PAYMENT FOR ENVIRONMENTAL SERVICE TO ENHANCE RESOURCE

USE EFFICIENCY AND LABOR FORCE PARTICIPATION IN MANAGING

AND MAINTAINING IRRIGATION INFRASTRUCTURE, THE CASE OF

UPPER BLUE NILE BASIN

A Thesis

Presented to the Faculty of the Graduate School

of Cornell University

in Partial Fulfillment of the Requirements for the Degree of

Master of Professional Studies

By

Habtamu Tilahun Kassahun

August 2009

© 2009 Habtamu Tilahun Kassahun

ABSTRACT

Using the contingent valuation method, this research project explores how

irrigation beneficiary households in the Upper Blue Nile Basin of Africa value

irrigation water to enhance agricultural productivity. Research in this area is

important because soil degradation and sedimentation threaten the livelihoods of many

populations in the region. Furthermore, mitigation measures require continual large

investment costs both in terms of human capital and financial resources. The research

encompasses the analysis of data collected from 210 randomly selected household

heads in the Koga Watershed of the Upper Blue Nile Basin in Ethiopia.

The research reported herein has two major objectives. The first objective is to

explore the value of irrigation provided to households as an initial step towards the

development of a payment for environmental services (PES) program. Under this

broad objective, there are two specific goals. The first is to estimate households’

willingness to pay (WTP) to establish PES for upland soil and water conservation

measures that ultimately reduce sedimentation loading in the newly constructed

reservoir. The model results revealed that the aggregate expected WTP for the total of

7,000 hectares of irrigable land was 964,320 birr per year (9.65 birr equal $1 U.S.)

with a household utility-maximizing price of 192 birr per hectare of irrigable land per

year. The aggregate WTP was more than three times the annual budget allocated by

the Koga Irrigation and Watershed Management project to reduce sedimentation loads

(caused by upstream soil erosion) by 50 percent over the past 6 years. Thus, the

aggregate expected WTP by downstream users has a potential to compensate upstream

service providers and enhance resource use efficiency.

The second major objective of this research is to examine the magnitude and

determinants of labor supply behavior of farm households for the routine management

and maintenance of irrigation infrastructure in the Upper Blue Nile basin of Ethiopia.

For the total irrigable land area it is estimated that households could contribute an

estimated 468,784 person labor days per year. This would meet more than 30% of the

minimum annual labor requirement of the project for managing and maintaining of

irrigation infrastructures. A logit model analysis indicated that households’

willingness to contribute labor was influenced by education, age of the household

head, expectations about yields in irrigated agriculture, wealth of the household,

involvement in off-farm activities, time taken to walk to the nearest market, the

household’s dependency ratio and randomly assigned bid working days. Of these

determinant factors, an intervention measures for managing and maintaining irrigation

infrastructure through labor force participation should emphasize education about the

likely benefits of irrigated agriculture. To increase labor participation particularly for

new development projects, description of resource valuation scenario and future

benefits should be clearly explained to farmers. Furthermore, the number of person-

days allotted for conservation activities per hectare of irrigable land should take into

account the high elasticity of households’ willingness to contribute for the randomly

assigned bid working days.

iii

BIOGRAPHICAL SKETCH

Habtamu Tilahun Kassahun was born and raised in Ethiopia. Because of his

family’s work, he traveled and lived in various regions of the country. He obtained his

diploma in Veterinary Medicine with distinction in 1999 from Addis Ababa

University. From 2000 to 2001, Habtamu served as Assistant Veterinarian in one of

the remote and rural area of the Western Gojjam administrative zone of Amhara

National Regional State, Bureau of Agriculture. There, in addition to the routine tasks

of helping to prevent and treat animal diseases to improve livestock productivity,

Habtamu had a great opportunity to understand the real rural face of Ethiopia and the

pervasive nature of rural poverty.

While working as an assistant animal health instructor at Woreta College of

Agriculture, he received a BA degree in Economics with great distinction from Bahir

Dar University in 2007. Soon after, he applied for the new Masters program of

Integrated Watershed Management and Hydrology offered by the field of International

Agriculture and Rural Development at Cornell University on the engineering campus

of Bahir Dar University. Given the multidisciplinary nature of the program, Habtamu

has an interest in the application of economics to watershed issues. His agricultural

background proved valuable during both his coursework as well as during his field

research.

In his thesis, Habtamu addressed the issue of water disputes in a sub-watershed

context using prospective payments for environmental services scheme in the Blue

Nile Basin. He believes that the relationships between water users both locally and

globally should be governed by benefit-sharing. The ongoing dispute between Egypt,

Sudan and Ethiopia should also account for the external benefits that can be generated

along the Blue Nile River among different stakeholders.

iv

I would not have started this master program without Emebet Gizachew, who

drew my attention to this opportunity while we were celebrating our undergraduate

commencement day on July 7, 2007. She was a brilliant member of this program and a

close friend of mine for more than 9 years. I lost her life and love in a tragic car

accident last year. I dedicate this work to her.

v

ACKNOWLEDGEMENTS

It has been a privilege and a motivation to work with senior scientists from

Cornell University. I greatly acknowledge and thank the excellent guidance and

expertise of my supervisors, Professor David R. Lee, who provided the main scientific

framework and direction for this thesis, and Dr. Charles F. Nicholson, who made a

substantial contribution towards the completion of this thesis and furnished many

outstanding suggestions. His contribution and friendly approach are unforgettable.

I also greatly acknowledge Professor Gregory L. Poe (Cornell University) and

Professor Angela Neilan (Virginia Tech University) for their interest and contribution

during the early stages of my work particularly in developing the survey instrument.

Professor Tammo S. Steenhuis, who is a Director and principal investigator of

the Cornell Masters Program in Ethiopia, has made a great contribution towards the

success of my research. He was always eager and willing to help as Program Director

and as an advisor. I always found an image of my parents in his personality.

I am thankful to Tigist Alemayehu, who is expert at the Environmental

Protection Agency at Merawi, Ethiopia, for her valuable help during the field visit.

I would like to express my sincere thanks and gratitude to Dr. Amy S. Collick,

who generously provided me her precious time whenever I needed her help over the

past two years, in addition to her challenging duties as a coordinator of Cornell

University Masters Program at Bahir Dar University. She has been also personally an

enormous help for me.

It has been also my great fortune to have great Agricultural Development

Agents in the study area. In particular, I wish to cite Abita Genet, Asrat Ambelu,

Zeyitie Telayneh and Amare Gebeyehu. Without their help it would have been

difficult to get to a single farmer’s house.

vi

This thesis would not have been possible without financial support from

Cornell University, and transport services from the Koga Irrigation and Watershed

Management Project. I will not also forget my deepest gratitude to my brother Tesfaye

Tilahun, who covered lots of costs beyond those budgeted to enable the successful

completion of this thesis.

I want to acknowledge the Environmental Protection Agency at Merawi that

made land distribution data available and arranged experts to help me during the

reconnaissance survey.

I express my gratitude to the Ethiopian Economic Association and Ethiopian

Economic Policy Research Institute for providing me training on the latest STATA

software, which was a vital component of this thesis.

I also thank the Bureau of Agriculture and Rural Development, Woreta College

of Agriculture, for allowing me to pursue the masters degree program and for their

financial support.

I do not dare to imagine how things would have gone without Netsanet Alelign,

my wife. No words can express the deep gratitude I feel towards her for keeping me

physically, mentally and emotionally alive.

I want to convey thanks to those persons who, directly or indirectly, have

provided support in my research work and whose names I may have forgotten to

mention here.

A very special thanks goes to my dear friends and family: you were very

patient with me! Thank you for this and also for your support, love, and

understanding.

Finally, I thank God for his wonderful mercies to enable me complete my

studies successfully.

vii

TABLE OF CONTENTS

BIOGRAPHICAL SKETCH ............................................................................................. iii 

ACKNOWLEDGEMENTS ................................................................................................ v 

TABLE OF CONTENTS .................................................................................................. vii 

LIST OF FIGURES ............................................................................................................ ix 

LIST OF TABLES .............................................................................................................. x 

CHAPTER ONE: ................................................................................................................. 1 

PROJECT BACKGROUND, OBJECTIVES, ORGANIZATION AND SCOPE OF

THE STUDY ....................................................................................................................... 1 

CHAPTER TWO: ................................................................................................................ 4 

BACKGROUND, SITE DESCRIPTION, DATA SOURCES AND COMPILATION

METHODS .......................................................................................................................... 4 

CHAPTER THREE: .......................................................................................................... 12 

THE ECONOMICS OF ENVIRONMENTAL RESOURCE VALUATION - A

CONCEPTUAL FRAMEWORK FOR INTEGRATED WATERSHED

MANAGEMENT .............................................................................................................. 12 

CHAPTER FOUR: ............................................................................................................ 19 

PAYMENT FOR ENVIRONMENTAL SERVICES TO ENHANCE

ENVIRONMENTAL PRODUCTIVITY IN THE UPPER BLUE NILE BASIN ............ 19 

CHAPTER FIVE: .............................................................................................................. 63 

APPLICATION OF THE CONTINGENT VALUATION METHOD FOR LABOR

FORCE PARTICIPATION IN MANAGING AND MAINTAINING IRRIGATION

INFRASTRUCTURES ...................................................................................................... 63 

REFERENCES .................................................................................................................. 87 

APPENDIX ....................................................................................................................... 96 

viii

APPENDIX 1: QUESTIONNAIRE PREPARED FOR IRRIGATION BENEFICIARY

HOUSEHOLDS, KOGA WATERSHED, UPPER BLUE NILE BASIN, ETHIOPIA .... 96 

ix

LIST OF FIGURES

FIGURE 1: KOGA IRRIGATION AND WATERSHED DEVELOPMENT MAP ................................. 7

FIGURE 2: PARTIAL VIEW OF IRRIGATION COMMAND AREA LANDSCAPE AND

INTERVIEWED HOUSEHOLDS IN AMBO MESK ( A) AND ENGUTI KEBELE (B)

(SOURCE: SATELLITE IMAGE EXTRACTED FROM GOOGLE EARTH PRO AND OWN GPS

SURVEY DATA). ............................................................................................................. 9

FIGURE 3: INCONSISTENT RESPONSES BETWEEN DICHOTOMOUS CHOICE AND THE

FOLLOW UP OPEN ENDED QUESTION ............................................................................. 49

FIGURE 4: DISTRIBUTION OF “YES” RESPONSE AND AVERAGE MAXIMUM WTP FOR THE

DIFFERENT INITIAL BIDS ............................................................................................... 52

FIGURE 5: THE RELATIONSHIP BETWEEN EXPECTED AND PREDICTED PROBABILITY OF

WTP WITH BID VALUE ................................................................................................. 59

FIGURE 6: EXPECTED WORKING DAY CONTRIBUTION AND BID WORKING DAY TREND ......... 84

x

LIST OF TABLES

TABLE 1: OUTLIER IDENTIFICATION AND IMPUTATION ....................................................... 10

TABLE 2: SUMMARY OF EXPECTED SIGNS AND DESCRIPTIVE STATISTICS FOR SAMPLE

HOUSEHOLDS (N = 190) ................................................................................................ 47

TABLE 3: LOGIT PREDICTION OF HOUSEHOLD’S WILLINGNESS TO PAY TO SUPPORT

UPLAND SOIL AND WATER CONSERVATION PRACTICES FOR HOUSEHOLDS WITH

POSITIVE EXPECTATION FOR IRRIGATION FARMING AND WITHOUT 9% POSITIVE

EXPECTATION FOR IRRIGATION FARMING. .................................................................... 56

TABLE 4: DESCRIPTIVE STATISTICS SAMPLE HOUSEHOLDS (N = 198) ................................ 79

TABLE 5: LOGISTIC REGRESSION MODEL FOR WILLINGNESS TO CONTRIBUTE LABOR FOR

MANAGING AND MAINTAINING IRRIGATION INFRASTRUCTURE ..................................... 81

 

1

CHAPTER ONE:

PROJECT BACKGROUND, OBJECTIVES, ORGANIZATION AND SCOPE

OF THE STUDY

PROJECT BACKGROUND

The Koga Irrigation and Watershed Management Project is the first attempt by

the Government of Ethiopia to develop a large-scale irrigation scheme for rural

farmers. The African Development Bank (ADB) had financed a feasibility study and

technical proposal for watershed management and irrigation development in the Koga

watershed between 1992 and 1995. The project started with the construction of

infrastructure in 2002 and remains under construction with an expected completion

date of 2010. The Koga Irrigation and Watershed Management Project will harness the

water resources of the Koga River to irrigate approximately 7,000 ha of the command

area as well as to improve rain-fed agriculture, forestry, livestock, soil conservation,

and water and sanitation on some 22,000 ha of the upstream catchment area (ADF,

2001; personal communication, Koga Irrigation and Watershed Management

Representative).

The project area is experiencing rapid population growth and there is no

additional land to be brought into cultivation. Indeed, some of the land currently

farmed is located on steep slopes, which exacerbates soil degradation in the upper part

of the watershed. Ideally, this land should be returned to permanent vegetation cover

(personal observation and communication, Koga Irrigation and Watershed

Management Representative and other experts). If current trends in land use continue,

erosion from farmland will result in the soils becoming too shallow, thus undermining

reliable rain-fed cropping and increasing the siltation of the reservoirs used for

2

irrigation (Ministry of Natural Resources and Environmental Protection, 1995a,

1995b). Therefore, the protection and sustainable management of the watersheds is

important to stabilize the physical and biotic environment for the effective functioning

of the ecosystem, to sustain and improve the quality of life and to intensify

productivity in the area.

OBJECTIVES, SCOPE AND ORGANIZATION

To implement these management schemes in a watershed, collaboration and

integration of the government and various stakeholders are required. This research

project explores how beneficiary households of an irrigation project value irrigation

water in terms of labor and cash contributions to enhance agricultural productivity and

ensure sustainability of the resource base on which agriculture fundamentally depends.

In addition, this research uses a contingent valuation approach to generate information

for optimum decision making using both labor and money contribution as payment

vehicles.

Although watersheds provide various goods and services, this study focuses

explicitly on the value of irrigation water to its users. The contingent valuation

approach estimates the willingness to pay of irrigation beneficiary households to

support soil and water conservation practices in the upstream part of the watershed.

Contributions of cash and labor from irrigation beneficiary households have the

potential to reduce sedimentation loading in the reservoir and better sustain common

irrigation channels, if sufficient compensation is provided to upstream households who

would undertake much of the conservation activity. This study did not explore the

cash payments that upstream households would require to undertake soil and water

conservation practices. Nevertheless, this study provides a useful starting point for the

3

development of a payment for environmental services program. In addition, this study

explores the factors affecting valuation of irrigation water by beneficiary households

This thesis is organized into five different chapters. Chapter Two provides a

full physical description of the Koga Watershed as well as a general description of all

data sources used in the thesis, and data cleaning processes. Chapter Three addresses

the economics of environmental resource valuation as a conceptual framework for

integrated watershed management. Chapter Four explores the household valuation of

irrigation water as an initial step towards development of a payment for environmental

services program to reduce sedimentation loading in the reservoir and to protect

associated infrastructure. Finally, Chapter Five covers the application of the

contingent valuation method for labor force participation in managing and maintaining

irrigation infrastructure to ultimately get reliable and on-time irrigation water supply.

4

CHAPTER TWO:

BACKGROUND, SITE DESCRIPTION, DATA SOURCES AND

COMPILATION METHODS

BACKGROUND

Payment for environmental services program is of potential interest in

Ethiopia, where soil degradation and sedimentation threaten the livelihoods of the

many in the rural population. The average soil loss from farmland is estimated to be

100 tons/ha/year (Holden and Shiferaw, 2002; Hagos, 2003; Nedessa et al., 2005).

The effects of land degradation in some areas of the highlands may be large enough to

offset the yield gains from technical change (WDR, 2007). Soil losses are large in

Ethiopia due to topographic characteristics (many highly sloped lands in agricultural

production), high rainfall intensity during some months of the year and low vegetation

cover. High rates of soil erosion imply that sedimentation behind newly constructed

dams is expected to be large in the absence of continuous and appropriate soil

conservation measures. Sedimentation results in damage to downstream fields, river

channels, and capital infrastructure, (dams, water systems and irrigation channels),

thereby imposing heavy maintenance costs on downstream users (Colombo et al.,

2005).

Upstream land users have little reason to account for the downstream

consequences of their land use decisions (Kerr, et al., 2001; Kerr, et al., 2006). In the

past, the most common ways of reducing the onsite and offsite effects of soil erosion

in developing countries was through government or donor expenditures on

conservation activities with little or no community participation. However, absolute

dependence on government or donor funds without community involvement to carry

out environmental conservation is unlikely to be unsustainable. The massive

5

government-led conservation campaign in the 1970s and 1980s in Ethiopia, in

collaboration with international donors’ support (food for work), is an example of

failure due to lack of community involvement (Hoben, 1995; Bekele, 1997; Sheferaw

and Holden 1998; Beshah, 2003: Carlsson et al. 2005; Desta et al., 2005; Bewket,

2007). One example in Ethiopia from the 1980s is the large Borkena Dam in South

Wello, which was constructed before sufficient soil conservation measures were put in

place. Potential runoff and sedimentation rates were seriously underestimated, and

siltation of the multi-million birr1 dam occurred within one rainy season (Desta et al.,

2005).

Because soil erosion and related sedimentation are an important and pervasive

problem in Ethiopia, this research project explores the household valuation of

irrigation water using the contingent valuation method as an initial step towards

development of a PES to reduce sedimentation load on reservoir and protection of

associated infrastructure. Furthermore, this research project examines the magnitude

and determinants of labor supply behavior of farm households for the routine

management and maintenance of irrigation infrastructure in the Upper Blue Nile basin

of Ethiopia. The specific objectives of the study, are (1) to elicit willingness to pay

(WTP) of the irrigation beneficiary households for soil and water conservation

practices that reduce sedimentation loads in the reservoir, (2) to identify the

determinants of willingness to pay for environmental services using a binary logistic

model (In addition to reduced sedimentation, a PES program may also improve

agricultural productivity in upstream areas, but this potential benefit is not examined),

(3) to elicit the willingness to contribute labor supply of the irrigation beneficiary

households to manage and maintain common irrigation channels and support soil and

water conservation activities in the nearby upstream areas, and (4) to examine the

1 Ethiopian National Currency

6

determinants of factors of farmers’ willingness to contribute labor supply for the

protection of irrigation infrastructure. The application is to the Koga Watershed of the

Upper Blue Nile Basin of Ethiopia

SITE DESCRIPTION

This research project explores how the beneficiary households of an irrigation

project in Koga watershed value irrigation water, which can be used to enhance

agricultural productivity and ensure the sustainability of the resource.

The Koga Irrigation and Watershed Management Project is located in Amhara

Regional State, south of Lake Tana in the Upper Blue Nile Basin of Ethiopia (Lat. 110

10’ N to 110 25’ N, Long. 370 02’E to 370 17’ E) (Figure 1). The project area

comprises about 34,000 ha, of which 28,000 ha are within the Koga catchment. Only

1,000 ha of the irrigation command area are located within the catchment territory.

The remaining 6,000 ha are irrigation command area outside of the watershed

boundary to the North direction. The watershed is characterized by tapered, strongly

dissected highlands to the south, and a relatively flat plateau in the north (the dam site

and irrigation command area) as illustrated by the landscape features in Figure 1. The

rate of soil loss in the furthest upstream portions of the watershed exceeds the soil

formation rate (Ministry of Natural Resources and Environmental Protection, 1995b),

in part because of the severe deforestation in the 1970s and 1980s (GTF Project,

2007). Elevation ranges between 1800 and 3200 meters above sea level. The mean

annual rainfall over the study area is 1560 mm, of which 90% falls between May and

October (Ministry of Natural Resources and Environmental Protection, 1995a, 1995b).

7

Figure 1: Koga Irrigation and Watershed Development Map

8

DATA SOURCES AND DATA COMPILATION METHOD

Between July to October 2008, data were obtained from a survey in the

irrigation command areas of the Koga watershed. Those households who have land

within the boundaries of the irrigation command area were considered for the study.

The irrigation command area extends to seven administrative districts (Kebeles) but it

occupies lower area than the administrative Kebeles. In 2007 the number of household

heads in the seven administrative Kebeles were 10,654 (FDREMWR, 2007). However,

considering the command area within the administrative kebeles, the number of

irrigation beneficiary household heads was expected to be lower than the total number

of household heads in all the Kebeles.

A two-stage random sampling method was employed for the selection of the

respondents of the study. First, from a total of seven administrative Kebeles under the

irrigation command area, two Kebeles (Enguti and Ambo Mesk ) were randomly

selected to represent the total irrigation command areas. The number of irrigation

beneficiaryhousehold heads in Enguti and Ambo Mesk kebeles were 909 and 819,

respectively.

The identities of irrigation beneficiary households were obtained from

Agricultural Development office of each Kebeles. For the reliability of the list, land

distribution data from the Merawi Environmental Protection Agency, Ethiopia, served

as a comparison. After that, using systematic random sampling, approximately 12

percent of irrigation beneficiary household heads were selected from each Kebeles.

Figure 2 represents a partial view of the interviewed households as well as the

landscape of the irrigation command area. Compared to Ambo Mesk Kebele, the

sample households’ (yellow points) were closer to each other in Enguti Kebele

because of residents’ settlement locations.

9

(A) (B) Figure 2: Partial View of irrigation command area landscape and Interviewed Households in Ambo Mesk ( A) and Enguti Kebele (B) (Source: Satellite image extracted from Google Earth Pro and Own GPS survey Data).

10

Collected data were first coded in a SPSS 16 database (www.spss.com). After

data entry processes were completed, the variables of greatest interest were aggregated

and printed out for a visual consistency check. For further data cleaning and outlier

identification, the new STATA 10 application software (www.stata.com) was used.

Each variable was examined not only for outliers but also for the general acceptability

of the figures in national and regional wise. The inconsistent values were also cross-

checked with the questionnaire to identify data entry errors.

Twelve inconsistent responses between money willingness to pay (“yes”

responses) and a follow-up question (an open-ended maximum willingness to pay

question) were removed from the analysis, because there is no convenient way to deal

with them other than removing these responses. For the labor contribution data, four

inconsistent responses were removed.

Outliers in explanatory variables were modified using a combination of list-

wise deletion and regression based imputation method. The estimated explanatory

variable values were used to impute missing values if and only if it had positive

values. If the estimated explanatory variable had a negative value it was deleted. Table

1 summarizes the outliers discovered and replaced with imputation. Table 1: Outlier identification and imputation Explanatory Variable Total

ObservationOutliers Imputed

Per capita income 210 2 1 Practical irrigation farming experience 210 6 3 Dependent ratio 210 5 0 Cultivated land per household size 210 2 1 Per capita corrugated iron sheet 210 1 0 Total

210

16

5

Surprisingly, about half of the outliers among the explanatory variables were

from households who also had inconsistencies in the dependent variable. This also

11

supports the decision to delete the inconsistent responses from the dependent and the

follow up questions.

12

CHAPTER THREE:

THE ECONOMICS OF ENVIRONMENTAL RESOURCE VALUATION - A

CONCEPTUAL FRAMEWORK FOR INTEGRATED WATERSHED

MANAGEMENT

In a well-functioning market economy with a comprehensive property rights

structure, the market allocates resources efficiently in the sense that an owner of

resources with well-defined property rights has a powerful incentive to use that

resource efficiently. Well-defined property rights are characterized by universality,

exclusivity, transferability and enforceability (Tietenberg, 1984). Universality means

that all resources are privately owned and all entitlements are completely specified.

Exclusivity assumes that all benefits and costs as a result of owning and using the

resource accrue to the owner either directly or indirectly by sale to other.

Transferability means that all property rights are transferable from one owner to

another in a voluntary exchange, and enforceability implies that property rights are

secure from involuntary seizure by others. Although it is easy to state these

conditions, most environmental resources lack these well-defined property rights

characteristics and show some characteristics of public goods. A good is “public” to

the extent that it lacks one or more of these well-defined property right characteristics.

The degree to which these characteristics are lacking contributes to market

inefficiency in the allocation of environmental resources and complicates their

valuation.

To implement different policy strategies and to correct market imperfections,

ideally the Total Economic Value (TEV) of all the benefits provided by environmental

resources needs to be computed. TEV is derived from both use value and non-use

value. The use value refers to the value that individuals drive from using

13

environmental resource, while non-use values are the values derived from

environmental resources even if individuals themselves do not use them (Birol et al.,

2006).

For sound watershed management decisions about the use of soil, water and

vegetation in a watershed (subject to local agro-climatic and topographic conditions),

environmental resource valuation is an indispensable tool. Environmental economists

have developed various methods to estimate the TEV of environmental resources. The

most common environmental valuation methods suitable in the watershed context and

potentially applicable for this research are discussed in the following sections.

ENVIRONMENTAL RESOURCE VALUATION METHODS

Environmental valuation methods are classified into two broad categories

based on the elicitation techniques used. When a valuation technique considers related

or surrogate markets in which the environmental good is implicitly traded, it is

referred as a revealed preference method or indirect valuation method. Examples of

this valuation method include the travel cost method (TCM), the hedonic pricing

method (HPM), the production function method (PFM), the net factor income method

(NFIM), the replacement cost method (RCM), the market prices method (MPM), and

the cost-of-illness method (CIM). The second category of environmental resource

valuation methods is known as the stated preference method or direct valuation

method. These comprise survey-based methods that can be used either for those

environmental goods that are not traded in any market or for assessing individuals’

stated behavior in a hypothetical setting. The method includes a number of different

approaches such as choice experiment method (CEM), contingent valuation method

(CVM) and conjoint analysis (CAM) (Birol et al., 2006).

14

Revealed Preference Methods (Indirect Valuation Methods):

Hedonic Pricing Method

The hedonic pricing method (HPM) is used to estimate economic values for

environmental services that directly affect market prices. It has most commonly been

applied to variations in housing prices that reflect the value of local environmental

attributes. The HPM is explained based on Lancaster's characteristics theory of value

(Lancaster, 1966), which states that any good can be described as a bundle of

characteristics, and that the price of the good depends on these characteristics.

The HPM was developed further by Griliches (1971) and Rosen (1974) with

the assumption of an implicit price (shadow price) for each of the characteristics of

environmental resource attributes that allows individuals to value additional units of

such resources or services. Although the theoretical explanations of HPM were

developed more fully after 1966, some early HPM studies were published in the late

1950s. Milliman (1959) and Hartman and Anderson (1962) were the first to apply

HPM to the valuation of irrigation water. The method is still widely used for different

goods (Hamilton, 2007). Recent applications of HPM that address watershed

management issues include the effect of agricultural land use and externalities on the

value of land (Ready and Abdalla, 2005), agricultural land productivity (Maddison,

2000), valuation of irrigation water (Faux and Perry, 1999), climate change (Rehdanz,

2006; Rehdanz and Maddison, 2004; Maddison and Bigano, 2003; Pendleton and

Mendelsohn, 1998), the economics of soil conservation structures (Sekar and

Ramasamy, 1998) and flood control (Miyata, and Abe, 1994).

These studies showed that HPM is versatile and can be adapted to consider

several possible interactions between market goods and environmental quality. The

main limitation of this method is that the scope of environmental benefits that can be

measured is limited to goods for which the environmental product or service has a

15

direct linkage to a market. It does measure total economic value because of its

inability to measure (non-use) values and the requirement of detailed information on

market values for each characteristic. For valuation of irrigation water from the

proposed reservoir in the Koga watershed, this method cannot be applied because the

majority of proposed irrigation beneficiary households do not know how irrigation

water really affects physical output and its market value. Furthermore, water is

considered a free resource by most households in the watershed based on historical

and cultural factors. In this situation, it is more appropriate to develop the idea of a

hypothetical market to make irrigation beneficiary households aware of the need for

maintaining and protecting irrigation infrastructure for sustainable use of irrigation

and then to ask the valuation question directly.

Travel Cost Method

The TCM was first proposed by Hotelling (1931) and subsequently developed

by Clawson (1959) and Clawson and Knetsch (1966; cited in Birol et al., 2006). It is

used to estimate the value of recreational benefits generated by ecosystems or the

environment. It assumes that the value of the site or its recreational services is

reflected in the consumption behavior of related markets. In other words, the costs of

consuming environmental services are used as a proxy for its value. The basic

premise of the travel cost method is that the time and travel cost expenses that people

incur to visit a site represent the “price” of access to the site. Thus, consumption costs

include travel cost, entry fees, and onsite expenditures outlay on capital equipment

necessary for consumption. Therefore, peoples’ willingness to pay to visit the site can

be estimated based on the number of trips that people make at different travel costs.

This is analogous to estimating peoples’ willingness to pay for a marketed good based

on the quantity demanded at different prices (Hanley and Spash, 1993; Bateman and

Turner, 1993; Birol et al., 2006).

16

The TCM yields information on the use value of a recreational site as a whole

and its attributes based on market information. With reasoning similar to that for

HPM, TCM is not applicable for the current study. Furthermore, TCM is applicable

when the expenditures for projects to protect the site are relatively low.

Production Function Method

Similar to the above two revealed preference valuation methods, PFM

measures the use value of environmental resources or services implicitly from the

traded marketed good. The basic idea of the PFM is that the value of non-marketed

environmental goods and services that serve as inputs into the production of marketed

goods can be obtained implicitly from their marginal productivity in the production of

specified marketed goods (Birol et al., 2006). For example, sedimentation affects the

productivity of irrigated agricultural crops by decreasing the amount of water available

and timing of irrigation. Thus, the economic benefits of sediment reduction can be

measured by the increased revenues from greater agricultural productivity attributable

to sediment reduction. However, to capture this economic benefit, this would require

complete market information, including the costs of implementing different

biophysical structures for sediment reduction, the marginal physical products of all

inputs used in production of specific crop, and price information. Therefore,

considering these significant data constraints, it is not possible to apply PFM in the

current study.

However, from the perspective of households that employ conservation

structures and that might implement a payment for environmental services program –

for example, in communities residing in upstream parts of the watershed – the

production function method works better in pointing out the behavior of households’

decisions to adopt conservation structures associated with economic incentives

(Shiferaw and Holden, 2000).

17

Replacement Cost Method

RCM values the costs of replacing damaged assets, including environmental

assets, by assuming these costs are estimates of the benefit flows from averse

behavior. For example, the cost of sediments in reservoirs and associated irrigation

infrastructure can be used as a proxy for benefits accruing from managed ecosystems.

This method assumes that there are no secondary benefits arising from the

expenditures on environmental protection. This approach is not relevant in the current

application.

Stated Preference Methods (Direct Valuation Methods)

Choice Experiment method

One of the direct valuation methods is the choice experiment method (CEM),

which is also based on Lancaster’s characteristics theory of value (Lancaster, 1966)

and random utility theory (Thurstone 1927; McFadden, 1974; Mansky, 1977). In this

method, individuals are given a hypothetical setting and asked to choose their

preferred alternative among several alternatives in a choice set. Each alternative is

described by a number of attributes or characteristics by incorporating price as one of

the attributes along with other attributes of importance (Hanley et al., 1998; Alpizar et

al. 2001; Colombo et al. 2005; Birol et al., 2006).

CEM is a stated preference method appropriate when environmental attributes

are easily identified and differentiated to assess the relative impacts of different

environmental management options (Adamowicz et al., 1994; Colombo et al. 2005). In

our case, we are interested in measuring the value of sustainable irrigation water flows

under a single management option. The only attribute that randomly varies is price.

Therefore, in this case, another stated preference method known as the contingent

valuation method (CVM) can be better applied to measure economic value.

18

Contingent Valuation Method

CVM is the most commonly-used stated preference method both in developed

and developing countries. It is often used to estimate both use and non-use values for

all kinds of ecosystem and environmental services. It involves asking people directly

in a survey, how much they would be willing to pay (WTP) for specific environmental

services or how much they would be willing to accept (WTA) as compensation to give

up specific environmental services. It is called “contingent” valuation, because people

are asked to state their WTP or WTA, depending on hypothetical situations that

describe specific environmental service.

In summary, each of the valuation methods commonly used to value ecological

and environmental goods and services in a watershed context has its own data

requirements and limiting assumptions. One common limitation of all revealed

preference methods for the valuation of environmental resources is that they are based

on third-party calculations of the valuation of environmental resources, as they all are

computed from the supply side and don’t reflect “equilibrium” values derived from

full-fledged demand and supply functions. This may create issues for projects

involving implementation, particularly when financial resources and labor

contributions are required from community members. Considering the data

requirements and the nature of environmental services to be valued, CVM is used for

this study. Furthermore, in rural economies of developing countries where markets are

often imperfect and where preferences cannot often be revealed through market

mechanisms, CVM can be used and justified as the preferred approach (Holden and

Shiferaw, 2002). For more detail explanation of the CVM see Chapter Four.

19

CHAPTER FOUR:

PAYMENT FOR ENVIRONMENTAL SERVICES TO ENHANCE

ENVIRONMENTAL PRODUCTIVITY IN THE UPPER BLUE NILE BASIN

INTRODUCTION

Payment for environmental services (PES) is a market-based mechanism that

links environmental service providers and beneficiaries. The central principle of PES

is that those who benefit from environmental services should pay for the benefit they

have acquired from environmental services and that those who provide environmental

services should be compensated for providing them. During the last two decades,

several countries in the world have applied PES to restore and protect watershed

services. The beneficiaries are typically water users, and the service providers are

land users upstream in the watershed.

In Costa Rica, for example, Heredia town water users and hydropower

producer La Manguera SA pay to maintain and reforest the watershed to get reliable

water supply. In Colombia, irrigation water user groups and municipalities in the

Cauca valley are paying to conserve the watersheds that supply them with water.

Similar programs have also been observed in Mexico, Nicaragua and Ecuador to

protect watershed services (Pagiola et al. 2004a; Pagiola et al. 2007). Another example

of using PES to restore watershed services was implemented in New York State in the

late 1980’s. New York City was confronted with threats to water quality due to

changing agricultural practices and growing urbanization in the Catskills Watershed,

the watershed supplying the majority of the city’s water supply (Pagiola et al. 2004b).

PES to farmers in the watershed was a more cost-effective strategy to restore water

quality compared to building a multibillion dollar filtration plant. A recent study in

20

Ethiopia also showed the potential for PES to internalize watershed externalities

(Alemayehu, et al. 2008).

Despite this potential, implementation of PES for watershed conservation (in

Ethiopia as well as elsewhere in the world) has not been as widespread as it might be,

for a variety of reasons. Among other things, application of a PES scheme requires a

detailed study of a particular environmental service (Pagiola and Platais, 2007). Such a

study should determine the potential demand by beneficiaries of the environmental

services and their potential supply by upstream land users. On the demand side, the

important information to be obtained is what specific services are generated from the

environment, who benefits, and by how much. On the supply side, the key questions

concern how services are generated, who provides them, and how the services

provided would change if the watershed were managed to make payments to service

providers. This study examines the demand for environmental services because

identification and valuation of environmental services by beneficiaries is a top priority

for implementation of any PES program.

BASIC THEORY, PROBLEM, AND EMPIRICAL REVIEW OF

CONTINGENT VALUATION METHOD

In this study, contingent valuation method (CVM) is used to elicit irrigation

beneficiary households’ valuation of irrigation water to support upland soil and water

conservation practices in the Koga Watershed. CVM is the most commonly-used

stated preference method both in developed and developing countries. It is used to

estimate both use and non-use values for all kinds of ecosystem and environmental

services. It involves asking people directly in a survey, how much they would be

willing to pay (WTP) for specific environmental services or how much they would be

willing to accept (WTA) as compensation to give up specific environmental services.

21

It is called “contingent” valuation, because people are asked to state their WTP or

WTA, depending on hypothetical situations that describe a specific environmental

service (Chilton and Hutchinson, 2003; Birol et al., 2006).

Although CVM is the most widely used non-market valuation technique for

ecological and environmental resources, it has often been criticized based on concerns

about its validity and reliability (National Oceanic and Atmospheric Administration

(NOAA) 1993; Carson et al., 2001; Whittington, 2002; Venkatachalam, 2004).

Venkatachalam (2004), for example, has extensively reviewed what is called the

“embedding problem,” which refers to the wide range of variation in WTP values

estimated for the same good depending on whether the good is valued on its own or

valued as a part of a more inclusive package. The embedding problem has been a

concern regarding the reliability of CV studies in the past, but it is possible to address

the problem through careful survey design. More importantly, a clear description of

the hypothetical scenarios enables respondents to differentiate components of

environmental good or service and thus to minimize possible embedding problems

(Mitchell and Carson, 1989; NOAA 1993).

The level of information that is provided to respondents through the definition

of hypothetical “scenarios” not only affects the nature of the embedding problem but

also the general reliability of WTP values for a specific commodity. With different

amounts of information provided, there will be a disparity of WTP values for the same

environmental service or good (Bergsrtom et al. 1990; Gebre Egziabher and Adnew,

2007). Therefore, in designing a scenario, providing a clear and comprehensive

description of the environmental good under consideration is essential. This can be

facilitated through repeated pretesting, understanding specific local conditions and

correction of the questionnaire.

22

The linkage between the CV scenario (the hypothetical private or public good)

and the choice of elicitation procedure -- “open-ended” maximum WTP valuation

questions and “close-ended,” discrete “yes” or “no” valuation questions – also

determines the effectiveness of the instrument and the data quality (Whittington,

2002). The open-ended elicitation technique involves asking individuals what is the

maximum amount they are willing to pay for a specific commodity. The close-ended,

discrete “yes” or “no” valuation technique involves asking a WTP question to accept

or reject a predetermined bids value that potentially reflects the maximum willingness

to pay amounts of the respondents for a particular good. According to Whittington

(2002), the best elicitation techniques for hypothetical private and public goods are the

open-ended maximum WTP question and a closed-ended discrete choice valuation

questions, respectively. However, the effectiveness of the closed-ended format for

public goods can be affected during the pretest if open-ended maximum WTP

questions are used to determine the range of bids to use for close-ended, discrete

choice valuation questions. Therefore, Whittington (2002) suggests that the pretest

should be done with the CV scenario and the exact valuation questions used in the

final survey. Open-ended WTP valuation questions for public goods are inefficient

because the respondent needs to know that others are going to pay for the public good

before he or she can determine what he or she would be willing to pay. And this tends

to create large number of non-responses or “protest bids” since respondents either find

it difficult to answer or do not have incentives to provide honest answers.

The above argument also implicitly notes that the comparison of mean WTP

values generated from open-ended and closed-ended discrete choice valuation

questions should not be done for public goods, since the use of open-ended valuation

questions in public goods leads to underestimation of the value of the environmental

goods or services under consideration. This conclusion is also supported by the

23

findings of Kealy and Turner (1993) whose study compared a private good and a

public good and the elicitation methods chosen. They found that there is no statistical

difference between results derived from open-ended and discrete choice techniques for

the private good but a significant difference is found in the case of the public good2.

Another problem associated with elicitation format is what is called “starting

point bias” for dichotomous choice and bidding games3 (Boyle et al. 1985;

Venkatachalam, 2004; Aprahamian et al. 2007). Starting point bias arises in the

bidding game framework and under dichotomous choice when the initial bid

influences the respondent’s final bid. There is typically high correlation between the

initial bid and the final bid (for a lower initial bid value, a lower final value).

“Hypothetical bias” is also one of the frequently mentioned problems in CVM

estimation. It may arise if respondents are not familiar with the good under

consideration, so they do not reveal their true WTP. Hypothetical WTP values

frequently are found to be greater than the real WTP values (Neill et al. 1994;

Ahlheim, 1998; Bateman et al., 1999; Carson et al., 2001).

Another problem associated with CVM is “strategic bias,” which is a problem

for the valuation of public goods. For a public good, an individual will have an

incentive not to reveal his or her true preferences when confronted with questions of

WTP. This may lead to either free-riding or overpledging (Mitchell and Carson, 1989).

Overpledging occurs when an individual assumes that her or his stated WTP value will

influence the provision of good under question, but that the stated WTP would not

form the basis for any future pricing policy. On the other hand, free riding can occur

when an individual understates his or her true WTP for a public good on the 2 For more reason and justification on the disparity of WTP on different elastration formats see Venkatachalam, (2004). 3 In the bidding game, a respondent in a CV study is randomly assigned a particular bid from a range of predetermined bids. The bid assigned may be either a low or high level bid. The respondents are then asked to respond ‘yes’ or ‘no’ to that particular bid, and the process continues until “the highest positive response is recorded” (Boyle et al. 1985; Venkatachalam, 2004).

24

expectation that others would pay enough for that good to be provided. Therefore,

careful survey design is the fundamental requirement to avoid or minimize strategic

bias. To minimize strategic bias, the NOAA (1993) recommends close-ended discrete

chose valuation questions. Furthermore, Whittington (2002) stress the importance of

questionnaire design in the valuation of public goods to address free-riding, as

mentioned above.

The disparity between WTP and WTA valuation estimates has been an

accepted phenomenon in the CVM literature, in both theoretical and empirical studies

(Venkatachalam, 2004). Venkatachalam (2004) lists various reasons why WTP values

are almost always less than those from WTA measures. These include the income

effect, the substitution effect, property rights, transaction costs, broad-based

preferences, and respondents’ unfamiliarity with the valuation experiment as well as

the good. It is possible to minimize the disparity by providing adequate time for the

respondents to understand the issue under consideration (Coursey et al., 1987, cited in

Venkatachalam, 2004).

In summary, despite the major criticisms of the CVM, many scholars and

organizations have made efforts to improve the reliability and the validity of CVM

survey methods in both developed and developing countries, and have produced

working guidelines. Many of the problems associated with CVM surveys can be

reduced by careful study design and implementation of these guidelines. Therefore,

although CVM has its limitations, it is still an effective way to value environmental

goods and services if carefully designed and implemented. Furthermore, in developing

countries where markets are often imperfect and when preferences frequently cannot

be revealed through market mechanisms, CVM can be used as one solution (Holden

and Shiferaw, 2002).

25

CVM has been the most commonly applied valuation technique in Ethiopia,

particularly for valuation of forest resources, soil and water conservation, and for

valuation of animal disease prevention programs (Swallow and Woudyalew, 1994;

Holden and Shiferaw, 2002; Asrat et al., 2004; Jebessa 2004; Tessema and Holden,

2006). However, its application for the purpose of PES development is rare, as in most

countries. Alemayehu et al. (2008) studied the willingness of downstream users to

compensate upstream users to cover the costs of land management in the upstream

area in two micro-watersheds of the Blue Nile, namely the Koga (current study site)

and the Gumara, which is located 75 km away from the Koga Watershed to the north.

The combined WTP results (for both downstream and upstream micro-watersheds)

indicated that both upstream and downstream households were willing to pay for the

proposed management scheme, but the magnitude of the financing did not cover the

required amount for upstream soil and water conservation activity. Alemayehu et al.

(2008) found that the identity of the specific watershed was a statistically significant

factor affecting the willingness of downstream households’ to compensate those

upstream. They did not discuss or interpret this result, which has two implications

relevant for this study. The first implication is that aggregation of differently located

environmental services may create issues for the implementation of PES projects.

Alemayehu et al. estimated a mean WTP for the aggregate data set, but their method

of calculation meant that the WTP in the Gumara watershed was overestimated

whereas for the Koga it was underestimated. Their findings also raised the possibility

that the determinants of WTP differ in the two watersheds, but their analysis assumed

that any variation was captured in the single watershed dummy variable rather than in

other explanatory variables. In our study, we selected the Koga watershed because in

this watershed the reservoir and other irrigation infrastructures are more than 90 %

completed. Furthermore, training, demonstration and field visits are most often

26

delivered by the Koga Irrigation and Watershed Development Project for irrigation

beneficiary households in the watershed. However, in the case of the Gumara

watershed, nothing has yet started. This may increase the possibility of hypothetical

bias compared to the Koga watershed, and consequently could lead to an unreliable

estimation of WTP. Thus this study is confined to the Koga Watershed area.

LIMITATION OF PES

Although PES schemes are highly flexible and adaptable to markets for

watershed service, carbon sequestration, biodiversity conservation and other

environmental services, PES schemes face many difficulties and limitations. These

limitations, as summarized by Mayrand and Paquin (2004), include: their common

implementation in contexts where they are not the most cost-effective method to attain

the goals established; service providers, users and the service itself are sometimes not

properly identified; they are executed without a proper monitoring or control

mechanism; the costs of environmental services are set arbitrarily and do not

correspond to studies on demand and economic valuation of the resource; and their

design may not be based on previous socioeconomic or biophysical studies. In

conclusion, PES schemes are in their very early stages of development and consequently

the transaction costs remain very high. Transaction costs are expected to be very high

particularly in developing country contexts because of variations in infrastructure and the

institutional framework, imperfect information, and other factors.

DESCRIPTION OF THE STUDY AREA AND PROJECT BACKGROUND

Study Area and Project Background

The study area, the Koga Watershed, including irrigation command areas,

comprises about 34,000 ha, of which 28,000 ha is within the physical boundaries of

27

the Watershed (Figure 1). The watershed is characterized by tapered, strongly

dissected highlands to the south, and a relatively flat plateau to the north, including the

dam site and irrigation command area. The rate of soil loss in the furthest upstream

portion of the watershed exceeds the soil formation rate (Ministry of Natural

Resources and Environmental Protection, 1995b), in part because deforestation in the

1970s and 1980s was severe (GTF Project, 2007).

With support of the Ethiopian government and the African Development Fund,

the Koga Irrigation and Watershed Development Project has been working on the

development of irrigation infrastructure as well as on other watershed development

issues since 2002. The Koga Irrigation and Watershed Development Project covers

about 7,000 ha of irrigable land, and 22,000 ha of land watershed management in the

upstream part of the watershed. The watershed management component has been

working on livestock development, crop production, soil conservation, forestry

development, agricultural extension, health and sanitation promotion, and water

supply with total investment cost of 29,544 million birr4 since 2004. Of this, the

project allocated a total of 720,000 birr for six years for soil and water conservation, in

order to reduce sedimentation loads by 50% over a five- year period and extend the

project life to 50 years. Specifically, the budget has been used to purchase equipment

and materials for soil and water conservation work; there is no payment for labor. In

addition, about 30,000 birr per year (for a period of six years) has been allocated for

the training of farmers and agricultural extension workers on soil and water

conservation practices. Due to delays in implementation, the irrigation infrastructure (

the canal system) is still under construction and it is expected to be completed in

2009/2010. Irrigation agriculture is not practiced in the watershed yet due to the delays

4 1 US dollar equals to 9.65 Ethiopian Birr

28

in completion of the project activities. However, irrigation is not totally new to the

area. Before the construction of the big dam, about 595 ha of land had been used for

traditional small-scale irrigation agriculture (The Federal Democratic Republic of

Ethiopia Ministry of Water Resource (FDREMWR), 2007).

SOURCE AND USE OF DATA

Primary data on WTP of irrigation beneficiary households were collected using

random sampling procedures as discussed in Chapter Two. Information was collected

through personal interviews. The final survey sample encompassed 210 households.

A draft questionnaire for this purpose was first presented during focus group

discussions among the agencies involved in Koga watershed management and among

irrigation beneficiary households. The purpose of the focus group discussions was to

generate information that was used to refine the survey instrument for the contingent

valuation study, consistent with the guidelines in Whittington (2002). For the

agencies, the points for discussion included the current situation facing the watershed

and irrigation infrastructure, problems encountered in implementation of conservation

activities in the upstream parts of the watershed, and the activities that were at the time

incompletely implemented. This was because of insufficient funds and high yearly

expenses for watershed development activities in the upstream parts of the watershed.

For beneficiary households, the main points for discussion included: awareness of the

role of watershed protection such as forest, soil and water conservation to reduce

sediment loss and creating reliable water sources; their experiences with water

shortages for agricultural production; and methodological issues such as the

acceptable starting point and range of bids to be used to elicit willingness to pay, the

use of cash versus human labor contributions, the mode of payment of fees, and

acceptable ways of administering the revenues generated in a hypothetical market.

29

After this, the questionnaire was pre-tested repeatedly to evaluate its

effectiveness. Feedback from the pre-tests was used to revise the questionnaire,

especially in determining acceptable starting points and ranges of bids to minimize the

effect of starting point bias. The outcome of this effort is discussed in more detail

subsequently.

In the pretest and focus group discussions, we came to understand that

irrigation practices were not totally new in the area and the majority of beneficiary

households have had access to training and visits. Accordingly, the survey was

supported with illustrations to minimize the problem of hypothetical bias. As a result,

in this study we did not expect the influence of hypothetical bias on the survey results.

The head of the household was considered to be the unit of analysis for valuation of

irrigation water sustainability. Because we assumed that she or he was the ultimate

decision-maker with respect to financial matters, for public investment there might be

a possibility of joint decision making. However, to capture a spillover effect on family

decisions, we included two explanatory variables that influence household head

decisions in our model, including the highest schooling achieved within the family and

off-farm activity by any member of the household.

Secondary data were also collected from the Bureau of the Environmental

Protection Agency, the Bureau of Water Resource Development (BoWRD), the Koga

Irrigation and Watershed Development Project, and the Bureau of Agriculture (BoA)

of Amhara National Regional State (ANRS). In addition, land distribution data from

Environmental Protection Agency served as a comparison for the survey data.

30

VALUE ELICITATION FORMAT AND QUESTIONNAIRE DESIGN

Value Elicitation Format

Because protecting the dam from siltation and the irrigation canal are

considered public goods for irrigation beneficiary households, close-ended discrete

choice valuation questions (using the single-bounded dichotomous choice approach)

were used (Whittington, 2002). This method has been recommended by the NOAA

panel on contingent valuation (NOAA, 1993). It is the most popular method in the

contingent valuation literature because of its properties for incentive-compatible or

truthful revelation of preferences (Carson et al., 1996; Hanemann, 1994). According to

the NOAA panel, the most important advantages of this method are that ‘‘There is no

strategic reason for the respondent to do other than answer truthfully, although a

tendency to overestimate often appears even in connection with surveys concerning

routine market goods.’’

More specifically, in this study, the single-bounded dichotomous choice

approach with an open-ended follow-up question is applied. From market experience

in Ethiopia, the open-ended follow-up question is also the most frequent way to

bargain between buyers and sellers (Warolin, 1998; Asrat et al 2004). For example,

when the buyer is not interested in buying the commodity at the specified bid price,

the seller asks the buyer to tell him his maximum WTP for the specified commodity.

In the single-bounded dichotomous choice approach, the respondents are asked to state

only “yes” or “no” to a single bid from a range of predetermined bids that potentially

reflect the maximum willingness to pay amounts of the respondents for a particular

good (Mitchell and Carson, 1989). The follow-up question helps to identify

inconsistencies in answering closed-ended questions as well as to observe those

individuals who have positive WTP but below the proposed bid price range.

31

Furthermore, it enables assessment of whether a starting point bias exists or not in

randomly assigned bid values.

Questionnaire Design

The questionnaire was designed with careful consideration of the various

literatures referenced throughout this thesis. Considerable effort was also made to

increase the effectiveness of the questionnaire for households in the study area. To

elicit households’ valuation of irrigation water for sustainable agricultural

development, the survey visit included a brief introduction and initial background

questions, followed by presentation of the contingent valuation scenario for each

irrigation beneficiary household. Then, each household head was asked to pay a

specified amount of cash per year to keep the health of the dam and common irrigation

channels to assure a year-round reliable irrigation water supply; from seven alternative

bid values, one bid value was randomly assigned for each respondent. Finally, after

the response from the single-bounded dichotomous choice format, we asked an open-

ended follow-up valuation question and the reasons for inconsistencies, if any.

Furthermore, the result from the open-ended maximum willingness to pay follow-up

valuation question is served to check whether there is starting point bias among

different initial bids to the follow-up response. A sample questionnaire is included in

the Appendix.

MODEL SPECIFICATIONS, MEASUREMENT OF VARIABLES AND HYPOTHESES

Model Specification

In logit and probit models, the dependent variable takes on only two values that

represent the occurrence of an event (yes/no) or a choice between two alternatives. For

example, in our case, to model the choice status of each individual WTP for upland

soil and water conservation, the individuals differ in age, educational attainment,

32

experience, sex and other observable characteristics, which we denote as S. The

objective is to quantify the relationship between the individual characteristics and the

probability of household WTP for a randomly offered bid price. In the dichotomous

choice method, individuals are assumed to have utility functions, U, income (I), and a

set of conditioning factors (S):

U I; S

With the introduction of a proposed PES project, each individual is confronted

with a specified bid value, VWTP, which she/he could contribute toward assuring the

sustainability of a year-round irrigation water supply. It is assumed that the individual

will accept a suggested VWTP to maximize his or her utility under the following

condition and reject it otherwise (Hanemann, 1984):

U 1, I VWTP; S ε U 0, I; S ε

Here, ε and ε are independently distributed random variables with zero

means. Therefore, the probability that a household will decide to pay for the

sustainability of year round irrigation water supply is the probability that the

conditional indirect utility function for proposed intervention is greater than the

conditional indirect utility function for the status quo. Our dependent variable is

dichotomous, and equals 1 if the ith household is willing to pay money to support soil

and water conservation practices that reduce sedimentation loading in the reservoir,

the reservoir and 0 otherwise.

The general form of the estimation form is:

33

, , 1

where Y is the dependent variable, X is a vector of independent variables, β, is a

vector of parameters to be estimated, and ε is the error term. In practice, Y is

unobservable. What we observe is a dummy variable Y defined by

1, 0 U 1, I BID; S ε U 0, I; S ε , 0,

The probability that a household is willing to pay to assure the sustainability of

a year-round irrigation water supply is:

Pr ob 1| Pr ob 0

, 0|

,|

If the distribution is symmetric

Pr ob 1| Pr ob ,|

, , 2

where F is the cumulative distribution function (cdf). This provides an underlying

structural model for estimating the probability and it can be estimated either using a

probit or logit model, depending on the assumption on the distribution of the error

term (ε and computational convenience (Green, 2003).

34

In this study, both probit and logit models were adapted. The purpose of the

probit model is to calculate the mean WTP for the closed-ended format as stated by

Haneman et al. (1991) and to compare with the mean estimate derived from the logit

model (discussed later). The probit model mean estimation of Haneman et al. (1991)

only considers the bid values with no consideration of other factors which enables

household decision on willingness to pay. The logit model (because of its

mathematical convenience) is used to identify socio-economic factors that affect the

dichotomous choice WTP of households and enable to point out the mean willingness

to pay value associated with maximum aggregate willingness to pay. Therefore, by

choosing the logistic cdf in equation (2) for the logit model, the probability that the ith

household is willing to pay for the sustainability of year round irrigation water supply

is

Pr ob 1| , , , 3

is a linear function of n explanatory variables ( ), and expressed as:

If is the probability that the i-th household is willing to pay for the

sustainability of year round irrigation water supply, then 1 , the probability of not

willing to pay, is

11

1

Therefore, we can write

35

11

1

where 1⁄ is the odds ratio or the ratio of the probability that a household is

willing to pay for sustainability of year round irrigation water supply to the probability

that a household is not.

Taking the natural logarithm, we get the log of the odds ratio, which is known

as logit model.

4

If the error term ( ) is taken in to account the logit model becomes:

, 5

where β is an intercept which tells us the log-odds in favor of paying for the

sustainability of year-round irrigation water supply when the coefficients of all

included explanatory variable are assumed to be zero. β are slope parameters to be

estimated in the model, respectively. The slope tells how the log-odds in favor of

paying for the sustainability of year round irrigation water supply change as each

independent variable changes. Z is also referred to as the log of the odds ratio in favor

of paying for the sustainability of year-round irrigation water supply. In this study, the

above econometrics model (equation 5) is used to identify factors affecting the WTP

of a household for the sustainability of year-round irrigation water supply by using the

iterative maximum likelihood estimation procedure. To test the reliability and overall

fitness of the discrete choice model, we applied the likelihood ratio chi-square test

(Mukherjee, et al., 1998).

36

WTP Sensitivity Test Equations

From a policy point of view, the main interest for any analyst is to know what

the effect of a change in a given predictor would be on the outcome. Thus, it is

important to indicate the marginal effects in the logit and probit models, because these

differ from the reported coefficients for these models. The elasticity of the probability

that a household is willing to pay for the sustainability of year round irrigation water

supply subject to a given factor (e.g., explanatory variable) is calculated from the

expression for the partial derivative of the logistic cdf or as the discrete change in the

predicted probability when the variable of interest undergoes a discrete change. In

other words, for the derivative approach (marginal effect)

1| 1

11

11

1 , 6

This calculation is applied when is small; in other words, this can be

applied when we are interested in knowing the elasticity of willingness to pay at a

point with respect to unit changes in a continuous variable .

For the case of a dummy variable – e.g., a change from 0 to 1 – the formula is:

∆

1| , 1 1| , 0 7

The above two equations are used to explain how a change in the variable of

interest affects willingness to pay for the sustainability of year-round irrigation water

supply. The elasticity for a change in explanatory variable is computed holding fixed

the values of all variables at their sample’s mean values.

37

Mean and Aggregate WTP Estimation

Assuming the error term is distributed with mean zero and variance equal to

one, equation (2) takes the form of a probit model. The probit model in this study is

used to calculate irrigation beneficiary household’s mean willingness to pay for the

sustainability of year round irrigation water supply by regressing the willingness

variable on bid variable (Haneman et al. 1991, Gebre Egziabher and Adnew, 2007).

Then, divide the intercept ( ) by the coefficient associated with the bid value ( ). It is

also one of the reason why the probit model is used in WTP study for calculating the

aggregate and the mean WTP in a CV study. However, in this study, the probit mean

is not directly used to calculate the aggregate willingness to pay for the sustainability

of year round irrigation water supply. The probit mean is compared to the price that is

associated with the maximum expected aggregate WTP to observe how it far from the

expected aggregate WTP maximizing price. And it can be used as a measure of

aggregate WTP if and only if it has insignificant variation with the price that is

associated with the maximum expected aggregate WTP.

Assuming the probability of a household’s willing to pay for sustainable

irrigation water supply is a linear function of bid value, the following probit model is

specified to calculate the mean WTP:

Pr ob 1|

Then, mean WTP using the probit model as follows:

8

where: α is the constant term, and β is the bid coefficient.

38

Accordingly, to identify the price that was associated with the maximum

expected aggregate willingness to pay, we multiplied the probability of a household’s

willing to pay for the sustainability of year-round irrigation water supply (3) by the

amount of the price ranging from the minimum to the maximum bid price associated

with the probability . Therefore mathematically the expected willingness to pay

(EWTP) is expressed as:

BID 9

where, EWTP is expected willingness to pay.

Then, the expected aggregate willingness to pay (EAWTP) is obtained by multiplying

EWTP by the total irrigation command area (TICA) measured in Kada5; this gives:

10

Research Strategy

The main goal of this research is to elicit household’s willingness to pay to

support upland soil and water conservation practices for the purpose of assuring a

reliable year-round irrigation water supply. The response of households to the WTP

question forms the bases for our research overall strategy and predetermines the

dependent variable in the logistic regression of WTP on its determinants. The

information derived in this research should generate the demand for a sustainable

irrigation water supply, via estimation of the probability of household’s willingness to

pay to support upland soil and water conservation practices for different prices. This

provides information to decision makers on whether the funds raised from irrigation

5 1Kada=0.25ha

39

beneficiary households are enough to support the reduction of sediment loading in the

newly constructed dam. Estimating household’s WTP for a reliable irrigation water

supply also shows how it is sensitive to various factors. Among these are policy-

relevant variables which may influence implementation costs to increase the

probability of willingness to pay.

The overall estimation strategy of households’ willingness to pay to support

upland soil and water conservation practices involves three steps. The first step is the

specification and identification of variables that are likely to influence our dependent

variable, which is households’ willingness to pay to support upland soil and water

conservation practices for the purpose of assuring a reliable year-round irrigation

water supply. The identification of explanatory variables is based on the findings of

past studies, existing theoretical explanations, the authors’ knowledge of the farming

systems of the study area and farmers’ participation. Once this step is completed, the

second phase involves estimation and analysis. In the descriptive analysis, the

dependent variable (those households’ willingness to pay to support upland soil and

water conservation practices and those households who are unwilling) is used as a

category for defining willing and non-willing households for a mean difference

comparison. This gives us a preliminary indication of the difference in various

socioeconomic and demographic factors that affect household’s willingness to pay.

Finally, in this phase, we analyzed the logistic regression to examine the probability of

households’ willingness to pay and factors that affect it.

In the third and last step, we used the estimated regression model to identify

the price that was associated with the maximum expected aggregate willingness to

pay (as explained clearly in the methodology section). This gives us and, in turn,

policy makers, information about the overall capability of irrigation beneficiary

households to generate the financial resources that might be used to fund upland soil

40

and water conservation practices to reduce sediment loading in the areas of the newly

constructed dam.

Measurement of Variables and Hypotheses

Dependent Variable

The dependent variable in our WTP estimation was irrigation beneficiary

households’ willingness to pay to support upland soil and water conservation practices

for the purpose of assuring a reliable year-round irrigation water supply (WTPIW).

The variable is dichotomous; it is equals 1 if the ith household is willing to pay money

to support soil and water conservation in the upstream part of Koga Watershed, and 0

otherwise. In this dichotomous CV study, the response of households for the

hypothetical scenario is the bases for our fundamental research questions. Given that

supply of sustainable irrigation water, the dependent variable generate the demand (the

probability of households willingness to pay across different bids) for the irrigation

beneficiary households for soil and water conservation practices that reduce

sedimentation loads in the reservoir, to get reliable year round irrigation water supply.

This provides information to the decision makers whether the fund raised from

irrigation beneficiary households enough to support the sustainability of year round

irrigation water flow. Or whether there is a need for external fanatical support.

Furthermore, the dependent variable tells us how sensitive for various factors and

among several variables which variable is really policy relevant considering the cost

of implementation and to increase the probability of willing to pay to get reliable

irrigation water supply.

Explanatory Variables

It is assumed that the beneficiary household’s desire to maximize its expected

utility or profit (subject to various relevant constraints) determines its decision to vote

41

in favor of the proposed bid price. One consideration is whether the variables that

influence WTP are policy-relevant, that is, whether WTP can be influenced by various

interventions. Another issue is whether the WTP by downstream users provides

sufficient resources to compensate upstream service providers. Thus, the following 16

potential explanatory variables, which are hypothesized to influence households’

willingness to pay to support upland soil and water conservation practices, were

selected based on the findings of past studies, existing theoretical explanations, and

the authors’ knowledge of the farming systems of the study area. In addition, farmers’

participation during the interviews was also used to identify wealth indicator variables.

Bid value (VWTP) is randomly assigned price (in birr) for irrigation beneficiary

households agreed to pay or not that potentially reflect a household’s maximum

willingness to pay to get year round irrigation water flow per 0.25 ha of irrigable land.

Prices were first determined from repeated pretest and focus group discussion to

generate the true demand for sustainable irrigation water supply in the actual survey.

Accordingly, we used 7 alternative bid values to elicit irrigation beneficiary

households’ willingness to pay to support upland soil and water conservation

practices, which were 25, 31, 37, 43, 58, and 70 birr. An increase in bid value should

have a negative impact on households’ willingness to pay to support upland soil and

water conservation practices if irrigation water is considered as a normal good.

Education of household head. These were expressed as dummy variables:

EDUMMY1 (illiterate), EDUMMY2 (household head attained informal education and

is able to read and write), and EDUMMY3 (household head attained formal

education). Education increases farmers’ ability to get process information and use it.

So education is hypothesized to have a positive effect on farmers’ decisions to pay for

environmental services. This hypothesis was supported by the findings regarding

42

households’ willingness to pay in a previous environmental protection study in

Ethiopia (Tegegne, 1999).

Highest level of educational achievement within the household (FAMEDU).

The highest level of education achievement within the household is also hypothesized

to have a positive role in affecting WTP. This variable is used because education is

assumed to have a “spillover effect” on family decisions. For example, Asfaw and

Admassie (2004) separately applied the household head’s education and the highest

number of years of schooling completed by any adult member of the household in

deriving logit estimates of fertilizer adoption in Ethiopia. The highest number of years

of schooling completed by any adult member of the household became more

significant than simply the education of the head of the household.

Age of Household Head. This variable was also represented by dummy

variables: AGE51 (19-30), AGE52 (31-36), AGE53 (37-44), AGE54 (45-54), and

AGE55 (55-76). Each of these was generated from quintiles of the age of the

household head. For the dummy variable specification, AGE5N= 1 represents a

household head that belongs to the ‘Nth’ age category, 0 otherwise. N is represented by

numbers from 1 to 5 from youngest to oldest respectively. The effect of age can be

taken as a proxy for farming experience as well as a relevant planning horizon (Asrat

et al., 2004). Young farmers may have a longer planning horizon and hence may be

more likely to care about the sustainability of irrigation water. On the contrary,

younger farmers may lack farming experience and hence may assign a lower value to

it. To avoid the dummy variable trap, the youngest group (AGE51) is used as the

excluded category.

Practical irrigation farming experience (EXPER) - Practical irrigation farming

experience of a household is an essential element in the valuation of irrigation water. It

is hypothesized that those households who have longer experience (measured in years)

43

can more readily realize the benefits of irrigation farming and hence are likely to value

irrigation facilities more highly.

Wealth and income indicators. Farmers were asked to state the measure of

wealth used in the community based on their own perspective. The amount of

cultivated land, sales of surplus agricultural products and eucalyptus trees, livestock

holdings, house width (measured by the number of corrugated iron sheets used in

making the roof of the house), and households who have either horse or mule or both

with a cart were identified as a measure of wealth almost by all interviewed farmers.

We also used most of these measures of wealth and income indicators on a per capita

base. These are: per capita income from surplus agricultural output sales and income

from other sources (PCINCOME); per capita cultivated land (LANDPERHH); per

capita corrugated iron sheets (PCNCORR); and per capita livestock holdings

(PCTLU). Household which has either horse or mule or both with cart (HORMUL) is

a dummy variable. In the econometric analysis, off-farm activity is treated in two

different variables tested separately. The earnings from off-farm activities were

summed and expressed on a per capita basis, and engagement in off-farm income-

generating activities is also treated as a dummy variable.

Per capita income (PCINCOME) is measured in thousands of birr per year. All

marketed agricultural outputs (converted into monetary units by their respective

average prices) and income from off-farm activities by members of the household are

added. Then, to get PCINCOME, the total income was divided by the family size. The

probability that a household feels positively about the sustainability of irrigation

agriculture would likely increase as PCINCOME increases assuming that farmer has a

positive attitude towards irrigation agriculture. In other words, this implies that

poverty will reduce the probability of WTP.

44

Per capita cultivated land (LANDPERHH). This is measured in kada per

household size. An increase in cultivated land per household size expected to have a

positive impact on WTP by providing increased opportunities for surplus agricultural

production for sale.

Per capita corrugated iron sheets (PCNCORR) is measured by number of

corrugated iron sheets used in making the roof divided by household size. Carlsson, et

al. (2005) applied a dummy variable to represent the presence of a corrugated roof in

the valuation of community plantations in Ethiopia as a proxy of wealth. They found

that the effect of a corrugated roof was positive and statistically significant for WTP.

Because in our study area most households have a corrugated roofed house, we

suggest that the per capita measurement can give a better insight for wealth. Therefore,

PCNCORR may have a positive impact on household’s decision to pay for

environmental services.

Per capita livestock holding (PCTLUL) is measured by Tropical Livestock

Unit (TLU) per family size. One TLU is equal to 250 kg. The TLU values for different

species of animals are: 1 for camel; 0.7 for cattle; 0.8 for horse/mule; 0.5 for donkey;

and 0.1 for goat/sheep (ILCA, 1992; in Asrat et al. 2004). Depending on the strength

of the specialization in livestock farming, this variable may have either positive or

negative impacts on the valuation of environmental services. However, in the study

area both crop and livestock farming are treated equally, and PCTLUL is expected to

have a positive influence on WTPIW by contributing cash from the sale of livestock

and related products.

HORMUL is a dummy variable for which HORMUL = 1 is a household who

has either a horse or mule or both with a cart, and 0 otherwise. Cart horse and mules

are used for commodity transportation within the rural community, mainly for

transporting eucalyptus trees and sometimes to generate off-farm business and the

45

transportation of agricultural products to market. Therefore, we suggest that this

variable may have a plosive effect if it is linked to cash generation and marketing.

Off-farm activities (OFFFA). This is a dummy variable where OFFFA=1

indicates at least one member of the family participates in an off-farm business, and 0

otherwise. Participation of households in off-farm activities may have different effects

depending on their returns. If households believe that irrigation agriculture has a lower

expected return than the off-farm business, they may not place a high value on the

sustainability of irrigation agriculture. On the other hand, participation of households

in off-farm activities may contribute a positive effect on WTP by making cash

available, which would imply a justification similar to that for PCINCOME above.

Market access (MARTIME). Access to markets is measured as the time

required to walk to the nearest market. As the time to travel to gain market access

increases, this may increase the probability that a household would not be willing to

pay for a sustainable irrigation water supply.

Dependency ratio (DEPRATIO). This is the ratio of dependent household

members to the number of economically active family members. This variable is

expected to have a negative effect on farmers’ willingness to pay, because it reduces

the household’s ability to meet subsistence needs (Asrat et al. 2004).

Perceived trend in rain-fed agricultural productivity (TRENDAG). This is a

dummy variable. It takes the value of 1 if the household head believes that there has

been an increase crop yields (commonly grown crop such as maize, Teff, etc) per kada

of land during the past five years; 0 otherwise. The five year time horizon would

provide an adequate period to realize whether crop productivity reduction (if there was

any) was caused by changes in rainfall. Of course, one of the rationales of the

government for investing in large-scale irrigation in the area is the variability of

46

rainfall (African Development Fund, 2001). If a productivity decline is related to

rainfall, we speculate that farmers may value irrigation water more.

Secure rights of lifetime land use (LIFETUSE H). This is a dummy variable

which is a proxy for tenure security. The security of lifetime land use rights =1 if a

household think that he or she has the right to lifetime land use without any land

redistribution. The length of land use rights is vital for farmers who cultivate high-

value agricultural products like fruit and other perennials that require a longer growing

period. Thus, lifetime land use rights are expected to have a positive impact on the

valuation of irrigation water.

Female headed household’s (FEMALE). This is a dummy variable where 1=

the presence of a female-headed household, and 0 otherwise. In the study area, female-

headed households more often have access to different packages of agricultural

training relative to their small number. This may contribute toward a positive attitude

towards the sustainability of irrigation agriculture.

Household expectation towards irrigated agriculture (expectation). This is also

a dummy variable, which takes the value of 1 for a positive expectation of yields from

irrigation agriculture compared to rainfed agriculture, and 0 otherwise. Household’s

perceptions towards irrigation agriculture compared with rainfed agriculture is a

crucial component because our valuation takes place on the basis of a hypothetical

market. Those households who have a positive expectation of higher yields from

irrigation agriculture may value irrigation water more.

The availability of extension services have also an impact on the success of

irrigated agriculture. This would, in turn, affect the magnitude of the willingness to

contribute for the provision of environmental services. This is because it would make

the irrigation water more productive. However, it is difficult to get reliable data on this

variable, since irrigation agriculture has not yet started.

47

To facilitate the comparison of hypotheses and the model results, a summary of

expected signs and descriptive statistics is given in Table 2. This table presents

descriptive statistics for those households willing to pay to support upland soil and

water conservation (willing) and for those households who refused to pay the

proposed bid value (non-willing). Furthermore, the table presents combined

descriptive statistics for the total sample. The table also shows group means

comparison test (t-test) result for the two categories (willing and non-willing) of

respondents.

Table 2: Summary of expected signs and descriptive statistics for sample households (n = 190)

Variable

Sign % for 1 dummy variable

Mean n=190

Std. Dev

n=190

Willing (n=120) Non-Willing (n=70) Mean Diff

(t-test) Mean Min Max Mean Min Max

WTPIW ♦ 63.16 0.48 VWTP - 44.26 14.66 40.15 25 70 51.31 25 70 -5.43*** MWTP ♦♦♦ 36.19 26.93 49.28 25 200 13.76 0 50 PCTLU ± 0.69 0.35 0.72 0 1.75 0.64 0 1.66 1.62 PCNCORR + 9.6 5.09 10.38 0 22.5 8.26 0 23.33 2.83*** PCINCOME + 1.01 0.78 1.21 0 3.78 0.66 0 2.83 4.98*** LANDPERHH + 1.03 0.48 1.08 0 2.5 0.94 0 2.5 2.01** EXPER + 0.46 1.23 0.68 0 7 0.1 0 3 3.18*** MARTIME - 1.08 0.52 1.01 0 2.5 1.2 0.17 2.5 -2.44** FAMEDU + 5.62 4.1 6.23 0 12 4.59 0 12 2.70*** EDUMMY1 (Base)

40.53 0.49 0.29 0 1 0.60 0 1

EDUMMY2 26.84 0.44 0.33 0 1 0.17 0 1 EDUMMY3 32.63 0.47 0.38 0 1 0.23 0 1 HHSIZE ♦♦ 6.04 2.16 6.09 2 12 5.96 2 12 0.41 WORKINGHH ♦♦ 3.56 1.61 3.75 1 8 3.23 1 9 2.18** DEPRATIO - 0.82 0.52 0.75 0 2.5 0.94 0 2.5 -2.58** HORMUL + 31.05 0.46 0.32 0 1 0.3 0 1 FEMALE + 6.84 0.25 0.06 0 1 0.09 0 1 OFFFA + 40.53 0.49 0.52 0 1 0.21 0 1 TRENDAG - 35.26 0.48 0.26 0 1 0.51 0 1 LIFETUSE + 43.16 0.5 0.4 0 1 0.49 0 1 AGE ♦♦ 42.02 13.06 40.05 20 74 45.39 19 76 -2.76** AGE51 (Base) 23.16 0.42 0.25 0 1 0.2 0 1 AGE52 17.37 0.38 0.21 0 1 0.11 0 1 AGE53 20.53 0.4 0.22 0 1 0.19 0 1 AGE54 18.95 0.39 0.17 0 1 0.23 0 1 AGE55 20 0.4 0.16 0 1 0.27 0 1 Expectation + 90.53 0.29 1 1 1 0.74 0 1

t statistics ** p<0.05, *** p<0.01, ♦Dependent variable, ♦♦important variables indirectly incorporated into the logit model: HHSIZE is total household or family size, WORKINGHH is economically active family members, and AGE is age of household measured in years, ♦♦♦maximum WTP in birr per year

48

RESULTS AND DISCUSSION

This research project explores how much value a household places on

irrigation water and how socioeconomic and demographic factors affect this value as

an initial step towards the development of a PES to reduce sedimentation loading on

reservoir. Out of 210 sample households, 190 were analyzed, and 12 were removed

due to inconsistent responses and discrepancies between the dichotomous choice and

the follow-up open-ended responses. The remaining eight observations were also

removed due to outliers discovered from five explanatory variables (Table 1).

Figure 3 illustrates the relationship between inconsistent responses (lower

maximum willingness value than the bid value) to the bid value. As shown in Figure,

the inconsistent responses from dichotomous choice and the follow up open ended

question were getting more weight as a movement from 25 to 70 bid price. In addition

to this, the upward trend line also demonstrated the existence of a starting point bias in

the inconsistent responses which support our decision to reject all 12 inconsistent

responses from the analysis.

49

Figure 3: Inconsistent responses between dichotomous choice and the follow up open ended question

Descriptive Results and Discussions

Of the 190 sample respondents, 63% were found to be willing to pay the

proposed bid prices to assist upland watershed soil and water conservation practices to

reduce sedimentation loading, whereas the remaining 36.8% rejected the proposed bid

prices (Table 2).

Out of the total number of household heads, only about 7% were female.

Respondent’s ages ranged from 19 to 76 years old with an average of 42 years. About

10% of the respondents were below 27 years old, 80% were between 27 and 62 years,

and 10% were above 62 years old. The respective averages for willing and non-willing

households are 40 and 45 years. The age difference is significant at p < 0.01; younger

household heads were more willing to pay than the elder heads. Table 2 also shows

five dummy variables representing the age group of the household head which were

included in the logit model.

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80

Max

imu

m W

ilin

gnes

s to

pay

(B

irr/

Kad

a/ye

ar)

Bid Value (Birr/Kada/year)

Maximum WTP (Birr/Kada/year)

50

With an inverse relation to household head age, practical irrigation farming

experience significantly varied between willing and non-willing households, which

were 0.69 and 0.1 years respectively. Increases in experience positively affect

household’s willingness to pay for sustainable supply of irrigation water.

The average family size of the sample households, as well as the two groups,

was about 6 persons with a range of 2 to 12 persons per household. The average

number of economically-active family members was about 3.8 and 3.3 for willing and

non-willing households respectively. The average dependency ratios for willing and

non-willing households were 0.75 and 0.94, respectively. In other words, each

economically active individual supports approximately one economically inactive

individual. Though the mean results of the two groups seem close, the mean difference

significantly varies between respondents in the two groups.

Educational achievement is analyzed in to two groups. These are the highest

educational achievement within the household members and educational achievement

by household heads only. The greatest educational level attained within the household

members ranged from illiterate to grade 12, with a mean value of grade 6. About 10%

of households were illiterate (there is no any household member attending or

previously attended school), 40% of the households had at least one household

member attending primary school, 25% had a household member in grade 7 to 9, and

15% from grade 10 and 11. About 10% of the households had at least one family

member who had attended or completed grade 12. However, these figures changed if

the household head was the only one from the household with education. About 40%

of household heads were illiterate, 27% had informal education, and about 32%

attended some sort of formal education. The illiteracy rate in non-willing households

was twice that of willing households. Informal education skills were acquired from the

51

Ethiopian Orthodox Church, the current farmers training center and the pre-1991 basic

education campaign.

Per capita income from the sale of agricultural output and income from other

sources is the major indicator of wealth. Per capita income was found to be

significantly different between willing and non-willing respondents. The earnings of

willing households were approximately double that of the non-willing households

(1210 birr to 660 birr). It was also noted that about 52% of willing households

engaged in off-farm activities to earn additional income. Average per capita TLU

(total livestock units) holdings was about 0.69. There was no statistical difference here

between willing and non-willing households. Average per capita cultivated land was

almost one Kada in the community; this shows that the need for intensive cultivation

to sustain the livelihood and the rationality of making irrigation infrastructure

investments to be able to produce three times per year. The average number per capita

corrugated iron roof sheets was about 10 and 8 for willing and non-willing

households, respectively.

A decrease in the perceived trend in the agricultural productivity of land over

the last five years was indicated by about 65% of the total respondents, particularly for

Teff and maize production. Farmers also suggested the main causes of this decrease in

agricultural productivity. These included loss of soil fertility, manifested by an

increased need for fertilizer to obtain earlier yields, the variability of rainfall, and

shortages and delays in the availability of both improved seeds and fertilizer. About

35% of households agreed that agricultural output showed an improvement within the

past five years. As reasons for the perceived improvements, these households cited

use of a small plot of land, following agricultural development agent’s advice, and

practicing best management practices in terms of time of seeding, soil conservation,

weeding, harvesting and post harvest handling.

52

A close look at the average bid price between willing (40 birr) and non-willing

(50 birr) households suggests that the majority of household heads, who were asked to

pay a higher bid price to support upland soil and water conservation practices, were

refused to accept the a higher bid price. The distribution of yes and no responses along

the bid prices also illustrate our argument and hypothesis that states the probability of

‘yes’ responses decline with increased bid price Figure 4.

Figure 4: Distribution of “yes” response and average maximum WTP for the different initial bids

Figure 4 also illustrates the relationship between bid value to average

maximum willingness to pay to support upland soil and water conservation practice.

As mentioned at the questionnaire design section, the result from the follow-up

valuation question is served to check whether there is starting point bias among

different initial bids to the follow-up maximum willingness to pay response. The

average maximum WTP for the different initial bids increased as the initial bid

0

10

20

30

40

50

0 10 20 30 40 50 60 70 80

Ave

rage

Max

imu

m W

TP

(B

irr/

Kad

a/ye

ar)

Nu

mb

er o

f ye

s re

spon

ces

from

30

resp

ond

ents

Bid Value (Birr/Kada/year)

Yes Responces per bid value

Average Maximum WTP

53

increases. At first look, this seems to be a problem associated with a starting point

bias. However, a multiple comparison test of means failed to reject the null hypothesis

that all means are equal. Therefore, in this study there appears to be no starting point

bias.

Farmers were also asked about their expectation whether year-round irrigation

farming will have a positive or negative impact on their agricultural output. About

91% of households expected increased yields with irrigated agriculture. Those who

did not expect yield increases thought that growing crops two times in a dry season

will result in lower production in the rainy season as well as in subsequent seasons.

Similar findings were also documented by Tafesse (2007). Willing respondents had a

100% positive expectation towards irrigated agriculture (i.e., Expectation =1) (Table

1). That means there is not enough variation required for the computational algorithm

to capture the influence of expectations between willing households (those already

having a positive expectation for irrigation farming) in the logit regression model.

Accordingly, to examine the impact of expectations on households’ willingness to pay

to support upland soil and water conservation practices in the upstream part of the

Koga Watershed, we specified two models based on expectations: (1) We estimated

the model with those households who had only positive expectations for irrigation

agriculture. The model uses positive expectations as a bench mark and drops the

remaining 26% non-willing households (or about 9% of observations from the total

sample size) not having positive expectation for irrigation farming (Table 2). (2) In

the second specification, the model was estimated using both groups of households

since the sample size for those households not having positive expectation was too

small to be modeled in separate logistic regression models (Long, 1997). Therefore,

we examine the effect of expectation only with 9 percent non-positive expectations for

irrigation farming response included in the second estimates.

54

Results and Discussions of the Empirical Models

To elicit the key factors that determine the households’ willingness to pay for

upland soil and water conservation practices which ultimately reduce sedimentation

loading in the reservoir, two logistic regression models were estimated. The results are

presented in Table 3. In both models, the dependent variable assumes the value of 1 if

a household is willing to contribute the bid amount and 0 otherwise. The left side of

Table 3 (columns two to four) shows the results of the logit estimation of household’s

willingness to pay to support upland soil and water conservation practices (WTPIW)

for those households who had positive expectations for irrigation agriculture (a

sample size of 172 respondents). The right side (from column five to seven) shows the

logit estimation results for the households’ willingness to pay to support upland soil

and water conservation practices using the full sample of 190 households.

A comparison between the marginal effects of the two models indicates that

the logit prediction of WTPIW for those households who had positive expectation for

irrigation farming relatively less responsive for a unit change of the variables at their

respective means compared to the second set of estimates.

The estimated coefficient of the bid value (VWTP), which is the most crucial

explanatory variable of probability of WTP, was found to be statistically significant at

the 1% level with the expected negative sign. This indicates that the probability of

WTP to support upland soil and water conservation practices decreases (increases) as

the bid price increases (decreases) under the hypothetical market scenario. Keeping

the influence of other factors constant, a 1 Birr increase in the bid reduces the

probability of willingness to pay by 1.3%.

From the five categories of the household head’s age, the youngest group was

the excluded category. Age showed a similar behavioral pattern in both estimated

models. Except for the second youngest age group ((AGE52 ) from 31-36 years),

55

which had a positive impact on household’s WTP to support upland soil and water

conservation practices, the other three categories of age (AGE53, AGE54 and AGE55)

showed negative effects. The only effect that is statistically significant is for AGE55

(55-76 years) in the second regression model. Holding the influence of other factors

constant at their mean, the probability of willingness to pay decreases by 33.3 % if a

household head belongs to the oldest category (AGE55). This is likely due to the fact

that compared to the benchmark group, the oldest age group of households (age >

55years old) has a shorter planning horizon and is less likely give priority to the

sustainability of irrigation agriculture However, in the subgroup with a positive

expectation for irrigation agriculture, this oldest age variable was not statistically

significant.

Practical irrigation farming experience (EXPER) was positively significant at a

10% probability level in both specifications, consistent with a prior expectations. The

significant and positive relation between practical irrigation farming experience and a

household’s WTP to support upland soil and water conservation practices shows that

those households who have experience over relatively longer periods of time are more

willing to invest in the sustainability of irrigation agriculture over those households

who have relatively short periods of experience.

Per capita income (PCINCOME) had a positive and significant effect on the

household’s WTP to support upland soil and water conservation practices. This is

likely due to the fact that households with higher incomes have more flexibility in

being able to invest in the future sustainability of the local farming system. The

marginal effect of this variable indicates that an increase of 1,000 Birr in per capita

income results in a nearly 18% increase in a household’s WTP to support upland soil

and water conservation practices. With the introduction of irrigation agriculture, the

56

expected income of a household was estimated to double (African Development Fund,

2001). The result appears to be important variable for the implementation of PES.

Table 3: Logit prediction of household’s willingness to pay to support upland soil and water conservation practices for households with positive expectation for irrigation farming and without 9% positive expectation for irrigation farming.

Explanatory variables

Logit Estimate for Households with positive expectation for

irrigation farming

Logit Estimate with 9 % Households without positive

expectation for irrigation farming Estimated

coefficientst-stat Marginal

EffectEstimated

coefficientst-stat Marginal

EffectVWTP -0.0720*** -3.96 -0.011 -0.0738*** -4.41 -0.013PCTLU 0.6562___ 0.78 0.100 -0.0828___ -0.11 -0.015PCNCORR 0.0614___ 0.94 0.009 0.0456___ 0.85 0.008PCINCOME 1.0942**_ 2.13 0.166 1.0053**_ 2.16 0.178LANDPERHH 0.7286___ 1.05 0.111 0.6426___ 1.08 0.114EXPER 0.5901*__ 1.71 0.090 0.6584*__ 1.90 0.117MARTIME -0.6288___ -1.19 -0.096 -0.5813___ -1.22 -0.103FAMEDU 0.0430___ 0.52 0.007 0.1080___ 1.47 0.019EDUMMY2a 1.5413**_ 2.18 0.189 1.3901**_ 2.16 0.205EDUMMY3a -0.2127___ -0.30 -0.033 -0.1892___ -0.28 -0.034DEPRATIO -1.5258*** -2.81 -0.232 -1.4473*** -3.06 -0.256FEMALEa 2.6253*__ 1.84 0.196 1.5946___ 1.53 0.187OFFFAa 1.6827*** 2.65 0.235 1.7853*** 3.06 0.287TRENDAGa -1.7041*** -3.13 -0.297 -1.5838*** -3.22 -0.308LIFETUSEa -0.6270___ -1.13 -0.098 -0.3863___ -0.76 -0.069HORMULa -0.4595___ -0.74 -0.074 -0.3086___ -0.54 -0.056AGE52a 0.8158___ 0.96 0.105 0.8191___ 1.01 0.124AGE53a -0.6451___ -0.75 -0.110 -0.7089___ -0.86 -0.139AGE54a -0.3350___ -0.37 -0.054 -0.7065___ -0.84 -0.139AGE55a -1.1862___ -1.25 -0.219 -1.5713*__ -1.86 -0.333Constant 3.2794**_ 2.19 3.4546**_ 2.45 Observations 172 190 LR 2 (20) 93.74*** 118.09*** Prob > 2 0.0000 0.0000 Pseudo R2 0.4447 0.4722 Log likelihood -58.5363 -65.9963

***, **, * indicate statistical significance at the 99%, 95%, and 90% confidence levels, respectively. (a) Marginal effect is for discrete change of dummy variable from 0 to 1

The engagement of households in off-farm activities (OFFFA) was found to be

positively and significantly associated with the WTP to support upland soil and water

57

conservation practices. This is likely due to the effect of off-farm business on

household poverty reduction, as with that the effects of per capita income. The other

possible interpretation of this result is that if a household thinks that the time spent on

off-farm activities has a lower expected return than the irrigation farming, they may

more highly value the prospective sustainability of irrigation agriculture. Furthermore,

those households engaged in off-farm activities were disproportionately from the

youngest age group; they appear to consider this a complement to their decision to

invest in irrigation farming. With other variables at their respective means,

engagement in off-farm activity increases household’s WTP to support upland soil and

water conservation practices by nearly 29 percent.

Consistent with our earlier hypothesis, the household dependency ratio

(DEPRATIO) has a negative and significant effect on the WTP to support upland soil

and water conservation. The marginal effect indicates that increasing the number of

dependent household members (relative to the number of currently economically

active members (i.e., a one-unit marginal increase, reduces the probability of being

willing to pay by nearly 26%.

The perceived trend in rainfed agricultural productivity (TRENDAG)

negatively and significantly affected the households’ WTP. Holding other variables at

their respected mean, a perceived productivity improvement over the last five years

decreases the household’s WTP to support upland soil and water conservation by

approximately 31 percent. A possible explanation is that households perceive that the

productivity of irrigated agriculture may not be profitable considering payment

requirement to sustain year round irrigation water flow , and that there will be fewer

resources with which to pay for the sustainability of irrigation. However, considering

the real situation in the study area, in the past five years, the majority of irrigation

58

beneficiary households perceived a decrease in rainfed productivity. This has likely

led households to decide in favor of irrigation agriculture.

The presence of female-headed households (FEMALE) was positively related

to both estimates of WTP for irrigated agriculture investments; however, for those

households who have positive expectations of irrigated agriculture, this was significant

at 10 % probability level, confirming our prior expectation.

From the three categories of dummy variables representing educational level of

the household head, the illiterate group (EDUMMY1) was taken as the excluded

group. The informal education variable was positively and significantly correlated

with household’s WTP to support upland soil and water conservation practices.

Keeping other factors constant, compared to illiterate households, informally educated

households (EDUMMY2) are willing to pay at the rate of 20.5%. However the formal

education group was not significant compared to the illiterate group. The coefficient of

the highest level of educational achievement within the household (FAMEDU) was

positively correlated but insignificant.

The security of lifetime land use rights (LIFETUSE) was negatively and

insignificantly related to the households’ WTP to support upland soil and water

conservation practices. The possible reason for this result is that most farmers were

aware of the land redistribution plan of the government. Farmers were told about 20

percent of their landholdings located in the irrigation command area will be taken and

redistributed to land-less peoples. The land redistribution has been started in one

Kebele in 2008.

Aggregate WTP

One of the major objectives of this research was to estimate the aggregate WTP

for upland watershed soil and water conservation practices. Predicted percentage of

59

WTP and expected WTP to get reliable supply of irrigation water with alternative bid

value are illustrated in Figure 5.

When the initial bid value increases from 25 to 48 birr per Kada of irrigable

land, the expected WTP per Kada of irrigable land increases also, but at a decreasing

rate. At bids equal to 48 birr per Kada of irrigable land, the expected WTP attains its

maximum position equal to 34.44 birr per Kada of irrigable land per year with an

attached probability of 0.72. Then, it starts to fall and reaches 23.36 birr per Kada of

irrigable land per year at 70 birr bid value. At the probit mean estimate (55 birr), the

expected WTP was calculated to be 33.1 birr per kada of irrigable land per year, which

is lower than the maximizing bid value (48 birr). Therefore, the probit mean cannot be

used to calculate expected aggregate willingness to pay, because by lowering the bid

value about 7 birr from the probit mean it is possible to generate additional financing.

Figure 5: The relationship between expected and predicted probability of WTP with bid value

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80

Exp

ecte

d W

TP

P

red

icte

d P

erce

nta

ge o

f W

TP

Bid Value (Birr/Kada/year)

Expected WTP (Bir/Kada/year)

predicted Percentage of WTP (Birr/Kada/year)

60

To estimate the aggregate WTP, we used 48 birr per Kada of irrigable land

(192 birr/ha ) and the associated probability of household willingness to accept this bid

value(0.72). Therefore, with 28,000 Kada of irrigable land (7,000 hectare), the

aggregate expected willingness to pay was estimated to be 964,320 birr (Equation 10).

The aggregate WTP was more than three times the annual budget allocated by the

Koga Irrigation and Watershed Management project to reduce sedimentation loading

due to upstream soil erosion by 50 percent over the past six years. Thus, the aggregate

expected WTP by downstream users has a significant potential to compensate

upstream ecosystem service providers. Furthermore, this has the potential to enhance

resource use efficiency both in the downstream and upstream parts of the Koga

Watershed.

CONCLUSION AND RECOMMENDATION

This research explored the household valuation of irrigation as an initial step

towards the development of a payment for environmental services program that might

reduce the negative impact of sedimentation loading on the Koga reservoir and

associated structures in the Upper Blue Nile Basin of Ethiopia. Accordingly, two

econometric models (one for households who had positive expectations for irrigated

agriculture and one for the broader sample were estimated to elicit the key factors that

determine the households’ WTP to support upland soil and water conservation

practices. The influential factors that were revealed to be important are household

head’s education, per capita income, household dependency ratio, perceived trends in

rainfed agricultural productivity, the existence of off-farm activities, practical

irrigation farming experience, the magnitude of the bid value, household head age and

gender, and households’ expectations towards irrigation farming.

61

The effect of age on WTP was masked by the influence of positive

expectations for those households with positive expectation for irrigation agriculture.

However, for the broader sample, age was one of the most significant and influential

demographic variables. Therefore, working on the awareness of households towards

the benefits of irrigation farming is likely to have a possible solution for the

uncertainty associated with old age on household willingness to pay for reliable supply

of irrigation water. In contrast, both model formulations revealed that perceived loss

of rainfed agricultural productivity over a five-year time horizon was the most

influential factor that played a major role in determining households’ WTP. Holding

other variables at their respected means, a perceived loss in rainfed productivity is

estimated to increase household’s WTP to support upland soil and water conservation

by approximately 31 percent to get a reliable supply of irrigation water.

The model results were used to estimate an aggregate willingness to pay of

964,320 birr per year with a bid price of 192 birr/ha and associated probability of

paying the bid price equal to 0.72. The probability result shows that there is still a

possibility of increasing the aggregate expected WTP by manipulating the influential

determining factors. With the introduction of irrigation agriculture, assuming the

implementation of a PES system, the expected income of a typical household would

be estimated to double (African Development Fund, 2001). The probability of a

household’s WTP associated with this income level increase by nearly 18 percent,

which increase associated probability of paying the bid price and this increase the

aggregate willingness to pay.

As noted, the perceived loss in rainfed productivity is a policy-relevant

variable that influences an irrigation beneficiary household’s willingness to pay for the

sustainability of irrigated agriculture. A perceived loss in rainfed productivity increase

the probability of household’s WTP to get reliable supply of irrigation water by about

62

31%. The implication is that any plan for generation of financial resources from

irrigation beneficiary households should also consider factors that influence the

productivity of this system.

63

CHAPTER FIVE: APPLICATION OF THE CONTINGENT VALUATION METHOD FOR

LABOR FORCE PARTICIPATION IN MANAGING AND MAINTAINING

IRRIGATION INFRASTRUCTURES

INTRODUCTION

In rural settings where agriculture is the dominant sector, the marketed labor

supply is often negligible. A large share of work involves self-employment and other

informal labor market employment, not labor for paid wages or salary. Although many

of the poor are landless laborers who work for wages, the majority of poor households

do not supply labor to the market in many rural regions in developing countries

(Barrett, et al., 2008). Moreover, labor is often considered the most limiting

household resource in these settings, which is one reason why it is not offered to the

market. This implies that development projects that require labor supply in rural

settings often suffer from shortages even if they have the money to employ labor.

Thus, the willingness of the local population to supply labor can be a vital component

for the survival of development projects that demand more labor supply to accomplish

day to day activities (Jebessa, 2004). Furthermore, the available literature on

willingness to contribute (WTC) labor in developing countries emphasizes the

importance of incorporating labor in contingent valuation studies that consider the

pervasive nature of cash poverty (Asrat, et al., 2004; Jebessa, 2004; Tessema and

Holden, 2006; Hung, 2007).

For soil and water conservation projects in Ethiopia, Asrat, et al. (2004) and

Tessema and Holden (2006) emphasized the importance of labor over financial

contributions to enhance farmer participation and involvement in soil conservation.

Jebessa (2004) highlighted the importance of labor contributions in establishing

64

community-level tree plantation projects. In Vietnam, in the case of forest fire

prevention programs, the contingent valuation method for labor contributions was

found to be workable (Hung, 2007); that is, households are more willing to contribute

labor than money to prevent distraction of community forest. Furthermore, in various

part of Africa the application of CVM in estimating households’ labor contributions to

animal disease prevention strategies included labor contributions (Swallow and

Woudyalew, 1994; Echessah, et al. 1997; and Kamuanga 2001). However, for

irrigation infrastructure management and maintenance that demands considerable

labor, there is a lack of literature that deals with the labor supply behavior of rural

households. Thus, this study uses the contingent valuation method to explore the

household valuation of irrigation water to reduce sediment loading on the Koga

reservoir and to protect and maintain irrigation canals from sedimentation through

labor contributions. We apply this approach in the Koga Watershed of the Upper Blue

Nile Basin, Ethiopia

In the Upper Blue Nile Basin, where steep topography with high rainfall

intensity and low vegetation cover dominates, soil erosion has become a serious

problem (Amede, 2003; Berry, 2003; Hydrosult, Inc., et al. 2006b, and Awulachew, et

al., 2008). High rates of soil erosion imply that sedimentation behind the newly

constructed dams and infrastructures is expected to be high. Even if appropriate soil

conservation measures are applied in the upstream parts of the watershed, it is

impossible to reduce the sediment rate to zero. As a result, irrigation canals more often

fill with sediments from both onsite and offsite sources. Therefore, continuous

follow-up and removal of sediments from the irrigation infrastructure are

indispensable for assuring a continuous and reliable irrigation water supply. These

require the participation and coordination of a large labor force. Although alternative

approaches to obtain this labor exist, one approach is to solicit labor contributions

65

from the beneficiaries of the irrigation system. To evaluate the feasibility of this

approach, it is important to estimate the aggregate willingness to contribute (WTC)

labor from beneficiary households, and to understand the factors that influence an

individual household’s WTC. This research will: (1) elicit the willingness to

contribute labor supply of the irrigation beneficiary households in managing and

maintaining common irrigation channels and in supporting soil and water conservation

activities in the nearby upstream areas; and (2) examine the determinants of farmers’

willingness to contribute labor supply for the protection of the irrigation infrastructure.

Further, the analysis of such information is likely to help governments of developing

nations and international donors to identify salient community or household features

that would increase the targeting and subsequent success of sustainable project

implementation involving community labor participation. The study is also an addition

to the limited literature on the application of CVM to labor as a payment vehicle for

irrigation infrastructure management.

PROJECT BACKGROUND

The Koga Irrigation and Watershed Management Project is the first attempt by

the Government of Ethiopia to develop a large-scale irrigation scheme for rural

farmers. With the support of the Ethiopian government and the African Development

Fund, the project has been supporting the development of irrigation infrastructure

since 2002. However, due to delays, the project is still under construction and well

beyond its expected completion date of 2007. The reservoir has been constructed with

a maximum height of 21.5m to impound 77 million cubic meters (MCM) of water,

with a project design life of 50 years and the capacity to supply 7,000 ha of farmland

with irrigation water. The canal system remains to be fully constructed. The project

divided the canal system into two parts: (1) the construction of the main and secondary

66

canal conveyance system, and (2) the construction of a tertiary and quaternary canal

distribution system (African Development Fund, 2001; personal communication with

Koga Irrigation and Watershed Management Representatives). The construction of the

main and secondary canal conveyance system is entirely the responsibility of the

project, and its implementation has already begun. However, the construction of

tertiary and quaternary canal distribution system (430 km), which are designed to be

suitable for farm-level water management, is planned to be completed through labor

contributions from the irrigation beneficiary farmers as well as project financing from

around the demonstration areas (Personal Communication with Koga Irrigation and

Watershed Management Representatives; African Development Fund, 2001). As of

October 2008, the implementation of the work on the quaternary canal distribution

system (entirely the responsibility of the beneficiary households) had not yet begun.

Farm households’ willingness to contribute labor is likely to affect the sustainability of

the irrigation infrastructure as well as the success of the implementation of the project

itself.

WATERSHED RESOURCE VALUATION IN DEVELOPING COUNTRIES

Environmental resource valuation is an indispensable tool for making sound

watershed management decisions about the use of soil, water and vegetation in a

watershed subject to local agro-climatic and topographic conditions. Environmental

economists have developed various methods to estimate the economic value of

environmental resources, and they have classified them into two broad categories

based on the elicitation techniques used. When a valuation technique considers related

or surrogate markets in which the environmental good is implicitly traded, it is

referred to as a revealed preference method or indirect valuation method. The second

category of environmental resource valuation methods is known as the stated

67

preference method or direct valuation method. These survey-based methods can be

used either for those environmental goods that are not traded in a market or for

assessing individuals’ stated behavior in a hypothetical setting. In this case, the stated

preference method has an advantage over the revealed preference method by

incorporating a non-use value component, in addition to use value (Birol, et al., 2006).

Furthermore, in rural economies of developing countries where markets are often

imperfect and where preferences cannot be revealed through the market mechanism,

the stated preference method can be used as a solution (Holden and Shiferaw, 2002).

Considering these advantages we applied the stated preference method, specifically,

the contingent valuation method (CVM), to elicit labor contribution for maintenance

of irrigation water.

CVM is used to estimate both use and non-use values for all kinds of

ecosystem and environmental services (Mitchell and Carson, 1989). It involves

directly asking people in a survey how much they would be willing to pay (WTP) for

specific environmental services or how much they would be willing to accept (WTA)

as compensation to give up specific environmental services. In developing countries,

CVM has become an important tool for estimating WTP for public goods and

environmental resources. In these countries, the use of CVM more often incorporates

both labor and money as payment vehicles, particularly for soil and water

conservation, forest resources and animal disease prevention (the control of tsetse fly)

(Swallow and Woudyalew, 1994; Echessah, et al. 1997; Tegegne, 1999; Kamuanga,

2001; Jebessa, 2004; Asrat, et al., 2004; Tessema and Holden, 2006; Hung, 2007) due

to their labor intensity.

In response to criticisms of the CVM, many scholars and organizations have

made efforts to improve the reliability and the validity of CVM survey methods in

both developed and developing countries, and have produced working guidelines.

68

Many of the problems associated with CVM surveys can be reduced by careful study

design and implementation of these guidelines (National Oceanic and Atmospheric

Administration (NOAA), 1993; Carson, et al., 2001; Whittington, 2002;

Venkatachalam, 2004). Therefore, although CVM has its limitations, it is still an

effective way to estimate the values of environmental goods and services if carefully

designed and implemented. The CVM procedures used in this study have followed

these guidelines and procedures.

MATERIALS AND METHODS

Survey Design

Data from 210 randomly selected proposed irrigation beneficiary households in

the Koga Watershed in the Upper Blue Nile Basin of Ethiopia are used in this study.

Of 210 sample households, 198 were included in the analysis6. Four households were

excluded due to inconsistent responses between the dichotomous choice (willing to

contribute or not) and the follow-up open-ended question7. The remaining eight were

excluded due to outliers discovered in preliminary analysis.

Prior to administering the CVM survey, two major steps were taken to improve

the quality of the data. Separate focus group discussions were held among the

agencies involved in Koga Watershed Management and among the irrigation

beneficiary households. The second step was revision and repeated pre-testing of the

draft questionnaire based on the responses from the focus group discussion. Feedback

from the pre-test was used further to revise the questionnaire. Finally, face to face

interviews (guided by the questionnaire) were administered to irrigation beneficiary

households (the head of the household was the respondent) between August and

6 Detail on issues about the population and sampling technique were presented in Chapter Four. 7 The open-ended elicitation technique involves asking individuals what is the maximum amount they are willing to pay for a specific commodity.

69

October 2008. The questionnaire had five main components presented in the following

order: purpose of the study, general farming questions, questions on the use of

irrigation farming and perceived water scarcity, the valuation scenario and elicitation

questions, and socioeconomic characteristics (See Appendix 1 for details).

The valuation scenario was described to the respondents using photo

illustrations. Two photographs were used to show soil degradation caused by water

and sediment filled irrigation channels. In the elicitation question, a single-bounded

dichotomous choice question and open-ended follow-up question were used. In the

single-bounded dichotomous choice question, the respondents were asked to state

‘yes’ or ‘no’ regarding their willingness to pay a single bid selected from a range of

predetermined bids8 that potentially reflect their maximum willingness to contribute

labor (Mitchell and Carson, 1989). This method has been recommended by the

NOAA-panel on contingent valuation (NOAA, 1993).

EMPIRICAL MODEL SPECIFICATION

For a given working day’s contribution to managing and maintaining the

irrigation infrastructure in order to get a reliable irrigation water supply, individuals

have the choice either to accept the suggested donation (bid) level or to withhold their

participation. If labor is the principal or most limiting asset of the household, it is

assumed that the individual will accept a suggested working day contribution to

maximize his or her utility under the following condition or reject it otherwise

(Hanemann, 1984):

U 1, WDM VLWTP; S ε U 0, I; S ε 1

8 Five different starting working days (bids) were randomly assigned to respondents, such as 1, 1.5, 2, 2.5, and 3 working day per month for 0.25 ha irrigable land.

70

where U represents utility, WDM is total available working day per month, VLWTP is

work day contribution requirement per month for managing and maintaining irrigation

infrastructure, S is a vector of socioeconomic variables of an individual, and ε and ε

are independently distributed random variables with zero means.

Thus, the probability that a household will decide to contribute work days for

managing and maintaining irrigation infrastructure is the probability that the

conditional indirect utility function for proposed intervention is greater than the

conditional indirect utility function for the status quo (no intervention). Our dependent

variable is irrigation beneficiary households’ willingness to contribute work days for

managing and maintaining irrigation infrastructure; it is dichotomous, and equals 1 if

the ith household is willing to contribute the suggested number of work days per month

to reduce sedimentation loading in the reservoir and protect and maintain irrigation

cannels from sedimentation, and 0 otherwise. That is,

, , 2

where is the dependent variable, is a vector of independent variables, , is a

vector of parameters to be estimated, and is the error term. In practice, is

unobservable. What we observe is a dummy variable Y defined by

1, 0 U 1, WDM VLWTP; S ε U 0, I; S ε , 0,

The probability that a household is willing to contribute labor to managing and

maintaining the irrigation infrastructure for on-time and reliable irrigation water

supply is:

71

Pr ob 1| Pr ob 0

, 0|

,|

If the distribution is symmetric

Pr ob 1| Pr ob ,|

, , 3

The dichotomous choice format of the CVM question has a binary choice

dependent variable which represents a qualitative choice model. Assuming the error

term in Eq. (3) is distributed as a logistic function, the logit model arises. In this

framework, the probability that the individual will accept the proposed bid value

(VLWTP) can be expressed as the following logit model:

Pr ob 1| , , , 4

represents the log of the odds ratio in favor of contributing work days for the

sustainability of year-round irrigation water supply and is a linear function of n

explanatory variables ( ), and expressed as:

72

If is the probability that the ith household is willing to contribute labor to

managing and maintaining irrigation infrastructure, then 1 , the probability of not

willing to contribute labor, is

11

1

Therefore, we can write

11

1

where 1⁄ is the odds ratio or the ratio of the probability that the ith household is

willing to contribute labor to the probability that the household is not.

Taking the natural logarithm, we get the log of the odds ratio, which is known

as logit model:

5

If the error term ( ) is taken in to account the logit model becomes:

, 6

where is the intercept, which tells us how the log-odds in favor of contributing

labor when all included explanatory variables are assumed to be zero, and the

are the respective slope parameters to be estimated in the model. The slope tells how

the log-odds in favor of labor contributions change as the independent variables

change. is also referred to as the log of the odds ratio in favor of labor

73

contributions. The above econometric model specification (Equation 6) is used to

assess the significance of factors affecting the labor contributions of a household for

managing and maintaining irrigation infrastructure. Estimation of the model used the

iterative maximum likelihood estimation procedure. The likelihood ratio chi-square

test (Mukherjee, et al., 1998) was used to test the reliability and overall fitness of the

discrete choice model.

The log of the odds ratio of the logit estimates in (6) does not provide a direct

indication of the effect of each of the predictors on the change and direction of the

probability that a household is willing to contribute labor. The impact of a unit change

in an explanatory variable on the probability of contributing labor is computed at the

sample mean values for all variables.

Assuming the error term is distributed with mean zero and variance equal to

one, equation (3) takes the form of a probit model. The probit model in this study is

used to calculate irrigation beneficiary household’s mean willingness to contribute

work days to managing and maintaining irrigation infrastructure by regressing the

willingness variable on bid variable (Haneman et al. 1991, Gebre Egziabher and

Adnew, 2007). Then, divide the intercept ( ) by the coefficient associated with the

proposed bid value ( ). However, in this study, the probit mean is not directly used to

calculate the aggregate willingness to contribute work days for managing and

maintaining irrigation infrastructure. The probit mean is compared to the labor

contribution that is associated with the maximum expected aggregate willingness to

contribute to observe how it far from the expected aggregate WTC maximizing work

days. And it can be used as a measure of aggregate willingness to contribute work

days if and only if it has insignificant variation with the work days that is associated

with the maximum expected aggregate WTC.

74

Assuming the probability of a household’s willing to contribute work days for

sustainable irrigation water supply is a linear function of bid value, the following

probit model is specified to calculate the mean WTC:

Pr ob 1|VLWTP VLWTP

Then, mean willingness to contribute working days using the probit model

is given as follows:

7

where: α ( the constant term) and β (the bid coefficient) are derived from the probit

model above.

The probability computed from (4) was used to calculate expected willingness

to contribute work days (EWTC) that are associated with the minimum to maximum

bid values to generate the maximum possible work day allocation for maintenance and

management work:

VLWTP 8

If the probit mean provides a lower expected willingness to contribute labor

than the estimate of EWTC, the aggregate willingness to contribute labor (AWTC)

will be calculated using the maximizing bid value generated from (8). The probability

used in computing expected willingness contribute work days is not only based

on the proposed bid value (VLWTP) like that of the probit mean estimate but also

75

considers demographic and socioeconomic variables. This gives more trust in the

value of expected willingness to contribute labor.

9

where: TICA is the total irrigation command area measured in Kada (hector/ 4)9.

RESEARCH STRATEGY AND DATA DESCRIPTION

Research Strategy

The research strategy followed in estimation of household’s willing to

contribute labor to managing and maintaining irrigation infrastructure is exactly

similar to the overall strategy followed to estimate household’s willingness to pay in

Chapter Four. Therefore, we do not repeat this hear and refer the reader to the previous

discussion.

Data Description

The probability that a household is willing to contribute labor to managing and

maintaining irrigation infrastructure is expected to depend on characteristics pertaining

to the household, including the expected benefits of managing and maintaining

irrigation infrastructure and satisfaction of the existing alternative source of

production systems such as rainfed agriculture. Summary statistics and descriptions of

the variables used in the analysis with expected signs for variables used in the logit

estimates are reported in Table 4 Specific explanatory variables included in each of the

models were chosen based on the results of previous studies, first-hand knowledge of

conditions in the watershed and farmers’ involvement.

9 This is because our valuation question is based on 0.25 ha of irrigable land.

76

One variable hypothesized to be important is wealth. To define wealth for this

study, farmers participated in selecting community wealth ranking indicator variables.

Livestock holdings, house width measured by the number of corrugated iron sheets

used, the extent of cultivated land and its economic returns, and ownership of either

horses, mules or both with a cart were considered wealth indicators be almost all

community members interviewed. Except for a cart with mule or horse, which was

specified as a dummy variable, the other three wealth indicators variables were

converted to a per capita basis (livestock holdings were first converted to Tropical

Livestock Units (TLU)10). In addition to this, for an indicator of income, all marketed

agricultural outputs – converted into monetary units by their respective average prices

– and income from off-farm activities by all members of the household were summed

and calculated on a per capita basis. These have also been used as wealth and income

indicator variables in various environmental conservation CVM studies (Asrat, et al.

2004; Carlsson, et al. 2005; Mengistu, 2006; Tessema and Holden, 2006).

Engagement in off-farm activities was treated as a dummy variable and

expected to have a negative influence on the willingness to contribute labor to

managing and maintaining the irrigation infrastructure.

The age of the household head was one of the factors that have been suggested

to affect the willingness to contribute working days for managing and maintaining

irrigation infrastructure. However, the effect of age can be positive or negative

depending on length of farming experience and the existence of a short planning

horizon, respectively (Asrat, et al. 2004; Tessema and Holden, 2006). To capture this

behavior in the model, the age of the household head (measured in years) is used to

identify its appropriate age quintile using STATA software (www.stata.com). The

youngest age category was used as a benchmark in the logit model. Although

10 The TLU values for different species of animals are: 1 for camel; 0.7 for cattle; 0.8 for horse/mule; 0.5 for donkey; 0.1 for goat/sheep (ILCA, 1992; in Asrat et al. 2004)

77

experience with irrigation agriculture was hypothesized to have a positive impact on a

household’s decision to participate in conservation activities, we speculate that the

presence of older-age households may lower the likelihood of a household to invest in

soil and water conservation activities.

Two variables accounted for the expected effects of education: the highest

number of years of formal schooling completed by any household member (including

the head) in years and a dummy variable representing the education of the household

head. The first variable helped to distinguish whether there were intra-household

decisions influencing the willingness to contribute labor for conservation activities

(Basu, et al. 2000, in Asfaw and Admassie, 2004). For the second specification, the

categorical specification seemed to be a better approach to observe the effect of

education on the contribution of labor.

Perceived trends in rain-fed agricultural productivity are hypothesized to be

important determinants of household willingness to contribute labor, because this

implicitly forces households to trade off between rain-fed agriculture and irrigation

agriculture. Households were asked to evaluate the trend in agricultural productivity

over the last five years and their yield expectations for irrigation agriculture compared

to rain-fed agriculture. The five-year time horizon for evaluating agricultural

productivity trends is intended to assess farmers’ long-term perceptions of the

relationship between changes in rainfall and crop productivity (rather than short-term

variations due to a variety of factors). If farmers perceive that a decline in productivity

related to rainfall conditions exists, they may value irrigation water at a greater level.

Furthermore, our valuation takes place on the basis of a hypothetical market, so that

the expectations of households towards irrigated agriculture compared to rain-fed

agriculture may also affect their decision to participate in managing and maintaining

78

irrigation infrastructure. Those households who have positive expectations of yield

increases from irrigation may decide to contribute more labor.

RESULTS AND DISCUSSION

Characteristics of Irrigation Beneficiary Households

Summary statistics, the expected signs of variables influencing households’

willing to contribute labor, and descriptions of the variables used in estimation are

reported in Table 4. Of the 198 sample households about 60% were willing to accept

the stated bid amount of labor to manage and maintain common irrigation channels

and to support soil and water conservation activities in the nearby upstream areas.

About 6% of the respondents were not willing to contribute any labor, and the

remaining households agreed to contribute some number of working days between

zero to the proposed bid level of work days.

The age of household heads in the sample ranges between 20 to 76 years, with

a mean of 42 years. The average number of years of practical irrigation experience

was extremely low (0.49 years). Six percent of surveyed households were female-

headed. The mean household size was 6.14 with a dependency ratio (economically

dependent household members per number of economically active household member)

of 0.82. Average per capita livestock holdings was about 0.68 TLU, and about 31% of

the respondents have a horse or mule with cart for transportation. The mean number

per capita of corrugated iron sheets was found to be 9.46 with a standard deviation of

4.97. The average highest formal schooling completed by any household member was

found to be 5.71 years with a standard deviation of 4.06 years.

Table 4: Descriptive Statistics Sample Households (n = 198)

Variable Description Expected impact

WPTLF

% for 1 dummy variable

Mean Std. Dev.

Min Max

WPTLF 1 if household is willing to contribute proposed bid working days per month, 0 otherwise 60.10 0.49 0.00 1.00

VLWTP Bid value in working days per month - 1.97 0.71 1.00 3.00 PCTLU Per capita livestock holding in Tropical Livestock Unit (TLU) - 0.68 0.35 0.00 1.75 PCNCORR Per capita corrugated iron sheet used in making the roof (iron sheet per household size) + 9.46 4.97 0.00 23.33 PCINCOME Per capita income in thousands of birr + 0.97 0.73 0.00 3.42 LANDPERHH Cultivated land per household size (0.25ha/ household size) - 1.01 0.47 0.00 2.50 EXPER Practical irrigation farming experience in years ± 0.49 1.26 0.00 7.00 MARTIME Time taken to walk to the nearest market in hours - 1.08 0.51 0.00 2.50 HHSIZE Family size (number) 6.14 2.16 2.00 12.00 WORKINGHH Number of economically active household member 3.60 1.60 1.00 9.00 DEPRATIO Dependent ratio (economically dependent household member per WORKINGHH) - 0.82 0.52 0.00 2.50 FAMEDU Highest formal schooling completed by any household member in years + 5.71 4.06 0.00 12.00 EDUMMY1 1 if the household head is not educated, 0 otherwise Base 39.90 0.49 0.00 1.00 EDUMMY2 1 if the household attained informal education and able to read and write , 0 otherwise 26.26 0.44 0.00 1.00 EDUMMY3 1 if the household attained formal education , 0 otherwise 33.84 0.47 0.00 1.00 HORMUL 1 if the household who has either horse or mule or both with cart, 0 otherwise + 31.31 0.46 0.00 1.00 FEMALE 1 if the household head is female, 0 otherwise 6.06 0.24 0.00 1.00 OFFFA 1 if any household member of the family participate in off-farm business, 0 otherwise - 38.38 0.49 0.00 1.00 TRENDAG 1 if perceived trend in rain-fed agricultural productivity in the past five years improved, - 33.84 0.47 0.00 1.00 LIFETUSE 1 if a household think that he or she has life time land use right, 0 otherwise + 40.91 0.49 0.00 1.00 Expectation 1 if positive yield expectation from irrigation farming compared to rain fed agriculture + 90.91 0.29 0.00 1.00 AGE Age of the household head in years 42.17 12.83 19.00 76.00 AGEEXPER Age of household heads who have practical irrigation farming experience 41.09 10.03 27.00 62.00 AGE51* 1 if the 1st quintile of age of the household head (the youngest group) Base 22.22 26.82 0.42 0.00 1.00 AGE52* 1 if the 2nd quintile of age of the household head, 0 otherwise ± 19.19 34.00 0.39 0.00 1.00 AGE53* 1 if the 3rd quintile of age of the household head, 0 otherwise ± 21.72 41.47 0.41 0.00 1.00 AGE54* 1 if the 4th quintile of age of the household head, 0 otherwise ± 17.68 49.26 0.38 0.00 1.00 AGE55* 1 if the 5th quintile of age of the household head (the oldest group), 0 otherwise ± 19.19 62.37 0.39 0.00 1.00

* The mean value represent the age of the household head in the group.

79

80

When only household heads are accounted for educational achievement, about

40% of household heads were illiterate, 26% of household heads attained informal

education and were able to read and write, and about 34% of respondents attained

formal education.

The mean per capita cultivated land size among sample households was very

small, about 1 Kada (a quarter of a hectare). The average time taken to walk to the

nearest market was about 1.08 hours. Per capita income from all marketed agricultural

outputs and off-farm activities by any members of the household was about 970 birr

per year (about 101$ annually). About 40% of respondents earned additional income

from off-farm activities to support their livelihood.

Two-thirds of the households believed that in the past five years output gains

from rainfed agriculture had been decreasing and a large proportion of respondents

(91%) believe that irrigated agriculture will increase agricultural productivity. Those

who did not believe irrigated agriculture would increase yields mentioned that

growing crops two times in a dry season would likely result in less production in the

rainy season as well as in the subsequent seasons. Furthermore, this group was not

interested in any irrigation development activities.

Results of the Empirical Model

The impact of explanatory variables on the respondents' willingness to

contribute working days to manage and maintain common irrigation channels and to

support soil and water conservation activities in the nearby upstream areas was

examined using a logistic regression model (results in Table 5). The dependent

variable, WPTLF, in the model has a value of 1 if the household was willing to

contribute the proposed bid work days per month and 0 if the respondent rejected it.

The range of proposed bid work days was one to three day.

81

Table 5: Logistic regression model for willingness to contribute labor for managing and maintaining irrigation infrastructure

Explanatory variables

Estimated coefficients

Std. Err. Marginal Effect

VLWTP -2.370*** 0.038 -0.499PCTLU -1.876**_ 0.13 -0.395PCNCORR 0.129**_ 0.065 0.027PCINCOME 0.059___ 0.431 0.012LANDPERHH -0.716___ 0.296 -0.151EXPER -0.217___ 0.14 -0.046MARTIME -0.795*__ 0.207 -0.167DEPRATIO -1.976*** 0.075 -0.416FAMEDU 0.124*__ 0.084 0.026EDUMMY2a -1.180**_ 0.184 -0.2669EDUMMY3a -0.604___ 0.364 -0.131HORMULa 1.394**_ 2.353 0.2582FEMALEa 0.228___ 1.277 0.046OFFFAa -1.206**_ 0.17 -0.2621TRENDAGa -0.413___ 0.313 -0.089LIFETUSEa -0.0137___ 0.465 -0.003Expectationa 2.374**_ 10.048 0.5309AGE52a -2.485*** 0.068 -0.551AGE53a -3.583*** 0.025 -0.712AGE54a -3.279*** 0.035 -0.671AGE55a -4.764*** 0.008 -0.806Constant 9.374*** Observations 198______ LR chi2(21) 120.45____Prob > chi2 0.0000__Pseudo R2 0.4522__

***, **, * indicate statistical significance at the 99%, 95%, and 90% confidence levels, respectively. (a) Marginal effect is for discrete change of dummy variable from 0 to 1

The estimated coefficient of bid workday variable (VLWTP) was found to be

statistically significant at the 0.01 probability level with the expected negative sign.

This indicates that the probability of a ‘yes’ WPTLF response decreases (increases) as

the bid value of work day contributions increases (decreases) under the hypothetical

market scenario. Keeping other variables at their sample means, a one day increase in

the bid work day reduces the probability of WTC work days by nearly 50 percent.

This is also an indication of how labor is scarce in the region. Policy makers should

82

also consider this in allotting labor contributions for managing and maintaining of

irrigation infrastructures on a voluntary basis.

The coefficient of the variable representing expectations towards irrigation

agriculture (Expectation) has a positive sign, as expected, and a significant effect on

the dichotomous WPTLF response. Keeping other variables constant, a positive

expectation towards irrigated agriculture increases the probability of household’s

WTC labor for maintaining and managing of irrigation infrastructure by 53 percent.

The estimated coefficients of the age dummy variables were found to be statistically

significant at the 1% level as compared to the youngest age category. The negative

signs on the age dummies indicates that the probability of a ‘yes’ response on the

dichotomous WPTLF variable is likely to be higher in the youngest category than the

oldest. This is likely to be the effect of longer-term planning horizons of the younger

age group relative to the gains attributable to farming experience and an older age.

The coefficient of the variable representing the highest level of formal

schooling completed by any household member (FAMEDU) appeared to be

significant at 10% probability level with the expected sign. The implication of the

positive sign is that an increase in school grade achieved increases the probability of a

farmer to support the proposed voluntary labor contributions. The result suggests that

the existence of an intra-household effect on labor contribution decision of the

household head. On the other hand, household head education dummies show a

negative effect on the willingness to contribute labor as compared to the illiterate

group. However, only the coefficient on the informal education group variable was

significant at the 5% level indicating that the illiterate groups were more likely to

contribute labor for managing and maintaining irrigation infrastructure compared to

the informally educated groups. The possible justification for both specifications of

83

education may be due to the existence of intra-household positive externalities

(sharing the benefits of literacy) from the literate groups (Basu et al., 2000).

The coefficient on per capita livestock holdings was significant but negatively

affected the probability of a household’s willingness to contribute labor. The marginal

effect of per capita livestock holdings indicates that an increase of 1 per capita TLU

holding results in a 39.5 percent reduction in a household’s WTC labor. This is likely

due to the requirement for the amount of time for livestock management, which

competes with time for conservation work. Similarly, engagement in off-farm

activities reduces the probability of a household’s WTC working days by 26 percent.

The coefficient of the dependency ratio was found to be statistically significant

at a 1% probability level with the expected negative sign. Holding other variables

constant, an increase in the dependency ratio reduces the probability of WTC labor by

nearly 42 percent. The possible justification for the negative sign of the dependency

ratio is that with an increase in this variable, the work burden to manage home and

other farming activities with few economically active individuals in the household

increases, leading to less time available for managing and maintaining the irrigation

infrastructure.

Time taken to walk to the nearest market was considered a proxy to market

access. This coefficient was significant at a 10% probability level with the expected

negative sign. This indicates that the probability of a ‘yes’ response to the

dichotomous WPTLF decreases (increases) as the time taken to walk to the nearest

market increases (decreases) under the hypothetical market scenario. If farmers were

unable to sell surplus agricultural products in the nearest market, the opportunity cost

to sell such production may be become higher than the benefit, and consequently

farmers may refrain from participating in the production of surplus agricultural

commodities and may value irrigation water less.

84

The coefficient on the variable representing households having either horses or

mules or both with a cart (HORMUL) were found to be statistically significant with

the expected positive sign illustrating that those households who have HORMUL

spend less time traveling to market, and thus have more time available for

conservation activities. The coefficient on the number of per capita corrugated iron

sheets was significant and positively affected labor contributions.

Aggregate WTC

One of the major objectives of this research was to elicit the aggregate WTC

labor for irrigation beneficiary households to manage and maintain common irrigation

channels. The probability of WTC labor and expected WTC labor with alternative bid

offerings are shown in Figure 6. Both the expected WTC labor (Equation 8) and the

predicted probability of WTC labor (Equation 4) are plotted on the “Y” axis. The “X”

axis represents number of work day contribution per 0.25 ha of irrigable land per

month.

Figure 6: Expected working day contribution and bid working day trend

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 0.5 1 1.5 2 2.5 3 3.5

Exp

ecte

d W

TC

Lab

or

Pre

dic

ted

Pro

bab

ilit

y of

WT

C L

abor

Bid Value (day/Kada/Month)

Predicted probability of WTC (day/Kada/Month)

Expected WTC Labor (day/kada/Month)

85

As the bid value increases, the bid value diverges from the expected WTC

labor, but the expected WTC continues to increase to a maximum of 1.4 person days at

a bid value of 1.8 days. Higher bid values lead to a reduced willingness to contribute

labor. At the probit mean estimate of 2.3 work days contribution, the expected person-

day contribution was calculated to be 1.2 days, which is lower than the maximizing

bid value. Therefore, using the maximizing bid value (1.8 work day) and accounting

for all 7,000 ha of irrigable land, the expected aggregate WTC labor to manage and

maintain common irrigation channels and to support soil and water conservation

activities in the nearby upstream areas was estimated to be 39,065 person days per

month. This gives a yearly estimate of 468,784 person days. This would meet more

than 30% of the minimum annual labor requirement of the project for managing and

maintaining irrigation infrastructure.

CONCLUSIONS

Continuous follow up and removal of sediment from irrigation infrastructure to

ensure on-time and reliable irrigation water supplies requires considerable labor and

coordination. The willingness of households to contribute labor to maintain access to

irrigation water and knowledge of its determinants can be an important element in the

success of irrigation schemes. A principal finding of this study is that aggregate

willingness to contribute work days to support irrigation and soil and water

conservation activities in the Koga watershed is substantial, estimated at 468,784

person days per year. A useful extension would be a more detailed assessment of

labor needs for maintenance and perhaps a disaggregation of labor by task.

Farmers’ WTC labor was influenced by educational level, age of the household

head, the expected increased yields from irrigation, the wealth of household, off-farm

activities, the time taken to walk to the nearest market, the dependency ratio, and the

86

work days bid. However, the marginal effects indicate that changes in many of the

independent variables do not have meaningfully large impacts on the probability of

household labor contribution. Per capita livestock holdings, expected yields from

irrigated agriculture, the dependency ratio, and number of bid work days were

revealed as the most influential determinant factors of households’ willingness to

contribute labor. Of these, any plan for intervention in managing and maintaining

irrigation infrastructure through labor force participation should emphasize education

about the likely benefits of irrigation for agricultural production. To increase labor

participation particularly for new development projects, description of the project

scenario and future benefits should be clearly explained to farmers.

87

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APPENDIX

APPENDIX 1: QUESTIONNAIRE PREPARED FOR IRRIGATION

BENEFICIARY HOUSEHOLDS, KOGA WATERSHED, UPPER BLUE NILE

BASIN, ETHIOPIA

Habtamu T. Kassahun

(htk6@cornell.edu, or econvet@yahoo.com)

Payment for Environmental Service to Enhance Environmental Productivity and

Labor force Participation in Managing and Maintaining Irrigation Infrastructures,

the Case of Upper Blue Nile

Part I: Introduction

This questionnaire has been prepared to gather information about farming practices

and socioeconomic conditions of households in Koga Watershed. The research is

intended to develop a mechanism to help you in improving land and water

productivity through year round irrigation water supply in collaboration with you. The

information that you have delivered to the student will only be given to a third party

anonymously. In answering my questions, please remember that there are no correct or

wrong answers. I am just after your honest opinion.

Woreda: Mecha, Kebele ________________, Village/Got/ _____________________

Household Head Name __________________________________________________

Part II: Credit, Fertilizers, improved seeds and Labor Supply situation of Farm

Household

1. Do you have formal or in formal credit access whenever you want to borrow?

Yes No

A) If no, what is the reason? __________________________________________

B) If yes, how much have you borrowed in 2007 for agricultural production?

In cash

Commercial Bank (Birr)

Rural credit institutions (Birr)

Informal money lenders (Birr)

Others (Birr)

Total

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2. Have you used fertilizers for crop cultivation in 2007? Yes or No,

A) If no, why not? _____________________________________________________

B) If yes, how many kilograms of fertilizer have you used in 2007? ______________

3. Have you used improved seed in 2007? Yes/No

A) If no, why not? __________________________________________________

B) If yes, for which types of crops have you used improved seeds?

4. Do you currently have labor shortage for crop and livestock farming?

Yes or No

5. Have you ever employed wage labor daily for farming activities?

Yes or No

6. How much is the cost of labor during peak and slack working periods of the year

in your area?

Peak season _____Birr/person/ labor day,

Slack season _______ Birr/person/ labor day.

Part III. Land Use and Land Tenure System

7. How many kada (0.25 ha) of Land do you have land use right? _______________

8. How many piece of land do you have? __________________________________

9. How many kadas of Land did you cultivate (own and rent) in 2007? ___________

10. Do you know your land use rights and obligations? Yes or No If yes, could you

tell me about your land use rights and obligations? _________________________

Type of crops

Amount/kg Type of crops

Amount/kg Type of crops

Amount/kg

Type of crops

Amount/kg Type of crops

Amount/kg Type of crops

Amount/kg

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11. Do you believe you have a right to use your agricultural land without any

reduction on your land holding size throughout your life? Yes or No, please

explain? ___________________________________________________________

Part IV. Market access

12. How much time do you take to travel the nearest market to sell your agricultural

products? __________________________________________________________

Part V. Rain-fed and Irrigation Agriculture

13. Crop production in 2007 agricultural calendar

Crop Type Land used for Rain-fed Agriculture (kada)

Output (Quintals)

Land used for irrigation farming (Kada)

Output (Quintals)

For sell (Quintal)

Average Price per quintal

Barley Wheat Teff Finger Millet Sorghum Maize Pea Horse bean Linseed Lentil Sunflower Chickpea Noug Tomato Potato Beat-root Carrot Onion Garlic Cabbage Pepper

Tree In Kada Sell/year Pasture Eucalyptus

14. If you have practical irrigation farming experience, how many years do you have

__________________________________________________________________

15. Have you been advised in the use of irrigation farming management?

A) Yes B) No, by whom?

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B) BoA development agent

C) woreda experts

D) Others, please mention? ___________________________________________

16. How do you solve the problem of water scarcity due to variability of rainfall start,

duration, stoppage, and volume for crop production? _______________________

17. How would you explain the trends in your agricultural output over the last five

year per kada of land in rain-fed agriculture?

A) Decreasing, reason ________________________________________________

B) Increasing, reason _________________________________________________

18. If the answer is ‘’A’’ for the above question what do you think the causes of

decline in crop productivity? ___________________________________________

19. Are you a member of irrigation cooperative?

A) Yes B) No

20. What is your reason to be and not to be a member of irrigation cooperative? _____

21. Do you think year round irrigation farming increase agricultural output?

Yes or No

22. If yes, how many times do you think agricultural output increased, if you

compared with rain-fed agriculture per a given land?

A) 1 B) 1.5 C) 2 D) 2.5 E) 3 F) 3.5 G) 4 H) 4.5 I) 5

23. If no, what is your reason?

______________________________________________

Part VI. The CV Question

Maintaining the health of the dam and irrigation channels from sedimentation

as well as participating in managing conflicts within irrigation water users are required

to get year round irrigation water supply. In the upstream part of the watershed, soil

and water conservation work should be done in order to keep year round water flow

and to reduce the amount of siltation entering to your dam and damaging irrigation

channels. However, because of its distance from your residence area, it is impossible

for you to accomplish conservation activities and follow-up in the upstream areas of

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Koga Watershed. Therefore, all conservation activities in the upstream part of the

watershed should be done by communities residing in that part of the watershed.

However, since the dam is constructed for your benefit, no one can force upstream

residents to practice soil and water conservation activities; therefore, to encourage

participation households in soil and water conservation activities some incentives are

required. In the areas near your residence, it is possible to manage and maintain

common irrigation channels and to resolve conflicts that arise among irrigation users.

Therefore, to optimize long- and short-run benefits from irrigation water, irrigation

beneficiary households often contribute money and labor time to maintain the health

of the dam and irrigation channels.

24. Do you want to have an irrigation system to get year round water supply and to

produce three times per year? A) Yes B) No

25. If you are given irrigable land, will you be willing to vote for a irrigation

cooperative rules and regulation that will create a fund, if its passage will require

all irrigation users to contribute X (____) Birr/household/month/ 0.25 ha of land to

keep the health of the dam and common irrigation channels to get year round

irrigation water supply and to produce three times per year?

A) Yes B) NO

26. What is the maximum amount that you are willing to pay per kada of land for such

a project per year for ten years?

____________________________________________

27. If yes in Q25 and if the respondent WTP in Q. 25 is greater than in Q.26, then ask;

You said that you are willing to pay X(____) Birr (in Q.25) but when I ask you

your maximum amount willingness to pay you said _____Birr, which is less than

the amount you already agreed to pay previously. Why?

__________________________

28. If you are not willing to contribute any amount to the conservation activities,

please identify your reason/s

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A) I cannot afford to pay.

B) I think the government should finance the watershed management activities

C) I do not believe conservation and management activities will result in more

reliable water supply.

D) I do not fully understand the question.

E) Other reasons, please identify

_________________________________________

29. In addition to cash contribution, If you are requested to contribute X (____) labor

day per Kada of land per month to maintaining the health of the dam and irrigation

channels from sedimentation to get year round irrigation water supply, are you

willing to contribute, if its passage will require all irrigation users to contribute?

A) Yes B) No

30. What is the maximum number of days that you are willing to contribute for such a

project per month for ten years?

__________________________________________

31. If yes, in 29 and if the respondent WTP in Q. 29 is greater than in Q.30, then;

You said that you are willing to contribute X (____ ) labor day (in Q.29) but when

I ask you your maximum amount willingness to contribute labor you said _____

day which is less than the amount you already agreed to contribute. Why?

____________

32. If you are not willing to contribute any labor day for the conservation activities,

please tell me your reason/s

____________________________________________

33. If you are not willing to contribute any labor day for the conservation activities,

please tell me your reason/s

____________________________________________

34. What do you recommend to make irrigation water sustainable throughout the year?

__________________________________________________________________

___

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35. Do you believe sedimentation is a problem for the health of the dam and irrigation

channels in this area? A) Yes B) No

36. What do you want to cultivate, if you are given irrigable land?

Cereal Crops Barley, Wheat, Teff, Finger Millet, Sorghum, Maize,

Pulse Crops Pea, Horse bean, Lentil, Chickpea,

Oil Crops Sunflower, Noug, Linseed,

Horticultures Avocado, Mango, Orange, Papaya, Banana, Tomato, Potato, Beat-Root, Carrot, Onion, Garlic, Cabbage, Paper

Other Eucalyptus Pasture

Reason Reason Reason Reason Reason

VI. Socio-economic Information

37. Age ______

38. Gender: Male _____ Female_______

39. household head educational level ________

40. The largest level Educational attainment within the household (indicate year in the

bracket): (children, wife, husband, other relatives and other persons living in the

same house)

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A) No Education at all

B) Read and Write

C) Elementary level (___)

D) High school level (___)

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41. Household Size ______

42. Number of Disabled individuals in the family _____

43. Household size under 15 years old ______

44. Household size above 65 years old_______

45. Of what material have you constructed your home roof?

A) Straw/grass B) Corrugated iron sheet

46. If it is corrugated iron sheet,

A) How many sheets have you used in making the roof? _______

B) When did you get them? __________

47. How many livestock do you have? Cattle_____, Goat_____, Sheep_______,

Donkey/ Horse/ Mule ______, chicken________

48. Do you have other business (you or your family) other than agriculture (off-farm

activities) to support your livelihood?

A) Yes B) No, if no, proceed to Q 46.

49. If yes, in which type of businesses? _____________________________________

50. How much money do you earn per year from this activity?

_____________________

51. Please check the annual income bracket for your family income in Ethiopian Birr.

Include the earnings of all members of the family who are working or gainfully

employed, including you. Please be assured that the information you will reveal is

for research purposes only.

500 7000 15000 1000 9000 17000 3000 11000 19000 5000 13000 >=21000

52. How do you define someone’s level of wealth in your area?

____________________

53. According to community wealth ranking, what is your rank?

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A) Very poor

B) Poor

C) Middle Household Rich

D) Very rich