Post on 06-Mar-2018
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.
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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.
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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.
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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