Tesfaye and Brouwer 2012 Ecological Economics[1]

Ecological Economics 73 (2012) 168–178

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Ecological Economics

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Analysis

Testing participation constraints in contract design for sustainable soilconservation in Ethiopia

Abonesh Tesfaye, Roy Brouwer ⁎Department of Environmental Economics, Institute for Environmental Studies, VU University, Amsterdam, De Boelelaan 1087, 1081 HV, Amsterdam, The Netherlands

⁎ Corresponding author. Tel.: +31 20 5985608; fax:E-mail address: [email protected] (R. Brouwer

0921-8009/$ – see front matter © 2011 Elsevier B.V. Alldoi:10.1016/j.ecolecon.2011.10.017

a b s t r a c t
a r t i c l e i n f o
Article history:Received 3 January 2011Received in revised form 27 August 2011Accepted 17 October 2011Available online 25 November 2011

Keywords:Contract designChoice experimentParticipation constraintsSoil conservation

This paper focuses on contract design to improve the incentive structure of current coordination mechanismsrelated to sustainable land use management in the Ethiopian highlands. The main objective is to assesswhether, and if so under which terms and conditions, rural households are willing to enter into contractualagreements to invest in soil conservation measures on their land. Participation constraints are tested underdifferent soil erosion and institutional-economic conditions in a choice experiment targeting 750 ruralhouseholds. We show that contracts provided by local government peasant associations offering additionalcredit, land use security and extension services could be an effective means to increase the share of farmersimplementing soil conservation measures. However, trust in contract terms and conditions appears to playan important role. Farmers living in the most erosion prone areas are most likely to participate, while farmerstaking soil conservation measures already are less likely to enter into a contractual agreement with the localgovernment. Farmers not taking soil conservation measures will only do so if the contract price is lower thanor equal to the income losses suffered from soil erosion.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Land and soils in the Ethiopian highlands are among the most de-graded natural resources in East Africa (e.g. El-Swaify and Hurni,1996; Sonneveld, 2002). The annual soil erosion rate in the Ethiopianhighlands is estimated at about 1.5 billion metric tons (Taddese,2001). A large share of this top soil is washed away into the BlueNile. The degradation is caused by human pressures such as increas-ing populations, deforestation and unsustainable agricultural landuse practices (e.g. overgrazing), coupled with pressures like climatechange (e.g. higher intensity of rainfall). Recognizing that land degra-dation due to soil erosion is a major environmental and socio-economic problem, the Government of Ethiopia and DevelopmentAgencies have supported several efforts over the past decades to pro-mote soil conservation and environmental rehabilitation.

There is growing consensus though that many of these soil conser-vation programs in the past were disappointing and ineffective forvarious reasons. They used a flawed ‘environmental narrative’ to pro-mote large scale top-down interventions, gave inadequate consider-ation to farmers' perspectives, constraints, and local conditions,provided limited options to farmers, and in some cases even promot-ed unprofitable alternatives (e.g. Bekele, 2004; Shiferaw and Holden,1998 and 1999). Most importantly perhaps is that none of these pre-vious attempts paid adequate attention to the incentive-compatibility

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of the proposed government and non-governmental programs. Previ-ous studies conducted in Ethiopia confirm that the absence of incen-tives discourages farmers to adopt sustainable soil and waterconservation measures and even leads sometimes to the removal ofexisting measures. Studies by Shiferaw and Holden (1998), Osmanand Sauerborn (2001), Admassie (2000), Taddese (2001), Abera(2003), Gebremedhin and Swinton (2003), Ayalneh et al. (2006)and Holden et al. (2009) in different parts of the country indicatethat tenure insecurity related to fears of further redistribution ofrural lands is the principal factor behind farmers' unwillingness to in-vest in sustainable soil conservation measures. Shiferaw and Holden(1998), Bekele and Drake (2003) and Anley et al. (2007) furthermorefind that the diffusion of information about available technologicaloptions or rather the lack thereof has a significant effect on soil con-servation investment decisions.

This paper focuses on contract design to improve the incentivestructure of the current coordination mechanism of sustainable landuse management in the Ethiopian highlands. The highlands are partof the Blue Nile river basin and any intervention in current land usemanagement has a direct impact on the water services provided bythe river basin. Contracting is the most practical coordination mecha-nism and not unknown in Africa as commercial contract farming hasbeen a successful income generating driving force for smallholderfarmers (e.g. Grosh, 1994). In the study presented here we use con-tract theory (e.g. Bolton and Dewatripont, 2005) to address theissue of asymmetric and incomplete information regarding the on-site farmer costs of implementation of sustainable soil conservationmeasures on the one hand and the costs of current unsustainable

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169A. Tesfaye, R. Brouwer / Ecological Economics 73 (2012) 168–178

practices in terms of loss of crop yield and productive grazing land onthe other hand. The paper's main objective is to contribute to the de-velopment of an incentive-compatible contract design based on theidentification of key institutional-economic conditions needed to bein place for farmers to be willing to participate in such a contractualagreement and invest in soil conservation measures. We estimaterural farm household demand for contractual agreements under dif-ferent soil erosion and institutional-economic conditions using achoice experiment. More specifically in terms of Ferraro's (2008)screening contracts, we test in the choice experiment whether partic-ipation constraints are satisfied. Since no a priori knowledge and in-formation exist about farmer types and possible informational rents,incentive compatibility constraints cannot be tested. However, thechoice experiment is used as a means of screening contracts inorder to identify possible farmer types as a first step to informincentive-compatible contract design. To this end, proxies are usedto test farmer choice behavior under different cost (of measures)and price (of contracts) settings.

2. Contract Design and Tests of Participation Constraints

Despite the public interests and externalities involved, we treatthe sustainable management of land in our contract design primarilyfrom a private party perspective where farm households have every-thing to win or lose unless they take measures to sustain agriculturalpractices in the area where they live. The incentive is found in the pri-vate gains for farm households if they invest in sustainable soil con-servation measures. The private trade-off is between investing inland conservation and sustaining yield and income gains from theland in the short and long term. Not investing in soil conservationand erosion abatement will reduce yields further and result in in-creasing income losses. Although private benefits and sustainable in-come generation are also a function of rural infrastructures such asmarket access, prices and pricing policies for agricultural inputs andoutputs, including the opportunity cost of family labor (e.g. Pagiola,1999), this will be treated as given in our study.

The contract under evaluation here aims to facilitate and supportthe private investment decision. In terms of Sykutaand and Cook's(2001) three fundamental components of contract design (allocationof value, uncertainty and property rights), the principal in the design,a public entity (local or regional government) offering contracts on avoluntary basis to a wide variety of agents (farmers), provides thenecessary financial means to invest in sustainable soil conservationmeasures in return for the right to assert certain conditions with re-spect to land use management decisions and secure its rights overthe public benefits (including water services). These public benefitsinclude reduced downstream sedimentation, prevention of flooddamage, avoidance of discontinuities in hydropower generation dueto downstream siltation and general preservation of landscape eco-system functioning and biodiversity (Jägerskog et al., 2007). In ex-change, the private value (sustainable income generation fromagricultural yields) falls upon the farmers entering the contract.

The payment mechanism in this case is credit and the necessaryunderlying collateral provided by the principal, the provision costsof which are borne by the farmers and paid from the increase in in-come as a result of sustainable land use management practices. Themost important source of uncertainty in the investment decision isremoved through the allocation of land use certificates, securingfarmer land use over the contract period. However, some uncertaintyis still found on both sides of the transaction. Both the principal andthe farmers have limited information about the implementation andopportunity costs of the soil conservation measures and their effec-tiveness in improving water conditions and associated water servicelevels downstream on the one hand (size of the public benefits) andlong-term farm household income levels on the other hand (size ofthe private benefits). Moreover, the principal runs the risk of adverse

selection. Ideally, only those farmers participate who cause and facethe largest erosion problems and are able to introduce more sustain-able land use management practices in the least cost and most effi-cient way. This hypothesis will be tested in the choice experiment.

Farmers' willingness to enter into a contractual agreement mayalso depend on trust in authorities. A substantial literature (e.g.Cohen and Prusak, 2001; Knack, 2000; Knack and Keefer, 1997;Tyler and Degoey, 1995) demonstrates that trust in authorities is acrucial requirement for policy support and decisions to participatein these policies. Where relationships between community and gov-ernment institutions are characterized as high in trust, participationrates are expected to be higher. Government officials in societieswith higher trust may be perceived as more trustworthy and theirpolicies as more credible. As a consequence, people in such societiesmay be more inclined to adopt more appropriate time horizons inmaking investment decisions, for instance in relation to the selectionof long run optimal production technologies. Lack of trust often man-ifests itself in a high opt-out share in the choice experiment (seeSection 3). As we will show, trust in authorities also plays a role inthis study, although not through a disproportionate share of theopt-out alternative.

The contract design presented above is a significant deviationfrom the payments for ecosystem services literature where farmersare financially compensated for the environmental services they pro-vide (e.g. Engel et al., 2008). This is mainly due to the fact that thegovernment owns the land and farmers are merely given land userights. The contract specifies in most other cases the amount of landthat should be conserved and the compensation paid. The situationin Ethiopia is in that sense different from most payments for ecosys-tem services schemes. A few similar examples exist, but in thesecases contractual agreements are mandatory (Brouwer et al., 2011).

In order for the contract to be incentive-compatible and encourageparticipation of those farmers who are part of the problem and the so-lution, the payment should be at least equal to the landowner's op-portunity cost, while in order for the contract design to beeconomically efficient the compensation should not be higher thanthe value of the benefit provided (Corato, 2008). In this study, thecontract design is informed by the previous research findings in Ethi-opia listed before and experiences with payments for ecosystem ser-vices elsewhere where the success of such schemes was found todepend not only on tenure issues and access to technical assistance,but also on access to credit and existing institutional structures (e.g.Pagiola et al., 2005). Related to this, also the participation of poorerfarm households with no access to micro-credit facilities will betested.

In the choice experiment, farm households are offered a more orless continuous ‘menu of screening contracts’ (Ferraro, 2008) to iden-tify different types of landholders (market segments). Participationconstraints are tested in particular for farmers who (1) live in themost erosion sensitive areas; (2) take no soil conservation measures;and (3) whose income loss due to soil erosion exceeds the contractprice. The latter contract price includes both the costs of soil conser-vation measures and the costs of borrowing money.

3. Choice Experiment

Following Brouwer and Akter (2010) in the context of climatemicro-insurance, the contract design is tested empirically with thehelp of a choice experiment. Choice experiments have become in-creasingly popular in the environmental economics domain (Biroland Koundouri, 2008). However, almost no applications exist pertain-ing to soil erosion and land degradation, with the exception ofColombo et al. (2005). The latter assess the public value attached tothe negative impacts of soil erosion in two watersheds in SouthSpain by presenting the general public with policy alternativeswhich reduce desertification, protect water quality and biodiversity,

170 A. Tesfaye, R. Brouwer / Ecological Economics 73 (2012) 168–178

and safeguard rural jobs at the same time. Their experiment is notused to inform soil conservation contract design as in this casestudy. In this study, the choice experiment allows testing of appropri-ate mechanism configurations through the value attached to charac-teristic features of the contract design. Although hypothetical, theoutcome of the experiment provides important indicators to policyand decision-makers of the participation constraints of different sus-tainable land use management coordination mechanisms, i.e. theinstitutional-economic conditions needed to be in place for farmhouseholds to participate in proposed contractual agreements.

In this case, basic characteristics of the contracts include the spec-ification of the principal, contract duration and the terms and condi-tions under which the credit will be provided. Based on anextensive literature review, key informant interviews and pretestsof the design, two government organizations were used to test farmerpreferences for the two most likely principals, the Regional Bureau ofAgriculture and Rural Development falling under the Federal Ministryof Agriculture and Rural Development and at a local level the PeasantAssociations who are accountable to the Regional Bureaus and con-tributed to previous large-scale soil conservation policy implementa-tion efforts (Osman and Sauerborn, 2001). Non-governmental localfarmer cooperatives were also tested, but found to be not a realisticalternative, most importantly because they have no rights to issueland use certificates contrary to the Regional Bureau and Peasant As-sociation, and their diverse and sometimes weak organizationalstructure at individual community level. Given the positive correla-tion found in the literature between land certification and land in-vestment decisions in Ethiopia (Holden et al., 2009), land usecertificates are considered an important characteristic of the contract.Land use certificates are the registration of land that was previouslyused without formal title. The title gives the land holder usufructrights. The Ethiopian constitution states that all land belongs to thestate and cannot be sold or exchanged from hand to hand. However,in 2005 a Federal land use proclamation stated that farmers have aperpetual use right on their agricultural holdings. To this end, landuse certificates were issued. Ethiopian farmers have seen frequentland redistributions and are hence often uncertain about their landuse rights. Those farmers who do not yet have a land use certificatecan obtain one with the contract, while those farmers who alreadyhave a certificate are guaranteed that their certificate remains se-cured over the contract lifetime. After each contract period, the con-tract can be renewed again. Contracts are offered for the duration of1, 2, 3, 5 or 10 years.

In exchange for different credit and hence payback amounts (witha maximum credit of 3000 Birr1) and additional extension services,2

farmers commit to investing the money in soil conservation measuresand monthly pay-backs spread equally over the duration of the con-tract length at the prevailing market interest rate.3 Additional exten-sion services provided by trained local and regional extension officerswould be provided by the principal to (1) assist farmers in the con-struction and maintenance of the soil conservation structures, and(2) monitor compliance with the contractual agreements and avoidmoral hazard.4 The credit ceiling was fixed based on the limited avail-able information about the investment and maintenance costs of

1 Birr is the local currency in Ethiopia. In December 2009 US$ 1 was about 12.56Ethiopian Birr (Commercial Bank of Ethiopia, 2009). Per capita income in Ethiopia in2008 was US$ 280 (World Bank, 2009).

2 Additional extension services refer to more technical advice from local or regionalextension officers than the current average of at most once a year.

3 Respondents were told that they could borrow up to a limit of 3000 Birr for takingthe soil conservation measures. The monthly payback amounts reflect different creditamounts for different contract durations. The credit can only be used for financingthe implementation of the soil conservation measures.

4 An important advantage of the approach taken here compared to existing pay-ments for ecosystem service schemes where farmers are compensated for their landconservation efforts is the avoidance of the risk of free-riding.

possible soil conservation measures in the literature, available infor-mation about average income levels from theWorld Bank, and pretestinformation about credit uptake in the case study area.

Three possible soil conservation measures are identified in thecontract: soil bunds with grass strips, stone bunds and fanya juuwith grass strips. Soil and stone bunds are ridges and ditches madeof soil or stone, dug across the slope along the contour. Fanya juuare most labor intensive to construct and consist of terraces madeby digging a trench and throwing the soil uphill to form an embank-ment. These three measures are among the best known structuresused to prevent run-off and conserve soil and water in Ethiopia.Fanya juu are most effective in reducing soil erosion, followed bystone bunds and soil bunds (e.g. Herweg and Ludi, 1999). Grass canbe grown on the soil bunds and fanya juu to strengthen the soil con-servation structure. Shiferaw and Holden (1998) show that invest-ment and maintenance costs are on average highest for fanya juuand lowest for soil bunds.

The main components of the contract design are summarized inTable 1.

Alternative contractual designs are created by combining the sixcharacteristics presented in Table 1 based on their different possiblelevels. This yields 1440 possible combinations. Because farmers can-not be shown all 1440 different choice options, the number of possi-ble combinations was reduced to 162 choice tasks, which wereblocked in 18 versions of 9 choice tasks each based on a D-efficientmain effects statistical design procedure using Sawtooth Software. Abalanced overlap method was used to generate the design to approx-imate orthogonality conditions whilst allowing for predefined rela-tionships between attributes.

Each farmer in the survey (see next section) was randomly shownone of these 18 versions and answered 9 choice cards. Each choicecard shows two choice alternatives describing two different contrac-tual agreements along with the option to choose none of the two. Thelatter ‘opt-out’ option, as it was explained to farmers, implies facingincreasing soil erosion in the future and choosing not to invest extrain sustainable soil conservation measures with the help of the princi-pal through the conclusion of a contractual agreement. On the card itwas shown that the cost in this baseline alternative is zero. Farmerswho choose this ‘opt-out’ option are asked in a follow-up questionfor their underlying reasons. In order to make sure farmers have aclear understanding of the choice task, they are first asked to maketheir choice using an instruction card, allowing them to ask questionsabout the task before the experiment started.

For the choice experiment, interviewers were trained to memorizea standard text introducing the contractual agreement. A card dis-playing the contractual agreement and its specific characteristicswas used to help rural farm households understand (the objectiveof) the choice task. An example of a choice card is presented inFig. 1. The survey targeted rural residents with limited education.Therefore the attributes and their levels were conveyed on the choicecards to respondents with pictographs and as little text as possible inAmharic, the national language in Ethiopia. Each choice card was

Table 1Characterization of the contractual agreements in the choice experiment.

Main characteristic Detailed levels in contract

Principal (contract provider) Regional Agricultural BureauLocal Peasant Association

Contract length 1–2–3–5–10 yearsMonthly payment 50–100–150–200–250–300 BirrLand use certificate guarantee Yes, noSoil conservation measure Stone bund

Soil bundFanya juu

Additional extension service 1–2–4–6 times per year

Fig. 1. Example of a choice card.


printed on a separate sheet of paper, laminated, and bound togetherwith other choice cards into a spiral binder for multiple uses.

4. Statistical Choice Model

The choice model that informs this study has its roots in randomutility theory (e.g. Ben-Akiva and Lerman, 1985) and Lancaster's attri-bute based utility theory (Lancaster, 1991). The random utility ap-proach describes the utility of a respondent i's choice for alternativej Uij as consisting of a systematic (observable) component Vij and anerror (unobservable) component εij (Eq. (1)). Vij is usually specifiedas a linear function, additive in utility, where X is a vector of k attri-butes associated with alternative j – in this case the terms and condi-tions of the contract design – and β is the corresponding coefficientvector.

Uij ¼ Vij þ εij ¼ βXij þ εij ð1Þ

The standard choice model, the multinomial logit (MNL) model(McFadden, 1974), assumes that the random component of the

utility of the alternatives is independently and identically (Gum-bel) distributed (i.i.d.) with a type I extreme value (EV) distribu-tion. In the MNL model, substitution patterns are defined by theIndependence of Irrelevant Alternatives (IIA) restriction. This re-striction states that the relative probabilities of two alternativesare unaffected by other alternatives (Kanninen, 2007) and followsdirectly from the i.i.d. EV error terms. In addition, the responsive-ness to or preferences for attributes of different alternatives areassumed to be homogeneous across individuals. These assump-tions lead to a closed-form mathematical model that enables esti-mation through maximum likelihood (ML) procedures (e.g. Green,2003).

Over the past decades alternative modeling approaches have beendeveloped relaxing the IIA assumption, such as mixed logit models,including random parameter logit (RPL) and error-component (EC)models. Mixed logit models account for respondent differences (pref-erence heterogeneity) and repeated choices (Train, 2003). In order toaccount for preference heterogeneity, a vector of random coefficientsof the attributes Xk for individual i can be included in Eq. (1)representing individual preference variation (Eq. (2)). The utility


coefficients β vary according to individual (hence βi) with densityfunction f(β). This density can be a function of any set of parameters,and represents in this case the mean and covariance of β in the sam-ple population. Mixed logit models assume heterogeneity to be

Fig. 2. Location of the Gedeb

continuous over the interval spanned by the distribution for the pref-erence parameters (Scarpa et al., 2005).

Uij ¼ βiXij þ εij ¼ βXij þ f βð ÞXij þ εij ð2Þ

watershed in Ethiopia.

image of Fig.�2

Table 2Sample characteristics of respondents.

Household characteristic Machakel Gozamn Senan

Gender (% male) 85.2 97.2 95.6Average age 48 43 45Share illiterate (%) 45.6 42.4 47.2Average household size 5.4 5.7 5.5Average land size (ha) 1.2 0.8 0.7Average livestock holding (TLU) 5.4 5.2 3.2Average income from crop production (Birr/year) 13,934 7773 7069Average income from off-farm activity (Birr/year) 1441 1462 1626Share with access to micro credit (%) 16.4 14.4 10.4Share living below national poverty line (%) 30.4 50.4 52.4Share with land use certificate (%) 94.0 93.6 86.4Share with access to extension service (%) 90.0 92.3 92.0Share taking soil conservation measures (%) 69.6 67.6 62.8Average annual yield loss due to erosion (kg/ha)a 108 178 227

a Self-reported annual losses over the period 2006–2008.

5 For ease of reading, the outcomes of the statistical tests performed on relationshipsdiscussed in this section are not presented in the text. All test results are available fromthe authors upon request.


ECmodels accommodate correlation between the utilities of alterna-tives (Brownstone and Train, 1999). Correlation between alternatives isaccounted for by including an error component with zero mean in theutility function specification to allow for heteroskedasticity betweenthose alternatives that are likely to be correlated. Scarpa et al. (2005)recommend applying EC models when comparing less familiar (hypo-thetical) alternatives with better known (existing) ones (the opt-outin this case). A dummy variable dj taking the value 1 for each hypothet-ical alternative is included in the utility function of alternative j (Eq. (3)).λj is the parameter of the individual specific random error-componentand is assumed to have a standard normal distribution N[0,1].

Uij ¼ βXij þ f βð ÞXij þ λdj þ εij ð3Þ

Based on the choice design used in this case study, Eq. (3) can berewritten as:

Uij ¼ βkXijk þ f βkð ÞXijk þ βyYij þ λdj þ εij: ð4Þ

In this case study, alternatives are defined in terms of possiblecontract designs for farmers to take specific soil conservation mea-sures on their land through the provision of credit. The incentivecompatibility of the contract design is tested through the inclusionof different contractual terms and conditions (the attributes Xk pre-sented in Table 1 in Section 3), and accounting for famer characteris-tics Y in Eq. (4), such as their exposure level to soil erosion and theircoping capacity (e.g. do they take soil conservation measures alreadyand do they have access to credit facilities).

5. Case Study

The study is carried out in the Gedeb watershed, which is part ofthe wider Choke Mountain watershed, the largest watershed of theBlue Nile river basin in the north-east of Ethiopia, 300 km north ofthe capital Addis Ababa (see Fig. 2). The watershed covers an areaof 871 km2 with an estimated population of 495 thousand peopleaccording to CSA (2007), living in four administrative units calledworedas: Gozamn, Senan, Machakel and Debre Elias.

The area has humid to sub-humid climatic conditionswith an annu-al rainfall ranging from 920 to 1649mm. The temperature in thewater-shed varies between 7.5 and 22.5 °C depending on altitude. Agricultureis the most important economic activity and way of life for over 80% ofthe households living in the watershed. The existing farming system ismixed crop–livestock subsistence farming. Almost all farm householdshave livestock. The main crops grown in the area are tef, wheat, barley(including the local variety called engedo) and potato. Tef is one of themajor staple crops in the country. For sustainable socio-economic de-velopment of the area, soil and water conservation is vital.

The Gedeb watershed faces serious soil erosion problems due tounsustainable land use practices such as deforestation, intensive cropcultivation and overgrazing combined with increasing pressures frompopulation growth and increasing rainfall (Haileslassie et al., 2008).The estimated annual soil loss in the watershed ranges between 10 kgand 140 t per hectare depending on altitude and slope, with an averagethroughout the watershed of 9.1 t per hectare per year (Emrie, 2008).

The case study is conducted in three of the four woredas to accountfor different soil erosion exposure levels: Senan, Gozamn and Macha-kel. The elevation level of the Gedeb watershed ranges from 1500 to4000 m above sea level (MASL). Senan is high land (>3500 MASL),Machakel lowland (1500 MASL), while Gozamn is found in betweenlow and high land (2000-2500 MASL). Soil erosion problems are great-est in Senan, followed by Gozamn and least of a problem in Machakel.

In order to test the contract design through the presented choice ex-periment, a rural household survey was conducted in the three abovementioned woredas of the Gedeb watershed. Two hundred and fifty ran-domly selected household headswere interviewed in eachworeda in July

2009 after one round of focus group discussions in the case study area andtwo rounds of pretests. The questionnaire used for the survey consisted ofthreemain parts. Thefirst part dealtwith the farmers' socio-demographicand economic characteristics, the second part was about farmer percep-tion and attitude towards the soil erosion problems in the watershedand the last part included the choice experiment.

6. Farm Household Characteristics

The sample characteristics are presented in Table 2. A distinction ismade between the three woredas. Most of the 750 respondents inter-viewed (93%) were male household heads with an average age of45 years (respondents ranged in age between 18 and 95 years) and av-erage family size of 5, with a maximum of 12. A slight difference be-tween the woredas is observed with regard to mean age and familysize. However, these differences are not statistically significant.5 Morethan 40% of the sample is illiterate, only 23% had some kind of formalschooling while the rest is able to read and write without any formaltraining. The literacy rate is highest in Gozamn and lowest in Senan.

For 81% of the respondents farming is their principal occupation, 19%supplements their farm income with some form of off-farm activitysuch as the sale of firewood and petty trade. The average land size is0.9 ha, and significantly smaller in Senan and Gozamn than in Macha-kel. In the study area about 90% of the households reported to have aland use certificate. However, when asked about the current tenure sys-tem, a third stated to be dissatisfied because of the unfair and unequaldistribution of crop land among community members. Moreover, itappeared that land use certificates are sometimes issued to farmersunder the condition that they implement soil conservation measures.If they do not take measures or if they are unable to reduce soil erosionthey may still face the threat that their land will be taken away fromthem. In the study area many young and recently married couples arelandless, so even with the current distribution of land use certificates,part of the farmers in the sample believe that some kind of redistribu-tion of landwill be inevitable in the future as these landless young cou-ples cannot start and maintain a household without land. Landlessfarmers produce crops either on rented land or in the form of share-cropping. Sharecropping occurs primarily between landless householdsand landowners who lack sufficient family labor to cultivate crops.

Significant differences are found between woredas in terms offarm household income. Average farm income from crop cultivationis highest in Machakel and lowest in Senan. On the other hand, off-farm household income is higher in Senan compared to Machakeland Gozamn. Another important component of the farming system

7 We also tested different latent class models, but these appeared to fit the data poorly.Based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC),


in the study area is livestock holding. Almost all respondents (96%)own animals such as cows, goats and chicken. Total livestock holdingper farmer is aggregated into Tropical Livestock Units (TLU), whereone TLU equals 250 kg life weight. The average livestock holding inthe sample is 4.6 and varies significantly between Machakel andGozamn on the one hand and Senan on the other. Overall, 44% ofthe sample lives under the national poverty threshold value. This ishigher than the country's national share of 39% (CIA, 2009). In Macha-kel, the share of farmers living below the poverty line is lower thanthis national average, while in Gozamn and Senan just over half ofthe sample lives below the national poverty line.

A majority of the farmers (86%) do not use existing credit facilities,mainly provided by local micro-finance enterprises, most importantlybecause they lack the necessary collateral. No significant income differ-ences can be detected between those farm households who have accessto credit and those who do not. Of those respondents who have accessto credit 55% live above the poverty line. Those who have credit use itto purchase fertilizer, seed, and animal fodder. Extension services arealso available in the study area and around 90% of all farmers receivesome form of technical advice at least once a year from a developmentagent appointed by the government. The advice refers in most cases totechnical support related to soil and water conservation measures,ploughing, weed and pest control. Farmers in Senan perceive soil ero-sion as the most important problem undermining soil fertility. Sixtypercent reported to face severe to very severe soil erosion problems,whereas this was 47% in Gozamn and only 7% in Machakel. Farmerswere asked to carefully consider the impacts of soil erosion on theiryields and provide estimates of annual yield losses as a result of soil ero-sion only (and not any other factors) over the past three years. Thesequantified estimates based on farmers' own expert judgment confirmedqualitative perceptions across the three woredas. Annual per hectareyield losses are, on average, significantly higher in Senan (227 kg)than in Gozamn (178 kg) andMachakel (108 kg). These figures are con-sistentwith available average yield loss information at national level es-timated by the FAO (1986) and Hurni (1988). Across all woredas, theestimated annual damage costs due to soil erosion are, on average,20% of the total income generated by crop production, varying from10% inMachakel to 40% in Senan. These rough estimates based on farm-er self-reported market values of their yield loss seem inflated com-pared to experimental agronomic studies, which reported on-sitecosts of soil erosion for Ethiopia ranging from 2 to 7% of agricultural in-come (e.g. Bojo and Cassells, 1995; Sonneveld, 2002; Sutcliffe, 1993).

Two thirds of all farmers claim to take soil conservationmeasures. Thisshare ismore or less the same inMachakel andGozamn (69%), and slight-ly lower in Senan (63%). This is higher than expected. The high share ofrespondents claiming that they are taking soil conservation measures isexpected to be inflated by fears of losing their land use certificate, despitethe fact that itwas emphasized at the beginning of each interview that thesurvey was conducted independently of existing organizations, the an-swers of respondents would be treated completely confidential and notshared with anyone else and respondents would not be revisited after-wards by anyone to ask them questions about their replies.

Farmers who take soil conservation measures face significantlylower per hectare yield losses due to soil erosion than farmers who donot take any measures in the two most erosion prone areas Gozamnand Senan, while no significant difference can be detected in Machakelwhere yield losses are as expected substantially lower for both groups.6

Remarkably, no significant income differences can be found betweenfarm households who take soil conservation measures and those whodo not. One possible reason for this is that the self-reported agriculturalincome in the predominantly mixed farming systems also includesother on-farm income besides direct crop yield related income. Soil

6 Yield losses due to soil erosion in Machakel are about half of those in Gozamn, whereyield losses are 172 and191 kg/ha/year for farmerswith andwithout soil conservationmea-sures respectively. These numbers are 206 and 279 kg/ha/year for Senan, respectively.

bunds are used most often (68% of all the cases), followed by fanyajuu (18%) and stone bunds (14%). Stone bunds are used least becauseof the lack of sufficient stones. Soil bunds are most common in Senanand Gozamn, while fanya juu and soil bunds are most often applied inMachakel. An important reason for the more popular use of soil bundsis its low cost price (on average 700 Birr/ha/year). Fanya juu ismost ex-pensive (on average 1700 Birr/ha/year), while stone bunds are some-where in between (on average 1300 Birr/ha/year).

A rough cost–benefit analysis based on the available informationfrom farmers who take soil conservation measures about their imple-mentation costs (on average 1030 Birr/ha/year) and available infor-mation from farmers who do not take any soil conservationmeasures about their annual damage costs due to erosion relatedyield losses (on average 1750 Birr/ha/year) shows that it is economi-cally beneficial for farmers to protect their land. This suggests thatbased on private considerations, farmers have a strong incentive toparticipate in the proposed soil conservation contracts.

7. Choice Experiment Results

A majority of farmers felt sufficiently confident about and trustedthe terms and conditions of the contractual agreements they were of-fered in the choice experiment and were interested in concluding acontract to implement soil conservation measures on their land to re-duce soil erosion. Twenty-three of the 750 farmers (3%) refused toenter into a contract, either because they felt the current situationon their land is good enough (all these farmers lived in Machakelwhere erosion is less of a problem) or because they were unable to af-ford to borrow money. None of these respondents indicated that theydo not trust the authorities. The results of farmer choice behavior inthe choice experiment, including the 23 farmers who consistentlychose none of the two presented contracts, are presented in Table 3.Only those variables are presented that appeared to have a significanteffect at least at the 10% significance level on farmer choice behaviorfor one of the two contract alternatives. The number of observationsis slightly lower than 6750 (750 respondents times 9 choices) dueto missing values for some of the explanatory variables.

A combined random parameters and error component logit modelwas estimated in NLOGIT version 4.0 that accounts for the panel datastructure of the choice model.7 For efficiency purposes, the model isestimated using a Halton sequence of 100 replications in a quasi-Monte Carlo maximum likelihood simulation (Bhat, 2001). Themodel is highly significant (outcome of the model χ2 is 4943.148with 23 degrees of freedom) with a relatively high pseudo R2 forthis type of cross-section panel data analysis of almost 35%. Asexpected, no selection bias could be detected between the unlabelledcontract alternatives. The first option was chosen in 44% and the sec-ond option in 47% of all choice occasions. The ‘opt-out’ (none of thetwo) was chosen in the remaining 9% of the choice occasions. Theoutcome for the error component is highly significant at the one per-cent level and indicates the presence of heteroskedasticity, i.e.farmers perceived the two hypothetical contract alternatives distinct-ly different from the existing situation. The significant positive out-come of the alternative specific constant (ASC) implies that farmersprefer a change instead of no change from the current situation.

All attributes have a significant impact on choice behavior and arecharacterized by preference heterogeneity as can be seen from thesignificant standard deviations of the estimated random parameters,except the contract price which is included as a fixed parameter

four classes were identified, but we were unable to properly explain class membership inthe segment choice equations based on a stable set of significant variables. Attributeswereinsignificant in two of the four classes, while at most the location where the individualfarmer lives and whether the respondent belongs to the group of poorer farmers withno access to credit showed up as significant explanatory variables of class membership.

8 All farmer preference heterogeneity variables in Table 3 are included in the estimatedchoice model as an interaction term with the ASC except for the value attached to the at-tribute land use certificate by farmers with little or no trust in the contracts and the valueattached to the attribute soil bunds by farmers without soil conservation measures.

Table 3Estimated choice model.

Variable Coding Mean Standarddeviation

Coefficientestimate

Standarderror

Random parameters

Standard deviation Standard error

ASC 4.418⁎⁎⁎ 0.556Contract characteristics (attributes)If contract provider is Regional Bureau Dummy 0.332 0.471 −0.094⁎⁎ 0.048 1.197⁎⁎⁎ 0.098Contract length (years) Linear 2.821 3.298 0.023⁎⁎⁎ 0.007 0.066⁎⁎⁎ 0.014If contract includes land use certificate Dummy 0.333 0.471 1.423⁎⁎⁎ 0.090 2.438⁎⁎⁎ 0.125If soil conservation measure is soil bund Dummy 0.224 0.471 0.315⁎⁎⁎ 0.062 1.004⁎⁎⁎ 0.116If soil conservation measure is fanya juu Dummy 0.224 0.471 0.238⁎⁎⁎ 0.050 0.455⁎⁎⁎ 0.184Extension service frequency (visits per year) Linear 2.147 2.186 0.082⁎⁎⁎ 0.013 0.143⁎⁎⁎ 0.021Contract price (Birr/month) Linear 115.277 106.922 −0.003⁎⁎⁎ 0.0003

Farm household characteristics (preference heterogeneity)If farmer lives in Senan Dummy 0.333 0.471 0.784⁎⁎ 0.328If farmer lives in Machakel Dummy 0.333 0.471 −1.487⁎⁎⁎ 0.350If farmer already takes soil conservation measures Dummy 0.667 0.471 −0.967⁎⁎⁎ 0.313If low income and no access to credit Dummy 0.593 0.491 −0.572⁎⁎ 0.294Amount of livestock owned (Tropical Livestock Units) Linear 4.624 3.129 0.165⁎⁎⁎ 0.057Value land use certificate if there is no or little trust Dummy 0.102 0.303 −0.369⁎⁎⁎ 0.152If farmer takes no soil conservation measures and:Contract priceb income loss Dummy 0.169 0.374 1.014⁎⁎⁎ 0.102Soil conservation measure is soil bund Dummy 0.075 0.263 −0.218⁎⁎ 0.098

Model summary statisticsσ error component 3.696⁎⁎⁎ 0.222Log likelihood −4696.871McFadden R-squared 0.345N 6525

Significance levels: *** 1% ** 5%.


under the assumption of constant marginal utility of income. Follow-ing Hensher et al. (2005), the random parameters of all dummy vari-ables in Table 3 are estimated using a uniform distribution. Thecontract length and extension services are presented in Table 3 as lin-ear effects coded variables. Their random parameters are estimatedusing a normal distribution. Higher attribute levels are valued posi-tively. Only price has, as expected, a significant negative impact onthe probability that a contract is chosen. The negative impact of themonetary price attribute indicates that the trade-off presented tofarmers of paying for credit to enable them to implement measureson their land to reduce soil erosion through the provision of a con-tract functioned as foreseen.

A dummy variable is included for the regional bureau of agricul-ture (RBA), so the Local Peasant Association is the baseline category.Farmers prefer to enter into a contract with the local government in-stead of the regional government. Moreover, farmers prefer longer-term contracts and more intensive collaboration with extension offi-cers to get more frequently advice on soil conservation. Although amajority receives technical advice, current extension services aimedat reducing soil erosion are considered insufficient by most respon-dents. Parallel to the increase in frequency, the quality of the exten-sion service is expected to play an important role too.

Despite the fact that most farmers already have a land use certifi-cate, this contract attribute is valued highly by all farmers. An impor-tant reason for this may be farmer experiences with the long historyof land redistribution policies. Although farmers have been assignedland use certificates, they seem to lack trust that the certificates willguarantee future use rights and therefore attach a particularly highvalue to this attribute. The coefficient estimate is by far the highestfor all attribute dummy variables. A third of the sample also indicatedthat the current distribution of land is unfair and not sustainablegiven increasing demand for land from younger generations. No sig-nificant effect could be found when interacting the dummy for theland use certificate attribute with the dummy representing farmerswho already have a land use certificate. This means that farmerswith and without a land use certificate value the attribute equallyhigh. On the other hand, a significant negative effect is found for the

small share of respondents who have little or no trust in the proposedcontracts. This group values the land use certificate significantly less.

When examining the results for the type of soil conservation mea-sures, farmers prefer soil bund and fanya juu over stone bund, which isused as the baseline category. Soil bund is slightly more preferred tostone bund than fanya juu, but the difference between soil bund andfanya juu is not statistically significant (the Wald test statistic equals1.475; pb0.225). Hence, when given the choice, farmers are more likelyto implement a soil bund or fanya juu. Farmer preferences for soilbunds can be explained, because they are most commonly implementedthroughout thewatershed already, least labor intensive and least costly ofall threemeasures. Farmers also consider soil bunds the simplestmeasureto implement of all three measures. Fanya juu are more labor intensiveandmore expensive than soil bunds. Farmer preferences for thismeasurecan be explained because they are most effective in reducing soil erosion.A significant correction factor is found for farmers who do not take anysoil conservation measures yet when examining their interaction withthe soil conservation measures in Table 3. Compared to farmers who al-ready take measures, farmers without soil conservation measures stillprefer soil bund over stone bund (the negative coefficient −0.218 doesnot outweigh the positive coefficient 0.315 for soil bund), but significantlyless at the 10% level than in the case of fanya juu forwhich no such correc-tion factor canbe detected (theWald test statistic equals 2.795;pb0.095).Hence, soil bunds and fanya juu are most preferred overall, but farmerswho have no experience implementing soil conservation measures ap-pear to have stronger preferences for fanya juu than soil bunds.

Turning to the other non-attribute variables measuring preferenceheterogeneity, the choice experiment and contractual design seem topass the most important participation constraint tests (see Section 2).First, the location of the farmer plays a significant role in choice behav-ior. Gozamn, the midland woreda, represents the baseline category inthe model presented in Table 3.8 As expected, farmers living in

Table 4Market share simulation results (%).

Senan Machakel No contract Total

All farmers 82.5 10.4 7.1 100.0Farmers without SCM 90.1Farmers with SCM 4.4No contract 5.5Total 100.0

Soil bund Fanya juu No contract


Low income and no access to credit Higher income access to credit No contract


SCM: soil conservation measures.


Machakel where soil erosion problems are lowest are less likely to par-ticipate than farmers in Gozamn, while farmers living in Senan wheresoil erosion problems are highest are more likely to participate thanfarmers in Gozamn. Second, farmers who already take measures areless likely to conclude a contract, confirming that the contracts appealmost to the target groupmost in need of taking action, i.e. those farmersare expected to participate who face the biggest soil erosion problemsand do not take any measures yet.

Third, and perhaps the strongest test, those farmers who take nosoil conservation measures yet are more likely to participate if thecontract price is less than or equal to the income losses sufferedfrom soil erosion. The dummy variable has the value one if the equa-tion ‘contract priceb income loss’ applies. For farmers who alreadytake soil conservation measures, the expectation was that theywould be more likely to participate if their self-reported annual soilconservation implementation costs9 are lower than their annualyield losses due to soil erosion, i.e. if soil conservation efforts are al-ready beneficial. This would further avoid adverse selection as choiceswould be driven by economic rational considerations if only farmerswho already take soil conservation measures participate whose pri-vate benefits offset the investment costs in soil conservation. Howev-er, this variable was not statistically significant at the 10% level. Othervariables that were also tested to distinguish betweenmore or less ef-ficiently operating farmers, but were not statistically significant in-cluded farm size and crop income generated per hectare.

Finally, the contracts were expected to appeal to another importanttarget group, namely poorer farmers with no access to creditopportunities. The variable ‘low income and no access to credit’ is an in-teraction term between farm households with an annual income levellower than the sample average of 9900 Birr per year and no access tocredit facilities. Poorer households were expected to be less willing toenter a contractual agreement and pay for credit to finance soil conser-vationmeasures. However, providing these poorer households access tocredit was expected to offset this negative effect. The significant nega-tive sign indicates that poorer households without access to credit areless likely to enter into a contractual agreement than higher incomehouseholds with access to credit. The same effect is foundwhen includ-ing a dummy variable for respondents who do not have access to credit(without the interaction with low income). Wealthier households with

9 These implementation costs include the one time off investment costs in soil con-servation measures, the opportunity costs of productive land needed to be set aside forthe conservation structure and annual maintenance costs.

more livestock are more willing to conclude a contract, most likely be-cause they can afford to pay for credit. So, even if we control for house-hold wealth, the effect for farmers belonging to a poorer segmentwithout access to credit remains negative. No significant effect couldbe detected for any other demographic household characteristic (e.g.age of the household head, education and literacy rate).

8. Contract Market Shares

Based on the estimated choice model, market share simulations arecarried out (also in NLOGIT 4) to test how changes in the level of theinstitutional-economic incentives impact upon choice probabilities ofeach contract design. Different contract policieswere created to explorewhich mix of institutional-economic incentives generates the highestfarm household demand for a particular contract measured throughthe highest market share. The market shares are simulated for themain target groups, i.e. farmers living in different erosion prone areas,with and without soil conservation measures, with and without accessto credit facilities. The key results are presented in Table 4. The marketshares are estimated for the average farmer in the study area under theassumption that the contract will be offered for a period of 10 years anda monthly credit payback price of 175 Birr (the average price based onthe 6 price levels in the choice experiment) for which farmers obtaina guaranteed land use right (certificate) and technical advice from anextension service officer 4 times per year.

Table 4 first shows the market shares across all farmers whenvarying only the location, followed by the specification of the typeof measure in the contracts and different farm household characteris-tics. As expected from the choice model results presented in Table 3,the market shares are consistently highest for farmers without soilconservation measures. Also the share of farmers who are expectednot to conclude a contractual agreement is shown. Just over 80% ofall farmers concluding a contract would be found in Senan, 35% ofthe farmers would belong to a lower income group with no accessto credit, and choice behavior would not be very different across ei-ther soil bund or fanya juu if farmers would be allowed to choose be-tween these two types of measures in the contract specification.

In a second step, also a distinction is made between farmers withand without soil conservation measures. For instance, the marketshare in Senan increases to 90% if only farmers without soil conserva-tion measures would be accounted for, while the share of farmerswith soil conservation measures concluding a contract in Machakelis only 4%. Similar increases in market shares as a result of targeting


farmers without soil conservation measures can be observed whenexamining the results for lower income groups without access tocredit. An additional effect is also found if farmers without measuresare allowed to choose between types of measures. In that case, amuch larger share of farmers not taking soil conservation measuresis expected to implement the fanya juu.

Instead of varying only one or two factors during the market sim-ulation (keeping other influencing variables fixed), also scenarios canbe constructed where multiple variables change at the same time. Forexample, when offering the same contract as before and comparingchoice behavior of farm households with and without measures andaccess to credit, 55% of the market would consist of lower incomefarmers with no access to credit and who take no soil conservationmeasures compared to 37% higher income farmers with measuresand access to credit. Varying in this case also the location wherefarmers live, this market share increases further to 87% for farmhouseholds living in the more erosion exposed woreda Senan.

9. Discussion and Conclusions

The main objective of this paper was to assess whether, and if sounder which terms and conditions, rural households were willing toenter into contractual agreements to invest in soil conservationmeasures on their land in one of the most soil erosion affectedparts of the world. To this end, contractual agreements were intro-duced and participation constraints tested under different soil ero-sion and institutional-economic conditions in a survey-basedchoice experiment targeting 750 rural households, with the aim toinform improvement efforts of the incentive structure of currentland use management. The survey provided new insights into previ-ously unknown differences between farmers who take soil conser-vation measures and farmers who do not. Not only in terms ofindividual farm household characteristics, but also in terms of (i)the damage costs due to soil erosion, (ii) the implementation costsof soil conservation measures, and (iii) the effectiveness of soil con-servation measures in reducing yield losses. This allowed us to as-sess the economic efficiency and hence incentive behindinvestment decisions in soil conservation. The choice experimentrevealed important information related to demand for different con-tractual designs and market segments.

Important reasons why not all farm households take soil conserva-tion measures tested in this study include the lack of finance, knowl-edge about conservation practices and long-term land use security.Corresponding with the outcomes of previous studies, land use certi-fication and intensification of extension services are among the mostimportant conditions needed to be in place for farmers to commit tosoil conservation efforts. Farmers have strong preferences for con-cluding renewable longer term contracts.

Choice behavior showed that out of the three soil conservationmeasures most commonly implemented in the study area, farmersequally prefer soil bunds and fanya juu over stone bunds. Prefer-ences for soil bunds can be easily explained because they are theleast costly soil conservation measure. Fanya juu have been identi-fied in the literature as the most effective measure to abate soil ero-sion, but are also by far the most costly. Farmers not taking soilconservation measures prefer soil bunds less than farmers takingmeasures already, while such a significant difference cannot befound for fanya juu. Hence, despite their high implementationcosts fanya juu are the most preferred soil conservation measureamong farmers not taking any measures. When given the opportu-nity to choose between soil bunds and fanya juu, two thirds wouldchoose the latter, suggesting that when offered the financial re-sources to implement soil conservation measures, farmers mayalso consider their effectiveness and choose ‘value for money’. Itshould be noted that the soil conservation measures were explainedto all farmers participating in the rural household survey based on a

technical description provided by agronomic experts, but did notrefer to the costs and effectiveness of the measures. It was left upto farmers themselves to decide which measures would be most ap-propriate for their specific situation. We show that contracts pro-vided by local government peasant associations offering additionalcredit, land use security and extension services could be an effectivemeans to increase the share of farmers implementing soil conserva-tion measures, especially for those most in need of such measures.Less than 10% of the sample of 750 farm households had doubts orlacked confidence in the contracts' terms and conditions. The set-up of the contract design passed three of the four main participationconstraints. As expected, farmers facing higher soil erosion rates aremore likely to participate, while farmers who already take soil con-servation measures are less likely to participate and enter into acontractual agreement with the local government. This suggeststhat adverse selection will not play a major role when issuing con-tracts. Only the target group poorer farmers with no access to creditare less likely to participate. We show that farmers not taking anysoil conservation measures will do so if the contract price is lowerthan or equal to the income losses suffered from soil erosion sug-gesting rationality in their behavior.

Finally, in order to further inform the design of incentive-compatible contracts and enable the local government to target dif-ferent groups of farmers (market segments), a number of other fac-tors were tested with the help of the estimated choice model,including the operation costs of those farmers implementing soil con-servation measures and the productivity of different sized farmhousehold holdings not taking soil conservation measures. However,no significant effects could be detected for farmers operating at differ-ent levels of economic efficiency on their choice behavior for differentcontract designs. Here too further research is needed to increase gov-ernment level of understanding when trying to identify different tar-get groups of farmers for the purpose of the provision of incentive-compatible contracts. The analysis presented in this paper was unableto shed more light on possible segmentation of farmers beyond theirexposure rate to soil erosion measured through the location wherethey live and whether or not they take soil conservation measureson their land. At most, elicited preferences for implementing themost cost-effective measure fanya juu hint at some degree of incen-tive compatibility would contracts actually be concluded in the casestudy area.

Acknowledgements

This project is funded by the Netherlands Organization for Scien-tific Research (NWO-WOTRO) and part of the project ‘In Search ofSustainable Catchments and Basin-wide Solidarities; Transbound-ary Water Management of the Blue Nile River Basin’ coordinatedby UNESCO-IHE. Co-funding from the Institute for EnvironmentalStudies, VU University Amsterdam is gratefully acknowledged. Weare grateful to the Dean of the Faculty of Agriculture, Debre MarkosUniversity, who helped us with the practical organization of therural household survey and Dr. Melesse Temessgen and Mr. SebsibBelay from the College of Development Studies, Addis Ababa Uni-versity, for their support setting up the field work. A word of thanksalso goes to the interviewers recruited from Debre Markos Univer-sity, the students Ted Veldkamp and Jurre Tanja from VU UniversityAmsterdam for their help with the on-site pre-testing of the surveyand focus group discussions, and the PhD researchers and supervi-sors in the Blue Nile Hydro-Solidarity project for their useful com-ments on the study presented in this paper, in particular Prof.Pieter van der Zaag, Dr. Belay Simane and Dr. Workneh Negatu. Afinal word of thanks goes to Alfred Wagtendonk from the Institutefor Environmental Studies for his help with the maps presented inthis paper.


References

Abera, B., 2003. Factors influencing the adoption of soil conservation practices inNorthwestern Ethiopia. Discussion Paper No. 37. Institute of Rural Development,University of Gottingen, Germany.

Admassie, Y., 2000. Twenty Years to Nowhere. Property Rights, Land Management, andConservation in Ethiopia. The Red Sea Press Inc.

Anley, Y., Bogale, A., Haile-Gabriel, A., 2007. Adoption decision and use intensity of soiland water conservation measures by smallholders subsistence farmers in DedoDistrict, Western Ethiopia. Land degradation and Development 18, 289–302.

Ayalneh, B., Taeb, M., Mitsugi, E., 2006. Land ownership and conflicts over the use of re-sources: implication for household vulnerability in eastern Ethiopia. EcologicalEconomics 58, 134–145.

Bhat, C.R., 2001. Quasi-random maximum simulated likelihood estimation of mixedmultinomial logit model. Transportation Research Part B 35, 677–693.

Bekele, W., Drake, L., 2003. Soil and water conservation decision behavior of subsis-tence farmers in the Eastern highlands of Ethiopia: a case study of Hunde-Laftoarea. Ecological Economics 46, 437–451.

Bekele, W., 2004. Economics of soil and water conservation: Theory and empirical ap-plication to subsistence farming in the eastern Ethiopian highlands. PhD Thesis.Swedish University of Agricultural Science, Uppsala.

Ben-Akiva, M., Lerman, S.R., 1985. Discrete Choice Analysis: Theory and Application toTravel Demand. The MIT Press, Cambridge Massachusetts.

Birol, E., Koundouri, P. (Eds.), 2008. Choice Experiments Informing Environmental Pol-icy. A European Perspective. Edward Elgar Publishing, Cheltenham, UK.

Bojo, J., Cassells, D., 1995. Land Degradation and Rehabilitation in Ethiopia: A Reassess-ment. AFTES Working Paper No. 17. World Bank, Washington DC.

Bolton, P., Dewatripont, M., 2005. Contract Theory. The MIT Press, CambridgeMassachusetts.

Brouwer, R., Akter, S., 2010. Informing micro insurance contract design to mitigate cli-mate change catastrophe risks using choice experiments. Environmental Hazards9, 74–88.

Brouwer, R., Tesfaye, A., Pauw, P., 2011. Meta-analysis of institutional-economic factorsexplaining the environmental performance of payments for watershed services.Environmental Conservation 38 (4), 380–392.

Brownstone, D., Train, K., 1999. Forecasting new product penetration with flexible sub-stitution patterns. Journal of Economics 89, 109–129.

CIA (Central Intelligence Agency), 2009. The World Fact Book.Cohen, D., Prusak, L., 2001. In Good Company: How Social Capital Makes Organizations

Work. Harvard Business Press.Corato, L.D., 2008. Mechanism design for biodiversity conservation in developing coun-

tries. The University of York and the University of Padua. Working Paper No. 34.Colombo, S., Hanley, N., Calatrava-Requena, J., 2005. Designing policy for reducing the

off-farm effects of soil erosion using choice experiments. Journal of AgriculturalEconomics 56 (1), 81–95.

Commercial Bank of Ethiopia, 2009. International Banking Department. Exchange ratedaily report, Addis Ababa, Ethiopia.

CSA (Central Statistical Authority), 2007. Household Income, Consumption and Expen-diture Survey. Statistical Abstract.

El-Swaify, S.A., Hurni, H., 1996. Transboundary effects of soil erosion and conservationin the Nile river. Land Husbandry 1 (1/2), 6–21.

Emrie, K., 2008. Assessment of land use land cover change and land degradation ingedeb watershed, choke mountains. Unpublished M SC. Thesis.

Engel, S., Pagiola, S., Wunder, S., 2008. Designing payments for environmental servicesin theory and practice: an overview of the issues. Ecological Economics 65 (4),663–674.

FAO (Food and Agricultural Organization of the United Nations), 1986. Ethiopia high-land reclamation study. Final Report. FAO, Rome, Italy.

Ferraro, P.J., 2008. Asymmetric information and contract design for payments for envi-ronmental services. Ecological Economics 65, 810–821.

Gebremedhin, B., Swinton, M., 2003. Investment in soil conservation in Northern Ethi-opia: the role of land tenure security and public programs. Agricultural Economics29, 69–84.

Green, W.H., 2003. Econometric Analysis. Prentice-Hall International, Upper SaddleRiver (NJ).

Grosh, B., 1994. Contract farming in Africa: an application of the New Institutional Eco-nomics. Journal of African Economies 3 (2), 231–261.

Haileslassie, A., Hagos, F., Mapedza, E., Sadoff, C., Awulachew, S.B., Gebreselassie, S.,Peden, S., 2008. Institutional setting and livelihood strategies in the Blue Nilebasin: implication for upstream/downstream linkage. Working Paper 132. Interna-tional Water Management Institute, Colombo, Sirilanka.

Hensher, D., Rose, J., Greene, W.A., 2005. Applied Choice Analysis: A Primer. CambridgeUniversity Press, Cambridge.

Herweg, K., Ludi, E., 1999. The performance of selected soil and water conservationmeasures — case studies from Ethiopia and Eritrea. Catena 36 (1–2), 99–114.

Holden, S., Deininger, K., Ghebru, H., 2009. Impacts of low-cost land certification on in-vestment and productivity. American Journal of Agricultural Economics 91 (2),359–373.

Hurni, H., 1988. Degradation and conservation of the resources in the Ethiopian high-lands. Mountain Research and Development 8 (2/3), 123–130.

Jägerskog, A., Granit, J., Risberg, A., Yu, W., 2007. Transboundary water management asa regional public good. Financing development — an example from the Nile Basin.SIWI Report Nr. 20. SIWI, Stockholm.

Kanninen, B.J. (Ed.), 2007. Valuing Environmental Amenities Using Stated Choice Stud-ies. Springer, Dordrecht, the Netherlands.

Knack, S., Keefer, P., 1997. Does social capital have an economic payoff? A cross-country investigation. Journal of Economics 112 (4), 1251–1288.

Knack, S., 2000. Social Capital and the Quality of Government: Evidence from the Unit-ed States. World Bank Development Research Group, Washington D.C.

Lancaster, K., 1991. Modern Consumer Theory. Edward Elgar, Aldershot, UK.McFadden, D., 1974. Conditional logit analysis of qualitative choice behaviour. In: Zar-

embka, P. (Ed.), Frontiers of Econometrics. Academic Press, New York.Osman, M., Sauerborn, P., 2001. Soil and water conservation in Ethiopia. Review arti-

cles. Soils and Sediments 1 (2), 117–123.Pagiola, S., 1999. The Global Environmental Benefits of Land Degradation Control on

Agricultural Land. The World Bank, Washington, D.C.Pagiola, S., Arcenas, A., Platais, G., 2005. Can payments for environmental services help

reduce poverty? An exploration of the issues and the evidence to date from LatinAmerica. World Development 33 (2), 237–253.

Scarpa, R., Ferrini, S., Willis, K.G., 2005. Performance of error component models forstatus-quo effects in choice experiments. In: Scarpa, R., Alberini, A. (Eds.), Applica-tions of Simulation Methods in Environmental and Resource Economics. SpringerPublisher, Dordrecht, The Netherlands.

Shiferaw, B., Holden, S.T., 1998. Resource degradation and adoption of land conserva-tion technologies by smallholders in the Ethiopia highlands: a study in Andit Tid,Northern Shoa. Agricultural Economics 18, 233–247.

Shiferaw, B., Holden, S.T., 1999. Soil erosion and small holders conservation decision inthe highlands of Ethiopia. World Development 27 (4), 739–752.

Sonneveld, B.G.J., 2002. Land under Pressure: The Impact of Water Erosion on Food Pro-duction in Ethiopia. Shaker Publishing, Maastricht, The Netherlands.

Sutcliffe, J.P., 1993. Economic Assessment of Land Degradation in the Ethiopian High-lands: A Case Study. National Conservation Strategy Secretariat, Ministry of Plan-ning and Economic Development, Transitional Government of Ethiopia, AddisAbaba.

Sykutaand, M.E., Cook, M.L., 2001. A new institutional economics approach to contractsand cooperatives. American Journal of Agricultural Economics 83 (5), 1273–1279.

Taddese, G., 2001. Land degradation: a challenge to Ethiopia. Environmental Manage-ment 27 (6), 815–824.

Train, K., 2003. Discrete Choice Methods with Simulation. Cambridge University Press.Tyler, T., Degoey, P., 1995. Collective restraint in social dilemmas: procedural justice

and social identification effects on support for authorities. Journal of Personalityand Social Psychology 69 (3), 482–497.

World Bank, 2009. World Development Indicators Database. World Bank, WashingtonDC, USA.

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