other hand, Smith (1996) cautions that hypothetical bias can
also be large because of the nature of Contingent Valuation Method surveys. Careful
development of survey instruments (through initial preparatory work, focus groups,
cognitive interviews, and pretests); conscientious implementation of field work; and
rigorous econometric analysis that link the data to underlying theoretical models like utility
functions, can reduce hypothetically in a Contingent Valuation Method study (Gunatilake et
al., 2007). Another important reason behind the expressed reservations about the Contingent
Valuation Method is the potential divergence between responses and actual behavior.
21
Emerging evidence shows that predictions from “hypothetical” Contingent Valuation
Method scenarios seem to compare well with actual behavior (Cameron et al., 2002; Vossler
and Kerkvliet, 2003; Griffin et al., 1995). Choeet al. (1996) indicates that willingness to pay
values from Contingent Valuation Method is as robust as those from a revealed preference
model. Smith (1996) also points out that the Contingent Valuation Method will remain a
significant part of efforts to assess consumer preferences for nonmarket goods. Adamowicz
(2004) and Whitehead and Blomquist (2006) endorse this view and maintain that Contingent
valuation Method studies remain a key tool in generating data on non-market goods and
services for policy analysis. Based on the above arguments, this study chose to employ
Contingent Valuation Methods.
2.6. Empirical Studies on Willingness to Pay for Weather Index insurance
Weather index insurance is insurance that is linked to a weather index such as rainfall, rather
than a possible consequence of weather, such as crop failure. This subtle distinction resolves
a number of fundamental problems that make traditional insurance unworkable in rural parts
of developing countries. One key advantage is that the transaction costs are low. This makes
it workable under real market conditions both financially viable for private sector insurers
and affordable to small farmers. Unlike traditional crop insurance against crop failure, the
insurance company does not need to visit farmers’ fields, to determine premiums or to assess
damages. Instead the insurance is designed around rainfall data (for example). If the rainfa ll
amount is below the earlier agreed threshold, the insurance pays out. Since there is no need
for the insurance company to corroborate actual losses, payouts can be made quickly and
distress sales of assets avoided. This process also removes the ‘perverse incentives’ of crop
insurance, where farmers may actually prefer their crops to fail so that they receive a payout.
With index insurance, the payout is not linked to the crop survival or failure, so the farmer
has the incentive to make the best decisions for crop survival (Barnettet al.2007)
In recent decades weather-based insurance has been considered as a valuable alternative for
traditional crop insurance. The main advantage of the former is that it is better suited to
combat asymmetric information problems, i.e. adverse selection and moral hazard. An
22
additional important advantage of weather-based insurance is that it reduces considerably
transaction costs and thus allows a faster settlement of claims. The latter characteristic of
weather-based insurance makes it particularly relevant in the context of catastrophic event
management when the help must be provided within few days to a large number of affected
farms (Bokusheva R. and Conradt, S, 2012).
Ethiopia is characterized by great geographic and climatic diversity. The country has vast,
untapped agricultural potential, but the agricultural sector, dominated by small -scale
farmers with low productivity, is confronted with increasing population and food insecur ity,
very low and declining levels of agricultural productivity, and worsening natural resource
degradation (Demel 2002). Closeto 20 million Ethiopians are under the threat of famine
because of a poor rain season and will need food aid if they are to survive (Vidal, 2003).
There are many factors, which influence the farmer decision to buy a weather-based
insurance contract. In addition to factors evaluated in the context of traditional agricultura l
insurance such as farm’s socio economic characteristics, risk aversion, level of production
diversification, etc., for weather-based insurance the literature discusses the effect of
informal insurance, basis risk and model prediction uncertainties on the farmers’ demand
for insurance (Akter, 2011; Barnett, 2010; Bokusheva and Breustedt, 2012).
One should therefore expect increased specialization and high profits, as farmers focus on
maximizing the output of the insured crop, rather than on diversifying the weather risk
through the cropping system. The weather index based insurance will thus not only introduce
a more efficient and low-cost insurance but it will provide a more transparent and actuary
fair insurance products to the farmer. The provision of direct risk relief to farmers will enable
them to alter their production strategies towards maximizing output, rather than diversifying
risk, and to shift their demand for credit from consumption loans to investment loans. This
is likely to result in increased specialization and investment, and thus contribute to increased
profits and the wellbeing of the farmers in rain-fed areas (Ali, 2013).
23
Hill et al. (2011) examined which farmers would be early entrants into weather index
insurance markets in Ethiopia, were such markets to develop on a large scale. They did this
by examining the determinants of willingness to pay for weather insurance among 1,400
Ethiopian households that have been tracked for 15 years as part of the Ethiopia Rural
Household Survey. This provides both historical and current information with which to
assess the determinants of demand. They find that educated, rich, and proactive individua ls
were more likely to purchase insurance.
Risk aversion was associated with low insurance take-up, suggesting that models of
technology adoption can inform the purchase and spread of weather index insurance. They
also assess how willingness to pay varied as two key characteristics of the contract were
varied and find that basis risk reduced demand for insurance, particularly when the price of
the contract was high, and that provision of insurance through groups was preferred by
women and individuals with lower levels of education.
According to Berhane et al. (2013), weather risk remains a major challenge to farming in
poor countries that face frequent droughts. Recent evidence on index-based weather
insurance points to low take-up rates largely due to basis risk (i.e. residual risk left uninsured
by the index). Using randomized control trials, they study to what extent traditional groups
can be utilized to mitigate basis risk by retailing insurance through these groups. They find
that selling insurance through Iddirs, with pre-defined sharing rules, increases take-up
suggesting that groups are better placed to reduce basis risk. They also find that insurance
strengthens existing risk-sharing behavior within groups, for example, by improving access
to loans from the Iddir to cover crop losses and improving perceived ability to finance
emergencies. Insurance has also improved household welfare in the short term considered
in this study, albeit to a limited extent.
Trang (2013) strives to find out the willingness to pay of Vietnamese farmers based on
areayield index insurance, as well as the factors that influence it. He conducted a contingent
valuation study on a sample of Vietnamese farmers in Dong Thap province, using a double
bound dichotomous choice procedure. He measured farmers’ risk attitude by a gambling
24
game, while other interested variables were collected from a household questionnaire. By
using empirical analysis using a probit model for the willingness to join and an interval data
model for double bound dichotomous choice he found evidences that while most farmers
might be interested in joining the program, they will only be willing to buy the insurance at
a subsidized price. He also found that the perceived high probability of agricultural risk
occurrence, farmers’ risk aversion, wealth, education and complementary risk management
strategies such as the sale of assets statistically influenced the decision to take up insurance.
Finally he concluded that risk-averse farmers, those who do not grow rice in the risky season,
and those intending to sign an interlinked contract, statistically had a lower willingness to
pay. Andhouseholds with non-farm employment and savings, better knowledge of
insurance, and, surprisingly, the small farmers of the community were willing to pay more
for the insurance.
3. RESARCH METHODOLOGY
3.1. Description of the Study Area
The study was conducted in Adamitulu Jidokombolcha and Bora districts of Oromia
Regional State of Ethiopia. These districts are among the Central Rift Valley districts of
Ethiopia. These two districts are purposively selected based on the level of information on
weather index insurance program, hence making the study more appropriate. The altitude of
these areas ranges from 1500 to 2300 meter above sea level. East Shewa Zone extends
between 70 33’50” N – 90 08’56” N and from 380 024’10” E – 400 005’ 34” E. The Mid-
Rift Valley area experiences an erratic, unreliable and low rainfall averaging between 500
and 900 mm per annum. The rainfall is bimodal with the short rains occurring in February
to May and long rains from June to September (Kassieet.al. 2013)
25
East Shewa zone is among the 17 zones of the Oromia Regional State and is located between
80 12’48.7’’ (8.21350) north latitude and 380 50’ 53.1’’ (38.84810) east longitudes. East Shewa
zone has an average altitude of 1707 meters above sea level. The zone has total area of about
8370.90 square kilometer. Based on the 2007 census conducted by the central statistica l
Agency of Ethiopia (CSA), the total population of the East Shewa zone was 1356342 of
which 696350 are males and 659,992 are females. Adamitulu Jidokombolcha and Bora are
among the 13 districts of the zone.
Adamitulu Jidokombolcha district is located in east Shewa zone of Oromia Regional State.
Ziway town is the administrative center of the district located 167km to the southeast of
Addis Ababa, which is located in the central rift valley of Ethiopia at latitude and longitude
of 7°52′N 38°42′E It shares borderlines withSouthern Nations, Nationalities and Peoples’
Regional State (SNNPRS) in the west and North West, Dugda district in the north, Arsi Zone
in the east and Arsi-Negele district in the south (Fig.1).It has a total area of 1403.3 square
kilometer. The altitude of this district ranges from 1,500 to 2,000 m above sea level, with a
bimodal rainfall pattern, having a main rainy season from June to September and the annual
rainfall ranges from 650 to 750 mm (Central Agricultural Census Commission, 2003).
Crop production is the dominant agricultural practice in the area. Maize and haricot beans
are the major crops grown as small holding subsistence farming system in the district. Maize
is used mainly for food and haricot beans are a cash crop. Some farmers also grow sorghum,
Teff, Wheat and Barley. According to the 2007 national census report, Adamitulu
Jidokombolcha district has a total population of is 141,405. Out of the total population
120482 (85.2%) live in rural areas, 71,167 are male and 70,238 are females.
Bora district is located in East Shewa zone of Oromia Regional State. A total area of the
district is estimated at 48,469 hectares. Alemtena town is the administrative center of the
district located 117 km to south of Addis Ababa It has a latitude and longitude8.30°N
38.95°E with an elevation of 1,611 meter above sea level. It shares borderlines with Lume,
Lake Koka, and Dodota in the East, Dugda in the West, Liben in the North and Zeway
Dugeda and Lake Zeway in the South (Fig.1). According to the 2007 national census report,
Bora district has a total population of about 58,748. Out of the total population 47,345 live
26
in rural areas, 30,487 are male and 28,261 are females. Mixed farming is the dominant
household activity in the District and it is mostly confined to production of a few rain-fed
crops such as fruit and vegetable crops, wheat, maize, teff, barley, chickpeas, and haricot
beans. Fishing is also a common household activity.
Figure 1. Location of the study area
3.2.Sampling Techniques
A two stage sampling technique was used to select 147 sample households. Out of the
districts in East Shewa Zone that have almost similar climate condition, Adamitulu
Jidokombolcha and Bora districts were purposively selected because these areas are most
drought prone areas and based on the information available about weather index based crop
insurance. In the first stage, two kebeles were selected from each district based on the
information available on weather index insurance. In the second stage, the total number of
households in each kebele were listed and a total number of 147 sample households were
selected based on probability proportional to sample size of households in each kebele.
Table 2: Number of household and sample sizes
Woreda No of Kebeles Name of PAs No of HH in kebeles Sample HH
ATJK 2 Abinegermama 855 51
AnenoShisho 959 58
27
Bora 2 Tibesuti 323 19
Doyoleman 320 19
Total 4 2457 147
3.3.Data Type and Method of Data Collection
Data were gathered from primary and secondary sources. The primary data were collected
from sample households through a structured questionnaire using face to face interview. The
secondary data were collected from the existing government line departments and offices,
records of nongovernmental organizations.
The willingness data is collected through CV method, this method is also suited to solicit
consumers’ willingness to pay for a product that is not yet on the market (Alberini and
Cooper, 2000). In this method, the researcher creates a hypothetical market in a non-market
or new good. The values, which are generated through this hypothetical market, are treated
as estimates of the value of new good.
The initial bids were set based on the information obtained from the discussion made with
small group of farmers’ from both districts and Oromia insurance company which are
implementing pilot projects on weather index based insurance in the districts currently. The
insurance company already set the bid, which is 100 ETB per one policy. Based on this the
data collected with the structured questionnaire.
Method of Data Analysis
The data collected was analyzed using both descriptive statistics and econometric model.
Descriptive statistics such as mean, percentage, standard deviation and frequency of
appearance was used, whereas on the econometric approach adopted the ordered probit
model.
3.4. The Ordered Probit Model
28
Under the contingent valuation, appropriate bid elicitation approach has always been a major
issue. Respondent may not possess enough information; or they may consider that the
question is too invasive of their privacy, and therefore fail to provide exact dollar amount that
represents their WTP. This may lead to a completely erroneous responses giving rise to
invalid parameter estimates.
The contingent valuation questions usually follow dichotomous choice responses where
individuals are asked whether to vote (yes/no) for the proposed bid options (Herrisen and
Shogren, 1996). The dichotomous choice questions are found to be suffered from the
anchoring effect (Herrisen and Shogren, 1996) drawing invalid conclusion.
With the anchoring effect in consideration, multiple bound question gained popularity in the
recent years (welsh and Poe, 1998; Alberini, et al. 2003). The multiple bound questions
provide a list of bid amounts from where a respondent chooses to represent his WTP value.
Some researchers argue that providing a list of alternative bids reduces the focus of
respondents on single bid or sequential bids and therefore reduces the anchoring effect
(Whitehead, 2002; Roach and Boyle, 2002; Rowe et al., 1996). In addition, literatures also
established that the double and multiple bound questioning approaches increases the
efficiency of parameter estimates (Whitehead 2002; Alberiniet al. 2003).
In double and multiple bound questions, given the dichotomous type response, logit or probit
models have mostly been used on contingent valuation studies (Whitehead et al., 2001).
Alberiniet al (2003) used random effect logit model to estimate the WTP value from the
multiple bound contingent valuation technique. Similarly, Whitehead (2002) employed
random effect probit models on double, triple and multiple bound questions. The precision
of WTP value increased with multiple bond questions in contingent valuation approach
(Whitehead, 2002). Whitehead (2002) focused that the double bound questionnaire format
provide better estimates for true WTP when a starting value of an individual’s bid can’t be
assigned to represent the distribution of WTP values. Roach et al (2002) also claimed an
increased efficiency in parameter estimates with multiple bound questionnaire setting of
WTP value elicitation.
29
Due to such facts and anchoring effect in single bound question, a multiple bound questions
formatting was found to be attractive in this study.The multiple bound questionnaire setting
is preferred at least for two reasons:
• The tendency of “yea saying” to the given value even though the true WTP is
less/greater than the provided can be reduced (Roach et al. 2002),
• The double bounded dichotomous choice model provides asymptotically more effic ient
estimates than single bounded model (Hanemann, et al. 1991).
On this study, the three levels of payment categories were defined by an ordinal scale
response index where, j represent three categories of payments. If the respondents’ WTP
value is 100 Birr then j takes a value of 1; if the utility difference falls within 100 Birr and
300 Birr j is 2; and if the WTP value is greater than or equal to 300 Birr then j takes the
value of 3. The data allows estimation of parameters using probit models (Boccaletti and
Nardella, 2001; Jin et al., 2008). For econometric purpose, the latent value of WTP takes the
three values as follows (Johnston, 1999; Jin et al., 2008);
𝑊𝑇𝑃𝑖 = 1 𝑖𝑓 𝑊𝑇𝑃∗ ≤ 𝑦1
𝑊𝑇𝑃𝑖 = 2 𝑖𝑓 𝑦1 < 𝑊𝑇𝑃∗ ≤ 𝑦2
𝑊𝑇𝑃𝑖 = 3 𝑖𝑓 𝑊𝑇𝑃∗ ≥ 𝑦3
Where y represents unobserved threshold parameters that outline the interval where utility
difference falls and the 𝑊𝑇𝑃∗represents the utility difference.
Now based on the probability that the difference on utility falls between the proposed and
existing weather index based product is represented by;
𝑃(𝑊𝑇𝑃𝑖 = 1) = 𝑃(𝑊𝑇𝑃𝑖∗ ≤ 𝛾1 )
= 𝑃(𝑍𝑖𝛽 + 𝜀𝑖 ≤ 𝛾1 )
= 𝑃(𝜀𝑖 ≤ 𝛾1 − 𝑍𝑖𝛽)
= Φ(𝛾1 − 𝑍𝑖𝛽)=1- Φ [𝑍𝑖β – 𝛾]
Similarly, the probability that 𝑦𝑖=2 is;
𝑃(𝑊𝑇𝑃𝑖 = 2) = 𝑃(𝛾1 < 𝑊𝑇𝑃𝑖∗ ≤ 𝛾2 )
= 𝑃(𝛾1 < 𝑍𝑖𝛽 + 𝜀𝑖 ≤ 𝛾2 )
30
= 𝑃(𝜀𝑖 < 𝛾2 − 𝑍𝑖𝛽) - 𝑃(𝜀𝑖 < 𝛾1 − 𝑍𝑖𝛽)
= Φ(𝛾2 − 𝑍𝑖𝛽) -- Φ(𝛾1 − 𝑍𝑖𝛽)
= Φ(𝑍𝑖𝛽 − 𝛾1 ) - Φ(𝑍𝑖𝛽 − 𝛾2 )
And the probability that yi=3 is;
𝑃(𝑊𝑇𝑃𝑖 = 3) = 𝑃(𝑊𝑇𝑃𝑖∗ ≥ 𝛾2 )
= 𝑃(𝑍𝑖𝛽 + 𝜀𝑖 ≥ 𝛾2 )
= 𝑃(𝜀𝑖 ≥ 𝛾2 − 𝑍𝑖𝛽)
= Φ(𝑍𝑖𝛽-𝛾3 ) =1 - Φ[u2 - ziβ]
Where, pis a probability operator. Provided all these probability density functions for 𝜺𝒊, the
unknown model parameters that can be estimated by maximizing the following log likelihood
function;
𝑙(𝛾1 𝛾2 , 𝛽) = ∑ 𝑊𝑇𝑃i=1 log[Φ(𝛾1 − 𝑍𝑖𝛽)] + ∑ 𝑊𝑇𝑃i=2 log[Φ(𝛾2 − 𝑍𝑖𝛽) − Φ(𝛾1 − 𝑍𝑖𝛽)] +
∑ 𝑊𝑇𝑃i=3 log[Φ(𝑍𝑖𝛽) − 𝛾2 )]
The effects of changes in explanatory variables on the probability of WTP falling in a given
range are not explained by the estimated coefficients (Green, 2008) in case of probit models.
It is therefore, the effects of explanatory variables are expressed in terms of marginal effects
which can be derived as follows;
𝝏𝑷(𝑾𝑻𝑷=𝟏|𝒛)
𝝏𝒛 =-∅𝒁𝒊𝜷)𝜷
𝝏𝑷(𝑾𝑻𝑷=𝟐|𝒛)
𝝏𝒛 =(∅(−𝒁𝒊𝜷) − ∅(𝜸 − 𝒁𝒊𝜷))𝜷
𝝏𝑷(𝑾𝑻𝑷=𝟑|𝒛)
𝝏𝒛 =(∅(−𝒁𝒊𝜷) − ∅(𝜸 − 𝒁𝒊𝜷))𝜷
𝝏𝑷(𝑾𝑻𝑷=𝟒|𝒛)
𝝏𝒛 =∅(𝜸 − 𝒁𝒊𝜷))𝜷
The marginal effect is the slope of the curve that relates an explanatory variable to
P(WTP=j|z)controlling the effect of other variables (Long, 1997). The sign of marginal effect
is not required to be same as that of coefficients (Long, 1997).
31
3.5. Variable Definitions and Hypothesis
Dependent variable
Using a cheap talk method, respondents were informed about the hypothetical weather index
insurance services, which may require them to pay for weather index insurance. Based on
multiple bid design three bid categories were provided to the respondents. The respondents
were then asked to choose a category where his/her true WTP falls. Thus, the dependent
variable takes 1, 2, and 3 in line with their orderings.
The independent variables
Based on review of theoretical and empirical works, the following Socio-economic
characteristics of the households and institutional factors were considered in the model.
Age of the household: Age is continuous variable defined as the age of the head of farm
household at the time of interview measured in years. According to the study by Patrick
(1988) the age of the household was found to have negative effect on the demand for
insurance. Gineet al. (2007) also found that young farmers are more likely to purchase
insurance than elders. Therefore, in this study it is hypothesized that young farmers are more
likely to purchase insurance than elders.
Sex of household: This is measured as a dummy variable taking the value of 1 for male
headed household and 0 otherwise. The sex of the household head was included to
differentiate between male and female household heads in their participation of making a
decision on income distribution. In this study, it is hypothesized that male head households
are likely to purchase the insurance service than female head households. Therefore, it is
expected to affect willingness to pay for weather index based insurance positively. According
to Wan(2014) also found a significant relationship between gender and breeding sow
insurance uptake in China, but Danso-Abbeam et al.,(2014), did not observe any statistica l ly
significant relationship between gender and cocoa insurance uptake in Ghana.
32
Crop diversification: It is a continuous variable, which represents the diversification of
crops of the respondents in crop index unit (CDI). Farmers, diversify crops mainly to reduce
the risks associated with farming. A study conducted by Ginder and Spaulding (2006) found
that diversification increased participation in crop insurance in Northern Illinois, USA,
because crop insurance was considered as a coping mechanism for food security, production
and market risks. However, Sherrick et al. (2004) found that undertaking both livestock and
crop production and the reliance on off-farm income by farmers represented a form of
diversification, which contributed to the stability of overall income thus reducing the demand
for crop insurance in the USA. Furthermore, Knigh and Coble (2006) found that farmers who
practiced crop diversification were less likely to participate in crop insurance. In this study,
crop diversification was expected to negatively influence farmers‟ decision to participate in
the WIBI scheme in Huye District. This is because farmers diversify their enterprises as an
alternative way informally cope with weather-related risk.
Awareness of weather-index insurance: This is a dummy variable measured as 1 if the
farmer is aware of weather index insurance and 0 if not. The lack of information is a major
constraint to the purchase of insurance. Farmers who are aware of insurance have much more
information than farmers who do not. A positive relationship is thus expected between the
awareness of insurance and the willingness to purchase weather index insurance (Danso-
Abbeam et al., 2014). According to Alam, (2010) in Bangladesh confirms that understanding
and awareness about insurance was found as one of the key determinants and drivers of
insurance adoption.
Off-farm income: It is income from other non-farming income. It is a continuous variable
measured in Birr. A study conducted by Sukuraiand Readon (1997) showed that respondents
who received high amount of income from other non-farm activities are not interested in
participating in drought insurance. Therefore, households who have less amount of off- farm
income are expected to be more willing to pay for weather index based insurance.
Family Size: It is a continuous variable measured in number of people living under one roof.
Higher family size is accompanied with larger household expenditure, which consequently
33
depletes household cash resources. Sukurai and Readon (1997) have shown that as size of
household increase, demand for insurance decrease. In this study, size of household was
expected to have negative effect on the willingness to pay for the weather index based risk
insurance.
Education status of the household: Itis a continuous variable taking number of years of formal
education. Education may increase farmers’ ability to use information as well as practice.
Education has been shown to be positively related to farmers’ willingness to pay for soil and
water conservation practices (Paulos 2002). Therefore, it was hypothesized to have a positive
influence on farmers’ willingness to pay for weather index based insurance.
Saving money: This is a dummy variable takes the value as 1, if households save their money
0, otherwise. The result from the study by Aidoo et al., (2014) showed that farmers who used
savings as a coping strategy were likely to pay more for insurance. Therefore, in this study
saving money is expected to have a positive influence on the willingness to pay for weather
index based insurance.
Access to microfinance: It is dummy variable, which takes the value of 1, if the household
has credit access and 0 if not. A study conducted by Gineet al. (2007) indicates that insurance
participation was higher when households are less credit constrained. In this study, access to
credit was expected to have a negative effect on the demand for insurance and willingness to
pay for it.
Extension service access: It is a dummy variable, which takes a value of 1, if the farmer has
access to extension service and 0 otherwise. Access to extension service indicates to the
availability and existence of technical advices to smallholder farmers in the study area. In
this study, it was hypothesized that expected to affect willingness to pay positively.
Livestock holding (TLU): It is a continuous variable which represent livestock holding of
the respondent in tropical livestock unit. It was expected to influence the willingness to pay
of the household head either positively or negatively. In this study, it was expected to have
34
negative influence on the willingness to pay for index based insurance based on the study
conducted by Hiwot and Ayalneh (2014)
Ownership of radio of the household: This variable is a dummy variable, which takes the
value of 1 if household has radio and 0 otherwise. Radio is a source of information and can
enhance the ability of farmers’ access to different sources of information such as extension
service, credit service, use of new technologies, improved seed varieties, input price, output
price, crop protection, post-harvest handling techniques than those farmers don’t possess
radio. Thus, farmers who have radio might be able to have information earlier than those who
do not have. Therefore, in this study it was hypothesized that ownership of radio to affect
willingness to pay for index based insurance positively. A study conducted by Hiwot and
Ayalneh (2014) indicates that the households who had radio were more willing to pay for
rainfall index insurance.
Farm size: This refers to the area of plot of land allotted for crop production. The unit of
measurement for area is also different in different parts of the country; hence the data was
changed to hectare for smoothness. Accordingly, the hectare of plot of land used for crop
production was used in the analysis. According to the study conducted by Mebratu (2014),
households with large land size have less willingness to pay for insurance.
Land certification: This is also a dummy variable measured as 1 if the household is certified
and 0 otherwise. A study conducted by Abugri et al., (2017) the influence of land ownership
on WTP is positive. This finding agrees with the findings by Holden and Shiferaw (2002)
who concluded that land ownership is likely to increase farmers’ willingness to pay for
agricultural insurance since it guarantees security of tenure for them. In this study having
land certification were expected to have positive effect on the demand for insurance and
willingness to pay for it.
Table 3: Variables and their measurement included in the model
Description of Variable Unit of measurement Hypothesis
Age of household head Years -
Sex of the household 1=male, 0=female ±
Family size (No. of people living in one roof) In persons +
35
Education status of the household head Number of years of formal education +
Farm size Total land size in ha +
Access to microfinance 1=Yes, 0=No +
Access to Extension service 1= service user 0 ther wise +
Livestock asset measured in tropical livestock unit -
Diversification of crops Measured in crop index ±
Awareness of weather index based insurance 1=Yes , 0=No +
Land certification 1=Yes, 0= No +
Off-farm activity 1=Yes, 0=No -
Saving money 1=Yes, 0= No +
Own radio 1=Yes, 0= No +
4. RESULTS AND DISCUSSION
This chapter has two major sections. In the first section results of descriptive statistics on
socioeconomic factors is presented. In the second section results of the economic analysis on
farmers willingness to pay for weather index based insurance is presented.
4.1. Household Socioeconomic Characteristics
Regarding the continuous variables mean age of the respondent was found to be 39.61 with
the minimum 20 and maximum of 80 years. The mean age of respondents who are willing to
pay for weather-based insurance was 39.62 with 20 minimum and maximum of 80 years
while that of the not willing was 39.53 with minimum of 20 and maximum of 70 years
respectively. There was no statistically significant difference between farmers who are
willing and non-willing. The average family size was 7.6 with a minimum of 1 and a
maximum of 17 family members. The average family sizes of the willing respondents and
non-willing respondents were 7.52 and 8.16, respectively. There was no statistica l ly
significant difference between willing and non-willing respondents. The average farm size
of the respondents was found to be 2.32 with minimum of 0.25 and a maximum of 6 hectare
of land. The average TLU of the households was 5.87. The mean livestock holding of the
36
willing households was 5.89 units that of the non-willing households was 5.75 units. There
was no statistically significant difference between the two groups of households with respect
to anyone of the socio-demographic characteristics (Table 4).
Table 4: Socio-demographic characteristics of sample households
Source: own survey, 2016
From the total surveyed respondents 128 (87.1%) were willing to pay for weather index based
insurance whereas the rest 19 (12.9%) were not willing to pay for the service. Based on the
survey result, of the interviewed households 126 (85.71%) were male respondents while the
remaining 21 (14.29%) were female respondents. Out of willing respondents, 109 (85.16%)
were male respondents and 19 (14.84%) were female respondents, while out of non-willing
respondents 17 (89.5%) were males and 2 (10.5%) were female respondents. The average
education level of the sample respondents was found to be attended grade four. Out of the
total household surveyed 76.19%, saved their money. The result of chi-square test showed is
statistically significant difference in saving money of household heads between willing and
non-willing groups in favor of the latter at 5 percent probability level.
Of the total households surveyed only 91.84 % had contact with extension agents (Table 5).
There was no statistically significant difference between the willing and non-willing
households in their access to extension services. On the other hand, 47.62%, of the respondents
reported that they have engaged in off-farm activities and the rest 52.38% were not. There was
also statistically significant difference between willing and non- willing households ta 5
percent level of significant in terms of off-farm activities indicating that farmers’ engagement
in off-farm activities was high in the case of those farmers who were willing to pay for weather
index based insurances.
Variables
Willing to pay Not willing to pay Total mean
Std. Deviation t-value Mean
Std.
Deviation Mean
Std.
Deviation
Age
39.62
11.73
39.53
13.93 39.61 11.98 0.031 Education 4.35 3.22 3.58 3.04 4.25 3.20 -0.9817 Livestock Ownership (TLU)
5.89 5.06 5.75 3.31
5.87 4.86 -0.1175
Family size 7.52 3.14 8.16 3.66 7.60 3.20 0.8143
Farm size 2.32 1.55 2.37 1.46 2.32 1.53 0.1344
37
Table 5: Access to service (for dummy variables)
Variables
Categorie
s
Not willing to pay (n=19)
willing to pay (n=128) Total χ2 value
N % N % N %
Sex Female 2 10.5 19 14.84 21 14.29 0.252
Male 17 89.5 109 85.16 126 85.71 Land certification No 6 31.6 20 15.63 26 17.69 2.892
Yes 13 68.4 108 84.38 121 82.31 Saving No 8 42.1 27 21.09 35 23.81 4.026*
Yes 11 57.9 101 78.91 112 76.19 Access to credit No 2 10.5 12 9.38 14 9.52 0.025
Yes 17 89.5 116 90.63 133 90.48 Extension service No 2 10.5 10 7.81 12 8.16 0.163
Yes 17 89.5 118 92.19 135 91.84
Off-farm activity No 14 73.7 63 49.22 77 52.38 3.970* Yes 5 26.3 65 50.78 70 47.62
Source: own survey, 2016, Note: * means significant 10% probability levels,
Sources of Risk and Management Strategies Practiced
4.1.1. Risk coping mechanisms
The major risk coping mechanisms practiced by the sample households are depicted (Fig.2)
out of the total household surveyed 46.26 %, 36.73 and 33.33% of the respondents using
selling livestock to cope with risk of drought/shortage of rain fall, plant disease and
insect/pest respectively. On the other hand, 16.33%, 20.41% and 19.05% of the respondents
reported that they have used borrowing money to cope up with drought/shortage of rain, plant
disease and insect/pest risks respectively.
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00
Borow money
Sale livestock
Reduce consumption
Draw from saving
Depend on aid
Take insurance
Off-farm employment
Using Chemical
%tage of respondents
Co
pin
g m
ech
an
ism
Unexpected RF at harvesting Drought/Rainfall shortage Plant deasis Insect/Pest
38
Figure 2. Risk coping mechanisms practiced by sample households (%)
4.1.2. Risk management strategies
In order to cope with sources of risks below in table 6, rural households have developed
through time various risk management strategies, which only differ from place to place, and
among the farmers. Farmers in the study area practice using crop rotation and using tolerant
variety as a major risk management strategy. Using chemical, crop insurance, intercropping
and using irrigation were also strategies used by farmers.
Table 6: Major risk management strategies practiced by sample respondents (%)
Risk management strategy
Mechanism
Risk type
Insect/Pest Plant
disease
Drought/Rainfall
Shortage
Unexpected
RF
% % % %
Multiple cropping 0.68 - - -
Using Rotation 4.08 3.40 12.93 0.68
Using Irrigation 1.36 1.36 2.72 0.00
Crop insurance 0 0.00 8.84 3.40
Livestock & Crop diversification - - - 0.68
Ask for external support 1.36 0.68 2.72 0.00
Using Tolerant Variety 2.04 2.04 12.93 0.00
Using Chemical 14 30.61 2.04 1.36
Off-farm work - - 3.40 -
Intercropping 0.68 2.04 2.72 -
None 43.5 51.02 48.30 93.88
Source: own survey, 2016
4.2. Perception of Farmers towards Weather Index Based Insurance
The farmers were asked whether they perceive and aware of about weather index-based
insurance scheme or not. Most of the respondents answered that they are aware and
perceived about weather index-based insurance as indicated on table 7 below. Out of 147
respondents 104(70.75%) ware aware and perceived that weather index based insurance is
39
the best scheme to cope with climate induces. However, 43 (29.25%) of the respondents
were not.
Table 7:Perception and awareness of farmers towards weather index based insurance
Perception Frequency Percent
Yes 104 70.75
No 43 29.25
Source own survey, 2016
4.3. Econometric Results
Determinants of willingness to pay
Ordered probit model was used to estimate determinants of weather index based insurance.
While estimating the model the hypothesizing variables were tried for the existence of outliers,
heteroscedasticity and multicollinearity. Influential outliers were checked using STATA 14
and transformation of variables were done. Collinearity diagnosis was used to access the
presence of multicollinearity. There was no serious multicollinearity (Appendix 2). Robust
regression was used to correct for problem of heteroscedasticity.
The model was statistically significant at 1% level indicating the goodness of fit of the model
to estimate the parameters included in the model. Table 8 depicts results of the econometric
analysis of factors influencing household’s willingness to pay for weather index based
insurance. Accordingly, out of the 13 hypothesized explanatory variables, seven were found
to be statistically significant. The variables are age of the household head, family size, and
farm size of the respondent, crop index, owning radio, saving money and land certificat ion.
The sign of the estimated coefficients was consistent with the expected signs.
40
Table 8: Parameter estimates using ordered response models on stated WTP ranges
Variables Coef. RobustStd. Err.
Family size 0.149*** 0.0485
Farm size(LN) -0.445** 0.219
Education 0.0583 0.0385
Age (LNAGE) -0.993* 0.544
Livestock ownership (TLU -0.0342 0.0312
Money saving 0.887*** 0.276
Access to credit 0.585 0.435
Extension Service 0.354 0.394
Radio ownership 0.583** 0.264
Gender 0.301 0.327
Off-farm activity -0.0388 0.252
Land certification 0.598* 0.362
Crop index -1.133** 0.54
Awareness of WII -0.321 0.275
CUT1 -2.2** 2.296
CUT2 -1.408* 2.294
Number of observation = 133 Pseudo R square = 0.1816 Prob> chi2 = 0.0000 Wald chi2(13) = 55.89 Log pseudo likelihood = -102.30691
***, ** and * indicate statistical significance at 1%, 5% & 10% levels respectively
Age of household influenced the respondent’s willingness to pay negatively and it was
statistically significant at 10 percent level of significance. Earlier studies by Patrick (1988)
and Gineet al. (2007) have found similar results. Overall, as the age of household head
41
increases, the willingness to pay decreases significantly. Therefore, younger household heads
are more likely to be willing to pay for weather index based insurance compared to older
household heads. This may be explained by the fact that younger household heads have less
life experience on predicting weather conditions and tend to be risk averse. However, the
marginal effect on age shows that for each additional year in age of the respondent, the
probability of the willingness to pay for weather index based insurance increases at the first
and the second (low) level of willingness to pay but decreases at the third level (higher level
of WTP) by 0.557%, 0.458% and 1.01% respectively (Table 9). The implication could be that
households in the lower level are skeptical about the role of weather index based insurance
than those in the higher level.
The result has shown that family size variable is an important factor that influences the
respondent’s willingness to pay positively and it is statistically significant at 1 percent level
of significance. The marginal effects on family size shows a negative effect on first and second
level of willingness to pay, while it is positive for the third level of willingness to pay. This
shows that families with large family size have high probability of paying weather index based
insurance. Thus, the larger the household, the higher the WTP for insurance. This agrees with
the findings of Hill et al. (2013) that members of larger inter-dependent communities are more
likely to purchase insurance.
The result showed that saving money influences the respondent’s willingness to pay positive ly
and it was statistically significant at 1 percent level of significance. The marginal effects of
saving money shows a negative effect on first and second level of willingness to pay, while it
is positive for the third level of willingness to pay (Table 9). This shows that respondents who
are saving their money have probability of paying higher- level weather index based insurance.
This is in line with findings by Aidooet al., (2014) but contradictory to that of
Ramasubramanian (2012) who observed a negative effect of savings on farmers WTP for
insurance.
Ownership of radio by the household is another important factor, which is positively and
significantly significant at 5 percent level of significance, which is related to farmers’
willingness to pay for weather index based insurance. Radio enhances the ability of farmers’
42
access to information about risk management strategies allowing them to make informed
decision. The marginal effect on this variable shows that farmers who own radio have less
probability of paying the first and second (low) level of (WTP) but have high probability of
paying the third level of WTP, Hiwot T. (2010).
The coefficient for farm size had a negative and significant effect at 5 percent level of
significance. Suggesting that farmers having larger landholdings have less probability of
paying for weather index insurance. The marginal effect of land holding show a positive effect
on first and second level of WTP categories, while it is negative for higher WTP value (level
3).Thus, the individuals who have large size of land has less probability of paying for weather
index insurance, Mebratu(2014).
Crop index has negative and statistically significant influence at 5 percent level of significance
on the WTP of farmers. However, the marginal effects for the first and second two categories
(i.e. WTP=1 and WTP=2) are positive whereas it is negative on third level of willingness to
pay (WTP=3). The more respondents grow more crops the less probability of paying for
weather index insurance, which is the characteristics of the first and second category of
farmers.
The result has shown that land certification is an important factor that influences the
respondent’s willingness to pay positively and it was statistically significant at 10 percent level
of significance. This is in line with findings by Abugri et al., (2017).The marginal effect for
the first and second two categories (i.e. WTP=1 and WTP=2) have positive effects, but a
negative effect on third level of WTP. This variable also shows that farmers that possess land
certification have high probability of paying for weather index based insurance than those
farmers who do not possess.
43
Table 9: Marginal effects of ordered probit models on stated WTP ranges:
Variables
P(WTP=1) P(WTP=2) P(WTP=3)
Mean dy/dx dy/dx dy/dx
Family size -0.0301*** -0.0253** 0.0554*** 7.6 (0.0103) (0.00985) (0.018)
Farm size 0.0898** 0.0755* -0.165** 2.3 (0.0456) (0.0401) (0.0811)
Education -0.0118 -0.0099 0.0217 4.3
(0.008) (0.00681) (0.0144) Age (LNAGE) 0.201* 0.169* -0.369* 39.5
(0.115) (0.0971) (0.203) Livestock ownership (TLU) 0.0069 0.0058 -0.0127 6.0
(0.00618) (0.00553) (0.0115) Money saving -0.230*** -0.110*** 0.340*** 1.2
(0.0864) (0.0333) (0.102) Access to credit -0.152 -0.0761** 0.228 1.1
(0.139) (0.0374) (0.17) Extension service -0.0844 -0.0528 0.137 0.9
(0.108) (0.0497) (0.156) Radio ownership -0.120** -0.0949** 0.215** 0.5
(0.0578) (0.0442) (0.0956) Gender -0.0684 -0.047 0.115 0.8
(0.0818) (0.0475) (0.128) Off-farm activity 0.00784 0.00659 -0.0144 0.5
(0.0506) (0.043) (0.0936) Land certification -0.15 -0.0818** 0.232 1.144
(0.108) (0.0391) (0.141) Crop index 0.229** 0.193* -0.421** 0.4
(0.114) (0.0994) (0.202) Awareness of WII 0.0648 0.0545 -0.119 0.7
(0.0558) (0.0485) (0.102)
44
Note, The numbers in the Parentheses are robust standard errors
* p<0.05, ** p<0.01, *** p<0.001
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1. Summary and Conclusion
The main objective of this study was to identify factors affecting smallholder farmers’
willingness to pay for weather index based insurance in Central rift valley of Ethiopia.
This study tried to look in to socio economic, institutional and physical and other related
factors, which can affect farmers’ willingness to pay for weather index, based insurance.
Data were collected from 147 farm households drawn randomly from Adamitulu
jidokomblocha and Bora districts using structured questionnaire. Both descriptive statistics
and econometric model were employed to analyze the data. Contingent Valuation Method
(CVM) was employed to elicit farmers WTP for weather index based insurance.
The result from descriptive analysis showed that selling livestock as the major risk coping
mechanisms when they faced drought/shortage of rainfall. In addition to selling livestock,
borrowing money, reduced consumption, taking insurance, off-farm employment and draw
from saving were the main risk copping mechanism practiced by farmers.
The farmers have practiced different types of risk management strategies like using crop
rotation, tolerant varieties majorly, intercropping, off-farm employment, Using Chemical,
crop insurance, and using irrigation.
45
Of the total sample households 128 (87.1%) were willing to participate and the rest 19
(12.9%) were not willing to participate. Fourteen potential explanatory variables were
hypothesized to explain farmers’ willingness to pay for weather index based insurance. The
result of ordered probit model revealed that only seven potential explanatory variables were
used to identify willingness to pay among selected sample households at different
significant levels.
Age of household influenced the respondent’s willingness to pay negatively and it was
statistically significant at 10 percent level of significance. Therefore, younger household
heads were more likely to be willing to pay for weather index based insurance compared to
older household heads. However, the marginal effect on age shows that for each additiona l
year in age of the respondent, the probability of willingness to pay for weather index based
insurance increases at the first and the second (low) level of willingness to pay by 0.557%
and 0.458% respectively but decreases at the third level (higher level of WTP) by 1.01% .
The family size variable was an important factor that influences the respondent’s
willingness to pay positively and it was statistically significant at 1 percent level of
significance. The marginal effect on family size variable showed a negative effect on first
and second level of willingness to pay, while it is positive for the third level of willingness
to pay. This shows that families with large family size have high probability of paying
weather index based insurance.
Saving money influenced the respondent’s willingness to pay positively and it was
statistically significant at 1 percent level of significance. The marginal effect of saving
money show a negative effect on first and second level of willingness to pay, while it is
positive for the third level of willingness to pay. This shows that respondents who are
saving their money have high probability of paying for weather index based insurance.
The farm size had a negative and significant effect suggesting that farmers having larger
landholdings have less probability of paying weather index insurance. The marginal effect
of land holding size showed a positive effect on first and second level of WTP categories,
46
while it is negative for higher WTP value (level 3) Thus the individuals who had large size
of land had less probability of paying for weather index insurance.
Ownership of radio by the household is another important factor, which is positively, and
significantly affected at 5 percent level of significance related to farmers’ willingness to
pay for weather index based insurance. Radio enhances the ability of farmers’ access to
information about risk management strategies allowing them to make informed decision.
The marginal effect on this variable shows that farmers who own radio have less probability
of paying the first and second (low) level of (WTP) but have high probability of paying the
third level of WTP.
5.2. Policy Implication
The overall understanding of factors affecting smallholder farmers’ willingness to pay for
weather index based insurance would help policy makers and development workers to
design and implement the weather index based insurance service in sustainable and in
effective manner. Based on the findings of the study, the following points are suggested to
be considered as an important element in order to implement the service and enhance
farmers’ weather index based insurance utilization and effectiveness in the country.
Development policies should focus on the different Medias to create awareness and
understanding among farmers. Future policies promoting weather-index related insurance
mechanism should focus on farmer and non-farmer specific factors. Policy actions should
focus on the type of responses of the different categories of farmers.
47
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7. APPENDIX
7.1. Appendix 1. Tables
Appendix 1: Conversion Factor for Tropical Livestock Unit (TLU)
No. Animal Category Tropical Livestock Unit
(TLU)
1 Ox 1.1
2 Cow 1
3 Heifer 0.5
4 Bull 0.6
5 Calves 0.2
6 Sheep 0.01
7 Goat 0.09
8 Donkey 0.5
9 Horse 0.8
10 Mule 0.7
11 Poultry 0.01
Source: Storck, et at. (1991)
Appendix 2: Collinearity Diagnostics
VARIABLES VIF R2
FAMLYSZ 1.55 0.3566
FARMSZ 2.07 0.518
EDUHH 1.43 0.3027
AGE 1.8 0.4459
TLU 2.03 0.5084
SAVEMONEY 1.18 0.1496
CREDIT ACCESS 1.23 0.1871
EXTENSION SERVICE 1.09 0.081
OWN RADIO 1.28 0.2183
55
7.2. Appendix 2. Survey Questionnaire
HOUSEHOLD SURVEY QUESTIONNAIRE ON WEATHER INDEX INSURANCE
GENERAL INFORMATION
Date of interview Location: zone District Kebele
Name of enumerator Agro-climatic zone (1=Dega, 2= w/dega, 3= kola)
1. Name of the respondent Position in the household:_______
1) Head 2) Son/daughter 3) Spouse 4) Relative 5) others specify _______________
2. Age of respondent 3. Sex of respondent 1) male 2) female
4. Educational level__________ years completed, 0=illiterate
5. Marital status 1= Married 2= Single/unmarried 3= Divorced 4= Widowed
6. Experience in farming __(years) 7. Social position in the community so far? 1= Yes 0= No
8. If yes to Q7, type of responsibility__1) kebele leader 2) simple farmer 3) others specify
9. Do you have off farm income? 1) Yes 2) No, If Yes, How much is your off-farm income
in Birr/ year __________
10. Do you know the availability of weather station in the area? 1=Yes 2=No
GENDER 1.42 0.297
OFF-FARM ACTIVITY 1.35 0.2592
LANDCERTFKT 1.36 0.2672
CROP INDEX 1.41 0.2905
AWARENESWII 1.28 0.2198
Mean VIF 1.46
56
10.1. If yes, distance from home to the nearest weather station ______km or
(in walking minutes)
11. Do you have land certificate _______ 1) Yes 0) No
HOUSEHOLD CHARACTERISTICS AND COMPOSITION
Sex disaggregated data on family size
Sex of family
member Total 0-14 15-65 above 65
Male
Female
Total
OWNERSHIP OF ASSETS
Land ownership tenure system (2014/15)
Land category Total ha
Irrigated
land out of total (in ha)
Rented out ha
Shared out ha
Crops grown
1=tef, 2=H. bean, 3 Maize, 4=barley,
5=wheat, 6=sorghum, 7= horticultural crops
Season
1 main season 2=off
season 3=both)
1. Total own land
(2.1+3+4 +5)
X X
2. Cultivated (2.1+2.2+2.3)
X X
2.1 Own X X
2.2 Rented in X X
2.3 Shared in X X
3. Grazing land X X X X
4. Fallow land X X X X
5. Homestead X X X X
Livestock ownership
Type of
livestock
1=Yes
2= No
Number
available
Numbers
Sold out this
year
Number
Lost/died
If sold
Average
price?
Ox
Cow
Bull
57
Heifer
Calf
Sheep
Goat
Donkeys
Chicken
Bee hives
Mules
Horse
Others specify-______
Ownership of other assets
Item 1=yes
2=No
Number/quantity Estimated value
(Birr)
1.House type
Grass roofed
Corrugated iron
2. Water tanks
3. cart (animal draw)
4. Sickle
5. Sprayer
6. Wheel barrow
7. Bicycle
8. Tractor
9. Motorbike
10. Radio
11. TV
12. Farm tools (shovel, hoe etc…)
13. Water pump
14. Others specify
CROPS PRODUCTION IN 2014/2015 CROPPING YEAR
1. How many plots of farm land do you have in 2014 (2006/07 EC) , indicate the area
& plot characteristics
Plot
No.
What is the size of each
plot? (total
Average distance from
home (in
Are you the owner of this plot?
1= Yes 2=No
How did you acquire this plot? 1=inheritance 2=own
(purchased)
If plot is rented, how much did you
pay in cash for the last
58
area in
ha)
km or
min)
3=rented 4=government/
communal/ cooperative 5= shared in
cropping
year?
2. What was the amount of output you harvested last year (2006/07E.C)?
Crop
type
Area in
(ha)
Production
in (Qt)
Amount
sold in (qt)
Saved as
seed in (qt)
Amount
Consumed in (qt)
Amount left/
reserved in qt
Teff
Wheat
Barley
Maize
H.Beans
Sorghum
UNION MEMBERSHIP
1. Do you belong to any agricultural cooperative union? (1=yes 0=no) - ---If yes, answer the
subsequent questions
Name of cooperative
you have participated
Type of farmer’s
cooperative union
Codes A
The union function
Codes B
Position held in
cop/union Codes C
Year joined
Are you comfortable with the services
received from the union Codes D
Group name Group Function position year Satisfaction
Codes A Codes B Codes C Code D
1=Input supply cooperative
2=Producer marketing cooperative 3=Local
administration 4=Saving and credit
cooperative 5=Welfare/funeral cooperative
6=Water user’s cooperative
1=Produce marketing 2=Input access/marketing
3=Seed production 4=Farmer research group 5=Savings and credit 6=
Welfare/funeral club 7=Tree planting and nurseries 8=Soil &
water conservation 9=Faith-based organization 10= Input credit 11=Other,
specify………
1= chairman
2=Vice chairman 3= treasure
4=Secretary 5=Member
1= comfortable
2=not comfortable 3= partly no
say
59
7=Other,
specify…………….
Membership to local institutions/organizations (Social capital)
Are you member of the following institutions (either or both)?
Institution type 1. Yes 2. No
Iddir
Iquib
Farmer association
Farmer cooperative/credit and saving
Development group (1-to-----)
Others
2. Number of people you rely on for help in times of critical needs, such as crop failure?
1) Relatives in the village __________ 2) Relatives outside the village______
3) non-relatives in the village 4) Non-relatives outside the village 5)
others ___________ (eg. gov’t food handouts)
60
INDEX BASED WEATHER INSURANCE PARTICIPATION AND DEMAND FOR INSURANCE
Knowledge of insurance, Sources of Information and participation
1. Are you aware of any agricultural or crop insurance schemes available? (1=Yes 0=No) ------------------
2. Have you ever heard that insurance is one of the best schemes to cope with climate- induced risk in agricultural production in your
locality? 1=Yes 0=No
3. If yes to Q2 above
If yes, which
insurance schemes do you
know? Codes A
Main source of
insurance information
Codes B
Have you ever
participated (1=Yes 2=No)
If NO, why?
Codes
C
If YES, year first
participated
If YES,
for how many
years
For which crop did
you participate?
Why did you
prefer to be insured
for this crop?
Codes D
Did you buy the
policy in 2014/15
(1=Yes 2=No)
If No, give
reasons
Codes
C
If yes, which
insurance scheme did you
buy in 2014/15
Codes A
knowledge
information participation reason 1
Participation List of crops
Reason of selection
pay reson2 Preference 2
Codes A Codes B Codes C Codes D
1=MPC (multi peril crop insurance)
2=WII (weather index
insurance) 3=SPCI (single peril crop
insurance) 4=NPCI (Named Peril
Crop Insurance)
1=MoA/extension, 7=Neighbour/relative 2=farmers union
8=Radio/newspaper/TV 3=NGOs 9=On-farm
trials in own farm 4=Agricultural exhibitions 5=Farmer organizations
6=On-farm trials in another farm 10= from the nearby agri. Research center 11. Other,
specify…...……
1=policy not available 6= high premium price 2=No money to buy policy
7=No market for output 3=drought resistant variety
8=I don’t trust them 4=used intercropping 9=Not enough land
5=Lack information 10= used crop diversification
1= staple food 2= cash crop 3= most prone to drought
4= disease & pest incidence is high
5= others…..
61
4. If No. to Q2 above
2. Do you know institutions working in the area of insurance (be it crop insurance or WII)? 1= Yes 2= No; If yes, have you
received any support from any of these organizations? 1= Yes 2=No; If Yes, please name & specify their specific roles in the
insurance transaction process.
If not yet,
do you want to
participate? 1= Yes 2= No
If No,
ask why?
Codes
A
If Yes, in
whichschemes you want to
participate?
Codes B
For which
crop did you
participate?
Whydid
youprefer this crop
to be insured?
Codes C
Which
way ofdelivery
do you like?
Codes D
will you buy
the policy this season
1= Yes 2= No
If no
give reasons
Codes
A
If yes, how
much do you like to pay
2014/15
Codes E
How
often do you like
pay? Codes F
Willingness No
will 1
will 1 List of
crops
prefer Mode of
del.
Will 2 No
will 2
pay frequency
Codes A Codes B Codes C Codes D Codes E Codes F
1= is member of social self net program
2= informal insurances can be used
3= small pieces of land 4= don’t trust the insurers 5= has no attitude toward insurance
6= has no perception against risks
7= other specify
1=MPC (multi peril crop
insurance) 2=WII (weather
index insurance)
3=SPCI (single peril
crop insurance)
4=NPCI (Named Peril Crop Insurance)
1= staple food 2= cash crop
3= prone to drought
4= disease & pest incidence is high
5= others…..
1=agricultural cooperative
2=direct from insurance company
3=local chief 4=funeral organization
5=development agents
6=NGOs 7=micro finance offices
8= others specify
1= 100 birr/year
2= 200 birr/year
3= 300 birr/year 4=
400birr/year
1= weekly 2= every two
week 3= monthly
4= every four moth 5= every six
month 6= yearly
62
No. Name Type of support
1
2
3
4. With the existing risk scenario what is your willingness to pay at the 5. What is the amount of crop land you would like
beginning of the year for insurance coverage of crop failure to insure at the following premium rate/annum?
(assume total coverage)/kert
6. If the weather index insurance is birr for a growing season; how frequently in a season are you willing to pay the amount? 1.
Once in a season, 2. Two times in a season 3. Three times in a season 4. Four times in a year 5. Others specify (eg. in the form of
food for work)
Range of premium Willingness to insure crop land area (kert)
100-200 Birr/ season
200-300 Birr/ season
300-400 Birr/ season
400-500 Birr/ season
Farmer’s own premium
Price offer/premium/ Willingness (1=yes 2=no 3=indifferent 4=may be yes
5=maybe no )
No. of policies
100 Birr/hectare
200 Birr/ hectare
300 Birr/ hectare
400 Birr/ hectare
Farmer’s own premium
SOURCES OF INCOME
Your sources of income during the past 12 months (2006/07 E.C) and which three are the most
important?
Income source 1=Yes
2= No
Value in
Birr
If yes
If yes, Rank three Most
important sources of income
(1st , 2nd , 3rd)
Vegetable Crop sales
Other crops sales Cattle/oxen sales
Sheep/Goat sales Poultry sales
Daily labor/masonry Trade
PSNP(Productive Safety Net Program)
Remittances From mining (sale of stones and sand… etc)
Relief aid Sales from firewood, brick making, charcoal making etc
Regular employment Others (specify)
Total income (in 2006)
Access to finance
1. Do you save money? 1) Yes 0)No
2. If no, why?________________________________________
3. If yes, where do you save if you have some money?
1. Banks 2. Home 3. Credit and saving association 4. Cooperative
4. How do you normally finance input (fertilizer, seed, chemical) costs?
1. Own finance, 2. Loan from money lender3. Loan from credit and saving association 4.
From sale of livestock 5. Borrow from fellow farmers 6.others specify ____________
5. Which month of a year is financing needed (financial support)?
1. January – March, 2.April – June 3. July – September 4. October – December 5. ________
6. Is there any credit sources in your area? 1) Yes 2) No
7. If yes, how do you judge sources of credit you know? (Please rank 1, 2, 3)
Criteria of credit
source
Money
lenders
Credit
groups
Insurance
company
Microfinance
1. Unfavorable terms
2. Require collateral
3. High interest rate
4. Bureaucratic
5. Unfavorable loan
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repayment
6. First Insured one
8. Experience with formal credit used since the last 10 years_____________year
9. Experience with informal credit used since the last 10 years_____________years
10. What was the maximum loan amount you have acquired so far? ___Birr (if it is in kind try
to change to birr)
11. What types of collateral do you normally provide for formal credit?
1. No collateral, 2. Land 3.Group guarantee 4.Jewelry 5.Consign or/guarantor 6.Others
specify____
12. What types of collateral do you normally provide for informal credit?
1. No collateral, 2.Land 3. Group guarantee 4. Jewelry 5.Consign or/guarantor 6. Others specify
___
13. In which month are you expected to pay back loans?
1. January-March, 2.April – June 3. July -September 4. October - December 5.
(Other)_______
14. Have you ever experienced any default on your credit? 1) Yes 0. No
15. If Yes, why? 1. Crop failure 2.Livestock failure 3.Market failure 4. Other reasons ____
16. If Yes, which year?
17. Did you apply for credit (both cash and kind) in the last cropping year? (1=yes 0=no)-----
18. If NO, Why didn’t you apply for credit?
1=sufficient income 2= no collateral 3=previous debt 4=have own sufficient funds
5=other specify ______
19. If Yes, please give some details
Form of credit
applied
Person/
Institution applied from?
Amount
borrowed Birr
Interest rate
(%annually)
Purpose of borrowing
(1=cash 2=in-
kind)
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Access to Extension Service
1. Did you have any contacts with extension agents in the last year (2014/15)? 1= Yes, 2= No
2. How often did the extension agents visit you in the 2014/15 cropping season?
1) Weekly 2) Every two weeks 3) Monthly 4) Quarterly 5) once in a year 6)Other specify
3. Did you get any training on agricultural insurance schemes and advise? 1= Yes 2= No
If yes, who gave you the training? 1) Extension officers 2) Insurance companies 3)
Research institutions 4) Unions/cooperatives 5) others specify
4. How did you perceive the training and advises you got? 1) Very useful 2) useful
3) Not useful
5. Did you get any advisory service from extension officers (especially agricultural insurance)
1=Yes 2= No If no, why?
Risk and copping strategies
1. Which years in the past 10 do you recall as best, normal and bad for crop production?
(1=best year 2=normal year 3=bad year)
2. Please indicate your experience of typical year for best, normal and bad in the past 10
yearsand tell us the yield obtained against each crops produced by the household
Crop
type
Last season Best yield
obtained
Normal yield obtained
Worst yield obtained
Area(ha) Production(qt) Typical year
Qt/kert Typical year
Qt/kert Typical year
Qt/kert
Teff
Wheat Barley
Maize H.Beans
Sorghum
3. In a good season which month does rain fallstart ____________ and finish ___________
4. In a bad season which month does rainfall start _____________ and finish ___________
5. What do you mainly grow 1)Tef2) barley 3) wheat 4) Maize 5) Sorghum 6) H/bean 7) others
specify?
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
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Crop type Tef Barley Wheat Maize Sorghum H/bean
Total area planted last season (ha)
Total area affected last season due
to bad weather condition (in %)
6. Rank the following production risks according to their severity/negative impact to your production?
Risk type
Rank (1st–5th)
Risk Management
mechanism/s (Codes A)
Coping mechanism/s
(Codes B)
Perception about the risk factor in
future (Codes C)
Insect Pests
Plant Diseases
Rainfall–drought/shortage
High temperatures
Excessive rainfall
Unexpected rainfall at
harvest
Frequency of droughts
Frequency of floods
Excessive frost at harvesting
Code A
0. None1. Plant multip le
crops 2. Use rotations 3. Irrigation
4. Crop insurance 5. Livestock and crops diversification 6. Ask for
external support 7. Tolerant varieties 8. Intercropping
8.weather index insurance
9. Others specify _____________
Code B
0. None 1. Borrow money 2.Sale
livestock /assets 3. Reduce consumption 4. Draw from saving 5. Increase daily
labor 6. Depend on aid 7. Migrate 8. Take insurance 9.Ask for external support 10. Off-farm employment 11. Go for credit
12.others
Code C
1) increasing
2) same 3) decreasing
7. Which periods in the growing season are the most critical to have rainfall for a successful harvest?
1=Not important, 2=somewhat important, 3=very important, and critical
Planting Emergency/germination & leaf
dev.) Vegetative ( stem elongation)
Flowering Maturation
8. At what stage is drought affecting you more? 1) Time of land Preparation
67
2) Time of emergence 3) Seedling time 4) Flowering time 5) Others specify
9. What is the degree of drought when it happens? 1) Very high 2) high 3) medium
4) low 5) very low
10. On average, how often is the occurrence of drought at your area? 1) 1-2 years interval
2) 3-4 years interval 3) 4-5 years interval 4) 6-7 years interval 5) others specify
11. If the risk is drought, which of the crops is most prone? 1) Tef 2) barley 3) wheat
4) Maize 5) Haricot bean 6) Sorghum 7) others specify
Access to weather information
1. Do you have any access to weather forecast information? 1=Yes 2= No
2. What type of weather information do you have access to?
a) Start of rain b) End of rain c) Amount of rainfall d) drought/dry spell occurrence e) Wind f) Floods g) other (specify…………..)
3. Are these forecasts 1) seasonal 2) monthly 3) Ten days 4) Daily 5) other specify
4. From where are you getting weather information?
a) District Meteorological station b) Radio c) Local (indigenous) weather forecasting d) Extension officer e) Village meeting f) Researchers g) Village elders (IK) h)
NGOs working in our area i) Television j) Friends, family, neighbors k) Others; (specify)
5. From the weather information source mentioned above, which one do you consider most
reliable and adequate?
a) District Meteorological station b) Radio c) Local (indigenous) weather forecasting
d)Extension officer e) Television f) Researchers g) Unions/cooperatives h) Others
(Please specify)
6. How would you like to receive future weather forecast information? (Rank them)
a) District Meteorological station b) Radio c) Local (indigenous) weather
forecasting d) Extension officer e) Village meeting f) Local Newspapers g)
Researchers h) Village elders (IK) i) NGOs working in our area j) Television k)
Friends, family, neighbors l) others; (specify)………………………
7. Judging from experience, letting weather forecasts and information influence your crop
related decisions has been: 1) Extremely useful, 2) Useful 3) Not useful
8. Did you use any of the advice and information about when to plant crops from the above
sources that you mentioned 1=Yes 2= No
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Weather variability perception
9. Which coping strategy practices have you used in managing:-
(a) Erratic rainfalls
0) wait and see the outcome 1) Stopping urea application 2) thinning 3) Traditional water
conservation practices 4) improved water harvesting practices 5) others (specify) _____
(b) Drought
1) Planting drought tolerant crops 2) Retention of selected breeds 3) migrating to other places
4) purchase of feed 4) Conservation of water 5)Irrigation 6)others (specify)
__________________
10. What do you think can be done to improve the participation of insurance?
……………………………………………………………………………………………………
…………
11. What do you think can be done to improve delivery system of insurance?
………………………………………………………………………………………………
Any other suggestion
Over the last 10 years have
you Noticed
[Change]?
Adaptations you practiced for the last 10 years you noticed
Have you made adaptations to cope with
long-term Shifts in
rainfall?
1=YES 2=NO
1=YES 2=NO
a No change in rain a Changed crop variety
Less rain b Built a water harvesting
scheme
More rain c Bought insurance
d More frequent droughts d Irrigated more
e More frequent floods e Changed from crop to livestock
f Delay in the start of the
rainy seasons
f Increased number of
livestock
g The rainy seasons end sooner
g Reduced number of livestock
h No change in number of hot days
h Migrated to another area
Increase in hot days i Found off-farm jobs
Decline in hot days j Leased your land
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