Economic Value of Wondo Genet Forest in Domestic Water Supply of theCommunity of Shashemene Town

By Bamlaku Ayenew1, Yemiru Tesfaye2

1Hawassa University Wondo Genet Collage of Forestry and NaturalResources, Department of Natural Resource Economics and Policy;Email: baml07@yahoo.com

2 Assistance profecer at Hawassa University Wondo Genet Collage ofForestry and Natural Resources, Department of forestry; Email:tyemiru@yahoo.com

AbstractThe study attempts to determine the economic value of Wondo Genet forest (WGF) in domesticwater supply of the community of shashemene town by eliciting households’ willingness topay (WTP) using contingent valuation method in the form of Double Bounded DichotomousChoice technique followed by an open-ended question. Descriptive statistics and econometricBivariate probit models were applied to determine the mean and factors affecting WTP forforest conservation so as to improve water. A sample of 215 households was randomlyselected, and the survey was used for face to face interviews. Results of the study showed thatmost respondents were WTP for forest conservation. Response to the hypothetical scenariorevealed that sampled households expressed their WTP from the open ended elicitationmethod was 26.31 ETB per month added with water bills. The mean WTP for the doublebounded bivariate probit estimate with covariates ranged from 35 ETB to 30 ETB per monthfor the initial bid and for the follow up bid, respectively. The results of the model revealed thatSex, Education, income, have positive and significant effects on WTP while Age, Family size,Amount of bid have a negative and significant effect. In nutshell, it can be concluded that theeconomic value of WGF ecosystem for the population of Shashemene is substantial that theyare WTP approximately 1.8 million ETB per year for its conservation and sustained supply ofdrinking water. Significant socio-economic variables should also consider while designingforest conservation related project at a household level.

Key words: Ecosystem Services of Forests, willingness to pay,contingent valuation

Introduction

Forests provide importantecosystem services, beyondsupplying direct use products,such as timber, fiber, fuel wood,woody biomass for energy and the

non-timber forest products(NTFPs). These provisioningservices are often considered tobe the main forest ecosystemservices. However, theprovisioning services depend onsoil formation, photosynthesis,

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and nutrient cycling, which areso-called supporting services,provided by the forests [1].However, forests are under severethreat in many part of the world.Averages of almost 15 millionhectare of forest were lost everyyear during the 1990s, mostly inthe tropics Pagiola et al. [2].Ethiopia is among the countrieswhere millions of people dependon forests and forest productsfor their livelihoods [3]

The significant economic growththat Ethiopia has achievedparticularly over the past fewyears could also mean that unlessthe forest resources are managedproperly and attempts are made toincrease their size, the pressureon forests would increase, whichwould worsen the problem [4].Loss of forest has beenaccompanied by a loss ofecosystem services that forestsprovide [5]. As a result of this,more than one billion city-dwellers in the world lack accessto clean water Stolton et al. [6].So, multilateral agencies (e.g.,World Bank 2003), and nations(e.g., Miranda, et al., 2007) arepromoting and adopting newapproaches to finance forest and

watershed conservation/protectionso as to develop sustainable useand management of waterresources.

To improve watershed conditionsand to sustain water resources,sustainable forest management isconsidered as a key factor towater resources management inparticular and upland resourcesdevelopment in general Kerr et al.[7]. Stolton et al. [6] Study showsthat much of the world’s drinkingwater comes from catchments thatare or would naturally beforested. There appears to be aclear link between forests andthe quality of water coming outof a catchment, a much moresporadic link between forests andthe quantity of water availableand a variable link betweenforests depending on type, ageand constancy of flow.

Payment for EnvironmentalServices (PES) approaches attemptto use economic incentives withinregions to protect naturalresources while accommodatingagricultural production,forestry, drinking water supplyand tourism, [8]. Watershed PESprograms have been designed to

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use direct payments to compensateupstream resource users for theirnatural resource stewardship andchanges in land use that resultin ecological services todownstream beneficiaries [9].Direct payment approaches such asPES have been found to be cost-effective means for resourceconservation and sustainableecosystem management Ferraro et al.[10]. In Ethiopia there is verylittle experience except some PESprogram such as the watershedproject of the Eastern NileTechnical Regional Office (ENTRO)of the Nile Basin Initiative(NBI), support to localcommunities for managing theirsmaller watersheds by governmentand donors [11].

Wondo Genet natural forestcatchment is a primary source ofdrinking water for surroundingareas, including Hawassa andShashemene towns in addition toits immense economic benefits[12; 13; 14]. However, it is alsoa catchment of increasingdeforestation and severe resourcecompetition especially foragricultural land and waterleading to high occurrence ofresource conflicts. The main

resource degradations includeillegal felling of trees for fuelwood and construction materials,charcoal making, pit-sawing,expansion of agriculture andhuman-induced fire. For instance,during the past three decades,the forest area in catchment hasbeen shrinking by 72% by means ofillegal act Gessesse et al. [15].This has led to substantialdecline and deterioration of themost valuable ecosystem servicesof the forest to the surroundingpeople. In particular, thedegradation of the natural forestis affecting the hydrologicalcycle and ground water provisionof the catchment creating massiveimpact on the domestic waterusers of the two nearby towns;Hawassa and Shashemene. As aresult, the drinking watersupply, quantity and quality isbeing seriously affected. Oneimportant reason for the decade’slong degradation andineffectiveness of the variousefforts to sustainably conservethe Wondo Genet natural forest islack of critical knowledge on thevalue of the environmentalservices of the forest for localand national economy. This lackof appropriate economic valuation

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and awareness on the ecosystemservices of the forest hasnegatively affected decisionmaking and resource managementactivities at local, regional andnational levels.

Sustaining these ecosystemservices and maximizing theirbenefits thus entails propervaluation, recognition andincorporation of the value ofecosystem services in themanagement planning, utilizationand conservation activities anddecision making processes [16].However, the intangible nature ofthese services coupled withmarket failure for ecosystemservices has limited the maineconomic values of the forest totimber and fuel wood. This hasled to the undervaluation ornegligence of the valuable forestecosystem services. In Ethiopiain general and in Wondo Genet inparticular, the domestic watersupply service of the Wondo Genetforest is not economically valuedand remains unknown. As a result,the potential economiccontribution and participation oflocal water users in Shashemenetown for ensuring thesustainability of the forest

remains unknown and untapped. Theobjective of this paper is toinvestigate the economic value ofWondo Genet forest resource inensuring improved domestic watersupply service for the communityof Shashemene town, with thespecific objective to elicit anddetermining households’willingness to pay (WTP) for theforest ecosystem conservationusing CVM and to determine thefactors affecting households’WTP.

2. Materials and Methods The Study area

The town of Shashemene is foundin the southern Ethiopia inOromiya administrative regionalstates, lying at about 250 Kmsalong the major road betweenAddis Ababa and the town ofHawassa. Geographically, the townis located at 7011'09"N to7013'19" N latitude and 38035'02"E to 38037'05" E Longitude. Itlocated within the Great RiftValley system and is close to thelakes and holiday resorts ofHawassa town, Langano and theShala-Abiyata Park. Its altituderanges from 1,672 - 2,722 ma.s.l. Mount Abaro is the highest

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point 2, 580 m a.s.l. Accordingto the official CentralStatistical Survey 2001 E.C, thetotal population of Shashemenewas estimated be around 150,000by 39,474 households.

Water supply of the town

According to the informationobtained from the town’s waterservice office, the main sourceof water for the Shashemene towninhabitants is coming from WondoGenet forest watershed and Solaground water. The average dailywater production capacity fromWondo Genet forest is 157, 013m3/second. The water coming fromthe two areas is distributed tothe consumers through 1,290connections and six publicfountains. However, from time totime the amount of water comingfrom these two areas decreased,while demand of water increased.Now a day Shashemene town watersupply and sewerage Servicereported that the water supplydevelopment schemes are beingconstructed in different parts ofthe district. As a result, thetotal population with access topotable water in the town hasreached 87% in 2004 E.C.

Sampling Techniques and Sample SizeA two- stage random samplingtechnique was used when selectingsample respondent households forthe study. In the first phase,three kebeles were selected outof the eight kebeles which arewater beneficiaries of the watercoming from Wondo Genet forestcatchment for domestic purposes.The three kebeles are Didaboka,Abosto and Bulchana. In thesecond phase, proportionally withpopulation size, samplehouseholds were selected usingsimple random sampling techniquesfrom each of the selectedkebeles. Several rules-of-thumbhave been suggested fordetermining the minimumnumber of sample householdsrequired to conduct multipleregression analysis. The studyused a method developed by [17]to select the total sample sizefrom the total households. Hesuggested a rule-of-thumb that N≥ 50 + 8m, where N is minimumnumber of sample householdsrequired to conduct multipleregression analysis and m is thenumber of explanatory variablesused in the regression analysis.The explanatory variables used inthis study were ten. So that the

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minimum sample size is N ≥ 50 + 8x 10 ≥ 154. However, consideringthe benefits of large sample sizeto increase the quality of CVstudy, a total of 215 samplehouseholds were surveyed for thestudy.Data sources

Both secondary and primary datawere used for this study. Theprimary data were collected usingface to face interviews with theheads of the households. A CVMmethod in the form of double-bounded dichotomous choiceelicitation method with openended follow up question was alsoemployed to elicit households’WTP for forest conservation so asto improve the supply of water.The double-bounded dichotomouschoice format (yes-no, no-yesresponses) makes clear bounds onunobservable true WTP. Besides,the yes-yes, no-no responsesharpens the true WTP Haab et al.[18]. The double-boundeddichotomous choice format alsohelps to elicit more informationabout respondent’s WTP thansingle bounded format Arrow et al.[19]; Cameron et al. [ 20].

Preliminary Survey and Bids

Before the final survey wasconducted, pre-test and groupdiscussions were done. The pre-testing pilot survey wasconducted using 40 randomlyselected households in tworandomly selected kebeles. In thegroup discussion 15 people wereinvolved. The discussion was madewith ten potable waterbeneficiary households. Ofthese, two were STWSSS workers,and the remaining three wereamong the selected kebelesadministrators. Based on this,the starting point pricesidentified for WTP were 5, 15,25, 35, and 55 birr per monthadded with water bill. Usingthese initial bids, sets of bidswere determined for follow upquestions based on whether theresponse is “no” or “yes” for theinitial bid. If the respondentswere willing to take the offeredinitial bid, the follow up bid is15,25,35,55 and 75 birr; in caseof a “no” response to the initialbid, the follow up bid is 2.5, 5,15, 25, 35, birr respectively.Given this, the actual survey wasundertaken by dividing the totalsampled households randomly intofive groups.

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The survey was successfullycompleted with relatively smallnumber of protest zeros (about2%). These protesters providedwrong value and after checked forsample selection bias they wereexcluded from the data set. Thecriteria for selecting protestzero was based on the report ofthe NOAA Panel on contingentvaluation by Cameron et al. [20].Cameron et al. [20] suggested thata respondent actually willing topay the stated amount mightanswer in the negative if therespondent believes the proposedscenarios distributed the burdenunfairly, doubts the feasibilityof the proposed action andrefuses to accept thehypothetical choice problem.

Questionnaire Design

As stated in the previous sectionprimary data for the study weregathered using structuredquestionnaire through personalinterviews. So, a draftquestionnaire for this purposewas first prepared afterreviewing related literaturesreferenced throughout thisthesis, interviewing keyinformants and including the

researchers understanding of theissues. The survey questionnaireconsisted of four sectionsoutlined below: 1) Demographicand Socio-economic information 2)Water supply information 3) Levelof awareness regarding forest andforest benefits, and Wondo Genetforest watershed 4) Assessment ofWTP for forest protectionservices so as to improve watersupply.

Empirical Model Specification andAnalysis

Bivariate normal probabilitydensity functions are among thefamiliar bivariate distributionsemployed commonly bystatisticians crucially; theyallow for a non-zero correlation,whereas the standard logisticdistribution does not Cameron etal. [21]. Hence, the bivariateprobit model is used in thisstudy to estimate the mean WTPfrom the double boundeddichotomous choice. Forestimation of WTP, the bivarateprobit Model is used i.e. doublebound Dichotomous choice modeltakes the following form Haab etal. [18]. The jth contribution tothe Likelihood function is givenas

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Lj ( / t)= Pr(1 + 1j t1

, 2 + 2j t2)YN * Pr(1 + 1j t1, 2 + 2j t2)YY* Pr(1 + 1j t1, 2 + 2j t2)NN* Pr(1 +1j t1, 2 + 2jt2)NY……………………………..……….(4)

This formulation is referred toas the bivariate discrete choicemodelWhere: = mean value forwillingness to pay YY = 1 for a yes-yes

answer, 0 otherwise, NY =1for a no-yes answer, 0otherwise, etc.

And the jth contribution to thebivariate probit likelihoodfunction becomes.

Lj ( / t) = 12(d1j ((t1-1

)/1), d2j ((t2-2 )/2),d1jd2j).

Where: 12= Standardizedbivariate normal distributionfunction with zero means

Y1j=1 if the response tothe first question is yes,and 0 otherwise

Y2j=1 if the response tothe second question is yes,0 otherwise

d1j = 2y 1j-1, and d

2j= 2y2j-1 = correlation

coefficient

= standard deviationof the errors

This general model isestimated using the standardbivarate probit algorithms.Finally, the mean willingness topay (MWTP) from bivariate probitmodel was calculated using theformula specified by Haab et al.[18].

3. Results and DiscussionAwareness about Watershed andForest Benefit

Wondo Genet watershed is a sourceof four major streams namelyWosha, Worqa, Hallo and Lango andsmall springs which give variousservices for communities in andaround the watershed [22].Shashemene town has been usingwater from Wondo Genet watershedsince the 1974/5 [23]. In orderto investigate the awarenesslevel of the residents ofshashemene town regarding sourceof water supply to the town, theywere asked awareness question.Accordingly, the survey result ofthis study showed that themajority of respondents (80%)believe that the water providedby the Shashemene water districtto the households comes from

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Wondo Genet forest springs. Onthe contrary, about (7%) of thesample respondents think thatdeep well or water pump stationfrom various locations aresources of their drinking water.The remaining (13 %) do not knowwhere their water comes from. Outof the total sampled households,about 69.5 % them had informationabout Wondo Genet forestwatershed and are well informedwhat a watershed is and what itsfunctions are, while 30.5 % arenot aware of what a watershed isand what its functions are. Thoserespondents who knew about thewatershed were also asked fortheir view regarding theimportance of protecting forest.In addition to the watershedissue, all respondents were alsoasked for their view regardingthe forest benefits other than

providing fuel wood and otherwood products to the nearbycommunity.

Weather maintenance, soilprotection, and water supply andquality improvement are the threetop environmental servicesappreciated by the respondents,on the other hand, other servicesof the forest such as habitat forwild animals, recreation value,and carbon sequestration, areless appreciated environmentalservices. Moreover, theperception of householdsregarding forest and watershedbenefits is vital to understandthe level of knowledge andawareness of the community inhandling the problem regardingthe management of WGF watershedto ensure adequate supply ofwater.

Table 1: Respondents’ rank order of benefit and services of forest

Forest benefits and services Ranks (% of respondents)1

1st 2nd 3rd 4th 5th 6th 7th

Local weather maintenance and prevent occurrence natural hazard

46 15 11 10 8 9 6

Recreational value 2 1 3 11 2 26 14

Protect the soil erosion 18 41 13 12 10 16 11

1 Select the largest figure among the lists while moving down the column of table 7

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Increase water percolation and thusimprove water availability

12 20 40 15 9 11 16

Carbon sequestration , mitigating global warming

10 7 10 12 26 13 21

`Shade, cool and clean air 3 6 14 27 7 9 18

Residence for wild animals 9 10 11 13 38 16 14

Total 100

100

100

100

100

100

100

Source: Survey result, 2013

Perceptions on Forest CoverChange in the Watershed Area

About 93% of sampled householdsresponded that the forestcoverage in WGF watershed isdeclining. Respondentsacknowledged human induced fire,poverty, lack of environmentaleducation and socio-politicalfactors as the main top fourreasons of deforestation. On theother hand population growth,illegal extraction of wood anddrought were less driving causeof deforestation. This result isconsistent and supporting thefindings of [13], in which forthe year 1977, 13% of the WondoGenet Catchment was under natural

forest which has reduced to 2 %in 2000.

This study shows that forestdegradation is continuing inWondo Genet watershed due to anumber of internal and externalfactors noticed by localcommunity which may result inbringing about a number ofproblems including decline in thesupply of quality and quantity inthe study area and nearby areas.Similarly study conducted byWickama, et al. [24] (Undated)indicates that such type ofproblem has been occurred in Bagawaterhsed Lushoto District, inTanzania. According to thisstudy, the local community in

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Lushoto district faced scarcityof water in their village afterforest land was cleared andallocated for cultivation.Consequently, this makes theenlightenment of the valuationscenario and the hypotheticalmarket easier because they knewthe good to be valued closely andclearly. Hence, this is a callfor community to and otherstakeholders to look forsolution.

With regard to the solutions forthe problem of deforestation, thesample households are asked and;about 41 % of the respondentssuggested strong natural forestprotection and managing rules andregulations as a means to controlforest depletion. About 27 %choose tree planting and otherconservation activities; while 22% of the respondent supportedwider provision of environmentaleducation. Besides, about 7 %suggested for creation of another

source of income as a solution,and 3 % suggested family planningand reconciling population growthwith economic/welfare improvementas a solution to reducedeforestation and conservation ofthe watershed.

Households’ Responses of theDouble Dichotomous ChoiceValuation

Out of the total respondents 59(27%) of them opposed theproposed project. Therespondents’ reasons foropposition of the proposedproject were categorized in twomain divisions (table 6); whereasthe remaining 149 (70%)respondents were willing to payfor the project. Among thewilling respondents, 58 % and 37%responded their acceptance “yes”for first and second bid,respectively.

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Table 2: Reasons for unwillingness to pay for Wondo Genet forest watershed conservationValid zero bidder No. %I can’t afford to pay any additional amount towhat I am currently paying in the form of waterbill

21 9.76

Shortage of money to pay 24 11.16

I did not get piped water came to WGF area 2 0.93I don’t know the importance of forest for water service

4 1.86

Protest Zero bidder I think it should be the government that shouldfinance the forest protection and management so asto improve water service

4 1.86

I do not trust the institution who will manage the funds for this activity

2 0.93

Those who consume more water must pay to protect and manage forest areas

1 0.46

Only the rich households should pay 1 046Total 59 27Source: Survey result, 2013

The distribution of “yes” and“no” responses for the first andsecond bids across the differentinitial bids reveals that as theinitial bid gets higher, thefrequency of acceptancedecreases. This is by the factthat as the number of households

rises, the bid value declines asshown in the demand curve (Fig.1). By the same indication theaverage amount of the 1st bid is32.22 ETB per month but the valueincreased to 34.45 ETB for the2nd bid amount.

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Figure 1: Aggregate demand curve of Households WTP for forest conservationby domestic water users

Another interesting result isthat 63.79 % of the respondentswho accepted the1st bid gavesimilar response for the followup question and 69.05 % of thosewho rejected the 1st bid againrejected the 2nd bid. This couldindicate the presence of effectof the first response on responsefor the follow up question, whichis consistent with prior studiesdone by [25] on valuation ofmulti-purpose tree resource usingDBDC elicitation format andCameron et al.[21].

After the dichotomous doublebounded question, a follow upopen ended question was askedindependently. About 27 % of therespondents gave an amount lessthan the amount that they alreadyagreed to pay in the dichotomouschoice questions. The respondentsprovided different reasons. About64% of these respondents said

that first they thought the bidsoffered to them were imposed bythe government (despite theexplanation given about the aimof the survey) so that theydecided to agree but when theyfelt that it was a voluntarycontribution, they told their‘true’ willingness to pay.However, about 18% respondentsgave an amount higher than theamount they already agreed to payin dichotomous choice questionoffered bids. The main reasons ofthose respondents were theywanted a reliable water supplyfor future generations (29.53),and it is a good project becauseforest produces safeenvironmental services (70.47).While the rest 55 % of therespondents reported that theycould not afford more than whatthey stated because of inadequateincome, and they reported that

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the amount they agreed to pay wassatisfactory.

Determinants of Households’ WTP

Multivariate regression analysishelps analyze WTP responses bydetermining if WTP estimates areinternally consistent ortheoretically valid. This modelwould permit identification ofthe factors that influencerespondents’ WTP and checking ifdirections of effects areconsistent with theoreticalexpectations. However, prior torunning the bivarate probitregression model, the explanatoryvariables were checked formulticollinearity and the degreeof association through Varianceinflating factor (VIF2) andContingency coefficient (CC3).Based on the VIF, the data had no

2 Where, Ri2 is the

coefficient of determination in the regression of one explanatory variable (X) on the other explanatory variables (Xj).

3 where, C=

coefficient of contingency, χ2=Chi-square test and N= totalsample size

serious problem of multi colinearity. That is, the VIF ofthe continuous variables wereless than 10 (table 2), so thatall the continuous variables werepart of the regression analysis.The extent of association betweendummy variable was also computedusing contingency coefficient.The analysis shows that there isweak association between thedummy explanatory variables(table 2). Therefore, all thedummy variables were included inthe regression analysis.

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Table 3: Variance inflating factor for continues and contingency coefficient for dummy explanatory variables used in regression models.

Source: Survey result, 2013

The estimated result on factorsaffecting the households’willingness to pay for forestprotection/conservation so as toimprove water is presented in(table 3) and it shows both thesignificant and insignificantvariables. However, only thesignificant variables are

discussed since the result fromthe bivarate probit model in isnot indicating the magnitudeeffect of the explanatoryvariables on the probability thatrespondents accept or reject theinitial bids. Therefore, weestimated the marginal effects ofthe variables on the WTP (table 4

).

Table 4: Bivarate probit model resultModel one Model two

variables coef Rebust.

Std. Err p>z coef Rebust. Std.Err p>z

Age -0.01682 0.0974 0.061* -0.0266 0.1111 0.017**

Sex 0.78 0.2691 0.004*** 0.5866 0.2373 0.013**HHs -0.1477 0.0488 0.002** -0.04464 0.0544 0.413

Educ 0.047744 0.0291 0.026** 0.5829 0.0259 0.025**

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VIF of continuous explanatory variable

Contingency coefficients for dummyexplanatory variables

variables VIF variables Sex Awer lastAge Income Education HHsizeReysBill Bid 1Bid 2

1.371.511.551.311.602.493.443.29

Sex 1.0000 Awer 0.0415 1.0000last -0.0235 -0.0549 1.000

Aware 0.2451121 0.249206 0.325 0.068039 0.2334 0.771

Income 0.00036 0.0001 0.020** 0.00012 0.0001 0.036**Lsat 0.85088 0.2845 0.139 0.2977 0.2178 0.123

Reys 0.0130332 0.008559 0.128 -.00124 0.0083 0.882

Bill 0.0036 0.0042 0.362 -0.0013 0.0040 0.748

Bid1/2-

0.012673

0.0162 0.000*** -0.0061 0.0178 0.001***

-cons 0.4431 0.5162 0.431 0.182 0.53622 0.934Number of obs = 200 Prob>Chi2 = 0.000Log likelihood = -143.202 Chi 2 (1) = 67.66

Wald chi2(18) = 39573.85 Roh(p-value) = 0.87 Source: Survey result, 2013

Table 5: Marginal effect of explanatory variables variables Dy/dx Std.

Err.Variabl

es Dy/dx Std.

Err.

Age -0.0082 0.00354 Income 0.0010 0.00004Sex 0.1928 0.06915 Lsat 0.1051 0.07877HHs -0.0171 0.01768 Bill -0.0020 0.00133No. years residence

-0.0024 0.003 Awareness

0.0515 0.08030

Educ 0.0201 0.00846 Bid1/2 -03430 0.00578Source: Survey result, 2013

Age of the household head (AGE)had negative effect on thewillingness to pay of householdsfor forest ecosystem conservationpractices to improved watersupply service. The negative andsignificant correlation betweenage and WTP might be perhapsbecause of two reasons. Older agemay shorten planning time horizonand reduce WTP i.e. they expectless benefit from the investment

relative to young householdheads, given that the benefitsare generally longer term innature. On the other hand, olderhouseholds head might faceconstraint of money to pay forconservation of forest i.e. adulthouseholds are more financiallystable than old households. Themarginal effect estimates of(table 4) also shows, thatkeeping the influences of other

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factors constant, a one yearincrease in the age of thehousehold head reduces theprobability of accepting both the1st and 2nd bid by about 0.82% andwas happened to be significant atless than 10 % and 5% probabilitylevel in the 1st and 2nd equationrespectively. The empiricalfinding of the study isconsistent with perviousempirical studies Calderon et.al[26].

Sex: Sex is represented by dummyvariable (dummy = 1 for male and0 for female). The variable sexof the respondents is positivelyrelated to the probability ofrespondents’ WTP for forestecosystem conservation practicesto improved water supply of thetown (table 3). It is significantat less than 1 % and 5% in the1st and 2nd equation respectively.The result revealed that thosemale headed households were foundto be willing to pay than femaleheaded households. This can beexplained by the fact that maleshave participated more toooutdoor activities in relation toforest protection activity andothers compared to females. Theresult contradicts with the study

by Bogale et al. [27] and similarwith [28].

Family size (HHsize):- The resultof the model showed that therewas significant at 1% to affectthe probability of accepting theinitial bid and negativeassociation between family sizeand WTP for forest ecosystemconservation practices toimproved water supply. Themarginal effect estimates showthat all other factors keptconstant, a one person increasein the total family sizedecreased the probability ofwillingness to pay by 1.71 %.

Education level (educ.):-Education of the household headwas also significant at 5 %probability level to say “yes” toboth the 1st and 2nd bid. It had apositive relationship with thedependent variable showing thatas the education level of thehousehold head increases,willingness to pay for forestecosystem conservation increases.The marginal effect result showthat for each additionalincrement of education, theprobability of willingness of ahousehold to pay for the forest

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will increase by 2.0 %, ceterisparibus. One possible reasoncould be that more educatedindividuals are concerned aboutenvironmental goods includingforest in our case. This could bepossibly because educationincreases environmental awarenessand value for environmental goodssuch as forest. The findings ofWhittington et al. [29] which weredone on developing countries;with regard to households WTP forimproved water services indicatedthat better educated householdsare more WTP for improved waterservices.

Household monthly income (Income)as expected has positiverelationship with the households’WTP and statically significant at5% both in the 1st and 2nd

equation. This effect indicatedthat respondents with highermonthly income were more likelyto say yes to the 1st and the 2nd

bid than households with lowerincome. The results indicate thathouseholds with high income tendto reveal a high WTP for thepromised system than theircounterparts with low incomes.The marginal effect estimate forhouseholds monthly income

variable shows that a 1 Birrincrease in income of thehousehold will increase theprobability of households' WTPfor protection of forest toimproved water supply by 1.0 %.The result is consistent withGriffin et.al [30]. In contrarywith these finding, Baltodano etal. [31] has reported income didnot have an effect on thehousehold WTP for improved waterquality improvement.

Bids amount (bids):- Both bidvalues, one and two, havenegative coefficient as expectedand statistically significant at1% for the follow up question. Asthe bid amount increases, therespondents would be less willingto accept the scenario and thatis consistent with the law ofdemand. This is also evidencedthat, as one more Yes responsegiven for the second bid itresulted in a decrease of WTP by0.034 marginal effects (table 4).

Mean willingness to pay forforestry conservation

Double-bounded contingentvaluation model is used toestimate the mean WTP and itsdeterminants. There are two

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options of independent modelswhich can be used to estimatemean WTP. The models arebivariate model with nocovariates i.e. WTP checkedagainst the offered amount andbivariate model with covariatesi.e. WTP against socio economicfactors that can be affecting WTPfor forest conservation toimprove water supply service.Thus, before deciding on whichmodel to apply, it seemsimportant to compare theirresults which would help tocapture the true behavior ofpeople that expressed throughtheir preferences. Thus, theresult from the second model waspreferred for it uses to run themodel.

Cameron et al. [21] indicatedthat, the model which runs withdeterminant factors to estimatemean WTP4 WTP are more preferred4 The mean WTP from bivariate probit

model was computed using the formula

specified by [18] that is

Where = coefficient for the

constant term

ß = coefficient offered

bids to the respondent

for its high marginal valueaccuracy estimation forenvironmental changes. As aresult the mean WTP value ofconserving forest to improvedwater provision was ranged from35 and ETB to 30 ETB perhousehold per month, for theinitial bid (Fbid) and for thefollow up second bid amount(Sbid) respectively. This figureis higher than the meanwillingness to pay amount fromthe open ended question maximumWTP which was 26.31 ETB. This mayseem to indicate free riding andlack of base for answering WTPquestions under open endedformat.

Aggregate WTP for Conserving Forest to Improved Water Availability

According to Baltodano et al. [31]there are four important issuesto be considered regarding sampledesign and execution in order tohave a valid aggregation ofbenefits: population choice bias,sampling frame bias, samples noneresponse bias and sampleselection bias. Stratified randomsampling method was used for

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selecting respondents’ domesticwater user other than source fromWondo Genet area. Protest zeroresponses were excluded from theanalysis and a face to faceinterview method were used.Hence, none of the above biaseswas expected in the analysis.

If the bivariate probit model isestimated on a dichotomous choiceCV question with a follow up andthe parameter shows that eitherthe mean, or variance or bothdiffer between the initial bid-price and the follow up, theresearcher must decide whichestimates to use to calculate theWTP measure [18]. Hence, in orderto choose the appropriate WTPamong the two bivariateestimates, the data was examinedand the total amount for the YYand NN responses accounted forabout 66.5 % of the totalresponses. This means that the2nd bid amount was closer to the

unobserved true value of theindividual. For example, if thefirst random bid for theindividual is 5 ETB and therespondent accepts the first bidthen the 2nd bid becomes 15ETB,again, if the respondent acceptsthe second bid. This indicatesthat the respondents’ true WTP isgreater than or equal to 15 ETBso the 2nd bid will be a betterestimate than the 1st one. Thesame is true for NN answer. Evenfor the rest 33.5 % of the NY andYN responses, both the first andthe second bid amounts will haveequal chances to be closerestimates of the true value.Hence, using the second estimateof the double bounded bivariatemodel to calculate as meanwillingness to pay for WondoGenet forest ecosystem protectionthe aggregate benefits of thesociety is summarized in (Table5).

Table 6: Average and aggregate willingness to pay of households for conserving WGF Site Population

sizeMean WTP/month

TotalWTP/year

Didaboki 944 30 339726.3Abosto 973 30 350336.8Bulchana 961 30 345884.2 Total 2878 30 1035947

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Total households ofthe Town

39474 30 14210640

Source: Survey result, 2013

Aggregate WTP of the selectedkebele communities from theproposed to conservation offorest so as to improve watersupply was found to be 1, 035,947 ETB per year. Whereas, theaggregate WTP amount for thepopulation of Shashemene town toimproved Wondo Genet forestecosystem conservation was, 1,421, 0640 ETB per year. This isthe amount the whole householdsin Shashemene town are expectedto pay per year when thesuggested improved forestecosystem conservation programcomes true. If single bounded andopen ended WTP were used theestimate becomes 1, 657, 9080 ETBand 1, 246, 2731.28 ETB per yearrespectively.

4. Conclusion and recommendation More than 93 % of sampledhouseholds said that the forestresource is shrinking from yearto year due to environmentalproblem. The main cause of thisincidence is human induced fire,population growth, lack of

environmental education andsocio-political factors, illegalfelling of trees for fuel-woodand construction materials,charcoal making, pit-sawing, andexpansion of agricultural land.This also affects other resourcessuch as, the hydrological andsoil resources of the catchment,which will have a massive impacton the domestic water users ofnearby towns.

The annual aggregate WTP forWondo Genet forest conservationby the population of Shashemenetown was estimated to be1,035,947 ETB (second bid) and 1,657, 9080 ETB (first bid) peryear using double boundeddichotomies model. Whereas, theannual total WTP from open endedformat was also, computed at 1,246, 2731.28 ETB per year. Thisindicates that, the value of theforest for the people ofShasahemene is significant thatthey are willing to pay up to 1.4million birr per year.

The empirical findings on thedeterminants of WTP indicated

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that monthly income, age,household size, bids, and formaleducation are key factorsinfluencing the WTP. Therefore,understanding of socio-economiccharacteristics that influencedhouseholds WTP significantly is anecessary and first step toconserve forest so as to achieveimproved water.

Acknowledgments

First of all, I would like toexpress my limitless gratitude toAlmighty God together with hisVirgin mother St. Marry who helpedme in all circumstances. Next, myspecial thanks goes to my instructorand thesis advisor Dr. YemiruTesfaye for his valuable andconstructive professional adviceduring compiling this thesis. I amalso indebted to WGCF-NR,HwU forproviding me the scholarship tostudy Natural Resource Economics andPolicy at Wondo Genet College ofForestry and Natural Resources,Hawassa University.

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