Subjective vs. objective measures in the valuation of water quality

Journal of Environmental Management 130 (2013) 288e296

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Subjective vs. objective measures in the valuation of water quality Janne Artell*, Heini Ahtiainen, Eija Pouta MTT Agrifood Research Finland, Latokartanonkaari 9, 00790 Helsinki, Finland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 May 2012 Received in revised form 27 May 2013 Accepted 6 September 2013 Available online 3 October 2013

Environmental valuation studies rely on accurate descriptions of the current environmental state and its change. Valuation scenario can be based on objective quality measures described to respondents, on individual subjective perceptions or their combination. If subjective perceptions differ systematically from objective measures, valuation results may be biased. We examine the factors underlying the divergence between perceptions of water quality among summer house owners and the objective water quality classification. We use bivariate probit and multinomial logit models to identify factors that explain both the divergence between perceived and objectively measured water quality and its direction, paying special attention to variables essential in valuation, including those describing the respondent, the summer house and the water body. Some 50% of the respondents perceive water quality differently from the objective quality measures. Several factors are identified behind systematic differences between the perceived and objectively measured quality, in particular the water body type, the level of the objective quality classification and the travel distance to the site. The results emphasize the need to take individual perceptions into account in addition to objective measures in valuation studies, especially if the environmental quality of the study area differs considerably from the average quality in general. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Valuation Water quality Perceptions

1. Introduction The valuation of environmental goods and services relies on accurate descriptions of the status quo of the environment and its future changes. Natural sciences provide precise measurements of environmental conditions used as basis for policy decisions, but these measures do not necessarily coincide with individual perceptions. Perceptions differ between individuals and may diverge from scientific, objective data, especially in complex environmental issues (Kataria et al., 2011). As environmental goods are ultimately products of perception, systematic differences between scientific measures of environmental characteristics and their subjective counterparts affect valuation studies and their results (Whitehead, 2006). A comprehensive understanding of policy effects requires information of the actual quality change and how the change is perceived. This is especially true when there is a wide difference between these two measures. However, attaining information of both subjective and objective quality measures can be prohibitively expensive or even impossible. It is thus important to understand what links exist between objective monitoring data and subjective

* Corresponding author. Tel.: þ358 40 172 64 26. E-mail addresses: janne.artell@mtt.fi (J. Artell), (H. Ahtiainen), eija.pouta@mtt.fi (E. Pouta).

heini.ahtiainen@mtt.fi

0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.09.007

perceptions of environmental quality. Knowledge of the links between the two measures improves interpreting previous results and the design of future valuation studies. There are several benefits in using subjective data on environmental quality, either with or without objective measures. First and foremost, behavior is based on preferences formed from perceptions (Bockstael and McConnell, 2007; Poor et al., 2001). A precise description of people’s perceptions of environmental amenities should therefore provide the most accurate estimates of the values attached to these amenities. Objective measurements may also be inconsistent with complex and heterogenous perceptions of environmental quality producing biased benefit estimates (Marsh et al., 2011; Kataria et al., 2011). Secondly, subjective environmental quality perceptions may be easier and more cost-efficient to obtain than scientifically measured data if an environmental valuation method itself demands the use of surveys. Monitoring data that correspond to each individual’s environmental conditions are not always readily available and may suffer from being out of date or in the wrong location for the context of the study. The chosen method of valuation, and consequently of data collection, has a strong influence on the choice between objective and subjective quality measures. Especially in data-intensive valuation methods such as hedonic pricing, where the number of observations is large and the time frame of the study may extend to

J. Artell et al. / Journal of Environmental Management 130 (2013) 288e296

many years, existing data sources have an edge over the collection of subjective perception data, which is in many cases difficult and time-consuming (Bockstael and McConnell, 2007; Baranzini et al., 2010). In stated preference studies, the current practice is to describe the environmental status quo to the survey respondents, but an alternative is to gather and use subjective perceptions of environmental quality beyond the objective information (Marsh et al., 2011; Domínquez-Torreiro and Soliño, 2011). Such perception data can be inexpensively collected within a valuation survey. The literature exploring valuation and the effects of using objective vs. subjective measures of environmental quality is relatively limited and primarily originates from the revealed preference setting (Poor et al., 2001; Adamovicz et al., 1997; Jeon et al., 2005; Baranzini et al., 2010). Recently, Marsh et al. (2011) and Domínguez-Torreiro and Soliño (2011) have examined the issue in the context of choice experiments. Both studies find differences between perceptions and objective measures. However, little attention has been devoted to the underlying factors affecting the divergence between the measures. To identify cases where subjective measures are applicable in a policy relevant sense, it is important to know whose perceptions deviate from the objective measures, and in what kind of environmental settings this deviance is most notable. In this paper we focus on the ability of citizens to evaluate water quality and the underlying elements determining this ability. Beyond environmental valuation, the skill of citizens in evaluating water quality is an issue of importance in the development of environmental monitoring. Several studies have suggested that citizens could provide information relevant to environmental monitoring (Gouveija et al., 2004; Nicholson et al., 2002; Savan et al., 2003). In Finland, the environmental administration has developed a collaborative web-based system, “Lakewiki” (Finnish Environment Institute, 2012), to which the public can contribute by providing information and observations on lakes and their water quality. The purpose is to support the implementation of the European Union Water Framework Directive (WFD) (European Commission, 2000) by encouraging citizens to participate in the monitoring and protection of water bodies. Public participation plays a key role in the implementation of the WFD, in the form of encouraging active involvement, and ensuring consultation and access to background information. For assessing citizen monitoring, it is useful to know under what conditions their evaluations may differ from scientific measures. In the present paper, using data from a large-scale valuation survey sent to a sample of Finnish purchasers of private summer houses, we analyze whether perceived water quality differs from its scientific counterpart designed to resemble users’ perspectives. First, we identify factors associated with the existence of divergence between perceptions and objective measures. Second, we examine the direction of this divergence by analyzing under what conditions there is a tendency for the public to overestimate (underestimate) objective water quality. Based on our analysis, we discuss the possible implications for the design and interpretation of valuation studies. The paper is organized as follows. Section 2 discusses the previous literature on water quality perceptions and relates the results to the valuation of water quality. Section 3 describes the data, the objective and subjective measures of water quality and the methods used in the analysis. Section 4 presents the results and Section 5 provides discussion and conclusions. 2. Accuracy of water quality perceptions as a challenge for valuation There is a wide body of literature focusing on differences in environmental perceptions between individuals and showing the

289

importance of individual characteristics, such as sociodemographic factors (e.g. Flynn et al., 1994; Múgica and DeLucio, 1996; Bonaiuto et al., 1999), as well as the role of knowledge (Bell, 2001; Burton, 2004) and attitudes (Kaltenborn and Bjerke, 2002). Previously, self-reported environmental quality has been found to differ from objective quality such that the relationship is influenced by travel distance to the location and the type of environment, i.e. natural or built (Kweon et al., 2006). According to the literature, water quality perceptions are associated with individual characteristics such as socioeconomic factors, together with an individual’s location, setting and proximity to water bodies (Brody et al., 2004), environmental knowledge and attitudes (Danielson et al., 1995), and also factors related to the water bodies themselves (Steinwender et al., 2008). Steinwender et al. (2008) found subjective quality assessment by the public to follow most measured water quality indicators. Faulkner et al. (2001) demonstrated that people are fairly astute observers of water quality improvements, particularly those with the most frequent contact with a water body. Lepesteur et al. (2008) emphasized the role of personal experience and social exchange over factual environmental information in forming individual perceptions of water quality. Thus far, few valuation studies have analyzed the use of both objective and subjective measures of water quality. Poor et al. (2001) compared subjective and objective measures of water clarity in a hedonic property model. They observed a tendency to underestimate water clarity compared to the objective measure, and suggested that objective measures, with lower implicit price estimates, are better predictors of property sales prices. On the other hand, Jeon et al. (2005) found both objective and subjective measures of water quality to significantly affect the choice of a recreation site, and models including subjective water quality perceptions to outperform models excluding them. In the context of stated preferences for water quality improvements, Barton et al. (2009) compared respondents’ perceptions of water quality with scientific descriptions, and found that they were similar for 60% of respondents. Marsh et al. (2011) and Domínguez-Torreiro and Soliño (2011) investigated the effect of the status quo format, i.e. provided descriptions of the status quo vs. the status quo perceived by respondents, in choice experiments. Marsh et al. (2011) concluded that the majority of the respondents were able to assess the current water quality, and that these respondents generally had a higher income and education. Respondents who used their own perceived status quo had higher marginal willingness to pay values, but were generally reluctant to approve policies implying changes from the status quo. Domínguez-Torreiro and Soliño (2011) showed that 31% of respondents were unable to report their own subjective status quo for at least one of the attributes in the choice experiment and that 30% of their sample completely matched their perceptions with the provided status quo. Kataria et al. (2011) tested how disbelief in the description of the status quo and the proposed policy scenario affect welfare estimates, finding a non-negligible proportion of respondents disagreeing with the status quo, and observing that beliefs regarding the status quo affect the marginal willingness to pay estimates. They did not, however, examine which individual characteristics or features of the environmental good affect the tendency to disagree with the information presented in the survey. From the environmental valuation perspective, it is particularly interesting to examine how variables fundamental in valuation are associated with the accuracy of water quality perceptions. If perceived and objective quality differ systematically due to some individual or environmental attributes, the accuracy of the analysis may deteriorate.

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For all valuation methods, it is relevant to evaluate whether the accuracy of perceptions depends on the specific characteristics of a water body. Previous literature shows that perceptions are associated with actual water quality (Steinwender et al., 2008; Jeon et al., 2005), but in the case of valuation it is more important to understand whether and how the accuracy of perceptions is associated with the actual water quality or the type of water body. For stated preference methods, the essential factors are those describing the environmental good. Stated preference studies require careful specification of the reference quality and the after-policy quality, which together define the valued good. In the travel cost method, the environmental quality typically explains the number of trips to a site or the choice of a recreation site. If people systematically over- or underestimate the objective measure, the position or the slope of the demand curve will be biased in the analysis due to misspecification. Faulkner et al. (2001) found that for frequent observers, the accuracy of water quality perceptions converged on the objective measure, implying that frequent exposure affects the ability to assess environmental quality. In this case, the left side of the demand curve, associated with higher travel expenses and a lower number of visits, might be subject to uncertainty. In other words, consumer surplus estimates for a marginal quality change would be imprecise for the least frequent visitors. In the hedonic pricing method, environmental quality is an attribute that affects the price of a property, but it is also possible that the property price indirectly affects the perception of quality, thus creating an endogeneity problem. Price has been suggested to be a relevant cue for intrinsic quality when inadequate information is available for the consumer (Zeithaml, 1988). The association between price and perceived quality has varied greatly according to the product and individual, but most studies have found them to be positively related (Rao and Monroe, 1989; Völckner and Hofmann, 2007). Previous valuation studies have found heterogeneity in environmental preferences, e.g. Kosenius (2010) in the case of water quality conservation. Accounting for heterogeneity is important for modeling the preferences, but particularly so for equity considerations. The heterogeneity is not only constrained by the preference structure but also concerns the perceptions of environmental conditions. This implies that systematic consideration of equity can take advantage of the separation of heterogeneity in the accuracy of environmental perceptions and heterogeneity in preferences. Overall, identifying the settings and individual characteristics associated with the differences between objectively and subjectively measured water quality can be useful to researchers in environmental valuation by providing information on the respective methodological strengths and weaknesses of valuation methods, and in particular on the applicability of valuation based on either subjective or objective measures. 3. Data and methods

2004. The 20-page survey provided basis for valuing water quality using revealed and stated preference methods. In this study we focus on the water quality perception data from the survey. The survey was administered jointly through the Internet and by mail, and survey recipients were chosen from the official real estate market price registry maintained by the National Land Survey of Finland (National Land Survey of Finland, 2012). After a pilot of 200 property owners in November 2008, the final survey was sent to 2547 property owners between the end of 2008 and early 2009. Respondents were initially approached by letter with instructions on completing an online survey, followed by two further contacts with an additional possibility to reply by mail. Excluding out-ofreach respondents, the response rate in the final version of the survey was over 51%, i.e. 1249 responses e a high figure compared to previous response rates to mail and web surveys reported in the meta-analysis by Shih and Fan (2008). 3.2. Objective measure of water quality The objective measure of water quality used in this study was the general usability classification provided by the Finnish Environment Institute. The classification is based on the average suitability of water bodies for water supply, fishing and other types of recreation in Finland (Finnish Environment Institute, 2010). Several criteria are used in the classification, including the amount of chlorophyll-a and total phosphorus, the transparency, turbidity and color, the amount of oxygen, the hygienic quality of the water, the occurrence of algal blooms and the concentrations of harmful substances. The classification, illustrated in Fig. 1 on a map of Finland, is based on data from the period 2000e2003 and covers 82% of lakes, 16% of the length of rivers and all of the Baltic Sea coastal area within Finnish territory. The data closely reflect the average water quality before and at the time of summer house purchase, in addition to being the most recent available nationwide official objective data at the time of the survey. The usability classification includes five categories ranging from poor to excellent. Water bodies classed as poor or passable are not recommended for recreation, as there may be severe algal blooming, occasional fish deaths or actual health risks. Water bodies in the satisfactory quality class may have repeated algal blooming. This category also includes watercourses that are notably humic due to natural causes. Satisfactory water quality indicates that a water body is generally suitable for most recreational requirements. The good and the excellent categories have no restrictions for recreational use and, in an ecological sense, the water bodies are in or near their natural state.1 We linked each summer house and thus each respondent to a corresponding water usability class using GIS software. Only properties within 250 m from the nearest quality-classified water body were included in the sample to prevent assigning quality values to properties that may actually be located next to a neighboring, unclassified water body.2 Table 1 shows that owners of

3.1. The survey method In Finland, approximately 45% of the population has access to a summer house (Sievänen et al., 2007) with some 85% of these being located within 100 m from a water body (Nieminen, 2010). New summer house owners were selected as the study population, since they can be expected to remember their initial perceptions and possess experience and interest in assessing water quality, providing a good basis for our analysis. Summer house purchasers are not, however, fully representative of the Finnish population, as they are older and wealthier than the general population. The data originate from a valuation survey sent in December 2008 to all Finnish purchasers of private summer houses of the year

1 The general usability classification preceded the ecological classification of water bodies specified in the Water Framework Directive (European Commission, 2000). The two classifications differ in the respect that the usability classification reflects human needs, whereas the ecological classification is primarily based on biological quality elements. While these two classifications differ, they are closely related. 2 This constraint reduced the initial number of observations from 1249 to 837 observations, while item non-response in the survey reduced the final sample to 709 observations. The mean (median) distance from a quality-classified water body was over 1.5 (1.2) kilometers for the excluded observations. As approximately 85% of Finnish summer houses reside within 100 m from water (Nieminen, 2010), we felt the 250 m distance limit would remove water bodies unlikely adjacent to the observations from the analysis.

J. Artell et al. / Journal of Environmental Management 130 (2013) 288e296

Fig. 1. General usability classification of water quality in Finland in 2000e2003.

lakeshore and riverside properties are present in all five usability categories, while respondents with seashore properties lack both excellent and poor quality sites. Most lakes in the sample were of excellent or good quality, while rivers and coastal areas typically had a slightly poorer quality. 3.3. Subjective measures of water quality Two variables described the subjective perceptions of water quality in the analysis: the initial perception of the water quality at the time of purchasing the summer house in the year 2004 (WQINI), and the educated assessment of water quality at the time of the survey in the year 2008 based on the experience (WQEDU) Table 1 Distribution of the general usability classification categories across water body types in the sample. Objective measure Water body

Excellent

Good

Satisfactory

Passable or Poor

Total

Lake River Sea Total

233 2 0 235

222 10 32 264

76 20 66 162

36 5 7 48

567 37 105 709

291

obtained during the four years of property ownership. Both subjective water quality measures were defined to concern the water body adjacent to the summer house, and were measured in the same survey. See Appendix 1 for the translated questions for both variables used in the survey. WQINI was the respondent’s survey-reported initial perception of the water quality at the time of purchase, i.e. in 2004. While the response categories were identical to those of the objective measure, a five-step scale ranging from poor to excellent, the assessment itself was uninformed, i.e. the respondents received no information on the attributes of the water quality classification at that point of the survey. WQEDU represented an assessment of current water quality based on the respondent’s own experience during the ownership period. In the survey, the respondent was informed on how to assess four water quality factors prior to the response: water clarity, fish species, blue-green algae blooms and sliming of structures and equipment. The survey did not, however, provide the respondents any information about the objective water quality status at their properties. The factors and their levels (excellent, good and satisfactory) were described3 in detail, after which the respondent rated the water quality in terms of each factor. Alternatively, the respondent could state “I don’t know” for any of the four ratings. The categories ranged from excellent to worse than satisfactory, with the worse than satisfactory class corresponding to the passable and poor water quality in the objective measure. We calculated the mean of these four quality levels and rounded the result to the nearest integer to obtain the “educated assessment” quality category for the analysis. To facilitate the analysis with higher number of observations, the “I don’t know” replies in 93 cases were imputed with the sample mean value for the one to four missing quality factor levels before taking the mean value of the four factors.4 The initial impression (WQINI) was measured four years after the assessment in the purchasing situation. If a respondent could not remember his or her initial impression from the time of purchase, it is likely that the evaluation of the initial quality would come close to the quality perception at the time of the survey. Despite this, the initial evaluation took place without information on the water quality attributes. Thus, for those having difficulties to remember their impressions four years back in time, the two subjective measures could differ purely due to the water quality attributes provided in the survey. The association between the perceived and scientifically measured water quality was first examined with correlations. The two subjective measures of quality were correlated pairwise with the general usability classification. The correlation coefficients were all significant and their magnitude ranged from 0.476 to 0.534 (Table 2). The correlation was highest between the perceived water quality at the time of purchase and the general usability classification. Table 2 also reports the correlation between the subjective measures, showing that these measures were also significantly correlated. 3.4. Dependent variables We used the variables DIVINI and DIVGEDU to represent the divergence between the subjective and objective measure for the

3 The descriptions were connected to the general usability classification descriptions through water clarity, and more loosely through the three other quality factors as the classification was not specific on the occurrence of algae blooms, fish species composition or the sliming of structures. The choice of these variables stems from the need to use them in the valuation part of the survey. 4 Estimation results with non-imputed sample available by request from the author.

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Table 2 Correlations between subjective and objective measures. Correlated measures

Pearson correlation

Spearman’s rho

WQINI vs. objective WQEDU vs. objective WQINI vs. WQEDU

0.534*** 0.476*** 0.512***

0.534*** 0.480*** 0.524***

***

Table 3 The direction of the difference between the objective and perceived quality in the distribution of DRCINF and DRCEDU variables.

Significant at the 1% level.

initial and educated assessments, respectively, where 1 indicates a divergence. DRCINF and DRCEDU, the second set of dependent variables in our analysis, express the direction of this divergence for both subjective measures. Table 3 provides the distributions for the two variables. The variables were categorized to be positive and labeled as overestimations when the person’s subjective perception exceeded the objective water quality by any number of steps. Correspondingly, the negative category implied that water quality was underestimated i.e. the objective quality was lower than the subjective perception of quality. Naturally, the zero category implies identical subjective and objective measures. Table 4 presents the descriptive statistics for the dependent variables.

DRCINF Difference at the time of the purchase DRCEDU Difference at the time of the survey

Total

Subjective measure overestimates the objective by one or more steps

No difference

Subjective measure underestimates the objective by one or more steps

1

0

1

187 (26%)

347 (49%)

175 (25%)

709 (100%)

201 (28%)

342 (48%)

166 (23%)

709 (100%)

Table 4 Descriptive statistics for dependent variables (n ¼ 709). Dependent variable

Description

Mean

Std. dev.

3.5. Statistical models

DIVINI

0.511

0.500

0

1

We employed a two-stage approach to analyze which factors are associated with the difference between the objective water quality measure and subjective perception of water quality. In the first stage, a bivariate probit regression model was specified to determine whether the quality measures would diverge in a systematic fashion taking the common error structure between the initial and educated perceptions of an individual into account. The model provides a general view on how respondent characteristics and the environmental conditions affect reliable evaluations of water quality. We refer to this model as the divergence model. Significance tests for a single coefficient were based on the Wald test, and the likelihood ratio test was used to test the significance of the model. In the second stage of the analysis we examined the factors affecting systematic over- or underestimation of objective water quality. Both an ordered logit model and multinomial logit specification were tested as modeling alternatives. Ordered logistic regression assumes that the dependent variable is ordered with respect to some underlying latent continuous variable and the same coefficients describe the relationship across all categories. In a multinomial logit model, the categorical outcomes do not follow any intrinsic order, and the relationship between various outcome categories and independent categories are different across each category. We used the seemingly unrelated estimation (STATA, 2007) framework to take the common individual error structure between the two dependent variables into account. This model is referred to as the direction model.5

DIVEDU

1 if objective measure unequal to perceived at the time of purchase 1 if objective water measure unequal to perceived at the time of survey Direction of difference between the objective and perceived quality at the time of purchase Direction of difference between the objective and perceived quality at the time of survey

0.512

0.500

0

1

0.017

0.715

1

1

0.049

0.718

1

1

3.6. Independent variables Our analysis focused on explaining the difference between the objective and subjective water quality measures using variables relevant to valuation studies and equity analysis identified in the literature review. We also included other interesting factors that may affect the difference. The descriptive statistics for the variables are presented in Table 5. Naturally, some variables were of interest to all of the valuation methods. For example, perceptions may differ

5 As there were only a small number of observations where the respondent had estimated the water quality to be two or more steps above or below the objective water quality measure, using these as separate categories to study magnitude effects was impractical.

DRCINF

DRCEDU

Min

Max

according to the water body type, affecting the implementation of both revealed and stated preference studies. The type and description of the valued environmental good were found important for all valuation methods in the literature review. Thus, we tested in our models whether the type of water body affected the respondent’s perception of quality (SEA, RIVER, LAKE). For the travel cost method specifically, we identified travel costs and the frequency of recreation as factors affecting the estimation of the demand function. Therefore, we included in our models a proxy for the travel costs, namely the self-reported distance between the property and the respondent’s permanent residence (PROPDIST), and a dummy variable for respondents with fewer water recreation days during a typical summer than others, i.e. less than 15 days each summer (NWATERREC). In the hedonic pricing context, the literature review identified reports that have raised concern over the endogeneity problem, i.e. whether the price of a summer house affects the perceptions of water quality. Thus, the official sales price of each property was added to the analysis (PROPPRICE). To explore the factors affecting equity analysis we included income (INCOME), age (RESPAGE) and education (EDUC) variables6 in our models.

6 As education was strongly correlated with income, and provided no significant effect in the models when estimated separately from income, the variable was dropped from the subsequent analysis. After excluding the education variable an OLS model testing of multicollinearity using variance inflation factors (VIF) was conducted for both direction models. The VIF’s were between 1.06 and 1.81 for all variables indicating that multicollinearity was not a serious issue in the models.

J. Artell et al. / Journal of Environmental Management 130 (2013) 288e296

NUTS2 categories) (SOUTH, EAST, WEST, NORTH) for variability within the country.

Table 5 Descriptive statistics for explanatory variables (n ¼ 709). Description All valuation methods SEAa 1 for seaside properties RIVER 1 for riverside properties LAKE 1 for lakeside properties LOWUSAB 1 for passable or poor water usability classification HIGHUSAB 1 for excellent or good water usability classification a MIDDLEUSAB 1 for satisfactory water usability classification Revealed preference method variables NWATERREC 1 for respondents with less than 15 water recreation days at a summer house in a typical season PROPDIST Respondent-reported distance from home to the property, 100 km PROPPRICE Property price, 1000 euros (2004) Equity analysis variables INCOME Household’s gross monthly income, 1000 euros (2008) RESPAGE Age of the respondent EDUC 1 for university-level education Other variables of interest FAMIQ 1 for respondents familiar with local water quality prior to purchase IMPSIZED 1 if the size of the water body was an important factor in the purchasing decision IMPQUALD 1 if the water quality at the property was an important factor in the purchasing decision ISLAND 1 for island properties SOUTHa 1 for properties in Southern Finland EAST 1 for properties in Eastern Finland WEST 1 for properties in Western Finland NORTH 1 for properties in Northern Finland a

Mean

293

S.D.

Min

Max

4. Results 0.148 0.052

0.355 0.223

0 0

1 1

0.800

0.400

0

1

0.068

0.251

0

1

0.704

0.457

0

1

0.228

0.420

0

1

0.150

0.357

0

1

1.358

1.591

0

52.728

43.213

2

470

5.785

2.434

0

10

51.772 0.251

9.773 0.434

20 0

82 1

0.484

0.500

0

1

0.762

0.426

0

1

0.805

0.396

0

1

0.230 0.319

0.421 0.466

0 0

1 1

15.26

0.344

0.475

0

1

0.238

0.426

0

1

0.090

0.287

0

1

These dummy variables represent the points of comparison in the models.

In addition to the factors identified in the literature review, we included supplementary variables in our models. Prior knowledge of local water quality conditions is likely to affect the perception of water quality (FAMIQ). Respondents’ water related attitudes are an important issue for similar reasons; we used the perceived importance of the size of the water body (IMPSIZED) and of the water quality (IMPQUALD) in the purchasing decision to reflect the affinity of respondents to water quality. Local and regional conditions can also change the way perceptions differ from the objective measure. We included a dummy variable for island properties (ISLAND) to account for a tangible contact with water, and regional dummies (the European Union

4.1. Divergence models The bivariate probit model for the divergence between the initial quality perception and the objective measure (Table 6) confirms that multiple factors contribute to the difference. Results reveal that for both the initial assessment and, in particular, the educated assessment, the ownership of a property adjacent to a water body of poor quality (LOWUSAB) was significantly associated with divergent subjective and objective measures. Conversely, respondents perceived water quality more often the same as the objective measure when the water quality was good (HIGHUSAB). The lack of other significant variables with the educated assessment suggests that verbal education on how to assess local water quality can put respondents to the same starting point in the assessment, regardless of their background. Interestingly, the frequency of water recreation (NWATERREC) had no significant effect on the difference between the perceptions and the objective measure. For travel cost studies, it is noteworthy that longer distances between the home and the summer house (PROPDIST) were associated with a larger likelihood of divergence in the initial water quality assessment. Coupled with a significant and negative coefficient for local knowledge of the water quality (FAMIQ), the two results confirmed that prior familiarity with local conditions reduced divergence. Encouragingly for hedonic property price studies, the property price (PROPPRICE) was not a statistically significant attribute in either model. Furthermore, respondents considering water quality to be an important purchasing factor (IMPQUALD) were more likely to assess water quality the same as the objective measure. In contrast, the perception of those valuing the size of the water area (IMPSIZED) was more likely to diverge from the objective classification, although the effect was only weakly statistically significant. Table 6 Bivariate probit models for the existence of divergence between the objective and subjective water quality for the time of the purchase (DIVINI) and the time of the survey (DIVEDU). Bivariate probit regression

RIVER LAKE LOWUSAB HIGHUSAB NWATERREC PROPDIST PROPPRICE INCOME RESPAGE FAMIQ IMPSIZED IMPQUALD ISLAND EAST WEST NORTH Constant Rho Log likelihood Wald c2 (32) N

DIVINI

DIVEDU

Coef.

S.E.

Coef.

S.E.

0.566** 0.446** 0.735*** 0.273** 0.089 0.066** 0.001 0.035 0.004 0.216** 0.221* 0.400*** 0.226* 0.295** 0.266* 0.227 0.922***

0.265 0.180 0.244 0.137 0.144 0.032 0.001 0.021 0.005 0.099 0.133 0.139 0.124 0.136 0.140 0.199 0.368

0.295 0.119 1.024*** 0.714*** 0.204 0.022 0.001 0.017 0.004 0.157 0.135 0.038 0.111 0.009 0.186 0.112 0.573

0.263 0.176 0.297 0.138 0.149 0.032 0.001 0.022 0.005 0.100 0.134 0.141 0.126 0.139 0.143 0.197 0.369

0.356***

0.057

888 139.27 709

Individual coefficients are significant at the*** 1%, ** 5% or *10% level. Heteroskedastic robust standard errors.

294

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Table 7 Multinomial logit model results for the direction and magnitude of the divergence between the objective and subjective water quality for the time of purchase (DRCINF) and the time of the survey (DRCEDU). Reference level: perception is equal to objective quality. Model for DRCINF Coef.

Model for DRCEDU S.E.

Overestimation RIVER LAKE NWATERREC PROPDIST PROPPRICE INCOME RESPAGE FAMIQ IMPSIZED IMPQUALD ISLAND EAST WEST NORTH Constant

0.859* 1.520*** 0.047 0.006 0.002 0.013 0.022** 0.358* 0.191 0.365 0.265 0.387 0.699*** 0.214 1.808***

Log likelihood LR c2 Pseudo R2 N

689 106 0.071 709

Coef.

S.E.

Underestimation 0.481 0.298 0.274 0.074 0.002 0.041 0.010 0.194 0.255 0.277 0.240 0.277 0.268 0.410 0.717

0.432 0.305 0.262 0.125** 0.003 0.108** 0.009 0.377* 0.374 1.187*** 0.516* 0.403 0.139 0.704* 0.420

Coef.

S.E.

Overestimation 0.608 0.368 0.293 0.057 0.003 0.046 0.009 0.199 0.263 0.262 0.267 0.271 0.293 0.392 0.734

0.640 1.570*** 0.292 0.000 0.003 0.021 0.012 0.371* 0.104 0.269 0.79 0.465* 0.007 0.179 2.224***

Coef.

S.E.

Underestimation 0.469 0.291 0.280 0.062 0.002 0.040 0.010 0.194 0.250 0.258 0.246 0.276 0.256 0.394 0.691

1.064 1.978*** 0.399 0.011 0.001 0.036 0.018* 0.179 0.302 0.295 0.145 0.080 0.510* 0.229 3.222***

0.862 0.637 0.286 0.068 0.003 0.046 0.010 0.205 0.280 0.283 0.256 0.271 0.296 0.439 0.966

677 133 0.089 709

Individual coefficients are significant at the *** 1%, ** 5% or *10% level. Results compared to ‘no difference’ category, i.e. DRCINF ¼ 0 (DRCEDU ¼ 0). Log likelihood, LR c2 and Pseudo R2 descriptors from separate multinomial logit models. Heteroskedastic robust standard errors reported by suest e seemingly unrelated estimation procedure (STATA, 2007).

Coastal water quality was initially more often assessed differently from the objective quality than the quality of other water bodies (LAKE, RIVER), whereas in the educated assessment model the effect was no longer present. The result indicates that providing information on the specific levels of water quality can overcome water body specific divergences in evaluations. Initial perceptions were less likely to diverge for respondents with properties on an island (ISLAND). Intuitively, this could be due to the direct contact with water, in one form or another, if the respondent has visited the property prior to the purchase. Regional effects (EAST, WEST) were also present in the model for initial assessment. There may be multiple explanations for these effects, as the regions are very large and differ both in the prevalent surface water types and socioeconomic attributes in general. 4.2. Direction models We tested the applicability of an ordered logit approach to the direction model with the Brant test of parallel regression assumption. The Brant test failed for initial water quality perceptions, whereas, for the informed, or the “educated”, quality assessment, the responses followed an ordinal scaling. This implies that with additional information the difference between perceptions and the actual quality is more aligned in a monotonic fashion.7 However, to maintain comparability between the two models we used the more general multinomial specification for both types of perceptions in our analysis. We identified variables associated with the tendency to either over- or underestimate the objective measure of water quality using a multinomial logit model (Table 7) for both the initial and educated perception. The standard errors are based on seemingly unrelated post-estimation procedure8 that links the error structures of both models to obtain a more efficient estimation. As in the divergence models, both time, i.e. perceived water quality at the

time of the survey (2008) as compared to the year of property purchase (2004), and the information on assessing water quality appeared to make divergence less dependent on explanatory variables than the uninformed alternative. Although the goodness-offit measures are relatively low, the models provide information of the variables that associate significantly with the tendency to overor underestimate the water quality. The estimated models suggest that owners of riverside (RIVER) and lakeshore (LAKE) properties were less likely to initially overestimate objective water quality than seashore property owners. After assessment education, lakeshore property owners were still less likely to overestimate water quality, but also significantly more likely to underestimate the quality compared to seashore property owners. Significant difference between riverside property owners and seashore owners was no longer present after education. Increased distance to the summer house (PROPDIST) increased the likelihood of underestimating water quality at the initial assessment. Few variables describing the individual had a significant effect on the direction of the divergence. In the initial assessment higher income (INCOME) was associated with smaller likelihood to underestimate the objective water quality. The models showed a shift with respect to age (RESPAGE): in the initial assessment, older respondents were less likely to overestimate water quality, whereas after assessment education, they were more likely to underestimate the objective water quality. Respondents with prior knowledge of the local water quality (FAMIQ) had perceptions that were consistent with the objective measure in the assessment of water quality in both models. In a similar vein, the initial water quality assessments were less likely underestimated if the property was located on an island (ISLAND) or if water quality was important in the purchasing decision (IMPQUALD).

5. Discussion and conclusions 7 8

We thank an anonymous reviewer for this insight. STATA (2007).

In this study we provide knowledge on the links between the objective and subjective measures to improve the interpretation of

J. Artell et al. / Journal of Environmental Management 130 (2013) 288e296

results from previous and the design of future environmental valuation studies. We find that assessing the level of water quality is not necessarily an easy task for individuals, even with personal quality experience that owners of summer houses typically have. We used two distinct sets of individual perceptions of water quality for the analysis. The first measure was based on an initial impression concerning water quality that can be considered as a typical citizen’s perception of surface water quality without any given prior information. The second evaluation can be considered as more experienced, as it was based on the knowledge that summer cottage owners had accumulated during their ownership, and on information they were provided on assessing water quality using four attributes that are also applied in the official water usability classification. Independent of experience or education on water quality, roughly one-half of the surveyed respondents assess water quality similarly to the general usability classification that was used as an objective measure. Our study provides information on the other half of the sample, whose perceptions of water quality are dissimilar to the objective measure. The results show that quality perceptions are more likely to diverge from the objective measure if the objective quality is poor. As an interesting feature, lakeshore and riverside property owners are typically less likely to overestimate water quality compared to the owners of seashore properties. This suggests that at low water quality levels the usability of water for recreation may not be hindered as much as the official water quality measure would indicate. This finding has an impact in valuation studies that primarily use objective measures. For example, Boyle et al. (1999) found a diminishing rate of return of benefits for improved, objectively measured water quality, i.e. that there is a call for protecting good quality sites. Our results add weight to the previous finding: the non-linearity between benefits and quality could, in fact, be stronger when measured with subjective water quality. People may not respond to scenarios depicting poor initial water quality as expected, or on the other hand, to scenarios presenting only fractional improvements when objective water quality indicators are used in valuation scenarios. The fact that the water body type influenced the perception of water quality with and without education has implications for valuation studies assessing multiple types of water bodies at the same time. After education lakeshore property-owners are particularly more likely to perceive water quality worse than the official classification. As a further research topic, it would be interesting to examine whether people have different scales for water quality depending on the type of water body, i.e. whether people utilize similar attributes in perceiving water quality in lakes, sea areas or rivers. Divergence between the initially assessed subjective measure and the objective measure increased with the travel distance to the summer house, consistent with findings in the previous literature (Brody et al., 2004). In travel cost studies, this might cause uncertainty, especially on the left part of the demand curve. From the hedonic pricing viewpoint, the results were promising, as the accuracy of quality evaluation was independent of property prices. In stark contrast to Faulkner et al. (2001), who found the individual importance of environmental issues to reduce the accuracy of evaluation, our study finds respondents considering water quality as an important purchasing factor e accounting for fourfifths of the sample e to be more likely to have convergent perceptions with the objective measure without education on how to assess water quality. This result raises implications for all valuation methods, but especially the hedonic pricing method; the use of objective water quality measures can be supported if most respondents are expected to consider water quality as an important purchasing factor. The fact that 20% of respondents did not view

295

water quality as important indicates that endogenous sorting in the property market may be present, i.e. property owners indifferent to water quality concentrate in poorer quality areas. We found indications that the initial subjective water quality perception was related to some individual-specific variables, but the effects were largely lost after the respondents were educated in water quality assessment. This has importance in considering the heterogeneity of valuation results and equity issues. We found weak evidence for higher income to decrease the probability of underestimation for an uneducated assessment, while education was not found to have any statistical effect. Marsh et al. (2011) found respondents with higher incomes and educational levels to be typically better able to give their own assessment of water quality. Similarly to Steinwender et al. (2008), we also observed age to have an effect on perceptions: interestingly, the younger generation overestimated the objective water quality in the initial water quality assessment, whereas after education, the older generation was more prone to underestimate the quality. The results of our study demonstrate summer house owners are able to provide fairly reliable information on local water quality in terms of the science-based water usability classification. This is an encouraging result for the advent of inexpensive ways for citizens to provide decentralized environmental monitoring information, such as Lakewiki (Finnish Environmental Institute, 2012). However, from the monitoring point of view, it is problematic that some evaluations differ systematically from the objective measures, especially for low water quality, which needs to be improved. Here the focus was in usability classification that is conceptually more close to people’s perceptions than the ecological classification of water bodies specified in the Water Framework Directive (European Commission, 2000). In this sense, concerning the ecological classification, we can expect more serious systematic divergences between perceptions and the objective state. The results emphasize the need to use individual perception, particularly in cases where the environmental quality of the site differs considerably from the average quality in general. In addition, valuation can rely on subjective perceptions in situations where individuals are motivated to form a perception of environmental quality, and when the researcher is able to significantly contribute to the respondents’ understanding on how to assess the environmental quality. However, in our study educating the respondents did not specifically reduce divergence, but rather made the factors behind divergence harder to explain through models. In other cases our results lead us to confirm that researchers will benefit from incorporating both the scientific accuracy of measurements and individual-specific perceptions of environmental quality in their studies. Appendix 1. Water quality perception questions presented in the survey Initial water quality assessment (used in formation of WQINI): How did you assess the water quality to be at the time of purchase?

Educated water quality assessment (used in formation of WQEDU): What is your opinion on the current status of the water body you use at your summer house?

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J. Artell et al. / Journal of Environmental Management 130 (2013) 288e296

Please use the following definitions when answering the question.

Quality factor

Quality factor variation Excellent

Good

The bottom can be seen from the depth of between 1 and 2 m Typical catch Fish species Typical catch composition includes pike, perch includes pike, perch and cyprinids and salmonoids (e.g. whitefish and trout) None Observed on 1e4 Blue-green days annually algal blooming Sliming No sliming Slow slime formation Water The bottom can be transparency seen from the depth of over 2 m

Satisfactory The bottom can be seen from the depth of less than one meters Typical catch includes cyprinids, few pike and perch

Observed on 5e15 days annually Fast slime formation

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