When policy implementation failures affect public preferences for environmental goods: Implications for economic analysis in the European water policy

Ecological Economics 169 (2020) 106523

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When policy implementation failures affect public preferences for environmental goods: Implications for economic analysis in the European water policy

T

Ángel Pernia,*, Jesús Barreiro-Hurléb, José Miguel Martínez-Pazb,c a

Departamento de Economía General, Universidad de Cádiz, Cádiz, Spain Instituto Universitario del Agua y del Medio Ambiente (INUAMA), Universidad de Murcia, Murcia, Spain c Departamento de Economía Aplicada, Facultad de Economía y Empresa, Universidad de Murcia, Murcia, Spain b

ARTICLE INFO

ABSTRACT

Keywords: Contingent valuation Preference stability Good ecological status Mar Menor Water Framework Directive European Union Spain

The Water Framework Directive (WFD) establishes that Programme of Measures (PoMs) to manage aquatic ecosystems have to be assessed to determine whether the benefits obtained from the good ecological status outweigh the costs. Stated preference methods have been widely applied to estimate non-market benefits of improved ecological status, assuming that respondents declare their true preferences that are stable over time. However, evidence on preference stability is mixed in the literature. The objective of this paper is to study preference stability towards water bodies improvement. As the WFD implementation has been delayed and have proven to be a greater challenge than expected, we focus on the effects of policy implementation failures on preference stability. We analyse two contingent valuation surveys to assess environmental values for the case of the Mar Menor (SE Spain) in years 2010 and 2017. We find higher protest response rate, changing indirect utility functions towards good ecological status and decreased WTPs in 2017. It indicates that public valuation might fail to adhere to rational economic premises when public authorities fail to reach environmental objectives and/ or PoMs are not correctly implemented. Decision making based on stated preferences should carefully consider potential biases emerging from management performance.

1. Introduction As the first layer of European water policy, the Water Framework Directive (WFD) establishes the objectives, instruments and tools for the integrated management of river basins (EC, 2000). The WFD's main objective was to achieve the good ecological status (GES) in water bodies by 2015. For this purpose, River Basin District Plans (RBDPs) defining specific objectives and Programme of Measures (PoMs) to conserve and improve aquatic ecosystems (i.e. rivers, lakes, transitional and coastal waters) were developed and should be revised every six years. According to the WFD, the design and selection of measures have to consider stakeholders´ perceptions and public preferences. Moreover, water authorities have to conduct ex-ante evaluations to assess whether the costs of the PoMs are proportionate, i.e., the benefits obtained from the good ecological status outweigh the costs of the measures (MartinOrtega, 2012).

⁎

The comparison of costs and benefits can be qualitative and quantitative (Martin-Ortega et al., 2015). While the former can be conducted through expert judgments and stakeholder active involvement (Perni and Martínez-Paz, 2013), the second requires the use of economic valuation techniques (Marre et al., 2016). As most of the costs and benefits related to improving aquatic ecosystems are not directly traded in markets, stated preference methods, such as contingent valuation and choice experiments, can play a key role for assessing water quality and quantity improving measures (Feuillette et al., 2016). These methods simulate hypothetical markets via questionnaires in order to quantify the non-market benefits or costs of a given action. A streamlined stated preference valuation method requires respondents to declare their willingness to pay (WTP) for the improvement of a water body, which can then be used as estimates of the non-market benefits of such an improvement. Then, these estimates can be used in cost-benefit analyses to select the best PoM in terms of economic efficiency (Metcalfe

Corresponding author. E-mail addresses: [email protected] (Á. Perni), [email protected] (J. Barreiro-Hurlé), [email protected] (J.M. Martínez-Paz).

https://doi.org/10.1016/j.ecolecon.2019.106523 Received 31 July 2019; Received in revised form 10 October 2019; Accepted 28 October 2019 0921-8009/ © 2019 Elsevier B.V. All rights reserved.

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et al., 2012). As these methods capture public preferences and perceptions (Sarvilinna et al., 2018), stated preference methods are also coherent with WFD principles on public participation.1 Consumer theory (Lancaster, 1966) provides the underlying foundations for stated preference methods. According to this theory, respondents declare their true preferences which are assumed to be stable over time. Otherwise, if WTPs estimates varies, ceteris paribus any of the standard economic drivers of demand, then the CBA is no longer valid to assess policy implications of changes in environmental quality (Czajkowski et al., 2016), i.e., improved water ecological status. Preference stability serves as an indication of the methodological validity and reliability of stated preference methods (Bishop and Boyle, 2019). Evidence on preference stability is mixed. While most empirical research studying the temporal stability of WTP values concludes that preferences are stable (McConnell et al., 1998; Carlsson et al., 2014; Price et al., 2017) in particular in the absence of extreme events and for short periods of time (Bliem et al., 2012), some studies suggest that WTP estimates may change over time (Schuhmann et al., 2019). Factors that drive temporal unstable preferences are the changes in the variables that co-determine the demand of an environmental good (e.g. income), changes in the baseline quality against improvements are provided, the change in knowledge regarding the characteristics of a good or the personal experience with it (Czajkowski et al., 2016). Some authors have pointed out specific causes that may alter individuals’ WTP. Loureiro and Loomis (2017) found that WTP estimates for environmental protection are not stable when individuals are exposed to economic downturns that lead to changes in personal income and consumer confidence. Brouwer et al. (2008) concluded that preferences may change over time due to increased public awareness and information levels through extensive media exposure, which relates to the fact that, in economic valuation, as respondents become better informed, the new information significantly influence their choices (Oppewal et al., 2010). Evidence related to the specific case of WFD implementation is lacking. Preference stability is particularly relevant due to the relatively long planning horizon of the directive (i.e. RBDPs are six years long). Despite the major scientific, technical and economic efforts undertaken to characterize the European water bodies, promote public participation and develop PoMs, achieving the WFD objective remains a challenge in numerous basins. During the first WFD implementation cycle for the period 2009–2015, the number of surface water bodies achieving a good ecological status only increased by 10% (Voulvoulis et al., 2017). According to the EEA (2018), around 40 % of the surface water bodies are in either good or better ecological status compared to the initial conditions, being lakes and coastal waters in better status than rivers and transitional waters. Some PoMs have been delayed because of funding constraints (EEA, 2018), and others have proven to be a greater challenge than expected for public authorities due to conflicts between stakeholders (Perni and Martínez-Paz, 2017). Public preferences towards WFD implementation might vary not only due to changes in the socioeconomic context, but also as a response to increased information on water bodies status (Kataria et al., 2012) that, in many cases, provides evidence of limited policy effectiveness. Political inefficiency may also increase protest behaviour of more informed individuals. If respondents distrust public

administrations, they may declare political, social, moral o emotional beliefs, rather than economic preferences. This might imply further methodological issues in the sense that respondents might be not revealing their true preferences, but rather protest against the economic valuation survey. Systematic protest behaviour arising across a large portion of the respondents can have a significant influence on the economic analysis (Remoundou et al., 2012) and it can also evolve due to changes in the socio-ecological system over time (Uehara et al., 2018). Against this background, the objective of this is paper is to study whether public preferences towards good ecological status of water bodies are stable over time, in particular, when public authorities fail to implement the WFD. For this we conduct a comprehensive analysis of preference stability across time focusing on three measurements: (i) individuals´ utility functions, (ii) willingness to pay estimates and (iii) protest behaviour. While previous studies have focused on the economic foundations of stated preferences methods, we place our analysis in the specific framework of the European water policy and add an explicit analysis on a non-economic phenomenon, protest behaviour. Further, this study will shed light about the implications of unstable preferences on the analysis of disproportionate costs of the WFD and on the transferability of non-market cost and benefits across time. With regard to water resources, preferences stability has been addressed for flood alleviation and wetland conservation (Brouwer and Bateman, 2005), and effects of water quality on public health (Brouwer, 2006). Studies analysing temporal stability of preferences in the specific WFD context are rather limited. One exception is the work by Bliem et al. (2012), but the time frame against which the study evaluates preference stability is rather short (i.e. one year), thus not being able to capture the effect of public administration performance on achieving GES. This research assesses environmental values at different relatively distant points in time (i.e. seven years) for the case of the Mar Menor coastal lagoon (SE Spain), where public authorities have not fully implemented measures that allow the Mar Menor to reach improved environmental conditions. Public preferences and WTPs for improving ecological status are analysed using two contingent valuation surveys delivered in 2010 and 2017. The remaining of this paper is organized as follows. First, we present the case of the Mar Menor coastal lagoon, describing past and current situation of this water body and how public authorities have faced its eutrophication. We then present our research hypothesis, econometric approach and data. The results focus on bi-variate and multi-variate analysis of our two samples in order to test for preferences stability. The discussion section put in perspective our main findings according to other studies on the topic. Finally, we provide a set of conclusions with the aim of guiding policy makers and practitioners to better apply the WFD economic principles. 2. The Mar Menor coastal lagoon: an example of delayed implementation of the WFD The Mar Menor (SE Spain) is one of the largest hypersaline coastal lagoons in Europe (Fig. 1). It is located in the Segura River Basin District (SRBD) and covers 135 km2. The lagoon is separated from the Mediterranean Sea by a sand bar that is 20 km long and between 100 and 900 m wide. It is connected to the Mediterranean Sea by several channels, some of which have been widened to allow the passage of boats. The Mar Menor is inhabited by singular flora and fauna adapted to high salinity (ranging from 42 to 47 PSU) and temperature (ranging from 12 °C and 30 °C). Other habitats are also well represented in the surroundings, including crypto-wetlands, coastal saltpans and dunes (Martínez-Fernández et al., 2014). Given its unique environmental features, the Mar Menor is protected at regional, national and international level. The lagoon is included in the Ramsar List of Wetlands and it is designated Specially Protected Area of Mediterranean Importance, Special Protection Area for Birds and Site of Community

1

Despite academic recommendations on using stated preference methods, applications by national water agencies are not common in the Member States yet. RBDPs and reports by national water agencies on the WFD implementation usually indicate that environmental non-market benefits are “more difficult to quantify” than costs, and estimates from academic research are reported only occasionally when available (NEAA, 2008; DEFRA, 2014). One exception would be the close collaboration between academia and the water agencies in the United Kingdom, which resulted in the major study on environmental benefits of the WFD implementation using stated preference methods to date (Metcalfe et al., 2012). 2

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Fig. 1. Location of the Mar Menor coastal lagoon. Source: Authors' own elaboration

agriculture, water abstraction and practices. The SRBD authority developed a new SRBD plan for the period 2015–2021 keeping the same objectives as in 2008 (CHS, 2016), i.e. from moderate ecological status to good status in 2027. The PoMs established to improve the Mar Menor contains basic and additional measures. Basic measures refer to measures established by existing basic legislation and include full wastewater treatment in sensitive areas (Urban Waste-Water Treatment Directive 91/271/CEE) and application of codes of good practices by farmers in vulnerable zones (Nitrates Directive 91/271/CEE). Additional measures aim to reduce nutrient load further to reach GES. In both plans, the managing authority proposed the same measures: (i) wastewater treatment plants connected to a submarine emissary to fully eliminate sewage spills; (ii) storm tanks to collect urban wastewater; (iii) cleaning of the Rambla del Albujón, the main ephemeral watercourse that discharges into the lagoon, to increase nutrient retention; (iv) sewer systems and desalinization of irrigation returns; and (v) installation of a set of wells around the lagoon to abstract and treat polluted groundwater. While the three first measures are executed or being implemented, the last two have not been initiated at the time of drafting this paper (July 2019). The interested reader can refer to Perni and Martínez-Paz (2013) for the specifics of the design and effectiveness of these measures. In order to respond to legal and social demands, the public authorities proposed new measures for the current policy horizon (i.e. 2015–2021) that still need implementation. These measures include environmental restoration of the Rambla del Albujón, installing green filters in ramblas discharging into the lagoon and improved inspection of illegal discharges. Furthermore, an expert panel was set up to supervise and monitor the Mar Menor status (CHS, 2016). The SRBD plan for the 2015–2021 period (CHS, 2015) establishes a three-steps plan to implement the PoMs additional to those basic measures that are compulsory by law (e.g. wastewater treatment plants and codes of good practices). These additional measures are eligible for disproportionate cost analysis (Martínez-Paz et al., 2013). The first step covers the 2015–2021 period and includes the installation of storm tanks and the set of wells around the lagoon. The total investment costs amount to 57.7 million euros and the equivalent annual cost, including maintenance and operational costs, is estimated at 4.7 million euros per year. The second step corresponds to the next planning horizon cycle, i.e. 2021–2027, and includes the construction of additional storm tanks,

Importance (BORM, 2018). The main pressures on the Mar Menor are agriculture, tourism and mining (Conesa and Jiménez-Cárceles, 2007). The lagoon receives runoffs from the Campo de Cartagena basin, which accounts for 45,000 ha of intensive agriculture. This sub-basin is drained by several ephemeral watercourses (ramblas) that transport nutrient-enriched water and sediments from cropping areas to the lagoon (Garcia-Ayllon, 2018), as well as a significant amount of mining wastes (Dassenakis et al., 2010). Moreover, groundwater that is also polluted by nutrients is extracted and treated for irrigation, and the residual high-salinity wastewater is again reinjected to the aquifer or directly discharged in the ramblas, intensifying pollution in the lagoon. Last, the massive tourist affluence and the insufficient wastewater treatment capacity contribute to increase nutrient loads. All these increased nutrient concentrations lead the lagoon to eutrophication and generate algal blooms and jellyfish proliferation (Pérez-Ruzafa et al., 2019). In the first SRBD plan implementing the WFD, the Mar Menor ecological status was classified as “less than good with a tendency to deteriorate” (CHS, 2008). This first plan aimed just at reversing the trend by 2015 (moderate ecological status) and reaching a good ecological status either in 2021 or 2027, a decision that was justified under the disproportionate costs principle established in the WFD (CHS, 2008). Despite the modest ambition, public authorities failed to meet their target and eutrophication did not remit. Research by the Spanish National Oceanographic Institute proved that eutrophication continued to reduce the surface covered by benthonic macrophytes in the Mar Menor. While in 2014 there were 13,780 ha covered by Cymodocea nodosa, Ruppia cirrhosa and Caulerpa prolifera, this surface was reduced by 85% in 2016 due to harmful algal bloom and it resulted in major social concern (Garcia-Ayllon, 2018). This situation, together with the delayed WFD implementation (e.g. the measures were not fully applied yet and the public knew that situation), generated a profound public controversy regarding Mar Menor management, and it caused public demonstrations to support the lagoon conservation, to warn about the fact that the wetland could be at an irreversible environmental crossroad and to demand administrative action and political responsibilities. Social networks were widely used to spread the Mar Menor environmental challenges by NGOs and other stakeholders. The agricultural sector also stated their concerns on the impacts of higher controls on 3

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the environmental restoration of the Rambla del Albujón and other wetlands as well as the installation of desalination facilities to treat polluted irrigation returns. The expected investment costs for this period are 71.1 million euros, which yield to 6.2 million euros in terms of equivalent annual costs. The last stage covers the period 2027-2033. The planned investment is 11.8 million euros for the installation of the last storm tanks (i.e. annual equivalent costs up to 0.7 million euros). Although out of the scope of this study as they were proposed after the survey was conducted, the most recent measures that are being proposed include control of irrigated crop areas in zones close to the lagoon (i.e., five percent of each farm area is to be left uncultivated and prohibition of the growing of vegetables less than 100 m from the coastline) and creation of conservation structures for indigenous plants around farm perimeters (CARM, 2017; BORM, 2018).

scenario description was the inclusion of the new measures added in the 2017 PoM2 . The first scenario refers to the application of the basic measures, which would allow the lagoon to a achieve a moderate ecological status. The second scenario considers the application of basic and additional measures that would result in the lagoon achieving a good ecological status. The WTP questions were expressed according to these two scenarios using a mixed format: one dichotomous question followed by two open-format questions. First, respondents were asked whether they were willing to pay using an increase in the water bill intended for the Mar Menor environmental recovery as a payment vehicle. Interviewees were asked for the motive behind their yes or no responses. Respondents reluctant to pay who argued that the improvement “is the responsibility of the government” or that “an additional charge on the water bill is inappropriate” were classified as protest responses. Second, respondents were asked about their maximum WTP for each improvement scenarios (i.e. moderate and good scenarios). Data was collected via face-to-face interviews. The first survey was conducted in April 2010 and the second survey in April 2017. A stratified simple random sampling was performed by districts of the Region of Murcia. The sample sizes were 344 and 498 for the 2010 and 2017 surveys, respectively.

3. Study design 3.1. Survey and data collection Surveys for preference stability analysis fundamentally cover three aspects: sampling approach, preference elicitation method and time span between samples. Sampling strategy can consist in repeating the same survey either on independent random samples (Loureiro and Loomis, 2017) or on the same respondents using the test-retest approach (Matthews et al., 2017). Preference elicitation methods include contingent valuation (Schuhmann et al., 2019), choice experiments (Lew and Wallmo, 2017) and multi-criteria analysis (Lienert et al., 2016), and temporal dimension may range from very short (e.g. days) to long periods (e.g. 20 years) (Skourtos et al., 2010). In this research, we conduct the same contingent valuation survey on two independent random samples in years 2010 and 2017. As most applications tend to confirm preference stability regardless the method applied (Lew and Wallmo, 2017), it can be assumed that the elicitation method (i.e. contingent valuation versus choice experiments) will not impact the hypothesis testing. Therefore, other factors rather than methods determine instable preferences over time (e.g. time between surveys, contextual determinants or changes in the variables that co-determine demand). In conclusion, evidence shows that unless external factors change preferences are stable. Years 2010 and 2017 are adequate to test for preference stability towards WFD objectives and implementation according to the own WFD planning horizons. The first PoMs had to enter into force by 2009 and to be revised for the 2015–2021 period. In our case study, the measures aimed at improving the Mar Menor in 2009 were published before distributing the questionnaires, so public and stakeholders could be informed about the measures proposed to revert the Mar Menor coastal lagoon to improved ecological status (CHS, 2008, 2016). In the 2017 survey, the worsening environmental conditions, with harmful algal blooms episodes, were already visible and perceivable by the public and stakeholders (Garcia-Ayllon, 2018). Further, the increasing use of social media has boosted the available and accessible information about the Mar Menor issues. The questionnaire covered different aspects about respondents´ knowledge and preferences towards Mar Menor: (i) perceptions, opinions and visit frequency, (ii) contingent valuation questions, (iii) follow-up questions regarding individuals´ willingness to pay to differentiate between protest and true-zero responses, (iv) respondents´ ecological commitment evaluation and (v) socio-demographic characteristics. The questionnaire is described in Perni et al. (2011). Prior to the valuation questions cheap talk scripts were read to the interviewees and information leaflets were provided to assure a minimum common level of knowledge. Interviewees were asked to value two environmental quality improvement scenarios based on the PoM, which were kept constant in both surveys as environmental objectives were not reached in the first policy horizon and the PoM was practically the same. The only change between the 2010 and 2017

3.2. Empirical hypothesis and modelling approaches This research is structured according to the standard contingent valuation analysis (Johnston et al., 2017). First, we focus on protest behaviour and we analyse whether protesters and their socio-economic drivers have changed between 2010 and 2017. Second, we assess whether respondents’ indirect utility functions and WTPs have significantly shifted in that period. Regarding protest behaviour, we expect that social concerns have changed and are reflected in the respondents´ decision to accept the market-based valuation exercise, leading to significant difference in the number of protest answers and in their determinants. To examine whether propensity to protest has increased, we test for differences in the share of protest responses. Protest believes are identified as described above. The null hypothesis is expressed as follows:

H0 : Protest2010 = Protest2017

(1)

We further test whether the factors driving protest responses are constant over time, i.e., if they are statistically identical. We formulate the following logit model where the dependent variable is Prob (Protest = 1) :

Prob (Protest = 1) =

exp(x ) 1 + exp(x )

(2)

Where x denotes the full set of explanatory variables that are used to estimate the regressors by Maximum Likelihood. The null hypothesis assumes equality in the vector of coefficients:

H0 :

2010

=

(3)

2017

Following Swait and Louviere (1993), we conduct the likelihood ratio procedure to test for differences in the vector of coefficients in three different estimated regressions:

LR =

2[LRpooled

(LR2010 + LR2017)]

(4)

Where LRpooled , LR2010 , LR2017 are the values of the maximized log-likelihood function for the pooled (restricted model), 2010 and 2017 regressions (unrestricted models), respectively. The next analysis is restricted to respondents participating in the hypothetical market, i.e., non-protest individuals. For these individuals, 2 Environmental restoration of ramblas, installing green filters and improved inspection of illegal discharges.

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the welfare of a given environmental quality level can be expressed through an indirect utility function as follows:

U = V (j , Y , Z , H ) +

Table 1 Summary of main hypothesis and test statistics. Source: Authors´ own elaboration

(5)

j

Where V(·) represents the systematic part of the utility, which depends on income (Y), other individual characteristics (Z) and resource characteristics (H); j denotes environmental quality level, where j = 0 indicates the status quo and j = 1 improved-quality level. The error term j represents other non-observable variables that determine individual’s utility. Assuming that the individual must pay for reaching j = 1, it holds:

V (0, Y0, Z , H0) +

0

= V (1, Y0

WTP , Z , H1) +

1

WTP , Z , H1)

V (0, Y0, Z , H0)

exp( V ) exp(x ) = 1 + exp( V ) 1 + exp(x )

(6)

0

+ x + u, u|x

WTP = max(0, WTP *)

Normal (0,

2)

2010

=

2017

(7)

14.0671

2017

2010

=

2017

WTPs Values WTPMod

Mod Mod WTP2010 = WTP2017

WTPGood

Good Good WTP2010 = WTP2017

Test Value (p-value) −2.285 (0.022) 5.881 (0.554)

14.0671

16.518 (0.021) 13.842 (0.054) 24.523 (0.001)

1.6492

0.881 (0.384) 2.990 (0.004)

3.3. Empirical models Protest responses (“Protest”) are coded using a binary variable that takes value 1 if the respondents express a protest believe (see above) in year t. This dummy is regressed against a set of socio-demographic and attitudinal variables collected in the survey as presented in Eq. (13) and explained in Table 2.

(8)

Prob(Protestit = 1) = +

5 Userit

+

0

+

1 Incit

+

2 Work it

+

3 Univit

+

4 Link it

(13)

6 ECit

The empirical model specification for the WTP answers is defined in Eq. (14), which is applied to estimate the logit (we omit the logit transformation) and the Tobit models outlined in Section 3.2:

WTPitm =

(9)

0

+

(10)

+

1 Incit

+

2 Workit

+

3 Univit

+

4 Link it

+

5 Userit

6 ECit

(14)

Where the dependent variable is the WTP for the individual i at period t, m denotes the binary WTP of the non-protest respondents (WTPBinary), and the WTP for the moderate (WTPMod) and good (WTPGood) ecological status resulting from the application of basic and basic plus additional measures. The explanatory variables entered on the right hand-side of the econometrical models are explained in Table 2. The EC is an index that measures the respondent´s environmental commitment. It can refer to affective (AEC), verbal (VEC) or real (REC) ecological commitment. It is measured using a five-point Likert scale applied to a set of items/affirmations in the questionnaire (min/disagree = 1; max/agree 5). For example, to declare VEC, the respondent had to score, among others, this statement “I would stop buying products from companies that pollute continental or coastal waters, even if it proved inconvenient for me”. Once all the items are scored in each category, we calculate the arithmetic average to construct the index. See Perni et al. (2011) and Perni and Martínez-Paz (2017) for further details about these indexes. We run an additional set of econometric models using the subsample of Users in order to explore whether perceived water quality influences protest behaviour and willingness to pay. Eqs. (15) and (16) show the specification followed for this sub-sample:

(11)

where 2010 is the vector of coefficients in 2010 and 2017 2017, respectively. If the null hypothesis is accepted through the LR test (Eq. (4)) using the logit and the Tobit specifications, then we will have consistent evidence of time-invariant preferences. Our last hypothesis deals with the magnitude of the WTPs stated by the respondents in the contingent valuation exercise. We test the hypothesis of equal WTPs between the two time periods (McConnell et al., 1998):

H0 : WTP2010 = WTP2017

=

± 1.96

Tobit WTPGood

The latent variable WTP * follows the classical linear model assumptions. Eq. (10) implies that WTP equals WTP* when WTP * 0 , and WTP = 0 , otherwise. regressors are estimated by Maximum Likelihood. Similar to Loureiro and Loomis (2017) who also test for preferences stability using two independent randomly distributed samples, our first hypothesis is that the indirect utility model coefficients are equal across years:

H0 :

Protest2010 = Protest2017 2010

Critical Value (z, χ2, t)

Tobit WTPMod

Where x denotes the full set of explanatory variables that are used to estimate the regressors by Maximum Likelihood. When the respondents declare their WTP amount, the literature frequently suggests using the Tobit model, as this WTP is zero for a nontrivial fraction of the respondents and is approximately continuously distributed over positive values. The Tobit model represents the observed response (e.g. WTP) in terms of an underlying latent variable:

WTP * =

Protest Behaviour Share of protest responses

Utility Functions Logit WTPBinary

Using a dichotomous (i.e. yes or no) response, Eq. (7) yields the logit model, where the probability of an affirmative answer (Prob (WTP Binary = 1) ) is given by the logistic function:

Prob (WTP Binary = 1) =

Null Hypothesis

Drivers of protest behaviour

where WTP denotes the compensating variation, i.e. the adjustment in income after the environmental change that makes the individual indifferent between the status quo and the improved-quality level. The error terms (ε1, ε0) are assumed to be independent and identically distributed random variables with zero means. When respondents are asked to elicit their preferences for an environmental improvement, their answer is based on the difference between the indirect utility functions:

V = V (1, Y0

Preferences Assumption

(12)

Table 1 summarizes the hypothesis developed in this section as well as the results of the statistical tests that will be presented in the following sections.

Prob (Protestit = 1) = 0 + 1 Incit + 2 Workit + 3 Univit + 4 Linkit + 5 Qualityit + 6 ECit

(15)

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Table 2 Summary statistics for socio-demographic and attitudinal variables in the 2010 and 2017 samples with and without protest responses. Source: Authors´ own elaboration Variable - Description

Full sample

Excluding protest

Mean (s.d.) or Proportion

Mean (s.d.) or Proportion

2010

2017

2010

2017

45.98 (16.02) 0.49 0.32 1,917 (1,201) 653 (424) 0.50 0.76 0.52 0.64

38.38 (16.12) 0.50 0.53 1,825 (1,225) 542 (410) 0.50 0.72 0.43 0.83

44.93 (16.66) 0.46 0.37 1,986 (1,073) 659 (385) 0.52 0.81 0.62 0.70

35.50 (15.66) 0.49 0.58 1,905 (1,301) 574 (452) 0.45 0.77 0.48 0.86

Contingent valuation variables Protest - Binary var. taking value 1 if the respondent rejects the valuation exercise WTPBinary - Binary WTP (Yes = 1) WTPMod - WTP for a moderate status (€/year)

2.00 (0.95) 4.62 (1.97) 4.31 (0.70) 3.54 (0.78) 2.26 (0.77)

1.79 (1.01) 2.82 (1.80) 4.44 (0.74) 3.14 (1.06) 2.54 (1.08)

1.95 (0.93) 4.63 (1.98) 4.38 (0.68) 3.64 (0.77) 2.29 (0.76)

1.75 (0.98) 2.78 (1.68) 4.47 (0.72) 3.34 (1.03) 2.66 (1.05)

0.43 0.48 –

0.51 0.35 –

WTPGood - WTP for a good status (€/year)

–

–

– 0.84 20.11 (23.18) 35.34 (39.60)

– 0.72 18.25 (21.12) 25.59 (28.63)

Socio-demographic and attitudinal variables Age - Respondents’ age (years) Gender - Binary var. taking value 1 for males Univ - Binary var. taking value 1 for bachelor graduates or higher Inc - Household income (€/month) Incpc -Household income per capita (€/month) Work - Binary var. taking value 1 for workers User - Binary var. taking value 1if respondent visited the lagoon in the last two years Link - Binary var. taking value 1if respondents live or work in lagoon surroundings Know – Binary var. takin value 1 if the respondent declares to have a medium or high knowledge about the Mar Menor Trust - Cardinal variable measuring trust in administration from 1 (min.) to 5 (max.) Quality - Cardinal variable measuring perceived water quality from 1 (min.) to 10 (max.). Only Users. AEC, VEC, REC - Indexes ranging from 1 to 5 that indicate the respondents’ environmental commitment in the affective, verbal and real dimensions, respectively.

WTPitm = 0 + 1 Incit + 2 Workit + 3 Univit + 4 Linkit + 5 Qualityit + 6 ECit

(16)

Link (z = 2.573; p-value = 0.010), Know (z=-6.279; p-value = 0.000), Quality (t = 11.716; p-value = 0.000), Trust (t = 3.487; pvalue = 0.005) and the three EC indexes (AEC [t=-2.413; pvalue = 0.016], VEC [t = 6.007; p-value = 0.000], REC [t=-4.089; pvalue = 0.000]). The second sample is younger and more educated. Although average household income has not significantly changed, the lower income per capita in 2017 may represent a small income adjustment in the period 2010-2017. However, considering that the most recent Spanish economic downturn ended in the third quarter of 2013, the decrease in per capita income estimated from the sample is mainly driven by increased household size. Changes in the variables Know, Link, User and Trust provide some first insights about changes in the public preferences towards Mar Menor. The results show that individuals were better informed about the Mar Menor status in 2017 than in 2010, which is consistent with the higher use of the social media and social network services in the period. This more generalized access to information may have influenced public perceptions about the Mar Menor management and therefore preferences. As individuals access more information about the poorer environmental conditions in the lagoon, they may decrease their propensity to visit the lagoon and establish some kind of links such as developing an economic activity or living near the lagoon or owning a second residence in the area. This perception is corroborated by the fact that the assessment of water quality by users significantly decreased between 2017 and 2010. In order to measure how respondents trusted administrations (Trust), they were asked about their perception on public administrations´ awareness and efficacy regarding the Mar Menor management using a five-point Likert’s scale. The respondents gave an average score of 2.00 in 2010 and 1.79 in 2017, showing that trust in administration is diminishing over time. Finally, although we

Where Quality indicates the perceived water quality by Mar Menor users, who were asking to score the coastal lagoon water quality using a ten-point scale (min = 1, max = 10). All the other explanatory variables are the same as in Eqs. (13) and (14). Explanatory variables were chosen based on the expected theoretical drivers of stated WTP. As we model different independent variables the final set of explanatory variables were selected as to assure the best-fit for the three models. Moreover, variables were tested for absence of multicollinearity before selecting the final models reported. 4. Results 4.1. Descriptive statistics for socio-demographic and attitudinal variables Table 2 shows the descriptive statistics of the main variables used in this analysis for 2010 and 2017 samples both with and without protest responses. Means and standard deviations are reported for cardinal variables, while binary variables are reported as proportions. As the sample excluding protest responses yields very similar results for the descriptive statistics, in the following we focus on the full samples’ descriptive statistics to take advantage of their higher statistical powerful. Whereas both samples can be considered equal for variables such as Gender (z=-0.285; p-value = 0.775), Inc (t = 1.077; p-value = 0.282), Work (z = 0.000; p-value = 1.000) and User (z = 1.295; pvalue = 0.195) in 2010 and 2017, other variables present differences for a 0.05 significant level including Age (t = 6.740; p-value = 0.000), Univ (z= -6.029; p-value = 0.000), Incpc (t = 3.806; p-value = 0.000), 6

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Table 3 Binary logit model for protest responses. Source: Authors’ own elaboration Prob (Protest = 1)

Model 1 2010 Coef. (Std. Error)

Model 2 2017 Coef. (Std. Error)

Model 3 Pooled Coef. (Std. Error†)

Model 4 Pooled Coef. (Std. Error†)

Intercept

1.5240 *** (0.5796) −1.6267e-05 (0.0001) −0.0059 (0.2360) −0.3319 (0.2676) −0.7566 *** (0.2772) −0.1077 (0.3163) −0.3412 ** (0.1513)

1.4569 *** (0.3594) 0.0001 (7.8983e-05) 0.4130 ** (0.1877) −0.1719 (0.1934) −0.3170 (0.2051) −0.3086 (0.2268) −0,3275 *** (0.0920)

1.5753*** (0.2994) −6.0720e-05*** (6.2322e-05) 0.2477 (0.1449) −0,2048 *** (0.1488) −0.4914 ** (0.1623) −0.2433 *** (0.1831) −0,3566 *** (0.0764)

1.3769 *** (0.1379) −5.3696e-05 ** (2.6559e-05) 0.2487 (0.1958) −0.2613 *** (0.0892) −0.4741 ** (0.1984) −0.2503 *** (0.0836) −0.3317 *** (0.0068) 0.2117 *** (0.0036)

344 −222.7186 24.7284 0.0004 64.2%

498 −329.6496 30.7863 0.0000 58.6%

842 −555.3087 55.1027 0.0000 60.3%

842 −554.3632 56.9938 0.0000 59.6%

Income Work Univ Link User VEC Year2017 Model statistics N Likelihood value Chi-squared p-value PCP

*, ** and *** indicate P > |z| at 0.1, 0.05 and 0.001 significance levels, respectively. † Robust Standard errors clustered by sampling year. PCP = Percent correctly predicted.

detect significant difference in the EC indexes between samples, there is no a clear direction of the net change in environmental awareness. While AEC and REC increased over the period, VEC is slightly lower in 2017 than in 2010. It seems that, although environmental concerns are more important from an affective point of view (AEC) which is reflected in real habits (REC) in 2017, the respondents´ declared lowered intention to change their behaviour (VEC).

variable. 4.3. Changes in indirect utility functions and WTPs Our third hypothesis on preference stability focuses on respondents´ indirect utility functions. We conduct three LR tests to contrast whether the utility function regressors are equal across years. We test for equality in the vector of parameters in the logit model for WTPBinary. Then, we proceed using the vector of coefficients for the Tobit models employed for modelling WTPMod and WTPGood. As for the previous tests, the unrestricted models correspond to the estimations using single equations and separate data samples, whilst the restricted models are estimated using pooled data. While Table 1 summarizes all the contrasted hypothesis, Tables 4 to 6 report the specific econometric model estimates. In all the models, coefficients present the expected signs according to economic theory. In the following we focus on the hypothesis tests of the utility functions. Table 4 reports the binary logit models for WTPBinary. The LR test 2 = 16.5179; p-value = 0.0207), statistic is above the critical value ( 7,0.05 so we reject the null hypothesis of equality in utility functions, which means that the single equation models using separate data must be used for exploring WTPBinary determinants over time (Model 5 and Model 6). The regressors for Link and User are significant in both models. Coefficients values are lower in 2017 than in 2010, but one has to recall that they are not directly comparable due to the logit transformation used to compute the regressors. At the means of the rest of the regressors, the marginal effects of Link are slightly similar in 2010 (10.73%) and 2017 (13.84%), whereas for User they are notably lower in 2017 (5.18%) than in 2010 (15.61%), meaning that the influence of being user on WTPBinary drastically dropped in 2017. In addition, Model 8 including the dummy Year2017 also corroborates the systematic lower probability of WTPBinary in the second sample. All variables fixed in their mean value, the probability of being willing to pay in 2017 decreases by 10.3%. These outcomes suggest that preferences are instable towards the participation in the hypothetical market. Table 5 reports the Tobit regressions for WTPMod. The LR test

4.2. Changes in protest behaviour Our first hypothesis refers to the stability of protest behaviour for the period 2010-2017. As shown in Table 2, protest responses shares increased from 2010 (43%) to 2017 (51%). This difference is statistically significant at 0.05 level (z = -2.285; p-value = 0.022), rejecting the null hypothesis that protest behaviour is constant in the period. In order to test our second hypothesis about protest behaviour drivers, we conducted a LR test applying the likelihood values reported in Table 3. This table shows four models. Models 1 and 2 correspond to the two unrestricted models using 2010 and 2017 separately, whilst Model 3 represent the restricted model pooling data from both samples. The 2 = 5.881 (p-value = 0.554) below the LR test yields a value of 7,0.05 critical value of 14.067, signalling that there are not significant differences between the restricted and unrestricted models. Therefore, protest behaviour drivers are better described using the former. The probability of giving a protest response to the dichotomous WTP question shows an inverse relation with Inc, Univ, Link, User and VEC, which is coherent with what is expected. To evaluate whether there is a time-specific driver of protest behaviour that cannot be captured by the socio-demographic and attitudinal variables, we estimate Model 4 for the pooled sample including a dummy variable that takes value one for the 2017 observations (Year2017). As expected, the coefficient is significant and indicates systematic higher protest response rates for 2017. Setting each explanatory variable at its sample mean, the probability of protest behaviour increased by 5.27% in 2017. Level and significance of the other explanatory variables does not vary following the inclusion of this 7

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Table 4 Binary logit model for dichotomous willingness to pay (WTPBinary). Source: authors’ own elaboration. WTP Binary = Prob (WTP = 1)

Model 5 2010 Coef. (Std. Error)

Model 6 2017 Coef. (Std. Error)

Model 7 Pooled Coef. (Std. Error†)

Model 8 Pooled Coef. (Std. Error†)

Intercept

−1.9214 * (1.1291) 0.0002 (0.0003) 1.1931 ** (0.4990) 0.8396 (0.6070) 1.0402 * (0.5665) 1.2450** (0.5752) 0.3243 (0.2800)

−1.0881 * (0.5772)

−1.5736 *** (0.5260) 0.0002 ** (6.7328e-05)

−0.9728 (0.6877) 0.0002 *** (0.8800e-05) 0.3170 (0.4908) 0.7804 *** (0.1243)

196 −66.8491 40.7595 0.0000 85.2%

243 −133.1839 21.7318 0.0014 74.9%

Inc Work Univ Link User VEC Year2017 Model statistics N Likelihood value Chi-squared p-value PCP

0.0001 (0.0001) −0.0431 (0.3058) 0.6564 (0.3102) 0.7261 ** (0.3329) 0.2605 ** (0.3656) 0.3089** (0.1511)

0.3423 (0.5047) 0.5815 *** (0.0062) 0.9208 *** (0.1971) 0.5261 (0.3735) 0.3803 *** (0.0433)

0.8530 *** (0.1560) 0.5831 (0.4141) 0.3052*** (0.0051) −0.6833 *** (0.0322)

439 −208.2919 54.5445 0.0000 79.3%

439 −205.0697 60.9889 0.0000 78.8%

*, ** and *** indicate P > |z| at 0.1, 0.05 and 0.001 significance levels, respectively. † Robust Standard errors clustered by sampling year. PCP = Percent correctly predicted.

status are stable, although dropping the significance level to 0.1 would suggest weak preference instability. Note that although Model 10 is overall significant (p-value = 0.0259), practically none of the explanatory variables are significant when taken individually, which indicates that the model is not adequate to represent the 2017 utility function for WTPMod. Yet the pooled Tobit regression including the dummy Year2017 (Model 12) shows that WTPMod is lower in 2017 compared to 2010 due to time-specific factors other than those captured by the explanatory variables. Table 6 reports the Tobit regression models for the dependent variable WTPGood. The LR test indicates that the restricted and unrest2 = 24.523; pricted regressions are statistically different ( 7,0.05 value = 0.001), so we reject the null hypothesis of equal indirect utility functions in 2010 and 2017 valuations. Setting in the mean the significant variables common to Model 13 and Model 14, the results indicate that their partial effects are higher in 2010 (Univ = 16.0690; Link = 11.5150) than in 2017 (Univ = 8.7602; Link = 8.3640), suggesting that respondents have readjusted their utility function. The pooled regression model including the dummy Year2017 also indicates that the WTPGood is lower in 2017 due to other time-specific factors. Our fourth hypothesis related to the stability of the WTP estimates. Table 2 reports the average WTPs and standard deviations for each scenario. WTPs values diverge between years and are lower in 2017 than in 2010. Second central moments are also different between samples, as Levene´s test for equality of variances are rejected for both WTPGood (F = 5.305; p-value = 0.022) and WTPMod (F = 21.021; pvalue = 0.000). The WTPs standard deviations show that respondents´ WTPs were less disperse in 2017 showing a lower variability in the value of water improvements. According to Boyle (1989), it may be explained by the higher knowledge about the scenario being valued (e.g. Mar Menor status, measures and policy context). Assuming that variances are not equal, the Student´s t-test for equality of means yields significant differences for WTPGood (t = 2.990; p-value = 0.004), but not for WTPMod (t = 0.881; p-value = 0.384).

Table 5 Tobit models for willingness to pay for a moderate ecological status (WTPMod). Source: Authors’ own elaboration (WTP Mod )

Model 9 2010 Coef. (Std. Error)

Model 10 2017 Coef. (Std. Error)

Model 11 Pooled Coef. (Std. Error†)

Model 12 Pooled Coef. (Std. Error†)

Intercept

−18.6802 (13.0441) 0.0011 (0.0018) 13.4587 *** (3.64743) 13.3103 *** (4.0038) 8.0350 * (4.5324) 3.2088 (5.7404) 3.2723 (2.6778)

−1.0376 (12.2428) 0.0005 (0.0014) 0.8321 (3.6315) 11.0751 *** (3.7772) 5.7020 (3.9575) 4.7082 (4.6591) 0.0043 (2.6178)

−7.7129 (8.4124) 0.0010 ** (0.0004) 7.1374 (6.3746) 11.4361 *** (0.7954) 6.8568 *** (1.1860) 4.2021 *** (0.7027) 1.0803 (1.4869)

−5.6625 (11.4246) 0.0009 *** (0.0003) 6.7676 (6.1930) 12.5546 *** (1.1171) 6.1184 *** (0.9559) 4.2886 *** (0.5705) 1.2906 (1.6266) −5.1290 *** (0.3279)

196 −778.3867 46.5316 0.0000

243 −876.4807 14.3557 0.0259

439 −1661.7900 47.9510 0.0000

439 −1659.9050 51.7235 0.0000

Inc Work Univ Link User AEC Year2017 Model statistics N Likelihood value Chi-squared p-value

*, ** and *** indicate P > |z| at 0.1, 0.05 and 0.001 significance levels, respectively. † Robust Standard errors grouped by sampling year.

statistic is below the critical value and very close to pass the 0.05 sig2 = 13.842; p-value = 0.054). As a robustness test, nificant level ( 7,0.05 we re-estimated the restricted and unrestricted models omitting the variable AEC, as this variable is not significant in any of the models, and 2 = 12.687; p-value = 0.080). we obtained equivalent results ( 7,0.05 Therefore, we cannot reject the null hypothesis of equal utility functions and conclude that preferences regarding a moderate ecological 8

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4.4. Effects of water quality perception on users’ behaviour

Table 6 Tobit models for willingness to pay for a good ecological status (WTPGood). Source: Authors’ own elaboration (WTP Good)

Model 13 2010 Coef. (Std. Error)

Model 14 2017 Coef. (Std. Error)

Model 15 Pooled Coef. (Std. Error†)

Model 16 Pooled Coef. (Std. Error†)

Intercept

−31.5773 * (16.7482) 0.0026 (0.0031) 22.8598 *** (6.2294) 18.7382 *** (6.8289) 12.8320 * (7.6749) 10.9657 (9.8281) 5.8408 (3.9696)

−17.1668 * (9.9842) 0.0012 (0.0019) −1.8477 (4.8297) 12.8888 ** (5.0474) 11.2480 ** (5.2017) 8.1897 (6.2418) 4.5505 * (2.4383)

−29.1049 *** (9.1494) 0.0025 ** (0.0010) 10.5588 (12.4374) 14.0583 *** (2,4140) 13.0289 *** (1.1998) 8,61829 *** (1.0216) 6.3834 *** (0.9963)

−17.4689 (13.3706) 0.0022 *** (0.0007) −17.4689 (12.0313) 17.2920 *** (3.1240) 11.2837 *** (0,6145) 9.0837 *** (1.3129) 5.0918 *** (0.6804) −12.7618 *** (0.2925)

196 −865.8307 47.9663 0.0000

243 −925.3776 24.4658 0.0004

439 −1803.470 63.4742 0.0000

439 −1798.604 73.8865 0.0000

Inc Work Univ Link User VEC‡ Year2017 Model statistics N Likelihood value Chi-squared p-value

Applying the model specifications presented in Eqs. (15) and (16), we proceed with the sub-samples of users in order to assess whether valuation is driven by the changes in water quality perceptions. These sub-samples contain 260 observations for 2010 and 360 for 2017, of which 159 and 187, corresponds to respondents participating in the hypothetical market, respectively. Table 7 shows the descriptive statistics for the main dependent variables and Table 8 the results of the tested hypothesis. All model estimates are reported in the supplementary file annexed to this paper. Subjective assessment of quality by individuals can have a double impact on WTP. First, the change in the quality assessment changes the relative size of the improvement proposed. The lower the baseline quality the higher the magnitude of the good provided (Qm – Qbaseline increases as Qbaseline decreases). A second pathway relates to the relationship between the quality assessment and the trust in the managing authority responsible for delivering the quality improvements (Kataria et al., 2012). In 2017 the PoMs should have delivered already the moderate quality level (higher than the baseline quality in 2010). However, the subjective assessment of the quality of the waterbody by interviewees had been reduced by 40% (from 4.62 to 2.82, see Table 2). Therefore, in 2017 the quality improvement proposed was greater but also distrust on managing authorities was higher than in 2010. Users´ trust in public administrations yields an average score of 1.97 (s.d. = 0.94) in 2010 and 1.76 (s.d. = 0.76) in 2017 (t = 3.0712; pvalue = 0.001). Therefore, a reduction in the subjective quality can also lead to lower valuation of the good if provision is deemed uncertain or even improbable. Outcomes from this last set of models are all consistent with the findings of the previous section, yielding time-variant shifts in protest response shares, utility functions regarding WTPBinary and WTPGood as well as WTP for a good ecological status. Again, drivers for protest responses and WTPMod did not significantly change, but there are other time-specific factors that are captured for the variable Year2017. Results also show that there is an inverse relation between perceive water quality and protest behaviour, i.e. the higher the perceived quality, the lower the probability of providing a protest response. It seems that the more optimistic the respondent is about the Mar Menor ecological status, the more s/he is willing to participate in the contingent valuation exercise as s/he may believe that the improvement is

*, ** and *** indicate P > |z| at 0.1, 0.05 and 0.001 significance levels, respectively. † Robust Standard errors grouped by sampling year. ‡ WTPGood models behave better with this measure of ecological commitment. The shift is not relevant in the case of the WTPMod models. Table 7 Descriptive statistics for the contingent valuation variables (user sub-samples). Source: Authors’ own elaboration Variables

2010

2017

Share of protest responses WTPBinary (excluding protest responses) WTPMod (s.d.) WTPGood (s.d.)

0.39 0.90 21.11 (23.34) 37.92 (40.74)

0.49 0.75 19.19 (21.52) 27.72 (29.59)

Table 8 Summary of hypothesis and test statistics for sub-samples of users. Source: Authors’ own elaboration Preferences Assumption

Null hypothesis

Critical value (z, χ2, t)

Test value (p-value)

Water quality perception effect on dependent variable†

Protest behaviour Share of protest responses

Protest10 = Protest17

± 1.96

−2.4707 (0.0135) 4.5578 (0.7137)

–

14.8640 (0.0378) 13.3006 (0.0651) 18.1110 (0.0115)

2010 sample not significant 2017 sample willingness to participate in the market increases with perceived quality The pooled model indicates non-significant relation

0.7955 (0.4269) 2.6230 (0.0091)

–

Drivers of protest behaviour

10

=

17

14.0671

Utility functions Logit WTPBinary

10

=

17

14.0671

Tobit WTPMod Tobit WTPGood WTPs values WTPMod

Mod Mod WTP2010 = WTP2017

WTPGood

Good Good WTP2010 = WTP2017

†

1.6492

Protest behaviour increases as quality perception decreases

The single models indicate non-significant relation

–

The evaluation is done using either the independent regressions or the pooled regression based on the LR test results.

9

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probable to occur. Therefore, based on the impact on protest behaviour, we can discard the quality impact pathway and support the idea that baseline quality assessment relates to trust in the managing authority. As far as the actual valuation (WTPMod, WTPGood) results show that the total amount to contribute is controlled by other factors (e.g. sociodemographics, ecological commitment, time-specific and/or institutional factors) rather than water quality perception. Moreover, the fact that the net change in water quality improvement increases from 2010 to 2017 (as the baseline perception decreases and the moderate and improvement quality levels remain constant) while the valuation decreases tends to contradict a scope test, which also suggests that the impact of quality on valuation may be not through neoclassical value pathways, rather indirectly through the trust in the delivery of the improvements.

(Year2017) partially capture this aspect, admitting that it may also capture other confounded temporal factors. For instance, this variable may also capture that citizens visualize the more and more polarized conflict, which explains their changes in preferences; or how they are more pessimistic about the possibility of Mar Menor reaching a good ecological status. We also consider these aspects as public reactions to policy implementation failures. The decrements in WTP obtained in this study may be further explicated by a change in respondents´ behavior towards the Mar Menor environmental issues in the period 2010-2017. Whittington et al. (2017) explain that the notion of status quo and reference conditions determine how individuals experiment changes in environmental quality and how associated changes in welfare have to be addressed. While status quo is where respondents´ actually are, the reference condition is the state from which a shift in well-being (i.e. a gain or a loss) will be evaluated. According to the authors, the analyst has to consider the respondents´ perceived conditions in order to determine the economic valuation approach, i.e., the survey have to assume either a compensating variation or an equivalent variation in welfare and, consistently, either WTP or a WTA question. Disregarding this may lead to biased economic estimates. This study was designed under the assumption that the status quo equals the reference condition of the Mar Menor. This decision was made on the basis that most of the individuals had no experienced the reference condition of a pristine coastal lagoon, but a certain level of diminished environmental quality over a long period of time. Accordingly, a scenario on compensating variation through WTP questions basis was constructed. For the sake of comparison, we kept this assumption in the 2017 survey. However, it may have occurred that the respondents´ perception on status quo and reference conditions had changed over the past years, so that the status quo implied lower environmental quality than the reference condition. In this case, following Whittington et al. (2017), an equivalent variation scheme using willingness to accept questions probably would have resulted in higher economic estimates. Although this is clear from a methodological viewpoint, further research on the Mar Menor case study should reconsider how respondents perceive the status quo and how they give credibility to the improvement scenarios (Kataria et al., 2012) as well as the delivery uncertainty of the environmental outcomes (Glenk and Colombo, 2011). Regarding whether the benefits of improving the Mar Menor outweigh the costs, Martínez-Paz et al. (2013) conducted the cost-benefit analysis of the initial PoMs, using the WTPs value obtained in the first survey (e.g. year 2010), and concluded that the measures could not be described as disproportionately costly if the non-market benefits were accounted for. In addition, although out of the scope of this paper, we conducted a simplified cost-benefit assessment to show the implications of instable preferences that lead to lowered WTP. We follow these assumptions (Martínez-Paz et al., 2013): (i) only additional measures are assessed, assuming that the WTP for additional measures (ΔWTP) equals to WTPGood minus WTPMod, (ii) non-market benefits are estimated aggregating ΔWTPyear over the target population (population over 18 years of age of the Region of Murcia), (iii) additional measures costs include investment, operational and maintenance costs, and are included in the analysis according to the expected implementation date on the SRBD plan (CHS, 2015), (iv) non-market benefits are assumed to start in year 2022, after the implementation of the basic and the more relevant additional measures. As expected, the internal rate of return (IRR), which measures the profitability of the PoMs in relative terms, largely differ depending on the contingent valuation exercise. While the IRR equals to 8.3% if WTP estimates for the 2010 survey are used, this profitability indicator decreases to 2.2% for the 2017 WTP estimates.

5. Discussion This study has proved that the public preferences towards Mar Menor improvement have changed in the period 2010-2017. In a nutshell, we found higher protest response rate, changing indirect utility functions for the highest level of environmental quality (e.g. good ecological status) and decreased WTPs. Given that the survey and the elements of the contingent valuation exercise were not significantly different, how the management context has evolved, which represents a failure in the WFD implementation, helps to explain our findings. Our study has detected significant increases in protest behavior, reflected in the higher rate of protest responses recorded in the 2017 survey. According to the meta-analysis about protest behaviour in stated preference methods by Meyerhoff and Liebe (2010), increments in water bill tends to reduce protest behaviour, while face-to-face interviews and open format questions in contingent valuation studies increase protest responses probability. They also concluded that surveys to individuals living in countries with lower levels of institutional trust lead to higher protest rates. For these reasons, we expected a moderate presence of protest behaviour due to survey design and context. Our study contributes to the better understanding of protest behaviour isolating the effect of management context from the contingent valuation design, as all factors were constant but the survey period. The results reveal that the unachieved Mar Menor improvement is probably one of the most relevant reasons that lead the respondents to rely less on the administration ability to revert the deterioration tendency and, thus, to refuse more the valuation exercise. It also may lead to increased impact of status quo bias on stated preferences economic estimates (Barreiro-Hurle et al., 2018) Environmental economic valuation literature supports that preferences are stable over periods of about 5 years (Lew and Wallmo, 2017). Our results are also similar to those obtained by Brouwer and Bateman (2005) that conduct two identical surveys in a period of 5 years and found a significant decrease in WTPs. In particular, our results confirm that longer periods may result in detecting instable preferences, revealing at the same time that there are institutional concerns that can determine the direction of the shift. Remoundou et al. (2012) argued that the institutional context may impact on the economic estimates due to respondents´ insufficient conviction to the effective implementation of a policy or project. However, they found no statistically significant effects of the institution on welfare estimates when comparing the European Commission and an authority under the supervision of the National State government in Greece. Conversely, we gathered evidences from our case study showing that the valuation outcome might not be neutral to the institutional context, so that ineffective policy performance may decrease the marginal effects of some variables embedded in the individuals’ utility function as well as WTPs over time. Although we have not included specific questions on how individuals trust administration in the econometric models, we could assume that the time-dummy variable

6. Conclusions and policy implications This paper provides evidences on non-stable preferences towards 10

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good ecological status promoted by the Water Framework Directive that challenge the application of economic valuation methods to assess Programme of Measures. According to our results based on the Mar Menor coastal lagoon case study, when public authorities fails to reach environmental objectives and/or measures are not correctly implemented, the public valuation might fail to adhere to rational economic premises. When the non-market benefits estimations largely rely on the public acceptance of measures and institutional trust, as it occurs in stated preference methods, the economic analysis may misinform decision-making if the valuation conditions do not guarantee a robust preference elicitation by respondents, i.e., if the respondents´ declarations are individual judgements rather than strictly economic preferences. The point here is not to invalidate stated preference methods, but to prevent policy-makers and practitioners about the possible consequences of ineffective policy on public behaviour. If they seek for guiding policy based on these methods, they have to carefully consider potential biases emerging from their management performance. In spite of these concerns, the literature has largely supported the suitability of these methods to infer reliable and robust economic estimates (Carlsson et al., 2014; Liebe et al., 2012; Czajkowski et al., 2016; Matthews et al., 2017; Schuhmann et al., 2019). In line with Söderberg and Barton (2014), we believe that under certain circumstances, as the ones described in this paper, the economic estimates resulting from stated preference methods should be used as a qualitative indicator of political support to water policy objectives and measures rather than as reliable monetary measures of quality improvements. If they were assumed as purely quantitative estimates, then it is more likely that programmes of measures would not pass a benefit-cost analysis as long as WTPs decreases over time. When the difference between benefits and cost is not enough, a high margin of error around the benefits will make it difficult to justify the decision of reaching or postponing the WFD objectives. Therefore, complementary quantitative and qualitative policy assessment tools, such as multicriteria approaches and deliberative processes (Perni and Martínez-Paz, 2013; Martin-Ortega et al., 2015), would be highly recommended in order to conduct the Disproportionality Analysis established in the WFD to make exemptions to the good ecological status objective. In addition, uncertainty on cost and benefit estimates can be addressed using probabilistic simulation methods in cost-benefit analysis in order to increase the robustness of the economic evaluation indicators (Martínez-Paz et al., 2014). Despite the fact that our application is related to water quality, we believe that this recommendation goes beyond water policy and must be considered in the implementation of other policies. Additional testing with other policies would be required to confirm our belief. Further research on this field should attempt to examine whether these aggregated conclusions based on between-subject analysis could be confirmed or further explained in a within-subject framework. Probably, a public preference monitoring system, parallel to the water quality monitoring, would help on a better implementation of the WFD economic principles. Whereas from academic perspective the test-retest approach may be the suitable approach to fully control the experiment, it may be not operative from a managerial viewpoint as it would involve additional efforts (i.e. stimulate individuals to participate twice or more in the valuation exercise). In this line, economic valuation approaches that are plausible for the long-term management of water bodies still needs development and testing.

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Declaration of Competing Interest None. Acknowledgements Ángel Perni and José Miguel Martínez-Paz contribution to this paper was partly funded by project 20912/PI/18, financed by “Fundación 11

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