Experience, Financial Literacy, and the Propensity to Hold ... · financial literacy. While their...

WPG18-03 Experience, Financial Literacy 1

Experience, Financial Literacy, and the Propensity to Hold Income Protection Insurance by Country of Residence *+Gordon L Clark, *#Juncal Cuñado, and *Sarah McGill *Smith School of Enterprise and the Environment, Oxford University, South Parks Rd., Oxford OX1 3QY, UK; +Department of Banking and Finance, Monash University, Caulfield VIC 3145, Australia; #Department of Economics, University of Navarra, 31080 Pamplona, Spain. Abstract. In this paper, we report the results of a survey on the individual propensity to hold income protection insurance across 11 countries and focus on whether it is meaningful to assume that observed preferences for such products are driven by factors such as respondents’ experience of adverse health-related events and financial literacy. While country-specific effects dominate the empirical analysis, there is evidence of common preferences for those that have had similar experience and are higher-paid individuals within and between blocs of countries. It is also shown that financial literacy is not nearly as significant as a driver of respondents’ financial behaviour as its advocates suggest. Implications are drawn from our results for understanding the relationship between national institutions, individual behaviour, and welfare. Keywords. Behaviour, experience, financial literacy, national institutions JEL Codes. D12, G22, I31 Disclosure. This paper was made possible by the financial support of Zurich Insurance. The representative sample survey which underpins the study was designed by Sarah McGill and administered by Epiphany RBC (Amsterdam). The authors were wholly responsible for the analysis of the data, the interpretation of the results and the implications drawn thereof. Acknowledgements. The logic of this paper is owed, in part, to conversations with Julie Agnew, Nick Chater, Maurizio Faschetti, Paul Gerrans, Olivia Mitchell, Mike Oaksford, Peter Tufano, and the late John C Marshall. Research assistance was provided by Jakob Engel and Irem Kok. The authors are also pleased to acknowledge comments on the project and previous drafts of this paper by Selina Cohen, Jennifer Sabourin and Noel Whiteside. None of the above should be held responsible for the views and opinions expressed herein.


Introduction Economic geographers tend to privilege the local over the global when focusing upon the manifestations of wider processes. For example, in recent research on housing markets and labour markets, behaviour is measured and accounted for in particular places and/or in cities and regions (see Smith 2013; McDowell et al. 2008, 2009). To the extent research focuses upon the relationships between individuals and communities it is also assumed albeit oftentimes unremarked that people's attitudes and behaviour derive, in large part, from their location in space and time. Similarly, cognitive scientists privilege the local over the global when it comes to explaining observed behaviour. Fielder and Juslin (2006) put the issue succinctly noting individual behaviour is almost always "local" before it is "global" in that people are well-intentioned but “naïve intuitive statisticians”. Distinctions are also made between practical knowledge gleaned through experience and learning-by-doing and formal or codified knowledge which is a social resource albeit more abstract and principles-based (Thrift 1985). Whereas geographers tend to assume practical knowledge is good-enough for most people, most of the time, the judgement and decision-making (JDM) paradigm which dominates behavioural psychology links practical knowledge with a variety of systematic errors of reasoning including myopia and overconfidence (Kruger and Funder 2004). At issue is whether individual experience – what people do or what they observe others doing in relevant settings – is sufficient for planning for the future compared to more formal models of the world that provide frameworks for decision-making. Important for this paper is the research spawned by Leyshon et al. (1998) on finance, vulnerable communities, and the changing shape of the UK banking industry. In the next section, we provide a detailed assessment of the insights derived thereof including reference to what they define as practical knowledge and the concept of financial literacy which is deemed by many of its advocates as a means of transcending local knowledge in favour of the codified knowledge of how finance ‘works’ (see Lusardi and Mitchell 2014). Here, our reach is wider, taking in the behavioural predispositions of survey respondents as regards their demand for insurance across 11 countries. In doing so, this paper tests the significance of individual experience in different institutional settings compared to formal conceptions of financial knowledge and understanding. More specifically, we focus upon the effect of an adverse and unexpected event on a person’s welfare and the significance he or she then attributes to planning for the future. In doing so, we test whether this type of experience is more or less important than scoring well on tests of financial literacy when it comes explaining who holds a certain type of income insurance product by country of residence, by individual attributes and by household characteristics. We have in mind serious health-related events, physical and/or mental, that impair people’s capacity to earn a living short of forcing them out of the local labour market. This type of event is likely a shock to customary practice – that is, the normal ways in which people act on an everyday basis and evaluate options for the future. The purchase of insurance is a financial decision akin to the purchase of financial products that offer long-term pay-offs. But insurance products are also different on a number of counts. First, insuring against the ‘future’ can have no limit other than that imposed by an event and/or a failure to pay the yearly premium. Second, the purchase of insurance may be hard to justify given strong behavioural preferences for the present over the future and


the near future over an ill-defined future (Ainslie 2000). At issue, is whether having had experience of an event (or knowing someone who has had experience) that resulted in the unexpected interruption of earned income is important in predicting who holds income protection insurance and whether financial literacy is a significant predictor of holding income protection insurance with or without such an experience. Put slightly differently, do people have to experience such an event before they recognise the costs and consequences of a failure to hold income protection insurance? Does financial literacy make a difference? To answer these questions, we rely upon an international survey of the demand for income protection insurance conceived and executed in 2016. The authors were responsible for designing the survey instrument which was implemented in 11 countries including Germany, Italy, Spain, Switzerland, and the UK, Brazil, Mexico, and the USA, and Australia, Hong Kong, and Malaysia through representative and stratified samples of employed individuals aged 25-60 years. We test whether the effects of experience and financial literacy are conditioned by country-of-residence (a proxy for institutional context), age and gender (proxies for identity), and earned income, family status and the like (proxies for social position) (as implied by McDowell et al. 2008, 2009 and Sharpe 2007 amongst others). In the next section, we review Leyshon et al. (2004, 2006) research on financial decision-making in vulnerable communities. This is followed by an exploration of recent research on how people assess options and make decisions. Thereafter, we summarise the research project underpinning this study along with the relevant hypotheses. Two sections report on the empirical analysis and the results derived thereof referring, where appropriate, to related papers that provide comprehensive assessments of these results. The final section draws implications and conclusions from our research for understanding the relationship between practical reasoning and the international policy program designed to support and encourage informed financial decision-making. Practical and Codified Knowledge Leyshon et al.’s (1998) paper on the emerging landscape of financial services became a citation classic, setting the stage for research in economic geography on the global financial services industry. They focused upon the changing relationship between the consumers of financial services and UK savings and deposit banks in the face of new technologies designed to discount the reliance of banks upon spatially-extensive branch networks. Recognising that these networks were once important for closing the information gap between the consumers and providers of financial services, they argued that banks and other kinds of retail service providers were (now) able to “use databases to discriminate between different types of customer through indicators of past or likely future performance” (page 44). Of the issues discussed, two are especially important for contemporary research. One was their recognition that social welfare is determined, in part, by the ability of consumers to make effective choices when purchasing financial products. Linked to this issue was financial literacy. While their definition is elusive, financial literacy was associated with people’s ease and comfort when reading and interpreting financial information (page 31).1

1/. Drawing upon their research, an Australian bank recently defined financial literacy as "the ability to make

informed judgements and to take effective decisions regarding the use management of money" recognising that effective decision-making involves skill, knowledge, attitudes (including commitments) and behaviour (ANZ 2015, 1).


As is the case in recent research on the topic (see Lusardi and Mitchell 2014), they associated financial literacy with knowledge of financial concepts and products including credit, how interest rate charges work, repayment mortgages and week-to-week and month-to-month household budgeting. Leyshon et al. noted "for the majority of the population, a lack of financial literacy can, in relative terms, be extremely costly" (page 32). They also discussed how and why trust in financial institutions can ameliorate shortfalls in financial literacy. It was recognised that knowledge is required to sustain trust whether that is between individuals or between individuals and institutions. Here, they distinguished between "three different types of knowledge: unconscious knowledge, practical knowledge, and empirical knowledge" (italics in the original; page 36). This discussion drew upon Thrift (1985) where practical knowledge was deemed informal and based upon experience either directly or indirectly by watching others. By contrast, empirical knowledge was deemed to be formalised or rationalised ways of knowing that are organised and distributed in systematic ways across society. They don't discuss in any detail unconscious knowledge.2 Practical knowledge is allied with learning-by-doing (Arrow 1962). Empirical knowledge can be allied with codified knowledge, and may be validated by third parties such as professional organisations, learned societies, and government. Lusardi and Mitchell (2014, 6) defined financial literacy as "people's ability to process economic information to make informed decisions about financial planning, wealth accumulation, debt, and pensions." Drawing upon the US federal government’s 2004 Health and Retirement Study, Lusardi and Mitchell (2011) established a testing procedure anchored in academic research on topics such as portfolio diversification and compound interest. Their conception of financial literacy asks people to transcend everyday experience in favour of a principles-based decision-making framework which is believed to be (more) consistent with long-term financial well-being. In related papers, Leyshon et al. (2004, 2006) sought to better understand the social and spatial dimensions of financial literacy. In their 2004 paper it was suggested that the UK market for financial services had become segmented according to consumers’ financial sophistication. Sophisticated consumers were more likely to bypass conventional face-to-face modes of financial intermediation in favour of systems of intermediation based upon information and communication technologies. Those consumers not nearly as savvy in terms of their capacity to assess financial services and products were observed to rely upon local providers for a limited set of financial services. By their account, it was possible to identify distinctive ‘ecologies of finance’ wherein certain segments of the population in certain communities were ‘at risk’ to the predatory practices of providers. They also showed that financially sophisticated consumers could take advantage of the opportunities available in the market for financial services. Rather than being vulnerable to predatory pricing practices, these types of consumers could play-off competing providers in favour of their own interests. Since access to financial services does not require face-to-face intermediation, these types of consumers could reach beyond their immediate locality to the national marketplace. With the relevant financial knowledge and skills, these consumers could look behind proffered products for the underlying value-propositions (Leyshon et al.

2/. Thrift (1985, 373) explained unconscious knowledge as that which is inherited from the past and is “based

upon forgotten practices still remembered in the limning of action.”


2006). For this segment of the population, their command of financial knowledge intersects with experience to reinforce financial sophistication. Though not directly concerned with the market segmentation of financial services, Lusardi and Mitchell (2014) showed that financial literacy is highly correlated with individuals earned income, wealth, housing status, educational attainment, and household circumstance. Across the world, those that test-well on measures of financial literacy are relatively well-off compared to those not so advantaged and compared to those vulnerable to the vicissitudes of national and regional labour markets. That is, those well-positioned in their society are able to filter experience through the lens of financial knowledge to their advantage whereas those not so well-positioned have only experience to go by. Hence, Lusardi and Mitchell’s recommendations in favour of government programs to enhance people’s financial literacy. Knowledge and Decision-Making The judgement and decision-making (JDM) paradigm in behavioural psychology and cognitive science also disputes the value of practical knowledge. Experimental research suggests that people, if left of their own devices, tend to discount the future, tend to select data and evidence that confirms their predispositions, and tend to rely upon implicit and/or unacknowledged frames of reference that exclude relevant options (Kruger and Funder 2004). Rather than continuously evaluating their commitments, many people do not devote sufficient attention to decision-making until adverse events prompt a level of deliberation consistent with the costs and benefits of systematic decision-making (Baron 2008). One of the key distinctions found in JDM literature is between System 1 type thinking and System 2 type thinking. The former is often described in terms of intuitive solutions to immediate problems, drawing upon practical knowledge to produce effective if not optimal solutions. By contrast, System 2 thinking is deemed to be a form of deliberation which takes into account the underlying causes and consequences (codified knowledge) of what might appear to be a specific event or issue. It is assumed that System 2 thinking produces "better" solutions to problems especially if taken in the context of a string of related issues and decisions aimed at producing the best possible outcome. Inevitably, System 2 thinking is more expensive than System 1 thinking in terms of cognitive effort and resources (Kahneman 2011). While this distinction provides analytical clarity, it is nonetheless misleading on a number of counts. These two types of thinking are interrelated, one with the other. For many people, intuitive responses to immediate circumstances work up until such solutions violate their interests and commitments thereby prompting a switch from one mode of thinking to another. Switching between one mode of thinking to another requires calibration of the immediate and longer-term payoffs associated with one mode of reasoning compared to the other, just as it may involve cognitive and material resources which can be scarce and/or unevenly distributed in society. The willingness and capacity to switch to System 2 reasoning depends upon a person's cognitive disposition and social position (Sharpe 2007). Equally, it is arguable that practical reasoning has certain advantages, and can take the form of heuristics that interpolate these two systems of reasoning. Gigerenzer et al. (1999) suggested that heuristics distil people’s practical knowledge providing ready-made insights about their options including the likelihood of certain events occurring and re-occurring in


the future along with their likely consequences. As such, practical knowledge can evolve into a set of decision rules that can be updated and adapted as required. Note, however, the formation and evolution of heuristics are likely to be affected by the flow of relevant information and whether the costs and consequences of large and small events can be accommodated within existing norms and conventions. The argument in favour of System 2 thinking goes as follows. Incremental, rule-based decision-making runs the risk of reinforcing path dependency – reinforcing expectations where, in fact, expectations should be re-evaluated in the light of shifts and changes in underlying processes and systems. For those that can least afford disruptions in their material well-being, incremental adaptation whether rule-based or not may simply reinforce their commitment to a pathway which exposes them to the adverse consequences of more and more risk. In some developed economies, fully a third of working men and women are "located" in sectorial and spatial segments of the labour market that are vulnerable to exogenous shocks (OECD 2017). The lack of saving and community resources can amplify the negative consequences of practical reasoning. Even so, System 2 thinking requires effort. By relying codified knowledge of the world albeit conceived in terms of certain domains and/or reference groups, people face significant challenges when applying this type of knowledge to specific circumstances. One related issue has to do with recognition: that is, judging the significance of an event or set of events in relation to the relevant domain of codified knowledge. A related issue has to do with boundary conditions: that is, distinguishing between overlapping but different domains of codified knowledge such that confusion over their salience does not paralyse decision-making. Just as importantly, there is the issue of stubborn facts: where the application of codified knowledge is confounded by circumstances not easily accommodated within existing decision templates. These issues have long been recognised in the JDM literature. When Nisbett et al (1982, 111) reviewed the findings of relevant research they acknowledged that most of the time, tend to rely on experience. This was attributed, in part, to the fact that codified knowledge is, more often than not, “remote, pallid, and abstract.” By contrast, experience can be “livid, salient, and concrete” often prompting an immediate response given “an all too real world.” In our research, we are especially concerned about the impact of adverse unexpected changes in a person’s health on their earned incomes and hence their demand for insurance referencing “serious illness” such as cancer, heart disease and loss of hearing and sight as well as mental health issues. Analytical and Empirical Framework In much of the JDM literature, the focus of research is on the respective virtues and vices of codified knowledge and practical knowledge. Notwithstanding the insights derived from laboratory-based testing procedures, this research is often silent on three issues. First, there is little in the way of explicit recognition of the institutional context in which people frame expectations and make decisions about their future welfare. Second, while hinted at, especially by social scientists concerned to explain the welfare consequences of low levels of financial literacy, there is little in the way of research that demonstrates the relationship between individuals’ economic and social status and their capacity to make informed financial decisions. Third, there is little appreciation of the role that social relationships can play in encouraging System 2 thinking over System 1 thinking.


These issues are important for economic geographers (witness Leyshon et al. 2004, 2006), and for those concerned about the increasing financial vulnerability of workers in OECD countries (Avent 2016; Strauss 2017). Specifically, it is widely accepted that whether or not individuals rely upon practical knowledge over financial literacy depends, in part, on their institutional context. Likewise, it is supposed that an individual’s level of economic well-being provides certain capabilities and resources such that, on average, those better-off in society are more able to discount the pernicious aspects of experience when taking into account underlining conditions and opportunities. In a similar light, if a person acts both with respect to their own welfare and the welfare of others this may prompt a level of attention which discounts the “vivid” in favour of long-term well-being. In this respect, it is reasonable to assume that the effects of experience and financial literary on the propensity of an individual to hold income protection insurance is conditioned by (a) their institutional context, (b) their capabilities and resources, and (c) their commitments to others. These are qualifications as to the likely impact of different kinds of knowledge on financial decision making and recognition that behaviour is always socially embedded in terms of its conception and realisation (Thrift 1985, 366). These points are reflected in the hypotheses that frame our empirical analysis.

H1. Those lacking either financial literacy or, more precisely, knowledge and understanding of financial products are more likely to hold such insurance if they have experienced an event that has had a marked effect on their immediate welfare: the ‘vivid’ affect.

H2. Those that score well on tests or financial literacy and/or have knowledge and understanding of insurance products are more likely to hold income protection insurance whether or not they have experienced an event adversely affecting their longer-term welfare: the ‘attention’ affect.

H3. The demand for income protection insurance depends, in part, upon the institutional context in which people live and work whatever their experience and/or level of financial sophistication: the ‘institution’ affect.

H4. Those holding advantageous positions in their society as measured in terms of their earned incomes and labour market status are more likely to hold income protection insurance whatever their experience and/or level of financial sophistication: the ‘capabilities and resources’ affect.

H5. Those that act not only on their own behalf but also on behalf of others are more likely to hold income protection insurance whatever their experience and/or level of financial sophistication: the ‘social relationship’ affect.

We test these hypotheses using survey responses derived from a large, multi-country survey of the demand for income protection insurance. The survey was designed and implemented in early 2016 and was based upon representative sample surveys of employed individuals (25–60 years of age) in 11 countries including Germany, Italy, Spain, Switzerland, and the UK (Europe), Brazil, Mexico, in the USA (the Americas), and Australia, Hong Kong, and of Malaysia. As indicated above the survey was designed to understand better the demand for income protection insurance focusing upon what distinguishes those who hold this type of insurance from those do not taking into account respondents’ health and well-being, institutional context, sociodemographic attributes, and financial knowledge and literacy.


The survey instrument was comprised of 57 questions, some of which sought respondents’ information on basic issues such as gender (Q3), age (Q2), education (Q5B), and household income (Q5C). In other cases, the questions were multi-layered and provided respondents with answer options designed to standardise the data collected and, in some cases, test for their knowledge and understanding of relevant concepts including insurance (Q15) and financial literacy (Q45-Q47). The survey instrument is available from the authors. The collection of the survey data is described in more detail in the Appendix to this paper. After the first section devoted to collecting basic data on respondents, Q6 was posed in the following manner: “Do you personally have insurance (beyond obligatory government benefits), which would protect your income against in the following types of risk?” The risks identified included various forms of serious illness, physical and mental, and premature death. Since we were concerned to test the effects of unexpected events on respondents’ propensity to hold income protection insurance Q11 was posed in the following manner: “In your working life have you personally ever experienced loss of income due to any the following?” Thereafter, a list of eight physical conditions, two mental health issues, and two issues related to caring for others were identified as answer options. So as to establish respondents’ levels of financial literacy, in Section H they were asked to respond to Lusardi and Mitchell's (2011) test of financial literacy. Given that we have each respondent’s country of residence, we used this data to test whether country of residence was statistically significant when estimating a logit model predicting the propensity to hold income protection insurance across the entire database. In this case, each country entered the estimation procedure and, as such, was used to represent the salience affect. Elsewhere, we show for a small number of countries (Australia and Switzerland) that being a migrant worker was associated with a lower propensity to hold income protection insurance taking into account their attributes relative to similarly-placed respondents working in their country of origin (see Clark et al. 2018). To determine whether there was a ‘capabilities and resources affect’ on the propensity to hold income protection insurance respondents were required to questions regarding their employment status (Q3, Q4), their highest level education (Q5B), and level of household income (Q50). At issue was whether scoring above average on these indicators of socio-economic status was associated with a higher propensity of holding income protection insurance whatever their experience and/or level of financial literacy. This was followed with a series of questions regarding their personal circumstances and, in particular, asking respondents to indicate the number of people living in their household (Q50), who else lived in their household identifying spouse and partners, children, parents and others (Q50A), and the numbers of people reliant upon respondents’ earned incomes (Q51). Propensity to Hold Income Protection Insurance In Table 1 we provide a count of the number of survey respondents by country. Elsewhere, it has been observed that a representative sample of the UK population is in the order of 900-1000 respondents (Clark et al. 2012). Here, the number of survey respondents by country is less sensitive to the total population of each country than anticipated. The number of respondents from countries with large populations such as the USA and Brazil were such that these countries are underrepresented whereas countries with relatively smaller populations such as Australia and Switzerland are overrepresented in the analysis.


Sensitivity tests of the results by country and specific sets of questions indicated that no country was so unusual as to statistically affect the results.3 [Insert Table 1 About Here] The first step in the analysis was to undertake an ANOVA based on the means of respondents holding protection insurance by country of residence. Here, the null hypothesis was that the means were equal across countries. This was augmented, as indicated below, by a test of difference on the predicted margins of means made available through the logit model of all countries and variables with respect to the dependent variable. By analysing the variance of the mean percentage of respondents holding income protection insurance in each country, it was determined that there were significant differences across countries. This is prima facie evidence for the existence of a significant ‘institution’ affect on the propensity to hold income protection insurance. There was some evidence of the existence of three or four groups of countries. Inspection of the data provided in Table 1, suggested that Brazil, Germany, Italy, Spain, and the UK could be one group of countries (0.18–0.29); Australia, Switzerland, and Mexico (0.30–0.37) a second group; the USA (0.45) appeared as an outlier relative to its nearest neighbour and European countries; and Hong Kong and Malaysia (each 0.67) could be a fourth group. The second step was to estimate the ‘reference’ model where the dependent variable was whether or not a survey respondent held such an insurance product. Explanatory variables included (a) country of residence; (b) socio-demographic factors such as gender, age, income, whether the respondent was the household’s primary income earner, household size, and years of education; (c) employment factors, notably work status (full-time, part-time, or self-employed); and (d) financial literacy based upon the conventional set of questions used by Lusardi and Mitchell (2014). Most importantly, we used respondents’ self-assessed health status, whether they had previously experienced loss of income for a range of physical or mental health-related reasons and/or the premature death of a household breadwinner, and whether they knew someone who had experienced loss of income for similar reasons. Table 2 presents the summary statistics of these variables. [Insert Table 2 About Here] The results of estimating the reference model are presented in Table 3. As such, we can accept Hypothesis #1 (the ‘vivid’ affect) which holds that (adverse) experience is a significant predictor of holding income protection insurance and reject Hypothesis #2 (the ‘attention’ affect) which supposed that financial literacy is a significant predictor of holding income protection insurance. These are important results, reinforcing claims in the behavioural psychology and cognitive science literature to the effect that people more often

3/. Of the 11,584 survey respondents, 8.66 per cent (1003) indicated that they did not know whether they held

income protection insurance. Countries with lower than the medium proportion of “don’t knows” included Germany (5.41 per cent), Hong Kong (7.80 per cent), Italy (6.58 per cent), Malaysia (5.02 per cent), Mexico (4.96 per cent), and the UK (5.60 per cent). Those above the medium were Australia (11.54 per cent), Brazil (8.91 per cent), Spain (11.98 per cent), Switzerland (17.48 per cent) and the USA (10.64 per cent). In the next section, it is shown that predicting those who “don’t know” can be allied with predicting those who carry income protection insurance.


than not respond to events (System 1 thinking) rather than systematically plan for an unknown future (System 2 thinking). Notable in this regard was the level of significance of the parameters on physical and mental health, and knowing someone who had experienced such an interruption to income. [Insert Table 3 About Here] Notwithstanding different specifications of the model, the institution affect was also highly statistically significant across all countries. Therefore, we can accept Hypothesis #3. This is an important finding, suggesting that ‘local’ arrangements of welfare entitlements and benefits relevant to unexpected interruptions in earned income affect residents’ purchase income protection insurance.4 In effect, these types of arrangements determine the both the national and international scope of the market for this kind of insurance. With respect to Hypothesis #4, the ‘capabilities and resources’ affect, the results are consistent with the hypothesis and suggest that higher earning respondents were more likely than lower earning respondents to hold income protection insurance. This result is consistent with the following possibilities: these respondents had either (a) more to lose than lower paid respondents or (b) unlike lower-paid respondents would avoid relying upon government and employer support programmes or (c) had the capabilities and resources to assess the value-for-money of income protection insurance (consistent with System 2 thinking). As for Hypothesis #5, the ‘social relationship’ affect, the results suggest that this can be an important determinant of holding income protection insurance. But the statistical significance of the parameters on being either the sole or the primary household breadwinner and household size are weaker than other effects. Country-Specific Factors To understand the commonalities and differences in the results across countries and between groups of countries, the reference model was re-estimated for each country. The country-specific results are provided in Table 4-6 and are summarised in Table 7. Herein, a distinction is made between levels of significance for the estimated parameters on each explanatory variable, namely 99 per cent for entries in bold, 95 per cent for entries in italics, and 90 per cent for entries in neither bold nor italics. [Insert Table 7 About Here]

As indicated, there were fewer country-specific significant parameters on the explanatory variables than was the case in the pooled reference model. In the Australian case, for example, the parameters on respondents’ gender, household status, household size, years of education, gender of the primary wage earner, financial literacy, and self-reported health status were all found not significant. Statistically significant parameters were found on respondents’ age, income, work status (of limited significance) and whether respondents had experienced or knew someone who had experienced a loss of income.

4/. To complement our analysis of the institution affect, the Social Safety Protection index was obtained from

the World Economic Forum and included as an explanatory variable. The results suggest that national social safety systems are a factor behind these cross-country differences. However, we are not confident that this index is sufficiently robust to warrant inclusion in the reported results.


Loss of income due to an unanticipated physical and/or mental health-related event was shown to be significant in all but one country (Malaysia). But, it is also shown that experience of a loss of income due to physical impairment was only weakly statistically significant in Italy, and more significant in the UK and Germany. By contrast, for most other countries it was uniformly significant, albeit at the 95 per cent level of confidence. The European and American counties clearly differed from the East Asian countries in terms of the importance attributed to mental illness in driving the demand for income protection insurance. Even so, for Malaysia the parameter on this variable was not significant. Others’ experience of loss of income due to impairment was significant in most countries at the 99 or 95 per cent confidence levels but weaker for Malaysia and Mexico. For the UK (strong), the USA (medium), and Germany (weak), it was found that financial literacy was negatively associated with the propensity to hold income protection insurance. But, for Hong Kong and Malaysia financial literacy was significantly and positively associated with the propensity to hold such insurance. It is arguable that the negative results for the three developed economies reflects a judgement to the effect that respondents in each country believed that either (a) income protection insurance is not worth its likely cost, or (b) in two of the three cases, public insurance systems are a better option than private or employer-sponsored programs, or (c) the disclosure requirements associated with taking out income protection insurance are so onerous as to discount the value of this type of insurance. Each implies a level of deliberation at odds with experiential decision-making. With respect to Hong Kong and Malaysia, it is possible that each, in its own way, offers adequate government and employer benefits in the event of physical impairment. Respondents had the option to identify instances when their income was interrupted due to any of a range of physical or mental health problems. Respondents identifying a past experience of mental impairment as a key driver in holding income protection insurance could reflect fundamental shortfalls in the benefit systems of their respective countries. Alternatively, it is possible that the significance attributed to mental health as a driver of income protection insurance reflects something distinctive about everyday life, especially for younger respondents seeking success in highly competitive environments.5 Respondents’ income was statistically significant for 10 of the 11 countries. In this case, the positive sign on the parameter indicated that higher income respondents were more likely to hold income protection insurance than lower income respondents. As such, on a country by country basis, we can accept Hypothesis #4 although the ‘capabilities and resources’ affect is strongest for the Australia, Italy, Spain, the UK and Mexico than for the other countries. The exception was Switzerland. As indicated above, there are various ways of explaining the significance of this affect including respondents’ ability to pay the premiums, income-sensitive risk aversion, and more subtly a positive relationship between higher income and knowledge and understanding of financial matters. As for the Swiss exception, this could reflect the availability this type of insurance through employers without regard to earned income.

5/. Inspecting the data for outliers that might have biased the results revealed that the

proportions of younger and full-time employed respondents in Hong Kong and Malaysia were higher than in the other countries. Further details are available from the authors.


Evidence for the existence of a ‘social relationship’ affect (Hypothesis #5) on a country by country basis is more limited. Household size was relatively weakly significant for Spain, Switzerland, the UK and the USA while being the primary breadwinner in a household was weakly significant for Brazil, Hong Kong, and the USA. For Germany and Italy, being male and the sole breadwinner was significant weakly significant. Basically, the country-specificity of this affect suggests that it can take various forms depending upon countries’ institutional arrangements including the links between government programs and policies and family structure. Implications and Conclusions Experience frames expectations of what is possible in terms of that which is salient to the individual, in terms of the likelihood of relevant events, and in terms of the expected impact or consequences of such events. Imagine that those that rely upon practical knowledge are prompted by some external agent. On the other hand, those that take the time and make the effort to assess the merits or otherwise of taking out insurance use their experience but, most importantly, situate their experience in the context of wider knowledge of the costs and benefits of insurance and an appreciation of their long-term commitments. By this logic, financial literacy matters both in principle and in relation to how people understand their place in the financial world and all that entails for their longer-term welfare (Lusardi and Mitchell 2014). In this paper, it was found that experience dominates financial literacy. Most important was the experience of the respondent, less important was the experience of someone they knew. Having suffered the consequences of such an event, or having seen others affected, is consistent with behavioural notions of regret (looking backwards) and loss aversion (looking forward) (Kahneman and Tversky 1979). It is also consistent with theories of learning-by-doing and behaviour wherein experience out-weighs formal knowledge (Arrow 1962; Gertler 2003). This is one explanation for the lack of significance, in most cases, of financial literacy. Instead of assuming that it ipso facto establishes the relevance of an issue or product like income protection insurance, it seems that financial literacy is, at best, a tool that informs decision-making after the salience of the issue has been established by experience. Just as important, income matters: the higher (lower) a respondent’s income, the higher (lower) the propensity to hold income protection insurance. The conclusion to be drawn is that whatever the country, whatever an individual’s circumstances holding insurance is an issue of affordability. For those with a constrained budget and limited capacity to allocate a portion of current income to insure against the future, one response would be to hope for the best. That is, risk aversion is mediated by individuals’ capacity to ‘manage’ such an issue (compare Kahneman and Tversky 1979). In some countries, however, the weakness of the income affect suggests that government and/or employer-provided benefits provide sufficient insurance against such that it is less salient to many respondents (see more generally Bordalo et al. 2012). Throughout, we have suggested that country-specific institutions remain important as a lens through which to understand individuals’ welfare and prospects. We have also documented the commonalities and differences between respondents with respect to the demand for income protection insurance according to their countries of residence. In doing so, our paper is relevant to the on-going debate about the significance or otherwise of


neoliberalism and the extent to which working people in many developed economies have been exposed to the vicissitudes of global financial markets and local labour markets (see Harvey 2007; Langley 2008). Our paper is not a test of the significance of neoliberalism – given the challenges inherent in the implementation of the survey across countries it is appropriate to be circumspect about the implications to be drawn from our study. Nonetheless, our results offer insights about the possible ‘reach’ of neoliberalism (see Weller and O’Neill 2014), along with findings that deserve greater scrutiny. First, any country-specific explanation of the propensity to hold income protection insurance would be incomplete without reference to the shared attributes of individuals across countries. Second, those who hold income protection insurance are in the middle to upper echelons of income earners (by country). Our respondents are obviously dissimilar in terms of their earned incomes if we were to benchmark those incomes against the US dollar. However, they share similar social and labour market positions within the countries represented in our sample. It might be argued that this is evidence of a universal model of human behaviour that discounts geography in favour of the shared attributes of individuals. Even so, taking into account shared attributes leaves a significant ‘institution’ affect. Consider, as well, our search for commonalities between countries in terms of their residents’ propensity to hold income protection insurance. Recall that an important thread in social science research over the past 25 years has been around the theme of nation-state regimes of accumulation, governance, and welfare. Esping-Andersen (1990, 1999) argued that welfare states should be understood in terms of their shared traditions as well as their differences that give rise to very different institutions and policy practices. Hall and Soskice (2001) argued that nation-states could be grouped according to ‘varieties of capitalism’, an argument that economic geographers including Clark and Wójcik (2007), Peck and Theodore (2011), and more recently by Dixon (2014) have analysed and developed. The results showed that it is meaningful to distinguish between two groups of countries – European and East Asian – along with a third group which is probably a statistical artefact rather than the expression of shared institutional and/or national policy principles and practices. We found differences between groups of countries in terms of the propensity of individuals to hold income protection insurance represented at the low end by Germany (0.183) and at the high end by Hong Kong (0.675). In between, the USA’s propensity was (0.448). Residents of countries at the low end of the distribution either believe in, or have experienced, the integrity of government programmes and policies that underwrite their incomes, or feel that they cannot afford to pay the premiums for income protection insurance. At the high end of the distribution, our respondents make their own provision for income protection given the expected value of government benefits. In terms of the propensity to hold income protection insurance, the UK (0.214) was aligned with Germany and Italy rather than the mixed group, which included the United States (0.448). One explanation of the finding could be that many of our UK respondents, referencing the heritage of the UK welfare state rather than current circumstances, systematically underestimate their vulnerability. Another explanation may be that the rapid growth in UK employment since 1995, notwithstanding the consequences of the global financial crisis, has encouraged the average private sector employee to overestimate the probability of remaining employed. Our UK respondents may also believe that the NHS will carry the burden associated with any health-related interruption to earned income.


Presumably our USA respondents understand only too well the adverse consequences of significant interruptions in earned income. Appendix To ensure global reach and robust, timely data collection, we enlisted a private survey research firm to administer the survey. The method used for the survey research is called CAWI (Computer Assisted Web Interviewing). The CAWI engages respondents through a network of panel providers, thus allowing both global reach and access to local knowledge and expertise in panel management. All the panels with which the research firm engages are actively managed, proprietary to the providers, and maintained time (at least a decade). The providers’ panel management practices were compliant with market research industry standards (ESOMAR), and data protection and privacy laws. Panel members are individuals who participate freely in online surveys after enrolling voluntarily as a panel member. Recruitment takes place either through open enrolment, where individuals sign up to participate, or through invitations targeted at people who share demographic characteristics of interest to the panel providers. Recruitment is conducted via online marketing channels and direct email. Enrolment in the panel then takes place via a double opt-in registration process: prospective panellists first complete a registration form, including a consent form, and then confirm their registration by clicking on a customised link sent to them via an automatically generated email. To take part in a specific survey, panel members receive invitations via email, which include basic participation instructions and information about the approximate length of the survey. These invitations are designed to be ‘non-leading’: panellists receive no advance information about the topic or contents of the survey, only an estimated time of completion. Members of a panel receive rewards for participating in surveys according to a structured incentive scheme. The size of the reward offered as an incentive is determined by the length and complexity of the survey; in many cases it is also determined by the socio-demographic profile of panellists needed for a specific survey. For instance, highly qualified professionals would generally receive greater inducements for completing a survey related to their profession than for completing a survey on consumer purchasing decisions. The incentives provided for a participating in any project are identical for all respondents. Only panellists who complete a survey successfully receive incentives. These are tailored to local laws and preferences but generally include a ‘points’ programme, gift cards, vouchers, charitable contributions, and prize draws. Point’s which panellists can redeem points for other types of incentives, are standard in most countries. To ensure that the target audience for a study is selected from the available panel members, a screener section is normally included at the beginning of a survey (as was the case here; see discussion below). To ensure an otherwise representative sample within this target audience, a quota system based on nationally representative demographic variables such as age, gender, region, and income level is used in sample selection. It is worth noting that whereas panels in developed countries are not significantly skewed on these variables, in developing countries they are skewed towards urban areas where internet access is higher. References


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Table 1. Propensity to hold insurance by country of residence. Mean Std. dev. n

Australia 0.308 0.462 912

Brazil 0.247 0.431 940

Germany 0.183 0.387 980

Hong Kong 0.675 0.468 958

Italy 0.235 0.424 965

Malaysia 0.667 0.471 983

Mexico 0.371 0.483 978

Spain 0.286 0.452 911

Switzerland 0.334 0.472 850

UK 0.214 0.411 1180

USA 0.448 0.498 924

F=140.85***

Source: Authors. F is the statistic used to test the null hypothesis that all the means are equal; *** means significant at a 1 per cent level.

Table 2. Variable description and basic statistics

Variable Description Mean Std. dev. Min. Max.

Demand for insurance

Do you have insurance Binary variable (it takes the value 1 if the respondent does have insurance; 0, otherwise)

0.36 0.48 0 1

Socio-economic variables

Gender Binary variable( it takes the value 1 for male respondents, 0 for females)

0.48 0.50 0 1

Age (groups) 25–35 years 36–45 years 46–55 years 56–60 years

0.35 0.29 0.26 0.10

0.48 0.45 0.44 0.30

0 0 0 0

1 1 1 1

Income* Lower than average Equal than average Higher than average

0.36 0.39 0.25

0.47 0.48 0.46

0 0 0

1 1 1

Household size # people household 2.99 1.41 1 7

Education # years 14.79 4.86 0 25

Work status

Work status Full-time Part-time Self-employed Not employed

0.65 0.18 0.13 0.04

0.48 0.39 0.34 0.19

0 0 0 0

1 1 1 1

Health

Self-reported health status

Healthier than average Same as healthy Less healthy

0.26 0.62 0.12

0.44 0.49 0.32

0 0 0

1 1 1

Financial literacy

Financial literacy Correctly responded Q1 (interest rates) Correctly responded Q2 (inflation rates) Correctly responded Q3 (risk diversification) All three correct At least 1 do not know

0.66 0.55 0.74 0.38 0.20

0.47 0.50 0.44 0.49 0.40

0 0 0 0 0

1 1 1 1 1

Experience of loss of income

Experience (Have you ever experienced a loss of income due to any of these causes?)

Physical cause Emotional cause

0.10 0.25

0.30 0.43

0 0

1 1

Do you know someone who has experienced a loss of income due to illness or disability

Binary variable (it takes the value 1 if the respondent knows someone; 0, otherwise)

0.54 0.50 0 1

Country of residence

Country of residence Australia Brazil Germany Hong Kong Italy Malaysia Mexico Spain Switzerland UK USA

0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.11 0.09

0.28 0.28 0.29 0.29 0.29 0.29 0.28 0.29 0.28 0.31 0.29

0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1

* Respondents were divided into three different groups depending on their income relative to the mean income in their country of residence. Source: authors


Table 3. Demand for Insurance (estimated coefficients and marginal effects)

Explanatory

variables

Indicator Model 1 Model 2 Model 3

Coeff. Marginal effects Coeff. Marginal effects Coeff. Marginal effects

Gender Male 0.070 (1.26) 0.014 (1.27) 0.058 (1.14) 0.011 (1.15) 0.068 (1.10) 0.013 (1.10)

Age 25–35 years

36–45 years

46–55 years

0.577*** (4.75)

0.343*** (4.42)

0.145* (1.71)

0.112*** (4.97)

0.067*** (4.52)

0.028 (1.71)

0.533*** (4.55)

0.312*** (4.34)

0.131 (1.62)

0.103***(4.76)

0.060***(4.46)

0.025 (1.62)

0.412*** (4.51)

0.285*** (4.29)

0.144* (1.75)

0.077***(4.66)

0.053***(4.31)

0.027* (1.75)

Income Lower than av.

Equal

–1.045*** (–6.47)

–0.499*** (–4.44)

–0.203*** (–6.88)

–0.097***(–4.61)

–0.978*** (–6.13)

–0.477*** (–4.21)

–0.190***(–6.47)

–0.092***(–4.35)

–0.997*** (–6.61)

–0.485*** (–4.74)

–0.186***(–6.98)

–0.091***(–4.89)

Primary/

secondary

wage

earner

Only wage

Primary wage

Equal wages

Second wage

0.748**(2.31)

0.789**(2.48)

0.525 (1.59)

0.188 (0.60)

0.145**(2.30)

0.153**(2.47)

0.102 (1.58)

0.036 (0.59)

0.588*(1.92)

0.633*(2.14)

0.389 (1.24)

0.101 (0.35)

0.114*(1.91)

0.123*(2.13)

0.075 (1.23)

0.020 (0.35)

0.527*(1.90)

0.565**(2.05)

0.371 (1.26)

0.182 (0.66)

0.098*(1.88)

0.105**(2.04)

0.069 (1.25)

0.032 (0.65)

Household

size

0.099** (2.34) 0.019**(2.37) 0.099** (2.29) 0.019**(2.32)

0.061* (1.90) 0.011* (1.93)

Education # years 0.001 (0.15) 0.0002 (0.15) 0.002 (0.20) 0.0003 (0.20) 0.009 (1.39) 0.002 (1.36)

Work status Full-time

Part-time

Self-employed

0.319* (1.72)

0.121 (0.49)

0.075 (0.42)

0.062* (1.73)

0.023 (0.49)

0.015 (0.42)

0.347* (1.85)

0.124 (0.52)

0.065 (0.35)

0.065* (1.85)

0.023 (0.53)

0.012 (0.35)

Financial

literacy

–0.076 (–1.22) –0.014 (–1.24)

Health Healthier

Same as

healthy

0.402***(5.23)

0.220***(3.12)

0.075***(5.11)

0.041***(3.06)

Experienced

loss of

income

Physical

Mental

0.636***(6.45)

0.352***(5.64)

0.119***(6.69)

0.066***(4.63)

Know

someone

0.465***(10.00) 0.087***(10.57)

Country

effects

Yes Yes Yes Yes Yes Yes

Number

obs

10060 10060 10060 10060 10060 10060

Pseudo R2 0.1285 0.1285 0.1298 0.1298 0.1554 0.1554

Source: Authors. Standard errors are clustered by country. *, ** and *** mean significant at the 10 per cent, 5 per cent and 1 per cent significant levels.


Table 4. Demand for Insurance by Country (estimated coefficients and marginal affects)

Explanatory variables Indicator Country

Australia Mexico Spain Switzerland US(A)

Coeff. Marginal effects Coeff. Marginal effects Coeff. Marginal effects Coeff. Marginal effects Coeff. Marginal effects

Gender Male 0.202 (1.19) 0.039 (1.19) –0.110 (–0.71) –0.023 (–0.71) 0.137 (0.83) 0.026 (0.83) 0.243 (1.29) 0.051 (1.30) 0.046 (0.29) 0.009 (0.29)

Age 25–35 years

36–45 years

46–55 years

0.563** (2.03)

0.335 (1.22)

0.367 (1.39)

0.108** (2.05)

0.064 (1.22)

0.070 (1.39)

0.429 (1.41)

0.315 (1.01)

0.289 (0.93)

0.088 (1.42)

0.065 (1.01)

0.059 (0.93)

0.260 (0.92)

0.310 (1.14)

–0.163 (–0.58)

0.049 (0.92)

0.058 (1.14)

–0.031 (–0.58)

0.461 (1.64)

0.046 (0.16)

0.200 (0.71)

0.097 (1.65)

0.010 (0.16)

0.042 (0.71)

0.149 (0.55)

–0.036 (–0.13)

–0.300 (–1.20)

0.027 (0.55)

–0.006 (–0.13)

–0.057 (–1.20)


Equal

–1.370*** (–4.40)

–0.695** (–3.69)

–0.262***

(–4.57)

–0.133***

(–3.80)

–1.397*** (–7.08)

–0.903*** (–5.10)

–0.286*** (–2.45)

–0.185* (–1.76)

–0.94*** (–2.93)

–0.569* (–1.92)

–0.176***

(–2.93)

–0.107* (–1.92)

–0.051 (–0.20)

0.0581 (0.30)

–0.001 (–0.20)

0.012 (0.30)

–0.570** (–2.45)

–0.453** (–2.51)

–0.109** (–2.45)

–0.086** (–2.51)

Primary/

secondary wage earner

Only wage

Primary wage

Equal wages

Second wage

–0.148 (–0.34)

–0.247 (–0.55)

–0.538 (–1.15)

–0.637 (–1.39)

–0.028 (–0.34)

–0.047 (–0.55)

–0.103 (–1.15)

–0.122 (–1.39)

0.306 (0.46)

0.216 (0.33)

0.198 (0.30)

–0.141 (–0.21)

0.063 (0.46)

0.044 (0.33)

0.041 (0.30)

–0.029 (–0.21)

1.319 (1.60)

0.967 (1.17)

0.865 (1.05)

0.857 (1.05)

0.247 (1.60)

0.181 (1.17)

0.162 (1.05)

0.161 (1.05)

1.180 (1.04)

1.124 (0.99)

1.415 (1.24)

1.471 (1.29)

0.250 (1.04)

0.238 (0.99)

0.300 (1.24)

0.311 (1.29)

2.065* (1.91)

2.300** (2.13)

2.084* (1.91)

1.632 (1.50)

0.392* (1.91)

0.436** (2.13)

0.395* (1.91)

0.307 (1.50)

Household size 0.02 (0.39) 0.004 (0.39) –0.012 (–0.22) –0.003 (–0.22) 0.163** (2.31) 0.031** (2.31) 0.180** (2.38) 0.038** (2.38) 0.200*** (2.97) 0.038*** (2.97)

Education # years 0.013 (0.70) 0.002 (0.71) 0.030 (1.63) 0.006 (1.63) 0.020 (1.20) 0.004 (1.20) 0.028 (1.51) 0.006 (1.51) –0.010 (–0.59) –0.002 (–0.59)


Part-time

Self-employed

–0.454 (–1.17)

–0.873** (–2.26)

–0.791* (–1.92)

–0.087 (–1.17)

–0.167** (–2.28)

–0.151* (–1.93)

0.809 (1.26)

1.057 (1.60)

0.385 (0.60)

0.166 (1.26)

0.217 (1.60)

0.079 (0.60)

0.003 (0.01)

–0.322 (–0.70)

–0.109 (–0.23)

0.000 (0.01)

–0.060 (–0.70)

–0.20 (–0.23)

–0.075 (–0.13)

–0.091 (–0.15)

0.049 (0.08)

–0.016 (–0.13)

–0.019 (–0.15)

0.010 (0.08)

0.587 (1.23)

0.048 (0.10)

0.044 (0.08)

0.110 (1.23)

0.007 (0.10)

0.008 (0.08)

Financial literacy –0.046 (–0.53) –0.009 (–0.53) –0.128 (–1.58) –0.026 (–1.58) –0.09 (–1.07) –0.017 (–1.07) –0.052 (–0.57) –0.011 (–0.57) –0.280*** (–

3.21)

–0.053*** (–

3.21)

Health Healthier

Same as healthy

0.345 (1.32)

0.106 (0.45)

0.066 (1.33)

0.020 (0.45)

0.819** (2.05)

0.513 (1.29)

0.168** (2.05)

0.105 (1.29)

–0.038 (–0.14)

0.008 (0.03)

–0.007 (–0.14)

0.002 (0.03)

0.324 (1.13)

0.353 (1.35)

0.069 (1.13)

0.075 (1.35)

0.248 (0.86)

–0.195 (–0.73)

0.047 (0.86)

–0.038 (–0.73)

Experienced loss of income Physical

Mental

0.633** (2.26)

0.439** (2.39)

0.121** (2.29)

0.084** (2.41)

0.772*** (2.67)

–0.092 (–0.53)

0.158*** (2.67)

–0.019 (–0.53)

0.951*** (3.26)

0.435** (2.31)

0.178*** (3.26)

0.081** (2.31)

0.912** (2.49)

0.130 (0.65)

0.193** (2.53)

0.028 (0.65)

0.673** (2.07)

0.398** (2.07)

0.128** (2.07)

0.074** (2.07)

Know someone 0.617*** (3.69) 0.118*** (3.80) 0.302* (1.95) 0.062* (1.95) 0.505*** (2.93) 0.095*** (2.93) 0.459*** (2.71) 0.097*** (2.71) 0.419** (2.59) 0.080** (2.59)

Number obs 844 844 928 928 894 894 773 773 918 918

Pseudo R2 0.0992 0.0992 0.0989 0.0989 0.0735 0.0735 0.0424 0.0424 0.1850 0.185

*, ** and *** mean significant at the 10 per cent, 5 per cent and 1 per cent significant levels.

Source: Authors


Table 5. Demand for Insurance by Country: Germany, Italy and the UK (estimated coefficients and marginal effects)

Explanatory

Variables

Indicator Germany Italy UK

Model 3 Model 3 Model 3

Coeff. Marginal Effects Coeff. Marginal Effects Coeff. Marginal Effects

Gender Male 0.547*** (2.65) 0.070*** (2.65) 0.408** (2.26) 0.065** (2.26) –0.223 (–1.28) –0.031 (–1.28)

Age 25–35 years

36–45 years

46–55 years

0.895*** (2.64)

0.495 (1.41)

0.140 (0.41)

0.114*** (2.64)

0.063 (1.41)

0.018 (0.41)

0.029 (0.10)

0.058 (0.21)

0.046 (0.16)

0.005 (0.10)

0.009 (0.21)

0.007 (0.16)

0.822** (2.40)

0.685** (1.99)

0.603* (1.77)

0.116** (2.40)

0.097** (1.99)

0.085* (1.77)


Equal

–0.834** (–2.31)

–0.423** (–1.97)

–0.107** (–2.31)

–0.054** (–1.97)

–1.87*** (–6.59)

–0.845*** (–4.01)

–0.300*** (–

6.59)

–0.135*** (–

4.01)

–1.50*** (–4.77)

–0.511** (–2.43)

–0.211*** (–4.77)

–0.072** (–2.43)

Primary/

secondary

wage earner

Only wage

Primary wage

Equal wages

Second wage

–1.121 (–0.91)

–0.919 (–0.75)

–0.915 (–0.74)

–1.573 (–1.25)

–0.143 (–0.91)

–0.117 (–0.75)

–0.117 (–0.74)

–0.201 (–1.25)

–0.136 (–0.28)

–0.008 (–0.02)

–0.403 (–0.80)

–0.244 (–0.49)

–0.022 (–0.28)

–0.001 (–0.02)

–0.064 (–0.80)

–0.039 (–0.49)

–0.186 (–0.23)

–0.182 (–0.23)

–0.879 (–1.05)

–1.082 (–1.34)

–0.026 (–0.23)

–0.026 (–0.23)

–0.124 (–1.05)

–0.153 (–1.34)

Household

size

0.073 (0.83) 0.009 (0.83) –0.128 (–1.64) –0.020 (–1.64) 0.208*** (3.10) 0.029*** (3.10)

Education # years –0.008 (–0.38) –0.001 (–0.38) –0.012 (–0.67) –0.002 (–0.67) –0.028* (–1.67) –0.004* (–1.67)


Part-time

Self-employed

1.137 (1.42)

0.817 (0.98)

1.206 (1.42)

1.145 (1.42)

0.104 (0.98)

0.154(1.42)

0.851 (1.26)

0.875 (1.24)

0.930 (1.36)

0.136 (1.26)

0.140 (1.24)

0.149 (1.36)

0.303 (0.34)

–0.011 (–0.01)

0.020 (0.02)

0.043 (0.34)

–0.001 (–0.01)

0.003 (0.02)

Financial

literacy

–0.296*** (–

2.88)

–0.038*** (–

2.88)

–0.138 (–1.52) –0.022 (–1.52) –0.348*** (–4.11) –0.049*** (–4.11)

Health Healthier

Same as healthy

0.216 (0.60)

0.509 (1.62)

0.028 (0.60)

0.065 (1.62)

0.331 (1.00)

–0.037 (–0.12)

0.053 (1.00)

–0.006 (–0.12)

0.691** (2.42)

0.425 (1.62)

0.097** (2.42)

0.060 (1.62)

Experienced

loss of income

Physical

Mental

0.420 (1.30)

0.441** (2.00)

0.054 (1.30)

0.056** (2.00)

0.704* (1.88)

0.641*** (2.63)

0.113* (1.88)

0.102*** (2.63)

0.976*** (3.85)

0.006 (0.03)

0.138*** (3.85)

0.001 (0.03)

Know

someone

0.856*** (4.37) 0.109*** (4.37) 0.424** (2.45) 0.068** (2.45) 0.523*** (3.17) 0.074*** (3.17)

Number obs 953 953 929 929 1130 1130

Pseudo R2 0.1361 0.1361 0.1003 0.1003 0.1627 0.1627

*, ** and *** mean significant at the 10 per cent, 5 per cent and 1 per cent significant levels.

Source: Authors


Table 6. Demand for Insurance by Country: Hong Kong, Malaysia, Brazil (estimated coefficients and marginal effects)

Explanatory

Variables

Indicator Hong Kong Malaysia Brazil

Model 3 Model 3 Model 3

Coeff. Marginal Effects Coeff. Marginal Effects Coeff. Marginal Effects

Gender Male –0.239 (–1.54) –0.046 (–1.54) –0.009 (–0.06) –0.002 (–0.06) 0.117 (0.64) 0.020 (0.64)

Age 25–35 years

36–45 years

46–55 years

–0.105 (–0.24)

0.261 (0.60)

–0.146 (–0.33)

–0.020 (–0.24)

0.051 (0.60)

–0.028 (–0.33)

0.051 (0.10)

0.045 (0.09)

0.047 (0.09)

0.011 (0.10)

0.009 (0.09)

0.010 (0.09)

0.606 (1.43)

0.318 (0.73)

0.504 (1.14)

0.102 (1.43)

0.053 (0.73)

0.084 (1.14)


Equal

–0.436** (–2.31)

–0.132 (–0.63)

–0.085** (–2.31)

–0.026 (–0.63)

–0.630** (–2.21)

–0.220 (–0.72)

–0.131** (–2.21)

–0.046 (–0.72)

–1.216*** (–4.14)

–0.766** (–2.52)

–0.204*** (–4.14)

–0.128** (–2.52)

Primary/

secondary wage

earner

Only wage

Primary wage

Equal wages

Second wage

2.472*** (3.08)

2.413*** (3.05)

2.295*** (2.87)

1.974** (2.45)

0.482*** (3.08)

0.470*** (3.05)

0.447*** (2.87)

0.385** (2.45)

0.472 (0.91)

0.396 (0.78)

0.501 (0.97)

0.356 (0.70)

0.098 (0.91)

0.083 (0.78)

0.104 (0.97)

0.074 (0.70)

0.327 (0.79)

0.829** (2.06)

0.277 (0.65)

–0.196 (–0.45)

0.055 (0.79)

0.139** (2.06)

0.046 (0.65)

–0.033 (–0.45)

Household size 0.003 (0.04) 0.001 (0.04) –0.060 (–1.28) –0.013 (–1.28) 0.086 (1.31) 0.014 (1.31)

Education # years 0.019 (1.32) 0.004 (1.32) 0.029* (1.89) 0.006* (1.89) –0.004 (–0.22) –0.001 (–0.22)


Part-time

Self-employed

0.993 (1.04)

0.668 (0.67)

0.894 (0.87)

0.193 (1.04)

0.130 (0.67)

0.174 (0.87)

0.710 (1.36)

0.641 (1.06)

0.289 (0.53)

0.148 (1.36)

0.134 (1.06)

0.060 (0.53)

0.786** (2.05)

0.544 (1.25)

0.132 (0.34)

0.132** (2.05)

0.091 (1.25)

0.022 (0.34)

Financial literacy 0.220*** (2.75) 0.043*** (2.75) 0.221*** (2.76) 0.046*** (2.76) 0.047 (0.49) 0.008 (0.49)

Health Healthier

Same as healthy

0.572** (2.22)

0.259 (1.15)

0.111** (2.22)

0.051 (1.15)

0.447 (1.63)

0.468** (2.22)

0.093 (1.63)

0.098** (2.22)

0.063 (0.22)

0.021 (0.08)

0.011 (0.22)

0.003 (0.08)

Experienced loss of

income

Physical

Mental

0.221 (1.09)

0.548*** (2.98)

0.043 (1.09)

0.107*** (2.98)

0.208 (0.73)

0.295 (1.64)

0.043 (0.73)

0.061 (1.64)

0.446* (1.70)

0.613*** (3.11)

0.075* (1.70)

0.103*** (3.11)

Know someone 0.618*** (4.00) 0.121*** (4.00) 0.256* (1.74) 0.053* (1.74) 0.476** (2.28) 0.080** (3.17)

Number obs 942 942 958 958 791 791

Pseudo R2 0.0867 0.0867 0.0435 0.0435 0.1242 0.1242

*, ** and *** mean significant at the 10 per cent, 5 per cent and 1 per cent significant levels. Source: Authors

Table 7. Country-Specific Reference Models: Summary of Significant Findings

Explanatory

Variables

Countries

Germany Italy UK Australia Mexico Spain Switzerland USA Hong

Kong

Malaysia Brazil

Age +ve

Younger

+ve

Younger

+ve

Younger

Income +ve +-ve +-ve +-ve +-ve +-ve +-ve +-ve +-ve +-ve

Primary etc. +ve +ve +ve

Gender

Wage

Earner

+ve

Male

+ve

Male

Household

Size

+ve +ve +ve +ve

Work Status -ve PT -ve PT +ve FT

Financial

Literacy

-ve -ve -ve +ve +ve

Health +ve +ve

Healthier

+ve

Healthier

Experience +ve

Mental

+ve

Physical

Mental

+ve

Physical

+ve

Physical

Mental

+ve

Physical

Mental

+ve

Physical

Mental

+ve

Physical

+ve

Physical

Mental

+ve

Mental

+ve

Mental

Physical

Others’

Experience

+ve +ve +ve +ve +ve +ve +ve +ve +ve +ve +ve

Source: Authors

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