Journal of Environmental Management 131 (2013) 351e362

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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

Forming social capitaldDoes participatory planning foster trust in institutions? Susanne Menzel*, Matthias Buchecker, Tobias Schulz Swiss Federal Institute for Forest, Snow and Landscape Research, Economics and Social Sciences, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 July 2012 Received in revised form 22 August 2013 Accepted 5 October 2013 Available online 7 November 2013

Participatory planning that includes interest groups and municipal representatives has been presented as a means to deal with the increasing difficulty to reach arrangements due to progressively scarce land resources. Under dispute is whether collaborative forms of planning augment social capital or whether they might actually cause the destruction of such a valuable social commodity. In this paper we focus on trust in institution as a specific dimension of social capital because we argue that this is one of the effects the convenors of such participatory planning procedures are most interested in. We pursue a pre-post design and survey advisory group members of five on-going river-related planning processes in Switzerland. Controlling for generalised trust, we investigate how trust in institutions is affected over time by the quality of such processes and the degree of participation they offer. We find that generalised trust is highly correlated with initial levels of trust and so is process quality. Particularly the latter finding challenges the usually assumed direction of causality according to which process quality influences trust building. Additionally, we find a positive (non-significant) effect of process quality on changes in trust, while a higher degree of participation rather seems to hinder trust building. We suppose this indicates that under the conditions of limited time and resources more attention should be paid to how to improve the quality of participatory processes than putting much effort in increasing the degree of participation. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Pre-post design Flood prevention Participatory planning Process quality Stakeholder influence

1. Introduction With the rising likelihood of intense flooding events, climate change has altered the risk profile of land use along rivers. At the same time competition for the use of such land has increased, which has resulted in a higher potential for conflict between the users of river basins (Palmer et al., 2008). Being aware of increased risk of conflict due to climate change, public sector institutions have started to include climate change into existing conflict prevention mechanisms (European Commission, 2009; World Water Assessment Programme, 2009). Social scientists have argued and shown that participatory planning and adaptive management serve as means to manage climate change impacts and similar challenges (Conrad et al., 2011). Participatory planning can also be seen as a long-term approach to help in the creation of social outcomes such as social capital, which enhances the adaptive capacity of a community by enabling it to better deal with new challenges (Adger, 2003). Although this long-term perspective is

* Corresponding author. E-mail address: [email protected] (S. Menzel). 0301-4797/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jenvman.2013.10.010

not very common, social outcomes have gained recognition in the environmental management literature under the label of ‘relational outcomes’ (Pahl-Wostl and Hare, 2004). Conversely, others argue that stakeholder participation may in fact lead to the aggravation of conflicts rather than to their mitigation (see, e.g., Bullock and Hanna, 2008; Leach et al., 2002). In this case, it could be argued that collaborative efforts cause the erosion of social capital. We aim at contributing to the unresolved debate about whether social capital is formed or destroyed through participatory planning by suggesting that both points of view have their merits. We conduct a survey in the context of combined flood protection and river restoration projects in Switzerland in order to investigate what conditions for participation can increase or decrease trust. We concentrate on trust in institutions as the relevant dimension of social capital (Paxton, 2007), because our experience indicates that higher-level public administrative staff can better relate to and is more interested in this dimension than other aspects of social capital such as networks or norms. Although Swiss citizensdsimilar to Scandinaviansdare usually ranked rather high in international comparisons of generalised and institutional trust (Grönlund and Setälä, 2012), the maintenance of confidence in public administration remains a concern in Switzerland (Hoppner, 2009: 1047).

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An effective examination of the changes in the levels of trust would require a pre-post survey design. However the relevant literature generally lacks such research designs. We respond to this gap by comparatively investigating five on-going planning cases at two points in time. Inspired by the literature on the evaluation of participatory planning, we examine the degree of stakeholder participation (¼stakeholder influence) and two quality dimensions of the participatory process: transparency and fairness as well as the appreciation of stakeholder engagement. We focus on these dimensions because they are factors that might influence whether trust is created through participatory planning processes and because the convener of a process can influence these dimensions. Additionally we take into account the (expected) outcomes of the planning cases. 2. Theory 2.1. Social capital, trust, and participatory planning While Bourdieu (1986) and Coleman (1988) are widely credited with introducing the concept of social capital to the social sciences, Putnam et al. (1993) popularised it by disentangling three underlying dimensions: network structures, social norms of reciprocity and trust. Putman’s definition has been very influential in the planning literature (Pretty and Smith, 2004), where the relevance of social capital is of little dispute (Innes and Booher, 1999; Leach and Sabatier, 2005). In this vein, locally existing social capital has been examined as a variable to explain the likelihood of community members engaging in participatory planning (O’Riordan and Ward, 1997; Ohno et al., 2010). A significant part of the literature suggests, however, that social capital is not only an input into but also an output of participatory planning processes and that participation and social capital can reinforce each other (Jones et al., 2012; Menzel and Buchecker, 2013; Wagner and Fernandez-Gimenez, 2008). Most work on the relationship between participatory planning processes and social capital has either aimed at the comprehensive operationalization and measurement of this inherently latent concept (Wagner and Fernandez-Gimenez, 2008) or focussed on interpersonal trust within the participatory planning group as one particular aspect of social capital (Leach and Sabatier, 2005). In this article, we are exclusively interested in how trust in local and regional authorities, managing participatory flood prevention and restoration projects, developed for participants of such local planning groups. We thus focus on trust and do not consider the remaining dimensions of Putnam et al. (1993) conceptualization, i.e., social network structures and norms of reciprocity. We also do not engage in analysing interpersonal trust relationships among the members of the participatory processes as an outcome. While we acknowledge that these are important dimensions of social capital, we decided to concentrate on the dimension we consider most relevant for practical purposes. Paxton (1999) already emphasised institutional trust, i.e., citizens’ support of the political institutions in their jurisdictions,1 as one important dimension of the trust concept. In recent contributions, institutional trust has been identified as an important factor influencing the perceived benefit of policy options derived through local planning (Jones et al., 2012) and there is also evidence showing that conveners of

1 Trust in institutions itself is a multidimensional concept that captures citizens’ confidence in various state institutions: the police, the judiciary, the legislative, the government as well as the administration at different state levels, etc (Grönlund and Setalä, 2012). However, for our purposes, it is sufficient to examine and measure trust in those parts of the local and regional administration that are responsible for flood prevention and restoration projects.

participatory processes are strongly interested in how participants of such planning groups perceive and appraise the role of state actors in these processes (Hoppner, 2009). Putnam et al. (1993) and related authors (Oskarsson, 2010), however, assume a strong positive relationship between institutional trust and generalised trust, i.e., a default belief about other individuals’ goodwill. Interestingly, causal relationships between institutional and generalized trust have been claimed and tested in both directions (Nannestad, 2008). Because not considering generalised trust might result in spurious correlations, we employ it as an important control variable in our model. Considerable empirical work has assessed how levels of social capital relate to individual traits and group characteristics at a single point in time in the context of participatory planning. Yet thus far analyses of how social capital develops, is maintained or destroyed through repeated interactions, have been largely lacking (Wagner and Fernandez-Gimenez, 2008). Still repeated interactive processes are at the very heart of participatory environmental planning processes, which often concern relatively large and complex projects developed over long time spans. Although Carr et al. (1998) maintains that “trust and relationships built during the process as being [the] greatest benefit” (page 767) of participatory planning, social capital has not been sufficiently investigated “under development” (Wagner and Fernandez-Gimenez, 2008; Grönlund and Setälä, 2012; Paxton, 1999). Given that pre-post survey designs2 are rather challenging, this gap is not surprising. Exceptions are the studies by Garmendia and Stagl (2010) and Hoppner et al. (2007). Both studies actually conducted a repeated measurement survey; however the first focused on social learning and not on social capital and also did not attempt to provide an explanatory model. The second assessed how two particular forms of participatory planning events (workshop vs. information event) affected trust, but did not compare participatory processes more comprehensively. The study by Wagner and Fernandez-Gimenez (2009) is most comparable with what we have tried to accomplish here. However, they only mimicked a pre-post survey and asked about ex-post and ex-ante process characteristics and social capital indicators at one ex-post moment in time. We thus aim to advance this kind of research by using an actual pre-post research design and examining causal relations by comparing several participatory planning groups. Our main interest lies in establishing the impact of process characteristics that is, the degree of stakeholder participation and the quality of the participatory process (we call this degree and quality of participation in the remainder of the text), on the formation or destruction of trust in institutions. Wagner and Fernandez-Gimenez (2008: 341) found that “commitment and continuity; understanding, empathy, and respect; transparency; and dependability and predictability are important mechanisms for building social capital in collaborative settings.“ We base our investigation on a subset of concepts that have already been scrutinised as factors relevant for successful or adequate participatory planning. For an overview of these concepts see, e.g., Blackstock et al. (2007), Menzel et al. (2012) and Reed (2008). 2.2. Process features To represent process features we focus on four concepts we expected to contribute to the understanding of how trust levels develop over time. We chose these concepts, because earlier research had made use of them to carve out conditions for successful participatory planning. From the possible list of about 20

2 In a pre-post design data are gathered before and after an event or series of events. We use the term synonymously with repeated measurement.

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criteria (Menzel et al., 2012) we chose those that are relevant in the context and according to earlier work related to changes in trust. They are (i) stakeholder influence, (ii) perceived appreciation of engagement as well as (iii) transparency and fairness of the decision-making process. The fourth criterion, expected outcomes, we included due to the interdependence of relational features (such as trust) and substantial outcomes (such as agreements and implementations). Leach and Sabatier (2005) have brought this criterion to well-recognised attention and it also relates to the dual characteristic of social capital as input and output variable.

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transparency of rules and assumptions for insiders and outsiders (Rauschmayer and Risse, 2005; Wittmer et al., 2006), or transparency on how final decisions will be reached (Sheppard and Meitner, 2005). Fairness is one of the oldest normative criteria behind participation and Renn et al. (1995) picked it as one of their two evaluation criteria for participatory processes (along with competence). The criterion of transparency remains topical in research on participatory processes, e.g., in the context of increasing use of the Internet in participatory processes (Kelly et al., 2012), particularly in justice oriented research (Krutli et al., 2012). Our expectation was that the more a process is considered fair and transparent, the more it would build trust.

2.3. Stakeholder influence 2.6. Expected outcomes Stakeholder influence or the degree of participation is related to early debates on participative planning and to the notion that giving participants ample power to influence the process and its results leads to or even constitutes good participation (Arnstein, 1969). This concept has constantly accompanied the debate about participation and it is still doing so (Neef and Neubert, 2011)3 also with the term of ‘decision space’ (McDermott, 2009). However, the normative conception of influence leading to better participatory processes has not only been questioned theoretically (Sunstein, 2005). With the spreading of participatory approaches and the growing number of empirical studies, research on the motivation of potential participants has shown that only a minority is willing to participate at an intensity level that would justify giving much power to the stakeholders (Nadeau et al., 2007). Also, empirical evidence on the relationship between power of participants and outcomes points towards a moderate level of stakeholder influence leading to more successful processes (Dorcey and McDaniels, 2001). We thus hypothesise that giving power to stakeholders has a positive but weak influence on trust building in institutions.

Wagner and Fernandez-Gimenez (2009) find that perceived success is one of their most important predictors of the formation of social capital in the participatory planning groups they examine. As indicated in the theory section, it has been noted earlier that social capital can be seen as an input as well as an output of participatory planning processes (Beierle, 1998). Leach and Sabatier (2005), in their examination of 76 collaborative watershed management partnerships found that substantial outcomes might affect the social or relational outcomes. We also consider substantial outcomes to influence trust building. As the processes we investigated are still on-going we could not include achieved outcomes in our design. As we believe that participants can certainly anticipate what outcomes a process might produce, we decided to use the concept of expected outcomes as the fourth explanatory concept. We hypothesise that higher levels of expected outcomes are associated with more trust building. 3. Case selection, measurement, and types of analysis 3.1. Case selection and measurement

2.4. Appreciation

The second quality dimension we investigate relates to transparency and fairness. These are classical evaluation criteria for participatory planning and co-management, which have been extensively seen as critical for good participatory planning. Different forms of transparency have been investigated and deemed important: transparency of the process for the public (Rowe and Frewer, 2000),

Following a pre-post design we first identified river cases that were in a stage of being established. We did so by contacting civil servants and experts and interviewing them about projects in the preliminary stages. This way we identified seven cases of river engineering projects. In one case the leader was not willing to collaborate. In the other cases, namely, AA, BUE, HA, REU, RON, TOE we proceeded as described below (for details about the cases see Table A1 in the appendix). In order to measure the concepts laid out in the previous section, we distributed questionnaires among the members of advisory groups of these river related planning processes (referred to as cases or groups in the remainder of the text).4 To allow for repeated measurement, these questionnaires were distributed on two occasions to each advisory group, one in the beginning, shortly after the group had constituted itself (wave 0), and one after about one year of operation and two to three meetings (wave 1). The groups consisted of about 6e 40 people, depending on the size of the project and the number of interests affected. However, not all group members attended every workshop organised by the project management. Subsequently, we received fewer questionnaires than there were people involved in the groups. The projects were at different stages of realisation and unfortunately, for the case RON the process came to a halt and hence the project management declined a second survey round. For each concept, a number of questions were asked (see the list of concepts and corresponding questions in Table A2). Each concept is operationalised by computing an additive mean index from the

3 Neef and Neubert (2011) also emphasise the diversification of participatory approaches and that common typologies of participatory approaches, which suggest degree of participation to be a single scale, are not longer adequate.

4 Because in those advisory groups women are heavily underrepresented, we had decided not to include the gender question in the questionnaire, otherwise the anonymity of the answer would not have been guaranteed.

Already Halvorsen (2001) assessed public participation techniques for comfort, convenience, satisfaction, and deliberation and found that these dimensions positively influence the participants’ perception of the public agency’s trustworthiness. Since Halvorsen’s work, however, these obvious but little-established criteria have received little attention. We resume this work on subjective experiences including the experienced emotional states (such as anger or joy) of the participants, because we think that sensation and mood-related factors are often underrepresented in research despite their importance for decision-making and assessments (Ackerman et al., 2010). Following this earlier approach we included the ideas of ‘effort being appreciated’ and ‘emotional states experienced in the process’ as one of two dimension of process quality. Accordingly, we expected that processes in which participants’ efforts are appreciated, and in which they rarely experience unfavourable emotional states, would generate more trust than processes with the opposite characteristics. 2.5. Transparency and fairness

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respective variables. We treat these indices as having quasi-interval scales. The dependent variable trust in institutions consists of two variables measuring the trust participants expressed in the state administration responsible for the projects at different levels of government (municipality and canton). Three questions about the influence of the participants in the process were used to operationalise stakeholder influence. These were 1) the number of possible occasions to provide influence, 2) possibility to introduce new ideas and 3) success of new ideas. The quality of the process was measured on two dimensions of which the first, appreciation, is the emotional costs (e.g., caused by inefficiencies, lacking appreciation from the project management) arising from the process. The second dimension of process qualitydtransparency and fairnessdwas constructed from two items that measure how clear the impact of the advisory group was on decisions made and the relative influence of the actors. We used five items to construct the index for expected outcomes, which thus includes information about the acceptance of the project both within the advisory group and by the general public, as well as the practicability and progress of the project. Finally, as a control variable, we operationalised generalised trust using one standard question about how much other people can be trusted. In Table A2, we list Cronbach’s alpha for each construct. While these values indicate consistent scales for trust in institutions, stakeholder influence and expected outcomes, this is less the case for the two dimensions of process quality. However, we stick to these scales because they are theoretically well justified. 3.2. Types of analysis Generally, we assume that the measurements of the explanatory variable from the second wave are more appropriate to explain the changes in trust between the two waves than the values from the first wave. That is, we presume that the values respondents gave in the second wave actually represent more realistic assessments of the explanatory variables, since they were measured after the respondents could collect experience in their group. We present a descriptive analysis of trust-levels in the different cases at the first and the second wave and analyses of the bivariate relationship between trust and the single explanatory variables. Both steps are done for the full sample as well as for the sample of subjects with repeated measurement. An intuitively appealing approach to analyse the data would be to compute the change in trust levels between t0 und t1 and to relate this to our explanatory variables measured in t1. However, this type of analysis would result in a loss of information, since it drops the levels of trust at t0 and t1 from the analysis. In addition, it would restrict the sample to be analysed to the subjects with repeated observations. We hence follow a more sophisticated and thus less intuitively comprehensible approach. In such a model5 for panel-data (Singer and Willet, 2003: 80f), for each subject, the dependent variable measures levels of trust at t0 and t1,6 and the explanatory variables are interacted with a

5 Since we restrict ourselves to measuring the observations of the second wave only for the explanatory variables, our panel regression models do not involve time-varying predictors. 6 In a panel regression context, repeated observations for each subject are not independent and this requires to correct the standard errors of an OLS-regression analysis accordingly or to apply models with fixed or random subject specific intercepts (Singer and Willet, 2003). While we reject a ‘fixed effects’ model (for example by introducing dummy variables for n  1 subjects) because it would rule out explanatory variables at the subject level, this choice is not entirely to the discretion of the researcher: if one or more regressors correlate with the random intercept term, the estimates can be subject to serious bias. We hence apply the recommended “Hausman-test” (Cameron and Trivedi, 2005) to check, whether our data admit the application of the random coefficient panel regression model.

wave-indicator (wave-dummy  explanatory variables). Examining the coefficient of this interaction term allows judging, how the explanatory variable affects changes in trust over time.7 In our data, the subjects with repeated observations amount to 28 individuals.8 Given this small sample size, we first apply OLS regression to our data,9 before we test a random intercept panel regression model, in order to validate the results. In an additional step, we apply the panel regression model to the full (pooled10) data set, i.e., all questionnaires from the first and second wave. To be able to include all subjects that participated in the first and second wave, we have to relax the assumption that it is exclusively the level of the independent variables in t1 that can explain the change in trust between t0 and t1. Consequently, in the pooled analysis, we allow for an immediate relationship between the level of trust and the level of the independent variables at each observed point in time.11 For all models, further dummy variables are needed to identify group membership (we chose TOE as the reference group). Dummy variables for groups enable us to test, whether differences in trust levels exist across groups that are not captured by our explanatory variables. Similarly, a dummy variable identifies those subjects that have been members of the project management team, because they are very likely to have differing opinions. 4. Results 4.1. Descriptive results for the dependent variable Below we list boxplots for different samples that show the distribution, mean and median of the dependent variable for each wave and group. The means (thin horizontal lines) of trust levels for the different groups in the beginning and at the second wave of observation show that trust increased only in AA and TOE (see Fig. 1a (all subjects)). If we look at the subjects with repeated measurement (Fig. 1b), we see that trust levels also increased in REU. The statistical tests described in the caption of Fig. 1 show a significant increase in trust only for group AA (Fig. 1a and b) and a significant decrease for group HA (Fig. 1b). Interestingly, there is much variation in the answers for some cases: the means of the second wave of BUE and the first wave of TOE are influenced by some particularly low trust-values. 4.2. Bivariate relationships To examine the relationship between trust and process characteristics, we conduct a merely visual analysis of scatterplots that combine trust in institutions for waves 0 and 1 on the vertical and the explanatory variable at t1 on the horizontal axis. For each wave, a OLS regression line is drawn. While its slope helps to judge the strength of the relationship, we also report the fit of the regression line (R2). Figs. 2a and 3a confirm a positive relationship between the dependent variable (measured at both waves) with expected outcomes (measured at wave 1). While this relationship seems to be rather

7 The coefficients of the original variablesdwe call them ‘main effects’dindicate the relationship between explanatory and dependent variable for the first wave. 8 7 of these 28 did not answer the trust question at the second occasion. In order not to lose additional observations, we assumed that a missing value at the second wave indicates that no changes in trust occurred. 9 Applying different forms of ‘panel-robust’ standard error calculations (Cameron and Trivedi, 2005) does not change the results substantially and therefore we just report the plain OLS results. 10 In order to conduct a regression analysis that employs all individuals, the observations from both waves have to be pooled into one data set. 11 In the pooled OLS model, we also control for those subjects with repeated measurement by including a corresponding dummy variable.

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values indicate that there can be some confidence in the slope parameters. Similar observations can be made for most of the remaining variables and plots, although there are some exceptions. All explanatory variables seem to be correlated positively with trust in institutions at both waves, and in virtually all plots the slope of the regression line is steeper for wave 1. Stakeholder influence, however, is not as strongly and reliably correlated as expected outcomes. Appreciation is the only variable that actually shows a remarkable difference between the full sample (Fig. 2c) and the sample with repeated observations (Fig. 3c): for the former, the relationship with the dependent variable even weakens slightly in wave 1. However, the relationship is strongly positive for the latter sample, although it does not change much over time. For transparency and fairness the plots look actually quite similar to those of stakeholder influence. Additionally, if transparency and fairness is combined together with appreciation into an integrated index of process quality, the slope of this new variable turns out to be quite steep and the R2 rather high, but the relationship does not change much over time. 4.3. Multiple regression analysis

Fig. 1. Index for trust in institutions for the five groups at the first (t0) and second (t1) wave. Panel a: all subjects (N ¼ 141); Panel b: subjects with repeated measurement only (N ¼ 28). We test the differences between the distributions at t0 and t1 (for each group separately) using a t-test for independent samples based on all subjects (Fig. 1a) and a Wilcoxon-test for dependent samples (paired observations across time for subjects with repeated observations; Fig. 1b).

strong (the regression lines are rather steep), in both graphs, the slope becomes considerably steeper for wave 1 and correspondingly, we might suspect that expected outcomes (perceived at wave 1) actually are positively related to an increase in trust in institutions. The R2

In the next step, we test different types of regression models (Table 1) based on different samples and different explanatory variables, as explained in the method section. The results of Model 1, which is based on the pooled sample, strikingly differ from the remaining models in Table 1. The correlation between outcome expectations and trust in institutions is quite strong and significant in wave 0 and it remains similar for wave 1. For the group dummies, we find that relative to the reference group TOE, in the beginning, trust levels were lower in BUE and AA; only for BUE is this difference significant. The interaction of the group-dummies with the wave indicator shows decreases in trust for HA and REU that are not explained by the remaining variables in the model, although none of these interaction terms is significant. An important observation from Table 1 is that the interaction terms, which capture how the explanatory variables influence changes in trust from wave 0 to wave 1, are not significant. This indicates that in general, membership in these groups did not have a strong impact on whether trust in institutions changed over time. Of course, Models 2 to 5 are based on a rather small sample and this might add to the difficulty of observing significant results.12 We therefore also discuss coefficients that are not statistically significant at a conventional level in the remainder of this paper. Model 2 replicates Model 1 with the sample of subjects with repeated measurement. We find that generalised trust is the single statistically significant explanatory variable. Over time, this relationship weakens, indicating that on average, the high expectations are not fully confirmed by the experience in these groups. This decrease, however, is rather small. The results of Model 2 are quite different from those of Model 1 in other aspects. Most remarkably, expected outcomes is not significant and its quantitative impact at wave 0 is zero. Although the impact of this variable over time is now rather strong, it is insignificant. The correlation between stakeholder influence and trust in institutions wave 0 even becomes negative, although not significantly. Consistent with the scatterplots in Figs. 2d and 3d, transparency and fairness is clearly positively (but not significantly) correlated with trust in institutions

12 Employing so many variables results in rather low degrees of freedom for the analysis, however, diagnostic plots reveal that the residuals are largely normally distributed and there are no severe outliers.

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Fig. 2. Bivariate relationship between trust in institutions at t0 and t1 and the independent variables (a to e) at t1 for all subjects (N ¼ 151).

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Fig. 3. Bivariate relationship between trust in institutions at t0 and t1 and the independent variables (a to e) at t1 for the subjects with repeated measurement only (N ¼ 28).

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Table 1 OLS and random intercept panel regression models with levels of trust in t0 and t1 as the dependent variable.

Expected outcomes (M2eM5: t1 values only) Expected outcomes (M2eM5: t1)  wave Stakeholder influence (M2eM5: t1) Stakeholder influence (M2eM5: t1)  wave Appreciation (M2eM5: t1) Appreciation (M2eM5: t1)  wave Transparency and fairness (M2eM5: t1) Transparency and fairness (M2eM5: t1)  wave Compound process quality Process quality  wave AA AA  wave BUE BUE  wave HA HA  wave REU REU  wave Generalised trust Generalised trust  wave Answered both waves Not member of leading group Not member of leading group  wave Wave Constant Subjects N F-test Adj. R2 2*log-likelihood AIC Variance subject-level Error variance

M1

M2

M3

M4

M5

OLS

OLS

RE-ML

OLS

RE-ML

0.838*** (0.298) 0.139 (0.517) 0.215 (0.309) 0.600 (0.491) 0.446 (0.275) 0.267 (0.488) 0.130 (0.307) 0.721 (0.445)

0.216 (0.566) 0.637 (0.801) 0.324 (0.403) 0.266 (0.570) 0.636 (0.479) 0.069 (0.677) 0.634 (0.424) 0.200 (0.600)

0.216 (0.456) 0.637 (0.399) 0.324 (0.324) 0.266 (0.284) 0.636 (0.385) 0.069 (0.337) 0.634* (0.342) 0.200 (0.299)

0.133 (0.542) 0.593 (0.766) 0.293 (0.394) 0.249 (0.558)

0.133 (0.446) 0.593 (0.388) 0.293 (0.325) 0.249 (0.283)

1.220** (0.556) 0.244 (0.786) 0.047 (0.784) 0.096 (1.109) 0.161 (1.184) 0.102 (1.674) 1.228 (0.825) 1.251 (1.167) 0.264 (0.960) 0.213 (1.358) 0.971*** (0.292) 0.146 (0.412)

1.220** (0.457) 0.244 (0.398) 0.047 (0.646) 0.096 (0.562) 0.161 (0.974) 0.102 (0.848) 1.228* (0.679) 1.251* (0.591) 0.264 (0.790) 0.213 (0.688) 0.971*** (0.240) 0.146 (0.209)

1.200 (0.888) 0.350 (1.255) 1.120 (2.729) 0.063 (1.930) 27.000 54.000 5.222*** 0.602

1.200 (0.731) 0.350 (0.636) 1.120 (1.382) 0.063 (1.588) 27.000 54.000

0.681 (0.487) 0.061 (0.904) 1.492** (0.687) 0.610 (1.298) 0.739 (0.526) 0.948 (0.966) 0.413 (0.644) 1.526 (1.072)

0.103 (0.317) 0.165 (0.687) 1.497 (1.050) 2.488 (2.179) 0.558 (1.381) 136.000 127.000 3.497*** 0.270

0.247 (0.861) 0.201 (1.218) 0.085 (1.205) 0.061 (1.705) 1.257 (0.837) 1.236 (1.184) 0.418 (1.006) 0.294 (1.423) 0.932*** (0.302) 0.167 (0.428)

0.247 (0.693) 0.201 (0.607) 0.085 (0.970) 0.061 (0.850) 1.257* (0.674) 1.236* (0.590) 0.418 (0.810) 0.294 (0.000) 0.932*** (0.243) 0.167 (0.213)

0.299 (1.292) 1.298 (0.914) 1.045 (2.783) 0.078 (1.968) 27.000 54.000 4.667*** 0.592

0.299* (0.644) 1.298 (0.735) 1.045 (1.387) 0.078 (1.584) 27.000 54.000

148.147 198.147 0.653 0.944

148.243 195.643 0.691 0.950

***: p < 0.01; **: p < 0.05; *: p < 0.1; standard errors in parentheses.

already at wave 0 and this relationship does not change much over time. For appreciation the negative impact over time disappears. As far as the group effects are concerned, some of the coefficients change signs between Model 1 and Model 2. In order to validate the results in Model 2, we also test a random intercept model (Model 3).13 All coefficients are exactly the same for Model 3 and Model 2. In Model 3, in addition to the impact of generalised trust, transparency and fairness also becomes significant; this is also the case for the static and dynamic effect of group HA: relative to TOE, HA actually started at a significantly higher level of trust in institutions, which was then lost during the process. One interesting observation from Models 2 and 3 is that the two process quality dimensions have very similar effects for both initial level of trust and for change in trust. Correspondingly, Models 4 and 5 contain all the explanatory variables of Models 2 and 3, except we replaced the two process quality dimensions by the integrated index for process quality. The result is clear and consistent across the two statistical models: generalised trust and process quality are the two explanatory concepts that excel in terms of statistical significance and quantitative effect: both are clearly positively correlated with trust in institutions already at wave 0. Hence, people that have a high level of generalised trust and those that have perceived a high level of process quality during the process seem to be more likely to have started with higher levels of trust.

13 For all random-intercept regressions applied here the Hausman-test suggested, the random-intercepts model should be preferred over the fixed-effect model.

5. Discussion The first aim of this study was to investigate whether trust in institutions increases or decreases in participatory planning settings. For our sample in the descriptive analysis, we found differing levels and increases as well as decreases in such trust, depending on the observed planning case. However, the within-group variation is considerable and in the majority of the cases, trust in institutions did not change significantly. The second aim was to find out which characteristics of participatory planning processes contribute to the formation of institutional trust. We cannot conclude much with respect to this question, since the interaction of the explanatory variables with time never becomes significant. If we put aside statistical significance, the interaction coefficients reveal that stakeholder influence has a negative influence on changes in trust while the effect of process quality and expected outcomes is positive. The only instance of a significant change in trust in our regression models could be established for a single group: in HA, the members started with a significantly higher mean level of trust in institutions (as compared to the reference group) and over time, this trust decreased significantly. This effect, however, is specific for group HA and cannot be related to our explanatory concepts. Expectations regarding substantial trust-building effects in these processes can thus not be confirmed with our data. A central insight from our examination is that generalised trust was the most reliable and important predictor of trust in institutions. This result corroborates the findings of Wagner and FernandezGimenez (2009), that the initial level of generalised trust strongly determines the level of trust in institutions in participatory processes. This also corroborates the recent finding by Jones et al. (2012), indicating that at the individual level a higher level of

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generalised trust implies more positive perceptions of the benefits of projects. The results for stakeholder influence are very ambiguous, as the bivariate and the multiple regression analyses point in different directions.14 This implies that stakeholder influence is not a reliable predictor of changes in trust, which corresponds with earlier research on that topic (Sunstein, 2005). For the two dimensions of process quality the results are ambiguous for the pooled sample but quite consistently positive for the regression models that are based on the subjects with repeated measurement. Combining our two process quality variables reveals that the compound variable measures the only process feature that has a statistically significant impact. However, the significant positive effect does not concern the impact on the changes in trust from wave 0 to wave 1 but rather the static relationship. Thus, people, who judge the quality of the participatory process to be high at wave 1, were already at a high trust level at wave 0. We are thus unable to find a significant effect of process quality over time and this challenges the causal argument that high process quality leads to trust increase; it might as well be that people with higher trust in institutions tend to appraise process quality more positively. Finally, our results about output expectations are not very consistent across analyses and samples: while the bivariate and the multiple regression analysis of the pooled sample suggest that expectations formed in the process are strongly related to initial trust (which would lead us to suggest again that the causal relation is from trust to expectations) the regression models for the subjects with repeated observations indicate the reverse: output expectations formed in the process seem to have a quantitatively strong, although statistically insignificant, impact on changes in trust. We mention some caveats of the study before we proceed to the conclusion. For the pooled regression analysis, we have to keep in mind that the results are based on two different samples for the two waves. We have very little information on the reasons why some subjects answered the questionnaire only once but usually participation in the planning group (and thus partly also the decision to participate in the survey) depends on organisational changes in participating organisations (e.g., NGOs). The pooled sample however resembles more closely the one-shot samples employed in earlier studies (Leach and Sabatier, 2005) that have found outcomes to be an important predictor of levels of trust. It is thus interesting to compare the results of the pooled analysis, which suggest a static relationship between outcome expectations and levels of trust with the results based on the small sample, which give at least a weak indication of outcome expectations actually causing trust in institutions to increase. Also the analyses based on the small sample have some shortcomings. It appears likely that individuals who stay in those groups for a long time and respond twice to a questionnaire are rather special. They are probably more motivated, more experienced15 and therefore probably also less prone to revise their trust in institutions due to experiences in a specific participatory group. As a last qualifying remark we note that the relationships uncovered in this paper might actually be quite specific to the Swiss context and to issues of natural resource management. In Switzerland, levels of trust are relatively high and participatory venues at differentdbut particularly the municipaldlevel are quite

14 One has to keep in mind though, that the analysis in Table 1 is based on quite different samples than the scatterplots in Figs. 1-3, because the regression analysis drops the cases with missing values in one of the variables. 15 Additional cross-tabulation analysis with age and experience shows that the people with repeated answers were more experienced on average but not older than the remaining participants in either t0 or t1. We refrained from incorporating experience or age in our models due to the low degree of freedom.

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numerous. Consequently, changes in trust in institutions may not be strongly dependent on conditions in a specific participatory planning group. Our study has revealed that it is difficult to examine causal relationships in a quantitative framework even though it is based on repeated measurement data. This kind of research thus might profit from a strong qualitative component, as demonstrated earlier based on mixed method approaches (see, e.g., Buijs, 2009). This suggestion is also supported by the fact that we have indications for group-specific influences, pointing to events or factors that affected these groups and which we have not captured with our approach. 6. Conclusion Participatory approaches are increasing in importance in environmental planning and policy making. It is thus crucial to better understand how different characteristics of processes, such as the degree of participation and the quality of the participatory process, not only affect the substantial results and outcomes (flood protection in our example) of the projects, but also how these relate to relational outcomes, i.e., social capital and trust in institutions. Overall we conclude from our results that for settings similar to the ones examined in this study, no strong effects of the participatory processes on changes in trust should be expected. We have come to this conclusion by drawing on a quite unique pre-post survey design to evaluate the effects of participation characteristics on trust levels in advisory planning groups. However, our analysis reveals some interesting results with respect to how the design of participatory processes might influence social outcomes. To briefly summarise, we have found that our control variable of generalised trust is strongly correlated with levels of trust in institutions. We have thus discovered strong indications to conclude that high levels of generalised trust are closely connected to trust in institutions, with the latter being largely resistant to experiences in a specific process. In terms of the overall process quality we have found strong correlations with the initial level of trust in institutions, which might not only indicate that process quality positively influences trust building in institutions, but also that high institutional trust levels might lead to a more favourable assessment of process quality. Most strikingly, our results suggest that the level of stakeholder influence is rather negatively related to changes in trust in institutions. Hence, we conclude that extensive stakeholder influence does not per se constitute a ‘good’ participatory process, at least not in Switzerland. Although we did not explicitly link concepts of adaptive capacity and resilience of local communities with the concept of trust in institutions, we think that our study bears some implications for the wider literature on adaptive management and governance in the context of climate change. First, recent literature on social learning considers trust as a sub-category of social learning (Muro and Jeffrey, 2012). Hence, the results presented here seem not only relevant for the social capital literature but also for the social learning discourse. Unlike Muro and Jeffrey, who focus more on organisational aspects of planning groupsdwhich have some overlap with what we call process qualitydto explain social learning outcomes, we have also focused on stakeholder influence/ degree of participation and expected outcomes as explanatory variables. The overall small effects of stakeholder influence and the relatively strong impact of quality aspects in the context of our investigation point to the potential value of investigating the cluster ‘organisational and meeting quality’ including the appreciation of stakeholder engagement for positive social outcomes of collaborative planning. Further research could develop a better understanding of the concept ‘quality of collaborative planning settings’ in different contexts.

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Finally, in the most recent literature on the empirical assessment and comparison of performance of governance regimes in the context of adaptation to climate change, the degree of participation is listed as a performance indicator (Pahl-Wostl et al. 2012). As we have pointed out above, however, our results indicate that the quality of participation (e.g., fairness, appreciation of participants’ inputs, organisational dimensions of participation) is more relevant for positive planning outcomes than the degree of participation, which, as our results suggest, might even cause negative social outcomes. We thus think that those interested in adaptive planning

would be well advised to adopt a more pragmatic understanding of collaborative planning than the one typically employed today. We regard such a pragmatic and quality oriented approach to be particularly important when stakeholder participation is examined as a variable in the evaluation of governance performance, which is typically the case in recent literature. Emphasising more the quality of stakeholder involvement and not merely its scope in formal terms of co-determination would allow a more differentiated assessment of ongoing environmental management and governance activities.

Appendix Appendix I Table A1 Description of participatory planning cases Case e abbreviation

Case e full name

Closes municipality/ies

Key issues

AA

Alte Aarre

Worben, Studen, Dotzigen (Bern)

BUE

Buenz

Hendschicken (Aargau)

HA

Hasliaare

MeiringeneBrienz (Bern)

REU

Reuss

From Reussegg to the border of the canton (Lucerne)

RON TOE

Ron Toessegg

Ebikon (Lucerne) Teufen (Zurich)

Due to another water engineering project the flood probability in a part of the valley has increased. The aim of the project is to win additional space for purposeful flooding of agricultural areas and to create near to nature zones in and close to the river. Concerned interests: agriculture, municipalities, residents, road users, fisher According to most recent calculations municipalities downstream are not safe in case of HQ100 (or 300) flood events. The municipality as the aim to have a more near to nature local stream. Agricultural land is needed to widen the river. Concerned interests: agriculture, municipalities, residents, fisher, recreationists The existing river that has been channelled in the past has not enough capacity to deal with an HQ 300 flood event. The aim is to win additional space for purposeful flooding of agricultural area and to create more near to nature zones close to the river. Concerned interests: agriculture, municipalities, gravel plant, hydro power operator, residents, railway company, road users, fisher The (limited) existing space in the river bed causes floods downstream. The aim of the project is to win more space for the river and to create close to nature areas close to and in the river. Concerned interests: agriculture, municipalities, residents, fisher, recreationists Details not relevant Recreational and conservation interests compete in the water of the river Toess into the Rhine. The aim of the project was to specify and restrict different recreational activities to reduce conflict among them and to create space for animal and plant species. Concerned interests: fishing, camping, shooting, swimming, boating, conservation

Appendix II Table A2 Constructs and items Construct

Items

Response options (5-step ordinal scale if not mentioned otherwise)

Cronbach a

Variance component of first factora

Trust in institutions

“How high do you estimate your trust in the involved municipal authorities?” “How high do you estimate your trust in the involved cantonal authorities?” “The possibilities given to the participants of the advisory group to take influence on the decisions have been...”. “The participants of the advisory group have.” (e)

Values from 0 (no) to 10 (high)

t0: 0.779 t1: 0.765

t0: 82% t1: 81%

From “minimal” to “significant”.

t1: 0.808

t1: 73%

From “.just discussed existing alternatives.” to “.had far reaching opportunities to contribute new ideas” From “had a strong influence on the decision-making” to “had a weak influence on the decision-making” From “applies” to “does not apply”

t1: 0.663

t1: 50%

Stakeholder influence

“The viewpoints brought into the debate by the participants of the advisory group have.” Appreciationb

“I have the impression that my effort was not always appreciated.”(e) “I have the impression that too much comprehension for the preferences and opinions of the other participants was required of me.” (e) “How often did you feel that too much willingness to engage in the discussion was demanded from you?” (e)

Values from 0 (no) to 10 (high)

From “applies” to “does not apply”

From “often” to “seldom”

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Table A2 (continued ) Construct

Transparency and fairnessb

Expected outcomes

General trust

a b

Cronbach a

Variance component of first factora

From “very clear” to “not very clear”

t1: 0.450

t1: 65%

From “applies” to “does not apply”

t1: 0.899

t1: 71%

Items

Response options (5-step ordinal scale if not mentioned otherwise)

“How often did you wish somebody would have been briefer?” (e) “The role of the advisory group for the decisions making of the project team was.” Five-step ordinal scale from “Single participants had significantly more influence than others” to “All participants had about the same influence”. (e) “I think that the planning process has resulted in a broad acceptance of the final decision among the participants.” “The result we have acquired will lead to discontent in the general public.“ (e) “The result we have acquired will be implementable under the given time and budget constraints.” “The result we have acquired is a bogus compromise that eventually will not be implemented.” (e) “Because of the participatory process we have made significant progress in the planning.” “Do you think that most people can generally be trusted or that one cannot be cautious enough if confred with other people?” (e)

From “often” to “seldom”

From “applies” to “does not apply” From “applies” to “does not apply”

From “applies” to “does not apply”

From “applies” to “does not apply” From “one can trust” to “one can’t be cautious enough”.

Variance component of the first factor if a factor analysis is applied to the set of items of the construct. Cronbach a for the integrated “process quality” scale is 0.649, while the variance component of the first factor decreases to 37%.

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