Temporal stability of individual preferences for river restoration in Austria using a choice experiment

Journal of Environmental Management 103 (2012) 65e73

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Temporal stability of individual preferences for river restoration in Austria using a choice experiment Markus Bliem a, Michael Getzner b, *, Petra Rodiga-Laßnig a a b

Institute for Advanced Studies Carinthia, Klagenfurt, Austria Centre of Public Finance and Infrastructure Policy, Vienna University of Technology, Resselgasse 5, 1040 Vienna, Austria

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 May 2010 Received in revised form 16 February 2012 Accepted 21 February 2012 Available online 28 March 2012

Temporal stability of values (environmental preferences) is usually considered to be an indicator of the reliability of a valuation instrument because the values can be “reproduced” by follow-up experiments. The objective of this paper is to test temporal stability of individual preferences for river restoration by employing two identical choice experiments with a time difference of one year. We compared the results of two surveys carried out on the stretch of the Danube River between the Austrian capital of Vienna and the border to the Slovak Republic in 2007 and 2008. The choice experiment method considered economic costs and benefits of ecological improvements along the river, in order to value environmental resources. Using a multinomial logit and a mixed logit model for the two samples and a pooled sample, we found that preferences and willingness-to-pay estimates for program attributes are not sensitive to time. The results suggest that, in the absence of an extreme event, individual preferences are robust over a short time period. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Choice experiment Temporal stability Ecological restoration Water Framework Directive

1. Introduction The background to this paper is an international case study that was carried out as part of the EU DG research funded project AquaMoney.1 The main objective of the AquaMoney project was to develop practical guidelines for the economic valuation of environmental and resource costs and benefits related to water use and water services to be included in the economic analysis in the European Union’s Water Framework Directive (WFD) (Brouwer et al., 2009a). In general, it is not possible to value environmental resources in every single case due to scarce budgets and the major effort usually involved in designing and implementing instruments for environmental valuation (such as contingent valuation (CV), travel cost approaches, choice experiments). Benefit transfer is therefore considered to be an economical alternative to collecting primary data on public preferences for environmental resources. Benefits (environmental values in money terms) are transferred from a policy site to a study site, taking into account potential differences between these two sites, e.g. income differentials, preference heterogeneity, and diverse socio-economic characteristics at both the policy and study sites. Such a transfer can

* Corresponding author. Tel.: þ43 1 58801 280320; fax: þ43 1 58801 9280320. E-mail address: [email protected] (M. Getzner). 1 AquaMoney (SSPI-022723) (www.aquamoney.org). 0301-4797/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2012.02.029

involve different degrees of sophistication, from transferring values based on income differences to applying econometrically estimated willingness-to-pay functions of the study site to the socioeconomic characteristics of the policy site. The literature on benefits transfer suggests that this is not an easy task (cf. the recent reviews on benefit transfers by Wilson and Hoehn, 2006; Lindhjem and Navrud, 2008). One major question that arises both in transferring benefits from one site to another and in assessing the validity of valuation studies in general refers to temporal stability of environmental values (McConnell et al., 1998; Brouwer, 2000). There have only been a few studies assessing environmental values at different points in time. In one of the first studies on temporal stability,2 Cameron (1997) showed that respondents’ bids (mean willingness-to-pay (WTP)) in a contingent valuation (CV) survey for water quality improvements were not statistically different over a period of two years. She tested differences between WTP bids responding to an open-ended CV question in three surveys conducted in 1994, 1995 and 1997. While there are differences in mean WTP, these are nevertheless not statistically different. Brouwer and Bateman (2005) compared WTP values for flood control and wetland conservation between two identical surveys carried out in 1991 and 1996, finding a significant decrease in money

2 Other papers include Kealy et al. (1988, 1990), McConnell et al. (1998), Bergland et al. (1995), and Carson et al. (1997).

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M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73

values (WTP) over time. However, they suggest that the basic models estimating WTP, which mainly cover income, can be transferred over time, while more elaborate models taking into account additional determinants of WTP (other socio-economic or ad-hoc variables) are not transferable. Brouwer (2006) tests for differences and transferability of dichotomous choice WTP answers with the hypothesis that an extreme event would influence WTP. He found differences attributable to such events, while the results suggest that underlying preferences are stable over time. Bhattacharjee et al. (2009) test temporal stability of recreation demand with the result that estimates for demand for recreation at Iowa lakes are stable over time, mirroring different water quality levels. Parsons and Stefanova (2009) also found no significant differences in consumer preferences for recreation demand of mid-Atlantic beaches. Meyerhoff et al. (2010) present a test-retest procedure for onshore wind farms in Germany in a choice experiment and conclude there existed a fairly robust temporal stability between two surveys directed at the same sample of respondents with a one-year lag. Morrison and Bergland (2006) provide meta-analysis evidence that choice modelling can provide robust marginal values for environmental changes, for efficient use in transferral of benefits. Recently, Richardson and Loomis (2009) tested for changes in monetary value of biodiversity conservation over time in their meta-analysis. They compared findings of an earlier study (Loomis and White, 1996) with papers published since then. While their main purpose was to test for the reliability of benefit transfer between study and policy sites, an interesting attribute of their study was to include a variable denoting the year of the respective survey. Depending on the model and the estimation procedure, they produced inconclusive results. Some models exhibited coefficients for the study year that were significantly positive. This result suggests that, in comparing WTP studies (of different types and valuing the conservation of different species) over time, WTP values e ceteris paribus e seem to have increased. Temporal stability of environmental values can be viewed from different perspectives. One potential viewpoint is that environmental values collected at different points in time should lie within a comparable range, given the often-assumed stable (or only slowly changing) preferences of households for environmental amenities. Temporal stability of values could therefore be considered as an indication of the reliability of the valuation instrument (e.g. a CV survey), in the sense that the values elicited are not arbitrary values but can be “reproduced” by follow-up experiments (cf. Whitehead and Hoban, 1999). Different values at different points in time therefore call for caution, since the valuation instrument might produce values that arbitrarily change over time. On the other hand, changing environmental preferences may also lead to changes in environmental values elicited by the valuation instrument. The very change in values might therefore be regarded as an indication of reliability, insofar as it can generally be hypothesized that environmental values mirror environmental preferences. If values do not change due to different environmental preferences over time, a survey eliciting invariable values would instead measure other preferences than environmental ones (e.g. involving some “warm glow” effects). Other potential influences on WTP may also change over time, such as income. Therefore, it is necessary to correct for all measurable and arguable differences between surveys over time, and then determine which part of the difference cannot be explained. The current paper extends this literature and tests for temporal stability of environmental values by employing a choice experiment with identical questionnaires and choice sets at two different points in time. As an empirical example, we value different river restoration programs and their potential consequences, such as water quality improvements and flood risk reduction. Three primary questions were established:

- Did individual preferences change over time? - Do the environmental values lie in the same order of magnitude? - Based on the data from the two experiments, are the valuation functions different? The paper is structured as follows. In Section 2.1, we present the survey and the study area, and discuss the environmental policy background and the implementation of the choice experiment. In Section 2.2, we present descriptive and econometric evidence regarding willingness-to-pay for river restoration separately for the two choice experiments. Section 3 describes the methodology and gives detail a about the design of the choice experiment (CE) and the statistical model used in this paper. Section 4 summarizes and discusses the results, and is followed by the conclusions. 2. Study design 2.1. Background and description of the study area The current study has its origin in a European research project on the valuation of water resources (AquaMoney), concentrating on valuing environmental and resource costs and benefits according to the EU’s Water Framework Directive. Various water valuation studies were carried out in 10 different European countries, using different methodologies such as contingent valuation (CV) and choice experiments. The Austrian case study involved the implementation of a choice experiment eliciting willingness-to-pay (WTP) values for river restoration. The main water body concerned is the stretch of the Danube River between the Austrian capital of Vienna and the border to the Slovak Republic (approx. 50 km). Along the Danube River, the Donau-Auen National Park was established in 1996 to conserve ecologically valuable floodplains and wetlands. However, as the Danube River is also an important route for freight ships, it is partially channelized, leading to a limited hydrological dynamic. Therefore, additional river restoration measures have been proposed in the Danube catchment to transform the river to a more natural state by reconnecting it to its tributaries, and restoring the original floodplains in the catchment. Such river restoration improves water quality of the river and reduces flood frequency in the area. A map with the location of the study area is presented in Fig. 1. In order to value the river restoration in terms of its main impacts, that is, the reduction of flood risks and the improvement of water quality, a choice experiment was designed and implemented in 2007. The main objective of the survey was to elicit marginal WTP for these two impacts of river restoration along the Danube River. The results of the survey are documented in Brouwer et al. (2009b) and Bliem and Getzner (2008). The survey was also implemented, with some local adaptations but broadly identical wording, in study sites in Hungary and Romania in order to test for benefit transferability and comparability of values and value functions in an international comparison. However, the current paper uses the data of the Austrian case study, and compares the results with an identical Austrian survey carried out in the same regions one year later (2008). 2.2. Sample selection and respondents’ socio-economic characteristics A professional survey company implemented two identical surveys in November 2007 and in December 2008, using a web-based survey. Hereafter we will refer to these two surveys as “sample A” (2007) and “sample B” (2008). The sample for both surveys was segmented between people living in the Austrian federal states of Vienna and Lower Austria. A total of 526 people were recruited for

M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73

67

Fig. 1. Study area: Danube floodplains.

a pre-test, of which 109 completed the web-based questionnaire in autumn 2007. In addition,15 questionnaires were sent to experts, and face-to-face interviews were held with these experts to improve the structure and wording of the questionnaire. The questionnaire itself is documented in Bliem and Getzner (2008). Sample A was carried out in 2007 and 1977 people were invited to participate. The response rate was 25.59% (n ¼ 506 completed questionnaires) for sample A, and 23.31% (n ¼ 410) for sample B (total no. of people invited in sample B: 1759). The response rate has to be interpreted very carefully because it was calculated by the survey company to be the ratio between completed questionnaires and the overall number of people invited to participate in the online survey. Table 1 displays the socio-demographics of the two samples. Basically, the social and economic characteristics of both samples are similar to those of the population in Austria. The gender of respondents is very close to the Austrian average, with about 52% women and 48% men in the sample. The age structure of respondents in both surveys lies well within the distribution of the population of Vienna and Lower Austria, with the largest share of respondents aged between 30 and 50 years. The age category “>60” years is lower in both surveys compared to the total population. One explanation put forward in the literature is that, in a webbased survey method, the response rate of older people is generally low based on lack of experience with and access to the internet (Bech and Kristensen, 2009). The distribution of disposable monthly household income is also presented in Table 1. The median monthly household income falls in the income category V 1501e2000, which is slightly below the median household income in Austria (approx. V 2500 per month). Thus, the income distribution is skewed towards those with lower incomes, and lower income classes are slightly overrepresented in this web-based sample. The degree of similarity between the sociodemographic characteristics across both surveys can be formally

tested by the Chi-square test. The last column in Table 1 shows the results of the Chi-square test; age, sex and income distributions are not significantly different between the two samples. 2.3. Perceptions of water quality of the Danube River and experience with floods In a first block of questions, respondents were asked about their perception of water quality and personal experience with floods. A Table 1 Socio-demographics of respondents. Sample A (n ¼ 506)

(%)

Sample B (n ¼ 410)

(%)

Chi-Square (Significance Level)

c2 ¼ 0.0457

Gender

(p ¼ 0.831)

Male Age

242

47.83

199

48.54

c2 ¼ 0.105

(p ¼ 0.999)

14e19 years 20e29 years 30e39 years 40e49 years 50e59 years >60 years

50 90 105 112 93 56 506

9.88 17.79 20.75 22.13 18.38 11.07 100

40 73 85 89 75 48 410

9.76 17.80 20.73 21.71 18.29 11.71 100

c2 ¼ 3.432

Income

(p ¼ 0.634)

0e500 V 501e1000 V 1001e2000 V 2001e3000V >3001V No answer

31 54 156 98 67 100 506

6.13 10.67 30.83 19.37 13.24 19.76 100

21 45 118 84 45 97 410

5.12 10.98 28.78 20.49 10.97 23.66 100

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M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73

majority of respondents believed that current water quality is good or very good; about one quarter of respondents perceived water quality as poor or moderate, with slight differences in perceived water quality between sample A and sample B (Fig. 2). This difference is not significant (c2 ¼ 1.154; p ¼ 0.885). Mean water quality, as perceived by respondents, is lower than the Austrian Ministry of the Environment’s water quality assessment (Institute for Water Quality, 2008), which records a generally very good or good water quality for the Danube River in the study area. Generally, respondents are quite familiar with the current water quality of the Danube River. More than 84% of respondents in sample A and 81% in sample B indicated that floods had not personally affected them in the past. A minority of respondents had been affected by floods at least once, e.g. by having to dry up flooded basements, leave their homes, or repair damaged cars, furniture or houses. Personal experience with flooding showed no statistical significance at the 10% level of significance (c2 ¼ 2.697; p ¼ 0.11).3 Flood perception was also surveyed in the questionnaires. Expectations regarding future frequency of floods were rather optimistic in both samples. The majority of respondents did not expect to be affected by floods in the future, while about 30 percent of respondents expected to be affected once a year, or once every five years. 3. Methodology 3.1. Choice experiment design and econometric estimation of valuation functions The choice experiment (CE) was composed of three attributes: flood frequency, water quality and the cost price of the respective management option (see Table 2). Respondents were asked to choose between the current situation and two alternatives (management options). In the introduction, respondents were told that river restoration measures could improve the river’s nutrient retention capacity, and reduce (harmful) flood frequency. The extent of restoration measures (towards a more natural state) influences the improvement of water quality and expected reduction of flood frequency. Water quality was described in terms of diversity of aquatic life and potential recreational uses such as swimming, boating and fishing. A selection of multi-coloured pictograms was used to assist respondents to visualize different quality levels, starting from moderate to good and very good water quality. The differences between the three levels were explained in detail. Flood frequency was defined as the frequency of floods that will bring damage to communities and agricultural and industrial uses of areas downstream of the area of river restoration and renaturalization measures, using four levels (floods every 5, 25, 50 and 100 years, respectively). The lowest level for both attributesdwater quality and flood frequency, corresponded to the status quo. The monetary attribute was specified as an increase in respondents’ water bill to fund the water management program (by means of an annual contribution in addition to the water bill). The payment levels used in the choice experiment were 3, 10, 30 and 50 V. A main effect fractional factorial design, with 32 different choice sets blocked in eight different versions of four cards, was used. The design resolution was III, so no main effects are aliased with any

3 This potential difference cannot be explained by recent events since there was no major (damaging) flood event along the Danube River in the year between the surveys.

50 45.3 45.4

45 40 35 30 % 25

21.5

23.2 20.0 20.2

20 15

8.5

10 5

4.7

6.8

4.4

0 poor

moderate

good Sample A

very good

don’t know

Sample B

Fig. 2. Public perception of Danube water quality in sample A and sample B.

other main effects, but main effects are aliased with interactions (Montgomery, 2009).4 A total of 32 choice sets were assigned to eight blocks, such that each respondent was confronted with four choice questions. Each question consisted of a three-way choice: option A and option B, which gave an improvement in at least one attribute for a positive cost; and the zero-cost, zero-improvement status quo. The issue of dominant alternatives was taken account of without compromising the orthogonality conditions of the design and tested for in the above-mentioned pre-test of the experiment. Respondents who chose the current situation four times were asked to explain why. These respondents were confronted with a series of statements (e.g. “It is the task and responsibility of the government to protect the rivers”, or “The environment has the right to be protected irrespective of the costs to society.”) to identify and categorize possible protest bidders. Protest bids are usually excluded and the analysis is based only on individuals who report bids (cf. e.g. Dziegielewska and Mendelsohn, 2007). This may lead to biased estimators for the willingness-to-pay. Since the number of protest bids in both samples was very low (around 2%), no attempt to exclude them was undertaken. 3.2. Statistical model Choice experiments (CE) belong to the family of stated preference methods and is based on traditional microeconomic theory. CE combines Lancaster’s characteristics theory of value, and random utility theory (RUT). Lancaster asserted that consumers derive utility from the characteristics of the good, and not from the good itself (Lancaster, 1966). The value of ecological river restoration can, for example, be expressed as a sum of two exclusive categories of benefits: the impact of river restoration on water quality, and floodwater storage and the corresponding reduction of flood risk. The main proposition is that utility cannot be observed directly, but indirect valuation of consumer preferences is possible, with some degree of randomness (Manski, 1977). The utility function for a representative consumer can be written as:

Uij ¼ Vij þ 3 ij

(1)

4 Good practice in experimental design is essential for choice experiments in nonmarket valuation. The statistical efficiency of different designs was evaluated by means of Monte Carlo experiments by Ferrini and Scarpa (2007). Scarpa and Rose (2008) also dealt with the evaluation of design efficiency in discrete choice modelling. However, if reasonable a priori information is lacking, as it was in this case, in practice main effect fractional factorial designs are useful for linear models (Ferrini and Scarpa, 2007).

M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73 Table 2 Attributes and levels used in the choice experiment.

69

Table 3 Hausman test for IIA.

Level

Flood return period

Water quality

Cost price (V/household/year)

(1)

(2)

(3)

0 1 2 3 4

Once Once Once Once

Moderate Good Very good

0 3 10 30 50

Sample

c2-Statistic

Significance level

Sample A Sample B Pooled sample

7.75 12.33 16.82

p < 0.17 p < 0.03 p < 0.00

every every every every

5 years 25 years 50 years 100 years

where Vij is the systematic component of the utility held by consumer i for choice alternative j and 3 ij is the random or unobservable component. Therefore, the probability that individual i will chooses alternative “i” over alternative “j” is:


Probi ðjjCÞ ¼ Prob Vij þ 3 ij >Vik þ 3 ik ;

(2)

where C is the complete set of alternatives (Ben-Akivia and Lerman, 2000). In order to be able to estimate the probability of someone choosing an alternative, it is necessary to make assumptions about the distribution of the random term. It is usually assumed that the errors are Gumbel-distributed as well as independently and identically distributed (i.i.d.). Different assumptions about 3 ij lead to different choice models (Train, 2003). The Multinomial Logit Model (MNL) is the most frequently used model. Furthermore, the Independence of Irrelevant Alternatives (IIA) property states that the relative probabilities of two options being selected are unaffected by the introduction or removal of other alternatives. This follows directly from the independence of errors across the different options in the choice set. Violation of IIA requires more complex statistical models (e.g. random parameters (mixed) logit model, nested logit model) that relax some of the assumptions used. The most widely used test for violation of the IIA assumption is the Hausman test (Louviere et al., 2003). In addition, the responsiveness to the attributes of different alternatives is assumed to be homogeneous across individuals, i.e. preferences are assumed to be homogeneous. These assumptions lead to a closed-form mathematical model that enables estimation through conventional maximum likelihood (ML) procedures. Likelihood Ratio based tests can be used to test restrictions on the parameters or differences in parameters. The choice design hence results in the following conditional indirect utility form:

Vij ¼ al þ b1 Floodij þ b2 Qualityij þ b3 Pricejn þ 3 ij

(3)

In Eq. (3), alpha (a) is the alternative specific constant (ASC) and the betas (b) refer to the vector of coefficients related to the attributes flood return period (Flood), water quality (Quality) and cost price (Price). 4. Results5 Table 3 reports the results of the Hausman test for IIA (Hausman and McFadden (1984)). The test was performed for sample A (2007), sample B (2008) and the pooled sample merging the data of both surveys. In case of sample B and the pooled sample, the IIA assumption had to be rejected since the Hausman statistic is statistically significant below the 5% level. This highlights that estimating a multinomial logit model for sample B or the pooled sample could yield misleading results. In the absence of IIA, the mixed logit model can be used. It allows for variation in preferences across individuals and adjusts for error correlation across individual choices (Hensher

5

LIMDEP with NLOGIT4.0 was used for model estimation and statistical tests.

et al., 2005). It makes it possible to determine potential sources of any heterogeneity that may exist. Unexpectedly, for sample A it is not necessary to abandon the IIA assumption, since the Hausman statistic is not statistically significant: this is related to the insignificance of the random parameter in sample A for the variables denoting both a good water quality and flood frequency. Before discussing the results of the different models it is worth taking a closer look at the two samples. Table 3 shows that for sample A, the multinomial logit model is sufficient, assuming that the preferences are homogenous, while for sample B a mixed logit model is needed, allowing for preference variation across individuals. So, the two samples call for different models, although: -

-

-

the data come from two identical surveys with similar response rates; the two samples do not differ statistically significantly with regard to socio-economic factors such as gender, age and income; and the two samples do not differ statistically significantly with regard to perception of water quality.

The estimated models presented in Table 4 do not include any covariates. In a set of models not presented here, a variety of variables were included in the model specification, but did not show a relevant impact on the estimates. These variables were: perception, future visit, distance, income, visitor and education. A detailed description of the variables can be found in Appendix. Table 4 reports the simplest models that include three attributes termed: flood frequency, water quality (good and very good), and the price; in addition, the alternative specific constant was added to the estimation. Dummy coding was used for the categorical water quality (good and very good), with moderate water quality being the baseline category. For flood frequency, the lowest flood return period (once every five years) was the baseline category. Hence, a positive coefficient estimate was expected between the probability of choosing an alternative and flood frequency. A higher value means a lower flood frequency, resulting in a higher choice probability. Ignoring statistical indifference concerning the IIA assumption for sample A, Table 4 presents the estimates of the multinomial logit models (columns (2), (4), (6)) and of the mixed logit model (columns (3), (5), (7)) for sample A, B and the pooled sample. Looking at the multinomial logit estimates (columns (2), (4), (6)), the four attributes in all three samples had the expected signs and are all statistically significant well below the 1% level. Flood frequency, water quality good and water quality very good, had positive signs. This implied that respondents have preferences for choice sets that reduce flood risks and improve water quality. The negative sign of price indicated that respondents prefer lower water bills. Although the results are very close, there is one point at which sample A is different from sample B and the pooled sample. For sample A, the alternative specific constantdrepresenting all other determinants of utility for each option not included by the attributesdis not statistically significant, whereas it is for sample B and the pooled sample. Turning to the mixed logit model (columns (3), (5), (7)) it can be seen that the four attributes in all three samples have the expected

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M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73

Table 4 Model estimates. Sample A (1)

(2)

Sample B (3)

Pooled Sample

(4)

(5)

(6)

(7)

Variable

Multinomial Logit

Mixed Logit

Multinomial Logit

Mixed Logit

Multinomial Logit

Mixed Logit

ASC Flood frequency Water quality good Water quality very good Price Standard deviation flood frequency Standard deviation water quality good Standard deviation water quality very good Log Likelihood AIC BIC CAIC N

0.157 [0.098] 0.004*** [0.001] 0.938*** [0.103] 1.587*** [0.103] 0.021*** [0.003] e

0.188 [0.103] 0.004*** [0.001] 0.991*** [0.111] 1.718*** [0.141] 0.023*** [0.003] 0.003 [0.007]

0.256** [0.104] 0.004*** [0.001] 0.744*** [0.110] 1.435*** [0.110] 0.023*** [0.003] e

0.329*** [0.114] 0.005*** [0.002] 0.816*** [0.145] 1.722*** [0.200] 0.029*** [0.005] 0.010** [0.004]

0.202*** [0.071] 0.004*** [0.001] 0.849*** [0.075] 1.518*** [0.075] 0.022*** [0.002] e

0.245*** [0.001] 0.005*** [0.001] 0.931*** [0.089] 1.713*** [0.120] 0.025*** [0.003] 0.007** [0.003]

e

0.057 [0.436]

e

0.815 [0.502]

e

0.312 [0.456]

e

0.974*** [0.326]

e

1.326*** [0.443]

1916.868 1.922 1.936 1.927 2000

1916.869 1.923 1.945 1.931

1594.516 1.960 1.978 1.967 1632

1591.585 1.960 1.987 1.970

1.134*** [0.276] 3513.583 1.938 1.946 1.940 3632

3507.551 1.936 1.950 1.941

***1% significance; **5% significance; *10% significance, standard errors in [].

signs and are all statistically significant well below the 1% level. With respect to the chosen attributes, both models generated similar results. This confirms that people are valuating and are ready to pay for water quality improvement. Moreover, improvements are rated more highly when the associated costs are lower. Looking at the estimates for the standard deviations of the random parameters it can be seen that: -

-

-

the standard deviation of water quality good is not statistically significant for all three samples; the standard deviation of water quality very good is statistically significant for all three samples; and the standard deviation of flood frequency is not statistically significant for sample A, but is so for sample B and the pooled sample.

These results highlight on the one hand that one major component of preference heterogeneity is preference towards water quality very good and that the preference heterogeneity in sample B is larger than in sample A. It therefore seems that preferences are more homogenous in sample A. On the other hand, the random parameter water quality good can be collapsed to its mean, indicating no heterogeneity. If the parameters of the models are identical in numeric values, then it would be acceptable to estimate the model based on the pooled data set and use it for both samples. Moreover, it would mean that preferences did not change over time. The extent of similarity between parameters can be tested by the maximum likelihood analogue of the Chow test (Chow, 1960), which tests the difference between parameters across two samples:

b0i ðSample AÞ  b0i ðSample BÞ ¼ 0

(4)

The results of the Chow test are shown in Table 5. For the multinomial logit, as well as the mixed logit model, the equality of the parameters cannot be rejected. The Chi-square values are small and the significance levels are well above 10%. This means that that the underlying choice models of sample A and sample B are not significantly different from each other. In other words, the underlying indirect utility functions do not differ statistically significantly, or the preference did not essentially change over time in this case in full knowledge that issues of confounding of scale and preference parameters are not covered this way. A comparison of

choices would require revealing the scale of utility or scale parameter as a mechanism for making the results of two datasets comparable (Hensher et al., 2005, p. 75). Swait and Louviere (1993) point out that scale factor differences must be isolated before comparing parameters of two or more datasets. Brouwer et al. (2010) used the findings of Swait and Louviere (1993) and considered different scale parameters when analyzing repeated choice experiments. Table 6 presents estimates of implicit prices or willingness-topay and the corresponding standard errors. These values are the amount of money individuals are willing to pay for the given improvement (Column (2)). These values are based on a ceteris paribus assumption, that is, all other parameters are held constant except the attribute for which the implicit price is being calculated. All implicit prices are significantly different from zero at a 1% level of significance. Looking at the results for the multinomial model (Columns (3), (5), (7)), the estimates indicate that for sample A the respondents are willing to pay around V 0.2 in addition to their water bill for a reduction in flood frequency by one year. Furthermore, respondents are willing to pay about V 44.5 to improve water quality from moderate (status quo) to good and about V 75.3 to improve water quality from moderate to very good. There are similar results for sample B and the pooled sample. The biggest difference in estimates between samples can be identified for a change in water quality from moderate to very good. The highest willingness-to-pay was found for sample A. Moreover, it can be seen that the sample A showed the largest standard deviations for good and very good water quality. In order to estimate the standard deviations for the mixed logit models, the willingness-to-pay was simulated for each respondent using conditional and constrained parameter estimates (Hensher et al., 2005, p. 691). The results for the mixed logit model

Table 5 Chow test for parameter equality. Multinomial Logit

Mixed Logit

(1)

(2)

(3)

(4)

c2

Significance level

c2

Significance level

2.199

0.138

2.197

0.138

M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73

71

Table 6 Implicit Prices (Willingness-to-pay) estimates. Sample A (1)

(2)

Sample B

Pooled Sample

(3)

(4)

(5)

(6)

(7)

(8)

Variable

Improvement

Multinomial Logit

Mixed Logit

Multinomial Logit

Mixed Logit

Multinomial Logit

Mixed Logit

Flood frequency Water quality good

Per year From moderate to good From moderate to very good

V 0.20*** [0.048] V 44.49*** [6.529]

V 0.19*** [0.036] V 39.48*** [10.275]

V 0.17*** [0.050] V 31.80*** [5.462]

V 0.18*** [0.036] V 27.66*** [6.524]

V 0.19*** [0.035] V 38.40*** [4.240]

V 0.19*** [0.035] V 32.49*** [7.879]

V 75.31*** [8.387]

V 78.34*** [38.333]

V 61.30*** [6.842]

V 59.85*** [28.396]

V 68.63*** [5.407]

V 68.21*** [32.487]

Water quality very good

***1% significance; **5% significance; *10% significance, standard deviation [].

indicated that the estimates of the two samples are very close to each other, and even closer to the estimates of the multinomial logit model. For the change from moderate to good water quality, the estimates of the mixed logit model are smaller, but the corresponding standard deviation is larger than for the multinomial logit model. The most noteworthy result is that the mixed logit model shows an extremely large standard deviation for the parameter estimates for the change from moderate to very good water quality, although the estimates are quite similar. This is in line with the previous finding that one major component of preference heterogeneity is the preference towards very good water quality. In order to present a richer picture of the determinants of respondents’ choices, and to explore the validity of the current choice experiment, Table 7 presents the results of two best-fit ML models separately for samples A and B. Referring to the discussion above, preference heterogeneity was accounted for by including ‘water quality good’, ‘water quality very good’ and ‘cost price’ as random parameters. Interaction terms between these random parameters and other parameters in effect decompose any heterogeneity observed within the random parameters, offering an explanation as to why the heterogeneity exists. In both samples, random effects were detected for water quality good, water quality very good and cost price. The mean sample

population parameter estimates are statistically significant and have the correct sign. Looking at the derived standard deviations of the random parameters, it can be seen that in both samples the significant standard deviation belongs to water quality very good. In sample A, the standard deviation of cost price was also significant. The random parameter water quality good was observed to have an insignificant spread. It can be collapsed to its mean, indicating no heterogeneity. Moreover the interaction term between water quality (very) good and the covariate income is statistically significant in sample A but is not so in sample B. For sample B, the heterogeneity in the mean parameter estimate between water quality very good and income suggests that, across the sampled population, the sensitivity to water quality decreases with income. For sample B, the parameter estimate of the interaction between water quality (very) good and income is statistically not significant, and is around zero. In fact, the ML models for sample A and B were quite similar. The estimated effects are similar in size and sign. The flood frequency effect was significant in both sample A and sample B. Respondent perception of current water quality has a significant effect on the value attached to water quality improvements (indicated by the significant interaction between the variables water quality good and water very good and perception). The same applies for future

Table 7 Mixed logit model for sample A and sample B. Variable

Random parameters

Nonrandom parameters

Heterogeneity in mean

Standard deviations

Model fit

Water quality good Water quality very good Cost price ASC Flood frequency Cost price  income Water quality good  distance Water quality very good  distance Water quality good  future visit Water quality very good  future visit Water quality good  perception Water quality very good  perception Visitor Education Water quality good: income Water quality very good: income Cost price: income Water quality good Water quality very good Cost price Log Likelihood LR Test Adjusted R square n

***1% significance; **5% significance; *10% significance.

Sample A

Sample B

Coefficient

Std. err.

Coefficient

Std. err.

1.816 2.260 0.042 0.039 0.005 0.003 0.004 0.001 0.625 1.166 0.0105 0.009 0.240 0.516 0.095 0.095 0.000 0.0002 1.168 0.019 1877.147 631.366 0.140 2000

0.308*** 0.3325*** 0.008*** 0.154 0.002*** 0.001*** 0.002** 0.002 0.270** 0.270*** 0.003*** 0.003*** 0.151 0.268* 0.041** 0.041** (fixed) 4.689 0.347*** 0.009**

0.743 1.774 0.037 0.0353 0.005 0.002 0.002 0.004 0.328 0.851 0.014 0.010 0.464 0.349 0.001 0.001 0.000 0.776 1.111 0.010 1560.319 465.231 0.125 1632

0.363** 0.374*** 0.009*** 0.162 0.002*** 0.001 0.002 0.002** 0.278 0.260*** 0.004*** 0.003*** 0.160*** 0.280 0.050 0.050 (fixed) 0.705 0.422*** 0.015

p < 0.001

p < 0.001

72

M. Bliem et al. / Journal of Environmental Management 103 (2012) 65e73

visits, i.e. whether respondents would visit the case study sites more often in the future if water quality would be improved. In the first case, a negative relationship exists, meaning that respondents who perceive water quality as good already value water quality improvement less. In the latter case, a significant positive effect on choice behaviour is found: respondents who said they would visit more often if water quality were to be improved are more likely to pay for ecological river restoration benefits. Furthermore, a significant distance-decay effectdby including the variable distancedwas found for water quality improvement. Household income interacts significantly with the cost price only in sample A. As expected, higher income groups are more likely to choose river restoration as their most preferred option at a higher price than lower income groups. Finally, respondents who had previously visited the study area and respondents with a higher level of education are more likely to favour ecological restoration. Although most of the estimated effects in sample A and B are similar in size and sign, differences were also detected: the nonrandom variable ‘visitor’ was statistically significant for sample B but not for sample A. Concerning the variable ‘education’, the converse was true. The standard deviation of cost price had statistically significant standard deviation for sample A but not for sample B, which also led to statistically significant heterogeneity in mean in combination with water quality good and water quality very good. From these results, it is not clear whether or not respondents’ perception changed in the observed time period or not.

-

-

-

preferences in sample A were more homogenous than in sample B; the underlying indirect utility functions do not differ statistically significantly; and the marginal willingness-to-pay for the attributes of the choice experiment is in the same order of magnitude in the two surveys, corroborating the temporal stability of values for river restoration.

At the very least, no evidence was found that respondents’ perception in this choice experiment changed over time. The data were characterized by a remarkable stability. On the one hand, this may be caused by the well-balanced choice of the attributes and their levels. They were appropriate to precisely describe the situation for the respondents. Thus, it is assumed that the choice experiment itself and the questionnaire were of high quality. Data acquisition was also highly reliable. On the other hand, no extreme events (e.g. flooding) took place in the period between the two choice experiments, which could have had an effect on the respondents’ attitude. Since severe floods do not take place annually, the time span between the two choice experiments was may be too short. Brouwer and Bateman (2005) found differences when analyzing data with a time period of five years. It would be interesting for further analysis to repeat the choice experiment in the same manner after an extreme event or after a time period of more than three years. This provides a guide for future analysis.

5. Conclusions Acknowledgements In this paper we explore whether individual preferences change over a short time period. Using a number of tests, potential differences between two identical choice experiments implemented in two consecutive years were examined. The main findings are that: -

-

the chosen attributes have a statistically significant impact on preferences, as expected given the results of the pre-tests; turning from a multinomial logit to mixed logit model shows that one major component of preference heterogeneity is preference towards the variable ‘water quality very good’;

This study was carried out as part of the EU DG Research funded project AquaMoney (SSPI-022723) (www.aquamoney.org) and was also financially supported by the Research Council of Klagenfurt University. We are thankful to R. Brouwer for comments and suggestions, and to participants of the EAERE (European Association of Environmental and Resource Economists) 2009 conference in Amsterdam. Extremely helpful comments and suggestions by anonymous reviewers are also gratefully acknowledged. All errors are, of course, the responsibility of the authors.

Appendix A Table A. Variables used in the models. Variable

Description

Levels

Flood frequency

Probability of floods that will bring damage to communities and agricultural and industrial user of areas downstream of river restoration Water quality was described in terms of variety of aquatic life and recreational uses such as swimming, boating and fishing.

5, 25, 50 and 100 years

Water Quality

Cost price Perception Future visit Distance Income Visitor Education

Increase in the respondents’ water bill to fund the water management program (in the form of an annual contribution on top of the water bill). Respondents’ perception of current water quality. Engage in more activities (e.g. boating, walking) in the Danube national park if water quality improved Distance (euclidean) from home to the nearest recreational area in the Donau-Auen National Park Disposable monthly household income Frequency of annual visits in the Danube national park Respondents’ highest educational level

Moderate: Limited variety of aquatic species. Water can be used for agricultural purposes. Recreational use possible for e.g. boating, walking but not swimming. Fishing possible (but not for consumption). Good water quality: Wide range of fish and plant species. All recreational uses possible. Consumption of caught fish possible. Very good water quality: Near-natural stateefull range of natural species possible. All recreational uses possible. 3, 10, 30 and 50 V Poor ¼ 0; Moderate ¼ 1; Good ¼ 2; Very good ¼ 3 Yes ¼ 1; No ¼ 0 Distance measured in kilometres 10 categories from 0 to 250 V to more than 3.500 V visits ¼ 1; never visit ¼ 0 No education at all ¼ 0; Primary school ¼ 1 Professional training/high school/technical college ¼ 2; University ¼ 3

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