Ecological Economics 69 (2010) 528–538

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Ecological Economics

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Analysis

Heterogeneous preferences for water quality attributes: The Case of eutrophicationin the Gulf of Finland, the Baltic Sea

Anna-Kaisa Kosenius ⁎Department of Economics and Management, P.O.Box 28 (Koetilantie 5), FI-00014 University of Helsinki, Finland

⁎ Tel.: +358 50 4151 219; fax: +358 9191 58096.E-mail address: anna-kaisa.kosenius@helsinki.fi.

0921-8009/$ – see front matter © 2009 Elsevier B.V. Adoi:10.1016/j.ecolecon.2009.08.030

a b s t r a c t

a r t i c l e i n f o

Article history:Received 14 March 2008Received in revised form 26 August 2009Accepted 31 August 2009Available online 26 November 2009

Keywords:Preference heterogeneityChoice experimentEutrophicationMultinomial logit modelRandom parameters logit modelLatent class model

This paper examines the preference heterogeneity of the general public regarding water quality attributesand provides welfare estimates (WTP) for three nutrient-reduction scenarios that would improve the waterquality in the Gulf of Finland, part of the Baltic Sea. In the choice experiment (CE), the improvement isdescribed in terms of four water quality attributes: water clarity, abundance of coarse (non-attractive) fish,status of bladder wrack (a type of seaweed), and mass occurrences of blue–green algae blooms. The data areanalysed with multinomial logit (MNL), random parameters logit (RPL), and latent class (LCM) models. Theresults from MNL and RPL show that, on average for Finns, clear water is, relatively speaking, the mostimportant water quality characteristics followed by desire for fewer occurrences of blue–green algae. Theresults also show that the probability of expressing willingness to contribute to improvement of the waterquality increases with residential or recreational contact with the Gulf of Finland, a higher income thanaverage, a younger than average age, and lack of dependent children in the household. The LCM reveals thatopinions about the order of relative importance of attributes depend on age, household income, coastalresidence, and vacation home ownership. The present values for three nutrient reduction scenarios ofdifferent intensities ranging from 28,000 million euro to 54,000 million euro were calculated using meanwelfare estimates, discounted for perpetuity with a 2% rate.

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© 2009 Elsevier B.V. All rights reserved.

1. Introduction

Eutrophication, an accumulation of phytoplankton caused by theexcess amount of nutrients (phosphorus and nitrogen) from agricul-tural run-off and municipal waste water, is a worldwide environ-mental problem in both salt-water and fresh-water systems. Theexcess nutrients lead to excessive growth of plants that makes thewater more turbid, accelerates algae blooms, affects fish populations(among other species) and, when decomposed, often leads todecreased oxygen levels. Worldwide, there are many sea areas,lakes, and rivers that suffer from changes in ecosystem health inducedby eutrophication. Another aspect of eutrophication-related damageis the diminished recreational possibilities.

There is considerable literature about the valuation of benefit loss dueto eutrophication or the benefit from nutrient abatement. Both revealedpreference (RP) and stated-preference (SP) methods have been applied(see e.g., Kaoru, 1995; Whitehead et al., 2000; von Haefen and Phaneuf,2003; Hanley et al., 2006; Viscusi et al., 2008; Egan et al., 2009). The RPmethods (travel cost method [TCM] and hedonic pricing method [HP])are commonly used in the estimation of recreational benefits. Thesemethods utilize data from realmarkets and environmental data gathered

by environmental agencies. Based on the objective measures of waterquality (such as concentrations of nutrients or other biological orchemical measures), the related benefit estimates from RP methods arestraightforwardly applied to decision making, given that the recrea-tionists are responsive to themeasures not necessarily observable by laypersons. For this aspect, the application of survey-based SP methods(contingent valuation method [CVM] and choice experiment [CE])together with subjective water quality measures (such as the waterquality ladder) that are familiar to the respondents and aremore stronglyconnected to the value people place on the environment may improvethe validity of estimates. However, for decisionmaking, subjective waterquality measures must be linked to objective measures, which may becomplicated. (See Phaneuf and Smith, 2005).

The great advantage of SP methods over RP methods is that SPmethods are not limited to the evaluation of use values related to anenvironmental change. As for the alternative SP methods, in addition toproviding benefit estimates for selectedwater quality improvements, theadvantage of the choice experiment method over the contingentvaluationmethod is its ability to identify themarginal ratesof substitutionbetween different characteristics of an environmental change. This isespecially useful in the case of environmental goods possessing diversedimensions and values. (Bennett and Adamowicz, 2001).

Earlier SP literature on improved water quality offered variousdescriptions of water quality. For instance, CV studies have assessedthe value of reduced eutrophication (Söderqvist, 1996; Gren et al.,

Fig. 1. The map of the catchment area of the Baltic Sea, GUF = Gulf of Finland(source: HELCOM).

529A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

1997), of prevented asphyxiation (Goffe, 1995), of reducing theoccurrence of harmful algal blooms (Scatasta et al., 2003), and ofimprovement in sight depth (Söderqvist and Scharin, 2000). In the CEstudy, Eggert and Olsson (2009) described the improvement of waterquality with several characteristics: the level of fish stock, the qualityof bathing water and the biodiversity level. Their study indicated theexistence of preference variation among the general public for thecharacteristics of water quality.

This paper estimates the benefits associated with improving thecondition of the Gulf of Finland, the most eutrophied sub-basin in theBaltic Sea (Pitkänenet al., 2001). The coastal areas and thearchipelagoofthe Gulf of Finland are relatively important for recreation, since one-fourthof the Finnish population lives in coastalmunicipalities, and somethirty-thousand Finns own vacation homes along the coast (StatisticsFinland, 2006). Moreover, the Gulf of Finland is a primary fishing resortfor one hundred thousand recreational anglers (Pitkänen, 2004).

We apply an SP method, because the non-use dimension isundisputable in relation to the Gulf of Finland. For the Finns, there isno substitute for the Gulf. On a global scale, the Gulf is an ecologicallyunique body of brackish water (Pitkänen et al., 2001). Themultidimensionality of eutrophication calls for the use of choiceexperiment (CE), and we capture the dimensions by describingimprovement in the water quality in terms of changes in fourseawater characteristics: water clarity, abundance of so-called coarsefish species1 (Cyprinids), the state of population of a perennial macroalgae species bladder wrack (Fucus vesiculosus), and the number ofdays mass occurrences of blue–green algae (Cyanobacteria) bloomsare seen each year. Separate water quality characteristics aredifferently affected by policies directed to different nutrients(nitrogen or phosphorus). Thus, it is important to account formultidimensionality when deciding which policies to implement.

Another aspect of the valuation of an environmental good with (orwithout) multiple dimensions and values is the variation in thepreferences of the general public. We expect the preferences of Finnsfor water quality to vary considerably. To model preference heteroge-neity and to examine the associated distributions of welfare change inthe population,we analyse the datawith the standardmultinomial logit(MNL) with interactions with the observed individual characteristics,the random parameters logit model (RPL) (McFadden and Train, 2000;Hensher and Greene, 2003), and the latent class model (LCM) (Greeneand Hensher, 2003) which reveals the differences in the respondents'tastes for separate attributes. Besides the attribute heterogeneity, weexpect respondents having residential contact with the Gulf of Finland(i.e., living permanently and/or owning a vacation home on the coast)and with higher than average income to express higher WTP for waterquality improvement, consistent with economic theory. Our researchproduces an important component of the general view of the Gulfof Finland, namely the estimate of the benefits of water quality im-provement, and it may also serve as input for benefit transfer exercisesin the Baltic Sea and potentially worldwide (see e.g., Rosenberger andLoomis, 2003).

This paper estimates the magnitude of benefits from three selectednutrient reductions for Finns, and explores the sources of preferenceheterogeneity and its impact on the welfare estimates. The paper isorganized as follows: Section 2 describes the study area and the currentpolicy setting; Section 3 presents the valuation method and theapplication; Section 4 gives the results; and Section 5 concludes thepaper.

2. Study area and policy setting

The Baltic Sea (see the map in Fig. 1) is a semi-closed Europeaninland sea with rather low biodiversity and special unique geographic,

1 Coarse fish means fish that thrive in eutrophied conditions and are unattractive toanglers.

climatological, and oceanographic characteristics. Its catchment area issettled by 85million people, and the sea is very prone to an excess ofnutrients. The Gulf of Finland including its catchment area settled by20million people is the most eutrophied sub-basin of the Baltic Seabased on the ratio of nutrient load to surface area. The inflow ofnutrients to theGulf of Finland exceeds the average load to the Baltic Seaby 200–300% (Pitkänen et al., 2001). In the year 2000, the total loads ofnitrogen (N) and phosphorus (P) were 124,000 t and 6300 t, respec-tively (Rekolainen et al., 2006).

Both nitrogen and phosphorus are necessary for plant growth, butan excess of these nutrients leads to changes in the condition of theGulf of Finland, such as turbidity of water, intense growth of annualalgae harming the growth of perennial species such as bladder wrack(Fucus vesiculosus), harmful and potentially toxic algal occurrences,and changes in fish populations (improved growth of some speciesand increased numbers of fish and especially Cyprinids, small fish andfries) (Kiirikki and Blomster, 1996; Pitkänen, 2004). Generally in theGulf of Finland, the most important regulator of eutrophication isnitrogen (Tamminen and Kivi, 1996). However, phosphorus is alimiting nutrient for some blue–green algal or cyanobacteria species(Nodularia spumigena and Aphanizomenon sp.) that are able to fixnitrogen dissolved from the atmosphere (Pitkänen, 2004). Thesespecies produce blooms in the open sea.

530 A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

Several national and international actions and decisions aim atprotecting the Baltic Sea and the Gulf of Finland. Since 1974, theHelsinki Commission (HELCOM) has protected the marine environ-ment of the Baltic Sea. In 1992, the Commission adopted a 20-yearinvestment program to address the most polluting sources. TheFinnish Council of State has issued the decision-in-principles andestablished and revised water protection targets according to theassessment. The most recent Baltic Sea-wide program by HELCOM,the Baltic Sea Action Plan, aims at achieving a good environmentalstatus by 2021. (Action Plan…, 2005; HELCOM, 2007).

The implemented policies and abatement measures have notsucceeded in reaching the targets, either for nutrient reduction or forenvironmental improvement. Although the external nutrient load inthe year 2000 corresponded to about 60% of the load in the late 1980s2

(Rekolainen et al., 2006), the water quality did not improve. Onereason is the internal loading that accelerated in the mid of 1990s,supposedly owing to the increased area of anoxic sea bottom andstrengthened stratification (Pitkänen, 2004). Other causes potentiallyaffecting the development of the condition of the Gulf are temper-ature, wind, winter conditions, and salinity pulses (see e.g., Kiirikkiet al., 2003; Strategi för hav..., 2005).

According to the Baltic Sea ecosystem model (see Kiirikki et al.,2003), the long-term (30+ years) development of the Gulf of Finlandis highly dependent on where the nutrient reductions take place.Finnish national measures alone can improve the state of the coastalwaters, but international measures are needed to improve the entireGulf of Finland. The ecosystemmodel estimates the impact of nitrogenand phosphorus flows from land sources and adjacent sub-basins ontwo algal groups: “nitrogen-fixers” (i.e., cyanobacteria or blue–greenalgae in layman's terms), and “other phytoplankton” (as in Tyrrell,1999). The placement of reductions affects the biomasses of thesealgal groups differently (Pitkänen et al., 2007a,b).

In our study, we use different water quality characteristics toreflect the impact of reductions in nitrogen and phosphorus on thewater quality of the Gulf of Finland. The econometric specification andthe application of the valuation method, the choice experiment, aredescribed in the following section.

3. Method and application

Stated choice methods elicit the preferences of citizens forenvironmental goods that do not have a market price. In a choiceexperiment (CE) study, the respondents are asked to choose amongalternative policy options all characterized by attributes of environ-mental change. The “opt-out” option is included in the choice set as abase scenario for utility change calculations. The choice data and theprobabilistic choice model are used to estimate the marginal prices(or willingness-to-pay, WTP, or part-worth) for the attributes andwelfare estimates associated with outcomes or policies described interms of attributes. (Bennett and Adamowicz, 2001).

3.1. Econometric specification and derivation of willingness to pay

Until recently, the most often used probabilistic discrete choicemodel has been a multinomial logit (MNL) (or conditional logit) thatfeatures the independence of irrelevant alternatives (IIA) property.This means that the ratio of the probabilities of any two alternatives tobe chosen by the respondent are independent of the presence orabsence of any other alternative in the choice set (Hensher et al.,2005). The MNL assumes the equality of the utility functions across

2 The main reasons for the reduction are the Russian economic change thatdecreased the country's agricultural and industrial production, extensive reductionin fertilizer use in Estonia, and intensified nutrient removal in the most pollutingsources in Estonia as well as in Finnish point sources such as industry, fisheries, andsettlements.

the respondents, and thus results in homogeneous taste parameterestimates. Systematic (meaning linked to the observed characteristicsof the respondent) individual heterogeneity can be introduced intothe MNL model either by interacting socioeconomic variables withalternative-specific attributes or by dividing the sample into seg-ments. These approaches, however, may lead to many variables or themisspecification of groups since we use existing knowledge about thesources of preference variations (Hensher and Greene, 2003).

Increasingly popular models used in empirical applications are therandom parameters logit model (RPL) and the latent class model(LCM) (see e.g., Morey and Rossmann, 2003; Birol et al., 2006; Milonand Scrogin, 2006; Morey et al., 2006; Wang et al., 2007). Thesemodels introduce individual preference variation. By letting indivi-duals differ in respect to preferences, the RPL and the LCM relax the IIAproperty. The RPL addresses random heterogeneity in the model byassuming, for each individual's preferences, a continuous distribution.For the LCM, the parameter heterogeneity across individuals isformulated with a discrete distribution which is a function ofindividual characteristics. The respondents are classified into one ofthe classes (or segments) which remain latent for the researcher.Within each class, the preferences are homogeneous. (Greene, 2003).

The probabilistic choice model exploits the random utilityframework. The utility Ujn of the alternative j to the individual nconsists of the observable additive component Vjn and the unobserv-able component εjn:

Ujn = Vjn + εjn = α + ∑kβkxjkn + ∑mγmzmnα + εjn;

where α is the business-as-usual specific constant (one for the opt-out option and zero otherwise); βk is the coefficient for attribute kand has the distribution f(β); xjkn is the value of attribute k foralternative j; γm is the coefficient associated with the individual-specific or questionnaire-specific characteristics zmn (which interactwith α since they are constant over the alternatives); and the errorterm εjn, which is independent of other terms in the equation andindependently and identically distributed (IID) with identical type Iextreme value distribution, represents the unobserved heterogeneity.

The logit probability refers to the probability that the individualwill make the sequence of T choices specified as i={i1,…, iT}. Weapply the clustered specification that allows for repeated choices foreach respondent. Thus the logit probability Lni(β) is the product oflogit formulas of each choice of the respondent with the assumptionof the utility being linear in attributes:

LniðβÞ = ∏Tt = 1

eVniðβÞ

∑Jj = 1e

VnjðβÞ

24

35 = ∏T

t = 1eβ

′nxnit t

∑jeβ′nxnjt

24

35:

The probability Pni that the individual n makes the observedsequence of choices i is unconditional on the actually estimatedparameters and takes the following form

Pni = ∫LniðβÞf ðβÞdβ

where the logit formulas Lni(β) are averaged by weight given by thedensity f(β). The density can be specified either as discrete orcontinuous in β. The discrete distribution refers to the latent classmodel (LCM), according to which the population consists of thespecified number of segments (classes) in which the individuals havesimilar preferences. For the model with C classes, β takes C possiblevalues b1,…,bC, and the probability that β=bc is sc, that is, the share ofthe population in each class c. The continuous distribution f(β)related to the RPL can be specified, for instance, as normal, lognormal,triangular, gamma, or Rayleigh.

In the case of normal distribution, the density of β has the mean band the covariance W, both of which are estimated to determine the

Table 1Attributes and levels.

Attribute Description Attribute levels

Tax forhousehold(PRICE)

Tax for household oncea year for 20 years (€)

0, 5, 30, 70, 150, 350, 500

Best Middle WorstWater clarity(WAT)

Visibility of sea bottom(at 1 m depth)

Clearly visible Hardlyvisible

Not at allvisible

Number ofCyprinids(ROA)

Number of so-calledcoarse fish (Cyprinids)

Small Large Very large

Bladder wrack(BLW)

State of bladder wrackpopulation

Good A bitweakened

Weakeneda lot

Blue–greenalgae bloom(BGA)

Mass occurrences ofblue–green algae duringsummer

1–4 days 5–21 days 22–60 days

531A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

average taste of the respondents and the distribution of tastes in thepopulation. The choice probability Pni becomes:

Pni = ∫ ∏Tt = 1

eβ′nxnit t

∑jeβ′nxnjt

0@

1A

24

35ϕðβ jb;WÞdβ:

These simulated choice probabilities Pni are inserted in thesimulated log likelihood function to find the values of the trueparameter vector θ that maximize the function (Train, 2003).

For the selectedwater quality improvement, the expectedWTP (orconsumer surplus) of the individual n follows the standard utilitydifference expression (see e.g. Hanemann, 1984), which assumes theconstant marginal utility of income over population:

EðWTPnÞ = −1= βp½lnð∑ expðV1ÞÞ− lnð∑ expðV0ÞÞ�

whereβp is the parameter estimate of the cost,V1 is the utility evaluatedin the policy case, defined as changes in attribute levels relative to thebase scenario, the business-as-usual case V0. For the RPL model, theexpected WTPs for the individual n are obtained using unconditionalparameter estimates through simulation (Hensher et al., 2005: 688). Forthe latent classmodelwithC classes, thewelfaremeasures areweightedby the class membership, and themarginal utility of income is constantover the individuals grouped in each class c:

EðWTPnÞ = ∑Cc = 1πcf−1= βp½lnð∑ expðV1ÞÞ− lnð∑ expðV0ÞÞ�g

whereπc is the estimatedprobability the individualn of being addressedto the class c (Boxall and Adamowicz, 2002).

3.2. Application

The core of the CE questionnaire is a series of choice questions (orchoice situations or tasks). In our case, the respondents compared thepolicy options spelled out in terms of attributes that reflect theeutrophication status of the Gulf of Finland. Because eutrophicationleads to a large number of consequences, finding a set of attributesthat illustrates the broad and interdependent consequences and arerelevant to both policy makers and respondents (Bennett andAdamowicz, 2001) is important and requires a careful pilot phase.

We derived the attributes from information about the mostexperienced andmost uncomfortable consequences of eutrophicationfrom previous studies, our own pilot testing, and the indicators usedby the Finnish environmental administration. During several roundsof pilot interviews, representatives of the general public expressed tous their views on the consequences of eutrophication. Their answerswere then used to reduce the original list of consequences derivedfrom previous studies (Kiirikki et al., 2000, 2003). The pilot resultstogether with consultations with ecologists led to a choice of fourattributes (shown in Table 1): water clarity, abundance of so-calledcoarse fish species, the state of the macro algae species bladder wrackpopulation, and the number of days of mass occurrences of blue–green algae blooms each summer. The levels of attributes — best,middle, and worst— capture the range of current and potential futuretrends in the Gulf of Finland.

Water clarity and blue–green algae blooms were both familiar anduncomfortable for the general public, and the abundance of coarse fishpotentially harms recreational anglers. The participants in the pilotinterviews considered bladder wrack, a macro algae species, animportant habitat for the reproduction and development of smallfish. The monitoring of bladder wrack populations is part of thecoastal monitoring program required by the Water FrameworkDirective (WFD) (Bäck et al., 2006). The levels of the bladder wrackattribute correspond to descriptions of the ecological status in theWFD. The level “good” corresponds to the high and good states, the

level “a bit weakened” corresponds to the moderate state, and thelevel “weakened a lot” corresponds to the poor and bad states.

The 12-page questionnaire, conducted by mail, was constructed tofollow the survey practice recommended by Dillman (2002). Thebeginning of the questionnaire introduced the study area and theissue in question by examining each respondent's connections to theGulf of Finland, his experience of the harmful effects caused byeutrophication, and his perceptions of the current condition of theGulf of Finland, both in general and in terms of attributes and attributelevels used in the questionnaire. After the choice questions, infor-mation about answering the choice questions and about the res-pondents was collected.

The core section presented a future scenario of the Gulf of Finlandas it would appear without the implementation of new policies,referred to as the business-as-usual (BAU) option. The policy optionsprovided alternatives: the better conditions of the Gulf of Finlandbeing the result of international co-operation (a binding agreement)and a tax imposed for 20 years. International co-operation and relatednational measures were not specified in detail, because the pilotquestionnaire, mailed to one hundred respondents, showed that Finnsare very sensitive to how improvements are implemented. Instead,we mentioned that the measures would be implemented andallocated between the sources of nutrients according to the mostrecent research, and, if needed, the decisions would be updated. Thetax was chosen as a payment vehicle because the good to be valued isinternational in nature, while decisions concerning it are to be madeat the national level.

For the choice questions, 72 choice tasks (see the example in Fig. 2)were created with the help of Sawtooth Software's balanced overlapprocedure, which allows for some overlap of the attribute levelswithin the choice task. The choice tasks with dominant alternativeswere modified. Each respondent faced six choice situations, and ineach, picked the preferred alternative from two policy alternatives(Agreement A, Agreement B) and the business-as-usual (BAU)alternative. The BAU was described as the most damaging for theGulf's future, that is, it offered the worst levels of all the water qualityattributes. In addition, the price attribute was included in the options.

4. Results

4.1. Descriptive statistics

A sample of 1900 Finns, ages 18 to 80 and drawn from the CensusRegister,was stratifiedon thebasis of residential contactwith theGulf ofFinland in order to test hypotheses of the differences in the WTPsbetween non-coastal residents, part-time coastal residents who ownvacation home in coastal municipalities, and permanent coastal

Fig. 2. An example of a choice set.

532 A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

residents. After twomailing roundswith a reminder in between and theelimination of questionnaires with missing answers to the questionsrelevant for analysis, the response rate was 42.7%. Those who did notwish to supportmeasures to improve the Gulf, i.e. chose the BAUoption,and gave the following reasons: “polluters should pay”, “other countriesare responsible for the improvement of the Gulf of Finland” and “I don'tbelieve that the measures would improve the condition of the Gulf ofFinland,”were identified as protest zeros. This group, 10.5%of responsesreceived, was excluded from the analysis.

Table 2 compares the descriptive statistics of the sample with thecorresponding statistics of the Finnish population. For almost all

Table 2Descriptive statistics of the data and corresponding data on the Finnish population.

Sampleaverage

Finnishaveragea

Sample size 726 5255580

Socio-demographic characteristicsAge (OLD)b 51.48 45.88Gender (% of males) 51.79 49.55Household size 2.34 2.14Dependent children in the household(% with dep.ch.) (DEPCH)b

12.32 41.18

Education (% with polytechnic or universitydegree and above)

36.70 13.84

Unemployment (% without employment) 2.64 5.35Income (net, thousand €/year) (HINC)b 37.8 31.0

Characteristics related to the Gulf of FinlandCoastal residence (% living in coastalmunicipalities) (COA)b

63.09 23.47

Vacation home ownership (% of owners onthe coast of the Gulf of Finland) (VAC)b

32.78 2.44

No residential contact to the Gulf of Finland (%) 32.92 –

Recreational angler (%) 29.20 –

Heard from the media about the issue (%) 96.05 –

Spent free time on the coast (%) 71.97 –

Perception of the current state of the Gulf(1=excellent, 5=very poor), modal answer

4=poor –

Experienced harm from eutrophication (%) 55.54 –

Water turbidity causing lots of nuisance/nonuisance at all (% of respondents)

13.09/0.69 –

Numbers of coarse fish (Cyprinids) causinglots of nuisance/no nuisance at all (%)

4.41/11.02 –

Weakened bladder wrack population causinglots of nuisance/no nuisance at all (%)

16.94/5.79 –

Blue–green algae causing lots of nuisance/nonuisance at all (%)c

29.51/2.82 –

a The data for the Finnish population are from Statistics Finland (2006).b Themeans are not equal at the 5% level according to the t-test. These characteristics

are used as variables in the econometric models.c The measure of nuisance caused by blue green algae is the mean value of

perceptions about “blooms in the open sea,” “large amounts of algae,” “small amountsof algae,” and “potential health risk.”

demographics, the null hypothesis of the t-tests for equality of meanscannot be rejected at the 5% level. Only the null hypotheses regardingage, the share of households with dependent children, income, coastalresidence and vacation home ownership were rejected. Thus, becausethe sample population, on average, is older, has a higher income, andincludes fewer households with dependent children as well as hasmore coastal residents and vacation home owners than the generalFinnish population, the WTP estimates aggregated for the Finnishpopulation are not fully representative without weighting theparameters obtained from the model.

In the lower part of Table 2, the characteristics related to the Gulfof Finland imply that the respondents knew the study area ratherwell. Two-thirds (63.09%) live permanently on the coast, and one-third (32.78%) own a vacation home on the coast. About one-third(32.92%) of the respondents have no residential contact with the Gulfof Finland. Almost all (96.05%) have heard about the Gulf from themedia, and as many as two out of three (71.07%) have spent free timeon the coast. One-third (29.20%) had fished in the Gulf of Finlandduring the year 2005. The current state of the Gulf of Finland wasconsidered as rather unsatisfactory, the most common perception ofthe Gulf being “poor”. Slightly more than a half (55.54%) hadexperienced nuisance due to eutrophication. Among those respon-dents, the perceptions of the magnitude of the nuisance caused bydifferent consequences of eutrophication differed. Compared to theblue–green algae blooms, water turbidity was less often perceived ascausing both “lots of nuisance” (the high extreme of the 4-point scale)and “no nuisance at all”. This implies that, while blue–green algaeblooms do not cause nuisance for so many respondents, the nuisanceonce experienced is considered substantial. The same applies to theweakened bladder wrack population. The nuisance due to the numberof coarse fish was least often perceived.

4.2. Models

The MNL and RPL models were estimated using Limdep 9.0 Nlogit4.0, and the LCM model was estimated with Latent GOLD Choice 4.5.Because the LCM accounts for the series of six choice situationspresented to each respondent, we applied the cluster approach to themodelling of the MNL and RPL as well. In the models, the variable BAUis the alternative-specific constant, specified one for the business-as-usual alternative and zero otherwise. The constant (BAU) captures allthe variation in preferences not explained by the variables of themodel. The continuous variable PRICE refers to the price attribute. Thewater quality attributes (see Table 1), i.e. water clarity, the number ofcoarse fish, state of bladder wrack population, and days of blue–greenalgae occurrences in summertime (WAT, ROA, BLW, and BGA) areeffects-coded dummy variables for the considerable improvement inwater quality attributes compared to the baseline (BAU). Foridentification purposes, we had to eliminate from the model the

533A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

effects-coded dummy variables for small improvements in waterquality attributes, that is, the middle levels of qualitative attributes.Because the worst levels of the attributes referring to the BAU optionwere not included in the policy options, the model with all qualitativevariables failed to converge.

The starting point for modelling was the basic multinomial logit(or the conditional logit) model with only alternative-specific attri-butes as the explanatory variables (Hensher and Greene, 2003). Weended up studying the main effects only because the interactioneffects of the attributes did not fit the data well. To account forheterogeneity in the sample and to uncover whose welfare is affec-ted by changes in the environmental state (Boxall and Adamowicz,2002), we tested various socio-demographic variables as interac-tions with either the alternative-specific constant or the attributes ofthe alternatives. The former captures heterogeneity in choosing thepolicy vs. the business-as-usual option and the latter, in differentattributes. Of the tested socio-demographic interactions, we reportonly the significant ones here.

4.2.1. Multinomial logitThe likelihood ratio test for improving the MNL by the inclusion of

socio-demographic characteristics as interactionswith the BAU showed(χ2(5)=102.25 p<0.0001) the superiority of the interaction MNLmodel over the basic MNL model, as the chi-square statistics (102.25)exceed the critical limit for 5 degrees of freedom (11.07). Table 3 showsthe results. The overall fit of the BAU-interaction model measured byMcFadden's ρ2 was fairly good (0.18), and themodel predicted 51.1% ofthe choices correctly.

The significance of all attribute coefficients aswell as the coefficientsof socio-demographic interaction variables indicates that all selectedattributes significantly affected the choice and the respondent char-acteristics explained the differences in the tendency to choose thepolicyoption vs. the BAU option, i.e., whether the respondents are willing topay for protecting of the Gulf of Finland. The negative sign of the BAUindicates the higher utility when choosing the policy alternative insteadof the business-as-usual alternative and thus, on average in the sample,the tendency to choose the policy alternative. In the questionnaire, we

Table 3The results of multinomial logit (MNL) and random parameters logit (RPL) with BAUinteractions.

Variable/model MNL RPL

BAU −1.302 (0.119)a −3.679 (0.453)a

St. Dev. BAU – 3.399 (0.229)a

BAU*COA −0.295 (0.106)a −0.005 (0.386)BAU*VAC −0.379 (0.123)a −1.144 (0.444)b

BAU*OLD 0.211 (0.109)c 0.541 (0.393)BAU*DEPCH 0.474 (0.129)a 0.785 (0.469)c

BAU*HINC −0.793 (0.102)a −1.707 (0.364)a

PRICE −0.007 (0.000)a −0.012 (0.001)a

Water clarity WAT 0.318 (0.045)a 0.606 (0.081)a

St. Dev. WAT – 1.071 (0.118)a

Coarse Fish ROA 0.212 (0.045)a 0.271 (0.071)a

St. Dev ROA – 0.631 (0.146)a

Bladder wrack BLW 0.095 (0.043)b 0.153 (0.072)b

St. Dev. BLW – 0.889 (0.119)a

Blue–green algae BGA 0.232 (0.043)a 0.375 (0.090)a

St. Dev BGA – 1.660 (0.119)a

Number of obs 3946 3946Log likelihood −3222.325 −2680.221Log likelihood (0) −3937.713 −4335.124Pseudo R 0.18168 0.38174Correct predictions 51.14% 51.40%

IIA Test Chi-sqrd value

Alternat. A dropped 73.54a

Alternat. B dropped 82.76a

BAU dropped 0.05

Standard errors are in parentheses. a,b,cSignificant at 1(5)10% level.

did not specifically ask for themotivation for expressing a positiveWTP,but the popularity of policy optionsmay reflect concernover the currentpoor condition of the Gulf as well as willingness to act in order to avoidan even poorer condition in the future.

The signs of coefficients of interaction variables confirmed ourhypotheses that stronger preference for water quality improvementwas shown by coastal residents (COA) and vacation home owners(VAC) and by respondents with higher yearly income than theaverage (HINC). Moreover, the respondents who are younger thanaverage (non-OLD) and who do not have dependent children in thehousehold (non-DEPCH) were more likely to choose the policyalternative rather than the business-as-usual alternative. As explan-atory variables, we also tried education, recreational boat ownershipin the Gulf of Finland, being an angler in the Gulf of Finland, andexperience on nuisance caused by eutrophication.

The expected negative sign of the price coefficient implied higherutility from alternatives with lower price levels, while the positivesigns of water quality coefficients, as expected, imply higher utilityfrom alternatives with higher water quality levels. The comparison ofthe magnitudes of water quality coefficients revealed the highestrelative importance of water clarity (WAT) followed by blue–greenalgae (BGA), fish (ROA), and bladder wrack (BLW).

The common restriction of the MNL, the assumption of indepen-dence of irrelevant alternatives (IIA), was tested with the Hausmanand McFadden (1984) test (end of Table 3). Each of the threealternatives was eliminated in turn, and the performance of theresulting model was compared to the original (full) model. The nullhypothesis is that the rejection (or inclusion) of one alternative or achange in its attributes does not change the ratio of choiceprobabilities of other alternatives. The hypothesis could be rejectedfor the exclusion of policy alternatives (Agreements A and B)(p<0.01). For the exclusion of the BAU option (alternative C), thehypothesis could not be rejected indicating that the constant varianceassumption does not hold, and a more complex model than the MNLwith BAU interactions is needed (see Hensher et al., 2005, p. 601).

4.2.2. Random parameters logitThe RPL model in the last column of Table 3 was based on 1000

simulation draws. We assigned normal distribution to random waterquality parameters (WAT, ROA, BLW, and BGA) and the randomconstant BAU. The variable PRICE was specified as non-random.Thus, the preferences for the price of the policy were assumed to behomogeneous, that is, the marginal utility of money was assumed tobe constant over the sample.

The introduction of random preference variation improved themodel significantly (χ²(5)=1084.21, p<0.0001). Compared to theMNL model, the model fit measured by McFadden's ρ2 improvedconsiderably from 0.18 to 0.38, but the prediction power increasedonly modestly, from 51.1% to 51.4%. The signs of variables were as inthe MNL, but two interactions of BAU (coastal residence and olderthan average) were insignificant in the RPL specification.

A common feature for all water quality attributes (WAT, ROA, BLW,and BGA) was that the magnitudes of the coefficients for standarddeviations were greater than the mean coefficients. This indicatedrelatively large heterogeneity across the respondents for the waterquality attributes. Especially for the blue–green algae attribute therandom variation was four times larger than the mean estimateindicating a remarkable variation in preferences for reduced occur-rences of blue–green algae blooms. Both the order of relativeimportance of water quality attributes and the signs of socio-demographic variables were the same as in the MNL.

In MNL and RPL, no interactions of socio-demographic character-istics and attributes could be introduced without incurring a loss inthe significance of the other variables. The estimated variances of theRPL, however, reveal the unobserved preference heterogeneity. Inorder to reveal preference heterogeneity stemming from observed

Table 4Criteria for selecting the optimal number of classes.

Number of classes LogL R2 AIC BIC Parameters (P)

1 −3069.32 0.1908 6150.64 6177.53 62 −2481.75 0.4088 4999.49 5080.16 183 −2337.90 0.5216 4735.81 4870.26 304 −2259.29 0.5733 4602.57 4790.80 425 −2216.44 0.6240 4540.87 4782.88 546 −2193.62 0.6487 4519.25 4815.03 667 −2170.90 0.6650 4497.79 4847.36 788 −2153.05 0.6804 4486.10 4889.44 909 −2132.11 0.6803 4468.21 4925.34 10210 −2119.79 0.6940 4467.57 4978.47 114

AIC=Akaike Information Criterion:−2(LogL-P), BIC=Bayesian Information Criterion:−LogL+(P/2)*ln(N), N=653 (in total 3708 observations).

534 A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

characteristics of the respondents, we analysed the data with thelatent class model.

4.2.3. Latent class modelIn the LCM, the optimal number of classes was identified by

assessing the Bayesian Information Criterion (BIC), the AkaikeInformation Criterion (AIC), the R2, and the log likelihood statistics(see e.g., Swait, 2006) of the models with 1–10 classes (Table 4). Thelog likelihood and R2 as well as the AIC presented improvement as thenumber of classes increase. The BIC, instead, suggested by itsminimum value the 5-class model. The increase in the R2 from thebasic 1-class model (0.19) to the 5-class model (0.62) wasremarkable. The superiority of the 5-class model over the 4-classmodel was formally tested by the bootstrapping method andconfirmed, and the 5-class model was then further improved byremoving non-significant parameters and restricting the coefficientsof the bladder wrack attribute (BLW) to be equal over the classes assuggested by the Wald test statistics (Vermunt and Magidson, 2005).

The latent 5-class choice model (the upper part of Table 5) withthe explanatory power R2 of 0.66, and the ability to predict as much as83.2% of the choices correctly, provided remarkable improvementsover the MNL (0.18, 51.1%) and RPL (0.38, 51.4%) models (seeTable 3). The superiority of LCM over RPL was confirmed by the Ben-Akiva and Swait (1986) test for non-nested models. The nullhypothesis — that the model with the lower fitness measure, RPL, isthe correct model — was rejected (p<0.001) with the test statisticsvalue of −53.193. The positive (negative) signs of the statisticallysignificant water quality (price) coefficients were as expected.

The class membership model (the lower part of Table 5) includedthe same set of socio-demographic explanatory variables as the MNLand RPL models. These variables provided plausible explanations forthe membership of groups with similar preferences, but the ratherlow fit, R2 of 0.08, and the share of insignificant parameters suggestedthat the explanatory power of the model could improve by theinclusion of additional variables.

The results of the model supported the existence of preferenceheterogeneity in the sample, first of all, toward the contribution to theimplementation of nutrient reduction policy. The negative constants(BAU) of classes A, B, C, and E indicated the tendency to choose eitherof the policy options. In contrast, class D had higher preference forstaying in the business-as-usual situation. Concerning attributes, noneof them affected the choice significantly over all five classes, and therelative order of importance of water quality characteristics differedfrom one class to another.

3 The fitness measure is the Akaike Likelihood Ratio Index: 1-(Lj-Kj) /L0 where Ljrefers to LogL at convergence, Kj to the number of parameters, and L(0) to LogL at zero.The significance level at which the null is rejected is Φ -(-2Z*L(0)+K)1/2 , where Z isthe difference in fitness measures, and K the difference in the number of parameters.

Classes A and B reflected the opposite attitudes vis-à-vis therelative importance of attributes. The members of class A, which wemay describe as “Whatever improvement”, made their choice basedsolely on the price of the alternative, and none of the water qualityattributes significantly affected the choice. On the contrary, thechoices of the members of class B “Whatever price” were affected bythe water quality attributes and the price of the option wasinsignificant. The choices of classes C, D, and E were significantlyaffected both by price and by diverse combinations of water qualityattributes.

One-third (34%) of the respondents were assigned to class A, theprobability of which increased with non-coastal residence. Theprobability to be assigned to class B (11% of the respondents)increased with higher than average income. The order of relativeimportance of the attributes was blue–green algae followed by fish,water clarity, and bladder wrack. Also for the members of class C,“Blue–green algae and clarity” (27%), blue–green algae was relativelythe most important quality attribute followed closely by water clarity,while the choice was unaffected by the bladder wrack attribute. Theprobability of being assigned to this class increased with respondentswhowere younger than average, earned incomes higher than average,had a coastal residence, and owned a coastal vacation home.

Class D, “Business-as-usual please” (11%), was clearly distinct fromother classes in the sense that the members, on average, prefer theBAU option more than the policy options. The probability of classmembership increased with non-coastal residence and income belowaverage. Of the significant water quality attributes, water clarity wasrelatively more important than blue–green algae. Concerning class E,“Algae attributes” (16%), none of the variables of the class member-ship model explained the probability of being assigned to this classwhose members expressed the highest relative importance of bladderwrack.

4.3. Heterogeneity in WTP for nutrient reduction policies

The estimated parameters can be used to calculate either themarginal rates of substitution between the attributes defined by theratio of estimated parameters or the WTPs (the expected welfaremeasures) for the water quality scenarios, which may be more usefulfrom a policy viewpoint. Tables 6 and 7 present the WTPs of thegeneral public for three potential future conditions in the Gulf ofFinland, estimated from the MNL and RPL models (Table 6) and theLCM model (Table 7). The Delta method is used to estimate thestandard errors which are to be used in the calculation of confidenceintervals for WTPs.

Three scenarios presenting diverse intensities in nutrient reduc-tion are improvements over the business-as-usual (BAU) scenario,which represents the condition of the Gulf resulting from the failure ofthe Finnish Program for the Protection of the Baltic Sea with no newpolicy implementations (Kiirikki et al., 2003). The scenarios are basedon long-term forecasts of the Baltic Sea ecosystem model thataccounts for the effect of nutrient reduction on two algal groups:cyanobacteria and other algae (Pitkänen et al. 2007a,b).

Scenario 1 results from nutrient reductions in Estonia, Russia, andFinland according to the most stringent alternative in the Guidelinesfor Water Protection to 2015 (Rekolainen et al., 2006), i.e., a 7%reduction in the bioavailable loads of both N and P through measuresimplemented in Finland, Estonia, and Russia and the reduction ofatmospheric load. For Finland, this corresponds to the reduction of 33(44)% in total nitrogen (phosphorus) of the mean load of 1997–2002.In the ecosystem, this leads to a slight decrease (up to 15%) in thebiomasses of both cyanobacteria and other algae.

Scenario 2 demonstrates the intensified purification of wastewater (75% efficiency regarding N and 80% efficiency regarding P) inthe city of St. Petersburg, Russia. Compared to the year 2004, thiswould lead to a 49(53)% reduction in nitrogen (phosphorus) load

Table 5Model results from the latent class model.

Variable/class A B C D E

“Whatever improvement” “Whatever price” “Blue–green algae and clarity” “Business-as-usual please” Algae attributes”

Utility functionBAU −4.981 (0.368)a – −1.159 (0.657)b 1.846 (0.597)a −3.597 (0.561)a

PRICE −0.011 (0.001)a – −0.013 (0.002)a −0.044 (0.007)a −0.064 (0.011)a

WAT – 0.828 (0.183)a 1.262 (0.196)a 0.802 (0.267)a –

ROA – 0.910 (0.199)a 0.762 (0.163)a – –

BLW – 0.546 (0.159)a – – 0.546 (0.159)a

BGA – 1.086 (0.206)a 1.522 (0.221)a 0.685 (0.272)b 0.528 (0.258)a

Class membership functionConstant 1.257 (0.210)a −1.239 (0.391)a −0.279 (0.331) −0.116 (0.257) 0.379 (0.236)OLD 0.150 (0.220) 0.473 (0.332) −0.730 (0.249)a −0.271 (0.254) −0.164 (0.232)DEPCH −0.351 (0.279) 0.188 (0.345) −0.310 (0.288) 0.321 (0.289) 0.152 (0.272)HINC −0.407 (0.250) 0.706 (0.268)a 0.657 (0.237)a −0.614 (0.332)c −0.342 (0.290)COA −0.809 (0.251)a 0.373 (0.309) 1.241 (0.334)a −0.547 (0.244)b −0.258 (0.230)VAC −0.023 (0.242) 0.103 (0.279) 0.443 (0.233)c −0.163 (0.274) −0.360 (0.267)Latent class probs 0.34 0.11 0.27 0.11 0.16

No. of observations 3708No. of respondents 653Log likelihood −2234.66Correct predictions 0.832R² (choice model) 0.6645R² (class model) 0.0796

Standard errors are in parentheses. a,b,cSignificant at 1(5)10% level.

535A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

corresponding to a 5% decrease in bioavailable nitrogen and 17% inbioavailable phosphorus. As a result, the biomass of cyanobacteriawould decrease considerably (by 15 to 35%) and other algae woulddecrease slightly (up to 15%).

Scenario 3 is a combination of Scenarios 1 and 2 with the Polishcorrespondence to the nutrient-reduction-by-half target of the 1988/98 Ministerial Declaration, that is, 21% of nitrogen and 25% ofphosphorus compared to the mean loads in 1995–1999 of rivers andin 2000 for coastal point sources (such as industry and settlements).All these measures would considerably decrease (by 15 to 35%) boththe biomasses of cyanobacteria and other algae.

Since we lack a more detailed knowledge of the link between thewater quality indexes (objective measure) and the correspondingwater quality improvements as perceived by people (subjectivemeasure) as well as the actual links between the attributes in thecomplex sea ecosystem and the actual nutrient reductions needed forparticular improvements in water quality, we constructed thecombinations of the attributes so that they somewhat looselycorrespond to the algal groups of the ecosystem model (the second

Table 6Heterogeneity in WTP in the population: the MNL and the RPL models.

No. Scenario MNL RPL

Reduction inbiomass

Attribute levels Mean Low High Mean Low High

1 Other: <15 % WAT mid ROA mid 271.1 148.8 445.1 392.8 255.8 610.8Cyano: <15 % BLW mid BGA mid (10.7) (21.9) (23.1) (11.3) (11.3) (11.3)

2 Other: <15 % WAT mid ROA mid 287.6 165.3 471.6 405.2 268.1 623.1Cyano: >15 % BLW mid BGA best (11.1) (22.1) (23.3) (12.1) (12.1) (12.1)

3 Other: >15 % WAT best ROA best 332.0 209.8 516.0 448.1 311.1 666.0Cyano: >15 % BLW best BGA best (12.0) (22.5) (23.9) (12.9) (12.9) (12.9)

Individual characteristicsAge >45.88 yrs (OLD) ⁎ + − ⁎ + −Family with children (DEPCH) ⁎ + − ⁎ + −Yearly income >31,000 € (HINC) ⁎ − + ⁎ − +Coastal resident (COA) ⁎ − + ⁎ − +Coastal vacation home owner(VAC)

⁎ − + ⁎ − +

Standard errors in parentheses. ⁎An average Finn: 45.88 years old, no dependent children(prob. 1-0.4118), yearly income: 31,000 euros, non-coastal resident (prob. 1-0.2347), novacation home (prob. 1-0.0244).

and third columns of Table 6). The group “cyanobacteria” correspondsto the blue–green algae attribute, while the group “other algae” islinked to the bladder wrack attribute, because the share of littoralfilamentous algae increases at the expense of bladder wrack(Pitkänen, 2004). We assume water clarity and fish attributesimprove together with the bladder wrack attribute. The slightreduction in the algal biomass corresponds to theminor improvementin the attribute, that is, from the BAU level to the middle level,whereas the considerable reduction corresponds to the improvementto the best level. Because we included only those variables for the bestlevels of water quality attributes, we took a linear approximation ofthe utility in order to obtain the estimate for the middle levels of thequalitative attributes.

The WTP estimates from the MNL and RPL models (Table 6) areweighted for population representativeness. According to the t-test,all WTP estimates based on the MNL were significantly (p<0.001)lower than the corresponding estimates based on the RPL. The excessWTP of RPL reflects the random variation in WTP for improving water

Table 7Heterogeneity in WTP in the sample: latent class model.

Scenario LCM

No. Mean A B C D E

1 236.8 457.0 na 259.2 −25.2 64.6(6.2) (11.1) na (6.8) (1.2) (0.9)

2 254.6 457.0 na 319.2 −17.4 68.7(6.3) (11.1) na (7.8) (1.5) (1.1)

3 278.0 457.0 na 398.8 −8.2 73.0(6.7) (11.1) na (9.7) (1.7) (1.2)

Individual characteristicsAge >51.48 yrs (OLD) ⁎ ? ? − ? ?Family with children (DEPCH) ⁎ ? ? ? ? ?Yearly income >37,800 € (HINC) ⁎ ? + + − ?Coastal resident (COA) ⁎ − ? + − ?Coastal vacation home owner (VAC) ⁎ ? ? + ? ?

Share of respondents assignedto class

0.34 0.11 0.27 0.11 0.16

Standard errors in parentheses. ⁎An average respondent: 51.48 years old, no dependentchildren (prob. 1-0.1232), yearly income: 37,800 euros, coastal resident (prob. 0.6309),no vacation home (prob. 1-0.3278).

536 A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

quality in the Gulf of Finland. The low (high) WTP estimates werecalculated for a hypothetical Finnish household that has (no)dependent children, earns a yearly income below (above) 31,000 €,lives (not) and does (not) own a vacation home in a coastalmunicipality, and is older (younger) than 45.88. The differencesbetween the low/high WTP estimates and the mean estimates weresignificant (p<0.001), indicating the existence of preference hetero-geneity in the population.

With regard to theWTP estimates of the LCM, Table 7 presents theclass-weighted mean estimates and the class estimates. The meanWTP was lower than the mean estimates of the MNL and RPL(Table 6), but the estimates are not fully comparable. The reason isthat the LCM results were not weighted for population representa-tiveness because only one weighting variable could be introduced, butno cross-statistics on the socio-demographics were available. Despitethis shortage, the LCM results are useful, because they revealedaspects and features that were left uncovered by the MNL and RPL:first, the differences in the magnitude of overall WTP across house-holds and second, the differences in the order of relative importanceof water quality attributes (Table 5). The LCM also allowed fordifferent marginal utilities of income for separate classes.

Concerning the class WTP estimates, there was a group ofhouseholds (class A) that was, on average, willing to pay an annualtax increase of 427 euro for the improvement of the Gulf of Finlandregardless of the scale, and the probability for being in that groupincreasedwith non-coastal residence. Another interesting finding wasclass B, for which the price coefficient was insignificantly differentfrom zero. Thus, we were unable to estimate the mean WTP of themembers of this group. The size of this class with a potentially veryhigh WTP is probably smaller in the population than in the sample,since the average income of the sample exceeded the average incomeof the population.

The explanatory variables of the class membership refer to thecharacteristics of the respondent increasing the likelihood of beingassigned to the certain class. For class C, these characteristics werecoastal residency, vacation home ownership, younger than theaverage age and higher than the average income, and the character-istics were the most similar to the high WTP households of the MNLand RPL, although estimates of the LCM for the scenarios were muchlower ranging from 259.2 euro to 398.8 euro yearly. One feature leftuncovered by the MNL/RPL was the class with negativeWTP for waterquality improvement, which was represented by class D. The share ofthis class is probably larger in the population than in the sample,because the population consists of more non-coastal and lower-income households. The probability of being assigned to class E,whose members expressed a rather modest WTP ranging from 64.6euro to 73.0 euro, cannot be explained by the socio-demographicvariables included in the model.

To exemplify the national benefits of water quality improvementin the Gulf of Finland, Table 8 reports the present values for threescenarios that reflect the aggregated WTP of the Finnish populationconsisting of 2.405 million households (Statistics Finland, 2006) to

Table 8Present values (PV) and corresponding confidence intervals of benefits for Finns fromreduced eutrophication in the Gulf of Finland (in millions of €).

Scenario MNL RPL LCM

1 32,600 47,234 28,475(30,078–35,122) (44,571–49,897) (27,014–29,936)

2 34,584 48,725 30,616(31,968–37,200) (45,873–51,577) (29,131–32,100)

3 39,923 53,884 33,430(37,095–42,751) (50,844–56,924) (31,850–35,009)

PV=WTP/r⁎2.405. The constant discount rate r=2% refers to the growth rate of the GDP.The number of Finnish households is 2.405 million. 95% CIs in parentheses.

avoid the damage that occurs without new policy implementations.The mean household WTP estimates from Tables 6 and 7 werediscounted for perpetuity, because the long payment period(20 years) likely causes the respondents to interpret the tax aspermanent. The discount rate of 2% was used, because it representsthe growth rate of the GDP in Finland.

The MNL estimates for three scenarios ranged from 32,600 millioneuro to 39,923 million euro, while the RPL estimatesweremuch higher:from 47,234 to 53,883 million euro. The LCM estimates for threescenarios were themost conservative and the closest to each other. TheWTP estimate was 28,475 million euro for modest improvement to theGulf — hardly visible sea bottom, large numbers of coarse fish, asomewhat weakened bladder wrack population, and blue–green algaeblooms for 5–21 days each year. For improvement that is otherwisesimilar but the amount of blue–green algae blooms reduces to 1–4 days,the WTP estimate was 30,616 million euro. For improvement of allwater quality attributes to thehighest qualitative level, namely, a clearlyvisible sea bottom, small numbers of coarse fish, a good state of thebladder wrack population, and blue–green algae blooms for 1–4 dayseach year, the WTP estimate was 33, 430 million euro.

5. Conclusion

We conducted a choice experiment survey and estimated thewillingness to pay for the improvement of the water quality in theGulf of Finland, as characterized by four characteristics: the waterclarity, the abundance of so-called coarse fish (Cyprinids), thehealth of macro algae species bladder wrack (Fucus vesiculosus),and the mass occurrences of blue–green algae (Cyanobacteria). Weprovided welfare estimates (WTP) for Finnish households for threefuture scenarios and examined the impact of preference heteroge-neity on the estimates as well as the sources of heterogeneity,using the multinomial logit (MNL) and random parameters logit(RPL) models including interaction terms, and with the latent classmodel (LCM).

The yearly household WTP estimates from MNL and RPL modelsfor three nutrient reduction policies differing in terms of intensity ofreduction range from 149 to 611 euro for the most modest scenario,from 165 to 623 euro for the scenario with a focus on the reduction ofblue green algae blooms, and from 210 to 666 euro for the largestimprovement. Concerning the class-specific LCM estimates, thelowest WTP for all three scenarios is negative while the highestWTP estimate (for the class for which the scale of improvement is notimportant) is 457 euro. The discounted present values of the scenariosrange from 28,475 million euro to 53,884 million euro. That theestimates are relatively close to each other indicates that the Finnishpopulation has a great willingness to contribute to “doing something”to improve the unsatisfactory condition of the Gulf of Finland by theyear 2030. The tendency to prefer the policy options over staying onthe business-as-usual path suggests the importance to Finns ofprotecting the Gulf of Finland.

Shown by the MNL and RPL models, the implementation of anynutrient reduction policy especially increases the welfare of house-holds with close connections to the Gulf of Finland (residential orrecreational connections), higher income, young age, and nodependent children. Revealed by the latent class model, theimplementation of policies targeted to reducing the blue–greenalgae blooms (such as water purification in the city of St. Petersburg)increases especially the welfare of young, higher-income householdswith a permanent residence or a vacation home on the coast. Theindication is that especially those who derive use values from the Gulfof Finland focus on blue–green algae, a nuisance that rarely occurs buthighly inconvenient when present. Also revealed by the LCM, thelower-income and non-coastal households do not benefit from anyimprovement in the water quality in the Gulf of Finland, and preferthe business-as-usual option. This is consistent with the common

537A.-K. Kosenius / Ecological Economics 69 (2010) 528–538

phenomenon that the WTP increases with proximity to the environ-mental good to be valued.

To interpret the welfare estimations, complications due to the use ofsubjective water quality measures in the questionnaire rather thanobjective measures of nutrient levels and eutrophication as the basis fordecision making must be taken into account. Our illustrative linking ofthe qualitative attribute levels for water quality with reductions inbiomassesofdifferent algaegroups showsoneway todealwith this issue.

When it comes to the relative importance of water qualitycharacteristics as perceived by the general public, the averagerespondent regards clear water as the most important followed byscarce occurrences of blue–green algae blooms, small numbers ofunattractive fish, and the well-being of the bladder wrack population.The latent class model reveals other preferences: households with thehighest preference for improvement in the macro algae (bladderwrack) population as well as households focusing on the reduction ofblue–green algae occurrences.

These findings supporting heterogeneity for water quality attri-butes suggest the use of more flexible models than the basic MNL,but the issue of the superiority of one model over another is notstraightforward. Although the LCM showed excellence in terms ofstatistical measures of fit, the comparison requires a broader view.Concerning the specific data set regarding the improvement of waterquality in the Gulf of Finland, the undisputable advantage of the LCMis that it reveals the diverse orders of the importance of the waterquality attributes as well as the insignificance of some attributes.Thus, the LCM better captures the variation in preferences for specificattributes. However, because another aim of the study is to obtainmean welfare estimates from the population and weighting is anissue, the advantages of the RPL are superior. Finally, because wefound the heterogeneity issue for water quality attributes to be of highrelevance, we recommend the use of choice experiment in exercisesvaluing environmental goods with diverse dimensions and values,such as reduced eutrophication of marine environment.

Acknowledgments

This study is part of the project Non-Point Source PollutionEconomics (NOPEC) funded by the Academy of Finland (project no.115364). The author also thanks the Yrjö Jahnsson Foundation, theFinnish Cultural Foundation and the Otto A. Malm Donation Fund. Theresearch assistance of Heini Ahtiainen, is acknowledged as well asuseful discussions with Eija Pouta, PhD, at MTT Agrifood ResearchFinland. The comments of three anonymous referees improved thepaper considerably.

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