Comparing Public Preferences and Policy Responses to Flood Risks in Europe, Schemes and Mind Maps of Swedish

Download Comparing Public Preferences and Policy Responses to Flood Risks in Europe and more Schemes and Mind Maps Swedish in PDF only on Docsity! DOCTORA L T H E S I S Department of Business Administration, Technology and Social Sciences Division of Social Sciences Essays on the Economic Impacts of Floods and Landslides Elin Spegel ISSN 1402-1544 ISBN 978-91-7790-031-3 (print) ISBN 978-91-7790-032-0 (pdf) Luleå University of Technology 2018 E lin Spegel E ssays on the E conom ic Im pacts of Floods and Landslides Economics i Abstract This thesis consists of an introduction and four self-contained papers addressing aspects that are important for how the negative societal effects of natural disasters can be handled, using floods and landslides in the Gothenburg region in Sweden as examples. In paper I the valuation of the benefits of reducing the negative effects of floods; property damage, traffic disturbances and water supply security, were analysed, using a choice experiment. To understand what motivates individuals to contribute financially, the impact of individual differences in personality traits were also analysed. Data was collected via a web panel, the final sample consisted of 809 responses. The results showed that individuals’ were willing to pay to reduce the societal costs of floods, and that personality traits helped to explain heterogeneity in preferences. People scoring high on the personality trait including empathic and altruistic characteristics increased the individuals’ probability to support policies financially. These results indicate that further investments in flood risk reducing measures should be taken and that public support might increase if policy makers emphasize the welfare gained by society as whole, when designing flood management policies. In paper II the preferences for reducing the negative effects of floods, elicited in paper I, were compared to the preferences of public officials involved in flood risk management. Citizens will have to bear the consequences in the future, of decisions made by governments today. Therefore, it can be argued that decisions should reflect citizens’ preferences. By asking citizens and public officials to respond to identical choice-experiment surveys, it was possible to analyse whether priorities and monetary valuations of the negative effects of floods, namely, property damage, traffic disturbances and water supply security, differed. The overall finding was that public officials and citizens preferences were quite similar, and that both citizens and public officials were willing to pay to reduce the negative effects of floods. The results imply that decisions made within the public sector will likely not differ substantially from citizens’ preferences. In paper III the trade-offs between availability and affordability on the one hand, and incentives to reduce risks in their flood compensation schemes on the other, is studied in in four selected European countries compensation schemes for damages caused by floods, namely Sweden, England, France and the Netherlands. The compensation schemes currently in use in the selected countries vary, both in terms of to what extent they incentivise individuals to reduce flood risk and costs, and how compensation is distributed. This variation may be a result of a combination of differences in distributional value judgements, in the level of consequences of floods and in the importance of economic arguments. Finally, in paper IV individuals’ valuation of reducing the negative impacts of landslides, related to humans health, the environment, infrastructure, and important societal services, were analysed, using the choice experiment method. We also evaluated whether individuals valuations were sensitive to the level of risk of landslides. Data was collected via a web panel, the final data sample consisted of 504 responses. We found that reducing the risk of landslides had an overall a positive impact on individuals’ utility. The results also showed that individuals’ valuations were sensitive to risk: individuals’ willingness to contribute financially to policy programs aimed at reducing the risk of landslides decreased when the probability of landslides decreased. ii iii Table of contents Abstract……………………………………………………………………………………………………………………..i Acknowledgements…………………………………………………………………………………………………..v Preface……………………………………………………………………………………………………………………...1 Paper I: Spegel, E., 2016. Individuals’ preferences for reducing the societal costs of floods. Submitted to the Journal of Economic Behavior & Organization Paper II: Spegel, E., 2016. Valuing the reduction of floods - Public officials’ versus citizens’ preferences. Climate Risk Management 18, pp.1-14 Paper III: Spegel, E., 2017. The political economy of flood compensation schemes - addressing justice and incentives in selected European countries Paper IV: Spegel, E., Ek, K., 2018. Valuing the impacts of landslides: A choice experiment approach Appendix I: Questionnaire collecting data for the analyses in paper I and II Appendix II: Questionnaire collecting data for the analysis in paper IV vi 1 Preface 1. Introduction Climate change will increase the likelihood and magnitudes of extreme weathers in the future and natural hazards like floods and landslides will therefore also likely become more frequent and serious (IPCC, 2014). In terms of exposure, roughly 11 percent of the population of the world, equivalent to approximately 800 million people, are estimated to be exposed to flood risk1, and this number is expected to grow (UNISDR, 2011). Although Sweden historically has been relatively spared from serious consequences of floods (Swedish Civil Contingencies Agency, 2011), larger floods are expected to occur with increased frequency due to climate change, leaving in particular the south western parts of Sweden vulnerable to significant flooding (Government of Sweden, 2007). Floods may occur because of hydrological and geological characteristics. Precipitation can cause urban areas and areas along rivers to flood (Bates et al., 2008), and storms can give rise to flooding along coasts (Brakenridge et al., 2013). A landslide is another naturally occurring phenomenon that may induce floods (Kundzewicz et al., 2012), but it may also be caused by floods, as well as by earthquakes and soil erosion (Glade et al., 2004). Floods and landslides may generate significant negative impacts directly on humans (including loss of life), on properties and infrastructures, and on the environment. Although when it comes to floods, the most extreme with large number of fatalities mainly occur outside Europe (in particular in South Asia), the member states of the European Union are not exempted (Kundzewicz et al., 2013). During the period 1960-2016, almost 5500 people have lost their lives due to flooding in the European Union (The International Disaster Database, 2017). Floods and landslides also generate significant costs in the form of e.g. damaged property and infrastructure (Barredo, 2009). In the European Union, the annual economic losses due to flooding are estimated to 4.6 billion euro (Jongman et al., 2014). The economic losses of landslides are difficult to quantify, landslides often come with other natural hazards such as floods or earthquakes, and the costs arising from landslides are usually not considered separately (Hervás, 2003). The costs of both these extreme weather events and natural hazards are increasing (Lugeri et al., 2010), and the average 1 The estimated risk of an extreme weather event in a specific area is determined by its probability of occurrence and its expected consequences. 2 annual economic costs in Europe are expected to have increased fivefold by 2050 (Jongman et al., 2014). These increasing costs are partly due to changes in weather- and precipitation characteristics, another important contributing factor is the increased number of individuals and assets exposed to floods, not least because of urbanisation and concentration of economic activities in flood-prone areas (Bouwer, 2011; Jongman et al., 2012). The significance of floods and landslides varies greatly between the countries in the European Union. In total about 250 floods has occurred during the period 1960-2016 (The International Disaster Database, 2017), however the flood events has not occurred with the same frequency in all countries. Figure 1 gives a visual representation of the number of floods per 10 000 km2 per country between the years 1980-2016. From the figure, it is visible that the number of flood events per 10 000 km2 is considerably lower in countries such as Sweden, Finland and Latvia, in comparison to e.g. Bulgaria, Slovenia, Poland and Belgium. For landslides, the total number of events is unknown. However, by analysing national databases in the European countries, some dating back to the beginning of 1900, a rough estimate of about 600 000 landslides have been established (Van Den Eeckhaut and Hervás, 2012). It is not only the number of floods that varies between countries, so does also the severity in terms of fatalities and economic losses. In Sweden the consequences are Figure 1. Number of floods per 10 000 km2 per country 1980-2015 (Source:European Environment Agency, 2017) 5 well as the individual’s valuation of the protection of health and safety and material objects. Therefore, policy makers need to know how individuals value the risks and consequences of floods and landslides. By acquiring such knowledge, it is possible to estimate individuals’ valuation of the benefits of reducing the negative effects of floods and landslides. Because the importance of these benefits are usually unknown, the research in this thesis has to a great extent focused on trying to measure how individuals value the benefits of reducing the negative effects of natural hazards, specifically floods and landslides. Individuals’ valuation of measures taken to prevent these negative effects has therefore been analysed in papers I, II and IV, while paper III focuses on measures aimed at enabling society’s capacity to recover after an event. The benefits of reducing floods can be measured by how much individuals are willing to give up, (usually money but could also be measured in some other equivalence), to gain a reduction in flood- or landslide risk, or in the individual’s willingness to accept compensation to endure an increase in the current level of risk. The sum of the estimated individual benefits can then be compared to the associated costs of reducing flood- and landslide risks, and if the sum of benefits exceeds the cost of provision, the implementation of the project is said to be economically efficient (Samuelson, 1954). By making this comparison for the different policy options, policy makers can evaluate which alternative that is associated with the highest expected benefit, given the resources allocated to reducing floods and landslides. It is important to note that it is not the aim of the thesis to evaluate different policy programs aimed at reducing natural hazards, but rather to provide some of the information needed to make such evaluations. 2. Methodological approach and data collection 2.1 Estimating the value of reducing the negative impacts of floods and landslides If possible, we would like to infer the value of e.g. flood risk reduction from observations of behaviour. However many aspects of changed quality in the natural environment, including those imposed by natural hazards, are not reflected in the regular market setting, even though they may have a significant impact on the wellbeing of individuals. Failing to reflect the value of environmental goods and services may imply that resources spent on one type of flood protection measures may have come to better use spent on other types of measures (Bateman et al., 2002). Reasons why the valuation of the flood 6 and landslide risks may not be reflected in market prices are that they possess public good characteristics, which makes it difficult to construct market settings used to infer prices as for private goods (Freeman, 1993). When it comes to larger preventative measures, it is therefore unlikely that the value of preventing floods and landslides could be revealed from markets because of the public nature of measures taken to prevent larger floods and landslides. Inferring individuals’ valuation of goods from regular market settings may also imply that non-use values, and use-values not reflected in the market price, are unaccounted for. When relevant information about the value of a good cannot be observed from individuals’ decisions, it is possible to approximate the value by using existing data inferred from behaviour in other markets (revealed preference techniques) or by using hypothetical question approaches (stated preference techniques), which are intended to simulate the behaviour of individuals in the marketplace. Revealed preference techniques make use of observations of actual consumption decisions in the private market place (Freeman, 1993). For example, one way of trying to infer the value of reducing flood- and landslide risks may be to study the difference in prices between houses affected by different levels of flood- and landslide risks (the hedonic pricing method). In the areas where the studies were conducted, knowledge regarding the levels of flood- and landslide risk was however low. Therefore, the valuation of flood- and landslide risks may not be accurately reflected in property prices. In addition, using such a method could only infer the value individual place on reducing the negative impacts of floods and landslides, related to property damage. Floods and landslides can however come to imply negative consequences to human health and safety, the environment and infrastructure, as well property damage. The hedonic pricing method could therefore come to underestimate the value of reducing floods and landslides as the value individuals’ place of reducing such negative impacts of floods and landslides would not be included. Revealed preference techniques may also underestimate the value of non-priced resources because it fails to include non-use values. For these reasons revealed preference techniques was deemed inappropriate given the purpose of this thesis. Stated preference techniques, on the other hand, present an opportunity to capture the both use- and non-use values of reducing the negative impacts of floods and landslides. Stated preference techniques also make it possible to value hypothetical future scenarios. This feature makes stated preference techniques appropriate for measuring 7 the value of reducing the negative effects of floods and landslides. Valuing the effects of these natural hazards include taking into account the expected impact of future climate change, that is, the effects of something that has not yet been fully experienced, which cannot be inferred from actual behaviour. The main choice of stated preference methods is between the contingent valuation (CV) method and the choice experiment (CE) method (Bateman et al., 2002). The CV method allows for estimating the value of the total environmental good or service, by simply asking individuals to state their maximum willingness to pay, or accept, for a given change in the environmental quality. The CE method (Louviere and Hensher, 1982; Louviere and Woodworth, 1983) describes the environmental good or service as a bundle of characteristics, and their corresponding associated levels. Individuals are then given a set of alternatives, comprised of the characteristics and their associated levels, from which the individual is asked to make a choice. Since it is not the value of the flood or landslide per se that is the study object in the thesis, but rather their different consequences, the CE method is a better-suited method given the purpose; it is possible to estimate the marginal value of a change of each of the characteristics separately. By being able to estimate the marginal value of a change in the risk of different impacts of floods and landslides, rather than a discrete change in the risk of floods and landslides, the CE method may also provide policy makers with more useful information, since management decisions and project appraisals are many times concerned with changes in different types of impacts. The CE method can thus provide policymakers with valuable information, which can be used to help prioritize across different measures available. Therefore, the CE method is preferred. Stated preference methods also have disadvantages, including hypothetical bias. It has been shown that the stated WTPs are usually higher than actual WTPs due to the hypothetical nature of the payment commitment (Hensher, 2009). Ways of reducing the hypothetical bias include budget reminders and cheap-talk scripts (Cummings and Taylor, 1999; List, 2001). The CE method has however been shown to reduce this bias compared to the CV method (Murphy et al., 2004). Using willingness to pay (WTP) rather than willingness to accept (WTA) has also been shown to reduce the magnitude of the hypothetical bias (List and Gallet, 2001). A disadvantage of the CE-method is that there is a risk that respondents may find the choice tasks cognitively burdensome. This can lead to respondents using different 10 2.2 Collecting the data In order to estimate the value of reducing flood- and landslide risks, data on individuals’ preferences regarding these natural hazards needs to be collected. How data is collected has implications for the validity of the results. The data used in papers I, II and IV in this thesis was collected via a web panel containing approximately 90,000 Swedish citizens, from the company Norstat. For the data collected in papers I and II, a total of 3,641 respondents from the panel, residing in one of the six municipalities in the Gothenburg region (Ale, Göteborg, Kungälv, Lilla Edet, Mölndal and Partille) were invited to respond to the survey. For the data collected in paper IV, panel members residing in the municipalities around the Göta River (municipalities of Ale, Gothenburg, Kungälv, Lilla Edet, Trollhättan and Vänersborg) were selected to participate in the survey. In total 1894 panel members were invited to answer the survey. The web panel provides a fast and relatively cost efficient way of gathering data (Svensson, 2010). It also gives easy access to a large sample of respondents in the targeted geographical area. Using web surveys may also, in comparison to mail surveys, increase the quality and accuracy of data because of larger sample sizes, and fewer errors in data entry (which may more frequently appear in mail surveys as the researcher needs to transfer the answers from paper to computer) (Schillewaert and Meulemeester, 2005). Web surveys can also facilitate question branching, skipping patterns, forced answer prompts and audio-visual material. However, it should be noted that using a web panel also has its drawbacks from a statistical point of view. A key issue is that a web panel does not necessarily constitute a probability sample; that is, not every individual in the population was given an equal chance of being selected to the sample since only members of the panel can be selected to answer the survey (Couper, 2000). Members of the panel used in the papers in this thesis are however randomly recruited by telephone. By doing so, rather than having individuals to simply volunteer, as in the case of a web survey, the web panel members are recruited using a probability sampling. Other methods of data sampling that constitute a probability sample include regular mail and telephone. In comparison to a web survey, a web panel should therefore increase the probability of the sample being representative of the population. There may also arise other issues stemming from potential differences in characteristics of those who choose to be a part of the panel compared to the rest of the 11 population (Bethlehem, 2009). Such potential problems include professional survey- takers, differences in Internet usage between the sample and population, and self- selection bias. Differences in Internet usage between the sample and the population should however be a limited problem in Sweden as most individuals have access to Internet and use the Internet frequently.2 The concerns regarding professional or frequent survey-takers are that they are more likely to rush through surveys for the incentives given, thereby leading to less reliable data (Miller, 2007; Garland et al., 2012). The members of the panel used in this thesis are given incentives to answer the questionnaire; these consist of lottery tickets, or gift certificates. There is also concern that professional survey-takers may differ systematically from other respondents in terms of attitudes, opinions and beliefs, and thereby biasing the results (Casdas et al., 2006; Gittelman and Trimarchi, 2009). Existing research on this topic is however inconclusive, as evidence has been found both supporting (Nancarrow and Cartwright 2007; Toepoel et al., 2008; Walker et al., 2009) and not supporting (Chang and Krosnick, 2009; Kruse et al., 2010; Hillygus et al., 2014) problems of involving professional survey- takers. The issue of self-selection bias is a problem that most surveys are likely to suffer from to some extent. When Shillewaert and Meulmeester (2005) compared traditional (mail and telephone) and online data collection methods they found that online and offline data collection methods generated similar results in terms of attitudes and interests, but differed with regard to socio demographic characteristics. The respondents of the questionnaires used in this thesis were on average more educated and had a higher income, relative to the regional population averages. Shillewaert and Meulmeester (2005) found that both online and offline data collection methods generated samples that were different from national population data in terms socio-demographics such age, education and profession. Thus, it is always important to be careful when interpreting and generalising results, irrespective of sampling method. Another issue related to the representativeness, and thereby the validity of the results, is the response rate. In the samples collected and used in this thesis, the response rate was 22 percent, 24 percent and 27 percent (for the samples used in paper I, II and IV). The average response rate for other surveys in the same web panel is 23 percent.3 2 Whether regular telephone interviews can constitute a probability sample has however been questioned lately considering the growing number of households in Sweden without fixed phone lines (Svensson, 2010) 3 Personal communication with Norstat. 12 Thus, the response rates are in line with other surveys using the same web panel. Svensson (2010) states that a 20–30 percent response rate is common when using web panel surveys. The issue of a high non-response rate is thus not specific to these particular surveys, but rather a general issue when gathering data via web panels.4 However, since about 70 percent of the invited participants in the web panel chose to not respond to the questionnaires may have implications for the representativeness of the results, if the responses of those who answered the questionnaire differed from those who declined to answer the survey. There are studies on the topic (e.g. Curtin et al., 2000; Keeter et al., 2000; Merkle and Edelman, 2002) whose results suggest that smaller non-response rates do not necessarily alter survey estimates. However, a high rate of non-responses does at least increase the probability of statistical bias (Tomaskovic-Devey et al., 1994), including non-response bias (Groves, 2006). Non-response bias is a form of selection bias that stems from potential differences in observed and unobserved answers. Regarding the response representativeness of the collected samples used in this thesis, the gender and age distributions in the samples are similar to the regional averages reported by Statistics Sweden (2015, 2016). The respondents are however, on average, more educated then the regional population in general. Similar differences with respect to education have been found in previous Swedish studies (e.g. Ek and Persson, 2014; Svensson, 2010). An underrepresentation of individuals with low education, as well as low income, has been found to be a general issue when using online data collection methods as the mode for administrating surveys (Lindhjem and Navrud, 2011). Although the statistics for income levels are not entirely comparable (as the sample household income is given as a span rather than a specific number), the households in the samples have slightly higher incomes than the corresponding regional average. The regional population average does however lie within one standard deviation of the mean for the respondent sample. The sample collected is thus likely to be more representative to the regional population average in some respects than others are. These differences of course need to be borne in 4 When collecting the data for the thesis, a number of responses rather than a specific response rate, was set as a goal the for data collections. All members of the panels in the targeted geographical areas were then invited to answer the questionnaires. Since the panels for the targeted geographical areas of data collection had a relatively large number of panel members, a smaller percentage of panel members needed to respond to the reach the goal. In comparison, if the panel would have had a smaller number of panel members in the selected municipalities, a larger percentage of the individuals included in the panel would have needed to respond to reach the desired number of of responses, thereby the response rate would have increased. Thus, the size of the panel, given the set target of a certain number of responses, influenced the response rates. Response representativeness has been argued to be more important than response rates (Cook et al., 2000). 15 willingness to pay for reducing the negative effects of floods in the Gothenburg region. Furthermore, households in the region are shown to prioritise a reduction of water supply security above property damage due to floods, while traffic disturbances caused by floods are considered the least pressing of the three impacts. These results imply that policymakers should focus on flood-risk-mitigating measures that aim at reducing the negative effects of floods, and should prioritise addressing the issue of water supply security. Overall, although the respondents prioritise water supply security over property damage and traffic disturbances, in that order, heterogeneity in individuals’ preferences is found. The results show that personality traits to some extent help to explain heterogeneity in individuals’ preferences. It is found that the empathic, altruistic nature of the personality trait agreeableness increases the individual´s probability of supporting policies aimed at reducing the negative impacts of floods. This indicates that public support might increase if policy makers emphasize the welfare gained by society as whole, and not only the welfare gained at the individual level, when designing flood management policies. Paper II: Valuing the reduction of floods - Public officials’ versus citizens’ preferences The purpose of this paper is to analyse potential differences in citizens and officials valuation of reducing the negative effects of floods. Decisions made by governments today will affect the costs associated with floods faced by citizens tomorrow. Since citizens will have to bear the consequences of these decisions, it is reasonable that the decisions reflect citizens’ preferences. By having citizens and public officials respond to identical CE surveys, it was possible to analyse whether and, if so, how priorities and monetary valuation differed in respect of the different negative effects of floods, attributed by Property damage, Traffic disturbances and Water supply security. In order to conduct the analysis, a second sample consisting of 102 public officials, was added to the previously collected sample of citizens. The officials’ data was collected by sending out an email with a link to the same web questionnaire as the citizens answered. The group of public officials consisted of employees at the municipalities of Ale, Göteborg, Kungälv, Lilla Edet, Mölndal, and Partille and at the regional authority, the County Administrative Board of Västra Götaland. The group of public officials worked within departments responsible for urban planning, emergency management, water 16 production, environmental management and transport (including maintaining and planning for road networks and public transportation systems). Of the 421 public officials invited to answer the questionnaire, the final sample group consisted of 102 individuals, which corresponds to a response rate of 24%. When comparing the sample of public officials with the sample of citizens, public officials were more likely to have a university degree, they were younger, and they had higher income levels. There were also more women than in the citizen sample group. The overall finding of this paper is that public officials and citizens preferences seem to converge. In general, the estimated marginal WTP suggests that floods have a negative impact on the utility of both groups: both citizens and public officials were willing to pay to reduce such impacts. This implies that both public officials and citizens would benefit from further investments in flood-risk-reducing measures being made in order to limit the future costs caused by floods in the Gothenburg region. Furthermore, public officials generally seemed to make similar prioritisations as citizens, with water supply security being considered a more important matter to address than property damage, which was in turn considered more important that traffic disturbances. Further, public officials and citizens’ WTP for increasing the water supply security and decreasing property damage did not differ extensively. Given public officials’ central role in the decision-making process regarding flood risk management, and despite the rather limited of use of CBA, decisions made within the public sector will most likely not differ substantially from citizens’ preferences. The current study also found a statistically significant relationship between trust in government institutions and citizens’ WTP to reduce the negative effects of flood events. The citizen sample’s level of trust is rather low (averaging at around 20%) when it comes to believing that government institutions live up to their responsibility to handle the negative effects of floods. Given this rather low level of trust, it seems important for public officials to gain further legitimacy of their decisions relating to flood risk-management policies, but also their ability to raise financial resources to implement measures to reduce the negative effects of floods. The results further suggest that respondents – whether public officials or citizens – who reported being concerned about climate change were more likely to opt for an alternative that implied reducing the negative impacts of floods. About 60% of the citizen sample and 80% of the public official respondents reported that they were concerned 17 about climate change. The 20% difference could potentially be a result of a knowledge gap between public officials and citizens. Thus, there may be scope for public officials to gain further support for flood risk-management policies by increasing citizens’ knowledge of the negative effects of climate change in the region. Paper III: The political economy of compensation systems for floods - addressing justice and incentives in selected European countries The objective of this paper is to study how different European countries have addressed the trade-offs between availability and affordability on the one hand, and incentives to reduce risks in their flood compensation schemes on the other. As a response to the growing negative consequences of floods, the European Commission wishes to improve availability and affordability of flood insurance, but also to use insurance to promote risk reduction in order to reduce costs. Distributional aspects as well as economical aspects are thus emphasised by the EU. A compensation system where risk is born collectively can increase both availability and affordability, however, such a system may not necessarily encourage individuals to reduce risk. This suggests that pursuing availability and affordability in flood compensation schemes will typically come at a cost in term of weak incentives to promote risk-reducing behaviour. When designing compensation systems for floods, policy makers are left with the task of handling the trade-offs between the distributional aspects (availability, affordability), and economical aspects (incentives to reduce flood risk and related costs). How to weigh distributional aspects against the impact of incentives on costs may be dependent on what is considered a fair, equitable or a just distribution of compensation in society, such as equal compensation (Rawlsian) or equal opportunity to compensation (egalitarian), or simply whatever distribution that might arise from the market mechanism (libertarian). The costs and benefits from promoting availability and affordability at the expense of risk reduction is also affected by the individuals’ ability to protect themselves against floods, which in turn is a function of the level of consequences of floods in the particular country. The analysis is conducted by studying compensation systems for damages caused by floods in four selected European countries, namely Sweden, England, France and the Netherlands. The empirical material discussed in this paper is both of quantitative and qualitative nature. Qualitative data collection methods applied encompasses desk research including analysis of policy documents, legal texts and literature, including articles and books. 20 on average, more educated then the regional population in general. Although the statistics for income levels are not entirely comparable (as the sample household income is given as a span rather than a specific number), the households in the sample seem to have somewhat higher incomes than the regional average. The regional population average does however lie within one standard deviation of the mean for the respondent sample. This overrepresentation of individuals with higher incomes and education levels should be kept in mind when the results are interpreted. Our main finding is that reducing the risk of landslides has overall a positive impact on individuals’ utility. Individuals are further willing to pay to reduce the negative impacts of landslides. The results also demonstrate that in general, individuals prioritize preventing negative consequences for human health and safety over maintaining societal services and environmental status. Preventing damages to infrastructure is the least concerning area according to these results. The results imply that policymakers should prioritize measures aimed at preventing individuals from being affected (hurt or even killed), over maintaining societal services and environmental status, and measures targeting infrastructural damages when they decide on which measures to implement to prevent landslides. This ordering does however differ from the one made by the public authorities since they do not weight different impacts of landslides of the same scale differently, whilst our results indicate that the respondents do so. An important policy implication of the difference in prioritization is that under the current risk assessment made by the government, investment decisions will not correspond to the preferences of the citizens, thus the outcome will be socially inefficient. Finally, the results, concerning individuals risk preferences, show a tendency to choose a less “safe” option when the probability of the event decreased, that is, they were more inclined to choose an option which implied no financial contribution to policy programs aimed at reducing the risk of landslides when the probability of a landslide event was low (1 percent). 4. Main findings In this section, the main findings of the analysis carried out in the four appended papers will be presented. The findings from these papers include how the negative impacts of floods and landslides affect the wellbeing of people. The results from the appended papers also highlight the complexity of designing policies. It is important that policy decisions 21 correspond to individuals’ preferences to reach a resource efficient outcome, therefore knowledge of how different policy measures affect individuals’ wellbeing is needed. However, what constitutes a resource efficient outcome may have distributional effects that are considered to be more or less desirable, depending on what is considered just or fair. The severity of the consequences of floods and landslides may further limit the policy options available, as some policies, such as differentiated premiums, can only function properly under certain conditions. In papers I, II and IV, individuals’ valuation of reducing the negative impacts of floods and landslides have been studied. The general finding from these three papers is that the negative impacts of floods and landslides affect the wellbeing of people; there is a willingness to pay for reducing the expected negative effects of such natural hazards. These results imply that policymakers should invest in flood- and landslide risk mitigating measures that aim at reducing the negative effects of floods and landslides. The negative impacts of floods and landslides that were studied in paper I, II and IV, can broadly be sorted into four categories; the impact on the health and safety of humans, damages to infrastructure, damages to the environment, and damages to property. In all three papers individuals were found to consider the health and safety of humans to be the most important issue to address. Property damage and pollution to the environment were also important, however less so than the health and safety of humans. Finally, although individuals also value reductions in the negative impacts on infrastructure, it is considered the least pressing matter. The implications of these findings suggest that policy makers, when allocating resources to address the negative impacts of floods and landslides, should prioritize measures that aim at reducing risks towards the health and safety of humans. Paper II (and to some extent IV) deals with the representation of citizens’ valuation in policy decisions; the potential differences in prioritizations made by citizens and public officials regarding the management of the impacts of floods and landslides were analysed. The results are not unanimous. In paper II, the preferences of citizens were found to be similar to the preferences of public officials, whilst in paper IV the prioritisation of the impacts made by citizens and officials were found to differ. The implications of the difference in valuation are that studying the preferences of citizens to help aid in public decisions is important, since investment decisions made in accordance with the prioritisation of public officials may not necessarily correspond to 22 that of citizens. Policy decisions made without information regarding individuals preferences may therefore lead to socially inefficient outcomes. It is however possible that, at least part of the differences in valuation, revealed in paper IV, stems from the preferences of citizens and public officials not being measured by the same method, in comparison to paper II. In paper II, preferences were measured via identical choice-experiment surveys. In paper IV, the prioritisations made by citizens, estimated via a CE, was compared to the prioritisations of impacts, made by public authorities in their risk assessments of landslide risks (see Kiilsgaard et al., 2015). Part of the explanation may also lie in the fact that the risk assessment criteria’s used by the government does not only reflect the preferences of policy makers, but also expert opinions, as large parts of the risk assessment was done by, or assisted by experts. The preferences of experts have been found to differ from the preferences of citizens (Roger, 2013). Studying individuals’ preferences to help guide policy decisions is important, but it is also important to consider the distributional aspects associated with natural hazards, especially in light of climate change, as the expected increase in costs due to natural hazards could come to imply large costs for societies and their citizens. Policies such as differentiated premiums that are potentially able to reduce these costs, by increasing incentives to reduce risk for private actors, may imply low-income individuals living in areas with a high level of flood risk will not afford insurance. A compensation scheme, within which individuals instead bear the risks collectively, may not encourage individuals to reduce their risk exposure but it can increase the availability and the affordability of insurance also to high-risk and low-income individuals. These trade- offs between distributional and economical aspects was analysed, in paper III by studying the compensation schemes for damages caused by floods in four selected European countries, namely Sweden, England, France and the Netherlands. The variations in the design of compensation schemes that was found, are likely the result both of differences in distributional value judgements, the type and level of flood risk and economic arguments. The potential of incentive based schemes to reduce costs depends on the type and level of flood risk; they should be high enough so that the benefits from gaining information (about the risk) outweighs the transaction costs of achieving such knowledge, whilst being of the type so that the individual has the ability to reduce flood risk. Having the opportunity to use policy instruments such as 25 Government of Sweden, 2007. Official Reports of the Swedish Government: Sweden Facing Climate Change – Threats and Opportunities. 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Individuals’ preferences for reducing the societal costs of floods Elin Spegel Luleå University of Technology Economics Unit 971 87 Luleå Sweden E-mail: elin.spegel@ltu.se Abstract Due to climate change, damages caused by floods will increase in the future; thus, it is important to invest further in measures aimed at reducing the negative effects of floods. To prioritise among such measures and make resource-efficient decisions, knowledge of individuals’ preferences is needed. In order to analyse individuals’ preferences in this regard in the Gothenburg region, a choice experiment was conducted that related floods to water supply security, property damage and traffic disturbances. To gain deeper insight into how more general behavioural tendencies affect individuals’ preferences for floods, respondents’ personality traits were elicited. The results show that individuals’ utility is negatively affected by floods, and that they are willing to pay to reduce the societal costs of floods in the Gothenburg region. Furthermore, a reduction in the societal costs of floods related to water supply security is given priority over negative flood impacts on property and traffic. The results further show that personality traits help to explain heterogeneity in individuals’ preferences. It is found that the empathic, altruistic nature of the personality trait agreeableness increases the individual´s probability of supporting policies aimed at reducing the negative impacts of floods. This indicates that public support might increase if policy makers emphasize the welfare gained by society as whole, and not only the welfare gained at the individual level, when designing flood management policies. 3 econometric models employed. Section 3 provides the model results. Finally, section 4 discusses the main findings and their policy implications. 2. Case study and survey design The CE method is well established for estimating the value of changes in the characteristics of environmental goods and qualities. This method can value hypothetical future scenarios and allow for estimating the marginal value of change in each characteristic the CE includes. By using the CE method, this study aims at estimating the benefits derived from reducing the negative effects of floods. By being able to measure the marginal value of change rather than a discrete change in the aggregate environmental good, it is possible to estimate the relative importance of reducing the different characteristics of negative flood impacts. This provides policymakers with valuable information which can be used to help prioritise actions in this regard. 2.1 Description of the case study and attribute selection The flood impacts included in the CE are (i) Water supply security, (ii) Property damage, and (iii) Traffic disturbances. In addition, a (iv) Cost attribute, defined as a municipal annual fee paid for ten years by all households, represents a measure to fund a reduction of the impacts of flooding in the region. The choice to focus on the valuation of the effects of floods – rather than the valuation of a specific policy programme aimed at reducing floods – is based on the fact that individuals may choose an alternative not only for its anticipated outcomes in terms of the expected impacts of floods, but also as a result of a policy programme’s perceived qualities, such as its effectiveness, fairness and sustainability (Glenk and Fischer, 2010). It can be difficult, therefore, to distinguish between values and preferences for outcomes in terms of flood impacts, on the one hand, and values and preferences for policy programmes, on the other. For example, many policy programmes – flood risk management included (green roofs, wetlands, floating houses, etc.) may be valued according to their aesthetic and biological properties as well and, therefore, may capture the valuation of something other than flood impacts. The Gothenburg region lies in the south-western parts of Sweden. The region is defined here by its six municipalities, namely Ale, Göteborg, Kungälv, Lilla Edet, Mölndal, 4 and Partille, which have a combined population of about 760,000. Common features for the region are a high degree of annual precipitation (900 mm/year), but also shorter periods with heavy rainfall (Persson et al., 2011). Furthermore, strong westerly winds can give rise to high water levels along the coast, which can cause damage to the many residential homes and leisure activity facilities located close to rivers and shorelines (Källerfeldt et al., 2012). In the past decade alone, the region suffered floods in 2002, 2006, 2007 and 2011, during which primarily roads, railways and buildings incurred damage (Källerfeldt et al., 2012). In the years ahead, flood risks in the Gothenburg region are expected to increase beyond the national average. In addition, a preliminary assessment conducted in terms of the European Union Floods Directive (Swedish Civil Contingencies Agency, 2011) identified several cities in the region as being significantly vulnerable to fluvial flood risks. Indeed, in relation to climate change in general, flood risk stands out as one of the most significant issues that have the potential to affect society as a whole (Källerfeldt et al., 2012). A recent climate change analysis of the region by Persson et al. (2011) estimates that its annual precipitation will increase by between 10% and 30%. The same study predicts more frequent heavy rainfall, and an approximately 50–60% augmentation of the winter discharge into watercourses. Moreover, a 65–80 cm rise in sea levels – although partly offset by land elevation – is estimated along the coast. The selection of the aforementioned attributes, property damage, traffic disturbances and water supply security, is motivated because they are expected to imply significant costs to society. Furthermore, as highlighted by Källerfeldt et al. (2012) in their analysis of the societal consequences of climate change in the region, roads, railways, buildings and the drinking water supply are particularly vulnerable to future flood risks. Impacts on roads and railways include roads becoming flooded or washed away which in turn imply interruptions in traffic and potential traffic accidents. River front properties will also suffer from increased damages as a result of higher water levels, whilst other properties are more likely to become flooded more frequently due to surface water and overloaded sewerage systems (Källerfeldt et al. 2012). The drinking water supply is also expected to be affected by increased flooding (Swedish Water & Wastewater Association, 2007; Källerfeldt et al., 2012). The Göta river serves as the primary drinking water supply in the region, providing 700,000 individuals 5 with drinking water. Increased precipitation, heavy rainfalls and higher discharge levels in the watercourses implies risk of leakage from polluted areas and farmlands in to the rivers. Because the drinking water supply mainly consists of surface water, it is particularly vulnerable to microbiological contamination. In 2014, alternative drinking water supply sources other than Göta river were used for about 90 days (divided on 75 different occasions), due to microbiological impacts on the water supply security. However as many as 150 days per year has also occurred historically (Water Quality Association of the Göta river, 2014). These alternative drinking water supply sources are finite, and there have already today been occasions where these alternative sources have been used to their full extent. If nothing is done to resolve the issue, climate change could imply deteriorating water supply security implying drinking water shortages in the region (Källerfeldt et al., 2012). 2.2 Experimental design An efficient design was used to construct the choice sets. Priors for each attribute were estimated from the pre-tests and used to create the efficient design using the Ngene software. Unlike an orthogonal design, an efficient design does not only try minimise correlation between attribute levels, but also aims at generating model estimates with as small as possible standard (Rose and Bliemer, 2013). Efficient deigns also has the advantage of requiring smaller sample sizes and enabling smaller designs in terms of the number of choice sets. This implies that the efficient design, by using prior information, is able to avoid dominant alternatives, and achieve an appropriate level of utility balance among the alternatives in each choice situation. Because information (priors) is available with some degree of uncertainty, a Bayesian D-error approach, assuming normally distributed priors, was used. The final experimental design consisted of eight choice sets, which were presented to each respondent. Each choice set was comprised of three alternatives, including a Status quo alternative. The alternatives were presented to the individuals, who were asked to choose their most preferred alternative. 8 to the respondents. Both pre-tests resulted in small adjustments in the formulation of the questions and the text in the questionnaire. The questionnaire consisted of three parts. The first part was concentrated around the respondent’s previous experiences, knowledge, and concern regarding flood events. Respondents were for instance asked if they had been affected by a flood, if they knew of any measures being taken to reduce the negative effects of floods, and how they thought responsibility should be divided between local and national government agencies and individuals, and whether they considered these institutions to live up to their responsibilities. The second part included the CE. Respondents were provided with information on the consequences of floods on water supply security, property damage, and traffic disturbances in the region today, and the expected change in the future due to climate change. The respondents were informed that it is possible to reduce the negative consequences of floods, by taking different measures, but that it would imply costs which could be financed through an annual municipal fee paid (the coming ten years) by all households in the region. 9 Figure 1 Example of a choice set Choice set 1 Which of the following alternatives, A, B or C do you prefer? The alternatives describe a situation in 20 years’ time. Alternative C describes a situation where no measures against floods are taken, and implies a larger impact of floods than today. Choose one of the alternatives by ticking one of the boxes at the bottom of the Table. Alternative A – Measures taken Alternative B – Measures taken Alternative C – No measures Water supply security Number of days a year that the water from Göta river cannot be used for producing drinking water due to contamination. 100 days 100 days 200 days Damaged property Number of flood damaged properties a year. 10 properties 100 properties 100 properties Traffic disturbances Number of days a year with flooded, eroded or washed away roads and railways. 8 days 2 days 8 days Fee SEK a year to be paid for ten years. 500 SEK 100 SEK 0 SEK My choice [ ] [ ] [ ] The third part of the questionnaire collected information about the respondents’ socio- economic and demographic characteristics and personality traits. Each respondent’s personality traits were elicited by using the ten-statement BFI (BFI-10). 2.4 Personality traits The BFI-10 is a survey-based instrument consisting of ten statements constructed to measure the ‘Big Five’ dimensions of personality: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness (Rammstedt and John, 2007). Each dimension is measured by two statements, as depicted in Table 2. Respondents were 10 asked to rate the extent to which each of the ten statements described their personality on a scale ranging between 1 (Disagree strongly) and 5 (Agree strongly). The BFI was first constructed in the late 1980s by John et al. (1991), and consisted of 44 short-phrase questions (BFI-44). The BFI-44 has been found to accurately measure the ‘Big Five’ dimensions of personality across different cultures (McCrae and Allik, 2002; Schmitt et al., 2007). In order to provide a BFI measure in survey contexts where the respondent has limited time, the BFI-44 has been abbreviated to the BFI-10. The BFI-10 has been tested in several English- and German-speaking samples, and the results show that the BFI-10, relative to the BFI-44, derives reliable and valid results (Rammstedt and John, 2007). Thus, because the BFI-10 has been shown to accurately capture the five dimensions of personality whilst being quick and easy to answer, this shorter version has been used in surveys within the fields of psychology (Back et al., 2010; Erdle and Rushton, 2011) and economics e.g. Solino and Farizo (2014). Although the BFI-10 has not yet been formally validated in the Swedish setting, Zakrisson (2010) has validated the BFI-44 for that setting. The BFI-44 has also previously been used in studies conducted in Sweden in the field of psychology (Ekhammar et al., 2004; Lamb et al., 2002). Given that the BFI-10 has been shown to capture accurately the five dimensions of personality measured by the BFI-44, it is reasonable to assume that the BFI-10 should also be a meaningful measure of personality in the Swedish setting. The current study used the ten statements of the original BFI-10 with some minor revisions1. The English translation of the BFI-10 by Claesson et al. (2001), was used by Zakrisson (2010), in his validation of the BFI-44 mentioned above, and is presented in Table 2, as follows. 1 Six out of the ten statements were identical. Three out of the ten statements had only slightly different wording e.g. “I tend to see fault in others” instead I tend to find fault in others, or very close synonyms. The only case where the phrasing differs slightly is for the attribute agreeable where Claesson, Persson and Akrami (2001) phrased the question as being “generally trusting”, whereas the question in this study was phrased as “generally trusting people”. 13 the expectation of a positive relationship between Conscientiousness and individuals’ willingness to contribute towards reducing the negative effects of floods. The personality trait Openness is attributed to intelligence, imagination, an aesthetic sense, and being receptive to new ideas and changes. Thus, individuals characterised by Openness tend to be intellectually curious, open to new experiences, and appreciative of art (Costa and McCrae, 1989; Milfont and Sibley, 2012). Openness has also been related to a greater concern for others as well as higher levels of empathy and cognitive ability (Schwarts, 1992). Moreover, Openness has, like Agreeableness and Conscientiousness, been shown to be positively related to Environmental engagement (Hirsh 2010; Hirsh and Dolderman 2007; Milfont and Sibley 2012). Solino and Farizo, (2014) also found a positive relationship between high scores on Openness and the probability that a respondent reported being willing to contribute monetarily to a new environmental programme on forest management. However, unlike Agreeableness and Conscientiousness, the personality trait Openness has been found to be positively correlated to individuals willingness to undertake risky behaviour (Vollrath and Torgersen, 2002; Romero et al., 2012). Prior research does thus not give a unanimous direction of the expected relationship between personality traits and preferences for reducing the negative effects of floods. The relationship between environmental engagement and Openness potentially points towards a positive effect on individuals’ willingness to pay to reduce the societal costs of floods. However, since Openness has also been found to related to risky behaviour, it could imply that individuals inherent of this trait underestimates the risk and the negative consequences of floods, and thereby could be less willing to contribute towards reducing future costs of floods. Neuroticism encompasses traits such as anxiety, irritability, vulnerability and insecurity; individuals possessing this personality trait, therefore, tend to be nervous and to worry (Digman, 1990; John et al., 2008). Hirsh (2010) hypothesised that Neuroticism was associated with Environmental engagement due to neurotic individuals experiencing anxiety over the consequences of environmental degradation. However, based on empirical findings in previous literature, the link between Neuroticism and Environmental engagement is ambiguous. Milfont and Sibley (2012), for example, conducted three studies and found that Neuroticism was both positively (Study 2) and negatively (Studies 1 and 3) related to Environmental engagement. Moreover, while Hirsh (2010) and 14 Wiseman and Bogner (2003) found a positive correlation between Neuroticism and Environmental engagement, Solino and Farizo (2014) revealed that neurotic individuals were, on average, less likely to support an environmental programme. In respect of Neuroticism, therefore, previous findings do not unanimously support its correlation with Environmental engagement. The focus of this paper is the issue of flooding, which is one example of a form of societal risk. Given neurotic individuals anxious orientation it is possible that neurotic individuals experience more anxiety than others regarding the increased negative consequences of floods in the future. Neuroticism has also been found to relate to a more risk averse behaviour (Nicholson, 2005) Thus, neuroticism is hypothesised to have a positive effect on individuals’ willingness to contribute to policy programmes aimed at reducing the societal costs of floods. Individuals displaying high scores on the personality trait Extroversion tend to be energetic, assertive and outgoing, and adapt easily to social circumstances (Digman, 1990; John et al., 2008). In previous literature (Inglehart and Baker, 2000), Extroversion has been positively correlated with Self-expression, which in turn was associated with a relatively high degree of environmental concern. However, neither Hirsh and Dolderman (2007) nor Hirsh (2010) found any statistically significant relationship between Extroversion and Environmental engagement. Milfont and Sibley (2012) found a positive relationship between the two variables in one of their three studies, while Kim et al. (2014) found that acceptability of environmental carbon taxations were positively related to Extroversion. Empirical findings from previous studies related to environmental engagement are not unanimous, therefore, the theoretical interpretation of Extroversion offers no strong link either way for the current study in respect of indicating how extroverted individuals may choose in relation to reducing the negative effects of floods. Extraversion has however been found to have a positive effect on the individuals willingness to take risks, and thus extroverted individuals tend not to be risk avert. This relationship between risky behaviour and extroversion could imply that that individuals inherent of this trait underestimates the risk and the negative consequences of floods, thus they are less willing to contribute towards reducing future costs of floods. 15 2.5 Econometric model specification 2.5.1 The Random Parameter Logit Model The Random Parameter Logit (RPL) Model was chosen for the current study. This model is seen as a more flexible version of the Multinomial Logit (MNL) Model, as it allows the disturbance term to be random and to follow any distribution. A brief description of the RPL Model is provided below (for an in-depth overview, see Train, 2003; Hensher and Greene, 2003). An individual n (n = 1, …, N) faces T (t = 1, …, T) number of choice situations. In each choice situation, the individual is asked to choose among J (j = 1, …, J) alternatives. The utility U of individual n based on choosing alternative j in choice situation t is specified as: = ´ + (1) where is a vector of observed variables that capture the attributes of the alternatives and characteristics of the individual. Vector ´ represents the individual’s taste, and is the error component. Both ’s and are unobserved by the researcher. The individual is then assumed to choose alternative i, given that each alternative corresponds to a specific utility level, if and only if > , i.e. choosing the alternative which provides the individual with the highest expected utility. In the RPL Model, unlike the standard MNL Model, taste is allowed to vary across individuals in the population with distribution ( ) which depends on parameters . The parameters represent the mean and standard deviation of the ´s in the population, thus treating as a random instead of a fixed parameter. If ´ would be observable (and given that is assumed to be iid extreme value type 1), then the choice probability would be the standard logit: ( ) = ´ ´ (2) where ( ) is the logit probability evaluated at parameters . However, because is unknown and it follows a random distribution, the choice probability is instead defined as the integral of ( ), namely: = ´ ´ ( | ) (3) 18 With regard to socio-economic and demographic representation, Table 3 compares the mean derived for the collected sample with the regional population average, in respect of a selection of descriptive statistics. Table 3 Comparative descriptive statistics for the study sample and the regional population average Descriptive statistics Respondent sample Regional population Variable Mean Mean Women 49% 50% University degree 58% 42% Average age 50 years* 49 years* Monthly income per household 40,000–60,000 SEK 38,000 SEK Share of single households 27% 34% Statistics Sweden (2015) * Includes individuals over the age of 18. Regarding the representativeness of the sample, the descriptive statistics presented in Table 3 show that the gender and age distributions in the sample are similar to the regional averages reported by Statistics Sweden (2015), whereas the sample respondents are, on average, more educated then the regional population. Similar differences with respect to education have been found in previous Swedish studies (Ek and Persson, 2014; Ek and Söderholm, 2008). Although the statistics for income levels are not entirely comparable (as the sample household income is given as a span rather than a specific number), the households in the sample seem to have somewhat higher incomes than the regional average. This may, to some extent, be explained by the fact that the sample was more educated and had a smaller number of single households than the national average. Since a higher household income lessens economic constraints, these differences between the averages for the sample and the regional population may imply that the results are not entirely representative. In the process of determining the most appropriate final models, which is presented in section 3.2 several additional explanatory variables were tested, among which were education and income. However, the results 19 showed that none of these variables had any explanatory power. The differences visible in table 3, is therefore considered limited. Table 4 gives an overview of selected responses regarding respondents’ attitudes, their previous experience of floods and their socio-demographic characteristics. Table 4 Sample-specific statistics Descriptive statistics Variable Mean Standard deviation Minimum Maximum House 37% 0.482 0 1 Municipal water 91% 0.281 0 1 Commutes daily 53% 0.499 0 1 Commuting distance 14 km 14.837 0 99 Affected by low water quality 16% 0.361 0 1 Affected by traffic disturbances 73% 0.443 0 1 Affected by property damage 22% 0.415 0 1 Worried about climate change 61% 0.488 0 1 Worried about flooding 39% 0.448 0 1 Has been given information on flood risk in the region 26% 0.439 0 1 Has knowledge of proposed measures 19% 0.391 0 1 The survey included questions regarding respondents’ type of accommodation, drinking water supply and transport patterns. The results show that about 37% live in houses. Although individuals living in apartments can also be affected by flooding, especially if technical installations and storage units are kept in the basement, the extent of property damage faced by house owners is expected to be greater, i.e. leaving a house owner more exposed to the risk of property damage. About 91% of the respondents have their drinking water supplied by the municipality; therefore, they are exposed to the risk of water supply insecurity. The high percentage (91%) corresponds well to the regional average (Persson et al., 2011). Furthermore, about half of the sample (53%) commutes 20 an average of 14 km each day. It seems likely, therefore, that respondents in the sample could be affected by all of the above-mentioned societal costs of floods. A relatively large proportion of the respondents (73%) reported that they had been affected by traffic disturbances caused by floods, while only 22% had been affected by property damage due to floods, and only 16% had been affected by low-quality drinking water caused by flooding. Furthermore, although the region is currently already exposed to flooding, only 26% of the respondents had been given information on the flood risk in the region; moreover, only 19% had prior knowledge about regional flood-mitigating measures that had either been proposed or implemented. Table 5 Descriptive statistics relating to concern about environmental and societal risks Question: How worrying do you find the below-stated environmental and societal risks? Options Not worrying at all Not particularly worrying Neither/nor Quite worrying Very worrying Don’t know Climate change 4% 12% 24% 41% 19% 1% Flooding 4% 24% 33% 34% 4% 0% The respondents were also asked to state how worrying they found specific environmental and societal risks, i.e. the impacts of climate change and the risk of flooding. The results, displayed in Table 5, show that respondents were concerned about climate change as well as flooding. Furthermore, the respondents were more worried about the impacts of climate change in general than about flooding in particular. Table 6 Personality traits Personality traits Variable Disagree strongly Disagree Neither agree nor disagree Agree Agree strongly Extraversion 2% 14% 42% 32% 10% Agreeableness 3% 11% 50% 34% 2% Conscientiousness 0% 7% 39% 45% 9% Neuroticism 22% 45% 29% 3% 1% 23 traffic disturbances to an option with more. The Cost attribute is also negative and statistically significant at the 1% level, implying that the higher its cost, the less likely a specific alternative will be chosen. A negative Cost attribute thus imply that the respondents prefer low fees to high ones. The RPL Model also provides the standard deviation of each random parameter. The statistically significant standard deviations for the variables Cost, Water supply security, Property damage and Traffic disturbances imply that the coefficients vary across respondents. In other words, preference heterogeneity exists within the sample. The personality traits were interacted with the Status quo alternative. The personality trait Extroversion is statistically significant at the 1% level, and has a positive sign. A positive sign implies that the probability of choosing the Status quo alternative, i.e. of doing nothing to reduce the societal costs of floods, is increased if the respondent is an extrovert. Previous results (Hirsh and Dolderman, 2007; Hirsh, 2010; Milfont and Sibley, 2012; Kim et al., 2014) regarding Extroversion and its relation to environmental concern were mixed, and lend little advice on what to expect regarding this personality trait’s influence on preferences regarding negative flood-related effects in the current study. The relationship between risk and extroversion may however offer an explanation to the result. Extraversion has been positively related to individuals’ willingness to take risks (Vollrath and Torgersen, 2002; Romero et al., 2012). Potentially, it is the link to risk taking that drives the results, since individuals inherent of this trait may underestimate the risk and the negative consequences of floods, and are thereby found to be less willing to contribute towards reducing future costs of floods. The results also show that individuals with the personality trait Agreeableness – a result also statistically significant at the 1% level – are less likely to choose the Status quo alternative. Therefore, such individuals are more inclined to contribute to policy programmes that aim at reducing the negative effects of floods. This reading is supported by earlier empirical findings (Hirsh, 2010; Hirsh and Dolderman, 2007; Kim et al., 2014; Milfont and Sibley, 2012), who found a positive and strong relationship between Agreeableness and Environmental engagement. Agreeable individuals are also found to be risk averse (Nicholson et al., 2005). Because of the uncertainty inherent in predictions of how often floods occur, and the extent of the negative effects of floods, individuals who 24 are unwilling to undertake risky behaviour, should be more inclined towards contributing to policy measures aimed at reducing the negative impacts of floods. With respect to Openness, Conscientiousness and Neuroticism, the results showed no statistical significance, suggesting that these personality traits did not influence the probability that respondents would choose to contribute to a policy programme aimed at reducing the societal costs of floods. The results are not entirely unexpected, especially in the case of Neuroticism: in previous empirical results (Milfont and Sibley, 2012; Hirsh, 2010; Wiseman and Bogner, 2003; Solino and Farizo, 2014), it was hypothesised that more conscientious individuals should, on average, be willing to contribute to the societal cost of flood-impact-reducing programmes, as the benefits of investing today are only realised in the future. Nonetheless, this hypothesis could not be confirmed. A link between climate change concerns and the Status quo alternative could, however, be statistically established, namely at the 1% level. This result suggests that individuals who reported being worried about climate change were less likely to choose the Status quo alternative, implying they would thus opt for programmes reducing the societal costs of floods. These results are consistent with the findings of Veronesi et al. (2014) and Botzen and Van den Bergh (2012a, b). 3.3 Results from estimating a Latent Class Model When support for new flood risk management programs is being sought, it can be helpful to segment different groups, and try to target information accordingly. The LCM model provides an informative way of analysing preference heterogeneity by being able to segment respondents into such groups. A LCM was therefore estimated to analyse and explain preference heterogeneity further. The LCM analyses preference heterogeneity by categorising respondents according to a predetermined number of classes. The probability of a respondent belonging to a specific class is analysed via certain observed individual characteristics. When comparing the McFadden of the LCM (0.282) to the RPL Model (0.322), it appears as though the RPL Model offers greater explanatory power than the LCM. The performance of the LCM in relation to the RPL Model was further tested via the Ben- Akiwa and Swait (1986) test for non-nested models. The null hypothesis of the LCM, namely that it would provide a superior model fit, could not be rejected at the 5% level of 25 statistical significance. Thus, there is no evidence in favour of one model to another, according to the Ben-Akiwa and Swait test (1986). We will therefore go forward and analyse the results from the LCM model. According to Greene (2012), finding the appropriate number of latent classes for the LCM can be aided by different measures of model performance. To establish the number of latent classes, the Bayesian information criterion (BIC), the Akaike information criterion (AIC), the McFadden , and the log-likelihood function values were used. Nonetheless, determining the number of latent classes is not merely a matter of choosing the model with the lowest BIC and/or AIC. These criteria only serve as a guide to determine the number of latent classes, and therefore judgement, by the analyst, regarding the appropriateness of the final number of classes should also guide the decision (Swait, 1994). In the current study, the fit of the LCM was evaluated for six classes. If one looks at the log-likelihood function value (see Table 8), the BIC, the AIC and the all suggest that the higher the number of classes, the better the model fitted the data. However, using more classes also meant that the majority of classes consisted of a very small percentage of the sample. Some of the classes also had low statistical significance in terms of its explanatory variables. From the results, it was also hard to find any clear interpretation. Table 8 also shows that the greatest improvements in model fit are seen when going from one to two classes: in this regard, consider the log-likelihood function value (from -5931.84 to -5109.04), the BIC (from 5948.58 to 5165.65), the AIC (from 11853.68 to 10184.08) and the McFadden (from 0.052 to 0.282). After two classes, the change in BIC, AIC and the McFadden , is markedly smaller, suggesting that adding additional classes beyond the second class may not imply much improvement of the model fit. Thus, based on the analysis of Table 8, together with the sizes and interpretability of the latent classes beyond two classes, suggests that the model containing two classes provides the best fit. 28 Group 2 consisted of 19% of the respondents. This group valued increasing the security of the drinking water supply and decreasing the number of days of flood-related traffic disturbances. Group 2’s preferences differ somewhat from Group 1’s in that the former do not value any reduction of flood-related property damage. It is pertinent to mention here that the relevant attributes in the current study – Water supply security, Property damage, and Traffic disturbances – exhibit different degrees of public-/private-good characteristics. If issues such as congestion are disregarded, the reduction in Traffic disturbances could potentially be described as non- rivalrous in that one individual’s consumption of fewer days with traffic disturbances does not reduce the amount available to be consumed by another individual. It is, however, possible to exclude an individual from driving on the road, e.g. by imposing tolls. In addition, according to the Planning and Building Act (2010:90, ch.6, s.21), a municipality is responsible for building and maintaining the road network within detailed planned areas. Water supply security is similarly non-rivalrous, since one individual’s consumption of an increased water supply security does not affect another individual’s consumption of that good. However, since it is possible for a municipality to cut off the water supply to individuals, the water supply is excludable. Moreover, according to the Public Water Services Act (2006:412, s.6), a municipality is responsible for providing water supply and sewerage. Neither water supply security nor traffic disturbances can be described as a pure public good, therefore; nonetheless, both exhibit at least some public-good qualities. On the other hand, Property damage cannot be described as non-excludable or non-rivalrous, i.e. the protection of one property cannot be used for the protection of another. Of course, when more large-scale protection measures such as embankments are undertaken, they usually protect many properties. However, given that resources are assumed to be scarce, the protection of properties in one area is usually at the expense of protecting those in another area. Moreover, at least in theory, one could exclude some individuals, e.g. remove parts of an embankment. However, in reality, the exclusion of some individuals may be infeasible. Given that the Cost attribute is described as an annual municipal fee paid by all households in the region for ten years, it is possible that respondents in Group 2 believed that private goods should not be funded with public means, i.e. protecting private property from floods should not be financed via the municipal budget. Group 2 seems to 29 place greater value on public goods than private ones, therefore, at least when it comes to what flood-related issues they value. The variable “worried about climate change” and the personality dimensions are included as variables to try to explain the heterogeneity in the sample. Thus, these explanatory variables refer to respondent characteristics that may increase or decrease the probability of an individual belonging to a certain group. In the process of determining the most appropriate final model, several additional explanatory variables were tested, among which were Gender, Education, Age and Income. However, the results showed that none of these variables had any explanatory power. Similarly, testing for Experience, i.e. whether respondents had been affected by any of the three flood-related negative effects – Water supply security, Property damage or Traffic disturbances – could not explain the respondents’ preference heterogeneity. Finally, the individuals’ perception of their knowledge regarding the flood-related problems in the region was tested, but using the Knowledge variable did not help to explain the LCM group segmentation either. The class membership function shows that, in relation to Group 2, Group 1 is less extroverted, being statistically significant at the 5% level. Thus, individuals belonging to the group with a positive valuation of reducing the negative effects of floods are less extroverted, i.e. Group 1. The results of previous empirical research on the relationship between Extraversion and Environmental engagement have been ambiguous, but no earlier study has found the negative relationship indicated here. An explanation for this result may be found in the relationship between extroversion and risky behaviour, and the inherent uncertainty that surrounds natural hazards, such as floods. Extroversion has been linked to risk taking behaviour. Since individuals inherent of this trait underestimate the risk and the negative consequences of floods, and would thereby be less willing to contribute towards reducing future costs of floods, it is not unreasonable that individuals belong to the group who want to reduce these impacts should be less extroverted. Furthermore, respondents who scored high on Agreeableness were more likely to belong to Group 1, whose members valued reductions in all three attributes – Water supply security, Property damage, and Traffic disturbances – in comparison with Group 2, which did not value such reductions. This result was expected, and it builds on the 30 characteristics of individuals who score high on Agreeableness as being cooperative, sympathetic, generous and willing to compromise their own interests. Given that current and future generations are likely to benefit from reducing negative flood impacts, it is plausible that individuals displaying the personality trait Agreeableness should belong to the group characterised as wanting to reduce all negative impacts of floods, although they might not be able to benefit from such reductions directly (Group 1). This reasoning is also borne out by empirical studies (Hirsh, 2010; Hirsh and Dolderman, 2007; Kim et al., 2014; Milfont and Sibley 2012), which have found a positive and strong relationship between Agreeableness and Environmental engagement. Agreeable individuals has also been found to be risk averse (Vollrath and Torgersen, 2002; Romero et al., 2012). Risk averse individuals should be more inclined towards contributing to policy measures aimed at reducing the negative impacts of floods. The results of agreeable individuals explaining the class membership of the group of individuals who wants to contribute to reducing the negative impacts of floods, are therefore reasonable. Moreover, respondents who declared themselves concerned about climate change were more likely to belong to Group 1 than Group 2, i.e. exhibiting a positive valuation of reducing the negative impacts of floods. Further, support of this claim is presented by Veronesi et al. (2014), who found that being aware of climate change had a positive influence on the marginal WTP to reduce flood risks. 3.4 Marginal willingness-to-pay Since the estimates from the various model specifications, explicated above, are not directly comparable, one could use the parameter estimates from the RPL Model and the LCM to calculate the marginal WTP for the whole sample and for the two groups. In this way, one can assess respondents’ valuation of reducing the negative effects of future flooding in the Gothenburg region. As stated previously, the variables Water supply security, Property damage and Traffic disturbances are random parameters with a normal distribution.2 All other 2 The RPL Model allows the distribution to take any form, e.g. normal, lognormal or triangular. In order to determine what distributional form would fit the model best, the model was run with other distributional assumptions, including the uniform and triangular distribution. The results show that the log-likelihood 33 LCM were aggregated by simple summation of the price coefficients’ of Groups 1 and 2, times the expected percentage of individuals belonging to Group 1 and 2, respectively. By comparing the aggregate WTP of the LCM, to that of the RPL, it is visible that the LCM produces slightly higher estimates of the WTP for Water supply security and Traffic disturbances. For Water supply security the RPL model produces a marginal WTP of €0.7, whilst the aggregate LCM produces a WTP of €0.8. In the case of Traffic disturbances the RPL model produces a marginal WTP of €1.2, whilst the aggregate LCM produces a WTP of €1.5. For Property damage no difference in marginal WTP is found between the two models. The RPL model and the LCM thus seem to produce WTP that are relatively close to each other. Also, the relative order of marginal WTPs are the same for both models, in respect to the attributes. 3.4.2 Scenario-related WTPs for the RPL Model and the LCM According to the results in table 10, the marginal WTP estimates for reducing traffic disturbances is considerably higher than for reducing water supply security and property damage. The differences between the three attributes in question may first appear as large, but it is important to remember that the end-points, i.e. the levels used in the status quo and the change in attribute levels for the different scenarios vary considerably for each attribute in question. In order to relate the marginal WTP from Table 10 to the scenario attribute levels used in the CE, an overview is provided of the levels of change for each attribute from its respective end-point (see Table 11). The marginal WTP for reducing traffic disturbances refers to reducing, from eight to two, the number of days a year with flooded, eroded or washed-away roads and railways. The corresponding change regarding water supply security corresponds with reducing, from 200 to 100 (medium security) and from 200 to 50 (high security), the number of days a year that the Göta river cannot be used to produce drinking water. In respect of the attribute Property damage, the change refers to reducing, from 100 to 40 (medium damage and from 100 to 10 (high damage), the number of properties damaged by floods each year. 34 Table 11 Marginal willingness-to-pay related to various attribute levels, in SEK (Euros in parentheses) Variables RPL Model LCM Group 1 Group 2 Coefficient Coefficient Coefficient Water supply security – High (200–>50 days/year) 1,050 (108) 1,500 (153) 150 (15) Water supply security – Medium (200– >100 days/year) 700 (72) 1,000 (102) 100 (10) Property damage – Low (100–>10 properties/year) 270 (28) 360 (37) 0 (0) Property damage – Medium (100–>40 properties/year) 180 (19) 240 (25) 0 (0) Traffic disturbances – Low (8–>2 days/year) 72 (7) 90 (9) 102 (10) As can be seen in Table 11, the scenario-related marginal WTP from the RPL Model is €108 for high water supply security and €72 for medium water supply security. The scenario-related WTPs for the LCM are €153 (Group 1) and €15 (Group 2) for high water supply security, and €102 (Group 1) and €10 (Group 2) for medium water supply security. The scenario-related WTPs from the RPL Model are €28 and €19, respectively, for low to medium levels of property damage. For Group 1 of the LCM, the scenario-related WTPs are instead €37 and €25, respectively, for low to medium levels of property damage. Group 2, on the other hand, places no value on property damage. Traffic disturbances have a scenario-related WTP ranging between €72 (RPL Model) and €102 (Group 2). The scenario-related WTPs3 from the two models suggest that, in relation to taking no action against the societal costs of floods, the level of water supply security has the greatest impact on individuals’ utility, followed by property damage and then traffic disturbances. 3 The marginal WTP estimates assume a constant marginal utility. 35 3.4.3 Prioritisation of flood-related attributes Furthermore, besides the rather large differences in magnitude of marginal WTP also visible in Table 11, Groups 1 and 2 prioritise the relevant attributes quite differently. The results from Table 9 suggest that, although Group 2 did not value property damage, the groups still had similar priorities. The results from Table 10 indicate that, besides high water supply security, their preferences diverge in respect of each attribute. For example, Group 2 valued medium water supply security (€10) and a reduction in traffic disturbances (€10) equally, whilst Group 1 clearly favoured medium water supply security (€72) before reducing traffic disturbances (€7). Group 2 prioritised dealing with traffic disturbances before property damage, whilst Group 1 did not prioritise these two attributes in that way. With the scenario-related WTPs, the two groups’ preferences seem to be further apart, therefore. The scenario-related WTPs thus differ not only in terms of the flood-related attributes’ relative importance, but also in respect of differences within the sample. As described above, the marginal WTP estimates and the scenario-related WTPs have different units of measure. Nonetheless, the scenario-related marginal WTPs offer a more realistic view of change in comparison with the marginal WTP estimates: if policymakers are to make new investments, for example, it is unlikely that they would spend money to make small incremental changes such as reducing, by one house, the number of houses that suffer property damage caused by floods. From a policy perspective, therefore, the interpretation from the scenario-related WTPs is more informative. 4. Conclusions In this paper, public preferences for reducing the societal costs of floods in the Gothenburg region have been analysed via a CE characterised by the following negative impacts of floods: Water supply security, Property damage, and Traffic disturbances. To explain preference heterogeneity, but also to offer deeper insight into the relationship between personality traits and preferences for reducing the negative effects of floods, the CE respondents’ personality traits were elicited via the BFI-10. 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PLoS ONE 9(2), e89603. doi:10.1371/journal.pone.0089603 Paper II Contents lists available at ScienceDirect Climate Risk Management journal homepage: www.elsevier.com/locate/crm Valuing the reduction of floods: Public officials’ versus citizens’ preferences Elin Spegel Economics Unit, Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, 97187 Luleå, Sweden A R T I C L E I N F O Keywords: Flood risk reduction Choice experiment Trust in government Climate change concern Representation A B S T R A C T This paper analyses the preferences of public officials and citizens related to the impacts of floods in the Gothenburg region in Sweden. Citizens and public officials in the flood-prone region an- swered identical choice-experiment surveys characterized by the negative impacts of floods: property damage, traffic disturbances, and water supply security. By having citizens and public officials respond to identical surveys, it was possible to analyse whether and, if so, how priorities and monetary valuation differed in respect of the different negative effects of floods. The overall finding is that public officials’ and citizens’ preferences seem to converge, and that both citizens and public officials are willing to pay to reduce flood-related costs. Public officials have similar priorities to citizens, in that security of drinking water provision was given priority over property damage, while traffic disturbances were ranked lowest. In terms of their respective willingness to pay to avoid the negative impact of floods, public officials were willing to pay more than citizens to pay for securing the drinking water supply and for restoring damaged property, though these differences were not substantial. There are, however, some differences in preference between citizens and public officials: the latter preferred not to spend anything to reduce traffic dis- turbances caused by floods, whilst citizens were willing to do so. These results imply that deci- sions made within the public sector will not come to differ substantially from citizens’ pre- ferences. 1. Introduction In the light of climate change, with predicted increases in precipitation and flood risk in many parts of the world – including Sweden (IPCC, 2014), decisions made by governments today will affect the costs associated with floods faced by citizens tomorrow. Since citizens will have to bear the consequences of these decisions, it is reasonable that the decisions reflect citizens’ preferences. The aim of this paper is to analyse the preferences of public officials and citizens with regard to the impacts of floods. The meth- odology employed is the choice experiment (CE), which enables several aspects of flooding to be researched. By letting citizens and public officials in the flood-prone region of Gothenburg, Sweden, face identical CEs regarding the negative impacts of floods, possible differences in prioritisation of these negative effects can be analysed. In this CE, the negative impacts of floods are attributed to property damage, traffic disturbances and water supply security. Addressing the issue of flood risk in the Gothenburg region is important in order to limit the future costs caused by floods. Flood risks are expected to increase more than the national average (Swedish Civil Contingencies Agency, 2011), which implies that further investments in flood-risk-reducing measures need to be made. Although such measures have already been taken in the region, http://dx.doi.org/10.1016/j.crm.2017.08.003 Received 6 February 2017; Received in revised form 23 August 2017; Accepted 25 August 2017 E-mail address: Elin.Spegel@ltu.se. the County Administrative Board of Västra Götaland. The experts assisted with information and verified that the attributes were correctly described in the questionnaire. The experts consulted did not form part of the sample of public officials who were asked to answer the questionnaire. 2.2. Experiment design An efficient design, employing Ngene software, was used to construct the choice sets. Efficient designs make use of prior in- formation to improve the level of utility balance among the alternatives in each choice situation, relative orthogonal designs. This in turn reduces the sample size required to estimate a model. Because information (priors) is available with some degree of uncertainty, a Bayesian D-error approach, assuming normally distributed priors, was used. The final experimental design presented to each respondent consisted of eight choice sets. Each choice set comprised three alternatives (A, B and C), one of which – Alternative C – was a status quo option. Both respondent groups, i.e. citizens and public officials, were asked to choose, as individuals, which one of the three alternatives they preferred the most. The cost attribute consists of six levels. The attributes property damage and water supply security consists of three levels, and the attribute traffic disturbances consists of two levels (See Table 1). The choice of letting the traffic disturbances attribute only have two levels is motivated by the fact that respondents, during testing of the questionnaire, had difficulty making distinctions between more than two levels because of the magnitude of the attribute levels being rather small. Rather than increasing the distance between the attribute levels to unrealistic proportions, the choice was made to reduce the number of attribute levels to two levels. The medium levels of property damage, traffic disturbances and water supply security correspond to the prevailing impacts of floods. Because respondents were asked to value the reduction of future flood risk (20 years from the time of the survey), the medium and lower levels correspond to an improvement (i.e. decreased flood risks), while the higher levels imply the projected future flood risk with no intervention. Two of the alternatives in each choice set, i.e. A and B, correspond to a future scenario where measures are undertaken to decrease flood risk. These alternatives are associated with an increased annual cost. The third alternative, C, corresponds to a future scenario where no action is taken to decrease flood risk; hence, Alternative C is not associated with any additional cost. This non-cost option implies a higher flood risk than today. Thus, the Status quo alternative in this case represents the future if no additional measures against flood risk are taken, given the expected impact of the changing climate, rather than the presently prevailing situation. 2.3. The questionnaire A small focus group, consisting of individuals living in the region, was also used in order to evaluate the extent to which the selected attributes and their description were perceived as relevant and meaningful. The focus group and two pre-tests were em- ployed before the questionnaire was finalised in order to enhance the understanding of the survey questionnaire and the choice task. The focus group, comprising five citizens from the region – three women and two men, all aged between 28 and 38 – were asked to answer questions on how the negative effects of floods were perceived. The first of the two pre-tests, which was conducted on four women and five men from the region who were between the ages of 28 and 59, focused on the clarity of the questionnaire and the choice task. The second pre-test, which was conducted on a group of 30 university students in another part of the country, involved fine-tuning the questionnaire before it was sent out to the respondents. Both pre-tests resulted in small adjustments in the formulation of the questions and the text in the questionnaire. The final questionnaire consisted of three sections. The first was concentrated around the respondent’s previous experiences, knowledge, and concerns regarding flood risks and the expected impacts of flooding. For example, in this first section, both re- spondent groups were asked if they had been affected by a flood; if they knew of any measures being taken to reduce flood risks; how they thought the responsibility for taking measures to reduce the risks and consequences of floods should be divided between local and national governments, and between the public and private sectors; and to what extent they believed these institutions were living up to their responsibilities. The second section of the questionnaire included the CE. Both respondent groups were provided with current information on how flood risks negatively impacted on property, traffic and water provision, and the expected change in the future due to climate change. The respondents were informed that it was possible to reduce the negative consequences of floods by taking various mitigating Table 1 Attribute levels for choice alternatives. Attribute Description Attribute levels Alternatives A and B Alternative C (Status quo) Property damage Number of flood-damaged properties a year 10, 40, 100 100 Traffic disturbances Number of days a year with flooded, eroded or washed-away roads and railways 2, 8 8 Water supply security Number of days a year that the Göta River cannot be used to produce drinking water due to contamination 50, 100, 200 200 Cost Municipal fee in SEK to be paid each year for ten years 100; 300; 500; 800; 1000 0 E. Spegel measures, but that it would imply costs which could be financed through an annual municipal fee paid by all households in the region (during the coming ten years). Fig. 1 shows an example of an actual choice set used in the study. The third part of the questionnaire focused on the respondent’s socio-economic and demographic characteristics. 2.4. Data collection In March 2015, data was collected on both citizens and public officials. The citizens’ data was collected via a web panel containing approximately 90,000 individuals in Sweden. The members of the panel itself are randomly recruited by telephone, while individuals invited to answer the questionnaire were selected according to their geographic location and age. All individuals in the citizens group resided in one of the six municipalities in the Gothenburg region (Ale, Göteborg, Kungälv, Lilla Edet, Mölndal, and Partille), and were over 18 years old. 3,641 respondents were invited to answer the questionnaire. The final citizen sample consisted of 800 individuals, meaning that the response rate was 22%. The officials’ data was collected by sending out an email with a link to the same web questionnaire as the citizens answered. The group of public officials consisted of employees at the municipalities of Ale, Göteborg, Kungälv, Lilla Edet, Mölndal, and Partille and at the regional authority, the County Administrative Board of Västra Götaland. The group of public officials worked within de- partments responsible for urban planning, emergency management, water production, environmental management and transport (including maintaining and planning for road networks and public transportation systems). Public officials from these departments were considered a relevant group to target because, in their undertakings to prevent floods in relation to their specific policy area, they influence and shape flood risk management. Of the 421 public officials invited to answer the questionnaire, the final sample group consisted of 102 individuals, which corresponds to a response rate of 24%. For both respondent groups, therefore, the response rate was slightly above 20%. The average response rate for other surveys using the same web panel is 23%. A response rate of slightly above 20% also seems to be in line with other surveys using web panels in Sweden. Elgan and Leifman (2013) used the same web panel as in this paper to gather information regarding public health and alcohol problems in Sweden and reported a response rate of 35%. Svensson (2010) states that a 20–30% response rate, when using web panel surveys, is common. The response rate reported for the current study, therefore, does not differ significantly from what can be considered a common response rate when using web panels. Nonetheless, although the response rate is in line with what can be Choice Set 1 Which of the following alternatives, A, B or C, do you prefer? The alternatives describe a situation in 20 years’ time. Alternative C describes a situation where no measures against floods are taken, and implies a larger flood risk than today. Choose one of the alternatives by ticking one of the boxes at the bottom of the table. Alternative A – Measures taken Alternative B – Measures taken Alternative C – No measures taken Water supply security Number of days a year that the water from the Göta River cannot be used for producing drinking water due to contamination 100 days 100 days 200 days Damaged property Number of flood-damaged properties a year 10 properties 100 properties 100 properties Traffic disturbances Number of days a year with flooded, eroded or washed- away roads and railways 8 days 2 days 8 days Fee SEK a year to be paid for ten years 500 SEK 100 SEK 0 SEK ][][eciohcyM [ ] Fig. 1. Example of choice set. E. Spegel expected from using web panel surveys, the fact that 77% of the panel did not participate could have implications for the re- presentativeness of the sample. For example, the responses of those who answered the questionnaire may differ from those who declined to answer the survey. 2.5. Model specification In the survey, each choice set involved making a discrete choice among three options: Alternatives A and B corresponded to an improved future scenario, while Alternative C corresponded to a future scenario where no action was taken to mitigate flood risk. Each respondent was assumed to have chosen the alternative that provided him/her with the highest expected utility. 2.5.1. The Random Parameter Logit Model The Random Parameter Logit (RPL) Model can model choice as a function of the attributes of the alternatives, together with the respondents’ characteristics. The RPL Model is seen as a more flexible version of the Multinomial Logit (MNL) Model, as it allows unobserved factors to be random and follow any distribution. A general description of the RPL Model is provided below (for an in- depth description, see Train, 2009, and Hensher and Greene, 2003). An individual n (n = 1,…, N) faces t (t = 1,…, T) number of choice situations. In each choice situation t the individual faces a choice among j (j = 1,…, J) alternatives. Thus, the utility of individual n from choosing alternative j in choice situation t is specified as – = ′U β xjtn n jtn (1) where xjtn is a vector of observed variables that capture the attributes of the alternative j in choice situation t, faced by individual n. = + +β β z vΔ Γn n n (2) Vector βn represents the individual’s taste; zn represents observed heterogeneity, i.e. characteristics of individual n that influence the mean of the taste parameter, and vn captures unobserved heterogeneity, and is a vector of random variables with zero means and known variance. The matrix of the non-zero elements of the lower triangular Cholesky matrix is represented by Γ. The individual is then assumed to choose alternative i, given that each alternative corresponds to a specific utility level, if and only if > ∀ ≠U U j iin jn , i.e. choosing the alternative which provides him/her with the highest expected utility. In the RPL Model, unlike the Standard Logit Model, two types of variation in preferences are accounted for: the variation associated with individual specific characteristics (e.g. income), and a random unobservable preference heterogeneity captured by the standard deviation θ( ) of the distribution f β( ) of the β ’s in the population. If the standard deviation is statistically significant, the coefficient varies across individuals. In the MNL Model, preferences are able to vary according to individual specific characteristics but are otherwise assumed to be homogenous across individuals. Because βn is unknown and follows a random distribution, the simulated choice probability is defined as the integral of L β( )in n : ∫ ∑ ∏ ∏= ⎧ ⎨⎩ ⎛ ⎝⎜∑ ⎞ ⎠⎟ ⎫ ⎬⎭= = = + + + +P L R e e f β θ dβ1 ( | )in in r R t T j J β z v x j β z v x 1 1 1 ( Γ ) ( Γ ) n n in n n jn (3) where Pin is the simulated choice RPL probability evaluated at different values of β. Given a specified distributional form of f β( ) (such as normal, lognormal or triangular), the parameters θ of the distribution f β( ) can be estimated via a simulated maximum-likelihood estimation. We are, therefore, able to estimate the mean and standard deviation of the distribution of the populations’ taste for the attributes of the alternatives. Given R draws, one obtains simulated choice probabilities by averaging the logit expression over these draws. In this study Halton draws is used, as previous literature (see e.g. Bhat, 2001; Train, 2009) has found simulation variance to be lower when using Halton draws instead of standard random draws, thus making Halton draws more efficient. Furthermore, since each respondent faces t (t = 1,…,T) number of choice situations, in which individual faces a choice among j (j = 1,…,J) alternatives, the simulated choice probabilities are obtained by taking the products of the logit expressions over the T number of choice situations and J alternatives. 3. Results 3.1. Descriptive statistics With regard to socio-economic and demographic representation, Table 2 below compares the two groups in the sample with each other as well as with the regional average in respect of a selection of descriptive statistics. Regarding the representativeness of the citizen sample, the descriptive statistics show that, in comparison with the regional average reported by Statistics Sweden (2015), the gender and age distributions are similar. In respect of education, both sample groups are more educated, on average, in comparison with the regional population. Similar differences with respect to education have been found in previous Swedish studies (Ek and Persson, 2014; Ek and Söderholm, 2008). Although the income levels of the two sample groups and the regional population are not entirely comparable (as the sample groups’ household income is given as a span rather than a specific number), the households in the citizen sample seem to have somewhat higher incomes than the regional population average. Since a higher household income lessens economic constraints, these differences between the samples and the E. Spegel All respondents were given eight choice sets, each of which consisted of three alternatives. Each respondent answered all choice sets; thus, there are no missing observations. In the citizen sample, there were 809 respondents, who generated 6472 observations. For the group of public officials the final number of respondents was 102; thus, the sample constituted 816 observations. In the citizen sample, the Status quo alternative was chosen in 11% of the choice sets; in the public official sample, the Status quo alternative was chosen in 7% of these sets. The cost attribute Fee and the attributes Water supply security and Property damage are statistically significant for both groups. The coefficients all have negative signs, which is to be expected. Decreasing the water supply security, i.e. increasing the number of days for which the Göta River cannot be used to produce drinking water, should have a negative impact on the respondents’ utility and the probability of choosing an alternative. In addition, increasing the number of houses that suffer flood damage should also have a negative effect on the individual’s probability of choosing an alternative. Results for the Status quo option, which corresponds to doing nothing to reduce the negative effects of floods, are also statistically significant and negative, for both citizens and public officials, which implies a negative status quo bias. This entails that respondents in both groups refrain from choosing the Status quo. With regard to the Traffic disturbances attribute, increasing the number of days on which floods cause traffic disturbances has a statistically significant negative effect on the probability of citizens choosing a specific alternative. However, no such relationship is found for the sample of public officials. The estimated standard deviations of the random parameters of the RPL Model are statistically significant at the 1% level, except for traffic disturbances in public officials’ sample. Thus, heterogeneity among the respondents is found in both sample groups. The results of the RPL Model suggest that citizens and public officials do not differ substantially in their preferences if one looks at the signs and statistical significance of the estimated parameters. The only attribute for which the results of the two respondent groups differ significantly is Traffic disturbances. Although the results presented in Table 6 are informative in terms of differences in statistical significance, signs and hetero- geneity, they do not offer the opportunity to compare the relative importance of the various attributes and their respective levels. In order not only to make such relative comparisons, but also to be able to say something about the monetary valuation of reducing the negative effects of floods, the respondents’ marginal willingness-to-pay (WTP) for alleviating the negative impacts of floods has been calculated and analysed. These results are displayed in Table 7. The values evident in Table 7 may seem quite low at first glance, but this is due to the unit of measurement used. Thus, for Property damage, the interpretation of the marginal WTP is the value of reducing, by one house, the number of houses with property damage caused by floods, while the marginal WTP for Traffic disturbances relates to the value of reducing, by one day, the number of days with traffic disturbances caused by floods. The marginal WTP for Drinking water provision insecurity is interpreted as the value of decreasing, by one day, the number of days for which the Göta River cannot be used to produce drinking water. The WTP estimates show that, overall, both citizens and public officials are willing to pay to reduce the negative effects of floods; however, some differences also exist between citizens and public officials in respect of the valuation of the various attributes. For the citizen sample, the WTP for all three attributes is statistically significant at the 1% level: the WTP ranges from €0.3 for Property damage to €1.4 for Traffic disturbances. The sample of public officials, however, was only prepared to pay to reduce flood-related negative effects associated with water supply security and property damage. Both WTPs for Property damage and Water supply security are statistically significant at the 1% level. Apart from Traffic disturbances, therefore, the valuations by citizens and public officials seem rather similar, with both public officials and citizens willing to pay €0.8, for increasing the water supply security. For property damage, citizens were willing to pay €0.3 and officials €0.4. For citizens, the WTP estimate for reducing traffic disturbances is considerably higher than for improving water supply security and property damage. The differences between the attributes may first appear as large, but it is important to remember that the end- points – i.e. the levels used in the status quo and the change in the respective attribute levels for the different scenarios vary considerably for the attributes. In order to relate the marginal WTP from Table 7 to the scenario attribute levels used in the ex- periment, Table 8 gives the marginal WTP for each attribute, from their respective end-points. The marginal WTPs shown in Table 8 has been calculated by running the RPL models again using dummy coded variables for the attribute levels, and thereby calculating the marginal WTP of each attribute level. All variables except the cost attribute was normally distributed. See Appendix 1; Table B1 for the statistical models. The WTP for reducing traffic disturbances refers to reducing, from eight to two, the number of days a year with flooded, eroded or washed-away roads and railways. The corresponding change regarding water supply security corresponds to reducing, from 200 to 100 and from 200 to 50, the number of days a year that the water from the Göta river cannot be used for producing drinking water from 200 days to 100 days, and from 200 days to 50 days, a year respectively. For the attribute property damage the change refers to Table 7 Marginal willingness-to-pay to avoid negative impacts of floods. Marginal willingness-to-pay Citizens Public officials Variable Coefficient Standard error Coefficient Standard error Property damage in SEK(Euros)/property) 3 (0.3)*** 0.275 4 (0.4)*** 0.543 Traffic disturbances in SEK(Euros)/day 14 (1.4)*** 2.187 1 (0.1) 3.850 Drinking water provision insecurity in SEK(Euros)/day 8 (0.8)*** 0.366 8 (0.8)*** 0.892 ***, **, * indicate significance level at 1%, 5%, and 10% E. Spegel reducing the number of damaged properties from 100 to 40 properties, and from 100 to 10 properties, a year. The scenario-related WTPs3 from the RPL Model show that, compared with taking no action to reduce future flood risk, i.e. maintaining the status quo, Water supply security has the greatest impact on both the citizens’ and public officials’ utility, with a WTP of €109 for citizens and €107 for public officials in respect of a highly secure provision of drinking water. For a medium water- provision security scenario, citizens are willing to pay €71, and public officials €69, in comparison with taking no action and maintaining the status quo. The scenario-related WTPs for Property damage are smaller than those for Water supply security. For the scenario where property damage is low, citizens are willing to pay €30 and public officials €31; for property damage on a medium scale, the WTPs are €31 for citizens and €33 for public officials, respectively. Finally, although citizens awarded Traffic disturbances the lowest priority, this attribute had a scenario-related WTP of €8. Apparent in Table 8 is that public officials, on the other hand, did not have a statistically significant WTP. The WTP estimates of Table 7 and the scenario-related WTPs from Table 8 suggest that both citizens and public officials are willing to pay to reduce flood-related costs. With regard to reducing water supply security and property damage, public officials’ and citizens’ WTPs do not differ extensively. Traffic disturbance is the only attribute where the two sample groups’ preferences differed substantially. From the scenario-related WTPs in Table 8, it is visible that citizens’ prioritisations were the same as those adopted by public officials, with top priority being given to reducing water supply security before addressing property damage, followed by dealing with traffic disturbances. As described above, the WTP estimates and the scenario-related WTPs clearly have different units of measure. Nonetheless, in relation to the WTP estimates, the scenario-related WTPs give a more realistic view of change. If policy- makers are to make new investments, it is unlikely that they would spend money making small incremental changes such as reducing the number of houses, with property damage caused by floods, by one house. Thus, from a policy perspective, the interpretation from the scenario-related WTPs is more informative. This result suggests that citizens and public officials make similar prioritisations regarding the negative effects of floods. As stated above, most citizens (89%) and public officials (93%) seem to choose an option that implies reducing the negative effects of floods. Although the individuals choosing the status quo compose a smaller portion of both samples, it is still interesting to further investigate citizens’ and officials’ probability of choosing the Status quo alternative. If it is possible to identify variables that can explain why individuals do not want to reduce the negative effects of floods, this information could be used to gain further public support for new investments aimed at reducing flood-related damage. In addition, although public officials and citizens seem to have quite similar preferences, there are still some smaller differences in magnitude and statistical significance. Potentially, variables explaining the probability of choosing the status quo could be indicators of variables that could make citizens and public officials’ preferences converge or diverge. In order to investigate the probability that citizens and public officials would choose the status quo, several explanatory variables were interacted with the Status quo alternative (i.e. the alternative specific constant); the RPL Model, with the attributes coded as continuous, was once again run with two separate models, one for each respondent group.4 The results in Table 9 notably reveal that the coefficients for Water supply security, Property damage, Traffic disturbances and Cost, after the inclusion of more explanatory variables, still display the same signs and statistical significance. The preferences over the attribute levels seem to be stable across specifications. For the citizen sample, gender influences the respondents’ probability of choosing the status quo: women were less likely than men to choose the alternative of not contributing towards reducing the negative effects of floods. This result is in line with findings from previous studies (Cameron, 2005; Devkota et al., 2014). No such effect is found in the sample of public officials. Table 8 Marginal willingness to pay related to the attribute levels (in SEK, Euros in parentheses). Variable Marginal willingness-to-pay Citizens Public officials Coefficient St. error Coefficient St. error Property damage – Low (100 → 10 properties/year) 307*** (31) 27.471 317*** (33) 52.185 Property damage – Medium (100 → 40 properties/year) 298*** (30) 50.354 316*** (32) 95.698 Traffic disturbances – Low (8→ 2 days/year) 81*** (8) 18.767 −5 (0.5) 32.387 Water supply security – High (200 → 50 days/year) 1064*** (109) 57.027 1043*** (107) 122.613 Water supply security – Medium (200 → 100 days/year) 691*** (71) 43.584 671***(69) 85.186 ***, **, * indicate significance level at 1%, 5%, and 10% 3 The marginal WTP estimates assume a constant marginal utility. 4 Notably, in the process of finding the final model, several explanatory variables were interacted with the Status quo variable. For citizens as well as public officials, neither a university degree nor knowledge of floods had any explanatory power. The effect of the respondents’ income, which was also tested, was not statistically significant for either sample group’s probability of choosing the status quo; testing for the effect of age produced similar results. Earlier studies, such as those by Ågren et al. (2006) and Johansson-Stenman (2008), have found that these more socio demographic and knowledge-related variables provide explanations for discrepancies between the decisions taken by officials and the preferences of the general public. This study can conclude that neither of these categories of variables were found to have any statistically significant effect on either citizens’ or officials’ probability of choosing the status quo of doing nothing to reduce the negative effects of floods. E. Spegel The only explanatory variable that has a similar impact on both groups is climate change: concerns about climate change reduce the probability of choosing the Status quo alternative in both samples. Moreover, if one includes individual differences in perceptions about climate change as an explanatory variable in the model, the Status quo variable becomes statistically insignificant. Thus, it seems that climate change concerns are an important driver for the negative status quo bias. These results are consistent with previous findings (Botzen and Van den Bergh, 2012a,b). Citizens’ probability of choosing the Status quo alternative is further statistically reduced if they have trust in the state. In other words, citizens are more likely to choose a costly option – which implies doing something to reduce the social costs of floods – if they are confident that state actors (government, national authorities) live up to their responsibility to prevent the negative effects of floods in respect of property damage, traffic disturbances and water supply security. For the sample of public officials in the current study, no such statistically significant relationship between trust in government institutions and the probability of choosing the status quo was found. Both sample groups were also asked about the extent to which they trusted that citizens would fulfil their responsibility to prevent the negative effects of floods with regard to property damage, traffic disturbances and water supply security. For the citizen sample, trusting the individual to live up to his/her responsibility increases the probability that citizens would choose the Status quo alter- native. This result is statistically significant at the 5% level. This outcome seems reasonable: if citizens trust that private individuals live up to their responsibility to reduce the negative effects of floods, less needs to be done in this regard by public institutions. For the sample of public officials, no statistically significant relationship was found between trust in the individual and the probability of choosing the Status quo alternative. Finally, the three variables concerning the citizens’ and public officials’ previous experience of the relevant flood-related events – namely property damage, traffic disturbances and water supply security – were included in the RPL Model. It was hypothesised that respondents’ willingness to accept higher costs to reduce the negative impacts of floods would be influenced if they had been affected by such impacts. For the sample of public officials, the results show no such statistically significant relationship. For the citizen sample, however, individuals that had experienced low water quality and drinking water shortages were more likely to choose the status quo than those who had not. This result seems somewhat surprising at first, given that experiencing a shortage in drinking water is expected to have a negative effect on an individual’s well-being. However, it is possible that an individual’s actual experience of an interrupted drinking water supply was regarded as being less of a problem than an imagined interruption. The coefficient is only statistically significant at the 10% level. 4. Conclusion This paper has analysed the preferences of public officials and citizens regarding the negative impacts of floods. Citizens and public officials in the flood-prone Gothenburg region answered identical CE surveys characterised by the following negative impacts of floods, namely property damage, traffic disturbances and water supply security. By having citizens and public officials respond to identical surveys, differences in prioritisation amongst the negative effects of floods and their monetary valuation were analysed. In general, the estimated marginal WTP suggests that floods have a negative impact on the utility of both groups: both citizens and public officials were willing to pay to reduce such impacts. This implies that both public officials and citizens would benefit from further investments in flood-risk-reducing measures being made in order to limit the future costs caused by floods in the Gothenburg region. Furthermore, public officials generally seemed to make similar prioritisations to those that citizens did, with water supply security being considered a more important matter to address than property damage, which was in turn considered more important that traffic Table 9 Estimated Random Parameter Logit Model including explanatory variables6 (standard errors in parentheses). RPL Citizens Officials Variable Coefficient Standard deviation Coefficient Standard deviation Water provision security −0.176 (0.001)*** 0.019*** −0.035 (0.006)*** 0.024*** Property damage −0.004 0.001)*** 0.012*** −0.013 (0.005)*** 0.012*** Traffic disturbances −0.033 (0.007)*** 0.096*** −0.003 (0.020) 0.046 Fee 0.002 (0.000)*** 0.004*** −0.004 (0.001)*** 0.003*** Status quo (stq) −0.154 (0.467) Fixed −3.602 (2.849) Fixed Woman*stq −0.595 (0.166)*** Fixed 0.019 (0.697) Fixed Climate change*stq −0.361 (0.074)*** Fixed −0.71532 (0.294)** Fixed Trust in state*stq −0.548 (0.132)*** Fixed −0.131 (0.351) Fixed Trust in municipality*stq 0.084 (0.128) Fixed 0.741 (0.490) Fixed Trust in citizens*stq 0.248 (0.100)** Fixed 0.514 (0.527) Fixed Affected by water provision 0.248 (0.133)* Fixed 0.088 (0.824) Fixed Affected by property damage −0.028 (0.143) Fixed 1.069 (0.662) Fixed Affected by traffic disturbances 0.119 (0.119) Fixed −0.429 (0.515) Fixed Log likelihood value −4774.309 −421.535 Mc Fadden R2 0.329 0.436 ***, **, * indicate significance level at 1% 5% and 10%. E. Spegel