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AN ECONOMIC ANALYSIS OFRECREATIONAL FISHING ANDENVIRONMENTAL QUALITY CHANGES INTHE UPPER OLDMAN RIVER BASINDavid O. Watson , Wiktor L. Adamowicz & Peter C. BoxallPublished online: 23 Jan 2013.

To cite this article: David O. Watson , Wiktor L. Adamowicz & Peter C. Boxall (1994) AN ECONOMICANALYSIS OF RECREATIONAL FISHING AND ENVIRONMENTAL QUALITY CHANGES IN THE UPPER OLDMANRIVER BASIN , Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 19:3,213-225, DOI: 10.4296/cwrj1903213

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AN ECONOMIC ANALYSIS OF RECREATIONALFISHING AND ENVIRONMENTAL QUALITY

CHANGES IN THE UPPER OLDMAN RIVER BASINA p pl i cati o n/Ap p I i c ati o n

Submitted September 1 993; accepted April 1 994Written comments on this paper will be accepted until March 15, 1995

David O. Watsonl, Wiktor L. Adamowicz2 and Peter C. Boxall3

AbstractA discrete choice travel cost model, based on data collected from a survey ofrecreational anglers, was used to estimate changes in recreational fishing benefitsat sites in the Upper Oldman River region of Alberta resulting f rom the constructionof a dam. The quality attributes which affect the choice of site include the potentialto catch fish (catch rate and size of fish), access, and the size of the water body.Construction of the dam and creation of the reservoir reduces recreational fishingbenefits of the area. The annual value of this reduction ranges from annual lossesof $96 239 to $30 545 depending on model specification, and whether the valueof travel time is included. The Alberta government efforts to mitigate the dam'seffect by improving fish habitat in remaining reaches may improve the welfare ofanglers to levels equal to or greater than the original benefits. The mitigation effort,assuming a success rate considered probable by various experts, results in anannual gain in welfare of from $209 499 to $22 971 depending on the modelspecification, and whether the value of time is included. The approach used toexamine the dam and its mitigation plan illustrates the concept of "no-net-loss" ineconomic terms rather than physical terms.

R6sum6Un programme sur le choix discret des co0ts de voyage, bas6 sur des donn6esrecueillies lors d'un sondage effectu6 aupres des pdcheurs r6creatifs, a 6t6 utilis6pour estimer les changements dans les avantages de la p6che r6cr6ative dcertains emplacements de la r6gion Upper Oldman River en Alberta r6sultant dela construction d'un barrage. Les attributs, lesquels affectent le choix de l'em-placement, inclus le potentiel de capture (taux de capture et grosseur despoissons), I'accds et l'ampleur de l'espace aquatique. La construction du barrageet la cr6ation du r6servoir r6duisent les avantages de la p6che r6cr6ative de lar6gion. La valeur annuelle de cette r6duction se situe entre $96 239 et $30 545en pertes annuelles, d6pendant de la specification du programme et de la valeurdu voyage d consid6rer. Les efforts du gouvernement de l'Alberta d att6nuer l'effetdu barrage en am6liorant l'habitat du poisson dans les endroits restants peuventam6liorer le bien-etre des p6cheurs d des niveaux egaux ou sup6rieurs compar6aux avantages initiaux. feffort d'att6nuation, assumant un taux de succds con-sid6r6 probable par divers experts, r6sulte en un gain annuel de bien-dtre sesituant entre $209 499 et $22 971 d6pendant de la sp6cification du programme

1.

z.J.

Research Associate, Department of Rural Economy, University of Alberta.Present Address: Department of Natural Resources Canada, Edmonton, ABAssociate Professor, Department of Rural Economy, University of Alberta, Edmonton, ABNontimber Valuation Economist, Canadian Forest Service, Department of NaturalResources Canada, Edmonton, AB

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ainsi que la valeur du temps. L'approche utilis6e pour 6valuer le barrage et sonplan d'att6nuation illustre le concept d' "aucune perte nette" en termeseconomiques plutot qu'en termes physiques.

IntroductionIn 1985 the Government of Albefta an-nounced the construction of a dam on theOldman River which would flood portionsof the Oldman, Crowsnest and Castle riv-ers and create a large reservoir. Thecreation of the reservoir was deemed nec-essary by government for reasons ofirrigation water supply, municipal watersupply, and flood control. However, por-tions of the flooded rivers, in their originalstate, were also highly esteemed forrecreational fishing. The Federal Environ-mental Review of the project (FEARO1992, p.18) highlighted this by stating:

The Oldman River and its tributaries, theCastle and Crowsnest Rivers, havebeen described as 'the blue ribbon troutstreams'. Surveys upstream from thedamsite suggest that 60% of the highquality habitat for adult brown trouI,62kof the high quality habitat for adult moun-tain whitefish and 75% of the high qualityhabitat for adult rainbow trout in these' three rivers was inundated bv the reser-votr.

Very little attention was paid during theplanning and subsequent reviews of theeffects of environmental changes on thebenefits of recreational fishing in the UpperOldman River basin. The need to addressthis issue was outlined in the followingstatement (Erythana Ventures Corp.,1 e91 ).

...a number of reports have also beenprepared with respect to the effect of thedam on fisheries and on vegetation, bothin the river valley and in the river itself.However, the majority of these reportsdo not explicitly review the effects of thedam upon recreational fishing and rec-reational uses of riparian vegetation andgenerally do not address socio-eco-

214

nomic issues, but rather focus upon bio-physical considerations.

A challenge facing any assessment ofchanges in values associated with recrea-tional fishing is that the activity provideslargely nonmarket benefits to participants.This means that fishing opportunities arenot traded in markets and thus the eco-nomic benefits do not have associatedprices. Estimation of these nonmarketbenefits involves methods that require de-tailed data from anglers who use the area.This information was not readily availablebefore and during the construction of thedam. In this study, we use the results of acomprehensive survey of anglers con-ducted during the construction of the dam,to estimate the dam's imoacts on recrea-tional fishing values.

The Oldman River DamThe Oldman River Dam was constructedon the Oldman River, downstream of theconfluences with the Crowsnest and Cas-tle rivers, about 15 km northeast of thetown of Pincher Creek. The dam will storespring runoff and supply a constant flow ofwater during the summer months for irriga-tion and municipal uses downstream. Atthe full reservoir supply level (FSL), thedam will cause flooding ot 21.9 km of theOldman River, 9.1 km of the CrowsnestRiver, and 12.8 km of the Castle River. Thetotal area of the reservoir at FSL will be2420 ha.

The imoact of the dam on recreationalfishing was expected to be negative be-cause its most direct and obvious affectsare the flooding of 43.8 km of rivers in thearea to create the reservoir. Since the res-ervoir is not expected to be productive forgame fish populations (FEARO,1992) thereservoir cannot be considered as a sub-stitute for the rivers for fishing. This sug-gests a complete loss of recreational

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fishing value for this portion of the region.The Alberta government recognized theimportance of the recreational fishery andis involved in efforts to mitigate the effectsof the dam. Mitigation may be examinedfrom either a physical or economic view-point. A physical approach would deter-mine if there was any change in the habitatbefore and after the project, while an eco-nomic approach would determine if therewas a net loss or gain of recreationalvalue. This study highlights the economicapproach by examining changes in eco-nomic benefits associated with the oro-posed mitigation effort.

Benefit Measurement andRecreation Demand ModelsMost of the benefits associated with rec-reational fishing in Canada are nonmarketin nature because the activity for the vastmajority of participants is not priced in amarket. In order to calculate the changesin recreational fishing benefits due to theconstruction of the dam, a nonmarket valu-ation technique called a discrete choicetravel cost model (TCM) was used. This isa variant of the traditional travel cost modelcommonly used in the past (Adamowicz,1991). The discrete choice form of theTCM is based upon research reported inthe transportation literature (Domencichand McFadden, 1975; Ben-Akiva and Ler-man, 1985). These models, also calledrandom utility models (RUM), are usefulfor investigating situations where individu-als face a discrete rather than a continuousset of choices. Because of this property,the models have been used to investigatethe choice of specific sites related to rec-reation, and have been incorporated intothe broader category of travel cost models.These models have the advantage of be-ing established within a utility maximizingframework. ln this framework a recreation-ist selects a site that yields the highestutility (satisfaction) based upon the char-acteristics of the choice of sites available.

Canadian Water Resources JournalVol. 19, No. 3. 1994

Description of Discrete Choice orRandom Utility ModelsThe level of utility a recreationist receivesis represented by V and is defined as afunction of the attributes of the fishino sitechosen, Q, as in:

Vin =V(Qin) (1)

where Qin is a vector of attribute values forsite i as viewed by recreationist n. The setof available recreation sites is denoted byC. Site i will be chosen bv the recreationistonly if:

Vin > Vn, for all j *r; i, j e C (2)

Utility in this model is modelled as arandom variable and any observed incon-sistencies in choice behaviour are as-sumed to result from observationaldeficiencies on the part of the researcher(McFadden, 1981; Smith, 1989). Morespecifically, the random utility of recrea-tionist n selecting any specific recreationsite can be expressed as the sum of ob-servable and unobservable comoonents ofthe total utilities. In other words:

V;n = V;n f e;n, (3)

where vin is the systematic or observablecomponent of the utility of choosing site i,

and e;6 is the random component referredto as the stochastic disturbance. The orob-ability that site i will be chosen, nn(i), isequal to the probability that the utility ofchoosing site i, Vin, is greaterthan orequalto the utilities of choosing all other sites inthe choice set or:

lrn(i) = Pr lvin f €in ) yn + eln; > je C i

(4)

The utility function was specified as alinear function of the site attributes, or

Vin = Br f BzXinz * BsXing, + BrXinr, (5)

where the Xink ?fe measures of site quality,and the Bs are unknown parameters.

The multinominal logit model arisesf rom the assumption that the disturbances,€in, dre distributed as type I extreme values(Maddala, 1983; Stynes and Peterson,1984). In this case, nn(i), is determined by:

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nn1i.1 = -exP" for je C (6)

I "^P""j.c

The statements of site-choice prob-

abilities are used to derive a likelihoodfunction that is maximized to yield parame-ter estimates (Ben-Akiva and Lerman,1985). These are the parameters of theindirect utility function. The model is esti-mated using maximum likelihood tech-niques (Ben-Akiva and Lerman, 1985).

Welfare TheoryThe parameters of the indirect utility func-tion are used to calculate moneYequivalents of recreational fishingchanges which in economic terms arecalled welfare measures. The moneyequivalents of these changes arise fromtravel costs, which are related to the dis-tance an individual must travel to fish atsites in C and are assumed to be weaklycomplementary with the nonmarket bene-fits. Since the discrete choice modelestimates probabilities of sites chosen byan angler given variables in V, a change inany variable is reflected in changes in site-choice orobabilities. Given that thesechanges involve different travel costs,measuring changes in associated welfareinvolves estimating the amount individualsmust be compensated to remain at thesame utility level as before the qualitychange. Hanemann, (1982; 1984) hasshown that if the multinominal logit form ofthe random utility model is chosen, theformula for the welfare impact (Compen-sating Variation (CV) of a quality change)is (suppressing the subscript n on V):

1-CV=-'ln() exp(Vio))u-' ieC

- In rl exP (Vir )) (7)

ieC

where p is the marginal utility of income,Vio is the level of utility in the initial state (orquality level) and Vir is the level of utility inthe subsequent state. Hanemann (1982)

shows that the value for p is equal to -1

times the coefficient on the travel cost pa-

216

rameter in the discrete choice travel costmodel.

Data Col lection/SurveYDesignThe data for this model were obtained f rom

a mail survey conducted jointly by the Uni-versity of Alberta and the Alberta Fish andWildlife Division (hereafter called AFW).The survey concerned the 1990 fishingseason, and was conducted during thewinter of 1990/91. The sampling universefor the survey involved Albertans who pur-

chased Alberta fishing licences for the1990 fishing season. A random samplingmethod was used to obtain a sample sizeof 5000 license holders. From this 5000,there were 211 5 responses to twomailouts. The questionnaire focused on

fishing in southern Albefta (Fish Manage-ment Areas 1 & 2) at 77 o'f the mostimportant sites identified by AFW staff.About 1000 respondents indicated theyfished in southern Alberta during 1990.Further information on the survey andquestionnaire can be found in Adamowiczet al. (1992).

Respondents were asked to provide adetailed diary of up to 15 fishing trips takenduring the season. This diary required therespondent to provide the site of the trip,the date, fishing success, and the speciesof fish sought. The discrete choice TCMwas constructed using information from737 trips taken by 236 anglers to 19 desig-nated sites in the Upper Oldman region.Raising this sample to the total populationof anglers using the region was achievedusing the survey return statistics. This wasdone by estimating the number of anglersby residence from the survey and inflatingthis to the total population of each residen-tial centre.

Site QualitiesDetermining the variables that influencethe quality of a fishing experience is diffi-cult. Responses to various questions onthe survey, opinions from experts, and re-views of the literature were used to derivea list of 40 candidate variables for inclusioninto the indirect utility function. Fisheries

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staff at the Southern Region office of AFWprovided values for the 40 variables for the19 sites in the region. In this paper, eight ofthese variables are used in the travel costmodel.

Two variables of particular importancewere the catch rate and size of fish caughtat each site. AFW used creel surveys andknowledge of the fish populations at thesites to formulate some values for thesevariables. Because these variables are im-portant in examining the mitigation effortand are subject to interpretation, a secondset of fish-catch and fish-size measureswere obtained from Mr. J. O'Neil, a fisher-ies biologist with R.L. & L. EnvironmentalServices Ltd. of Edmonton currently work-ing on the fishery in the region.

The site quality list provided by AFWcontained information on different soeciesof fish. Using information provided by re-spondents to the survey on the speciessought, and a creel survey (Hildebrandand O'Neil, 1992), it was useful to createtwo separate variables: one for the catchrate of rainbow trout, and another for allother species grouped together.

Other variables selected for estimationof the models were based on knowledge ofthe criteria used by anglers for site selec-tion which was gathered in the survey(Adamowicz et al., 1992). Distance to thesite was chosen both because it wasthought to be important, and the fact thatthis type of model cannot measure eco-nomic benefits without travel costs, whichwere determined from the distance to thesite from home. The variables of streamreach length and lake area were includedto reflect the size of fishing areas and thepossibility of uncongested angling.

Campgrounds were included becauseaccessible camping is important whenfishing areas are distant from anglers'homes. Thus someone living within a shortdistance of the site would likely go homefor the evening, whereas someone whomust travel several hours may want tocamp. Avariable combining these two (dis-tance multiplied by camping spaces) wascreated.

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Alternative soecific constants wereused to account for some of the differ-ences in utilities not explained by qualityattributes. ldeally one would wish to in-cluded an alternative specific constant forall (but 1) sites. However, collinearityamong the constants and other explana-tory variables precluded the use of theentire set. Instead, constants were in-cluded for sites that had unusual charac-teristics. While several combinations ofalternative soecific constants were exam-ined, in several cases the parameterswere insignificant. The models presentedin this paper contain three alternative spe-cif ic constants, one for each of sites 1 , 10,and 11 . These constants are hypothesizedto capture effects that are specific to theparticular sites. For example, sites 1 and11 have two of the highest congestionrates, while site 10 has the lowest conges-tion level; site 10 contains a major popula-tion centre in the area; and site 11 containsa scenic water fall. These constants arestatistically significant and improve thepredictive power of the model. Neverthe-less, we consider models with and withoutalternative soecific constants in the follow-ing analysis.

A summary of the variables used in thestatistical models described below are:

DIST: The measured distance from theresidence of the angler to the fish-ing site.

DISTCAMP: A variable created by multi-plying DIST by the number ofcamping sites available at the fish-ing site.

PARKING: A value of 1 if parking wasavailable and 0 if not.

SIZECOT: A categorical measure on ascale of 1-10 of the size of fishcaught. Two sets of these ratingswere provided, one by AFW and theother by R.L.& L. EnvironmentalServices Ltd.

RAINBOW: The catch rate/h of rainbowtrout.

OTHRCATX:The catch rate/h of fish otherthan rainbow trout.

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AREALAKE: Surface area of a lake in ha.lf site is not a lake a value of 0 wascoded.

LONGCRIK: Length of the reach of thestream or river in km. lf the site isnot a river, a value of 0 was coded.

CC1, CC10, CC11: Alternative specificconstants for sites 1, 10 and 11,where 1 was coded for the appro-oriate site and 0 otherwise.

ResultsModel EstimationMaximum likelihood estimation of the mult-inominal logit models was undertakenusing LIMDEP, version 6.0 (Greene,'l 992). Separate models were estimatedbased upon the different values for fishcatch and fish size provided by AFW andO'Neil. Each model was then estimatedincluding the alternative specific con-stants. The results of these four differentmodels are shown in Table 2. The modelsare slatistically significant^as indicated bythe 1'values. The larger X'values indicatethat the models based on values fromO'Neil are somewhat "better" than AFWbased models. The difference is less obvi-ous when alternative soecific constants

are included. The parameters have f-val-ues that indicate they are significantlydifferent than 0. The signs of the estimatedcoefficients of the parameters are all in theexpected direction: the coefficient for DISTis negative as expected indicating that,everything else held constant, anglers pre-fer lower travel costs and less travel time;all other variables have positive coeffi-cients. DISTCAMe which incorporatesDIST, is positive due to the influence ofcamping spots. An increase in the value ofany one attribute (except distance) at anysite, with all other variables held constant,will increase anglers' utility and increasethe probability that the affected site will bechosen for a trip.

Fishing Quality Changes andWelfare MeasuresThere are some obvious effects of the damon quality attributes of sites used for fish-ing in the area. One is the shortening of thelength of the reach of the three rivers af-fected. This change of length of stream isdirectly measurable for the sites affectedand can be incorporated in LONGCRIK asa quality change in the analysis. Another isthe creation of the reservoir which could be

Table 1: A Summary of Changes in Fish Habitat in Southern Alberta due toConstruction of the Oldman Dam and Subsequent Mitigation Efforts

Sitel Original

habitat

(r')

Lost Habitat % Change

habitat constructed with dam(m2) (.') alone2

% Change % Change % Change

with dam & with dam & with dam &

mitigation mitigation mitigation

e5%)3 (50%) (75%)

1

I11

13

Total

45907 0

151 076 123 063

1787 0

45700 20 950

104 938 78 663

349 408 222676

3500 0

20 225 -81.40

30 661 0

61 858 -45.80

30 590 -75.00

146 834

1.90-78.10

428.90-12.OO

-oI.tu

3.81 s.70

-74.70 -71 .40

857.89 1286.80

21 .80 55.67

-60.40 -53.10

t Only the sites that underwent a change in habitat are listed.2 Calculation for 7o change is: (result minus original / original) X 100, where result is equal to

original minus lost.3 Calculation for % change for differing success levels is: (result minus original / original) X 100,

where result is equal to original minus lost plus consiructed multiplied by percent effectiveness.

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Table 2: Results of Multinomial LogitChoices for Anglers FishingAlberta

Models Describing Recreational Fishing Sitein the Oldman River Area in Southern

Coefficient(f-ratio)

Variable

Model 11 Model 2r Model 32 Model 42

DIST

DISTCAMP

PARKING

SIZECOT

RAINBOW

OTHRCATX

AREALAKE

LONGCRIK

cc1

cc1 0

cc1 1

Log-Likelihood Test(x')

-0.0216530

c5.537)

0.000071 4

(4.139)

0.75621(6.511)

0.15932(4.386)

1.4877(6.0e1)

0.7831 5

(5.000)

0.01 0307(6.165)

0.0'1 9374(4.5e6)

-0.026401

(-6.522)

0.0001451(8.70e)

-0.024856

(-6.003)

0.0001 0273(6.34e)

0.33577(0.063)

o.22191(e.366)

0.91629(8.7e7)

0.6291 0

(4.4oo)

0.01 1431

(6.e 10)

o.016712(3.ee4)

-o.026428

(-6.531)

0.0001 334

(7.731)

0.15742(4.3e1)

0.42616(2.160)

0.52220/2 noo\

o.012526(7 -171)

0.01 6924(3.703)

0.62981(2.281)

0.70871(4.028)

0.5491 I(2.573)

378.989302.O32

o.12554(3.051)

0.39829(1.383)

0.58538(3.261)

o.0132299(7.748)

0.018804(4.o25)

0.98209(5.BBB)

1.0209(7.242)

1.0883(6.708)

374.194 365.821

1 These models are based on values for the SIZECOTobtained from Alberta Fish and Wildlife.

2 These models are based on values for the SIZECOTfrom O'Neil.

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RAINBOW, and OTHRCATX variables

RAINBOW and OTHRCATX variables

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considered as the addition of a new site tothe fishing areas in the region. Since thereservoir will not be oroductive for fish(FEARO, 1992), it will not be consideredas a new or substitute site and does notenter the analysis as a quality change.

Perhaps the most important change isthe ootential effect of the dam on catchrates in the remaining sections of the threerivers affected. Future (post-dam) catchrates were estimated by assuming a linearrelationshio between catch rate and fishhabitat available. The amount of habitatconsidered high quality for adult trout spe-cies affected by the dam in terms of actualphysical changes due to either flooding ormitigation construction was used. Pre-damhabitat was available from R.L.& L. Envi-ronmental Services Ltd. (1986) who meas-ured the amount of habitat on the threerivers affected (Crowsnest, Oldman, andCastle) (Table 1). Post-dam habitat at af-fected sites was assessed by subtractingamounts lost in areas flooded by the dam.The amount of habitat constructed throughthe mitigation structures was then addedusing various levels of success of the miti-

Table 3: Estimates of the Annual Value of Changes in Fishing Quality due toconstruction of the oldman River Dam With and without Travel Timeand at Various Success Levels of Subsequent Habitat Mitigation Efforts

Model Mitioation Success Level

o"k 25% 75%

gation program. O'Neil suggests 75"k isprobably the best estimate of the successof the structures. White (1991) has a differ-ent opinion of the mitigation work. Be-

cause of these differences of opinion arange of percentage levels of successwere utilized ranging trom 25ok Io 75"k(Table 1). Changes in catch-rate due to thedam were determined assuming a linearrelationshio between habitat and catch-rate and considering the ranges in mitiga-tion success. All resulting estimated rateswere adjusted down if they were higherthan a value considered by AFW staff as afirst class catch-rate.

Measures of the change in welfare forthe four models were calculated usingEouation 7. In order to calculate thechange in benefits for the angling popula-tion, the change in benefits from each resi-dence to each of the sites was determined.This was oerformed at each of three suc-cess levels for the mitigation work (Table

3). The dollar value of travel to the site wasdetermined by converting distance to costsusing the estimated cost of operating amid-size car in Alberta in 1991, which is

-58 246.5

(-96 239.1)1

-32221 .B

(-53 239.2)

-37 580.5

(-62 093.3)

-30 545.2(-50 469.0)

29 036.6

(47 976.3)

-16 807.8

(-27 771.1)

-14 332.2

(-23 680.7)

-18 462.4

(-30 505.0)

86 092.9

(142249.0)

-3 283.5

(-5 425.3)

6 376.6

(10 535.e)

,6 978.4

(-11 530.3)

126 794.9

(209 49e.8)

7 206.4

(11 e07.0)

60 454.6

(ee e87.5)

22971 .6(37 e55.3)

1 lmpacts including the value of time are shown in parentheses

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$O.22lkm ($0.35/mile). This value, timesthe round trio distance f rom the home townto the site, was included in the formula.Aggregate welfare measures were gener-ated by extrapolating the sample whofished in the Oldman area (236 o'f 2115anglers) to the angling population (about7000 of 63 000 license holders).

There is some debate in the literatureover the selection of a value for the timespent travelling in travel cost models(Shaw, 1992; Bockstael ef al., 1987;McConnell, 1985). In order to gauge thesensitivity of the welfare measures to theinclusion of a value for time, the measureswere calculated both with and without timevalues. For the time value, it was assumedthat the angler could have been working,so an average manufacturing wage ratewas used. The wage rate was provided bythe Alberta Bureau of Statistics, andamounted to $574lweek. A work week of40 hours was assumed to obtain an hourlyrate. The average speed of travel was as-sumed to be 80 kph. The hourly wage ratedivided by the average speed, multipliedby the round trip distance, was included inthe formula for cost when a value for timewas desired. These methods of calculatingtotal travel costs (both actual costs andtime) are common in the travel cost litera-IU re.

Each of the four models identify a wel-fare loss to anglers using the Upper Old-man River basin due to the construction ofthe Oldman River Dam (Table 3 column 2).Depending on the model used, and includ-ing the value of time, the annual welfareloss ranges from about $96 239 to $50 469.The models based on values provided byO'Neil show a smaller loss than the modelsbased on values provided by AFW. Themodels with alternative soecific constantsshow a smaller loss than models withoutthem.

The welfare changes were estimatedfor each of three positive mitigation suc-cess fevels:25.50. and757" success. Forall four models, mitigation eventually pro-duces a welfare gain (Table 3). For Model1 positive gains occur atthe 25o/o successlevel; for Model 3 gains occur at the 50%

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level; and for Models 2and4 the 75% levelof success is necessary for the changes tobe positive. These differences highlight thesensitivity of welfare estimates to the in-

corporation and magnitudes of particularvariables. Those models that do includealternative specific constants (2 and 4) re-quire larger mitigation success levels toresult in gains. Notice that even thoughhabitat area after mitigation has decreased(Table 1), net gains from the mitigationoccur. This arises because the economicbenefits deoend on the location of the miti-gation effort as well as lhe characteristicsof the sites. Some of the sites actually gainmore fish habitat as a result of the mitiga-tion work than they originally had (Table 1).

All four models show about a two fold in-crease in the absolute value of either thewelfare loss or gain associated with theenvironmental change when the value oftravel time is included. Based on predic-

tions and statistical performance, models 2and 4 appear to be superior. However, allfour models are presented to illustrate thevariability in the welfare effects.

Capitalized Value of WelfareChangeThe welfare effects described in Table 3represent annual changes due to the con-struction of the dam. Capitalization of theannual welfare change can be performedusing the assumption that there would beno additional annual changes, and thatthese values accrue in perpetuity. Assum-ing a discount rate of 5oklarale commonlyused in such studies (Filion ef a/., 1990)and employed in much of the originalbenefit cost studies (Anderson and Associ-ates, 1986)l the losses due to damconstruction range from $2 000 000(model 1, including the value of time) to$600 000 (model 4, excluding the value oftime). These losses are significant andshould have been included in the bene-fit/cost analysis undertaken to determine if

the dam should have been built. However,the magnitude of the recreational fishingloss due to the construction of the dam isrelatively small when compared to the

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other costs and benefits associated withthe project.

The losses due to dam constructionafter mitigation efforts are somewhat lowerthan the values listed above. ln fact75"/"mitigation success implies net benefitsfrom the project rather than losses. Forexample, assuming 75% mitigation suc-cess model 1 (including the value of time)estimates a net benefit due to the environ-mental changes of about $4 000 000, whilemodel 4 (excluding the value of time) pro-vides a benefit of about $450 000. All othercapitalized values can be calculated fromTable 3. The fact that mitigation results inbenefits ratherthan losses, however, mustbe examined in light of the cost of themitigation effort.

The comparison between the amountspent on mitigation and the capitalizedvalue of the welfare change will be made intwo directions. First, the comparison willbe made between the amount spent andthe loss that occurred from the dam con-struction alone; and second between theamount spent and the gain that occurredfrom the mitigation effort. The reason forthe two approaches rests on some of theassumptions used in welfare economicsconcerning loss calculations. Briefly, oneof the assumptions is that a loss can becalculated in the same way as a gain. Thatis, that the amount a oerson would be will-ing to pay for a gain is equal to the amounthe/she must be comoensated for a loss.This assumption has been challenged inresearch conducted by Knetsch (1 990).

In order to calculate the net benefits ofmitigation the "starting point" is first shiftedby the initial loss (due to the dam) beforethe calculation is made. In other words, theloss occurred (i.e. fishing sites were dam-aged), and then a second effort is made toimprove on this new situation. The mitiga-tion efforts cost approximately $5.5 million.Depending on the model, the success rate,and the interest rate used, some casescome close to a break-even point or evena net gain. For example, for Model 1 (themodel producing the highest benefit esti-mates), with a 5% interest rate, 75% suc-

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cess level of mitigation, and the value oftime included, the result would be:

loss with dam alone: $1 924782benefit from mitigation: +$4 189 996final gain: $6114778

Comparing this benefit of $6.1 millionwith the $5.5 million spent on mitigationsuggests that the gain is greater than themoney spent. This example is a specialcase - in all the other oossible scenarios ofcombinations of interest rate. model. timevalue of money and success rate, if thepurpose was to create a gain then mitiga-tion spending was higher than the resul-tant benefit gain. In particular, ourpreferred models, 2 and 4, produce themost conservative measures of the bene-fits of mitigation (and losses due to damconstruction). However, the figure of $5.5million also includes work other than justthe habitat construction, and there areother benefits stemming from the spendingthat are not accounted for here. Theseother benefits could include recreationalactivities other than fishing at the camp-grounds constructed. There are also otherrecreational losses related to the projectas detailed in FEARO (1992).

DiscussionThe travel cost models develooed in thisstudy reveal that the dam caused a loss inwelfare for people who fish for recreationin the Upper Oldman region. This findingcannot necessarily be transferred to par-ticioants in recreational activities such ashunting, hiking and wildlife viewing. Thedam may cause reductions in welfare forthese users, but the magnitude of thesechanges are not estimated here. Theremay also be certain values placed on thepreservation or existence of the originalOldman Basin habitats and resources thatmay have been negatively affected by theconstruction. On the benefit side, the res-ervoir itself may be the source of newrecreational values. The net effects of thelosses from construction less the benefitsof the reservoir comprise the net non-mar-ket effects of the dam.

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There may be other economic impactsof the dam on fishing that are not de-scribed in this study. These involve possi-ble shifts in the frequency and distributionof visits to fishing sites which may haveeconomic effects through changes in ex-penditure patterns. Changes in trips pre-dicted by the discrete choice model can beused to determine indirect economic im-pacts of environmental changes. For ex-ample, one effect could be increasedeconomic activity in the towns of the UpperCrowsnest valley. With more visits pre-dicted to this area, there is potential fornew visitors to purchase goods and serv-ices during their trips and generate eco-nomic activity.

The notion of "no-net-loss" is examinedin this study from an economic viewpoint.Physical notions of no-net-loss considerthe maintenance of physical stocks (habi-tat, species number, etc.). Economic no-net-loss would involve the maintenance ofthe economic benefit flow. These twonotions need not be identical. For exam-ple, the physical quantity used in this studywas the hectares of trout habitat. Trouthabitat could be constructed to maintainthe ore-dam levels. The economic benefitfrom these habitats, however, may or maynot be enough to counteract the lossesdue to the dam. The benefits deoend onthe location of the new habitats and otherfactors (scenery, campgrounds, etc.). Theconcepts of economic and physical no-net-loss should both be examined in the evalu-ation of a mitigation policy. Mitigationbenefits will be related to the constructionof habitat and/or the construction of facili-ties, campgrounds, or improvements ofother attributes. As well, the costs of suchefforts, relative to the benefits generated,should be considered.

The study points out several limitationsin the use of this type of model in welfareestimation and policy planning. The first isthe lack of limnological knowledge on thebiophysical relations affecting the catchrate of fish. In part, this can never be totallyresolved, as it partly depends on the skilllevel of anglers. The linear specification ofthe discrete choice model is also a limita-

Canadian Water Resources JournalVol. 19, No. 3. 1994

tion. The sensitivity of the welfare esti-mates to the value of travel time in this typeof model was also identified. The use of theresults is also limited by other factors, out-side of the choice of model type. One ofthese is the appropriate discount rate touse in comparing the mitigation expendi-tures with the welfare loss that occurred.

The need for accurate data on qualityattributes, universally accepted levels ofagreement on such factors as the properdiscount rate. and the orobable success ofhabitat mitigation work has been high-lighted. The results and empirical prob-lems encountered in this studv identifyfruitful ground for future research in policyanalysis methodologies. The sense of theanalysis described in this study, and somesolutions to the questions raised, wouldmake similar examinations of future pro-jects easier and reduce levels of contro-versy surrounding such projects.

AcknowledgementsWe thank Frank Bishop (Alberta Fish andWildlife Services) and Jim O'Neil (R.1. & L.

Environmental Services Ltd.) who pro-vided imoortant technical data andsuggestions concerning fishing in thestudy area. Della Clish of the Fish andWildlife Division and Dennis O'Leary of theLand lnformation Services Division alsoprovided important support and guidance.We thank two anonymous reviewers andthe associate editor for helpful suggestionson the manuscriot. This research wasfunded by Alberta Fish and Wildlife Serv-ices and the Buck for Wildlife - FisheriesManagement Enhancement Program.

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Hanemann, W.M. 1982. Applied WelfareAnalysis with Quantal Choice Models.Working Paper No. 173. Depadment of Ag-ricultural Economics. Universitv of Califor-nia, Berkeley.

Hildebrand, L. and J. O'Neil. 1992. OldmanRiver Dam Project: Angler Creel and Opin-

ion Survey, Crowsnest River 1990. DraftReport prepared by R.L.& L. EnvironmentalServices for Alberla Public Works, Supplyand Services. Edmonton, Alberta.

Knetsch, J.L. 1990. "Environmental Policylmplications of Disparities between Willing-ness to Pay and Compensation DemandedMeasures of Value." J. Environ. Econ.Mgmt., 18:227-237.

Maddala, G.S. 1983. Limited Dependentand Qualitative Variables in EconometricsCambridge University Press. Cambridge,Mass.

McConnell, K.E. 1985. "The Economics ofOutdoor Recreation." ln: Handbookof Natu-ral Resources and Energy Economics. A.Y.Kneese and J.L. Sweeney (eds.). Vol ll:667-722. Elsevier Science Publishers Ltd.,New York.

McFadden, D. 1981 . "Econometric modelsof Probabilistic Choice." ln: Structural Analy-sis of Discrete Data with Econometric Appli'cations, C.F. Manski and D. McFadden(eds). MIT Press. Cambridge Mass.

R.L.& L. Environmental Services. 1986.Fisheries Resources Upstream of the Old-man River Dam. Report prepared for AlbertaEnvironment, Planning Division, Edmonton,Albefia.

Shaw, W.D. 1992. "Searching forthe Oppor-tunity Cost of an Individual's Time." LandEcon.,68:107-115.

Smith, V.K. 1989. "Taking Stock of Progresswith Travel Cost Recreation Demand Mod-els: Theory and lmplementation." MarineRes. Econ.,6:279-310.

Stynes, D.J. and G.L. Peterson. 1984. "AReview of Logit Models with lmplications forModelling Recreation Choices." J. Leis.Res., 16:295-310.

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Endnotes:1. Personal communication with J. O'Neil, R.L.& L. Environmental Services Ltd.,

Edmonton, Alberta in an interview with senior author.2. This information was provided by the Alberta Motor Association.3. Knetsch (1990) states that the compensation value is several orders of magnitude

higher than the willingness to pay. The exact difference can vary with the scarcityof the good in question, but a general figure used is that compensation needs tobe 3-4 times the willingness to pay.

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