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Geographies of trust: Socio-spatial variegations of trust in insurance
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Geographies of trust: Socio-spatial variegations of trust in insurance Bruce Tranter , Kate Booth ⁎
University of Tasmania, Australia
ARTICLE INFO
ABSTRACT
Keywords: Home and contents insurance Trust Australia Queensland Survey Geodemographics
Trust is commonly understood as a mechanism that acts to improve transaction efficiency, or as a structural characteristic of organizations. It is a 'good' thing that can be built and harnessed for economic success. However, this type of conceptualisation maintains trust as a discrete and internally stable entity that is universally applied and cordoned off from socio-spatial complexities. In this paper, we present an empiric on trust in the insurance industry with an eye to reports on declining trust in banks, financial institutions and government. Analysing data from two Australian surveys, we map the social and political correlates of having house and contents insurance, consider how knowledge of insurance related issues and trust in insurance companies is associated with house and contents insurance, and measure how much trust Australians place in insurance companies relative to other public institutions. We use our findings as a spring broad for considering more spatialised understandings of trust and conclude by providing signposts for further geographical trust research - the first, qualitative investigations of the spaces and places of trust and its correlates, and the second, using geodemographics for mapping trust's socio-spatial variegations. In this we contribute to research into the socio-spatial variegations of financial processes and technologies, as well as contributing to work on institutional trust.
1. Introduction Public trust in many institutions has been declining steadily in many advanced industrialised countries according to international survey data (Edelman, 2018). Large banks in Australia are a good example of this process, with a series of scandals rocking the banking industry in recent years forcing even a conservative government to initiate a major government inquiry (i.e. a Royal Commission) into the banking industry, further diluting trust in this sector (Eyers, 2018). Public trust in government itself, and in politicians more broadly has also declined in many countries (Edleman, 2018; Hooghe and Oser, 2017; Dalton, 2005). We focus on another critically important financial institution – the insurance industry. While quantitative social scientists have administered a range of national surveys on trust and confidence in institutions, to our knowledge they have not examined insurance companies vis a vis other public institutions. This is somewhat surprising given the power and influence of this sector (Lobo-Guerrero, 2010). It is also surprising given the well-established relationship between trust and risk (e.g. Beck, 1992; Giddens, 1990) – a relationship that appears highly pertinent to insurance as a risk management tool based on ‘trusting’ economic transactions (Booth, 2018). Trust has been observed as acting as a kind of economic and social lubricant. Rational prediction is one strategy for dealing with complex
⁎
future events but, as George Simmel observed, this by itself is insufficient (cited in Lewis and Weigert, 1985: 969). However, in embracing risk and doubt, …trust succeeds where rational prediction alone would fail, because to trust is to live as if certain rationally possible futures will not occur. Thus, trust reduces complexity far more quickly, economically, and thoroughly than does prediction (Lewis and Weigert, 1985: 969). Frederiksen (2014) maintains that when encountering uncertainty, trust can be an alternative ‘strategy’ to risk: “Trust… is a way to stabilise expectations and make risk considerations less relevant” (Frederiksen, 2014: 134). This relationship between risk and trust appears to be supported by studies that have found the more trust people have, the less risk they perceive (e.g. Siegrist et al., 2005). With trust enabling deliberation and action in the face of complexity and uncertainty (Knight et al., 2001) and fostering economic growth (Siegrist et al., 2005), increasing concern about risk and declining levels of trust in advanced industrialised nations (Doyle, 2007) have led to speculation of an erosion or collapse of social and economic function. Beck (1992) and Giddens (1990), observing this correlation between risk and trust, have characterised these as ‘risk societies’ – societies increasingly preoccupied with risk which leads to significant changes in
Corresponding author at: Social Sciences, Private Bag 22, University of Tasmania, Hobart, Tasmania 7000, Australia. E-mail address: [email protected] (B. Tranter).
https://doi.org/10.1016/j.geoforum.2019.07.006 Received 5 March 2019; Received in revised form 2 July 2019; Accepted 4 July 2019 0016-7185/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Bruce Tranter and Kate Booth, Geoforum, https://doi.org/10.1016/j.geoforum.2019.07.006
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social and political structures and function, both positive and negative. Turning our attention to insurers, we (1) map the social and political correlates of having house and contents insurance, (2) consider how knowledge of insurance related issues, and trust in insurance companies is associated with having house and contents insurance, and (3) examine how trust in insurance companies ranks alongside trust in other public institutions. We achieve this by drawing upon two data sources – a nationally representative sample of Australian adults, and a large sample of young adults from the Australian state of Queensland. Analysing national survey data, we examine empirically the social and political correlates of having house and contents insurance, and how trust in and knowledge of insurance companies is related to having house and contents insurance. Analysing Queensland data, we consider the level of trust in insurance companies compared to banks and financial institutions, courts and the legal system, the Australian government, universities and police, and map the social and political background of trust in these institutions. In interpreting these survey results, we draw upon conceptualisations of trust that move beyond what Çalişkan and Callon (2010) identify as the trust ‘black-box’:
between, for example, institutional structures and systems, and trust. It also enables an exploration of the factors that may generate or diminish trust. As Çalişkan and Callon (2010) highlight, economists and economic sociologists have placed great stress on trust in the everyday maintenance of markets, particularly the role trust plays in coordinating markets when uncertainty is high. This has, in part, led to a proliferation of research to better understand, build and mobilise trust for economic gain, with trust perceived as a key driver of information creation, knowledge exchange and innovation (Murphy, 2006). It is generally assumed that trust is ‘good’ and building trust will produce positive business and economic outcomes (e.g. Hamm et al., 2016). Previous research indicates the importance of trust in householder insurance decision-making, specifically how low levels of trust in insurers may lead to underinsurance (Booth and Harwood, 2016). Some households choose not to insure because they do not trust insurers or appear to downgrade their insurance cover on the supposition that insurers will not deliver full payouts. These observations appear to be supported by other research indicating that those who report lower levels of trust in others are less likely to have house and/or contents insurance (Booth and Tranter, 2018). Building trust in insurers – following the rationale outlined above – seems to be required if householders are to continue to purchase insurance or purchase an adequate level of insurance cover. However, Knight et al. (2001) observe that the production of trust is always entwined within systems of power and control. In the provision of services, systems of control can be developed and regulated to gain customer confidence and trust, and customer and provider trust occurs within structures and norms that condition behaviour and determine outcomes. In relation to trust issues in the virtual marketplace, ‘the task of the various corporate actors is to come up with novel legal, social or technical mechanisms of control and trust production’ (Knight et al., 2001: 318, emphasis added). The co-production of trust within instruments and mechanisms of control is infused with hidden and explicit articulations of power. For insurance, trust has traditionally been associated with the principle of utmost good faith – uberruma fides (Lobo-Guerrero, 2010). This principle embodies the assumption that trust is a necessary precondition for an insurance transaction to take place between, for example, a householder and an insurer. Insurance ‘trades in trust’ (LoboGuerrero, 2010):
…our objection to trust is that it is used as an undifferentiated explanation of coordination that black-boxes maintenance operations and socio-technical devices, instead of demanding that these be studied. It would be useful to develop further studies of market emotions that grant a key role to materialities in the production of these very emotions (Çalişkan and Callon, 2010: 21). Trust may be operationalised in surveys (as we have) and calculated and tracked along linear temporal trajectories (as decreasing or increasing). However, this invariably (re)produces an ontological asymmetry that maintains trust as a discrete and internally stable entity that is universally applied and cordoned off from related social and spatial complexities. In acknowledging that this limitation applies to our own empirical analysis, we use these observations as a spring broad from which to consider more relational and material – and thus, spatially variegated – understandings of trust. In this we contribute to addressing a second research lacuna: the more general lack of research into the socio-spatial variegations of financial processes and technologies. As Hall (2010) observes regarding research on the cultural economies of finance, while the global has been conceived as composed of spatial variegations constituted within diverse social worlds and not as a uniform distribution of finance and economics, little work has been undertaken into the way finance technologies are (re)produced in specific places. Financial practices and discourses can appear abstract and universalising with little attachment to place, yet as Pike and Pollard (2010: 38) observe, there is an “inescapable geographic construction, context, and rootedness of financial networks and practices”. In this paper, we provide a signpost for further geographical trust research. It is important to note that we do not set out to review the broad and diverse trust literatures, nor canvass the multiple and occasionally overlapping registers of trust embodied within these literatures, as this has been addressed extensively by others (e.g. Barbalet, 2009; Colledge et al., 2014; Frederiksen and Heinskou, 2016; Lewis and Weigert, 1985; Lucas et al., 2015; Murphy, 2006; Pytlik Zillig and Kimbrough, 2016; Withers; 2017).
…that both parties to the insurance transaction provide relevant and sufficient information that will help them make an informed decision on the insurability of the event to be covered, in the case of the underwriter, and the usefulness, value, and relevance of the insurance product on offer in the case of the buyer (Lobo-Guerrero, 2013: 26). However, given that utmost good faith is a principle in law that requires particular modes of behaviour and reporting on behalf of both parties, Lobo-Guerrero (2013) argues that in many cases trust, as mediated by law, ‘is not a given but an outcome of the insurance relationship. It is actively and continuously produced through the faithful interaction between client and insurer…’ (Lobo-Guerrero, 2013: 24). Trust in this sense ‘is not a given value or a principal for interaction but an outcome of social, cultural, political and economic relationality which needs to be actively and continuously produced’ (Lobo-Guerrero, 2013: 35); the principle of utmost good faith is therefore characterised as ‘trust’ a posteriori. The complexity for morality and calculation embodied within utmost good faith is far from benign, as by ‘making trust evident, insurance practices discipline and “police” the behaviour of individuals and collectivities’ (Lobo-Guerrero, 2010: 243). In relation to trust and the mis-selling of insurance products in the United Kingdom, LoboGuerrero (2013) goes on to describe that:
2. Trust and insurance As introduced above, trust is commonly understood as a mechanism that acts to improve transaction efficiency between parties, or as a structural characteristic of organizations and networks (Murphy, 2006). Trust is assumed to be a ‘thing’ that exists prior to the transaction, formed and acted upon through rational individual cognition, and relatively stable; trust as fixed, transferable and universal. This conceptualisation of trust allows cause and effect claims to be made
The principle of uberrimae fides becomes in this respect, not ‘a ready2
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made region that needs to be respected’, but ‘a proposal to manufacture’ trust through relationships in which the disclosure of material facts is forced upon the counterparty through a complex set of moral technologies (Lobo-Guerrero, 2013: 35).
typically used as a term in relation to others, whereas researchers tend to refer to confidence in institutions, such as universities or governments (Job, 2005: 2). 4. Data and method
He describes the process of disclosure of information on behalf of the consumer as an act of confession premised on coercion and control deployed through the unequal distribution of power. This a complex and iterative subjectification, in which individuals are not only on the receiving end of coercion but are conceived of as ‘coerced selves’. In other words, individuals impose on themselves the expectation and acceptability of confession and associated domination (Lobo-Guerrero, 2013). While insurers make calculations and projections based on archival data and probabilities, as described by Collier (2008) and O’Malley and Roberts (2014), even insurance is produced through other ‘strategies’ or contextual factors when dealing with future uncertainty and risk. These strategies embody both trust and control (Knight et al., 2001), providing impetus for researching public trust in insurance companies.
We analyse data from the 2017 Australian Survey of Social Attitudes (AuSSA), a national social survey designed to be representative of the Australian adult population. The sample was randomly selected from the Australian Electoral Roll ‘by federal electoral division in proportion to the size of the division versus the total enrolled’ (Evans, 2018). A sample of 5000 people was selected and administered in 4 waves of 1,250 by mail survey. After ineligibles were removed, a response rate of 28 per cent was achieved (n = 1,317). The Queensland data are drawn from a single wave of the Social Future and Life Pathways study, known as the ‘Our Lives’ survey (Our Lives, 2018), a longitudinal survey of young people from the state of Queensland, Australia (n = 2,030). The Our Lives survey was first administered in 2006 to a state-wide sample of school students aged 12/ 13. Subsequently, a further 5 survey waves have been administered. The survey consists of a set of core questions with modules added to particular waves. As in Waves 4 and 5, data collection for Wave 6 was conducted as a combination of online surveys (82%) and Computer Assisted Telephone Interviewing (18%). The sample collected in 2017 included respondents aged 23/24, representing 29 per cent of the original Wave 1 sample. In Wave 6, questions relating to trust in insurance companies, and type of house and contents insurance respondents had, were included in the survey for the first time. These Queensland data are unique as they not only allow us to compare trust in insurance companies relative to other institutions, but also to consider a large cohort of young Australians (aged in their mid-twenties), a stage of life when housing related issues are highly salient, including the importance of home and contents insurance.
3. Research questions In the empirical sections that follow we explore how trust is associated with having house and contents insurance in an advanced industrialised country – Australia. In Australia, there are no property or disaster insurance schemes (McAneney et al., 2016) and the insurance of homes and their contents is the responsibility of householders and private insurers, with governments acting as insurers of last resort particularly following large-scale adverse events (de Vet et al., 2019). Around 84 per cent of Australian homeowners have house insurance, 85 per cent have contents insurance, and 79 per cent both house and contents insurance (Booth and Tranter, 2018). Insurance patterns are unevenly distributed, with city-dwellers less likely to have house and contents insurance than their rural counterparts. In this context, we ask: What are the most important indicators of having house and contents insurance coverage in Australia? To what extent are interpersonal trust, and public trust in insurance companies associated with having house and contents insurance, and how trusting are Australians of insurance companies, relative to other public institutions? Based on the literature on institutional and interpersonal trust, and trust in insurance companies more specifically, we have the following research expectations. Insurance coverage will be higher among those:
4.1. Dependent variables We operationalise several dependent variables. The first is based on a question from the AuSSA that was also included in the Queensland Our Lives survey: ‘Thinking about your main place of residence, which of the following best describes the type of insurance cover that you or someone who lives with you has purchased? The residence is covered by…’ The variable contrasts those who have both house and contents insurance (scored 1), with respondents who have either house or contents insurance, or are not insured (scored 0). This dichotomous dependent variable is modelled using binary logistic regression analysis in Table 2. The Queensland study included questions on trust in institutions in several survey waves. However, in the 2017 survey an ‘insurance companies’ item was added to the list, providing a unique comparative measure of trust in insurance companies vis a vis other institutions. How much trust do you have in the following groups…? (response categories: 1 no trust at all; 2 not very much trust; 3 quite a lot of trust; 4 a great deal of trust). Several ordinal dependent variables are operationalised from these questions – trust in…insurance companies, banks and financial institutions, courts and the legal system, the Australian government, universities and police. We analyse these dependent variables in Table 3, using ordinal logistic regression. Analysis are conducted using SPSS version 24. Missing data are coded to their respective reference categories for dichotomous independent variables, while for continuous variables, missing data are replaced using SPSS linear interpolation.
1. With higher interpersonal trust 2. More trusting of insurance companies 3. With higher ‘confidence’ in their own knowledge of insurance related issues 4. On higher incomes, women, and the tertiary educated We examine these questions empirically, following a description of our data and the research strategies we adopt. In operationalizing trust, we employ Job’s (2005: 4) differentiation between ‘rational’ and ‘relational’ forms of trust. Important for his concept of rational trust is the assumption ‘that to trust presupposes consideration of information or knowledge about the other’ (Job 2005, 4). This invokes the concept of risk (intrinsic to this type of trust), because rational trust involves calculating the risks associated with trusting others. Our focus on trust in insurance companies clearly involves elements of risk, both for those seeking to be insured, and of course for insurance companies. Alternatively, relational trust ‘is based on belief or faith in the goodness of others’ (Job, 2005: 4). Relational trust manifests as a ‘trusting disposition’ that tends to be relatively stable and is imbued in childhood (Job, 2005, 4). Relational trust is important when we seek to measure generalised trust in others, as opposed to trust in specific individuals or institutions. In fact, trust is
4.2. Independent variables Several independent variables are operationalised for the regression models based upon national AuSSA data in Table 2. Trust in insurance companies is measured using the following question: Using the following scale ranging from 0 to 10, where 0 means “No trust at all” and 10 means 3
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“Complete trust”, please indicate how much trust you personally have in insurance companies? (mean 4.24; standard deviation 2.43) centred at its mean for regression analysis. We measure the perceived risk of experiencing a natural disaster using the question: What is the risk of a natural disaster (e.g. bushfire, cyclone, major storm or flood) striking your local area? (responses: very high; high; moderate; low; very low). We contrast very high + high = 1 with moderate or less = 0. An additive knowledge scale was constructed from three questions:
House and contents insurance levels are slightly higher among those with higher levels of interpersonal trust, although the percentage differences are not large, while large city dwellers are less likely to be insured than those living in other locations. Political party identification, a proxy for moral progressivism/conservativism, is also associated with insurance, with Greens identifiers least likely to be insured (56%), and Liberal/Nationals (coalition) identifiers most likely to have insurance cover (87%), although partisan differences are weaker among home owners. Australian born home owners (92%) are more likely to be insured than those born elsewhere (83%). The perceived risk of national disasters was a non-significant association with insurance, while believing in anthropogenic climate change is associated with lower levels of insurance, although not significantly so among home owners. Not surprisingly, we found around 90 per cent those who own or are purchasing their home have home and content insurance, compared to only 26 per cent in private rental, and 14 per cent in public rental accommodation.
a. How confident are you that you know what is covered by your house and contents insurance policy? b. In the case of loss, how confident are you that you know the costs for rebuilding your house? c. In case of loss, how confident are you that you know the costs for replacing your contents? The scale items (i.e. from strong agreement to strong disagreement) were reverse scored so that higher scores represent greater confidence in self-assessed knowledge of house and contents related issues. The scale is reliable (Alpha = 0.76) with a mean of 9.05 and standard deviation of 2.03 and centred at its mean for regression analysis. Using the same survey question as we employ here, in previous research Bean (2005, 139) found interpersonal trust to be a ‘better indicator of social capital than political trust or trust in government’. In both the national and Queensland data interpersonal trust was measured with the question: Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people? (response categories: most people can be trusted scored 1; you can’t be too careful in dealing with people scored 0). A variable measuring public confidence in house and contents insurance cover in the case of a natural disaster was included in both the AuSSA and Our Lives surveys: If a natural disaster (e.g. bushfire, cyclone, major storm or flood) strikes your area, how confident are you that your insurance will cover all your building and contents replacement costs? (response categories: not at all confident; not very confident; somewhat confident; confident; very confident). Several dummy (1/0) independent variables are operationalised, including respondent sex (men = 1), age (18–29 = 1; 30 + reference category); non-tertiary education = 1; high income (i.e. National sample household income $150,000+ =1; Queensland sample $80,000+ =1), born in Australia = 1; separated, divorced or never married = 1. Perceived risks of natural disaster are modelled as very high or high = 1 compared to moderate or less = 0. Believing that climate change is occurring mainly due to anthropogenic causes = 1 is contrasted with other climate beliefs = 0, while Coalition (i.e. Liberal or National Party) political identification = 1 is contrasted with other political identifications = 0.
5.1. National regression results We employ binary logistic regression analyses to examine the net associations between a dichotomous dependent variable – having house and contents insurance – and several independent variables (Table 2). Given that house and contents insurance is associated primarily with home owners or buyers, as is apparent in Table 1, we restrict our analysis to home owners and home buyers. To illustrate the associations between insurance coverage and various background variables, we introduce independent variables in five models. The first introduces demographic variables (i.e. respondent sex, age groups, non-tertiary education, household income, location [i.e. live in large city], marital status and country of birth). Model 2 examines generalised trust in others; trust in insurance companies, and self-assessed insurance knowledge. Model 3 measures perceptions of the risks of a natural disaster in the respondents’ location, and attitudes toward anthropogenic climate change. Model 4 introduces Liberal/National party identification compared to other political parties or non-identifiers, while in Model 5 all independent variables are included simultaneously. The results for Model 1 suggest several statistically significant sociodemographic associations are apparent at the 95 per cent level of confidence, or greater. The first model also has by far the largest Nagelkerke R2 of 0.16 compared to models 2 to 4, suggesting social background effects are important for understanding who takes up house and contents insurance. The odds ratio of 0.3 for the youngest age group aged 18–29 indicates the odds of having house and contents insurance for the youngest age category are approximately 5 times lower than those for older Australians. This finding holds after controlling for respondent sex, educational achievement, income level, location, marital status and country of birth. The odds of being insured for those living in a large city (0.4) are lower than other locations, while the separated, divorced or never been married have far smaller odds of being insured than other marital statuses (OR 0.3). Being born in Australia is associated with higher odds of being insured compared to being born overseas (OR 2.8), however, other indicators of including education and high income, are non-significant in Model 1. Model 2 examines elements of trust and confidence. Interpersonal trust and confidence in one’s knowledge of house and contents insurance are both significantly associated with the dependent variable. The positive odds ratios indicate that being more trusting of others and having greater knowledge of insurance issues, are associated with higher levels of house and contents insurance cover. Trust in insurance companies per se, has a very weak negative (0.97), but non-significant association with house and contents insurance. Perceptions of high disaster risk, while exhibiting a positive odds ratio, is non-significant predictor of having house and contents
5. Bivariate results The bivariate results presented in Table 2 show the associations between several independent variables and the house and contents insurance binary dependent variable, with results also presented for respondents who own or are purchasing their homes. Gender differences in the uptake of home and contents insurance are minimal, with 74 per cent of men and 73 per cent of women insured. The youngest 18 to 29 cohort are least likely to be insured (54%), although 69 per cent of younger home owners are insured. Tertiary education, or the lack of it, seems to have no discernible association with home and contents insurance. Those in the highest income bracket are most likely to be insured, although income has little impact on insurance among home owners. However, among home owners, those who are married or in de facto relationships (93%) or are widowed (93%), are far more likely to have house and contents insurance than people who have never married (74%). 4
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Table 1 House and contents insurance by social background (per cent).
Table 2 House and contents insurance by social background (odds ratios).
All
Home owners
Model
1
2
3
4
5
Men Women
74a 73a
88a 90a
Men
0.8
–
–
–
0.8
Age 18–29 30–44 45–59 60–69 70+
54a 72b 76b 84c 85c
69a 88b 92b 93b 93b
Age (30 + referent) Aged 18–29
1 0.3**
– –
– –
– –
1 0.5*
Degree Non-tertiary
1 0.9
– –
– –
– –
1 0.8
Degree Non-tertiary
73a 75a
89a 90a
Household Income < =Aus$150 K Household Income > Aus$150 K Missing on income
1 2.0 0.8
– – –
– – –
– – –
1 2.0 0.8
Household Income $0 - $30 K Aus $30-$60 K Aus $60-$100 K Aus $100-$150 K Aus $150 K+ Missing on income
73a,b 76a,b 77a,b 75a,b 86b 70a
91a,b 88a,b 93b 92a,b 95a,b 86a
Marital status Married/de facto Separated Divorced Widowed Never married
85a 53b 64b 84a 57b
93a 89a,b 86a,b 93a 74b
Interpersonal trust Most people can be trusted You can’t be too careful
78a 70b
92a 88b
Location Big city Big city suburb Town/small city Country village Farm/country home
67a 74a,b 75a,b 74a,b 80b
82a 90b 90b 95b 97b
Party ID Labor Coalition Greens No ID Born in Australia Born elsewhere Total
72a 87b 56c 70a 75a 70a 74
90a,b 93b 97a,b 86a 92a 83b 89
Which do you believe? CC mainly anthropogenic CC happening but ‘natural’ CC not happening Don’t know
70a 80b 91b 79b
88a 91a 92a 91a
Risk of natural disaster High/very high Moderate or less
80a 73a
92a 89a
Housing tenure Own outright Mortgage Private rental Public rental Boarding, living at home, other Total
90a 89a 26b 14b 65c 74
90a 89a – – – 89
Live in a large city
0.4**
–
–
0.4**
Separated, divorced, never married Other marital status
0.3*** 1
– –
– –
– –
0.3*** 1
Born in Australia Born elsewhere
2.8*** 1
– –
– –
– –
2.7*** 1
Most people can be trusted Trust in insurance companies (0–10) Self-assessed insurance knowledge (scale)
– – –
1.9** 0.97 1.16***
– – –
– –
1.6a 0.98 1.15**
Risk of natural disaster High/very high Moderate or less
– –
– –
1.5 1
– –
1.1 1
Climate change anthropogenic
–
–
0.6a
–
0.8
Party ID (other/none referent) Coalition ID
– –
– –
– –
1 2.0*
1 1.4
Pseudo R2 N
0.16 (950)
0.05 (950)
0.01 (950)
0.02 (950)
0.19 (950)
Notes: Estimates are for home owners and mortgagees only. Dependent variable: house and contents insurance = 1; no insurance = 0. Source: Australian Survey of Social Attitudes (2017). a p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001.
6. Queensland analyses Our Lives researchers have collected data on trust in Australian institutions approximately every two years since 2006. In 2017, for the first time, an additional item measuring insurance companies was added to their list, so the insurance companies item is unique to that survey year. In Fig. 1, for each institution we combine the percentages of those who express ‘a great deal of trust’ with ‘quite a lot of trust’. Studies of institutional trust in Australia have shown that particular institutions (i.e. police, universities, courts) tend to elicit relatively high levels of trust over time. However, we find the findings for insurance companies to be quite striking. As expected, and consistent with other studies, in 2017 young Queenslanders placed very high levels of trust in the police (87%), and relatively high trust in universities (72%) and courts and the legal system (66%), although they are somewhat less trusting of environmental groups (55%). Also, not expectedly, banks (45%) and the Australian government (35%) are trusted by relatively few Queenslanders, in both cases fewer than half of those surveyed. Longitudinal data from the Our Lives survey (not shown) indicate that while trust in the police has remained high at over eighty per cent since the first survey in 2006, trust in banks declined from 57 per cent in 2015 to 45 per cent in 2017 (Our Lives, 2017). We suspect trust in large banks may be lower than trust in smaller ‘community banks’ and credit unions, but lack access to nuanced data to test this expectation. It is nevertheless surprising, that only 25 per cent of Queenslanders aged 24 to 25 express a great deal or quite a lot of trust in insurance companies. Again, we suspect that trust in insurance companies would vary
Note: different subscript letters within each variable signifies statistically significant differences between variable categories at the 95% level or better. Source: Australian Survey of Social Attitudes (2017).
insurance, while believing in anthropogenic climate change has a significant association with insurance, but only at the 90 per cent level. Relative to Model 1 (that contains more independent variables), the contribution of Models 2 – 4 have far smaller Nagelkerke R2 statistics, suggesting that social background effects are relatively useful predictors of insurance. In the full model (Model 5), age, location in a large city, marital status, country of birth and confidence in insurance companies all have statistically significant associations with house and contents insurance at the 95% level or better (Nagelkerke R2 = 0.19). 5
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Trust in Institutions (%)
100 87
90 80
72 66
70 60
55 45
50
35
40
25
30 20 10 0 Police
Universities
Courts and the Groups working legal system to protect the environment
Banks and financial institutions
Australian Government
Insurance companies
Fig. 1. Trust in institutions among young Queensland adults.
further, according to type of insurance cover, a question that requires further research. Nevertheless, when it comes to trust, young Queenslanders place insurance companies in a distant last place relative to other Australian institutions.
indicator of institutional trust. Those who believe most people can be trusted are more likely than others to trust each of these institutions, consistent with the findings of Tranter and Skrbis (2009). Holding a post-secondary certificate or diploma is associated with higher odds of trusting insurance companies (OR 1.4), but lower odds of trusting the courts (OR 0.6), the Australian government (OR 0.7) and universities (OR 0.8). Higher income is positively associated with trust in institutions, but only weakly and non-significantly for all institutions apart from the courts (OR 1.4). Finally, political party affiliation is an important correlate of institutional trust. Similar to Rudolph and Evans’ (2005) on political trust, Liberal and National party identifiers are more trusting than those who identify with other political parties, with the exception of trust in universities. Secondary school has inconsistent associations across institutions, with the signs of association varying across the dependent variables. For example, those who last attended government schools have slightly higher odds of trusting insurance companies (OR 1.2), but lower odds than former independent or Catholic school students for trust in the courts (OR 0.7) or the Australian government (OR 0.7). Finally, those
6.1. Queensland regression results In Table 3 logistic regression analysis model trust in the institutions appearing in Fig. 1. The aim is to compare social background effects for trust in insurance companies comparted to banks and financial institutions, courts and the legal system, the Australian Government, universities and police. These dependent variables have an ordinal structure (i.e. a great deal of trust = 1; quite a lot of trust = 2; not very much trust = 3; no trust at all = 4) and are therefore modelled using ordinal logistic regression (i.e. SPSS GENLIN). Some associations are relatively consistent with institutional trust across most institutions. Women tend to be more likely than men to trust the institutions modelled here, apart from the courts/legal system, and the Australian government. Interpersonal trust is an important Table 3 Trust in insurance companies and other institutions (cumulative odds ratios). Insurance ***
Banks
Courts
***
Aus. Govt.
Universities
Police
Women
1.5
1.6
0.88
1.02
1.4
1.4**
Government School Other Secondary school
1.2* 1
1.0 1
0.7** 1
0.7** 1
1.04 1
1.1 1
Certificate or Diploma Other post-secondary Education
1.4** 1
1.1 1
0.6*** 1
0.7** 1
0.8* 1
0.9 1
Income $AUD80K+
1.2
1.2
1.4*
1.4
1.2
1.3
Most people can be trusted
1.4***
1.6***
2.6***
2.4***
2.0***
1.8***
No House or Contents Insurance
0.8*
0.8*
1.2
1.1
1.1
0.7***
Party ID (referent other or none) Coalition ID
1 1.7**
1 1.8***
1 1.6***
1 2.1***
1 1.1
1 1.8***
N
(2,013)
(2,013)
(2,013)
(2,013)
(2,013)
(2,013)
Notes: Dependent variables have an ordinal structure: 1 = no trust at all to 5 = a great deal of trust. Source: Our Lives (2017) a p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001. 6
**
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who do not have house and contents insurance are slightly less trusting of insurance companies (OR 0.8), banks (OR 0.8) and also the police (OR 0.7).
insurance because of perceived risks, we would expect to find a significant positive association here. There are likely also other factors contributing to feelings of dependency on insurance other than trust and risk per se. Reflecting Lobo-Guerrero’s (2013) observations of social and moral coercion embedded within insurance, Johnson (2013b) identifies a process of normatization within insurance practices. Social relations are ‘coaxed’ to produce and reinforce certain realities within which insurance is propagated and promulgated. Booth and Harwood (2016) and Lo (2013), for example, both identify social norms as contributing to decisions to purchase house and contents insurance. Dependency on insurers may, in part, be a refraction of social interdependency more generally – specifically within financialised societies like Australia where more and more ‘diverse domains of life are reorganized around principles of risk management, accounting, and financial speculation’ (Grove, 2012: 149). Despite our findings, and social survey results that appear to demonstrate decline in trust (e.g. Hooghe and Oser, 2017), Frederiksen and Heinskou (2016: 389) observe that ‘while there is little doubt that risk and calculation within linear time are increasing, this does not mean that trust is decreasing’. They argue there remains ample space for trust, and that there is perhaps even more room for trust as risk calculation increases – as a means of dealing with the growing uncertainty such calculation brings. In other words, if the relational complexity of trust holds (i.e. Lobo-Guerrero, 2013), trust may be reconfiguring, and reconfiguring in different ways in relation to different institutions and different places. For example, in the face of wildfire risk, interviewed residents experience varying manifestations of hope, anxiety, uncertainty and morality that contribute to ‘situated insurantial moments’:
7. Discussion In this analysis we consider three main research questions; what are the most important indicators of house and contents insurance coverage in Australia; to what extent is interpersonal trust, and public trust in insurance companies associated with having house and contents insurance; and how trusting are Australians of insurance companies, relative to other public institutions? Like Booth and Tranter (2018), we found demographic factors to be important indicators of taking out house and contents insurance. However, after controlling for a range of correlates, strong and significant associations at the national level were only found for living in large cities, country of birth, marital status and to a lesser extent, age. In Australia, city dwellers are less likely to have house and contents insurance, while the Australian born are far more likely to be insured than those born overseas, a finding worthy of further study. Australians who are separated, divorced or have never married are less likely than others to be insured, while even among home owners, younger Australians are less likely to have house and contents insurance. Importantly, these findings hold after controlling for sex, income and educational attainment, although the latter variables were not important predictors of insurance cover nationally. In addition, confidence in one’s knowledge of insurance related issues is positively and significantly associated with having house and contents insurance. However, we are unable to establish whether this is a causal relationship, or if it is, which way the causal arrow points. Does self-assessed knowledge of insurance precede taking out insurance, or does self-assessed knowledge increase as part of an on-going relationship with an insurer? Or, alternatively, as suggested by Lobo-Guerrero (2013), are these factors co-produced in more complex ways? Based upon previous research findings on insurance (e.g. Booth and Tranter, 2018) and on interpersonal trust in Australia (e.g. Bean, 2005; Tranter and Skrbis, 2009), we also expected that people who express higher levels of interpersonal trust (i.e. who mostly trust other people), should also be more likely to trust insurance companies, and therefore also more likely to purchase (in this instance) house and contents insurance. To an extent these expectations hold. High interpersonal trust is positively associated with trust in insurance companies, as it is with the other institutions that we examine through the Queensland data. However, interpersonal trust is at best weakly associated with taking out house and contents insurance nationally, and among young adults in Queensland. Similarly, placing high trust in insurance companies, does not translate into a greater likelihood of purchasing house and contents insurance. This lack of association may reflect the power dynamics associated with trust and insurance (Lobo-Guerrero, 2013). With many – at least in the western world – reliant on certain forms of insurance such as house and contents, people may feel they have little choice but to be insured, even if they do not necessarily trust insurance companies. This reliance and dependency could be conceived and reported as trust, but as Wynne et al. (2007: 35) observe, ‘tacit dependency may be misinterpreted as if it signified a more positive trust’. ‘Trust’ in the context of insurance appears to be more akin to dependency rather than to trust per se. As Szerszynski (1999: 248) describes, expressions of trust are often ‘an expression of fatalistic dependency on institutions, of a lack of choice but to trust’. Householders may not trust insurers and insurance more generally (Booth and Harwood, 2016), but have no choice but to place some kind of trust in their own insurer. Yet this observation pertaining to fatalistic dependency, to an extent, appears counterbalanced by our finding that perceptions of natural disaster risks are not significant predictors of having house and contents insurance at the national level. If people feel they need to have
…insurance is constituted within the complexity and fluidity of the social. At particular moments located within particular places, insurance takes on differing forms and functions (Booth and Harwood, 2016: 49). Booth and Harwood (2016) locate trust – or more specifically a lack of trust in insurers – within these ‘insurantial moments’. Several scholars approach trust in more relational and distributed terms. These have largely developed in response to theories that assume a correlation between trust and risk with the assumption that these are both binary and linear. Frederiksen and Heinskou (2016) employ the Deleuzean concept of becoming and explore the temporality of trust. They observe that ‘the uncertainty trust deals with, is connected to process experience rather than expectations of the future’ (2016: 374). Resonating with Frederiksen and Heinskou (2016), Murphy (2006) provides an understanding of trust as an emplaced, evolving and iterative process, rather than a ‘thing’. For Murphy (2006: 429), trust is a ‘sociospatial process enacted by agents through relations mediated by structural factors, power differentials, emotions, meaning systems, and material intermediaries’. This, we believe, signposts two lines of enquiry for geographers of trust. The first, focusses on nuanced, qualitative and ‘place-based’ conceptualisations of trust and its correlates – including confidence, control, dependency, distrust and faith, and embedded processes and materialities of building and regulating trust, maintaining dependency and ruminating distrust. These can relate to relationships between publics and institutions, and also within and between institutions and institutional discourses and practices: how these are variously and dynamically assembled in relation to different institutions (e.g. insurers, banks, government) and events (e.g. government inquiries, elections, instances of institutional misconduct or expressions of good faith, insurance claims). For example, Johnson (2013a) observes how purveyors of microinsurance surreptitiously hope for weather events that trigger payouts to maintain the trust and ‘friendship’ of poor farmers. Elsewhere, insurance decision making is informed by faith in traditional weather forecasting divined from goats’ entrails. Such observations indicate how trust is constituted within and constitutes insurantial 7
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spaces and places. The second line of enquiry – more closely reflecting the data and analysis employed in this paper – is the application of geodemographics for mapping socio-spatial distributions of trust and insurance. Geodemographics is rooted within the Chicago School’s interest in socio-spatial classification of urban areas and utilises population data to characterise local places accordingly (Singleton and Longley, 2009). For example, Singleton et al. (2016) classify consumer internet usage, enabling them to map the resilience (or otherwise) of local shopping precincts to online shopping trends. Our findings of significant associations between demographic, and insurance and trust variables potentially enable similar extrapolation across urban populations (at least in Australia and Queensland). Given demographics are spatially variegated, insurance and trust are also likely to be unevenly distributed. If those in particular places are more or less trusting, and more or less insured there are implications for (amongst other things), disaster resilience. Others also associate living in cities with house and contents underinsurance (Booth and Tranter, 2018), and lower socio-economic status with living in disaster-prone areas (Sewell et al., 2016). Geodemographics offers a useful approach for more fully understanding both the spatial distribution and spatial intersections of these factors.
rationalization. Economy Soc. 37 (2), 224–250. Doyle, A., 2007. Trust, citizenship and exclusion in the risk society. In: Law Commission of CanadaLaw Commission of Canada (Ed.), Risk and Trust: Including or Excluding Citizens. Fernwood Publishing, Black Point, pp. 7–22. Edelman trust barometer, 2018 (Accessed 12/12/2018) https://www.edelman.com/ trust-barometer. Evans, A., 2018. Australian Survey of Social Attitudes 2017, Australian Consortium for Social and Political Research: Canberra. Eyers, J., 2018. Royal commission erodes trust in banks: new poll. Financial Review (accessed 8/12/2018) https://www.afr.com/business/banking-and-finance/royalcommission-erodes-trust-in-banks-new-poll-20181025-h172v9. Dalton, R., 2005. The social transformation of trust in government. Int. Rev. Sociol. 15 (1), 133–154. De Vet, E., Eriksen, C., Booth, K., French, S., 2019. An unmitigated disaster: shifting from response and recovery to mitigation for an insurable future. Int. J. Disaster Risk Manage. 10 (2), 179–192. Frederiksen, M., 2014. Trust in the face of uncertainty: a qualitative study of intersubjective trust and risk. Int. Rev. Sociol. 24, 130–144. Frederiksen, M., Heinskou, M.B., 2016. Trusting the other or taking a chance? An investigation of chance and trust temporalities. Time & Soc. 25 (2), 374–392. Giddens, 1990. Consequences of Modernity. Polity Press, Cambridge. Grove, K., 2012. Pre-empting the next disaster: Catastrophe insurance and the financialization of disaster management. Security Dialogue 43 (2), 139–155. Hall, S., 2010. Geographies of money and finance I: cultural economy, politics and place. Prog. Hum. Geogr. 35 (2), 234–245. Hamm, J.A., Hoffman, L., Tomkins, A.J., Bornstein, B.H., 2016. On the influence of trust in predicting rural land owner cooperation with natural resource management institutions. J. Trust Res. 6 (1), 37–62. Hooghe, M., Oser, J., 2017. Partisan strength, political trust and generalized trust in the United States: An analysis of the General Social Survey, 1972–2014. Soc. Sci. Res. 68, 132–146. Job, J., 2005. How is trust in government created?: It begins at home, but ends in parliament. Aust. Rev. Public Affairs 6 (1), 1–23. Johnson, L., 2013a. Catastrophe bonds and financial risk: Securing capital and rule through contingency. Geoforum 45, 30–40. Johnson, L., 2013b. Index insurance and the articulation of risk-bearing subjects. Environ. Planning A: Economy Space 45, 2663–2681. Knight, D., Noble, F., Vurdubakis, T., Willmott, H., 2001. Chasing shadows: control, virtuality and the production of trust. Organisation Stud. 22 (2), 311–336. Lewis, J.D., Weigert, A., 1985. Trust as a social reality. Soc. Forces 63 (4), 967–985. Lo, A., 2013. The likelihood of having flood insurance increases with social expectations. Area 45 (1), 70–76. Lobo-Guerrero, L., 2010. Insurance, climate change, and the creation of geographies of uncertainty in the Indian Ocean Region. J. Indian Ocean Region 6 (2), 239–251. Lobo-Guerrero, L., 2013. Uberrima fides, Foucault and the security of uncertainty. Int. J. Semiot. Law 26, 23–37. Lucas, C., Leith, P., Davison, A., 2015. How climate change research undermines trust in everyday life: a review. WIREs Clim. Change 6, 79–91. McAneney, J., McAneney, D., Musulin, R., Walker, G., Crompton, R., 2016. Governmentsponsored natural disaster insurance pools: A view from down-under. Int. J. Disaster Risk Reduct. 15, 1–9. O’Malley, P., Roberts, A., 2014. Governmental conditions for the economization of uncertainty: Fire insurance, regulation and insurance actuarialism. J. Cultural Economy 7 (3), 253–272. Our Lives, 2017. Our Lives Wave 6 Technological Report 2017, https://ourlives.org.au/ wp-content/uploads/2018/10/Our-Lives-Technical-Report-Wave-6.pdf (accessed 12/12/2018). Our Lives, 2018. Our Lives: The Social Futures and Life Pathways Project, https:// ourlives.org.au/research/#surveys (accessed 12/12/2018). Murphy, J.T., 2006. Building trust in economic space. Prog. Hum. Geogr. 30 (4), 427–450. Pike, A., Pollard, J., 2010. Economic geographies of financialization. Economic Geography 86 (1), 29–51. Pytlik Zillig, L.M., Kimbrough, C.D., 2016. Consensus on conceptualizations and definitions of trust: Are we there yet? In: Interdisciplinary Perspectives on Trust. Springer International Publishing, pp. 17–47. Rudolph, T., Evans, J., 2005. Political trust, ideology, and public support for government spending. Am. J. Political Sci. 49 (3), 660–671. Sewell, T., Stephens, R.E., Dominey-Howes, D., 2016. Disaster declarations associated with bushfires, floods and storms in New South Wales, Australia between 2004–2014. Sci. Rep. https://doi.org/10.1038/srep36369. Siegrist, M., Gutscher, H., Earle, T.C., 2005. Perception of risk: The influence of general trust, and general confidence. J. Risk Res. 8 (2), 145–156. Singleton, A.D., Dolega, L., Riddlesden, D., Longley, P.A., 2016. Measuring the spatial vulnerability of retail centres to online consumption through a framework of e-resilience. Geoforum 69, 5–18. Singleton, A.D., Longley, P.A., 2009. Geodemographics, visualisation, and social networks in applied geography. Appl. Geogr. 29 (3), 289–298. Szerszynski, B., 1999. Risk and trust: the performative dimension. Environ. Values 8, 239–252. Tranter, B., Skrbis, Z., 2009. Trust and confidence: a study of young queenslanders. Aust. J. Political Sci. 44 (4), 659–678. Wither, C.W.J., 2017. Trust – in geography. Prog. Hum. Geogr. https://doi.org/10.1177/ 0309132516688078. Wynne, B., Waterton, C., Grove-White, R., 2007. Public Perceptions and the Nuclear Industry in West Cumbria, Report RT 94.3, Lancaster: Centre for the Study of Environmental Change, Lancaster University. https://pdfs.semanticscholar.org/ b1d8/6d1b4e473d2fc0bccb36e008fb5df11fec86.pdf.
8. Conclusion Our findings support existing research on trust and insurance, particularly in relation to socio-demographic associations (Booth and Tranter, 2018). However, this research also adds new insights. First, insurance companies rank at the bottom of the pile when it comes to relative institutional trust – even lower than banks and the Australian government. Further, personal trust in insurance companies is not associated with higher levels of insurance cover in Australia. Our trust related findings are nuanced, however. Rather than trust in insurance companies per se, it is confidence in one’s knowledge of insurance related issues that is associated most strongly with house and contents insurance. Those who are confident they know what is covered by insurance policies, and the costs of rebuilding and replacing contents are most likely to have house and contents insurance. Further research is required into ‘actual’ as well as perceived knowledge of insurance, and we acknowledge the limitations of operationalising Job’s (2005) conception of trust in surveys. However, our findings provide important signposts for further geographical research, suggesting a focus on more nuanced conceptualisations of trust in future studies of insurance, as well as the application of geodemographics for mapping the sociospatial variegations of trust and insurance. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoforum.2019.07.006. References Barbalet, J., 2009. A characterization of trust, and its consequences. Theory and Society 38, 367–382. Bean, C., 2005. Is there a crisis of trust in Australia. In: Wilson, S., Meagher, G., Gibson, R., Denemark, D., Western, M. (Eds.), Australian Social Attitudes: The First Report. UNSW Press, Sydney, pp. 122–140. Beck, U., 1992. Risk Society: Towards A New Modernity. New Delhi, SAGE. Booth, K., 2018. Profiteering from disaster: Why planners need to be paying more attention to insurance. Planning Practice & Res. 33 (2), 211–227. Booth, K., Harwood, A., 2016. Insurance as catastrophe: A geography of house and contents insurance in a bushfire prone area. Geoforum 69, 44–52. Booth, K., Tranter, B., 2018. When disaster strikes: Under-insurance in Australian households. Urban Stud. 55 (14), 3135–3150. Çalişkan, K., Callon, M., 2010. Economization, part 2: a research programme for the study of markets. Economy Soc. 39 (1), 1–32. Colledge, B., Morgan, J., Tench, R., 2014. The concept(s) of trust in late modernity the relevance of realist social theory. J. Theory Social Behav. 44 (4), 481–503. Collier, S., 2008. Enacting catastrophe: preparedness, insurance, budgetary
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Contents lists available at ScienceDirect
Geoforum journal homepage: www.elsevier.com/locate/geoforum
Geographies of trust: Socio-spatial variegations of trust in insurance Bruce Tranter , Kate Booth ⁎
University of Tasmania, Australia
ARTICLE INFO
ABSTRACT
Keywords: Home and contents insurance Trust Australia Queensland Survey Geodemographics
Trust is commonly understood as a mechanism that acts to improve transaction efficiency, or as a structural characteristic of organizations. It is a 'good' thing that can be built and harnessed for economic success. However, this type of conceptualisation maintains trust as a discrete and internally stable entity that is universally applied and cordoned off from socio-spatial complexities. In this paper, we present an empiric on trust in the insurance industry with an eye to reports on declining trust in banks, financial institutions and government. Analysing data from two Australian surveys, we map the social and political correlates of having house and contents insurance, consider how knowledge of insurance related issues and trust in insurance companies is associated with house and contents insurance, and measure how much trust Australians place in insurance companies relative to other public institutions. We use our findings as a spring broad for considering more spatialised understandings of trust and conclude by providing signposts for further geographical trust research - the first, qualitative investigations of the spaces and places of trust and its correlates, and the second, using geodemographics for mapping trust's socio-spatial variegations. In this we contribute to research into the socio-spatial variegations of financial processes and technologies, as well as contributing to work on institutional trust.
1. Introduction Public trust in many institutions has been declining steadily in many advanced industrialised countries according to international survey data (Edelman, 2018). Large banks in Australia are a good example of this process, with a series of scandals rocking the banking industry in recent years forcing even a conservative government to initiate a major government inquiry (i.e. a Royal Commission) into the banking industry, further diluting trust in this sector (Eyers, 2018). Public trust in government itself, and in politicians more broadly has also declined in many countries (Edleman, 2018; Hooghe and Oser, 2017; Dalton, 2005). We focus on another critically important financial institution – the insurance industry. While quantitative social scientists have administered a range of national surveys on trust and confidence in institutions, to our knowledge they have not examined insurance companies vis a vis other public institutions. This is somewhat surprising given the power and influence of this sector (Lobo-Guerrero, 2010). It is also surprising given the well-established relationship between trust and risk (e.g. Beck, 1992; Giddens, 1990) – a relationship that appears highly pertinent to insurance as a risk management tool based on ‘trusting’ economic transactions (Booth, 2018). Trust has been observed as acting as a kind of economic and social lubricant. Rational prediction is one strategy for dealing with complex
⁎
future events but, as George Simmel observed, this by itself is insufficient (cited in Lewis and Weigert, 1985: 969). However, in embracing risk and doubt, …trust succeeds where rational prediction alone would fail, because to trust is to live as if certain rationally possible futures will not occur. Thus, trust reduces complexity far more quickly, economically, and thoroughly than does prediction (Lewis and Weigert, 1985: 969). Frederiksen (2014) maintains that when encountering uncertainty, trust can be an alternative ‘strategy’ to risk: “Trust… is a way to stabilise expectations and make risk considerations less relevant” (Frederiksen, 2014: 134). This relationship between risk and trust appears to be supported by studies that have found the more trust people have, the less risk they perceive (e.g. Siegrist et al., 2005). With trust enabling deliberation and action in the face of complexity and uncertainty (Knight et al., 2001) and fostering economic growth (Siegrist et al., 2005), increasing concern about risk and declining levels of trust in advanced industrialised nations (Doyle, 2007) have led to speculation of an erosion or collapse of social and economic function. Beck (1992) and Giddens (1990), observing this correlation between risk and trust, have characterised these as ‘risk societies’ – societies increasingly preoccupied with risk which leads to significant changes in
Corresponding author at: Social Sciences, Private Bag 22, University of Tasmania, Hobart, Tasmania 7000, Australia. E-mail address: [email protected] (B. Tranter).
https://doi.org/10.1016/j.geoforum.2019.07.006 Received 5 March 2019; Received in revised form 2 July 2019; Accepted 4 July 2019 0016-7185/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Bruce Tranter and Kate Booth, Geoforum, https://doi.org/10.1016/j.geoforum.2019.07.006
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social and political structures and function, both positive and negative. Turning our attention to insurers, we (1) map the social and political correlates of having house and contents insurance, (2) consider how knowledge of insurance related issues, and trust in insurance companies is associated with having house and contents insurance, and (3) examine how trust in insurance companies ranks alongside trust in other public institutions. We achieve this by drawing upon two data sources – a nationally representative sample of Australian adults, and a large sample of young adults from the Australian state of Queensland. Analysing national survey data, we examine empirically the social and political correlates of having house and contents insurance, and how trust in and knowledge of insurance companies is related to having house and contents insurance. Analysing Queensland data, we consider the level of trust in insurance companies compared to banks and financial institutions, courts and the legal system, the Australian government, universities and police, and map the social and political background of trust in these institutions. In interpreting these survey results, we draw upon conceptualisations of trust that move beyond what Çalişkan and Callon (2010) identify as the trust ‘black-box’:
between, for example, institutional structures and systems, and trust. It also enables an exploration of the factors that may generate or diminish trust. As Çalişkan and Callon (2010) highlight, economists and economic sociologists have placed great stress on trust in the everyday maintenance of markets, particularly the role trust plays in coordinating markets when uncertainty is high. This has, in part, led to a proliferation of research to better understand, build and mobilise trust for economic gain, with trust perceived as a key driver of information creation, knowledge exchange and innovation (Murphy, 2006). It is generally assumed that trust is ‘good’ and building trust will produce positive business and economic outcomes (e.g. Hamm et al., 2016). Previous research indicates the importance of trust in householder insurance decision-making, specifically how low levels of trust in insurers may lead to underinsurance (Booth and Harwood, 2016). Some households choose not to insure because they do not trust insurers or appear to downgrade their insurance cover on the supposition that insurers will not deliver full payouts. These observations appear to be supported by other research indicating that those who report lower levels of trust in others are less likely to have house and/or contents insurance (Booth and Tranter, 2018). Building trust in insurers – following the rationale outlined above – seems to be required if householders are to continue to purchase insurance or purchase an adequate level of insurance cover. However, Knight et al. (2001) observe that the production of trust is always entwined within systems of power and control. In the provision of services, systems of control can be developed and regulated to gain customer confidence and trust, and customer and provider trust occurs within structures and norms that condition behaviour and determine outcomes. In relation to trust issues in the virtual marketplace, ‘the task of the various corporate actors is to come up with novel legal, social or technical mechanisms of control and trust production’ (Knight et al., 2001: 318, emphasis added). The co-production of trust within instruments and mechanisms of control is infused with hidden and explicit articulations of power. For insurance, trust has traditionally been associated with the principle of utmost good faith – uberruma fides (Lobo-Guerrero, 2010). This principle embodies the assumption that trust is a necessary precondition for an insurance transaction to take place between, for example, a householder and an insurer. Insurance ‘trades in trust’ (LoboGuerrero, 2010):
…our objection to trust is that it is used as an undifferentiated explanation of coordination that black-boxes maintenance operations and socio-technical devices, instead of demanding that these be studied. It would be useful to develop further studies of market emotions that grant a key role to materialities in the production of these very emotions (Çalişkan and Callon, 2010: 21). Trust may be operationalised in surveys (as we have) and calculated and tracked along linear temporal trajectories (as decreasing or increasing). However, this invariably (re)produces an ontological asymmetry that maintains trust as a discrete and internally stable entity that is universally applied and cordoned off from related social and spatial complexities. In acknowledging that this limitation applies to our own empirical analysis, we use these observations as a spring broad from which to consider more relational and material – and thus, spatially variegated – understandings of trust. In this we contribute to addressing a second research lacuna: the more general lack of research into the socio-spatial variegations of financial processes and technologies. As Hall (2010) observes regarding research on the cultural economies of finance, while the global has been conceived as composed of spatial variegations constituted within diverse social worlds and not as a uniform distribution of finance and economics, little work has been undertaken into the way finance technologies are (re)produced in specific places. Financial practices and discourses can appear abstract and universalising with little attachment to place, yet as Pike and Pollard (2010: 38) observe, there is an “inescapable geographic construction, context, and rootedness of financial networks and practices”. In this paper, we provide a signpost for further geographical trust research. It is important to note that we do not set out to review the broad and diverse trust literatures, nor canvass the multiple and occasionally overlapping registers of trust embodied within these literatures, as this has been addressed extensively by others (e.g. Barbalet, 2009; Colledge et al., 2014; Frederiksen and Heinskou, 2016; Lewis and Weigert, 1985; Lucas et al., 2015; Murphy, 2006; Pytlik Zillig and Kimbrough, 2016; Withers; 2017).
…that both parties to the insurance transaction provide relevant and sufficient information that will help them make an informed decision on the insurability of the event to be covered, in the case of the underwriter, and the usefulness, value, and relevance of the insurance product on offer in the case of the buyer (Lobo-Guerrero, 2013: 26). However, given that utmost good faith is a principle in law that requires particular modes of behaviour and reporting on behalf of both parties, Lobo-Guerrero (2013) argues that in many cases trust, as mediated by law, ‘is not a given but an outcome of the insurance relationship. It is actively and continuously produced through the faithful interaction between client and insurer…’ (Lobo-Guerrero, 2013: 24). Trust in this sense ‘is not a given value or a principal for interaction but an outcome of social, cultural, political and economic relationality which needs to be actively and continuously produced’ (Lobo-Guerrero, 2013: 35); the principle of utmost good faith is therefore characterised as ‘trust’ a posteriori. The complexity for morality and calculation embodied within utmost good faith is far from benign, as by ‘making trust evident, insurance practices discipline and “police” the behaviour of individuals and collectivities’ (Lobo-Guerrero, 2010: 243). In relation to trust and the mis-selling of insurance products in the United Kingdom, LoboGuerrero (2013) goes on to describe that:
2. Trust and insurance As introduced above, trust is commonly understood as a mechanism that acts to improve transaction efficiency between parties, or as a structural characteristic of organizations and networks (Murphy, 2006). Trust is assumed to be a ‘thing’ that exists prior to the transaction, formed and acted upon through rational individual cognition, and relatively stable; trust as fixed, transferable and universal. This conceptualisation of trust allows cause and effect claims to be made
The principle of uberrimae fides becomes in this respect, not ‘a ready2
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made region that needs to be respected’, but ‘a proposal to manufacture’ trust through relationships in which the disclosure of material facts is forced upon the counterparty through a complex set of moral technologies (Lobo-Guerrero, 2013: 35).
typically used as a term in relation to others, whereas researchers tend to refer to confidence in institutions, such as universities or governments (Job, 2005: 2). 4. Data and method
He describes the process of disclosure of information on behalf of the consumer as an act of confession premised on coercion and control deployed through the unequal distribution of power. This a complex and iterative subjectification, in which individuals are not only on the receiving end of coercion but are conceived of as ‘coerced selves’. In other words, individuals impose on themselves the expectation and acceptability of confession and associated domination (Lobo-Guerrero, 2013). While insurers make calculations and projections based on archival data and probabilities, as described by Collier (2008) and O’Malley and Roberts (2014), even insurance is produced through other ‘strategies’ or contextual factors when dealing with future uncertainty and risk. These strategies embody both trust and control (Knight et al., 2001), providing impetus for researching public trust in insurance companies.
We analyse data from the 2017 Australian Survey of Social Attitudes (AuSSA), a national social survey designed to be representative of the Australian adult population. The sample was randomly selected from the Australian Electoral Roll ‘by federal electoral division in proportion to the size of the division versus the total enrolled’ (Evans, 2018). A sample of 5000 people was selected and administered in 4 waves of 1,250 by mail survey. After ineligibles were removed, a response rate of 28 per cent was achieved (n = 1,317). The Queensland data are drawn from a single wave of the Social Future and Life Pathways study, known as the ‘Our Lives’ survey (Our Lives, 2018), a longitudinal survey of young people from the state of Queensland, Australia (n = 2,030). The Our Lives survey was first administered in 2006 to a state-wide sample of school students aged 12/ 13. Subsequently, a further 5 survey waves have been administered. The survey consists of a set of core questions with modules added to particular waves. As in Waves 4 and 5, data collection for Wave 6 was conducted as a combination of online surveys (82%) and Computer Assisted Telephone Interviewing (18%). The sample collected in 2017 included respondents aged 23/24, representing 29 per cent of the original Wave 1 sample. In Wave 6, questions relating to trust in insurance companies, and type of house and contents insurance respondents had, were included in the survey for the first time. These Queensland data are unique as they not only allow us to compare trust in insurance companies relative to other institutions, but also to consider a large cohort of young Australians (aged in their mid-twenties), a stage of life when housing related issues are highly salient, including the importance of home and contents insurance.
3. Research questions In the empirical sections that follow we explore how trust is associated with having house and contents insurance in an advanced industrialised country – Australia. In Australia, there are no property or disaster insurance schemes (McAneney et al., 2016) and the insurance of homes and their contents is the responsibility of householders and private insurers, with governments acting as insurers of last resort particularly following large-scale adverse events (de Vet et al., 2019). Around 84 per cent of Australian homeowners have house insurance, 85 per cent have contents insurance, and 79 per cent both house and contents insurance (Booth and Tranter, 2018). Insurance patterns are unevenly distributed, with city-dwellers less likely to have house and contents insurance than their rural counterparts. In this context, we ask: What are the most important indicators of having house and contents insurance coverage in Australia? To what extent are interpersonal trust, and public trust in insurance companies associated with having house and contents insurance, and how trusting are Australians of insurance companies, relative to other public institutions? Based on the literature on institutional and interpersonal trust, and trust in insurance companies more specifically, we have the following research expectations. Insurance coverage will be higher among those:
4.1. Dependent variables We operationalise several dependent variables. The first is based on a question from the AuSSA that was also included in the Queensland Our Lives survey: ‘Thinking about your main place of residence, which of the following best describes the type of insurance cover that you or someone who lives with you has purchased? The residence is covered by…’ The variable contrasts those who have both house and contents insurance (scored 1), with respondents who have either house or contents insurance, or are not insured (scored 0). This dichotomous dependent variable is modelled using binary logistic regression analysis in Table 2. The Queensland study included questions on trust in institutions in several survey waves. However, in the 2017 survey an ‘insurance companies’ item was added to the list, providing a unique comparative measure of trust in insurance companies vis a vis other institutions. How much trust do you have in the following groups…? (response categories: 1 no trust at all; 2 not very much trust; 3 quite a lot of trust; 4 a great deal of trust). Several ordinal dependent variables are operationalised from these questions – trust in…insurance companies, banks and financial institutions, courts and the legal system, the Australian government, universities and police. We analyse these dependent variables in Table 3, using ordinal logistic regression. Analysis are conducted using SPSS version 24. Missing data are coded to their respective reference categories for dichotomous independent variables, while for continuous variables, missing data are replaced using SPSS linear interpolation.
1. With higher interpersonal trust 2. More trusting of insurance companies 3. With higher ‘confidence’ in their own knowledge of insurance related issues 4. On higher incomes, women, and the tertiary educated We examine these questions empirically, following a description of our data and the research strategies we adopt. In operationalizing trust, we employ Job’s (2005: 4) differentiation between ‘rational’ and ‘relational’ forms of trust. Important for his concept of rational trust is the assumption ‘that to trust presupposes consideration of information or knowledge about the other’ (Job 2005, 4). This invokes the concept of risk (intrinsic to this type of trust), because rational trust involves calculating the risks associated with trusting others. Our focus on trust in insurance companies clearly involves elements of risk, both for those seeking to be insured, and of course for insurance companies. Alternatively, relational trust ‘is based on belief or faith in the goodness of others’ (Job, 2005: 4). Relational trust manifests as a ‘trusting disposition’ that tends to be relatively stable and is imbued in childhood (Job, 2005, 4). Relational trust is important when we seek to measure generalised trust in others, as opposed to trust in specific individuals or institutions. In fact, trust is
4.2. Independent variables Several independent variables are operationalised for the regression models based upon national AuSSA data in Table 2. Trust in insurance companies is measured using the following question: Using the following scale ranging from 0 to 10, where 0 means “No trust at all” and 10 means 3
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“Complete trust”, please indicate how much trust you personally have in insurance companies? (mean 4.24; standard deviation 2.43) centred at its mean for regression analysis. We measure the perceived risk of experiencing a natural disaster using the question: What is the risk of a natural disaster (e.g. bushfire, cyclone, major storm or flood) striking your local area? (responses: very high; high; moderate; low; very low). We contrast very high + high = 1 with moderate or less = 0. An additive knowledge scale was constructed from three questions:
House and contents insurance levels are slightly higher among those with higher levels of interpersonal trust, although the percentage differences are not large, while large city dwellers are less likely to be insured than those living in other locations. Political party identification, a proxy for moral progressivism/conservativism, is also associated with insurance, with Greens identifiers least likely to be insured (56%), and Liberal/Nationals (coalition) identifiers most likely to have insurance cover (87%), although partisan differences are weaker among home owners. Australian born home owners (92%) are more likely to be insured than those born elsewhere (83%). The perceived risk of national disasters was a non-significant association with insurance, while believing in anthropogenic climate change is associated with lower levels of insurance, although not significantly so among home owners. Not surprisingly, we found around 90 per cent those who own or are purchasing their home have home and content insurance, compared to only 26 per cent in private rental, and 14 per cent in public rental accommodation.
a. How confident are you that you know what is covered by your house and contents insurance policy? b. In the case of loss, how confident are you that you know the costs for rebuilding your house? c. In case of loss, how confident are you that you know the costs for replacing your contents? The scale items (i.e. from strong agreement to strong disagreement) were reverse scored so that higher scores represent greater confidence in self-assessed knowledge of house and contents related issues. The scale is reliable (Alpha = 0.76) with a mean of 9.05 and standard deviation of 2.03 and centred at its mean for regression analysis. Using the same survey question as we employ here, in previous research Bean (2005, 139) found interpersonal trust to be a ‘better indicator of social capital than political trust or trust in government’. In both the national and Queensland data interpersonal trust was measured with the question: Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people? (response categories: most people can be trusted scored 1; you can’t be too careful in dealing with people scored 0). A variable measuring public confidence in house and contents insurance cover in the case of a natural disaster was included in both the AuSSA and Our Lives surveys: If a natural disaster (e.g. bushfire, cyclone, major storm or flood) strikes your area, how confident are you that your insurance will cover all your building and contents replacement costs? (response categories: not at all confident; not very confident; somewhat confident; confident; very confident). Several dummy (1/0) independent variables are operationalised, including respondent sex (men = 1), age (18–29 = 1; 30 + reference category); non-tertiary education = 1; high income (i.e. National sample household income $150,000+ =1; Queensland sample $80,000+ =1), born in Australia = 1; separated, divorced or never married = 1. Perceived risks of natural disaster are modelled as very high or high = 1 compared to moderate or less = 0. Believing that climate change is occurring mainly due to anthropogenic causes = 1 is contrasted with other climate beliefs = 0, while Coalition (i.e. Liberal or National Party) political identification = 1 is contrasted with other political identifications = 0.
5.1. National regression results We employ binary logistic regression analyses to examine the net associations between a dichotomous dependent variable – having house and contents insurance – and several independent variables (Table 2). Given that house and contents insurance is associated primarily with home owners or buyers, as is apparent in Table 1, we restrict our analysis to home owners and home buyers. To illustrate the associations between insurance coverage and various background variables, we introduce independent variables in five models. The first introduces demographic variables (i.e. respondent sex, age groups, non-tertiary education, household income, location [i.e. live in large city], marital status and country of birth). Model 2 examines generalised trust in others; trust in insurance companies, and self-assessed insurance knowledge. Model 3 measures perceptions of the risks of a natural disaster in the respondents’ location, and attitudes toward anthropogenic climate change. Model 4 introduces Liberal/National party identification compared to other political parties or non-identifiers, while in Model 5 all independent variables are included simultaneously. The results for Model 1 suggest several statistically significant sociodemographic associations are apparent at the 95 per cent level of confidence, or greater. The first model also has by far the largest Nagelkerke R2 of 0.16 compared to models 2 to 4, suggesting social background effects are important for understanding who takes up house and contents insurance. The odds ratio of 0.3 for the youngest age group aged 18–29 indicates the odds of having house and contents insurance for the youngest age category are approximately 5 times lower than those for older Australians. This finding holds after controlling for respondent sex, educational achievement, income level, location, marital status and country of birth. The odds of being insured for those living in a large city (0.4) are lower than other locations, while the separated, divorced or never been married have far smaller odds of being insured than other marital statuses (OR 0.3). Being born in Australia is associated with higher odds of being insured compared to being born overseas (OR 2.8), however, other indicators of including education and high income, are non-significant in Model 1. Model 2 examines elements of trust and confidence. Interpersonal trust and confidence in one’s knowledge of house and contents insurance are both significantly associated with the dependent variable. The positive odds ratios indicate that being more trusting of others and having greater knowledge of insurance issues, are associated with higher levels of house and contents insurance cover. Trust in insurance companies per se, has a very weak negative (0.97), but non-significant association with house and contents insurance. Perceptions of high disaster risk, while exhibiting a positive odds ratio, is non-significant predictor of having house and contents
5. Bivariate results The bivariate results presented in Table 2 show the associations between several independent variables and the house and contents insurance binary dependent variable, with results also presented for respondents who own or are purchasing their homes. Gender differences in the uptake of home and contents insurance are minimal, with 74 per cent of men and 73 per cent of women insured. The youngest 18 to 29 cohort are least likely to be insured (54%), although 69 per cent of younger home owners are insured. Tertiary education, or the lack of it, seems to have no discernible association with home and contents insurance. Those in the highest income bracket are most likely to be insured, although income has little impact on insurance among home owners. However, among home owners, those who are married or in de facto relationships (93%) or are widowed (93%), are far more likely to have house and contents insurance than people who have never married (74%). 4
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Table 1 House and contents insurance by social background (per cent).
Table 2 House and contents insurance by social background (odds ratios).
All
Home owners
Model
1
2
3
4
5
Men Women
74a 73a
88a 90a
Men
0.8
–
–
–
0.8
Age 18–29 30–44 45–59 60–69 70+
54a 72b 76b 84c 85c
69a 88b 92b 93b 93b
Age (30 + referent) Aged 18–29
1 0.3**
– –
– –
– –
1 0.5*
Degree Non-tertiary
1 0.9
– –
– –
– –
1 0.8
Degree Non-tertiary
73a 75a
89a 90a
Household Income < =Aus$150 K Household Income > Aus$150 K Missing on income
1 2.0 0.8
– – –
– – –
– – –
1 2.0 0.8
Household Income $0 - $30 K Aus $30-$60 K Aus $60-$100 K Aus $100-$150 K Aus $150 K+ Missing on income
73a,b 76a,b 77a,b 75a,b 86b 70a
91a,b 88a,b 93b 92a,b 95a,b 86a
Marital status Married/de facto Separated Divorced Widowed Never married
85a 53b 64b 84a 57b
93a 89a,b 86a,b 93a 74b
Interpersonal trust Most people can be trusted You can’t be too careful
78a 70b
92a 88b
Location Big city Big city suburb Town/small city Country village Farm/country home
67a 74a,b 75a,b 74a,b 80b
82a 90b 90b 95b 97b
Party ID Labor Coalition Greens No ID Born in Australia Born elsewhere Total
72a 87b 56c 70a 75a 70a 74
90a,b 93b 97a,b 86a 92a 83b 89
Which do you believe? CC mainly anthropogenic CC happening but ‘natural’ CC not happening Don’t know
70a 80b 91b 79b
88a 91a 92a 91a
Risk of natural disaster High/very high Moderate or less
80a 73a
92a 89a
Housing tenure Own outright Mortgage Private rental Public rental Boarding, living at home, other Total
90a 89a 26b 14b 65c 74
90a 89a – – – 89
Live in a large city
0.4**
–
–
0.4**
Separated, divorced, never married Other marital status
0.3*** 1
– –
– –
– –
0.3*** 1
Born in Australia Born elsewhere
2.8*** 1
– –
– –
– –
2.7*** 1
Most people can be trusted Trust in insurance companies (0–10) Self-assessed insurance knowledge (scale)
– – –
1.9** 0.97 1.16***
– – –
– –
1.6a 0.98 1.15**
Risk of natural disaster High/very high Moderate or less
– –
– –
1.5 1
– –
1.1 1
Climate change anthropogenic
–
–
0.6a
–
0.8
Party ID (other/none referent) Coalition ID
– –
– –
– –
1 2.0*
1 1.4
Pseudo R2 N
0.16 (950)
0.05 (950)
0.01 (950)
0.02 (950)
0.19 (950)
Notes: Estimates are for home owners and mortgagees only. Dependent variable: house and contents insurance = 1; no insurance = 0. Source: Australian Survey of Social Attitudes (2017). a p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001.
6. Queensland analyses Our Lives researchers have collected data on trust in Australian institutions approximately every two years since 2006. In 2017, for the first time, an additional item measuring insurance companies was added to their list, so the insurance companies item is unique to that survey year. In Fig. 1, for each institution we combine the percentages of those who express ‘a great deal of trust’ with ‘quite a lot of trust’. Studies of institutional trust in Australia have shown that particular institutions (i.e. police, universities, courts) tend to elicit relatively high levels of trust over time. However, we find the findings for insurance companies to be quite striking. As expected, and consistent with other studies, in 2017 young Queenslanders placed very high levels of trust in the police (87%), and relatively high trust in universities (72%) and courts and the legal system (66%), although they are somewhat less trusting of environmental groups (55%). Also, not expectedly, banks (45%) and the Australian government (35%) are trusted by relatively few Queenslanders, in both cases fewer than half of those surveyed. Longitudinal data from the Our Lives survey (not shown) indicate that while trust in the police has remained high at over eighty per cent since the first survey in 2006, trust in banks declined from 57 per cent in 2015 to 45 per cent in 2017 (Our Lives, 2017). We suspect trust in large banks may be lower than trust in smaller ‘community banks’ and credit unions, but lack access to nuanced data to test this expectation. It is nevertheless surprising, that only 25 per cent of Queenslanders aged 24 to 25 express a great deal or quite a lot of trust in insurance companies. Again, we suspect that trust in insurance companies would vary
Note: different subscript letters within each variable signifies statistically significant differences between variable categories at the 95% level or better. Source: Australian Survey of Social Attitudes (2017).
insurance, while believing in anthropogenic climate change has a significant association with insurance, but only at the 90 per cent level. Relative to Model 1 (that contains more independent variables), the contribution of Models 2 – 4 have far smaller Nagelkerke R2 statistics, suggesting that social background effects are relatively useful predictors of insurance. In the full model (Model 5), age, location in a large city, marital status, country of birth and confidence in insurance companies all have statistically significant associations with house and contents insurance at the 95% level or better (Nagelkerke R2 = 0.19). 5
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Trust in Institutions (%)
100 87
90 80
72 66
70 60
55 45
50
35
40
25
30 20 10 0 Police
Universities
Courts and the Groups working legal system to protect the environment
Banks and financial institutions
Australian Government
Insurance companies
Fig. 1. Trust in institutions among young Queensland adults.
further, according to type of insurance cover, a question that requires further research. Nevertheless, when it comes to trust, young Queenslanders place insurance companies in a distant last place relative to other Australian institutions.
indicator of institutional trust. Those who believe most people can be trusted are more likely than others to trust each of these institutions, consistent with the findings of Tranter and Skrbis (2009). Holding a post-secondary certificate or diploma is associated with higher odds of trusting insurance companies (OR 1.4), but lower odds of trusting the courts (OR 0.6), the Australian government (OR 0.7) and universities (OR 0.8). Higher income is positively associated with trust in institutions, but only weakly and non-significantly for all institutions apart from the courts (OR 1.4). Finally, political party affiliation is an important correlate of institutional trust. Similar to Rudolph and Evans’ (2005) on political trust, Liberal and National party identifiers are more trusting than those who identify with other political parties, with the exception of trust in universities. Secondary school has inconsistent associations across institutions, with the signs of association varying across the dependent variables. For example, those who last attended government schools have slightly higher odds of trusting insurance companies (OR 1.2), but lower odds than former independent or Catholic school students for trust in the courts (OR 0.7) or the Australian government (OR 0.7). Finally, those
6.1. Queensland regression results In Table 3 logistic regression analysis model trust in the institutions appearing in Fig. 1. The aim is to compare social background effects for trust in insurance companies comparted to banks and financial institutions, courts and the legal system, the Australian Government, universities and police. These dependent variables have an ordinal structure (i.e. a great deal of trust = 1; quite a lot of trust = 2; not very much trust = 3; no trust at all = 4) and are therefore modelled using ordinal logistic regression (i.e. SPSS GENLIN). Some associations are relatively consistent with institutional trust across most institutions. Women tend to be more likely than men to trust the institutions modelled here, apart from the courts/legal system, and the Australian government. Interpersonal trust is an important Table 3 Trust in insurance companies and other institutions (cumulative odds ratios). Insurance ***
Banks
Courts
***
Aus. Govt.
Universities
Police
Women
1.5
1.6
0.88
1.02
1.4
1.4**
Government School Other Secondary school
1.2* 1
1.0 1
0.7** 1
0.7** 1
1.04 1
1.1 1
Certificate or Diploma Other post-secondary Education
1.4** 1
1.1 1
0.6*** 1
0.7** 1
0.8* 1
0.9 1
Income $AUD80K+
1.2
1.2
1.4*
1.4
1.2
1.3
Most people can be trusted
1.4***
1.6***
2.6***
2.4***
2.0***
1.8***
No House or Contents Insurance
0.8*
0.8*
1.2
1.1
1.1
0.7***
Party ID (referent other or none) Coalition ID
1 1.7**
1 1.8***
1 1.6***
1 2.1***
1 1.1
1 1.8***
N
(2,013)
(2,013)
(2,013)
(2,013)
(2,013)
(2,013)
Notes: Dependent variables have an ordinal structure: 1 = no trust at all to 5 = a great deal of trust. Source: Our Lives (2017) a p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001. 6
**
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who do not have house and contents insurance are slightly less trusting of insurance companies (OR 0.8), banks (OR 0.8) and also the police (OR 0.7).
insurance because of perceived risks, we would expect to find a significant positive association here. There are likely also other factors contributing to feelings of dependency on insurance other than trust and risk per se. Reflecting Lobo-Guerrero’s (2013) observations of social and moral coercion embedded within insurance, Johnson (2013b) identifies a process of normatization within insurance practices. Social relations are ‘coaxed’ to produce and reinforce certain realities within which insurance is propagated and promulgated. Booth and Harwood (2016) and Lo (2013), for example, both identify social norms as contributing to decisions to purchase house and contents insurance. Dependency on insurers may, in part, be a refraction of social interdependency more generally – specifically within financialised societies like Australia where more and more ‘diverse domains of life are reorganized around principles of risk management, accounting, and financial speculation’ (Grove, 2012: 149). Despite our findings, and social survey results that appear to demonstrate decline in trust (e.g. Hooghe and Oser, 2017), Frederiksen and Heinskou (2016: 389) observe that ‘while there is little doubt that risk and calculation within linear time are increasing, this does not mean that trust is decreasing’. They argue there remains ample space for trust, and that there is perhaps even more room for trust as risk calculation increases – as a means of dealing with the growing uncertainty such calculation brings. In other words, if the relational complexity of trust holds (i.e. Lobo-Guerrero, 2013), trust may be reconfiguring, and reconfiguring in different ways in relation to different institutions and different places. For example, in the face of wildfire risk, interviewed residents experience varying manifestations of hope, anxiety, uncertainty and morality that contribute to ‘situated insurantial moments’:
7. Discussion In this analysis we consider three main research questions; what are the most important indicators of house and contents insurance coverage in Australia; to what extent is interpersonal trust, and public trust in insurance companies associated with having house and contents insurance; and how trusting are Australians of insurance companies, relative to other public institutions? Like Booth and Tranter (2018), we found demographic factors to be important indicators of taking out house and contents insurance. However, after controlling for a range of correlates, strong and significant associations at the national level were only found for living in large cities, country of birth, marital status and to a lesser extent, age. In Australia, city dwellers are less likely to have house and contents insurance, while the Australian born are far more likely to be insured than those born overseas, a finding worthy of further study. Australians who are separated, divorced or have never married are less likely than others to be insured, while even among home owners, younger Australians are less likely to have house and contents insurance. Importantly, these findings hold after controlling for sex, income and educational attainment, although the latter variables were not important predictors of insurance cover nationally. In addition, confidence in one’s knowledge of insurance related issues is positively and significantly associated with having house and contents insurance. However, we are unable to establish whether this is a causal relationship, or if it is, which way the causal arrow points. Does self-assessed knowledge of insurance precede taking out insurance, or does self-assessed knowledge increase as part of an on-going relationship with an insurer? Or, alternatively, as suggested by Lobo-Guerrero (2013), are these factors co-produced in more complex ways? Based upon previous research findings on insurance (e.g. Booth and Tranter, 2018) and on interpersonal trust in Australia (e.g. Bean, 2005; Tranter and Skrbis, 2009), we also expected that people who express higher levels of interpersonal trust (i.e. who mostly trust other people), should also be more likely to trust insurance companies, and therefore also more likely to purchase (in this instance) house and contents insurance. To an extent these expectations hold. High interpersonal trust is positively associated with trust in insurance companies, as it is with the other institutions that we examine through the Queensland data. However, interpersonal trust is at best weakly associated with taking out house and contents insurance nationally, and among young adults in Queensland. Similarly, placing high trust in insurance companies, does not translate into a greater likelihood of purchasing house and contents insurance. This lack of association may reflect the power dynamics associated with trust and insurance (Lobo-Guerrero, 2013). With many – at least in the western world – reliant on certain forms of insurance such as house and contents, people may feel they have little choice but to be insured, even if they do not necessarily trust insurance companies. This reliance and dependency could be conceived and reported as trust, but as Wynne et al. (2007: 35) observe, ‘tacit dependency may be misinterpreted as if it signified a more positive trust’. ‘Trust’ in the context of insurance appears to be more akin to dependency rather than to trust per se. As Szerszynski (1999: 248) describes, expressions of trust are often ‘an expression of fatalistic dependency on institutions, of a lack of choice but to trust’. Householders may not trust insurers and insurance more generally (Booth and Harwood, 2016), but have no choice but to place some kind of trust in their own insurer. Yet this observation pertaining to fatalistic dependency, to an extent, appears counterbalanced by our finding that perceptions of natural disaster risks are not significant predictors of having house and contents insurance at the national level. If people feel they need to have
…insurance is constituted within the complexity and fluidity of the social. At particular moments located within particular places, insurance takes on differing forms and functions (Booth and Harwood, 2016: 49). Booth and Harwood (2016) locate trust – or more specifically a lack of trust in insurers – within these ‘insurantial moments’. Several scholars approach trust in more relational and distributed terms. These have largely developed in response to theories that assume a correlation between trust and risk with the assumption that these are both binary and linear. Frederiksen and Heinskou (2016) employ the Deleuzean concept of becoming and explore the temporality of trust. They observe that ‘the uncertainty trust deals with, is connected to process experience rather than expectations of the future’ (2016: 374). Resonating with Frederiksen and Heinskou (2016), Murphy (2006) provides an understanding of trust as an emplaced, evolving and iterative process, rather than a ‘thing’. For Murphy (2006: 429), trust is a ‘sociospatial process enacted by agents through relations mediated by structural factors, power differentials, emotions, meaning systems, and material intermediaries’. This, we believe, signposts two lines of enquiry for geographers of trust. The first, focusses on nuanced, qualitative and ‘place-based’ conceptualisations of trust and its correlates – including confidence, control, dependency, distrust and faith, and embedded processes and materialities of building and regulating trust, maintaining dependency and ruminating distrust. These can relate to relationships between publics and institutions, and also within and between institutions and institutional discourses and practices: how these are variously and dynamically assembled in relation to different institutions (e.g. insurers, banks, government) and events (e.g. government inquiries, elections, instances of institutional misconduct or expressions of good faith, insurance claims). For example, Johnson (2013a) observes how purveyors of microinsurance surreptitiously hope for weather events that trigger payouts to maintain the trust and ‘friendship’ of poor farmers. Elsewhere, insurance decision making is informed by faith in traditional weather forecasting divined from goats’ entrails. Such observations indicate how trust is constituted within and constitutes insurantial 7
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spaces and places. The second line of enquiry – more closely reflecting the data and analysis employed in this paper – is the application of geodemographics for mapping socio-spatial distributions of trust and insurance. Geodemographics is rooted within the Chicago School’s interest in socio-spatial classification of urban areas and utilises population data to characterise local places accordingly (Singleton and Longley, 2009). For example, Singleton et al. (2016) classify consumer internet usage, enabling them to map the resilience (or otherwise) of local shopping precincts to online shopping trends. Our findings of significant associations between demographic, and insurance and trust variables potentially enable similar extrapolation across urban populations (at least in Australia and Queensland). Given demographics are spatially variegated, insurance and trust are also likely to be unevenly distributed. If those in particular places are more or less trusting, and more or less insured there are implications for (amongst other things), disaster resilience. Others also associate living in cities with house and contents underinsurance (Booth and Tranter, 2018), and lower socio-economic status with living in disaster-prone areas (Sewell et al., 2016). Geodemographics offers a useful approach for more fully understanding both the spatial distribution and spatial intersections of these factors.
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8. Conclusion Our findings support existing research on trust and insurance, particularly in relation to socio-demographic associations (Booth and Tranter, 2018). However, this research also adds new insights. First, insurance companies rank at the bottom of the pile when it comes to relative institutional trust – even lower than banks and the Australian government. Further, personal trust in insurance companies is not associated with higher levels of insurance cover in Australia. Our trust related findings are nuanced, however. Rather than trust in insurance companies per se, it is confidence in one’s knowledge of insurance related issues that is associated most strongly with house and contents insurance. Those who are confident they know what is covered by insurance policies, and the costs of rebuilding and replacing contents are most likely to have house and contents insurance. Further research is required into ‘actual’ as well as perceived knowledge of insurance, and we acknowledge the limitations of operationalising Job’s (2005) conception of trust in surveys. However, our findings provide important signposts for further geographical research, suggesting a focus on more nuanced conceptualisations of trust in future studies of insurance, as well as the application of geodemographics for mapping the sociospatial variegations of trust and insurance. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoforum.2019.07.006. References Barbalet, J., 2009. A characterization of trust, and its consequences. Theory and Society 38, 367–382. Bean, C., 2005. Is there a crisis of trust in Australia. In: Wilson, S., Meagher, G., Gibson, R., Denemark, D., Western, M. (Eds.), Australian Social Attitudes: The First Report. UNSW Press, Sydney, pp. 122–140. Beck, U., 1992. Risk Society: Towards A New Modernity. New Delhi, SAGE. Booth, K., 2018. Profiteering from disaster: Why planners need to be paying more attention to insurance. Planning Practice & Res. 33 (2), 211–227. Booth, K., Harwood, A., 2016. Insurance as catastrophe: A geography of house and contents insurance in a bushfire prone area. Geoforum 69, 44–52. Booth, K., Tranter, B., 2018. When disaster strikes: Under-insurance in Australian households. Urban Stud. 55 (14), 3135–3150. Çalişkan, K., Callon, M., 2010. Economization, part 2: a research programme for the study of markets. Economy Soc. 39 (1), 1–32. Colledge, B., Morgan, J., Tench, R., 2014. The concept(s) of trust in late modernity the relevance of realist social theory. J. Theory Social Behav. 44 (4), 481–503. Collier, S., 2008. Enacting catastrophe: preparedness, insurance, budgetary
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