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

6. REFERENCES

Alam .J.M., Nazneen.M and Chowedhury S.F. 2010. Demand for weather index insurance in

coastal Areas of Bangladesh.

ARD (Agriculture and Rural Development World Bank). 2011. Weather index insurance for

agriculture: Guidance for Development Practitioners.

Aidoo, R., J.O. Mensah, P. Wie and D. Awunyo-vitor.2014. Prospect of Crop Insurance as a

Risk Management Tool among Arable Crop Farmers in Ghana. Asian Economic and

Financial Review 4(3): 341-354.

Akter, S. and N. Fatema. 2011. The role of microcredit and micro insurance in coping with

natural hazard risks. Contributed paper, 18th Annual Conference of the European

Association of Environmental and Resource Economists, June 29 – July 2, Rome.

Alberini, A., Alberto Longo, StefaniaTonin, Francesco Trombetta and Margherita Turvani.

2003. The Role of Liability, Regulation and Economic Incentives in Brownfie ld

Remediation and Redevelopment: Evidence from Surveys of Developers, FEEM

Working Paper 7, Milan, January. Available at www.feem.it

Albererini, A., K. Boyle, and M. Welsh.2003. Analysis of contingent valuation data with

multiple bids and response options allowing respondents to express uncertainty.

Journal of Environmental Economics and management, 45: 40-62.

Ali, Akhter. 2013. Farmers’ Willingness to Pay for Index Based Crop Insurance in Pakistan:

A Case Study on Food and Cash Crops of Rain-fed Areas.

Alderman, H., and T. Haque. 2008. Insurance Against Covariate Shocks, The World Bank:

Working paper #95.

48

Anderson, J. R., Dillon,J. L., and Hardaker, J.R., 1977. Agricultural decision analysis: Lowa

State University press.

Anderson, D.R.. 2001. All Risks Rating Within a Catastrophe Insurance System. Journal of

Risk and Insurance,43: 629-651.

Anderson, J., F. and Brown, R., L. FSA. 2005. Risk and Insurance: Education and

Examination Committee of the Society of Actuaries, pp.21-05.Second printing, Printed

in U.S.A.

Arrow, K., Solow, R. Portney, P.R. Leamer, E.E. Radner, R. and Schuman, H. 1993. Report

of the National Oceanic Atmospheric Administration (NOAA) Panel on Contingent

Valuation. Federal Register, 58(10): 4602–4614.

Asafu-Adjaye, J. 2000. Environmental Economics for Non-Economists. World Scientists

Publishing Company Ltd, New Jersey, USA, pp.101 - 130.

Barnett, B. 2010. Challenges with Developing Weather Index Insurance for Rural Areas of

Lower Income Countries. Weather Derivatives and Risk, Collaborative Research

Center,27-28 January 2010. Risk, Humboldt University of Berlin.

Barnett, B. and O. Mahul. 2007. Weather Index Insurance for Agriculture and Rural Areas

in Lower Income Countries. American Journal of Agricultural Economics, 89:1241-

1247.

Bennet, J. and Blamey, R. 2001. The Choice Modelling Approach to Environmenta l

Valuation. Edward Elgar Publishers. Ltd. UK,pp. 1-69.

Binswanger-Mkhize, H. 2012. Is there too much hype about index-based agricultura l

insurance? Journal of Development Studies, 48(2):187–200.

doi:10.1080/00220388.2011.625411.

Boehlije, M.D. and V.R. Eidman.1994. Farm Management. Wiley, New York.

Bokusheva R. and S. Conradt. 2012. Catastrophic crop insurance effectiveness: does it make

a difference how yield losses are conditioned? Paper prepared for the 123rd EAAE

Seminar.

Brookshire, D. and Whittington, D. 1993. Water Resource Issues in the Developing

Countries. Water Resources Research, 29(7):1883–88.

49

Budak D. B; Budak, F and Kaciraoo.2010. Livestock producer’s needs and willingness to

pay for extension service in Adana province of Turkey. Journal of Agricultural

research, 1187-1190.

Carter, M., Galarza, F., & Boucher, S. 2007. Underwriting area-based yield insurance to

crowd-in credit supply and demand. Savings and Development, 19–21March 2007.

Research Center on International Cooperation of the University of Bergamo

Chankova, S., Sulzbach,S., and Diop,F. 2008. Impact of mutual health organizations :

evidence from West Africa. Health and Policy Planning, 223:264-276.

Chantarat et al. 2009. Willingness to pay for index based livestock insurance: results from a

field experiment in northern Kenya.

Danso-Abbeam, P., Addai , K.N. and Ehiakpor , D. 2014. Willingness to Pay for Farm

Insurance by Smallholder Cocoa Farmers in Ghana. Journal of Social Science for Policy

Implication, Vol (2) No. 1 Pp 163-183

Debraj Ray.1998.Development Economics, Princeton University Press

Demel Teketel. 2002. Evolution and strategic plan of agricultural research in Ethiopia. Paper

presented at the second committee of directors’ retreat of the Association for

Strengthening Agricultural Research in Eastern and Central Africa (ASARECA),

Kigali, September 30–May 4

Dennis Holden, Peter Hazell, Anthony Pritchard, 1991. Risk in Agriculture. Proceedings of

the Tenth Agriculture Sector Symposium. World Bank Washington, D.C. USA.

Dillon J.L and J.B. Hardaker, 1993. Farm management research for smallholder farmers’

development. FAO Farm Management Series. FAO, Roma Italy. (6):302

Dimitri, C. and Green, C. 2002. Recent Growth Patterns in the U.S. Organic Foods Industry,

U.S Department of Agriculture, Economics Research Service. Agriculture Information

Bulletin No. 777.

Dunlap, A. 2006. Insurance basics. NC State University: College of Agriculture and Life

Sciences.

Eyob Meherrete. 2009. Innovations in insuring the poor, Providing weather index and

indemnity insurance in Ethiopia.

50

Financial Consumer Agency of Canada. 2011. Understanding Insurance Basics. Retrieved

from http://www.globalpacific.com/system/files/UInsurance-eng.pdf (Accessed 10

April 2016).

FAO (Food and Agricultural Organization). 2014. Rural infrastructure and Agro Industries

division.

Giné , X., Townsend, R. and Vickery, J. 2008. Patterns of rainfall insurance participation in

rural India. http://wber.oxfordjournals.org.

Gunatilake, H., J.-C. Yang, S. Pattanayak, and K.A. Choe. 2007. Good Practices for

Estimating Reliable Willingness-to-Pay Values in the Water Supply and Sanitation

Sector. Economics and Research Department, Asian Development Bank, 17.

GuushBerhane, D. Clarke, S. Dercon, R.V .Hill, and Alemayehu Seyoum Taffesse. 2013.

Insuring against the Weather: ESSP RESEARCH NOTE 20.

Hanemann, M.W., Loomis, J.B. and Kanninen, B.J. 1991. Statistical Efficiency of Double -

Bounded Dichotomous Choice Contingent Valuation. American Journal of

Agricultural Economics.73 (4): 1255-1263.

Hanemann, W.M. and Kanninen, B. 1996. The Statistical Analysis of Discrete Response CV

Data. Working Paper Number 798. Department of Agricultural and Resource

Economics and Policy. University of California, USA. 123.

Hanky, N., Shogren, F. and White, B. 1997. Environmental Economics in Theory and

Practice. Palgrave Macmillan Publishers, New York, pp. 357 – 418.

Hanley, N. and Spash, C. L. 1993. Cost-benefit Analysis and Environment, Edward Elgar

Publishing Limited, England, pp. 53-56.

Hardaker, J. Banri., Ruud B.M. Hurnie and Jock R.Anderson, 1997. Coping with risk in

agriculture. CAB International London, pp.1-10.

Hazell, P. 1992. The appropriate role of agricultural insurance in developing countries.

Journal of International Development, 4(6): 567–581.

Holden ST, Shiferaw B. 2002. Poverty and land degradation: peasants’ willingness to pay

to sustain land productivity. The adoption of natural resource management practices:

improving sustainable agricultural production in Sub-Saharan Africa. New York:

CABI Publishing; p.91–102.

51

Hurley et al. 2004. “Estimating Willingness-To-Pay Using a Polychotomous Choice

Function: An Application to Pork Products with Environmental Attributes Selected

paper prepared for presentation at the American Agricultural Economics Association

Annual Meeting, Denver, 1-4 August 2004.

IAIS-CGAP (International Association of Insurance Supervisors and Consultative Group to

Assist the Poor). 2007. Issues in Regulation and Supervision of Micro-insurance.

Working Group.

Joyce, Theodore.1994. Self-selection, prenatal care, and birth weight among blacks, whites,

and hispanics in New York City, The Journal of Human Resources 29(3): 762-794

Kanninen B.J. 1993. Optimal Experimental Design for Double-Bounded Dichotomous

Choice Contingent Valuation. Land Economics, 69:138-146.

Kassie, B.T., R.P. Rötter, H. Hengsdijk, S. Asseng, M.K. Van Ittersum, H. Kahiluoto, and

H. Van Keulen. 2013. Climate variability and change in the Central Rift Valley of

Ethiopia: challenges for rain fed crop production. J. Agric. Sci. 152(01): 58–74

Kimenju, S.C. and De Groote, H. 2005. Consumer willingness to pay for Genetically

Modified Food in Kenya. Agricultural Economics, 38: 35-46.

Lusk, J.L. and Hudson, D. 2004. Willingness to pay Estimates and their Relevance to

Agribusiness Decision Making. Review of Agricultural Economics, 26 (2): 152-169.

Mitchell, R.C. and R.T. Carson. 1989. Using Surveys to Value Public Goods: The

Contingent Valuation Method. Baltimore Johns Hopkins University Press.

Monteiro Monjardino de Souza, D and Lucas, M.R.V. 2001. Conjoint measurement of

preference for traditional cheese in Lisbon. British Food Journal, 103(6): 414 424.

Moon et al. 2002. Willingness to pay for environmental practices: implications for eco-

labeling, Land Economics,78(1): 88-102

Morduch, J. 1995.Income Smoothing and Consumption Smoothing. Journal of Economic

Perspectives, 9:103-114.

MoWR (Ministry of Water Resource of the Federal Democratic Republic of Ethiopia). 2007.

Climate Change Adaptation Program of Action (NAPA) of Ethiopia. Addis Ababa,

Ethiopia.

Murphy, M., Cowan, C., Henchion, M. and O’Reilly, S. 2000. Irish Consumer Preferences

for Honey: a Conjoint Approach. British Food Journal, 102(98): 585-598.

52

NahuSenay Araya. 2011. Weather Insurance for Farmers: Experience from Ethiopia.

Conference on New Directions for Smallholder Agriculture 24-25 January 2011,

Rome, IFAD HQ.

N. Islam .2007. Can Microfinance Reduce Economic Insecurity and Poverty? By How

Much and How? Development Policy Analysis Division, Department of Economic

and Social Affairs, United Nations, New York

Nketiah-Amponsah, E. (2009). Demand for health insurance among women in Ghana: Cross

Sectional Evidence. International Research Journal of Finance and Economics,

(33):179-191

Paul Mosley. 2009.Assessing the success of micro insurance programs in meeting the

insurance needs of the poor

Quagrainie, K. 2006. IQF Catfish Retail Pack: A Study of Consumers’ Willingness to Pay.

International Food and Agribusiness Management Review, 9(2): 75-87.

Rajib S., Fuad M., and Aminul I. 2013. Disaster Risk Reduction Methods, Approaches and

Practices. Doi: 10.1007/978-4-431-54252-0.

Ramasubramanian, A. J. 2012. Willingness to Pay for Index Based Crop Micro insurance in

India. NEUDC,Darmouth College, USA.

http://www.dartmouth.edu/~neudc2012/docs/paper_19.pdf (accessed May 12, 2015).

R. V. Hill, J. Hoddinott. and N. Kumar. 2011. Adoption of Weather Index Insurance:

Learning from Willingness to Pay among a Panel of Households in Rural Ethiopia.

Roach, B., K. J. Boyle, and M. Welsh. 2002. Testing bid design effects in multiple-bounded

contingent valuation,78 (1): 121-131.

Rosenzweig, M. R., and H. P. Binswanger.1993. Wealth, Weather Risk and the Composition

and Profitability of Agricultural Investments. The Economic Journal, 103:56-78.

Singh, G. 2010. Crop Insurance in India: Indian Institute of Management Ahmedabad- 380

015, India, W.P. No. 2010-06-01.

Smith, V., & Watts, M. 2009. Index based agricultural insurance in developing countries :

Feasibility, scalability and sustainability. Gates Foundation.

Stephen A. Abugri, Joseph Amikuzuno. Edward B. Daadi. 2017. Looking out for a better

mitigation strategy: smallholder farmers’ willingness to pay for drought-index crop

insurance premium in the Northern Region of Ghana.Doi:10.1186/s40066-017-0152-2

53

Subhasis B. and Navin K.R. 2017. Signifying Development: A Search on Finance, Health

and Employment. Laxmi publishing, 2017.

Trang, N., M. April. 2013. Willingness to Pay for Area Yield Index Insurance of Rice

Farmers in the Mekong Delta, Vietnam.

USAID (United States Agency International Development). 2006. Index insurance for

Weather Risks in Lower-income Countries.

UN (United Nations). 2007. Developing Index-Based Insurance for Agriculture in

Developing countries‖, Sustainable Development innovation briefs, Issue 2.

UNDP (United Nation Development Program). 2013. Annual report in Ethiopia.

Venkatachalam, L. 2004. The Contingent Valuation Method. Environmental Impact

Assessment Review, 24: 89-124.

Vidal, J. 2003. Ethiopia’s worst famine in 20 years. The Guardian,18 April 2003.

Vossler, C.A. and Kerkvliet, J. 2004. A Criterion Validity Test of the Contingent Valuation

Method: Comparing Hypothetical and Actual Voting Behavior for a Public

Referendum. Journal of Environmental Economics and Management, 45:631–49.

Wan, W. 2014. Farmers’ willingness to pay for breeding sow insurance: evidence from

China’s Hubei Province. A thesis submitted for the requirement of an MSC degree in

Agricultural Economics, University of Kenturcky, USA

Welsh, M. P. and G. L. Poe. 1998. Elicitation effects in contingent valuation: comparison to

a multiple bounded discrete choice approach. Journal of Environmental Economics

and Management, 36: 170-185.

World Bank. 2007. World Development Report: Agriculture for Development . Washington

D.C.: The World Bank.

World Bank. 2005. Managing agricultural production risk innovation in developing

countries. Report No. 32727-GLB. The International Bank for Reconstruction and

Development. Washington, D.C.

World Bank.2000. World Development Report 2000/01: Attacking Poverty.

Yesuf, M. and Bluffstone, Randy.2008. Risk Aversion in Low-Income Countries:

Experimental Evidence from Ethiopia. International Food Policy Research Institute.

Washington, DC.

54

Yoo, S. & Yang, H. 2001. Application of sample selection model to double- bounded dichotomous choice contingent valuation studies, Environmental and Resources Economics 20: 147–163

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

64

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)

65

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

66

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

68

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

69

........................................................................................................................................................

......................................................................................................................