Carl Andreas Fossick Ströberg

Master’s thesis

Social Vulnerability Index-assessment for Iceland

Advisor: Kristinn Hermansson, Ph.D.

University of Akureyri Faculty of Business and Science University Centre of the Westfjords Master of Resource Management: Coastal and Marine Management Ísafjörður, August/September 2018

Supervisory Committee

Advisor: Kristinn Hermansson, Ph.D.

External Reader: Matthias Kokorsch, Ph.D.

Program Director: Catherine Chambers, Ph.D.

Carl Andreas Fossick Ströberg Social Vulnerability Index-assessment for Iceland

45 ECTS thesis submitted in partial fulfilment of a Master of Resource Management degree in Coastal and Marine Management at the University Centre of the Westfjords, Suðurgata 12, 400 Ísafjörður, Iceland

Degree accredited by the University of Akureyri, Faculty of Business and Science, Borgir, 600 Akureyri, Iceland

Printing: Háskólaprent, Reykjavík, September 2018

Declaration

I hereby confirm that I am the sole author of this thesis and it is a product of my own academic research.

______Carl Andreas Fossick Ströberg

Abstract

Although much attention has been given to urbanization and rural development issues in Iceland, their effect on communities vulnerability to environmental hazards has so far been given less attention. This study assesses the impact that urbanization in Iceland has on the socio-economic conditions that commonly affect the preparedness for, susceptibility to, and ability to recover from the possible impact of environmental hazards. The study applies a social vulnerability index-assessment to identify how a variety of socio-economic variables combine to produce different levels of social vulnerability in Icelandic municipalities. Six components, consisting in total of 17 socio-economic variables, were identified as either increasing or decreasing social vulnerability through different combinations across the country. Results indicate that that urbanization in Iceland has a larger scale effect of decreasing social vulnerability for a majority of its population which is urbanizing, while increasing it for the small minority that reside in municipalities suffering most from demographic and economic decline. Whereas demographic instability, unemployment, and/or a lack of permanent residents tended to increase social vulnerability the most, an unhealthy economic climate was a more common contributor across the country as a whole. A healthy economic climate and conditions on the housing market was identified as the strongest factors for lowering social vulnerability, mainly, but not exclusively in densely populated urban areas.

Útdráttur

Á meðan málefni er varða þéttbýlismyndun og dreifbýlisþróun á Íslandi hafa vakið talsverða eftirtekt þá hefur skort á umfjöllun um tengsl þeirra varðandi varnarleysi samfélaga gagnvart umhverfishættum. Þessi rannsókn metur þau áhrif sem borgarvæðing á Íslandi hefur á þær félags-hagfræðilegu aðstæður sem venjulega hafa áhrif á undirbúning, áhættu um og hvernig samfélög jafna sig eftir mögulegar umhverfishættur. Í rannsóknin er beitt mælikvarða á félagslegt varnarleysi til segja til um hvernig félags-hagfræðilegar breytur geta í sameiningu valdið mismiklu félagslegu varnarleysi í Íslenskum sveitarfélögum. Sex þættir, sem samanstóðu af 17 félags-hagfræðilegum breytum, áttu hlut í því að annað hvort auka eða draga úr félagslegu varnarleysi í gegnum mismunandi samsetningar. Niðurstöður gefa til kynna að þéttbýlismyndun á Íslandi gegnir veigamiklu hlutverki í að draga úr félagslegu varnarleysi fyrir meirihluta fólks sem býr í samfélögum sem ganga í gegnum þéttbýlismyndun, en eykur félagslegt varnarleysi fyrir þá fáu íbúa í sveitarfélögum sem ganga í gegnum lýðfræðilega og efnahagslega hnignun. Á meðan lýðfræðilegur óstöðugleiki, atvinnuleysi, og/eða skortur á íbúum með varanlega búsetu voru þær breytur sem áttu til með að auka á félagslegt varnarleysi sem mest, þá var erfitt efnahagsástand algengari áhrifaþáttur á landið til heildar. Stöndugt efnahagsumhverfi og aðstæður á húsnæðismarkaði þóttu áhrifamestu þættirnir í að draga úr félagslegu varnarleysi, en þó ekki einir og sér á þéttbýlli svæðum.

Table of Contents

List of Figures ...... vii

List of Tables ...... viii

1 Introduction ...... 1 1.1 Research Question ...... 2 1.2 Study Area ...... 2

2 Social Vulnerability and its Application in Iceland ...... 5 2.1 Social Vulnerability: concepts and applications ...... 5 2.2 Social Vulnerability-assessment and demographic trends in Norway ...... 9 2.3 Social Vulnerability in Iceland ...... 11 2.3.1 Social Vulnerability and Demographic trends in Iceland ...... 11 2.3.2 Age and Social Vulnerability ...... 14 2.3.3 Economy and Social Vulnerability ...... 18 2.4 Urbanization and Social Vulnerability in Iceland ...... 24

3 Method: Designing a Social Vulnerability Index for Iceland ...... 25 3.1 Design of Social Vulnerability Indicator Dataset ...... 25 3.1.1 Spatial scale, resolution, and measurement units ...... 25 3.1.2 Social vulnerability indicators ...... 26 3.1.3 Multicollienarity, uniqueness, and confounding factors ...... 35 3.2 Principal Component Analysis ...... 36

4 Results ...... 41 4.1 Principal component summary ...... 41 4.2 Components Influence and Spatial Distribution ...... 43 4.2.1 Component 1. Demographics ...... 43 4.2.2 Component 2. Housing and population density ...... 44 4.2.3 Component 3. Infrastructure and family size ...... 45 4.2.4 Component 4. Economic ...... 46 4.2.5 Component 5. Temporary residents and migration ...... 47 4.2.6 Component 6. Unemployment ...... 48 4.3 Social Vulnerability Index-assessment for Iceland ...... 49

5 Discussion ...... 53 5.1 Limits for assessing Social Vulnerability for Iceland...... 53 5.1.1 Data-set construction and component identification in a new context ...... 53 5.1.2 Scales ...... 57 5.2 Social Vulnerability in Iceland ...... 60 5.2.1 Which are the causes for social vulnerability in Iceland? ...... 60 5.3 Urbanization and Social Vulnerability Patterns ...... 63

6 Conclusions...... 67

References ...... 69

Appendix A ...... 77

Appendix B...... lxxxiii

List of Figures

Figure 1. Study area. The maps depict the 8 regions and the borders of the 74 municipalities for which social vulnerability will be measured ...... 3

Figure 2 Population changes (%) in Icelandic municipalities from 1998 to 2017 ...... 15

Figure 3 Median age and population change in icelandic municipalities between 1998 and 2017...... 16

Figure 4 Median age change in Icelandic municipalities ...... 17

Figure 5 Median age change in Icelandic municipalities between 1998 and 2017 ...... 17

Figure 6 Population change against distribution of ITQs in icelandic regions from 1998 to 2017 ...... 21

Figure 7 Change in the distribution of ITQs within icelandic regions and municipalities from 1998 to 2017 ...... 22

Figure 8 Map of component scores for component 1: Demographics ...... 43

Figure 9 Map of component scores for component 2: Housing and population density ..... 44

Figure 10 Map of component scores for component 3: Infrastructure and family size...... 45

Figure 11 Map of component scores for component 4: Economic...... 46

Figure 12 Map for component scores for component 5: Temporary residents and migration ...... 47

Figure 13 Map of component scores for component 6: Unemployment ...... 48

Figure 14 Map of social vulnerability in Iceland ...... 49

vii List of Tables

Table 1 Age and population changes in Icelandic regions: 1998-2017 ...... 13

Table 2 Variables used to produce a social vulnerability assessment for Icelandic municipalities ...... 34

Table 3 Social vulnerability classification ...... 38

Table 4 Social vulnerability component summary ...... 42

Table 5 SVI-scores and component scores for the 5 most socially vulnerable and the 5 least vulnerable municipalities ...... 50

Table 6 Comparing Social Vulnerability Scores, Demographic data, and ITQ ...... 51

1 Introduction

Urbanization in Iceland has over the last decennia affected socio-economic development in more urban and rural parts of the country differently. While municipalities around the capital of Reykjavik have experienced stronger economic growth and one of the largest relative population increases in any Nordic country (Grunfelder, Rispling & Norlén, 2016), others have instead experienced different degrees of demographic or economic decline (Ragnarsson et al., 2017). Such changes, besides affecting the quality of life for inhabitants across the country, could also have more indirect consequences as pre-existing socio- economic conditions and changes to these are commonly regarded as influencing communities’ resilience to environmental hazards (Chen, Maki & Hayashi, 2014; Cutter, 2012).

Research on the socio-economic aspects of environmental risk management, termed social vulnerability, suggest that municipalities that are struggling socio-economically are often more vulnerable than others to environmental hazards (Adger et al., 2004; Benson & Clay, 2004; Cutter, 2012; McCarthy et al., 2015; Singh, Eghdami & Singh, 2014).

If this would be the case also in Iceland, it could imply that urbanization as a phenomenon might affect how communities can prepare for, handle, and recover from environmental hazards, and change these abilities over time. Although Iceland has been regarded as one of the safer countries in the world (Gardschagen et al., 2014), adapting to the socio- economic changes brought on by urbanization might be needed to remain so over the long term future.

The effects that socio-economic developments can have on a society’s vulnerability to environmental hazards are rarely mentioned in Icelandic environmental risk-management, perhaps because it has not yet been considered an issue of concern. Regardless of ones stance within the ongoing discourse on rural and urban development in Iceland, the ability to mitigate any negative effects that urbanization might have on safety of people in all parts of the country should not be neglected. It is therefore relevant to consider how the ability to handle the negative effects of potential environmental hazards could be affected when municipalities experience changes to the socio-economic conditions that help uphold such abilities.

1 1.1 Research Question

The aim of this study is to identify socio-economic factors and measure their impact on social vulnerability in Icelandic municipalities, and to further estimate how such an impact might change over time. This is achieved through the application and evaluation of a Social vulnerability index-assessment (SVI-assessment) for Iceland. The study is structured through two subsequent research questions:

 What are the limitations for producing a SVI-assessment for Iceland?

 How does urbanization influence the social vulnerability of Icelandic municipalities?

A SVI-assessment serve as an exploratory analytical tool that applies a broad range of socio-economic data to identify factors that affect society’s resilience to environmental hazards, and to measure how these factors impact different areas of the country. A wider understanding of how socio-economic factors influence the vulnerability to environmental hazards provides important knowledge on how to better prevent and mitigate risks that can cause harm to people, socio-economic functions, and the built environment. It can contribute to strategies aimed at improving the conditions of especially vulnerable communities, and to counteract developments that increase or produce new risks over time. The study therefore serve a dual-purpose of (1) broadening our understanding of how socio-economic developments in Iceland might change its vulnerability to environmental hazards, while also (2) producing information on the relationships between socio-economic processes and conditions, and how these function to affect municipalities in Iceland differently.

1.2 Study Area

Iceland is commonly divided into 8 regions, mainly used for statistical purposes, and 74 municipalities for which most local services are governed. Emergency services are coordinated to within 9 civil protection districts which follow a similar divisions as that in figure 1, with the exception of Vestmannaeyar which function as an individual district. Figure 1 depicts the 8 regions, and the borders of the 74 municipalities, as to provide a broader overview of the structure and scale of these divisions. The country has a relatively small population of 338.349 of which a majority live in the south-west, within commuter

distance to the capital of Reykjavík. With a population density of 3 inhabitants per sq. km Iceland is considered as having the 5th lowest mean population density in the world. The population density in urban areas, commonly located to the coastline is however often significantly higher (EEA, 2015), whereas large parts of the inland are uninhabited. Outside of the direct vicinity of the capital region, Akureyri serve as the Figure 1. Study area. The maps depict the 8 regions and largest population center of the the borders of the 74 municipalities for which social vulnerability will be measured north-coast, Ísafjörður in the north-west, and Egilsstaðir in the east (Ragnarsson et al., 2017). The size and composition of municipalities have changed a number of times over the last 30 years, mainly through mergers of smaller and less populated municipalities into more populated ones (Hagstofa Íslands, 2017a). The study applies a municipal scale to measure the social vulnerability of all municipalities in the country. The 8 regions are be used mainly as a structure within which municipalities can be regionalized and referenced, and as to better describe socio- economic phenomena at a broader scale.

2 Social Vulnerability and its Application in Iceland

Chapter 2 reviews theoretical concepts and their application within the field of vulnerability-studies, focusing on social vulnerability in particular. Such a review serve to identify potential issues that could impact social vulnerability in Iceland, and how an assessment of these can help develop the field of vulnerability-research. The chapter puts emphasis on large scale socio-economic and demographic changes related to urbanization, a factor that is seldom considered in environmental risk assessments. Such is done through discussing the changes to the socio-economic landscape of Iceland and how these changes would impact any eventual risks stemming from environmental hazards over the long term.

2.1 Social Vulnerability: concepts and applications

The concept of vulnerability as applied within environmental risk assessment-studies serve to explain the notion that people and places which are equally exposed to similar hazardous phenomena can be affected to different extents based on their degree of vulnerability (Cutter, 2012; Esnard & Sapat, 2014; Ranci, 2010). In cases involving environmental hazards, vulnerability is often broken down into two separate albeit interrelated concepts, the ‘biophysical vulnerability’, and the ‘social vulnerability’. While biophysical vulnerability concerns the physical attributes of the environment and how these could behave to impact the health and safety of populations, social vulnerability instead focus on how the socio-economic circumstances of said populations can affect or be affected by risks stemming from biophysical hazards. Once both perspectives on vulnerability have been assessed they are often combined to allow for the overall place vulnerability of a chosen area to be estimated.

Estimating the probability, magnitude, and sites of environmental hazards, or presence of physical factors that can cause hazardous events are commonly targeted to assess the biophysical vulnerability of a place. Such hazards can range from short term or sudden events e.g. storms, landslides, avalanches, or floods, to long term or large scale processes such as sea-level rise, soil erosion or droughts. However, the biophysical vulnerability

5 alone cannot provide much concise or practical information regarding how such hazards might impact societies, as it does not describe what these events would impact and therefore what the consequences of that impact could be to us. Instead, it is today recognized that many, if not most of the negative effects related to environmental hazards can be traced to pre-existing socio-economic conditions of the populations that they affect (Benson & Clay, 2004; Chen, 2014; Cutter, Boruff & Shirley, 2003; Cutter, 2012; Esnard & Sapat, 2014; McCarthy et al., 2015), implying that risks of negative impacts attributed to environmental hazards are to a large extent socio-economically and politically determined.

The term social vulnerability as operationalized within place vulnerability-studies, e.g. Adger et al., (2004), Armas & Gavris (2013), Cutter (2012), Dwyer et al. (2004), Fatemi et al. (2016), Holand & Lujana (2013), Lee (2014), Oxfam (2012), Rapaport et al. (2015), is used as a theoretical and methodological framework to identify and measure the influence of socio-economic factors on the preparedness for, susceptibility to, and ability to recover from the possible impact of environmental hazards. The term relates to a resilience which in this paper defines the ability of a societys socio-economic systems, both physical and non-physical, to absorb stresses caused by hazardous environmental events. Investigating the societal aspects of environmental risk management helps us understand why some groups or communities are disproportionally more vulnerable than others (Cutter, 2012; Singh, Eghdami & Singh, 2014). Examples of where social vulnerability have been assessed include: flooding in Chiayi County, Taiwan (Lee, 2014) and São Paulo, Brazil (Roncancio & Nardocci, 2016), climate change induced hazards along the Gulf coast, US (Oxfam, 2010), drought in South Africa (Muyambo et al., 2017), air pollution in the Yangtze River Delta Region, China (Ge et al., 2017), seismic hazards in Iran (Zebardast, 2013), and Italy (Frigerio et al., 2016), storm surges, flooding, and mass movements in Norway (Holand & Lujana, 2013), and riverbank erosion in Bangladesh (Monirul-Alam et al., 2017).

Assessing social vulnerability through the application of SVI-assessments has become more common within the field of environmental risk management over the last 20 years. Part of the reason for this development can be attributed to the way in which it provides a means to measure and compare the relative vulnerability of different areas, and explain the varying underlying causes in an easily communicable manner. The methodology is based on an exploratory statistical approach wherein principal component analysis is used to

reduce a large number of socio-economic variables, all of which are recognized to influence social vulnerability, by grouping them into a smaller number of components in which the variables correlate. Because all the socio-economic variables within a component behave in a relationship where the values of a variable can be roughly estimated through the values of the others, the components can be applied to identify and measure broader socio-economic phenomena without compromising the use of a large data-set of variables. Based on which variables that measure to act together as a component, they can indicate a variety of socio-economic phenomena. Commonly identified components tend to range from demographics, public or private finances, social capital, infrastructure, housing markets, to political power (Cutter, 2012; Cutter & Emrich, 2017; Dwyer et al., 2004; Curatolo & Wolleb, 2010; Thomas et al., 2013; Singh, Eghdami & Singh, 2014; Rapaport et al., 2015; Fatemi et al., 2016; Krawchenko et al., 2016), although these often vary based on place-specific circumstances. If the variables in a component all affect social vulnerability in the same direction i.e. positively or negatively, the component can act as a relative measurement of social vulnerability that can be used to compare its influence across different parts of the study area. As such, the SVI-assessment give an broad indication of the factors that increase and decrease social vulnerability, and provide a relative measurement of where these factors are more or less influential. Ultimately, these measurements are combined to indicate the overall social vulnerability of all parts of the study area, based on how their vulnerability deviates from the overall mean.

A conclusion shared by most SVI-assessments, and one which has long been firmly cemented into the theoretical foundation of vulnerability-research is that places and populations deemed most vulnerable to environmental hazards are, in a majority of cases, also those that are struggling socio-economically (Cutter, Mitchell & Scott, 2000; Cutter, Boruff & Shirley, 2003; Cutter, 2012; McCarthy et al., 2015; Thomas et al., 2013). A healthy socio-economic climate instead often coincide with a better ability prepare for, handle, and recover from potential losses caused by hazardous events (Cutter 2003; Cutter et al. 2012; McCarthy et al., 2015). Viewing vulnerability to environmental hazards as a state that reacts to socio-economic changes would suggest that urbanization, one of the most considerable such changes in the world today, should have a large, yet less understood impact on the health and safety of the people it affects.

7 While most studies that have applied a SVI-methodology have been conducted in less economically developed regions where the frequency and magnitude of environmental disasters are often more widely felt, few have targeted regions with similar socio-economic conditions to those of Iceland, a country that is considered mostly and fairly safe to live in (Gardschagen et al., 2014; Hall et al., 2008; Harjanne et al., 2016; Jóhannesdóttir, 2011). Over the last decades Iceland and its neighboring countries have experienced large scale changes to their demographic and socio-economic landscape, such as urbanization, de- industrialization, and a related depopulation of the countryside (Bjarnason, 2014; Bjarnason & Thorlindsson, 2006; Böhme, 2002; Friðriksson & Kristinsson, 2016; Grunfelder et al., 2016; Hörnström et al., 2015; Karlsdóttir & Ingólfsdóttir, 2011; Karlsdóttir et al., 2012; Karlstad & Lie, 2008; Leknes et al., 2016; Magnusson, n.d; Ragnarsson et al., 2017). The influence of such changes on social vulnerability, and indirectly also place vulnerability, have seldom been acknowledged in environmental risk assessments (Hall et al., 2008; Holand & Lujana, 2013; Jóhannesdóttir, 2011), which are often aimed at providing a more short-term perspective on vulnerability that can help mitigate acuter issues. A better understanding of how socio-economic processes and circumstances interact to affect social-vulnerability in Iceland should provide useful insights for future strategies to adapt to socio-economic changes while maintaining a high resilience towards environmental hazards.

In cases like Iceland, where social vulnerabilities have not been assessed before, and where the available data suited to performing a SVI-assessment can be limited, the analysis and possible interpretations of results are especially sensitive to data-input. The choice of socio-economic variables used to measure social vulnerability strongly influence the components or factors the assessment can identify, and thereby ultimately the validity of the assessment as a whole. It is therefore important to review previous research performed in regions with similar characteristics to Iceland, as to create a theoretical and methodological foundation on which a SVI-assessment can be constructed.

Holand and Lujana’s (2013) SVI of Norway, and Krawchenko et al.’s (2016) and Rapaport et al.’s (2015) studies on ageing and social vulnerability in Nova Scotia, Canada, are some of the few that have turned their focus towards the socio-economic issues that could affect the social vulnerability of those communities around the north Atlantic that are experiencing a changing industrial landscape, urbanization, and depopulation. Of these few

studies, the environmental and social-economic characteristics in Norway, although not alike in every aspect, are similar enough to facilitate a shorter review of SVI-assessments performed there (Holand, Lujana & Rød, 2011; Holand & Lujana, 2013) and which could provide a theoretical and methodological foundation for assessing similar issues in an Icelandic context.

2.2 Social Vulnerability-assessment and demographic trends in Norway

In 2011 Holand, Lujana, & Rød produced a SVI-assessment for Norway following standardized guidelines by Cutter, Boruff, and Shirley (2003), guidelines originally designed to fit a North American socio-economic context. The study was later adapted and replicated in 2013 as to better reflect Norwegian circumstances, wherein conceptual changes were made to the choice of variables used to represent socio-economic processes, and the interpretation of how these influence social vulnerability there. Norway and Iceland are to a large extent exposed to similar environmental hazards (Gabrielsen & Lacasse, 2015; Holand & Lujana, 2013; Jóhannesdóttir, 2011), with the exception of the volcanically related activities present in Iceland. As the most common biophysical hazards are similar, i.e. storms, avalanches, land and mud-slides, rock-falls, and floods, the choice of socio-economic variables applicable to social vulnerability-studies in Norway might also be suited as a theoretical and methodological foundation to construct a more valid assessment of Iceland.

In Holand & Lujana’s adapted assessment (2013) a dataset of 33 variables for each of the 431 municipalities was collected and used to identify which of the variables correlated and together acted to either increase or decrease social vulnerability in different municipalities. Based on these variables the assessment measured and extracted nine components, referred to as factors, which were most influential on social vulnerability: (1) Age and social status, (2) Marginal social groups, (3) Low-skill employment, (4) Urban areas, (5) High density populations, (6) Demographic instability, (7) Share of renters, (8) Municipal finances, (9) Aging infrastructure, and (10) Availability and quality of medical and social services.

Similar to Iceland, Norway is also experiencing a large-scale rural to urban-shift (Karlstad & Lie, 2008; Leknes et al., 2016; Ragnarsson et al., 2017), and many of the factors identified by Holand and Lujana (2013) are likely to be impacted by this process, both in

9 more rural and more urban municipalities. Mentions of the possibility of this changing the social vulnerability of municipalities over the long-term is absent in most Norwegian reports and development-strategies (Karlstad & Lie 2008, Leknes et al., 2016), and only briefly discussed in regards to climate change (NOU 2010:10).

A majority of the municipalities that were deemed as having a high social vulnerability in Norway have populations of under 4000 inhabitants, and have experienced a population decline of between -12% to -34% between the years 1990 and 2017 (Holand & Lujana, 2013; Statistisk sentralbyrå, 2017). Larger population centers were also commonly measured as more socially vulnerable, although these instead showed increased population numbers during the same period, e.g. Trondheim (137.346 to 190.464 individuals or +38%), Oslo (458.364 to 666.759 individuals or +45%). Less populated municipalities with high social vulnerability also show an increased share of inhabitants at 67 years of age or older, by between +1% and +63% (Statistisk sentralbyrå, 2017), while the share of this age group had declined somewhat in urban municipalities. This would imply that the impact of aging and declining populations on social vulnerability is an issue in smaller, more sparsely populated municipalities, whereas population increase is an issue in quickly growing urban regions to which the younger populations often migrate. If these demographic changes continue, the chance exist that their social vulnerability would continue to increase as well. In Norway there are however also examples of municipalities wherein a lower social vulnerability has been maintained regardless of e.g. population decline and ageing, and where it has also been reversed through other e.g. economic means (Leknes et al., 2016). Municipalities that measured a lower vulnerability by Holand and Lujana (2013) vary in population from around 1000 in Loabák - Lavangen to ca. 75.000 in Tromsø (Statistisk sentralbyrå, 2017). Municipalities with low vulnerability show less clear demographic patters over time, with population changes between 1990-2017 ranging from +19% in Hattfjelldal, to -24% in Lebesby (Statistisk sentralbyrå, 2017). Low-vulnerability municipalities also show no consistent pattern of aging, where the percentage of people at the age of 67 or above have both increased +46% in Storfjord/Omasvuotna (+2,9% population increase) and decreased by -26% in Snillfjord (-16,9% population decrease) between 1990-2017.

This would imply that the current demographic developments in Norway act foremost to increase social vulnerability in both shrinking and growing municipalities, while other

socio-economic factors could play a larger role in decreasing it. These suggestions can in part be supported through Leknes et al. (2016) who report that a combination of employment, demographics, and wealth decides the prerequisites for small and medium- sized communities to develop in Norway. The location of land-based facilities and employment opportunities within or related to the oil/gas- or fishing-industries are thought to play an important role in maintaining economic growth and socio-economic development in less populated municipalities, where those that have no such opportunities have generally fared worse than those who do (Kommunal- og moderniseringsdepartementet, 2016; Leknes et al., 2016). The comparison implies that municipalities which suffer from aging and population-decline can maintain a lower social vulnerability through other, mainly economic means. These implications are of relevance to the larger socio-economic and demographic changes taking place in Iceland, and how these might impact the social-vulnerability of municipalities there differently.

2.3 Social Vulnerability in Iceland

Biophysical vulnerabilities in Iceland are well studied and assessed at regular intervals (Almannavarnadeild Ríkislögreglustjóra, 2017; Harjanne et al., 2016; Jóhannesdóttir, 2011). Although the width of these assessments encompass most environmental hazards, their focus tend to be mainly on biophysical risks, leaving the socio-economic aspects somewhat in the background. Such a focus could limit our understanding of how socio- economic forces influence environmental risks in Iceland, and the ability to predict and mitigate new risks that could emerge over the coming decades. The following section will review how the most common factors related to social vulnerability fit into an Icelandic context, and how a SVI-assessment would have to be adaptded to the circumstances of the study area.

2.3.1 Social Vulnerability and Demographic trends in Iceland

Demographic compositions and changes to these are deemed as having an essential influence on social vulnerability and therefore to the production of a SVI-assessment. Demographic influences generally concern population growth/decline, the distribution of age groups, family structures, and migration patterns, amongst others (Adger et al., 2004; Carter et al., 2016; Chen, Maki & Hayashi, 2014; Cutter, 2012; Donner & Rodríguez, 2008). Large and fast population growths concentrated to smaller areas are often

11 considered a contributing factor to increased social vulnerability (Adger et al., 2004; Armas & Gavris, 2013; Cutter, Boruff & Shirley, 2003; Dwyer et al., 2004), while the effects of population decline and sparsity have so far been less studied.

Between 1998 and 2017 the total population of Iceland increased by 24,2% from 272.381 to 338.349 (Byggðastofnun, 2017). During this period Höfuðborgarsvæðið/The Capital- area grew by 31,7%, and today host 64% of Iceland’s population (Hagstofa Islands, 2017). No signs indicate any significant slowdown of this growth in the near future (Hagstofa Islands, 2016; Ragnarsson et al., 2016, 2017; Sverrisdóttir & Guðjónsson, 2017; VSÓ, 2017). Factors that have been attributed to an increased social vulnerability in urban areas, such as a higher share of low-skill employment, growth of urban areas, high density populations, and a higher share of renters, as measured by Holand & Lujana (2013) are to different degrees present also in Iceland’s rapidly growing urban regions, and particularly the municipalities around Reykjavík (Sverrisdóttir & Guðjónsson, 2017; VSÓ, 2017). Should this development continue, factors that increase social vulnerability in urbanizing municipalities as measured by Holand & Lujana (2013) might act to increase it also in the urbanizing municipalities in Iceland.

As can be seen in table 1 below, 6 out of the 8 regions have experienced a population increase since 1998. The 2 regions that stand out by showing significant decreases are Norðurland vestra and Vestfirðir (Westfjords) in which the population has declined by - 11,5% and -19,2% respectively. The regional spatial scale would imply that most areas of the country have grown population-wise when in fact a majority of Icelandic municipalities have been subject to population decline. Population-changes on a municipal scale as depicted in figure 2. (Tables for demographic changes on a municipal scale available in the appendix A) show that the growth within and between municipalities is more concentrated to the south-western part of the country. 33 (44,6%) municipailties have experienced a population increase, while 41 (55,4%) have experienced a population decline (Hagstofa ìslands, 2017).

Table 1 Age and population changes in Icelandic regions: 1998-2017

Pop: % of Pop: % of Pop- Median Median 1998 total 2017 total Change age age pop pop % 1998 2017 1998 2017 36,3 Iceland 272381 338349 + 24,2 % 32,2 (+4,1) Höfuðborgar 36,1 164606 60,4 % 216878 64,1 % + 31,7 % 32,5 svæðið (+3,6) Norðurland 38,1 28417 10,4 % 29685 8,7 % +4,5 % 32,2 Eystra (+5,9) 37,5 Suðurland 22832 8,4 % 27528 8,1 % +20,6 % 31,9 (+5,6) 33,9 Suðurnes 15715 5,8 % 23993 7,1 % + 52,6 % 30,3 (+3,6) 36,2 Vesturland 13924 5,1 % 15929 4,7 % +9,8 % 32,1 (+4,1) 37,3 Austurland 9946 3,6 % 10310 3 % +3,6 % 32,6 (+4,7) Norðurland 39,6 8090 3 % 7156 2,1 % -11,5 % 32,4 vestra (+7,2) 38 Vestfirðir 8556 3,1 % 6402 1,9 % - 19,2 % 31,1 (+6,9) (Source: Hagstofa Ìslands 2017)

At the other end of the urbanization-process, smaller communities which are subject to population decline often experience different degrees of socio-economic hardships (Berglund, Johansson & Molina, 2005; Bjarnason & Thorlindsson, 2006; Bjarnason, 2014; Friðriksson & Kristinsson, 2016; Karlsdóttir et al., 2012; Ragnarsson et al., 2016, 2017). This development is often acknowledged in rural studies, but seldom in connection to how it can affect municipalities resilience to environmental hazards or related health and safety concerns. As was the case in Norway, population decline is likely of more importance in an Icelandic context than what is usually the case in vulnerability studies, which are often produced for regions where this is a less common issue. Sparsely populated areas within which built environments are fewer, smaller, and further between might contribute to a situation where isolating factors can decrease the preparedness for, ability to handle, or recover from environmental hazards. Comparing population numbers and population change in Icelandic municipalities show how, similar to Norway, population decline is an issue that mainly affect municipalities with an already relatively small population. If the 41

13 municipalities with a shrinking population would depopulate further fewer people in these areas would be affected by possible environmental hazards occuring there, but the already vulnerable demographic groups that are commonly staying, e.g. older generations, would possibly become more exposed.

To date, a number of reasons behind centralization- and depopulation-trends in Iceland have been suggested through research covering a large variety of more or less interrelated issues such as labour markets/employment (Bjarnason & Thorlindsson, 2006; Bjarnason, 2014; Grunfelder et al., 2016), movement/concentration of individual tradable quotas for fisheries (Benediktsson & Karlsdóttir, 2011; Magnusson, n.d.), gender-related work/educational opportunities (Karlsdóttir & Ingólfsdóttir, 2011; Karlsdóttir et al., 2012), isolation and spatial planning (Böhme, 2002) amongst others. Population changes are only a part of what can be described as the demographic aspect of social vulnerability, and cannot by itself tell us very much about how and why demographics impact social vulnerability. To begin with, the effects of, and reasons behind population changes become somewhat clearer when related to age distributions and age changes.

2.3.2 Age and Social Vulnerability

The composition of age groups in a municipality can have a direct influence on social vulnerability as young and elderly are considered more vulnerable age-groups in the occurrence and aftermath of hazardous events (Carter et al., 2016; Krawchenko et al., 2016; Rapaport et al., 2015). Older populations, and particularly those who reside in care facilities are disproportionally affected by hazardous events (Johnson et al., 2014; Rapaport et al., 2015). Similarly, a larger proportion of younger age groups, mainly those dependent on adults, are considered to increase social vulnerability (Cutter, Boruff & Shirley, 2003; Fatemi et al., 2016; Frigerio et al., 2016). Age can also influence social vulnerability indirectly as heavily unbalanced age compositions in municipalities can affect aspects related to a community’s socio-economic health and future demographic development (Adger et al., 2004; Cutter, Boruff & Shirley, 2003).

Figure 2 Population changes (%) in Icelandic municipalities from 1998 to 2017

15 In Iceland, all but two municipalities, Mýrdalshreppur and Skagabyggð, have experienced an ageing of its population between 1998 and 2017. A more meaningful statistic is therefore to consider the relative ageing between municipalities. Looking at such data can give a better indication of how ageing function in relation to other demographic processes in Iceland. Data on median age and its changes between 1998 and 2017 were calculated and plotted against population changes (%) over the same period (see figure 3). These numbers show how the

20 municipalities which have grown the most population-wise are also 15

2017 often amongst the youngest in the - country. Most of the 10 municipalities that have experienced a population decrease 5 are instead amongst the oldest.

0 Further data on median age of all -50 0 50 100 municipalities in 1998 and 2017

Median age change (years) 1998 (years) change age Median -5 are presented in the appendix A. Population change (%) 1998-2017 Municipalities

Figure 3 Median age and population change in icelandic municipalities between 1998 and 2017

Indications on possible relationships between ageing and population changes in Iceland can also be seen through comparing figure 2 depicting where populations have increased (%), and figures 4 and 5 depicting the median age and where this has increased. Municipalities in the south-west are generally younger than municipalities in other parts of the country, Tálknafjarðarhreppur in the Westfjords, and Fjarðabyggð in Austurland being notable exceptions. Such numbers suggest Figure 4 Median age change in Icelandic municipalities that while stronger ageing might have a larger impact on social vulnerability in less populated municipalities, such might not be the case in quickly growing ones where populations are often younger.

A majority of municipalities in Iceland experience both population decline and a simultaneous ageing of its population, although the pace with which this occur differ between municipalities. Because these two processes are related to increased social vulnerability, it could imply that such an increase Figure 5 Median age change in Icelandic now occur on a larger national municipalities between 1998 and 2017

17 scale. If such would be the case, municipalities showing a more stable demographic development might be less socially vulnerable than those that experience faster and more significant changes. Municipalities within which the population is both declining and ageing do however contain a significantly lower share of the total population of the country. 41.693 (12,3%) individuals live in municipalities with a population decline (41 municipalities), while 296.656 (87,7%) live in municipalities experiencing population increase (33 municipalities). Of these 240.871 (71,2% of the total population) live in Höfuðborgarsvæðið/the Capital area or neighboring Suðurnes, implicating the scale and direction of Icelandic urbanization and how it also affect the age composition across the whole country.

However, as was the case in Norway, other socio-economic factors e.g. the presence of economic activities can affect the impacts that demographic circumstances have on social vulnerability and help to moderate its effects. Demographically unstable municipalities are therefore not necessarily more socially vulnerable than others if they show a relatively healthy economic climate. Furthermore, the reasons why demographic changes occur are very often part of a feedback effect related to other, e.g. economic and social factors, which are also integral parts of SVI-assessments.

2.3.3 Economy and Social Vulnerability

The review of social vulnerability in Norway suggested that demographically unstable municipalities that were given a low vulnerability commonly performed better economically. Such a statement would be in line with general theory on social vulnerability stating that a healthy economic climate can increase the ability prepare for, handle, and recover from potential losses caused by environmental hazards (Cutter, Boruff & Shirley, 2003; Cutter, 2012; McCarthy et al., 2015). It is therefore motivated to review some of the foremost economic influences that could be of relevance to an Icelandic SVI-assessment.

Similarly to Norway, financial risk sharing mechanisms in Iceland exist to provide financial support against natural hazards through mandatory insurance schemes provided by the central government (Harjanne et al., 2016). These schemes can act to offload much of the financial burden from municipalities and private parties in the aftermath of any hazardous events. This type of protection is positive in regards to social vulnerability as it can reduce risks related to material losses and protect capital invested through the built

environment. However, these mechanisms do not remove all financial responsibility from municipalities, whereas many mitigation or resilience efforts, e.g. civil protection, health, spatial planning, education, and structural mitigations are still funded, to different degrees, by the municipalities themselves (Viðlagatrygging Íslands, 2017). Even with insurance schemes and mitigation measures in place, the effects of an unhealthy economic climate can indirectly impact demographic change, property values, and social services over the long term, and thereby also social vulnerability (Benson & Clay, 2004; Bienert, 2014; Cross, 2001; Esnard & Sapat, 2014; Heinz Center for Science, Economics, and the Environment, 2000; McCarthy et al., 2015; Önder, Dökmeci & Keskin, 2004).

Disturbances caused by hazardous events can have a significant impact on the future socio- economic development in a municipality. This is especially the case within single sector economies where economic vulnerabilities increase through reliance on one industry (Benson & Clay, 2004; Shucksmith & Brown, 2016). In vulnerability research, oil development, fishing, and tourism are commonly used as examples of such (Cutter, Boruff & Shirley, 2003; Lee 2014; Oxfam 2012). These sectors tend to contribute to high income levels and prosperity when booming. However, if such an industry is negatively affected by environmental hazards or harder times, recovery is often significantly more difficult and can take longer time, even with insurance policies in place (Benson & Clay, 2004; Cutter, 2012). Industrial activities can however also be considered important for economic resilience, although such is especially the case in municipalities with an otherwise diverse economic landscape wherein risks related to single sector economies are lower (Benson & Clay, 2004; Esnard & Sapat, 2014; Magnusson, n.d; Shucksmith & Brown, 2016).

An economic factor that separates Iceland from many other countries where SVI- assessments have been conducted is the suggested influence that Individual tradable fishing-quotas (ITQ’s) have on local economies, and therefore also on the socio-economic condition of municipalities across the country. Similarly to Norway, the fishing-sector is considered to be of national significance (Grétarsson, 2011), and should not be ignored as a possible factor influencing social vulnerability in Iceland. Although the importance of fisheries in terms of its contribution to the total economy has decreased over the last 20 years, employment opportunities within the sector is an important asset for Icelandic coastal communities, and particularly smaller communities where the range of other employment opportunities are often more limited (Bjarnason & Thorlindsson, 2006;

19 Grétarsson, 2011; Magnusson, n.d.). Icelandic fisheries and fishery-dependent communities were less affected by large-scale economic downturns such as the 2008 banking crisis when compared with many other economic sectors and communities (Benediktsson & Karlsdóttir, 2011). Access to quotas might therefore help to moderate social vulnerability in a similar manner as coastal and sea-based industries have done in Norway. However, the loss of ITQ’s being traded to companies and operations based elsewhere in Iceland have had, and still have an impact on both local public economy and private economy (Benediktsson & Karlsdóttir, 2011). Municipalities that are economically dependent on fishing operations as an employer and which have at some point lost quotas often suffer from economic decline, rising unemployment, declining property-values and demoralization (Bjarnason & Thorlindsson, 2006). Loss of jobs within this sector has also been seen to affect the possibility to maintain essential social services such as schools and infrastructure (Karlsdóttir & Ingólfsdóttir, 2011). As such, ITQ’s could be considered to provide a vital economic lifeline for many municipalities, while simultaneously contributing to economic uncertainty and risks, in cases where fisheries exist as a main employer.

Fishing quotas have since their introduction in the 1980’s concentrated towards a few larger scale operations (Benediktsson & Karlsdóttir, 2011). An increased number of communities have therefore been affected over the long term through losing access to marine resources and job opportunities. This development is suggested to have had an effect on migration patterns within the country as younger generations, families, and migration workers tend to seek jobs and education elsewhere when there are fewer opportunities to be found locally (Bjarnason, 2014; Karlsdóttir et al., 2012). Between the years of 1998 and 2017 the regions that have increased their population the most tend to also have increased their share of the total ITQ. The change in total ITQ shares, as cod equivalent, compared to population change within municipalities between 1998 and 2017 are depicted in figure 6. These numbers do not allow for any causal relationship or direction to be determined, but rather suggest a broader scale movement of ITQs and populations which follow a roughly similar pattern.

Suðurnes 50

40 Höfuðborgarsvæðið

) Suðurland 20

2017 Vesturland - 10

Norðurland Eystra Austurland (1998 0 -6 -5 -4 -3 -2 -1 0 1 2 3 4 -10 Norðurland vestra

-20

Population change (%) within Icelandic regions regions Icelandic (%) within change Population Vestfirðir

Y) Y) -30 ( (X) Change in percentage (%) of total ITQs within regions (1998-2017)

Figure 6 Population change against distribution of ITQs in icelandic regions from 1998 to 2017

Figure 7 depicts the changes in distribution of the total ITQ, for all fish as cod equivalent, between the years 1998 and 2017, in map format. The map shows how regions in the south-west of the country have increased their share of quotas while other regions, mainly those further from Höfuðborgarsvaeðið/The capital area have decreased their shares. However, the movement of fishing quotas are less clear on a municipal scale than on a regional scale, and have both increased and decreased in municipalities within all regions. Furthermore, there seem to be no clear statistical relationship between population change on a municipal scale and ITQ-movement between the years of 1998 to 2017. Yet, claims that the movement of ITQs affect some, especially smaller or rural municipalities, make this an important variable to include in a SVI-assessment. Further knowledge regarding the relationships between demographics, social vulnerability and ITQs would be beneficial both within the context of vulnerability-research and to shed further light on any eventual relationship between ITQs and socio-economic development on a broader scale.

Another economic factor considered to affect social vulnerability, and which can in part be attributed to urbanization is the housing market (Bienert, 2014; Cutter, 2012; Holand & Lujana, 2013). Housing deprivation, increased property values, and increased rent costs, thought to affect social vulnerability, are common developments in urbanizing areas across northern Europe (Berglund, Johansson & Molina, 2005; Grunfelder, Rispling & Norlén,

Figure 7 Change in the distribution of ITQs within icelandic regions and municipalities from 1998 to 2017 2016; Karlstad & Lie, 2008), and have affected municipalities in and around Höfuðborgarsvæðið/the capital area of Iceland where population growth has been the strongest (Baldursdóttir et al., 2017; Ragnarsson et al., 2016, 2017; Sverrisdóttir & Guðjónsson, 2017; VSÓ, 2017). Most properties in Iceland are insured through

government funded and private disaster insurance schemes meant to protect private actors from material losses caused by environmental hazards (Harjanne et al., 2016; Viðlagatrygging Íslands, 2017). If considered in isolation from other factors, high property values would therefore relate to a decreased social vulnerability through indicating wealth in the form of capital collateral protected from material losses. Similar insurances are present also in a Norwegian context (Holand & Lujana, 2013) wherein house value measured to decrease social vulnerability.

However, in urban areas such as Höfuðborgarsvæðið/the Capital area where housing shortages and affordability are growing concerns (Sverrisdóttir & Guðjónsson, 2017; VSÓ, 2017), high costs might also have a broader effect on other aspects of social vulnerability, creating a situation where higher house values might have a less clear effect on social vulnerability at large. The costs of buying real property in urbanizing municipalities has increased unproportionally to salaries since 2011 (Baldursdóttir et al., 2017). Higher costs of living, caused partly by increasing costs to buy or rent property could act to increase the financial burden on inhabitants, which might instead increase social vulnerability (Cutter, Boruff & Shirley, 2003; Cutter 2012; Rufat et al. 2015). The effects of urbanization on the Icelandic housing market, within the context of vulnerability research, is not clear cut and could be either a potential future concern, or a safeguard regarding vulnerability to environmental hazards. A SVI-assessment would shed further light on how variables concerning the housing market correlate, and therefore give an indication on its positive or negative influences on social vulnerability.

At the other end of the urbanization process, lower or decreasing house values in socio- economically struggling municipalities could possibly act to increase social vulnerability. Many depopulating municipalities in Iceland, which are also struggling socio- economically, risk being subject to decreasing property values if they become less attractive places to live, work, or invest in over time (Baldursdóttir et al., 2017; Karlsdóttir et al., 2012). Decreasing property values would put further financial strain on inhabitants in cases where capital collateral in the form of real estate or real property decline over time. Even with insurance measures against environmental hazards in place, the financial means to prepare for, handle, and recover from hazardous events are likely to be lower in such municipalities. Although this is an issue that has been discussed in regards to rural development (Bjarnason & Thorlindsson, 2006; Bjarnason, 2014; Carter et al., 2016; Cross, 2001; Shucksmith & Brown, 2016) it has yet to be connected to a possible effect on the vulnerability to environmental hazards.

23 2.4 Urbanization and Social Vulnerability in Iceland

A somewhat controversial question that is likely be of increased relevance during the upcoming decade’s concern how much that could or should be invested in long-term and oftentimes costly resilience measures or development strategies for areas with weak economic development and small and decreasing populations. Briefly reviewing aspects of the socio-economic development in Iceland over the last 20 years through the concept of social vulnerability reveals a possibility that municipalities that are most affected by urbanization are also likely those where social vulnerability might change to a larger degree. Some of these municipalities could risk being subject to an increased social vulnerability over the long-term future. Environmental hazards could also emerge as a new risk-factor in areas where it isn’t considered so today, should the social vulnerability increase to above a point where a municipality cannot any longer sufficiently prepare for, handle, or recover from possible environmental hazards. However, as of today, no studies exist to sufficiently validate these assumptions, implying the need for more thorough analysis on which further, and more detailed studies can be based. In order to identify and counteract any possible developments towards and increased risk to the health and safety in municipalities that would be affected, the driving factors behind social vulnerability in Iceland need to be better understood. A SVI-assessment can provide further insight into possible driving forces, their correlations, and to what degree these affect social vulnerability of all municipalities differently. Should the results show that the common methodology for producing a SVI-assessments is not optimal for analysing these issues in Iceland, the study would instead provide important clues as to why this is the case, and how to progress to produce a more reliable type of assessment.

24 3 Method: Designing a Social Vulnerability Index for Iceland

The production of a SVI-assessment for Iceland is structured as a 3 step process:

1. Design of input-dataset through indicator construction, data collecting, and data- standardization

2. Applying data-set through Principal Component Analysis in SPSS

3. Calculating SVI-Scores and mapping results of SVI-assessment through ArcGIS- application

3.1 Design of Social Vulnerability Indicator Dataset

To produce a SVI-indicator dataset, the causes thought to impact social vulnerability in Iceland has to be defined, and the means by which these can be measured have to be decided upon. The input data applied in SVI-assessments are preferably from standardized data sources such as regularly updated census data. The use of standardized data sources ensure measurement reliability and reproducibility through allowing consistency in the method and metrics used to produce and update data (Cutter & Emich, 2017). In the case of Iceland, where no appropriate standardized dataset (e.g. census surveys) is available at a municipal spatial scale, and for the whole country, secondary data is instead collected from multiple sources. A SVI-assessment applies a large variety of variables to measure social vulnerability, of which some have been reviewed in an Icelandic context in chapter 2. The use of many variables from multiple sources instead of a standardized data-set require a more thorough review of the dataset construction to ensure and clarify its degree of validity and reliability, and to ease reproducibility.

3.1.1 Spatial scale, resolution, and measurement units

An initial consideration for constructing a SVI-assessment is the spatial scale and resolution used to represent the study area. The choice of spatial scale defines the detail with which social vulnerability can be measured and represented, the availability of data used for indicators, and impacts the margin of error of such data. SVI-assessments

25 therefore compromise between increased margins of error inherent to e.g. the smaller sample sizes of smaller spatial units (Cutter & Emich, 2017; Grey, 2017; University of South Carolina, 2017), or loss of detail and generalization when using larger spatial units. In Iceland, data is commonly presented in one of 4 different spatial scales: (1) national- scale, (2) regional scale, (3) municipal scale, and (4) urban/rural divisions (Hagstofa Íslands 2017a, 2017b). The most detailed scale at which data is collected to cover the whole country is the municipal level, consisting of 74 sveitarfélög/municipalities. Although population-numbers vary widely between municipalities, the municipal scale ensures a sufficiently low margin of error stemming from sample sizes used to produce indicators, while avoiding issues of generalization or ecological fallacies caused by applying larger spatial units such as a regional scale. Most of the data used as variables in the SVI-assessment apply a total population sampling, meaning that the small populations of some municipalities will not impact the margins of error when compared to more populated ones. Common methodological guidelines for principal component analysis, the main statistical tool used in a SVI-assessment, consider an analysis of 74 spatial units sufficient (Costello & Osborn, 2005; Grey, 2017). However, general recommendations for SVI-assessments are to use at least 100 units for comparative reasons (Cutter & Emich, 2017). Where a study area consist of fewer than 100 units common practice is to expand the study area outwards by adding neighbouring units until this threshold is reached. In the case of Iceland, which is limited to 74 units/municipalities, expanding the study area would not be possible. As no other feasible sub-division exist for which data is available, municipal scale is considered the only applicable option, and the consequences of applying this scale will be discussed further in regards to how results can be interpreted. Data available at finer spatial resolution can be adapted to fit the municipal scale, while data at coarser e.g. regional scales can’t. In cases where variables cannot be constructed at appropriate spatial scales, the exclusion of these, or the use of proxy-data will be evaluated in further detail in regards to how this could affect the reliability and validity of the results.

3.1.2 Social vulnerability indicators

22 socio-economic variables are used to cover various aspects of demographics, economics, and the built environment, all of which together indicate the relative degree of social vulnerability in municipalities. A majority of variables are selected based on general guidelines provided through Cutter, Boruff & Shirley (2003), Cutter & Emrich (2017), and

Holand & Lujana (2013), while a smaller number are based on circumstances more specific to Iceland. All variables are briefly reviewed below and given a code with which they will be referred to in the principal component analysis. The dataset and its variables are summarised in table 2 on page 33.

Age Structure

Three variables consider age structure, (1) percentage of population ≥ 65 years of age (OLD_POP), (2) percentage of population ≤ 5 years of age (YOUNG_POP), and (3) Change in median age from 1998-2017 (MEDAGE_CH). An overbalance towards older and/or younger age groups can affect the mobility of a population e.g. its ability to evacuate, and the ability to care for this larger share of the population, both during and in the aftermath of possible hazardous events (Cutter, Boruff & Shirley, 2003; Chen, Maki & Hayashi, 2014; Johnson, Ling & McBee, 2014; Lee, 2014). A higher percentage of elderly and/or younger individuals in a municipality therefore serve to increase social vulnerability (Carter et al., 2014; Donner & Rodriguez, 2008; Fekete, 2009; Holand & Lujana, 2013; Krawchenko et al., 2016; Lee, Hong & Park, 2017; Martins, 2012; Rapaport et al., 2015). Municipalities that are aging particularly fast when compared to the national mean are also considered more vulnerable and can indicate an increasing impact of ageing over time (Krawchenko et al., 2016; Rapaport et al., 2015).

Family structure

Two Variables consider family structure, (1) percentage of single households (SINGL_HHOLDS) and (2) crude birth-rates (BIRTH_RATE). High birth rates and a large share of single family households indicate a larger number of dependents (Cutter, Boruff & Shirley, 2003). Higher shares in either variable generally affect the financial and practical capability to care for dependants during and in the aftermath of hazardous events (Armas & Gavris, 2013; Heinz Center for Science, Economics, and the Environment, 2000; Holand & Lujana, 2013) and can therefore increase social vulnerability. Data on family sizes, a commonly applied variable, is not available for Icelandic municipalities and cannot be applied to the assessment.

27 Population change and distribution

Four variables consider population changes and distribution, (1) population growth from 1998 to 2017 (POP_GROWTH), (2) net migration from 1998 to 2017 (NET_MIG), (3) population density (POP_DENS), and (4) Summerhouses per capita (SUMM_HOUSES).

A strong population growth is commonly assumed to increase social vulnerability through increasing crowdedness, decreasing the amount of available quality housing, and can lead to difficulties for communities to adapt to rapidly changing demographic circumstances (Donner, 2008; Heinz Center for Science, Economics, and the Environment, 2000; Holand & Lujana, 2013; Siagian et al., 2014). A measurement of a twenty year-period is used as to represent long-term and consistent changes, and to avoid short term or temporary trends. A concept that has been less explored in vulnerability research is the impact that population decline or outmigration could have on demographic instability and social vulnerability. These processes can indirectly cause increased physical isolation, a loss of tax-base, and in the long term act to reinforce socio-economic decline, all of which would increase social vulnerability (Cross, 2001; Donner & Rodriguez, 2008; Rapaport et al., 2015; Stockdale, 2006;). Population decline is therefore measured as increasing social vulnerability. A very high net migration is a contributing factor to strong population growth, and can serve to reinforce its effects, while negative net migration would instead serve to reinforce population decline in cases where these two coincide (Cross, 2001; Cutter & Emrich, 2017). Therefore, the effects of net migration on social vulnerability have to be interpreted in regards to how it is measured to correlate to other variables and the strength of such a correlation.

Population densities reflect the degree of urbanization and crowdedness, or if low, population sparsity and physical isolation. Environmental hazards can be thought to have a larger impact where more people are concentrated within a smaller area (Lee, 2014), while low population densities would imply that fewer people in an area would be affected. People who live in remote or sparsely populated regions can however be considered more exposed due to isolating factors which can complicate communication, evacuation procedures, and other safety measures in cases of emergency (Cross, 2001). As such, although fewer people would be affected by a hazardous event in sparsely populated areas, those that are would in many cases be considered less resilient. Both high and low density populations are therefore measured as increasing social vulnerability. Municipalities that

cover a large area can in some cases have a high population density if a majority of its population is concentrated to one or a few communities, which would not show in a population per square kilometre measurement. This if especially the case in municipalities that cover larger areas of unpopulated highland.

Seasonal tourists and other temporary residents are considered more vulnerable than permanent residents as they often lack tighter and more local social networks, and do not hold a comparable local knowledge to that of permanent residents (Cutter, Boruff & Shirley, 2003; Lee, 2014). Such knowledge would range from understanding local risks, orientation in local surroundings, to the communications, functions and services of relevance in cases of emergency (Becken & Hughley, 2013; Cutter, Boruff & Shirley, 2003; Lee, 2014; Martinez-Graña et al., 2016). Data on tourist numbers and the amount of temporary residents in municipalities is not available. A higher amount of per capita summerhouses is instead used as a proxy to roughly indicate the share of temporary residents, where a higher per capita value measure to increase social vulnerability.

Economy

Five variables are included which relate to economic circumstances affecting social vulnerability, (1), median income (MED_INCOME) (2), unemployment (UNEMPL), (3) debt to revenue ratio of municipalities (DR_RATIO), (4) diversity in the range of private services (SERV_DIV), and (5) ITQ-shares as cod equivalent located within the municipality (ITQ).

The median income within municipalities relate to socio-economic status and indicate individuals financial ability to absorb and recover from losses caused by hazardous events (Cutter, Boruff & Shirley, 2003; Lee, 2014; Stockdale, 2006; Singh, Eghdami & Sing, 2014; Tunstall, Tapsell, & Fernandez-Bilbaoet, 2007; World Bank Group, 2014). Lower median income and higher unemployment-rates have a negative effect on this ability and are therefore measured as increasing social vulnerability. Similarly, high median incomes and low unemployment are measured as decreasing social vulnerability. Unemployment in Iceland tend to fluctuate between seasons, much due to the seasonal nature of employment in the fishing industry (Hagstofa Íslands 2017b). This result in higher unemployment rates during winters and lower rates during the summer seasons. The percentage of mean unemployment in all sectors over 2016 is therefore used to measure unemployment.

29 The debt-to-revenue ratio of a municipality indicate the margins by which municipalities have the financial means to prepare for and provide against the effects of hazardous events that are not covered by financial risk sharing mechanisms (Benson & Clay, 2004; Cutter, 2012; Holand & Lujana, 2013). A high debt-to-revenue ratio is considered to increase social vulnerability through lowering such margins (Holand & Lujana, 2013). The ministry of interior commonly applies combined measurement of municipal agencies, companies, and administrative units of which the municipality owns >50% or more to measure debt-to- revenue ratios (502/2012; Alþingi, 2011), and which will therefore be applied in this analysis aswell.

A lower diversity of private services available within a municipality can serve to increase social vulnerability, especially in more remote municipalities (Shucksmith & Brown, 2016). A more diverse economic landscape can instead help reduce isolating forces, and also serves as a proxy indicator for socio-economic well-being and economic resilience of municipalities (Berglund, Johansson & Molina, 2005; Holand & Lujana, 2013; Karlstad & Lie, 2008; Shucksmith & Brown, 2016). The indicator for diversity of private economic services apply data based on the mapping of economic services across Iceland produced in 2014 (Byggðastofnun, 2014). The document details the presence of 8 categories and 58 types of private services in communities across all regions of the country. Values for diversity are calculated as the percentage of services existing in a municipality compared to the total possible amount of services measured, 58 different types. The measurement should be considered sensitive to physical distances as less populated municipalities within commuter distance to larger ones, e.g. Reykjavík or Akureyri, are likely to have fewer services themselves (Ragnarsson et al., 2017). It is also likely that the distribution has changes somewhat since the measurement was conducted in 2014.

ITQ-shares have been suggested to play an important role for municipalities through providing economic and demographic stability (Benediktsson & Karlsdóttir, 2011; Bjarnason & Thorlindsson, 2006; Grétarsson, 2011; Karlsdóttir & Ingólfsdóttir, 2011). The nature of this influence is however still under debate, and is somewhat problematic to measure. The ITQ as a variable measuring social vulnerability is not common in SVI- methodology as this circumstance is somewhat specific to Iceland. It is therefore included as a means to shed further light on possible statistical correlations between socio-economic development and social vulnerability in the context of Iceland specifically. If results

indicate that higher ITQ-shares relate to a low diversity of economic services, it would indicate tendencies of mono-economy in such municipalities, which could act to increase social vulnerability. If larger ITQ-shares instead coincide with a broader range of economic services and/or higher income levels, it would instead be measured as decreasing social vulnerability.

Housing market

Three variables consider residential property and housing markets, (1) house values in municipalities (HOUSE_VALUE), (2) mean house-value to median income-ratio (H_MI_RATIO), and (3) rent-costs to median-income-ratio (R_MI_RATIO).

Mean house values (HOUSE_VALUE) are measured for every municipality as an indicator of social vulnerability. Property and real estate can serve to decrease social vulnerability through acting as capital collateral that improve the ability to recover from the possible effects of environmental hazards (Holand & Lujana, 2013). Because of the structure of insurance schemes and financial risk sharing mechanisms in Iceland, this capital collateral is to a large degree protected from possible material losses. Higher than average house values are therefore measured to decrease social vulnerability. In Iceland, house values and income do not always follow a linear relationship (Baldursdóttir, 2017; Sverrisdóttir & Guðjónsson, 2017), implying that further financial strain can be put on individuals in municipalities where costs of buying or renting property are unproportionally high in relation to the median income (Cutter, Boruff & Shirley, 2003; Holand & Lujana 2013; Singh, Eghdami & Sing 2014). Therefore, the mean income to mean house value ratio (H_MI_RATIO) will be applied as a variable to measure the relative “affordability” of housing. A negative value for this variable is measured to increase social vulnerability. A higher house-value to income-ratio instead measure as decreasing social vulnerability, as it would indicate that costs are lower in relation to mean income. The rent to income ratio (R_MI_RATIO) follows a similar rationale, where low values indicate rent costs that are higher in relation to the mean income in municipalities, thereby increasing social vulnerability. Using mean house and rent values entail a risk of increasing the margin of error, especially in less populated municipalities where the number of listings used to produce mean values could be lower.

31 Political power as a variable is included in standard SVI-assessment methodological frameworks (Cutter & Emich, 2017). Similar to the conceptual changes made by Holand & Lujana (2013), a variable measuring civil involvement is herein measured through municipal election turnout, where a high turnout is related to a decreased social vulnerability. As such, it should not be considered a measure of political power, but instead as an indicator of social commitment and responsibility, which can serve to decrease social vulnerability (Bjørklund, 2002; Holand & Lujana, 2013; Lee, 2014). Social commitment and responsibility relate to the strength and cohesion of social networks in municipalities, and is thought to help increase the social resilience and the ability to recover from hazardous events (Holand & Lujana, 2013; Lee, 2014).

Infrastructure and lifelines

Variables measuring infrastructure and lifelines are concerned with the built environment and includes, (1) airplane runways (AP_RWAYS), (2) harbours (HARBOURS), (3) road evacuation possibility (ROAD_OUT), and (4) capacity of local care facilities, day centres/retirement homes (MED_SERV).

Airports and access to runways can serve as lifelines, both in cases of emergency by servicing affected areas, and as a service-node integrating local economies and populations with the rest of the country. Airports with runways shorter than 800 meters do not fulfil the requirements for hospital or emergency transport (Isavia, 2012, 2017). Neither are they used for regular commercial travel to help decrease isolating forces (Isavia 2012; Karlsdóttir et al. 2012; Ragnarsson et al. 2016: 2017). The number of runways longer than 800 meters, per capita, is used as an indicator where a higher per capita value decrease social vulnerability.

Harbours (HARBOURS) can also serve as important socio-economic function by allowing the possibility for numerous economic activities that provide employment and wealth, thereby indirectly decreasing social vulnerability over the long term (Buch-Hansen, 2013; Forsætisráðuneytið, 2016). Harbours tend to differ significantly in size, and thereby also in their application and operating capacity. For the purpose of measuring social vulnerability, small-boat harbours are not included in the measurement, leaving 3 categories to represent the variable: Large fishing harbours (Stór fiskihöfn), medium sized fishing harbours (meðalstór fiskihöfn), and boat harbours (bátahöfn), as categorized by Vegagerðin (2017).

As there is no consequent way of conceptualizing how the size of, or per capita amount of harbours affect social vulnerability, the indicator will use a combined measurement of harbours per square kilometer, indicating a generally closer vicinity/accessability, and concentration of harbours.

Road evacuation routes (ROAD_OUT) refer to the amount of roads exiting a municipality measured as per capita, where a lower value indicate that less pressure would be put on available routes in cases where evacuation is needed (Cutter & Emich, 2017; Holand & Lujana 2013). A low value is measured as increasing social vulnerability while a high value is measured as decreasing it.

A better availability and quality of medical services and assisted living (MED_SERV), often measured as the capacity at local care facilities, day centres, and retirement homes, is considered to decrease social vulnerability (Cutter & Emich, 2017; Holand & Lujana 2013). The presence of such services is commonly measured through the share of the population that are currently living in care facilities, or with any type of assisted living. Such data is not available on a municipal level and can therefore not be applied in the assessment whereas other indicators instead need to be used. In the case of Iceland, an important factor to consider is the availability and capacity of local services, which tend to differ between municipalities. Comparing the total population in the municipality with the available health care services and living facilities for individuals requiring assistance can instead work as an indicator on both availability, and the possible pressure put on such services in the case of a hazardous event occurring. Medical assistance- and assisted living- places per capita is therefore applied as a variable, in which a lower value would indicate a lower capacity, thereby increasing social vulnerability, and a high value indicate a higher capacity i.e. lowering social vulnerability.

33 Table 2 Variables used to produce a social vulnerability assessment for Icelandic municipalities

Variable Name Description Effect on SOC-V Sources % of population <5 Hagstofa Íslands, YOUNG_POP High value = + years of age, 2016 2017a % of population >65 Hagstofa Íslands, OLD_POP High value = + years of age, 2016 2017a Change in median age Hagstofa Íslands, MEDAGE_CH High value = + between 1998-2017 2017a % population change High value = + Hagstofa Íslands, POP_GROWTH between 1998-2017 Low value = + 2017a % Net immigration Hagstofa Íslands, NET_MIGR Low value = + from 1998-2016 2017a % Single parent Hagstofa Íslands, SINGL_HOUSEH High value = + households, 2016 2017a High value = + Hagstofa Íslands, BIRTH_RATE Crude birth rate 2016 Low value = + 2017a Inhabitants per sq/km, High value = + Hagstofa Íslands, POP_DENS 2016 Low value = + 2017a Median income (1000s High value = - Hagstofa Íslands, MED_INCOME ISK), 2016 Low value = + 2017b % mean unemployment Hagstofa Íslands, UNEMPL High value = + 2016 2017b

Mean house value, High value = - Stjórnarráð Íslands HOUSE_VALUE 2016, (1000s ISK per 2017a; Þjóðskrá sq/m) Low value = + Íslands 2017a Hagstofa Íslands, Ratio between median 2017b, Stjórnarráð income 2016 and mean High value = + H_MI_RATIO Íslands 2017a; house value (sq/m) per Low value = - Þjóðskrá Íslands month 2016. 2017a Hagstofa Íslands, Ratio between median 2017b, Stjórnarráð income 2016 and mean High value = + R_MI_RATIO Íslands 2017b; rent value (sq/m) per Low value = - Þjóðskrá Íslands month 2016. 2017b Summerhouses per Þjóðskrá Íslands SUMM_HOUSES High value = + capita 2017c

Samband Íslenskra Debt-to-Revenue ratio High value = + DR_RATIO Sveitarfélaga 2016; % (A & B Hlútir) Low value = - 2017b Share (%) of total ITQ High value = - ITQ Fiskistofa 2017 (cod equivalents) Low value = + % presence of 58 types High value = - Byggðastofnun SERV_DIV of economic private 2014 services Low value = + % turnout for local High value = - Samband Íslenskra CIV_INV election 2016 Low value = + Sveitarfélaga 2017a Per capita number of High value = - Landmælingar ROAD_OUT roads leading out of Íslands 2016 municipality Low value = + Harbours per square HARBOURS High value = - Vegagerdin 2017 kilometre Flugsafn Íslands Airport runways High value = - 2017; Icelandic AP_RWAYS (>800m length) per Aerodromes 2017; 1000 inhabitants Low value = + Isavia 2012. Places at nursing homes, daytime care High value = - Velferðarráðuneytið MED_ASSIS and living facilities per Low value = + 2016 1000 inhabitants

3.1.3 Multicollienarity, uniqueness, and confounding factors

Multicollinearity-issues arise in cases where variables are strongly interrelated, and would therefore measure as having an unproportioned impact on social vulnerability by reinforcing the measured values of one another. If cases of multicollinearity being identified during initial PCA-runs, variables can be excluded if deemed necessary and the analysis performed again, in order to retain reliability of the measurements.

Uniqueness refer to individual variables that do not relate strongly enough to other variables used to perform the analysis. Such variables might affect social vulnerability, although not in a larger socio-economic context where it relates to the behaviour of other variables. This might give rise to validity-issues where false assumptions could be made regarding correlations between socio-economic variables. A single variable can still exist as a component i.e. factor in the component summary if it explains a large percentage of the variance in the dataset, which is described in further in section 5.2 below.

35 A number of variables that are commonly applied in SVI-assessments were not available for use due to lack of data. This unaccounted for data could act as confounding variables that increase variance or introduce bias. Such variables include: employment loss, age of houses and infrastructure, occupations, education, social dependence, special needs populations, and industrial development. It is not possible to reliably determine to what degree these factors influence the results of the assessment. Although, it is considered likely that some of these might act as external factors.

3.2 Principal Component Analysis

Following standard SVI-methodological guidelines presented by Cutter, Boruff, and Shirley (2003), and Cutter & Emrich (2017) a principal component analysis (PCA) is performed using the dataset presented in table 2 above as input.

When aiming to measure the relationships between many variables in a large dataset such as the one applied in this study, the number of pairwise correlations would be too big, and dispersion matrixes would be too large to be interpretable in any meaningful way. A PCA is designed as an exploratory multivariate technique to group the initial set of 22 variables into a smaller number of principal components or ‘artificial variables’ (Lærd Statistics, 2017), based on how the original variables align against each other. The result is a less complex dataset that can still explain most of the variation in the original input data (Statsoft, 2017). The process commonly result in 6 to 8 principal components which together explain at least 70 % of the total variance in the input data (Cutter & Emrich, 2017). By removing unnecessary complexity inherent to larger datasets and identifying variables that behave in unison, the PCA allows groups of variables, i.e. components, to represent a broader phenomenon such as e.g. social status, housing markets, demography, or economy, depending on which variables that measure as correlating. As such, many variables can be measured through one value, or component score, instead of individually.

These principal components consist of variables that behave in a pattern or correlation to each other, and if these have been determined to affect social vulnerability in a similar direction i.e. positively or negatively, the component can be used as a more coherent measurement of social vulnerability than if all variables in these component were interpreted pairwise or separately. A PCA identifies components in subsequent steps. The first component identified will account for more of the variation in the dataset than any

other. The second component will explain most of the variation in the dataset that is left unexplained by the first component. This process continues until the total variance explained by all identified components is considered sufficient, often above 70% (Costello & Osborne, 2005; Cutter & Emrich, 2017).

The variables within a component can load either high (positively) or low (negatively), where stronger loadings in any of the two directions indicate that a variable explains a larger part of the variation within a component. If the direction of the component loadings for all variables in a group is consistent and don’t imply simultaneous negative and positive effects on social vulnerability, the component can be given a positive or negative cardinality i.e. overall negative or positive influence on social vulnerability. Should the loadings point to both increase and decrease social vulnerability, further interpretation based on place specific circumstances of the study area might be needed.

In order to perform the PCA all variables are standardized as either percentage (%), per capita, mean/median values, or density (per square kilometers). The data-set is then examined to identify and locate missing values or entry errors, using descriptive functions in SPSS. A PCA will not run with missing values (Cutter & Emrich, 2017), and where such occur the mean value for that variable in the whole study areas is inserted. This will not alter the distribution within variables, and therefore doesn’t affect further stages of the assessment. Due to lack of available data, mean values were inserted for: (1) DR_RATIO in Skorradalshreppur, (2) HOUSE_VALUE in Breiðdalshreppur, Fljótsdalshreppur, Svalbarðshreppur, Húnavatnshreppur, (3) SUMM_HOUSES in Sveitarfélagið Garður, Akranes, and (4) SERV_DIV in Kjósarhreppur, Skorradalshreppur, Hvalfjarðarsveit, Eyja- og Miklaholtshreppur, Tjörneshreppur, Þingeyjarsveit, Svalbarðshreppur, Borgarfjarðarhreppur.

When all data has been standardized and examined for errors or missing values, a Barlett’s test of sphericity (BTS) and a Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is performed to ensure homogeneity and sample adequacy. Result should show > 0,6 for KMO and < 0,05 for BTS in order for the analysis to proceed. If these tests come back satisfactory, all variables are again standardized through a z-score transformation in SPSS. This produces a new dataset wherein all variables have a mean value of 0, and a standard deviation of 1. The transformation increases reliability when detecting correlations, and

37 allows for the data-set to be interpreted in regards to how values in each variable deviate from the mean (Costello & Osborne, 2005). The PCA is performed using varimax rotation (100 iterations) and Kaiser Criterion (100 iterations) for component selection. This reduces the tendency for a variable to load highly on more than one component (Cutter & Emrich, 2017). Once these steps have been performed and the data-set is considered suitable for processing, the principal components can be retrieved.

The first step in interpreting results is the production of a component score summary table, in which the characteristics of the principal components are presented in regards to how they affect social vulnerability. Variables that loaded strongly (above ,5 or below -,5) are given a component loading, indicating whether higher than average values or lower than average values correlate with the component loadings of the other variables in the component, and whether this relates to an increase or decrease in social vulnerability. If variables do not load strongly enough in any component they are removed from the dataset, and the PCA performed again until all variables used as input show acceptable component loadings.

Once the social vulnerability component summary is complete, the scores are mapped through a GIS-application to depict the distribution and relative impact of individual components across the study area, based on standard deviations as depicted in table 3.

< 1,5 Std. Dev. Very High 1,5 – 0,50 Std.Dev. High -0,50 – 0,50 Std.Dev. Medium 0,50 – 1,5 Std.Dev. Low > 1.5 Std.Dev. Very low Table 3 Social vulnerability classification

The component scores are thereafter used in an additive model to compute the overall social vulnerability scores for the municipalities. The additive model works through adding or subtracting values for each component to calculate SVI-scores for all municipalities depending on the components cardinality/influence on social vulnerability. The components are weighted equally as there is no reliable way quantifying why and how

much larger the effect of one component would be in regards to others (Cutter & Emrich, 2017). The additive model uses the equation below, although the cardinality (+ or -) and number of components (C) are yet to be retrieved:

푆푉퐼 = 퐶1 + 표푟 − 퐶2 + 표푟 − 퐶3 + 표푟 − 퐶4 + 표푟 − 퐶5 + 표푟 − 퐶6

The output is an overall social vulnerability score for each municipality, which is used as input data to produce a choropleth map representing the overall social vulnerability of all municipalities in Iceland. The scores are mapped according to the 5 grade classification based on standard deviation, as specified above. Therefore, the map will only depict the relative social vulnerability of municipalities, and cannot be interpreted as absolute or stand-alone measurements.

4 Results

4.1 Principal component summary

Six components were extracted by the PCA and summarized in table 4 in the order of their explained variance. The components together measured to explain 74,14% of the total variance within the dataset, which was considered satisfactory for producing a social vulnerability component summary. 17 of the 22 variables used in the PCA measured component loadings above ,5 or below -,5 and were acknowledged as dominant variables within their assigned component.

Five variables were excluded during initial runs of the PCA. Young population (YOUNG_POP), depth-to-revenue ratio (DR_RATIO), civil involvement (CIV_INV), and airport runways (AP_RWAYS) were excluded due to not measuring high enough component loadings within any of the 6 identified components. This does not imply that these variables might not affect social vulnerability at all, but that they cannot, when measured with the other variables used, jointly explain increased or decreased social vulnerability as part of components i.e. factors. Neither do they explain a large enough variance of the dataset by themselves to be considered as single variable-components. Harbours (HARBOURS) were excluded due to risk of multicollinearity that would have affected the reliability of the SVI-assessment negatively. Harbours and ITQ exist through a relationship that is not mainly explained by socio-economic factors, but rather by the fact that even if all harbours do not hold ITQs, such can only be located to vessels in municipalities with harbour infrastructure. The 2 variables therefore measured as having an unproportioned impact on social vulnerability through reinforcing each other.

Cardinalities were assigned to each of the 6 components based on the component loadings of their dominant variables. Dominant variables measure to correlate in the direction implied by their component loadings wherein positive loadings indicate above average values and negative loadings indicate below average values. Component 1 indicated a correlation between (1) above average age of the population, (2) below average birth rates, (3) above average median age change, and (4) below average population growth. As these are all circumstances that are considered to increase the social vulnerability of

41 municipalities the component was given a positive cardinality. A similar rationale was used to apply cardinalities to components 3-6.

Component 2, comprised of variables related to housing and population density contained component loadings where interpretations were more ambiguous. Higher than average house values relate to decreased social vulnerability while the 3 other dominant variables measure in directions that increase vulnerability. However, house values measured the highest component loading indicating that it explains a larger share of the variance in the component. Because of Icelandic disaster insurance schemes, functioning to a similar effect on social vulnerability as that of Norway (Holand & Lujana, 2013), higher than average house values were interpreted to have a larger influence on decreasing vulnerability than the other variables influence on increasing it. This interpretation and its effects on measuring social vulnerability in Iceland is developed upon further in chapter 5 where results are discussed more in depth.

Table 4 Social vulnerability component summary

% of Dominant Load Component Cardinality Name Variance Variables ing Explained OLD_POP ,926 BIRTH_RATE -,684 1 + Demographics 18,680 MEDAGE_CH ,652 POP_GROWTH -,542 HOUSE_VALUE ,905 Housing and H_MI_RATIO -,881 2 - population 16,229 -,571 density R_MI_RATIO POP_DENSE ,550 Infrastructure MED_ASSIS ,873 3 - and family 11,98 ROAD_OUT ,871 structure SING_HOUSEH -,515 MED_INCOME ,745 4 - Economic 11,22 SERV_DIV ,732 ITQ ,720 Temporary SUMM_HOUSE ,824 5 + residents and 8,686 ,534 migration NET_MIGR 6 + Unemployment 7,347 UNEMPL ,855 Total Variance 74,14 Explained

4.2 Components Influence and Spatial Distribution

The section present maps showing the spatial distribution and degree of influence exerted by individual components across the study area. These maps allow for a more detailed picture of how components influence social vulnerability unevenly across the country, and can serve to facilitate a more in-depth discussion on how to decrease it in municipalities where it might be of concern. Component scores for each municipality are classified using a 5-grade scale based on standard deviation, as specified in table 3 (page 38). The component scores and social vulnerability-scores for all municipalities are available in the appendix A.

4.2.1 Component 1. Demographics

Four variables related to demographic instability were measured as correlating, and served to explain 18,68 % of the variance in the data-set. An above average age of the population (OLD_POP) was measured to correlate with below average birth rates (BIRTH_RATE), above average median age change (MEDAGE_CH), and below average population growth (POP_GROWTH), all of which increase social vulnerability. Municipalities that are less affected by these circumstances were given the lower scores in figure 8 below, while those that measured as more affected were given higher scores. The four highest scoring

Figure 8 Map of component scores for component 1: Demographics

43 municipalities for component 1 were Tjörneshreppur (4,28), Árneshreppur (3,04), Helgafellssveit (1,94), and Skorradalshreppur (1,7), while the lowest scoring municipalities were Ásahreppur (-1,92), Sveitarfélagið Vogar (-1,54), Grindavíkurbær (-1,29), and Reykjanesbær (-1,23). Population decline and ageing affect municipalities across most parts of the country, whereas the southwest of Iceland measured generally lower values. Three of the four lowest scoring municipalities are located in the Suðurnes region, while the highest scoring municipalities were all located to different regions across the country.

4.2.2 Component 2. Housing and population density

Four dominant variables were identified to produce component 2. Component loadings indicated above average house value (HOUSE_VALUE), lower than average house value to median income ratio (H_MI_RATIO), lower than average rent value to median income ratio (R_MI_RATIO), and higher than average population density (POP_DENSE) to correlate. The component, serve to explain 16,229 % of the total variation in the data-set, which is the largest share not explained by component 1. Figure 9 indicate that the effect of higher house values for decreasing social vulnerability is unproportionally strong in Höfuðborgarsvæðið/The Capital area. The component measured to increase social

Figure 9 Map of component scores for component 2: Housing and population density

vulnerability in municipalities across most regions. Vestfirðir/The Westfjords and Austurland are however more negatively affected on a broader scale, when compared to other regions. The four municipalities wherein the component increased social vulnerability the most were Sveitarfélagið Skagaströnd (-2,52), Tálknafjarðarhreppur (- 1,96), Hvalfjarðarsveit (-1,63), and Bolungarvík (-1,56). The four municipalities where the component measure to decrease social vulnerability the most were Seltjarnarnes (3,38), Kópavogur (2,17), Hafnarfjörður (1,72), and Mosfellsbær (1,72), all within the Höfuðborgarsvæðið/Capital area.

4.2.3 Component 3. Infrastructure and family size

Component 3 named infrastructure and family size identified 3 variables, a higher than average number of places at nursing homes, daytime care and living facilities per capita (MED_ASSIS_PLACES), lower than average pressure on roads exiting municipalities (ROAD_OUT), and a lower than average number of single households per capita (SING_HOUSEH). The 3 variables together explain 11,98% of the variation in the dataset. Component loadings all indicate a direction to decrease social vulnerability, whereby the component was given a negative cardinality. Higher scores are concentrated to either

Figure 10 Map of component scores for component 3: Infrastructure and family size

45 population centres where populations are larger or municipalities with only one or very few exit routes. The municipalities in which component 3 measured to increase vulnerability the most were Árneshreppur (-1,03), Sveitarfélagið Vogar (-0,8), Sandgerði (- 0,72), and Borgarfjarðarhreppur (-0,72), while measuring to decrease it the most in Skorradalshreppur (1,88), Helgafellssveit (1,26), Fljótsdalshreppur (1,13), and Eyja- og Miklaholtshreppur (0,95).

4.2.4 Component 4. Economic

The fourth component identified through the PCA analysis measured 3 economically oriented variables as correlating, an above average median income (MED_INCOME), higher than average diversity of available private economic services (SERV_DIV), and an above average share of ITQs (ITQ). The component explain 11,22 % of the total variance in the dataset. Component loadings are all positive and indicate that they measure in a direction to decrease social vulnerability, by which the component was assigned a negative cardinality. The four municipalities in which it increases social vulnerability the most were Húnavatnshreppur (-1,5), Grímsnes- og Grafningshreppur (-1,49), Hörgársveit (-1,4), and Eyja- og Miklaholtshreppur (-1,36), whereas it was measured to decrease it the most in

Figure 11 Map of component scores for component 4: Economic

Reykjavík (2,77), Vestmannaeyjar (2,19), Fjarðabyggð (2,15), and Seltjarnarnes (1,85). Figure 11 indicate that the component act to decrease social vulnerability mainly in population centres such as Reykjavík, Ísafjörður, and Akureyri, or smaller municipalities with a fairly high share of fishing quotas e.g. Snæfellsbær and Fjallabyggð. Municipalities in which component 4 measured to increase vulnerability are more often less populated and/or located further inland.

4.2.5 Component 5. Temporary residents and migration

Component 5 consist of variables measuring a higher than average amount of summerhouses per capita (SUMM_HOUSES) and an above average net migration (NET_MIG). The component serve to explain 8,686 % of the variation in the data-set, and as higher component loadings for both variables are related to an increased social vulnerability, the component was assigned a positive cardinality. As seen in figure 12, these circumstances are present to a larger degree in municipalities in and around the south-west, where Skorradalshreppur (5,91), Grímsnes- og Grafningshreppur (2,86), Kjósarhreppur (2,51), and Mosfellsbær (1,23) measured the highest scores in the country. The 4 lowest scoring municipalities were Akrahreppur (-1,46), Dalabyggð (-1,03), and

Figure 12 Map for component scores for component 5: Temporary residents and migration

47 Langanesbyggð (-0,98), and Reykjavík (-0,97). The variable measuring summerhouses per capita did not include accommodations such as hotels, guesthouses, and Airbnb accommodations which are a more common features in densely populated municipalities such as Reykjavík.

4.2.6 Component 6. Unemployment

Component 6 measure a single dominant variable, higher than average unemployment, which serve to explain 7,34 percent of the total variation in the dataset. Due to the high variance explained by the component, it was applied in the measurement of the overall social vulnerability, following standard methodological guidelines. High unemployment is related to an increased social vulnerability, giving component 6 a positive cardinality. The municipalities that scored the highest values were Langanesbyggð (3,04), Tálknafjarðarhreppur (2,92), Breiðdalshreppur (2,45), and Árneshreppur (1,71), while Skagabyggð (-2,73), Mýrdalshreppur (-1,83), Akrahreppur (-1,75), and Ásahreppur (-1,58) scored the lowest values. Unemployment tend to increase social vulnerability to a higher degree in municipalities in Vestfirðir/the Westfjords and Norðurland eystra, whereas lower values are spread fairly evenly across other parts of the country.

Figure 13 Map of component scores for component 6: Unemployment

4.3 Social Vulnerability Index-assessment for Iceland

The overall social vulnerability scores for municipalities were calculated through applying the component scores for each municipality to the additive model as detailed below:

푆푉퐼 = 퐶1 − 퐶2 − 퐶3 − 퐶4 + 퐶5 + 퐶6

The SVI-scores were classified according to standard deviation from the mean of the study area. The map and the social vulnerability scores used to produce it do not represent municipalities overall vulnerability to environmental hazards as it does not concern the physical vulnerability which is instead driven by environmental circumstances.

Figure 14 depict the spatial distribution of degrees of social vulnerability across all municipalities in Iceland. Socially vulnerable municipalities are present to a varied extent across most regions in the country, but is a more common feature in Vestfirðir/The Westfjords and Norðurland Eystra wherein only Akureyri is classified as less socially vulnerable. Higher social vulnerability was also measured in less populated municipalities surrounding Höfuðborgarsvæðið/The Capital area. Lower social vulnerability is concentrated to four municipal clusters of which the south/south-east cover the largest area, and of which the south-west is inhabited by the largest population.

Figure 14 Map of social vulnerability in Iceland

49 Table 5 SVI-scores and component scores for the 5 most socially vulnerable and the 5 least vulnerable municipalities

SVI- Municipality C1 (+) C2 (-) C3 (-) C4 (-) C5 (+) C6 (+) Score Árneshreppur 3,04 0,64 -1,03 -0,99 -0,21 1,77 5,97 Tjörneshreppur 4,28 0,70 -0,04 -0,70 0,45 0,34 5,11 Skorradalshreppur 1,70 -0,80 1,88 1,08 5,92 -0,52 4,93 Breiðdalshreppur 0,62 -0,80 -0,14 -0,95 -0,07 2,46 4,90 Kjósarhreppur 0,13 0,17 -0,45 -0,80 2,52 0,31 4,03 Mýrdalshreppur -0,44 0,18 0,21 -0,48 -0,72 -1,83 -2,90 Vestmannaeyjar 0,15 0,02 0,20 2,20 -0,76 0,12 -2,90 Seltjarnarnes 1,15 3,38 0,55 1,85 -0,45 1,72 -3,36 Akureyrarkaupstaður -0,23 0,93 -0,11 1,62 -0,52 -0,19 -3,38 Reykjavík -0,42 1,65 0,28 2,77 -0,97 0,07 -6,02

Table 6 shows the mean component- and social vulnerability-scores of the 5 social vulnerability-classifications based on the values of the municipalities assigned to each of them. The table is complimented with population-data to describe the demographic characteristics within each social vulnerability-class. This data can give indications on who and how many that live under different degrees of social vulnerability. The table show how 0,27% (907 individuals) reside in municipalities with a very high social vulnerability, whereas 2,5% (8.451 individuals) live in municipalities with a high social vulnerability. 38,8% (131.342 individuals) live in municipalities with a low social vulnerability, whereas 43,2% (146.184 individuals) live in municipalities with a very low social vulnerability. A large majority of the population (82%, 277.526 individuals) reside in municipalities that measured either a low or very low social vulnerability. The table further indicate how economic and housing-related factors act as main influences on decreasing social vulnerability in the municipalities that scored the lowest, whereas the factors acting to increase social vulnerability in the most vulnerable municipalities varies to a larger degree. The distribution of ITQs present within municipalities of each social vulnerability class do not follow a similar pattern, wherein a small majority of the quotas reside in municipalities with a low social vulnerability, while the least vulnerable municipalities hold only 0,2 percent of the total ITQs in the country.

Table 6 Comparing Social Vulnerability Scores, Demographic data, and ITQ

SVI- Social C1 μ C2 μ C3 μ C4 μ C5 μ C6 μ Municipalities Score vulnerability (+) (-) (-) (-) (+) (+) μ Very high 7 1,42 -0,47 0,05 -0,41 1,14 1,25 4,64 High 13 -0,21 -0,57 -0,33 -0,46 0,34 0,61 2,08 Medium 30 -0,02 -0,10 -0,09 -0,23 -0,13 -0,24 0,03 Low 21 -0,30 0,36 -0,02 0,45 -0,32 -0,52 -1,92 Very low 3 0,17 1,99 0,24 2,08 -0,65 0,53 -4,25 Social Median Median pop Total ITQ Total pop Mean pop vulnerability age change % Very high 907 129 48,2 -25,40 15,8 High 8451 650 39,1 -21,50 13,7 Medium 51465 1715 38,8 -7,75 18,2 Low 131342 6254 37,8 +3 50,9 Very low 146184 48728 37,0 +14,8 0,2

5 Discussion

The social vulnerability of Icelandic municipalities was measured to be influenced by 6 different components, together made up of 17 socio-economic variables. These components covered circumstances that relate to demographics, housing, infrastructure, economy, residential and migratory patterns, and unemployment, all of which are commonly recognized to influence social vulnerability. As all components measure to exert both positive and negative influence unevenly across the 74 municipalities, no one factor can be attributed as the general cause social vulnerability in Iceland. The results instead indicate where and how different factors act in combination to either increase or decrease social vulnerability, and provides clues as to how urbanization might impact such circumstances over time. Before reviewing the implications of the results in further detail, it is important to understand the limitations of what the assessment can tell us, limits which stem from the theory and methodology used to produce it, and how these could be adapted to fit an Icelandic context. Only once these limitations are clearly defined can the social vulnerability of Icelandic municipalities be discussed in a broader sense.

5.1 Limits for assessing Social Vulnerability for Iceland

5.1.1 Data-set construction and component identification in a new context

Due to the exploratory nature of a principal component analysis, it was considered well suited for application in a new context, as the means by which it identifies components help avoid preconceived ideas about how social vulnerability should function. However, these components could only be identified from the variables chosen to be included in the data-set. This implies that the validity is instead more sensitive to the data-set construction and the basis on which these variables were chosen. A number of aspects set Iceland apart from regions in which similar studies have been conducted before. Therefore, new variables had to be conceptualized and the commonly recognized influence that some components and variables have on social vulnerability had to be adapted or reinterpreted to fit this new context. The processes by which this was done is influential in deciding

53 possible outcomes of the analysis, and can lead to validity-issues where variables used to measure social vulnerability elsewhere might not have similar influences in the study area. A related limitation affecting the applicability of a SVI-assessment for Iceland was the scarcity of data available to produce a comprehensive data-set. A number of commonly applied variables were not available for this study, of which some are likely to have affected the outcome of the assessment. These variables can be grouped into three broad categories, economic, social, and housing, which are discussed separately below.

Unavailable economically oriented variables The economically oriented variables not available on a municipal scale concerned occupations and employment in municipalities, and the impact of agricultural activities. The statistics available on a national or regional scale could not be adapted to a finer municipal scale without causing ecological fallacies or over-generalization.

The share of occupations in different sectors within municipalities’ i.e. professional/managerial, clerical/labourer, and service sector would have served to indicate reliance on sectors of the economy that can be more or less vulnerable to disruptions caused by environmental hazards. Social vulnerability might therefore have measured higher in municipalities with a bigger share of employment in primary and secondary industries as these are often more reliant on physical infrastructure and environmental circumstances that can be disturbed by hazardous events. Such indicators would provide further information on the possible impact of agricultural activities e.g. sheep husbandry for either increasing or decreasing social vulnerability.

If employment loss i.e. the loss of employment caused by hazardous events had been available as a variable it is possible that it would have increased vulnerability in more populated municipalities where many economic activities and workplaces are situated, and in municipalities dependent on one or a few large employers. Similarly, a variable measuring the density of commercial and industrial development might have also increased social vulnerability in municipalities where more people work within a smaller or concentrated area, e.g. population centres and smaller industry-reliant municipalities.

Even though these variables were not available, it is possible that they might have had some impact as confounding factors on components or other variables that were included in the data-set. The presence of employment opportunities within different sectors might to

different degrees have affected median age, population growth, incomes, service diversity, migration, and unemployment in municipalities. One such municipality is Fjarðabyggð in Austurland where the presence of an aluminium plant might explain scores indicating healthier demographic and economic circumstances than surrounding municipalities in the east and north-east of Iceland, but also a heavier reliance on that particular industry to maintain such circumstances. It is therefore important to emphasize that component 4, named economic, indicate that median income, diversity of private economic services, and ITQs exist in a statistical relationship, and that it doesn’t measure the general economic situation in municipalities from any broader perspective.

Unavailable housing-related variables The second category for which a number of commonly applied variables were not available concern housing-related circumstances. Component 2 named Housing and population density could only represent such matters from a market perspective where housing values and costs were measured to be related to the population density in municipalities, likely as a result of circumstances in and around the Höfuðborgarsvæðið/the Capital area. Although these are factors that affect social vulnerability, and for which a clear spatial pattern can be discerned in figure 9, variables measuring the share of renters and the amount of old and empty houses would have provided stronger theoretical connection to how housing, as a broader phenomenon, affect social vulnerability by adding a more physical dimension to it.

It can be argued that component 2 allow for somewhat arbitrary interpretations regarding cardinality. Whereas higher house values act to decrease social vulnerability, the component loadings of the 3 other dominant variables would, from a strict theoretical perspective, act to increase it. As such, the component and its cardinality has to be reviewed more in depth and in relation to place-specific circumstances. The house cost/sq. meter to mean income ratio and the rent cost/sq. meter to mean income ratio in municipalities both measured negative component loadings, and coincided with higher house values. Although such a relationship might seem intuitively clear at first, these ratios do not imply that properties are always less affordable where house values are high, as median incomes also differ between municipalities. As such, the measured “affordability” of housing would in this study be measured as a function of higher incomes and lower costs which did not always measure to coexist. This suggest that multicollinearity is not the

55 reason behind component loadings for component 2 and that house cost or rent cost to income ratios are always lower where house values are higher. Instead, the significantly higher house values and relative costs for renting and buying property around Höfuðborgarsvæðið/the Capital area, as compared to most of the rest of the country would be the main reason for the strong contrasts seen in figure 9. Due to the insurance-schemes which serve to protect properties from natural hazards, it can be argued that higher house values, acting as a type of capital collateral protected from environmental hazards, should decrease social vulnerability more than the costs of renting or buying property would increase it by putting financial strains on people.

High population density is commonly related to an increased social vulnerability, but what high or low population density implies can vary widely between study areas. What is regarded as a high population density in an Icelandic context is generally lower than in most countries where similar assessments have been produced, even considering Reykjavík and its surroundings. Instead, component 2 can be interpreted through how lower house values also coincide with sparsely populated municipalities. It can be suggested that isolating circumstances would increase social vulnerability for people living in the more sparsely populated parts of Iceland, whereas living in closer vicinity to other people would decrease it, up to a point where it causes issues of crowding. People living under more isolated circumstances would also be more exposed in cases of hazardous events occurring, whereas lower house values in these municipalities would lower their ability to recover economically from eventual damages or disturbances. This would be the case especially in municipalities where component 1 demographics, 4 economic, or 6 unemployment, measure to increase social vulnerability as well.

Interpreting component 3 and 5 Components 3 named infrastructure and family structure, and component 5 named temporary residents and migration consisted of dominant variables wherein the relationships between these would seem less rooted in perceived real life phenomena. Component 3 measured 3 dominant variables, (1) per capita places at nursing homes, daytime care and living facilities, (2) per capita number of roads leading out of municipality, and (3) percentage of single parent households in 2016. The measured correlation between these variables does not imply a causality between them, i.e. the amount of roads leading out of a municipality per capita would likely not affect or be

affected by the share of single parent households in the same municipality. The aim of applying a principal component analysis was not to identify possible causalities, but to explore and find statistical correlations between many variables to produce a more manageable dataset to measure social vulnerability. As the component loadings of the variables within these 2 components all affect social vulnerability in a similar direction, i.e. positively or negatively, the components can still be applied to do so. The reason behind correlations might instead be attributed to coincidence or possible confounding factors affecting the dominants variables.

The most evident limitation for measuring social vulnerability in Iceland is the lack of available data suitable for a producing a SVI-assessment. It is therefore important to view the results only through how the components and the variables used in this assessment affect social vulnerability. The study would have benefitted from applying more variables in order to cover as many possible influences as possible. The fact that many variables were not available to be applied in the assessment does not mean that data on some of these are not collected by Icelandic agencies, but that it is often produced or made available at incompatible spatial scales. The means by which statistical data is produced and presented is in part caused by another limitation for producing a SVI-assessment for the country, related to scales.

5.1.2 Scales

Two matters were identified which related to how scales affect the production and interpretability of a social vulnerability assessment for Iceland. The first of these concern physical scales where the size, distances, administrative divisions, and settlement structure of Iceland produce circumstances which would necessitate further adaptions to SVI- methodology. The second matter relate to statistical measurements, ranges, and contrasts with other studies regarding the variables and their impact on social vulnerability.

Physical scales The size of Iceland, its administrative divisions, and settlement structure have created a situation where socio-economic functions and processes work through a variety of scales and relations, of which some are unrelated to the administrative borders of municipalities. In SVI-methodology the units e.g. municipalities for which social vulnerability is measured are often considered to function in isolation from each other, implying that their

57 relative location in regard to other municipalities and the study area as a whole would not affect SVI-scores. Generally, this would be the result of units’ larger physical dimensions as larger units might not allow or give cause for the type of widespread social or economic interactions between them that is the case in Iceland. Examples of such would be assessments performed in North America where the methodology was initially developed, and where such units are often much larger in size. Other times it is due to the choice of variables included in the data-set where the type of socio-economic variables applied function at other scales.

Because of the relative size of Iceland many socio-economic functions, especially economic services and employment, act at other spatial scales than the municipal. This implies that the presence of these in one municipality would affect their presence in others, and thereby possibly the reliability of such measurements. In order to develop a SVI- methodology that can measure social vulnerability in study areas similar to Iceland further considerations might be needed to regarding how variables could be applied at different spatial scales, and how these could be integrated without affecting the reliability of the measurements. The theory and methodology on which SVI-assessments are based rarely consider such circumstances.

Similarly, the physical size of municipalities can lead to over-generalization where the uneven settlement structure or population density within them are not considered. As can be seen in figure 14, the physical size of municipalities vary widely, where the most populated ones, concentrated to Höfuðborgarsvæðið/The Capital area and Akureyri are significantly smaller than many other. This implies that population density measurements in such municipalities are more accurate, whereas the settlement structure in larger but less populated municipalities across the country have resulted in a more generalized ones. Even though some municipalities cover larger areas their inhabitants are often not distributed evenly across space, and would instead oftentimes be concentrated to one or a few communities within the municipality. As the SVI-methodology is developed to measure circumstances within physical units such as municipalities or census tracts, measurements for larger spatial units would inherently risk over-generalization. One such example is the municipality of Ísafjarðarbær with a population of 3608 in 2017. The majority of the inhabitants in this municipality live in the more densely populated town of Ísafjörður, whereas a minority are distributed to smaller communities across the municipality. The

methodology did not allow for measuring the social vulnerability of different communities within the municipality, which is likely to have differed. This issue could possibly grow over the long term in cases where less populated municipalities merge to form new larger ones over time, making such measurements more generalized. Applying standard methodological frameworks to measure social vulnerability in Iceland might therefore also become more problematic. A possible solution would be to apply a community-scale to measure the social vulnerability of individual towns and urban areas, although such an assessment could not consider people living outside of communities, and would require data that is not readily available as of today.

Because of the issues reviewed above, the results produced in this study are better discussed by identifying patterns that can be traced back to certain socio-economic factors instead of comparing circumstances in individual municipalities. This approach would give more insight into how urbanization as a phenomena affect social vulnerability in the country as a whole, and where and for who social vulnerability might be of concern.

Statistical and measurement scales Most studies that have been performed to measure social vulnerability, and the theory and methodology developed through these studies have focused on regions of the world where risks stemming from environmental hazards are oftentimes much more severe. A large contribution to such risks tend to be stronger socio-economic inequality and much stronger contrasts within societies in such regards than what is generally the case in Iceland. This does not imply that some municipalities couldn’t be defined as more vulnerable than others. It would however suggest that the range or contrast between the most and the least socially vulnerable municipalities might have to be perceived as smaller than if the study would have been performed in many other parts of the world. These contrasts would also apply to individual variables used to measure social vulnerability, where standard deviation measurements can act to conceal how small the differences are between highest and lowest values in a variable. As the SVI-assessment depict relative differences, it is important to consider whether the range between the highest and lowest values justify it being included as a variable to measure social vulnerability.

Examples of variables that are commonly applied to measure social vulnerability, e.g. unemployment, or median income, would have a more obvious impact in study areas

59 where the range between the lowest and highest is significantly larger than in Iceland. The highest unemployment in Iceland, measured as the percentage of mean unemployment in all sectors over 2016 was 4,8% in Langanesbyggð. In other parts of the world where differences between unemployment rates might be significantly larger the effects of such might have a stronger impact on social vulnerability. In assessments performed in regions similar to Iceland, the impact that a variable could have in practical terms might have to be re-evaluated to consider if e.g. an unemployment rate of 4,8% is too low to have any practical impact.

Despite these issues regarding which values should be considered high or low enough to actually affect social vulnerability, the components still indicate where the combined presence of several circumstances which are acknowledged to increase and decrease social vulnerability are more or less prevalent and whether these exist as a pattern. Using the maps showing component scores, the SVI-scores, and demographic data can serve to give an indication on how urbanization influences social vulnerability, and how it will do so over the long-term future.

5.2 Social Vulnerability in Iceland

The section discusses how social vulnerability function in Iceland based on the characteristics of the identified components. The impact that urbanization might have on these components, and social vulnerability in Iceland as a whole is thereafter discussed.

5.2.1 Which are the causes for social vulnerability in Iceland?

Component 1 identified demographic variables concerning population change, ageing, and birth rates as influencing social vulnerability. Mean values for its component scores as shown in table 6 indicate that demographic circumstances is one of the strongest identified factors for increasing social vulnerability, and that it does so mainly in the 7 municipalities where it is classified as very high. With the exception of a few notable outliers such as Seltjarnarnes and Helgafellssveit, scores for component 1 are distributed closer to the mean across all other municipalities. This would imply that more stable or favourable demographic circumstances have a lesser impact on decreasing social vulnerability than what demographic instability has on increasing it in the most vulnerable municipalities. The 7 municipalities classified as having a very high social vulnerability have a combined

population of 907 (0,2% of the total population), and a mean population of 129 inhabitants. The median population change in these 7 municipalities is similar to that of the 13 municipalities classified as having a high social vulnerability, although in which demographics exerts a much lesser influence on increasing social vulnerability. This would suggest that although a relatively strong population decline can impact social vulnerability negatively, it does so mainly in combination with a particularly high mean age, and where the population is already fairly small. A reason for this would be the stronger impact that individuals can have on percentage change in lesser populated municipalities when compared to those that might have decreased by a larger number of inhabitants, but a smaller percentage. Lower birth-rates would reinforce the situation in lesser populated and ageing municipalities over the long term, and is likely a result of an imbalanced age structure due to a lack of younger generations.

Component 5, temporary residents and migration, measured as increasing social vulnerability, mainly in municipalities surrounding Höfuðborgarsvæðið/The Capital area where summerhouses serve a larger urban population. The component can be thought to relate somewhat to the demographic circumstances discussed above. A larger number of summerhouses per capita measurement would indirectly indicate fewer permanent residents in such municipalities, whereas a positive net migration would indicate that an increased number of people are settling in these areas. If a positive net migration continues, the impact that a lower share of permanent residents have on social vulnerability in these municipalities would likely decrease over time. The vulnerability of tourists in the case of a hazardous event occurring was not assessed in this study, but would be of increased relevance due to the growth of the sector in recent years.

Whereas demographics and temporary residents and migration had the strongest impact in regards to higher mean component scores, other components instead showed a wider presence in municipalities classified as either highly or very highly vulnerable. Component 4, Economic, and component 6, Unemployment showed values that increase social vulnerability in a majority of these municipalities, implicating that such circumstances are more common, albeit oftentimes, less impactful contributors to higher social vulnerability. Component scores representing economic circumstances in these municipalities indicate a lack in the diversity of services and needs, a lower median income, and lower ITQ shares.

61 However, as is revealed in figure 6, the mean share of ITQs in municipalities scoring high or very high social vulnerability is similar to that in municipalities with a medium vulnerability. This would imply that the connection between ITQs, median income, and economic diversity is instead a result of circumstances in municipalities scoring a low social vulnerability, and in which more than 50% of all ITQs are located. It could therefore be suggested that while economic diversity and higher mean income would play a larger role in maintaining a lower social vulnerability in the most populated municipalities where almost no ITQs are located, larger fishing quotas might compensate for a lack of these in the small and medium sized municipalities that are less socially vulnerable. Although the statistical relationships identified in this study are not enough to draw larger conclusions, they would suggest that a lack of ITQs do not necessarily increase social vulnerability, but that the presence of such can help decrease it. Such a suggestion would be in line with claims made by Leknes et al. (2016) and Kommunal- og moderniseringsdepartementet (2016) in Norway, that employment opportunities in e.g. fishing industries play an important role maintaining economic growth and socio-economic development in less populated municipalities.

A higher than average mean income and a larger economic diversity together measured a stronger impact on decreasing social vulnerability in the 3 least vulnerable municipalities, and is together with housing related circumstances one of the two main contributors to a very low social vulnerability. A large majority of the population reside in municipalities with these characteristics, and wherein the population growth is also strongest, and the median age the lowest in the country.

Component 2, representing circumstances on the housing market that would impact social vulnerability did not follow a similar pattern to that of the economic circumstances, and as figure 9 would indicate seem to be driven more by population numbers and municipalities relative locations in the country. Whereas the component act to decrease social vulnerability strongly in Höfuðborgarsvæðið/The Capital area and Akureyri, the two most densely populated urban nodes, its effect on increasing vulnerability is more widespread, but also more modest. The component would indicate that housing-related factors act to increase social vulnerability in municipalities that are located further from these two urban centres, particularly in the Westfjords and along the east coast. This seem to be the case

also in Ísafjörður, where the population is still far above the median for Icelandic municipalities, yet decreasing.

5.3 Urbanization and Social Vulnerability Patterns

The components identified in the SVI-assessment, and their measured influence on social vulnerability indicate how a variety of factors could both drive and respond to the wide scale urbanization in Iceland. Understanding such dynamics serve as important insights for predicting future developments and their effects, especially when developing policies and management guidelines aimed, in part, at maintaining a high resilience to environmental hazards for all the population. A social vulnerability index-assessment provide a first-step indication of how a large variety of socio-economic circumstances function as part of a system, and in which urbanization can be conceptualized as a feedback that both answers to and drives further socio-economic change. Social vulnerability would be considered one effect of these behaviors, and which this SVI-assessment would suggest to work in a somewhat unique way in Iceland when compared to many other countries.

Contrary to a general conclusion drawn by many SVI-assessments, the social vulnerability of more populated areas was measured as lower in Iceland than in e.g. Norway, from which part of the foundation for this assessment was based. Two reasons for this are methodological, i.e. the availability of data, and the way in which variables and components were calculated and interpreted to fit an Icelandic context. One other reason should however be attributed to actual differences in the population structure between the two countries. Where Norway has a number of population centers acting as the focal points for urbanization, and a significantly larger population, Iceland has mainly one such node centered around Reykjavík, and to a lesser degree also Akureyri. The size and population of Iceland would not likely not allow for more than one main focal point for urbanization. Höfuðborgarsvæðið/The Capital area is still much smaller population wise than many of its counterparts in e.g. mainland Europe, meaning that many of the issues that are deemed to cause increased social vulnerability in larger urban areas might not yet have grown to such an extent that they would cause similar issues here. The urbanization in Iceland therefore creates a situation resembling an urban/rural dichotomy between the south-west and the rest of the country. This dichotomy is however only partly discernible in figure 14,

63 depicting the overall social vulnerability scores, but is more clearly visible in maps showing the influence of individual components.

As the social vulnerability is measured in relative terms, comparisons between both ends of the vulnerability spectrum gives a better indication of broader patterns. Such contrasts show how urbanization mainly affect the social vulnerability negatively in already less populated municipalities from which people move, and only in certain aspects those to where people move. From a national perspective, urbanization can therefore be thought of as decreasing social vulnerability for a majority of the population, which reside in, or move to areas that are already more resilient, and which have a better preconditions to remain so. Today, a large majority of Iceland’s population already live in areas where the vulnerability was classified as very low or low. A further migration of people to urban areas would likely decrease social vulnerability for the overall population further, or possibly up to a point where more common urban issues such as crowding or social-spatial segregation might begin to increase it also in Iceland. Whether or not this would increase the overall risk to the population depend on the presence of environmental hazards that could threaten urban areas in the south-west.

The very small share of population which reside in municipalities that are experiencing the more negative effects of urbanization would instead become unproportionally affected through the combined impact of economic and demographic decline, unemployment, lowered house values, and isolation. These municipalities are the most relevant areas to consider in regards to managing social vulnerability, even though only 0,2% of the population reside within them. However, a number of municipalities show tendencies where circumstances are likely to become increasingly similar to those in the most vulnerable municipalities. These are mainly located to the north-west and the north east of Iceland, the regions that are most distant to Höfuðborgarsvæðið/The Capital area.

Managing these areas with a goal of a positive socio-economic development would also have a long term effect of decreasing social vulnerability, and thereby creating more resilient communities from a broader perspective. The results produced in this study do not create a clear path on how to do so, but can indicate how and to what extent different factors serve to help develop municipalities in a positive direction, and avoid socio- economic decline.

The fact that so few people live in municipalities with a high social vulnerability today should be considered positive, as any eventual resilience measures might be less extensive by having to accommodate fewer people. Over time however, questions might arise as to the long-term gains of investing in such measures if urbanization continues to empty these areas of permanent residents. The presence of ITQs seem to have helped only a few select municipalities maintain lower social vulnerability. It should however not be considered a fully adequate indicator of the effects that industrial development can have on the socio- economic climate in municipalities outside of the south-west. Further economic data would be needed to produce a more detailed and thorough picture of how primary and secondary sector industrial activities relate to other socio-economic circumstances and urbanization.

The SVI-assessment gives only an indication on where socio-economic circumstances that are commonly considered to increase social vulnerability are more or less present, and in what combinations. Such indications are meant to give a wider understanding of how these processes function, and where further place specific studies would be needed to confirm any possible risks. Furthermore, the SVI-assessment does not concern place vulnerability, implying that beside the effects that social vulnerability has from a socio-economic perspective, environmental hazards would have to be present in such areas for environmental risks to be considered.

6 Conclusions

The study assessed the feasibility of producing a SVI-assessment for Iceland. This was done as a means to determine the impact that urbanization could have on the socio- economic circumstances that affect social vulnerability.

Methodological limitations were identified affecting the possibility of producing such an assessment. These limitations concerned a lack of available statistical data that could be applied to encompass all aspects that are commonly used to measure social vulnerability. Such data concerned data related to employment and industrial development, social security and living conditions, as well as the housing market. Other limitations concerned issues of scale, both physical and from theoretical and statistical perspective. Physical scales concerned the size of Iceland, its administrative divisions, and how networks of interaction between these are not sufficiently considered in SVI-methodology. Theoretical and statistical scales related to interpretation of variables, and how conventional assumption in SVI-theory applies to the circumstances in study area. These limitations can be considered somewhat unique to Iceland when compared to previous studies, suggesting that the methodology for producing these types of assessments might have to be adapted further to improve its validity, robustness, and the usability of the results.

The SVI-assessment indicated that urbanization in Iceland has the long term effect of decreasing social vulnerability for a majority of its population which is urbanizing, while increasing it for the small minority that reside in municipalities suffering most from demographic decline as a result of it. This situation should be considered unique to Iceland, whereas many urban areas are otherwise commonly assumed to be more vulnerable. The study tied 6 components, consisting in total of 17 variables to be affecting social vulnerability. While demographic instability, unemployment, and/or a lack of permanent residents tended to increase social vulnerability the most, an unhealthy economic climate was a more common contributor across the country as a whole. A healthy economic climate and conditions on the housing market was identified to decrease social vulnerability the most, mainly, but not exclusively in densely populated urban areas. As such, and in order to maintain a high resilience to environmental hazards across the entire

67 country, the study would suggest that focus might have to be put on lesser populated municipalities with an ageing population, and those at risk of becoming so over the long term. Results indicate that managing economic activities in these municipalities can be thought of as not only a means to increase socio-economic resilience, but also as a way to indirectly reduce risks stemming from possible environmental hazards.

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76 Appendix A 1. Municipalities: Demographic data

Höfuðborgarsvæðið: Capital area

Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017

Höfuðborgarsvæðið 164606 216878 31,70% 32,5 36,1 Reykjavík 1073411 123246 14,80% 32,9 34,8 Kópavogur 19867 35246 77,40% 32,2 36,4 Seltjarnarnes 4602 4450 -3,30% 34,2 39,8 Garðabær 92162 15230 65,30% 33,4 38,1 Hafnarfjörður 17196 28703 67% 30,5 35 Mosfellsbær 5246 9783 86,50% 29,9 35,8 Kjósarhreppur 138 220 59,40% 33,9 47,5

1 Data for Reykjavík in 1998 consists of combined data from Reykjavík and Kjalarneshreppur which merged in 1998. 2 Data for Garðabær in 1998 consists of combined data from Garðabær and Sveitarfélagið Álftanes which merged in 2012/2013

Suðurnes

Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017

Suðurnes Total 15715 23993 + 52,6 % 30,3 33,9 Reykjanesbær 10405 16350 +57,1 % 30,8 33,7 Grindavíkurbær 2134 3218 + 50,8 % 29,3 33,6 Sandgerði 1319 1708 +29,5 % 27,2 34,4 Sveitarfélagið 1141 1511 +32,4 % 30,3 35,1 Garður Sveitarfélagið 716 1206 + 68,4 % 30,3 35,3 Vogar

77 Vesturland

Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017

Vesturland 13924 15929 +14,4 % 32,1 36,2 Akranes 5125 7051 +37,6 32,2 35 Skorradalshreppur 52 58 +11,5 % 33,8 45 Hvalfjarðarsveit 5451 636 +16,7 % 32,7 40,1 Borgarbyggð 32592 3677 +77,3 % 33,5 37,2 Grundarfjarðarbær 922 869 -5,8 % 27,9 35,9 Helgafellssveit 65 52 -20 % 34,2 44,4 Stykkishólmur 1258 1168 -7,2 % 32,3 37 Eyja- og 122 120 -1,7 % 38,5 45 Miklaholtshreppur Snæfellsbær 1743 1625 -6,8 % 30,4 35,8 Dalabyggð 8333 673 -19,2 % 36,7 43,1

1 Hvalfjarðarsveit was founded in 2004 through the union of Hvalfjarðarstrandarhrepps, Innri-Akraneshrepps, Leirár- og Melahrepps og Skilmannahrepps from which data for the year 1998 has been combined. 2 Data for Borgarbyggð in 1998 consists of combined data from Borgarbyggð and Álftaneshreppur, Borgarhreppur og Þverárhlíðarhreppur (merged in 1998), and Borgarfjarðarsveit, Hvítársíðuhreppur, Kolbeinsstaðahreppur and Skorradalshreppur which merged in 2006, 3 Data for Dalabyggð in 1998 was combined with data from Skógarstrandarhreppur (merged in 1998), and Saurbæjarhreppur (merged in 2006).

Vestfirðir, Westfjords Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017 Vestfirðir 8556 6402 - 19,2 % 31,1 38 Bolungarvík 1093 908 -17 % 31,4 37,5 Ísafjarðarbær 4423 3608 -18,5 % 30,3 38,4 Reykhólahreppur 335 282 -15,9 % 35,1 38,2 Tálknafjarðarhreppur 327 236 - 27,9 % 26 33,1 Vesturbyggð 1254 1030 -17,9 % 31,3 36,4 Súðavíkurhreppur 271 186 - 31,4 % 32,2 43,3

Árneshreppur 72 46 - 36,2 % 45,6 60,8 Kaldrananeshreppur 142 106 -25,4 % 32,1 44 Strandabyggð 6391 468 -26,8 % 34,7 41,6 1 Data for Strandabyggð year 1998 constitutes the combined data of Hólmavíkurhreppur, Kirkjubólshreppur, and Broddaneshreppur which merged to create Strandabyggð in 2006

Norðurland vestra Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017 Norðurland vestra 8090 7156 -11,5 % 32,4 39,6 Sveitarfélagið 43171 3932 -9 % 31,9 39,6 Skagafjörður Húnaþing vestra 1312 1174 -10,5 % 34,2 40,3 Blönduóssbær 975 866 -11,2 % 33,8 38,8 Sveitarfélagið 628 479 -23,8 % 28,7 38,3 Skagaströnd Skagabyggð 91 101 +11 % 44,7 42,9 Húnavatnshreppur 5482 408 -25,5 % 32,9 40 Akrahreppur 219 196 -10,5 % 38,4 40,8 1 Sveitarfélagið Skagafjörður was formed in 1998 through the merging of 11 smaller municipalities from which data has been combined. 2 Numbers for Húnavatnshreppur in 1998 consists of 4 former municipalities which merged in 2006

Norðurland Eystra

Pop: Pop: Pop-Change Median age Median age 1998 2017 % 1998 2017

Norðurland Eystra 28417 29685 +4,5 % 32,2 38,1 Akureyri 15428 18488 +19,8 % 31,9 36,5 Norðurþing 3471 2963 -14,6 % 31,7 42,1 Fjallabyggð 27311 2033 -25,5 % 33,4 43,7 Dalvíkurbyggð 2082 1831 -12 % 30,4 38,9 Eyjafjarðarsveit 936 1015 +8,4 % 31,8 39,1 Hörgársveit 6132 574 -6,4 % 33,8 40,6

79 Svalbarðsstrandarhreppur 342 451 +31,8 % 31 37,4 Grýtubakkahreppur 377 352 -6,7 % 31 39,7 Skútustaðahreppur 466 425 -8,8 % 34,4 36,5 Tjörneshreppur 78 59 -24,4 % 43,8 60,4 Þingeyjarsveit 11323 915 -19,2 35,7 45,6 Svalbarðshreppur 121 95 -21,5 % 32,8 39,7 Langanesbyggð 640 484 -24,4 % 33,9 36,4

1 Data for Fjallabyggð in 1998 consists of combined data from Siglufjörður and Ólafsfjarðarbær which merged to create Fjallabyggð in 2006 2 Data for Hörgársveit in 1998 consists of combined data from Skriðuhreppur, Öxnadalshreppur and Glæsibæjarhreppur (merged to create Hörgárbyggð in 2001), and Arnarneshreppur, which merged with Hörgárbyggð in 2010 to create Hörgársveit. 3 Data for Þingeyjarsveit in 1998 consists of combined data from Ljósavatnshreppur, Bárðdælahreppur, Hálshreppur and Reykdælahreppur (merged 2001), and Aðaldælahreppur which joined in 2008.

Austurland

Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017

Austurland 9946 10310 +3,6 % 32,6 37,3 Seyðisfjörður 802 650 -19 % 34,4 44,7 Fjarðabyggð 43691 4691 +7,4 % 32 34,9 Vopnafjarðarhreppur 852 645 -24,3 % 34 44,5 Fljótsdalshreppur 100 81 -19 % 38,3 45,8 Borgarfjarðarhreppur 152 116 -23,7 % 39 47,1 Breiðdalshreppur 302 182 -39,8 % 38,5 46,5 Djúpavogshreppur 540 452 -16,3 % 31,2 37,1 Fljótsdalshérað 28292 3493 +23,5 % 31,8 37

1 Data for Fjarðabyggð in 1998 consists of combined data from Neskaupstaður, Eskifjarðarkaupstaður and Reyðarfjarðarhreppur (merged to create Fjarðabyggð in 1998), and Austurbyggð (which was created through the merging of Búðahreppur and Stöðvarhreppur in 2006), and Mjóafjarðarhreppur, and Fáskrúðsfjarðarhreppur which all merged with Fjarðabyggð in 2006. 2 Data for Fljótsdalshérað in 1998 consists of combined data from Fellahreppur and Norður-

Hérað, and Austur-Hérað (which was created through the merging of Egilsstaðabær, Eiðahreppur, Hjaltastaðarhreppur, Skriðdalshreppur and Vallahreppur in 1998 and from which data has been combined)

Suðurland

Pop: Pop-Change Median age Median age Pop: 1998 2017 % 1998 2017

Suðurland 22832 27528 +20,6 % 31,9 37,5 Sveitarfélagið 24761 2187 -11,7 30,4 37,3 Hornafjörður Vestmannaeyjar 4628 4292 -7,3 % 30,9 38,3 Sveitarfélagið 54862 8471 +54,4 31,5 36,9 Árborg Mýrdalshreppur 526 562 +6,8 % 36 33,4 Skaftárhreppur 593 475 -19,9 % 38,3 40,3 Ásahreppur 141 256 +81,5 % 34,6 34,6 Rangárþing eystra 17023 1752 +3 % 33,1 37,7 Rangárþing ytra 13824 1537 +11,2 33,7 39,7 Hrunamannahreppur 693 773 +11,5 29,7 37,4 Hveragerði 1668 2483 +48,8 % 32,4 40,2 Sveitarfélagið Ölfus 1526 2005 +31,3 29,4 37,9 Grímsnes- og 2985 467 +56,7 % 35,2 38,7 Grafningshreppur Skeiða- og 558 594 +6,5 % 32 38,3 Gnúpverjahreppur Bláskógabyggð 7926 1026 +29,5 32,5 34,3 Flóahreppur 4967 648 +30,6 31,5 34,1

1 Data for Sveitarfélagið Hornafjörður in 1998 consists of combined data from Hornafjarðarbær, Bæjarhreppur, Borgarhafnarhreppur and Hofshreppur which merged in 1998 2 Data for Sveitarfélagið Árborg in 1998 consists of combined data from Selfossbær, Eyrarbakkahreppur, Stokkseyrarhreppur and Sandvíkurhreppur which merged in 1998 3 Data for Rangárþing eystra in 1998 consists of combined data from Austur- Eyjafjallahreppur, Vestur-Eyjafjallahreppur, Austur-Landeyjahreppur, Vestur-

81 Landeyjahreppur, Fljótshlíðarhreppur and Hvolhreppur which merged in 2002 4 Data for Rangárþing ytra in 1998 consists of combined data from Rangárvallahreppur, Holta- and Landsveitar and Djúpárhreppur which merged in 2002 5 Data for Grímsnes- og Grafningshreppur in 1998 consists of combined data from Grímsnes- and Grafningshreppur which merged in 1998 6 Data for Bláskógabyggð in 1998 consists of combined data from Þingvallahreppur, Laugardalshreppur and Biskupstungnahreppur which merged in 1998 7 Data for Flóahreppur in 1998 consists of combined data from Hraungerðishreppur, Villingaholtshreppur and Gaulverjabæjarhrepps which merged in 2006

82 Appendix B

Component scores and Scial vulnerability scores in order of most socially vulnerable to least vulnerable municipality

Municipalities C1 (+) C2 (-) C3 (-) C4 (-) C5 (+) C6 (+) SVI-Score

Árneshreppur 3,04 0,64 -1,03 -0,99 -0,21 1,77 5,97

Tjörneshreppur 4,28 0,70 -0,04 -0,70 0,45 0,34 5,11

Skorradalshreppur 1,70 -0,80 1,88 1,08 5,92 -0,52 4,93

Breiðdalshreppur 0,62 -0,80 -0,14 -0,95 -0,07 2,46 4,90

Kjósarhreppur 0,13 0,17 -0,45 -0,80 2,52 0,31 4,03

Kaldrananeshreppur 0,89 -1,25 -0,07 -0,53 -0,17 1,45 4,02

Tálknafjarðarhreppur -0,74 -1,97 0,20 0,00 -0,46 2,93 3,49

Borgarfjarðarhreppur 1,05 -0,75 -0,73 -0,88 0,11 -0,23 3,27

Vopnafjarðarhreppur 1,27 -1,26 -0,09 0,26 0,22 0,49 3,08

Grímsnes- og Grafningshreppur -0,80 0,43 -0,28 -1,49 2,86 -0,54 2,87

Sveitarfélagið Skagaströnd 0,04 -2,52 -0,21 1,26 0,44 0,77 2,72

Seyðisfjörður 0,88 -1,47 -0,13 0,46 0,03 0,30 2,35

Súðavíkurhreppur 0,29 -0,02 0,10 -0,89 -0,27 1,20 2,04

Langanesbyggð -0,98 -0,58 0,06 -0,37 -0,99 3,05 1,98

Eyjafjarðarsveit -0,34 0,15 -0,51 -1,08 -0,08 0,72 1,75

Sandgerði -1,08 0,02 -0,73 -0,67 0,22 1,03 1,56

Svalbarðsstrandarhrepp ur -1,09 0,04 -0,65 -0,80 0,57 0,65 1,55

Húnavatnshreppur 0,31 -0,48 -0,08 -1,51 -0,60 -0,38 1,39

Sveitarfélagið Vogar -1,54 0,73 -0,81 -0,87 0,93 0,98 1,32

lxxxiii Hvalfjarðarsveit -0,76 -1,63 -0,19 0,65 0,97 -0,15 1,22

Svalbarðshreppur -0,01 0,23 0,47 -1,24 -0,97 1,41 0,97

Fjallabyggð 1,27 -0,69 -0,11 0,98 -0,38 0,11 0,82

Hveragerði 0,40 1,50 -0,45 -0,57 0,45 0,40 0,77

Strandabyggð 0,24 -1,15 0,03 0,03 -0,33 -0,24 0,75

Bláskógabyggð -0,80 0,37 -0,19 -1,26 0,65 -0,41 0,53

Hörgársveit -0,17 0,24 -0,12 -1,41 -0,58 -0,01 0,53

Sveitarfélagið Ölfus -0,67 -0,04 -0,50 0,08 0,40 0,32 0,50

Sveitarfélagið Garður -0,92 -0,20 -0,39 -0,16 0,55 0,10 0,50

Dalvíkurbyggð 0,10 -0,34 -0,04 0,32 -0,50 0,80 0,46

Djúpavogshreppur -0,07 -0,80 -0,37 -0,37 -0,68 -0,35 0,45

Þingeyjarsveit 0,92 0,33 -0,09 -0,84 -0,60 -0,46 0,44

Grýtubakkahreppur 0,74 -0,28 -0,10 0,34 -0,24 -0,14 0,39

Bolungarvík -0,35 -1,56 -0,04 1,06 -0,08 0,23 0,34

Vesturbyggð -0,37 -1,05 -0,20 0,39 -0,33 0,18 0,34

Hrunamannahreppur -0,03 0,09 -0,28 -0,73 -0,09 -0,59 0,21

Skútustaðahreppur -0,81 -0,12 0,12 -0,59 -0,53 0,76 0,00

Blönduósbær 0,48 -0,80 -0,15 0,58 -0,23 -0,65 -0,03

Húnaþing vestra 0,58 -0,49 -0,01 -0,27 -0,63 -0,86 -0,13

Rangárþing ytra 0,01 -0,03 -0,14 -0,03 0,06 -0,42 -0,16

Norðurþing 0,70 -0,08 0,05 0,42 -0,24 -0,23 -0,17

Skeiða- og Gnúpverjahreppur -0,29 0,29 0,13 -1,27 -0,24 -0,56 -0,23

Ásahreppur -1,92 -1,01 -0,04 -1,05 1,09 -1,58 -0,32

Mosfellsbær -0,98 1,73 -0,72 0,04 1,23 0,27 -0,52

Flóahreppur -1,02 0,34 -0,23 -1,33 -0,19 -0,62 -0,61

Borgarbyggð -0,39 0,72 -0,64 -0,25 0,19 -0,61 -0,63

Reykhólahreppur 0,21 -1,02 0,50 -0,26 -0,39 -1,38 -0,78

Dalabyggð 0,84 0,39 0,10 -0,56 -1,04 -0,72 -0,85

Ísafjarðarbær 0,28 -1,01 -0,09 1,51 -0,42 -0,31 -0,87

Sveitarfélagið Árborg -0,39 0,88 -0,62 0,35 0,46 -0,33 -0,88

Helgafellssveit 1,94 0,68 1,27 -0,70 -0,22 -1,39 -0,91

Fljótsdalshérað -0,14 0,29 -0,51 0,42 -0,02 -0,76 -1,11

Reykjanesbær -1,23 0,77 -0,69 0,31 0,37 0,07 -1,19

Grundarfjarðarbær -0,35 -0,11 -0,15 0,17 -0,60 -0,44 -1,30

Stykkishólmur 0,23 0,18 -0,06 0,54 -0,42 -0,48 -1,32

Rangárþing eystra 0,09 0,37 -0,05 -0,40 -0,59 -0,95 -1,38

Eyja- og Miklaholtshreppur -0,83 0,41 0,95 -1,37 -0,96 0,39 -1,39

Garðabær -0,14 1,72 -0,46 0,72 0,57 0,13 -1,42

Sveitarfélagið Skagafjörður 0,46 0,06 -0,11 0,88 -0,59 -0,60 -1,55

Kópavogur -0,48 2,18 -0,62 0,68 0,78 0,19 -1,74

Skaftárhreppur 0,89 0,53 0,28 -0,61 -0,97 -1,47 -1,74

Fljótsdalshreppur -0,97 0,68 1,14 -0,45 -0,26 0,83 -1,77

Hafnarfjörður -0,84 1,73 -0,51 0,58 0,50 0,37 -1,77

Snæfellsbær -0,70 -0,96 0,11 1,50 -0,51 -0,26 -2,13

Sveitarfélagið Hornafjörður 0,02 -0,23 0,03 1,22 -0,57 -0,73 -2,30

Skagabyggð -0,29 -1,26 0,58 -0,52 -0,49 -2,74 -2,32

Akrahreppur 0,50 0,96 -0,13 -1,21 -1,46 -1,76 -2,34

Fjarðabyggð -0,64 -0,98 -0,12 2,16 -0,12 -0,61 -2,42

Grindavíkurbær -1,29 0,11 -0,36 1,27 0,03 -0,25 -2,52

Akranes -0,35 0,91 -0,17 1,78 0,14 -0,15 -2,88

Mýrdalshreppur -0,44 0,18 0,21 -0,48 -0,72 -1,83 -2,90

Vestmannaeyjar 0,15 0,02 0,20 2,20 -0,76 0,12 -2,90

Seltjarnarnes 1,15 3,38 0,55 1,85 -0,45 1,72 -3,36

Akureyrarkaupstaður -0,23 0,93 -0,11 1,62 -0,52 -0,19 -3,38

Reykjavík -0,42 1,65 0,28 2,77 -0,97 0,07 -6,02