The impact of health status on portfolio choice: Evidence from the
Netherlands
Master’s Thesis
MSc Finance
June 2020 Iana Merciuc S3180506 University of Groningen Faculty of Economics and BusinessSupervisor: dr. Carolina Laureti
Abstract
This thesis aims to assess the relationship between health status and portfolio choice in the Dutch context. It employs a unique longitudinal data set that enables one to investigate an extended time interval (1995-2018). The paper finds that health deterioration affects neither probability of holding risky assets nor the share of risky assets in the portfolio. Amongst the selected channels, the degree of risk aversion, net income and financial, and nonfinancial assets appear to have a significant impact on the relationship of interest. Furthermore, I investigate younger (<65) and older (≥65) respondents separately. The results of the two subsamples are somewhat similar in terms of the health effects and channel variables.
1 1. Introduction
Decisions related to portfolio composition determine wealth accumulation of households, which, subsequently, affect resource distribution and social wellbeing. The household sector plays a significant role in the economy as it captures a considerable share of total investment holdings. In 2016, total households’ financial assets in the EU amounted to €33,850 billion or 227.1% of GDP (Eurostat, 2018).
Studying portfolio composition in the household context is of particular interest due to the documented deviations from financial theories. Modern Portfolio Theory (MPT) hypothesises that rational investors construct their portfolio in a way that maximises the expected return given the volatility or, conversely, minimises the risk given the return (Francis and Kim, 2013). The optimal allocation to risky assets, in turn, will solely depend on their degree of risk aversion. Campbell (2006) claims that household behaviour is much more convoluted than predicted in textbooks. More specifically, the author argues that although most households are efficient investors, there is a significant portion of such households tending to make investment mistakes such as under-diversification and avoidance of risky assets. A vast body of literature tries to identify and explain potential factors that affect households’ decisions related to portfolio allocation. Amongst the assessed determinants are labour income (Guiso, Jappelli, and Terlizzese, 1996; Elmendorf and Kimball, 2000; Angerer and Lam, 2009), taxation (Poterba and Samwick, 2003), marital history (Ulker, 2009), and financial literacy (Chu et al., 2017). This paper will assess the effect of health status on portfolio choice, measured by the ownership of risky assets and the overall share of risky assets in the portfolio.
The current trends of increasing life expectancy and declining birth rates translate into a rising proportion of elderly (Mierau and Turnovsky, 2014). On average, older people exhibit a significantly higher prevalence of multimorbidity (Barnett et al., 2012). Nevertheless, individuals over 55 are also powered by a more substantial proportion of net wealth (Balestra and Tonkin, 2018). Because of the negative association between age and health, and economic significance of the senior citizens, it is crucial to understand how changes in health status are reflected in the allocation of wealth.
2 time (Atella, Brunetti, and Maestas, 2012). The unpredictable nature of these costs represents a background risk, which might push toward minimal risk-taking.
The relationship between health and portfolio choice is not vastly researched, most of the existing literature focusing mainly on the US (Rosen and Wu, 2004; Edwards (2008); Fan and Zhao, 2009; Love and Smith, 2010). This study intends to investigate the relationship in the Dutch context. I believe it will be insightful to assess a country with a different healthcare system to the US. The US adopts private insurance and private healthcare delivery; whereas, the Netherlands preserves universal insurance and private but heavily regulated healthcare (Bhattacharya, Hyde, and Tu, 2014). In contrast to the US, anyone residing or paying taxes in the Netherlands is obliged by law to have Dutch health insurance. Mandatory health insurance could reduce background risk and stimulate financial risk-taking (Christelis, Georgarakos, and Sanz-de-Galdeano, 2020), attenuating the impact of health deterioration. Therefore, it is expected to observe a less pronounced effect of poor health on portfolio choice in the Netherlands than in the US.
Furthermore, previous studies centre solely on elderly investors. The association between health status and portfolio composition might be more complex amongst younger individuals since health status determines their labour outcomes and employability. The younger generation also exhibits different characteristics that channel the relationship of interest. For instance, in my sample, participants younger than 65 are relatively more risk-seeking, face a longer investment horizon and possess fewer financial and nonfinancial resources. Therefore, the younger and older generations might entail different results. I investigate both subgroups to draw more generalised conclusions and fully exploit the available data.
I construct the sample using the DNB Household Survey. The richness of the data set allows for incorporating multiple determinants, such as psychological factors, financial and nonfinancial resources, and demographic characteristics. Furthermore, minimal alterations of the selected measures across the waves enable me to estimate the health effect over a prolonged period, namely 1995-2018. The study replicates the empirical approach of Love and Smith (2010). In particular, I employ a binary (Logit) and a censored (Tobit) regression model with three specifications: random effects, correlated random effects, and fixed effects.
3 2. Literature Review
2.1 Determinants of portfolio choice
Household portfolio choice has been extensively researched over the past few decades due to its significant implications for social welfare. Multiple papers attempt to explain and predict how households allocate their wealth between safe and risky assets, proposing a vast range of potential factors. Several researchers focus on demographic characteristics. For instance, Coile and Milligan (2009) investigate how households’ portfolio composition changes after retirement. They find that with age, households reduce the holding of most asset types (e.g. real estate, stocks, bonds); nonetheless, they tend to increase the fraction of assets invested in CDs and bank accounts. Marital history could also be an essential determinant of portfolio choice. Ulker (2009) argues that the likelihood of holding a specific asset and the share of wealth allocated to that asset would depend on past marital disruptions. Love (2010) also outlines the role of marital status and family composition in asset allocation decisions.
Additionally, numerous studies take psychological, behavioural and social aspects into account. Christelis, Jappelli, and Padula (2010), for example, analyse the relationship between cognitive abilities (numeracy, verbal fluency, memory) and stockholding. The authors establish a statistically and economically significant association driven by information barriers. Hong, Kubik, and Stein (2004), and Lu and Tang (2019) investigate the role of social interactions. Both papers suggest that households’ asset allocation decisions depend on the choice of their peers. Van Rooij, Lusardi, and Alessie (2011) propose financial literacy as a potential determinant. The study shows that households with limited financial knowledge are less likely to own stocks. Another characteristic is political activism. According to Bonaparte and Kumar (2013), politically active individuals are highly exposed to political and financial news, consequently, facing lower information costs and being more likely to participate in the stock market. The paper concludes that being politically active increases the probability of investing in stocks or mutual funds by 9%-25%. Furthermore, researchers have extensively investigated general personality traits (Conlin et al., 2015; Bucciol and Zarri, 2017). This study focuses on one additional characteristic, namely, health status.
2.2 Health and portfolio choice 2.2.1 Channels
4 Second, Edwards (2008) argues that health status might affect portfolio choice via the marginal utility of consumption. The effect, nevertheless, depends on the sign of the cross-partial derivative of utility. A negative sign implies that disabling health risks increase the marginal value of labour-saving activities (e.g. having a housekeeper instead of cleaning yourself). Provided that the cross-partial derivative is negative, health shocks increase the effective risk aversion and, consequently, minimise financial risk-taking incentives. The opposite effect emerges if the cross-partial derivative is positive, which denotes a positive association between health and enjoyment of consumption. An individual then increases the share of risky assets in the portfolio following a health shock.
Third, multiple papers (e.g. Fan and Zhao, 2009; Cardak and Wilkins, 2009; Atella, Brunetti, and Maestas, 2012) investigate out-of-pocket medical costs. These unforeseen expenses represent a type of undiversifiable background risk that discourages financial risk-taking. An individual might be incentivised to accumulate more wealth to offset any potential medical costs associated with unexpected health shocks. The resulting increase in precautionary saving, consequently, would reduce the demand for risky assets. Alternatively, Berkowitz and Qiu (2006) propose that financial wealth effects driven by deterioration of health could explain the association between health status and household portfolio composition. That is to say, health deterioration results in the depletion of financial wealth, which in turn will alter asset allocation in household portfolios. Other commonly investigated channels are income and bequest motives (Love and Smith, 2010).
2.2.2 Empirical evidence
5 Nevertheless, other studies detect little to no effect. For instance, Berkowitz and Qiu (2006) observe that the association between health and portfolio composition disappears after controlling for financial wealth. Love and Smith (2010) also identify that the relationship of interest is eliminated amongst single households if unobserved factors, such as risk appetite, information, patience, and motivation, are accounted for. Fan and Zhao (2009) find a strong association between health and total assets, financial assets, and nonfinancial assets. This relationship, however, vanishes when the fixed-effects model is employed. The paper concludes that only the deterioration of certain aspects of health (physical functions and heart attack or stroke) might induce households to choose safer assets. Cardak and Wilkins (2009) aim to test multiple determinants of household portfolio choice, such as marital status, educational achievements, liquidity and credit constraints, labour income, and health. The authors find that the effect of poor health is insignificant for all households after introducing time and risk preferences. There is a significant negative impact on the ratio of risky assets when investigating employed households.
The discussed papers are mostly single-country studies and, except for Cardak and Wilkins (2009), use data gathered from US households. Atella, Brunetti, and Maestas (2012) are the pioneers of cross-country analysis in Europe on the topic. They compare the effect of both objective and perceived health status on the household portfolio choice across 10 European countries. The authors present three main findings. Firstly, declining self-perceived health leads to less financial risk-taking. Secondly, households take into account both current and future health status when building their portfolio. Finally, health status affects portfolio composition in countries with weaker healthcare systems. Bressan, Pace, and Pelizzon (2014) follow the steps of Atella, Brunetti, and Maestas (2012), performing a cross-country comparison across 11 European countries. The authors find that poor self-perceived health is negatively associated with portfolio choice. The results for more objective health measures are not statistically significant.
Therefore, most studies (summarised in Table A1, Appendix) identify that deterioration of health modifies portfolio composition by inducing households to prioritise safer assets. The effect, however, disappears when incorporating various determinants (e.g. financial wealth, risk aversion, out-of-pocket medical expenses) or accounting for unobserved individual characteristics (e.g. patience, motivation). Relying on empirical evidence from the existing literature, I formulate the following hypotheses:
H0: There is no association between poor health and financial risk-taking.
6 3. Methodology
This section presents the econometric approach and is divided into two subsections. Firstly, I describe the selected models and their specifications. Subsequently, I discuss the variables incorporated into the model.
3.1 Empirical approach
The existing research on the topic proposes various econometric approaches. For instance, Edwards (2008) combines Tobit with random effects and instrumental variables. Fan and Zhao (2009) employ ordinary least square (OLS), fixed effects, and random effects. This paper follows the empirical strategy of Love and Smith (2010), which entails using a binary model (Logit) and a censored regression model (Tobit). Tobit is applied in the case of allocation of risky assets because it tackles the issue of clustering at 0, arising from the fact that most individuals hold no risky assets. Both models will comprise of three specifications: random effects (RE), correlated random effects (CRE), and fixed effects (FE).
The existing literature commonly uses RE due to potential efficiency gains. Nevertheless, its underlying assumption that the unobserved individual effects are independent of the explanatory variables rarely holds in practice. The selected determinants might correlate with other variables not included in the model (e.g. stress). Hence two additional specifications are applied as a robustness check.
The FE specification involves less restrictive assumptions and enables to control for unobserved time-invariant characteristics correlated with the independent variables (Brooks, 2019). However, by eliminating all time-invariant variables, one loses the ability to determine the effect of those included in the model. Generally, nonlinear fixed effects are computationally challenging and subject to incidental parameter problem (Greene, 2004). To overcome these issues, I use the conditional fixed effects logit. It efficiently eliminates the unobserved heterogeneity by conditioning on the number of successes (the dependant variable equals one). In the rest of the paper, the conditional fixed effects Logit is labelled as FE Logit.As for FE Tobit, I employ the censored fixed effect model proposed by Honoré (1992).
7 Following the example of Love and Smith (2010), the analysis starts with a rather parsimonious regression that includes only health measures and demographic characteristics. Subsequently, I control for the full set of variables. Incorporating the remaining determinants would help get a better understanding of the association between health status and portfolio composition, and the role of the selected channels. Furthermore, I assess the younger and older generations separately. As discussed in the next section, the two subsamples exhibit different characteristics (e.g. risk aversion, planning horizon, and financial resources), which might alter the magnitude of the relationship. The regression equation that underlines the relationship of interest is:
yit = " + healthit * %!+ &it * %" + '#+ (i + uit, (1)
where, yit – the dependant variables, a binary variable that denotes whether the respondent i owns any risky assets at time t (Logit) and the share of risky assets in a portfolio (Tobit); " – the intercept; healthit – the health status; &it – a vector of exogenous explanatory variables; '# –
time-specific effects; (i – a time-invariant individual-specific effect, and uit – the error term. Since multiple events (e.g. dot-com bubble, the great recession) could have affected portfolio composition during the assessed time frame, it is crucial to account for time-specific effects. Ideally, one would create a dummy variable for each year. However, the study covers quite an extended period, namely 1995-2018. Adding 24 dummies to the existing explanatory variables could generate problems with degrees of freedom, especially in the case of FE specification, where the number of observations declines substantially. To overcome this issue, I follow van Kippersluis et al. (2009) and construct period dummies. In particular, I use two-consecutive-year intervals instead of each two-consecutive-year, e.g. first time dummy equals one for two-consecutive-years 1995 or 1996, and zero otherwise.
Another potential concern relates to reverse causation; that is to say, portfolio choice might have an impact on health status rather than the opposite. The revere causality could lead to biased and inconsistent estimates. Nonetheless, Rosen and Wu (2004) argue that there are no sound reasons to believe that this is the case, particularly after accounting for the level of wealth. Similar conclusions are drawn by Love and Smith (2010), and Fan and Zhao (2009).
3.2 Variables
The following part discusses the variables included in the analysis. The composition and description of each variable are available in the Appendix (Table A2).
3.2.1 Dependent variables
8 any risky assets and zero otherwise. As for the Tobit model, the response variable represents the ratio of risky assets to total assets. The total assets are computed by adding financial (e.g. savings and deposit accounts, deposit books, bonds or mortgage bonds) and nonfinancial asset categories (e.g. real estate, cars, boats) available in every wave. I exclude checking accounts because several respondents report extreme negative amounts, which bias the risky assets ratio. 3.2.2 Health measures
Similar to Rosen and Wu (2004), and Love and Smith (2010), I incorporate both self-perceived and more objective measures of health. The former reflects how individuals “feel” about their general health condition at a specific point in time. The more objective measures usually relate to functional limitations, unhealthy habits or specific medical conditions. According to Doiron et al. (2015), self-reported health can predict future health, having a significant effect across all illness categories. Nevertheless, it exhibits a slightly lower predictive power than objective measures. The primary concern with self-reported health is that it is more prone to biases induced by individual characteristics (Van and Gerdtham, 2003; Jürges, 2007). To ensure the validity of the results, I use both subjective and objective measures.
The following statement measures the self-perceived health: “In general, would you say your health is:”. The answer choice ranges from 1 to 5, where 1 corresponds to “excellent” and 5 to “poor”. For objective measures, I employ the questions that ask whether the respondents smoke, consume more than four alcoholic drinks in a day or suffer from long illnesses/disorders/handicaps. These are dichotomous variables with possible values of zero and one, which correspond to “no” and “yes” respectively.
3.2.3 Other explanatory variables Wealth and income
Love and Smith (2010) argue that it is crucial to include wealth and income, outlining their role as potential channels. Deterioration of health could induce income loss and depletion of wealth, which would be reflected on portfolio choice. The authors also suggest separating total wealth into financial assets and nonfinancial assets, since the former are generally more liquid and could entail a more pronounced effect. Berkowitz and Qiu (2006) find that health shocks do affect financial and nonfinancial wealth asymmetrically. The two types of wealth are computed as the sum of the asset groups indicated in Table A2 (Appendix). Since both variables are positively skewed, I will use their logarithmic form.
Liabilities
9 financial difficulties and deter individuals from holding risky assets. The authors find that households with outstanding mortgage debt exhibit a reduction of 10% in the probability of stocks ownership and 37% in the probability of bond ownership. I compute liabilities by summing the first six debt components available in the questionnaire (Table A2, Appendix). To account for a potential nonlinear relationship with the dependent variables, I also add the quadratic form.
Risk preferences
A risk-averse individual is less likely to invest in risky assets; therefore, risk appetite directly affects investment decisions. Nevertheless, it could be another channel that bridges health and portfolio choice. As previously mentioned, Edwards (2008) proposes a theoretical model in which health shocks influence households’ asset allocation strategies by altering their effective risk aversion. That is to say, sickness might change an individual’s risk preference and, in result, affect the ultimate investment decision. The direction of the change depends on the sign of the cross-partial derivative.
To measure the degree of risk aversion, I use the question “What would you say was the risk factor that you have taken with investments over the past few years?”. The participants can choose among five possible answers, ranging from 1 – “I have taken no risk at all” to 5 – “I have often taken great risks”.
Planning horizon
Health might affect portfolio composition via time horizon, which is frequently linked to life expectancy. Ageing reduces the amount of time remaining until death and, thus, the investment horizon, incentivising individuals to invest in safer assets. The results presented in the existing literature are contradictory. Atella, Brunetti, and Maestas (2012) show that longer investment horizons lead to a higher probability of stock holding. In contrast, Rosen and Wu (2004), and Edwards (2008) do not identify any significant impact.
The questionnaire allows measuring planning horizon by directly asking the respondents to select the time range they consider when planning expenditures and saving. The answer choice includes five options: 1 – “the next couple of months”, 2 – “the next year”, 3 – “the next couple of years”, 4 – “the next 5 to 10 years”, and 5 – “more than 10 years from now”. Other control variables
10 and marital status. According to Atella, Brunetti, and Maestas (2012), and Edwards (2008), males and married individuals (couples) are more likely to hold risky assets than females and singles. Gender and marital status are also frequently associated with health itself (Cramer, 1993). Additionally, I assess the completed level of education. A higher level of education might influence portfolio composition due to the improvement of financial knowledge. It could also have an impact on health status, as more educated individuals are less likely to engage in health impairing behaviour. Lastly, I take into consideration the number of children in the household, which could affect portfolio choice via bequest motives (Edwards, 2008; Fan and Zhao, 2009).
4. Data
4.1 Data source DHS
I will use the DNB Household Survey (DHS), administered by the scientific research institute CentERdata (Tilburg University, the Netherlands). It provides yearly information on more than 2000 Dutch households from 1993 to 2018. Each year, the survey adds new participants to refresh the sample, as such, producing unique longitudinal data. The DHS consists of six questionnaires: general information on the household, household and work, accommodation and mortgages, health and income, assets and liabilities, and economic and psychological concepts. I employ all except accommodation and mortgages, as the information it contains is irrelevant for the study.
Multiple papers investigating household investment behaviour use the DHS (Dimmock and Kouwenberg, 2010; Kapteyn and Teppa, 2011; Bonaparte, Korniotis, and Kumar, 2014). The data set entails several advantages. Firstly, its richness allows for assessing the relationship between health and portfolio composition by incorporating various economic, psychological, and demographic characteristics. Secondly, the questions measuring these factors have proved to be relatively consistent throughout the years of the data collection, which enables me to assess an extended time interval, 1995-2018. Finally, the participants of the survey are of different age groups. Therefore, I can analyse both younger and older generations, contrary to other studies that investigate solely older individuals (Rosen and Wu, 2004; Love and Smith, 2010; Pang and Warshawsky, 2010).
4.2 Sample construction
11 appending them to form a panel data. Since the DHS uses an individual as the sample unit, I focus on the individual level rather than the household level. Each respondent is assigned a unique personal index computed as:
panelid = nohhold * 100 + nomem, (2)
where panelid – the personal index, nohhold – the household index, and nomem – the household member index. The resulting sample comprises of 11,983 individuals and 43,908 observations, which is roughly 3.664 observations per individual.
Several variables require minor adjustments. Until 2002, the currency used in the questionnaires is the Dutch guilder. I convert it to euro by using the fixed-rate (€1 = ƒ2.20371) proposed by the European Central Bank in 1998. Additionally, I recode the variables that have been altered in the subsequent questionnaires (e.g. more possible answers were added, the code assigned to an answer was changed). The answer choices such as “Other” and “I don’t know” are treated as missing values to avoid misleading results.
The constructed panel is not balanced since multiple households drop the survey for unspecified reasons. The process of exiting a panel study is commonly known as attrition, which left uncontrolled, might lead to biased estimates. However, Love and Smith (2010) state that a balanced panel is not needed if one observes sufficient longitudinal changes in the health and portfolio choice variables. The authors argue that the chosen empirical approach can efficiently capture the relationship of interest. Therefore, no additional measures are employed.
4.3 Descriptive statistics
Table 1 depicts the descriptive statistics of the variables incorporated into the analysis. The age of the participants ranges from 16 to 94, averaging at 51. Almost 55% of the respondents are males and 75% live with a partner. The respondents earn roughly €22,000 (net of tax), being relatively short-sighted when planning what part of their income to allocate for expenditures and savings. The mean of 2.087 for Risk preference indicates that the participants rarely take investment risks. It also should be noted that only 21% of the sample participate in the risky asset markets and risky assets represent 7.1% out of the total wealth.
12 Additionally, I divide the sample into two groups, younger (<65) and older (≥65). The last column of Table 2 indicates that the two subsamples significantly differ from each other on all variables. Based on the health measures Self-perceived and Long illnesses, younger respondents display better health conditions; nevertheless, a relatively higher percentage of young smoke and consume alcohol. They also appear to own less risky assets despite reporting a higher preference for investment risks. Furthermore, younger individuals consider a longer investment horizon and enjoy fewer financial resources than older individuals. Each of these characteristics would differently affect the association between health and portfolio choice; consequently, no aggregated prediction could be made.
Table 1. Summary statistics of the sample.
Variable name Obs. Mean SD Min Max
Health Self-perceived 43,908 2.115 0.725 1 5 Long illnesses 43,908 0.264 0.441 0 1 Smoking 43,908 0.237 0.425 0 1 Drinking 43,908 0.062 0.241 0 1 Dependent variables
Share of risky assets 35,500 0.071 0.191 0 1
Ownership of risky assets 43,908 0.208 0.406 0 1
Demographic characteristics Age (years) 43,906 50.823 16.03 16 94 Male 43,908 0.552 0.497 0 1 Number of children 43,905 0.763 1.106 0 7 Couple 43,908 0.745 0.436 0 1 Education (level) 41,110 4.734 1.505 1 7 Behavioural variables Risk preference 18,987 2.087 0.997 1 5 Time horizon 41,741 2.349 1.167 1 5
Income and wealth
Net income (TEUR) 34,286 22.027 21.197 -4.799 1,158.915
Financial assets (TEUR) 42,255 29.019 93.146 0 3,701.301
Nonfinancial assets (TEUR) 42,275 16.339 93.095 0 4,491.501
Liabilities (TEUR) 42,275 2.568 21.107 0 1,812.5
Note: I define risky assets as stocks and mutual funds. Nonfinancial assets comprise real estate, cars, motorbikes, boats, and caravans; whereas financial assets include the remaining asset categories of the questionnaire assets and liabilities available in every wave (deposit books, growth funds, stocks, bonds etc.). Liabilities comprise of six debt components, namely private loans, extended lines of credit, outstanding debts on hire-purchase contracts/debts based on payment by instalment/equity-based loans, outstanding debts with retail business, loans from family or friends, and study loans. The unit of measure is indicated in the brackets next to the variable name. The variables net income, financial and nonfinancial assets, and liabilities are expressed in thousands of euros (TEUR), rounded to the closest integer. The objective measures of health (Long illnesses, Smoking, and Drinking), Ownership of risky
13 Table 2. Summary statistics of the subsamples young and old.
Variable name Young (<65) Old (≥65) t-test
Obs. Mean SD Obs. Mean SD
Health Self-perceived 33,697 2.067 0.71 10,211 2.274 0.753 25.381*** Long illnesses 33,697 0.232 0.422 10,211 0.369 0.482 27.737*** Smoking 33,697 0.272 0.445 10,211 0.12 0.325 -31.898*** Drinking 33,697 0.064 0.245 10,211 0.054 0.227 -3.485*** Dependent variables Share of risky assets 27,074 0.059 0.171 8,426 0.11 0.239 21.389*** Ownership of risky assets 33,697 0.192 0.394 10,211 0.262 0.44 15.419*** Demographic characteristics Age (years) 33,697 44.43 12.232 10,209 71.927 5.559 220.343*** Male 33,697 0.524 0.499 10,211 0.642 0.48 20.989*** Number of children 33,694 0.985 1.171 10,211 0.031 0.205 -81.916*** Couple 33,697 0.739 0.439 10,211 0.763 0.425 4.84*** Education (level) 31,382 4.813 1.463 9,728 4.478 1.606 -19.242*** Behavioural variables Risk preference 14,086 2.099 1.004 4,901 2.054 0.975 -2.691*** Time horizon 31,666 2.359 1.192 10,075 2.316 1.084 -3.2***
Income and wealth
Net income (TEUR) 26,411 21.702 21.773 7,875 23.115 19.1 5.195*** Financial assets (TEUR) 32,369 21.844 69.122 9,886 52.513 143.947 28.935*** Nonfinancial assets (TEUR) 32,387 15.061 93.453 9,888 20.523 91.791 5.108*** Liabilities (TEUR) 32,387 2.99 23.415 9,888 1.187 10.319 -7.442***
Note: The table depicts the summary statistics for two subsamples, namely respondents younger than 65 and older than 65. In the last column, I present the results of the t-test for each variable, where *, **, *** denote significant mean differences between young and old at 0.1, 0.05, and 0.01 levels, respectively. I define risky assets as stocks and mutual funds. Nonfinancial assets comprise real estate, cars, motorbikes, boats, and caravans; whereas financial assets include the remaining asset categories of the questionnaire assets and liabilities available in every wave (deposit books, growth funds, stocks, bonds etc.). Liabilities comprise of six debt components, namely private loans, extended lines of credit, outstanding debts on hire-purchase contracts/debts based on payment by instalment/equity-based loans, outstanding debts with retail business, loans from family or friends, and study loans. The unit of measure is indicated in the brackets next to the variable name. The variables net income, financial and nonfinancial assets, and liabilities are expressed in thousands of euros (TEUR), rounded to the closest integer. The objective measures of health (Long illnesses, Smoking, and Drinking), Ownership of risky
14 5. Empirical results
The following section discusses the results of the regression analysis. Before estimating the models, I construct the correlation matrix and perform the Variance Inflation Factors (VIF) test to investigate the association among the explanatory variables (Table A3 and Table A4, Appendix). Most of the relationships are weak with correlation coefficient under 0.3. Only two pairs exhibit coefficients higher than 0.8, namely Liabilities - Liabiliies2 and Age - Age2. Moreover, the VIF of the age variables exceeds 40, indicating the existence of multicollinearity problem. A strong correlation is expected since the quadratic form derives from the linear one. Nevertheless, relying on Bressan, Pace, and Pelizzon (2014), and Berkowitz and Qiu (2006), I incorporate both forms to capture the nonlinear relationship with the dependant variables. I apply robust standard errors to account for heteroskedasticity. In the cases when the software does not support the robust option, standard errors are obtained using bootstrapping with 50 replications. Additionally, I test for the presence of autocorrelation following the approach proposed by Love and Smith (2010). The authors conduct a nonformal test that involves comparing the unadjusted and adjusted for clustering standard errors in a linear regression setting. The standard errors obtained with clustering are relatively higher; nonetheless, most of the variables retain their significance (except Age squared and Education). Therefore, it is reasonable to assume that the results are not severely affected by autocorrelation.
I start by assessing the underlying effect of health on the holding of risky assets in a parsimonious regression (only demographic characteristics). Subsequently, I investigate the effects after incorporating a full set of variables. The analysis is repeated for the old and young subsamples. To provide a more general overview of the health effects, I incorporate all health measures simultaneously. The results are, nevertheless, almost identical when considering one measure at a time (Table A8, Table A9 and Table A10, Appendix).
Estimating marginal effect with FE specification is computationally challenging. The censored fixed effect, FE Tobit, does not allow computing the marginal effect. As for FE Logit, marginal effects are of little to no value (Longhi and Nandi, 2015). To compare the direction and significance of the coefficients across all three specifications, I firstly report the estimates directly obtained from the regression analysis. Following, I present the average marginal effects of the health variables for the RE and CRE specifications.
5.1 Parsimonious regression
15 three specifications. Self-reported health is statistically significant only in RE Tobit and RE Logit. One unit increase in Self-perceived, denoting health deterioration, leads to 0.6 percentage points decrease in the probability of holding risky assets and 0.2 percentage points decrease in their share in the portfolio. The effect vanishes when accounting for unobserved individual effects with FE and CRE. Regarding more objective measures, Smoking has a significant negative impact in RE Logit and positive impact in FE Tobit and CRE Tobit. That is to say, smoking individuals are less likely to own risky assets; however, they exhibit a higher share of risky assets in the portfolio. It should be noted that the identified effects of both self-perceived and objective health measures are rather small to be economically significant. Furthermore, they are of lower magnitude than presented in the existing literature on the US (e.g. Edwards, 2008; Love and Smith, 2010). It could relate to mandatory health insurance in the Netherlands, which counteracts the effect of health.
Consistent with Love and Smith (2010), health effects are merely present even before incorporating the remaining variables. Therefore, I fail to reject the null hypothesis that states that there is no association between health and portfolio choice. Nonetheless, it might be insightful to assess the impact of the selected channels; thus, I also perform the full specification analysis.
Demographic characteristics considerably affect portfolio choice. Male is statistically significant across all specifications. That is to say, men hold a higher proportion of risky assets and, generally, are more likely to possess risky assets in the portfolio. Age appears to be significant only in RE and CRE Logit. The probability of holding risky assets increases as the individual gets older; however, the negative coefficient of the quadratic form indicates that the effect diminishes with time. Education exhibits a significant positive effect solely in RE specification of both models. The obtained signs of the regression coefficients are in line with Rosen and Wu (2004), and Edwards (2008).
5.2 Full regression
16 Table 3. RE, FE, CRE Logit and Tobit regressions (demographics only).
Variables Logit Tobit
RE FE CRE RE FE CRE Health Self-perceived -0.087* 0.021 0.019 -0.003** -0.005 -0.001 (0.051) (0.067) (0.067) (0.002) (0.009) (0.002) Long illnesses 0.019 0.001 0.007 0.003 0.003 0.002 (0.078) (0.079) (0.093) (0.003) (0.011) (0.003) Smoking -0.239** 0.149 0.161 -0.003 0.054** 0.012* (0.099) (0.133) (0.168) (0.003) (0.027) (0.006) Drinking -0.107 -0.086 -0.060 -0.003 -0.026 -0.005 (0.127) (0.173) (0.163) (0.005) (0.020) (0.006) Demographic characteristics Age 0.163*** 0.100 0.115** 0.001 0.003 0.002 (0.021) (0.067) (0.051) (0.001) (0.009) (0.002) Age2 -0.001*** -0.001*** -0.001*** 0.000 -0.000 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Male 1.772*** 12.202*** 1.740*** 0.027*** 0.950*** 0.025*** (0.093) (1.050) (0.095) (0.004) (0.013) (0.004) Number of children -0.122** -0.017 -0.003 -0.002 -0.008 -0.002 (0.048) (0.061) (0.098) (0.002) (0.019) (0.004) Couple -0.114 0.231 0.235 -0.009** -0.012 -0.002 (0.108) (0.185) (0.195) (0.004) (0.038) (0.007) Education 0.433*** -0.037 -0.046 0.012*** 0.018 0.004 (0.034) (0.075) (0.084) (0.001) (0.016) (0.004) Constant -11.322*** - -11.119*** -0.078*** - -0.032** (0.552) (0.525) (0.016) (0.015) Average marginal effects Self-perceived -0.006* - 0.001 -0.002** - -0.000 (0.004) (0.005) (0.001) (0.001) Long illnesses 0.001 - 0.000 0.002 - 0.001 (0.005) (0.006) (0.002) (0.002) Smoking -0.017** - 0.011 -0.002 - 0.008* (0.007) (0.011) (0.002) (0.004) Drinking -0.007 - -0.004 -0.002 - -0.003 (0.009) (0.011) (0.003) (0.004)
Time effects YES YES YES YES YES YES
Observations 41,110 12,872 41,110 33,609 33,609 33,609
Number of panel id 11,035 1,682 11,035 9,325 9,325 9,325
Chi2 995.20*** 6,955.45*** 1,100.22*** 593.09*** 10,582.07*** 960.56***
Hausman test Chi2(5) = 73.74***
17 The impact of the selected channels and other determinants on portfolio choice is depicted in Table 4. The coefficient estimates in Logit and Tobit strongly support the existence of a positive association between risk-seeking and holding of risky assets. The results also outline the importance of wealth. Financial assets have a significant positive effect across all specifications in both models; whereas, nonfinancial assets display a significant positive effect in Logit and negative effect in Tobit. In other words, nonfinancial assets are positively associated with the likelihood of holding risky assets and negatively associated with the ratio of risky to total assets. These results contradict the findings of Berkowitz and Qiu (2006), which state that both types of assets are positively related to financial risk-taking. Furthermore, it is important to emphasise that the effect of nonfinancial wealth is considerably smaller compared to financial wealth. Amongst other determinants, net income exhibits a significant negative impact in RE Logit, RE Tobit, and CRE Tobit, opposing Berkowitz and Qiu (2006), and Bertaut (1998) who find a positive association.
The impact of health status is partially eliminated after incorporating the remaining variables. In particular, Smoking, which was previously significant in the Logit and Tobit models, does not longer demonstrate to have any effect. Self-perceived health is still statistically significant, at 0.1 level, only in the RE Tobit. Deterioration of health by one unit is associated with a decrease in the proportion of risky assets by roughly 0.4 percentage points; nevertheless, its effects on the probability of holding risky assets vanishes. These results provide strong evidence in favour of the null hypothesis.
From demographic characteristics, age, gender, and education retain their significance. The age coefficients reverse the sign, indicating that financial risk-taking declines at an accelerating rate with age. Being a man still entails a higher probability of owning risky assets and a larger share of risky assets in the portfolio. Living with a partner and obtaining a higher level of education is marginally significant in RE Logit and RE Tobit; however, the significance disappears after accounting for unobserved heterogeneity. The rather insignificant effect of marital status is not in line with Love and Smith (2010), Bogan and Fertig (2013), and Edwards (2008), of whose findings emphasise that singles and couples should exhibit substantial differences as they pursue different investment strategies.
Table 4. RE, FE, CRE Logit and Tobit regressions (all variables).
Variables Logit Tobit
18
Variables RE Logit FE CRE RE Tobit FE CRE
Smoking -0.055 0.279 0.318 0.002 0.035 0.016 (0.157) (0.228) (0.276) (0.006) (0.027) (0.013) Drinking -0.056 -0.178 -0.050 -0.003 -0.034 -0.008 (0.206) (0.336) (0.275) (0.008) (0.024) (0.011) Demographic characteristics Age -0.097** -0.367*** -0.240*** -0.002 -0.015 -0.004 (0.039) (0.106) (0.084) (0.002) (0.012) (0.004) Age2 0.001*** 0.002*** 0.002** 0.000*** 0.000* 0.000 (0.000) (0.001) (0.001) (0.000) (0.000) (0.000) Male 0.739*** 9.339*** 0.532*** 0.023*** - 0.014** (0.165) (1.092) (0.185) (0.006) (0.006) Number of children -0.050 -0.108 -0.126 -0.003 -0.024 -0.009 (0.076) (0.173) (0.170) (0.003) (0.020) (0.007) Couple -0.422** 0.536 0.470 -0.020** -0.012 -0.006 (0.181) (0.354) (0.338) (0.008) (0.036) (0.016) Education 0.199*** 0.119 0.165 0.011*** 0.022 0.009 (0.052) (0.119) (0.151) (0.002) (0.016) (0.007) Behavioural variables Risk preference 1.132*** 0.541*** 0.596*** 0.037*** 0.033*** 0.020*** (0.065) (0.082) (0.078) (0.002) (0.009) (0.003) Time horizon -0.020 -0.025 -0.053 -0.002 -0.009** -0.003 (0.040) (0.052) (0.045) (0.002) (0.004) (0.002)
Income and wealth
Log net income -0.076** -0.024 -0.088 -0.005*** -0.008 -0.005**
(0.038) (0.056) (0.062) (0.002) (0.006) (0.002)
Log financial assets 1.278*** 0.942*** 1.141*** 0.020*** 0.096*** 0.019***
(0.078) (0.088) (0.088) (0.001) (0.011) (0.001)
Log nonfinancial assets 0.056*** 0.052* 0.071*** -0.005*** -0.018*** -0.005***
(0.017) (0.028) (0.025) (0.001) (0.004) (0.001) Liabilities 0.006 0.001 0.005 0.000 -0.000 -0.000 (0.006) (0.012) (0.008) (0.000) (0.001) (0.000) Liabilities2 -0.000 -0.000 -0.000 -0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Constant -7.181*** - -9.769*** -0.039 - -0.054 (0.983) (1.149) (0.036) (0.039)
Average marginal effects
Self-perceived -0.002 - 0.005 -0.004* - -0.003 (0.006) (0.007) (0.002) (0.003) Long illnesses 0.004 - -0.002 0.002 - -0.003 (0.010) (0.011) (0.002) (0.004) Smoking -0.004 - 0.019 0.001 - 0.011 (0.011) (0.017) (0.005) (0.009) Drinking -0.004 - -0.003 -0.002 - -0.005 (0.014) (0.017) (0.006) (0.008)
19
Variables RE Logit FE CRE RE Tobit FE CRE
Observations 14,278 4,474 14,278 12,911 12,911 12,911
Number of panel id 5,116 703 5,116 4,613 4,613 4,613
Chi2 645.59*** 690.12*** 624.32*** 2,175.95*** 1,038.63*** 18,779.07***
Hausman test Chi2(7) = 128.16***
Note: The dependant variables are Ownership of risky assets (Logit) and Share of risky assets (Tobit). *, **, *** denote significance at 0.1, 0.05, and 0.01, respectively. Robust (Logit: RE, CRE) and bootstrapped (Logit: FE; Tobit: RE, CRE, FE) standard errors are indicated in parentheses. The first part of the table presents the estimates directly obtained from the regression analysis. The second part of the table shows the average marginal effect of the health variables. The variables net income, financial and nonfinancial assets, and liabilities are expressed in thousands of euros (TEUR), rounded to the closest integer. The estimated coefficients of the time dummies and the mean time-varying variables employed in the CRE specification are not reported.
5.3 Subsamples
I divide the sample into younger and older respondents and repeat the regression analysis for each subgroup. Table A6 (Appendix) depicts the results when investigating solely younger respondents (<65). Long illnesses and Smoking are marginally significant in RE and CRE Tobit, respectively. Nonetheless, their average marginal effects are rather small, being less than 2 percentage points. When assessing older individuals (≥65), only Long illnesses is marginally significant in FE and CRE Tobit (Table A7, Appendix). Other measures of health do not display any statistical significance. Similar to the full sample, health effects are merely present. The analysis outlines several determinants of portfolio choice, namely Risk preference, Log financial wealth, Log nonfinancial wealth, and Log net income. Lower risk aversion entails a higher probability of owning risky assets and more risky assets in the portfolio, the magnitude of the relationship being somewhat more pronounced with younger individuals. Financial risk-taking is positively associated with financial assets in both models. Furthermore, it is positively associated with nonfinancial assets in Logit and negatively in Tobit. The effects of the wealth variables in the two subsamples are of similar magnitude, being slightly higher in the case of younger respondents. Net income is another significant factor that emerges only in the subsample composed of younger participants. It renders a negative association with the holding of risky assets.
20 6. Conclusion
This paper aims to investigate how health deterioration affects household portfolio composition. Multiple studies identify no significant relationship between health and the holding of risky assets. In contrast to the existing literature, which focuses mainly on older US households, I assess the relationship in the Dutch context and evaluate both younger and older individuals. My sample is constructed using the DNB Household Survey. The database provides unique longitudinal data, which allows for investigating a prolonged time interval (1995-2018) and including various psychological and economic determinants. I estimate health effects by replicating the empirical strategy of Love and Smith (2010).
Consistent with Berkowitz and Qiu (2006), Fan and Zhao (2009), Cardak and Wilkins (2009), and Love and Smith (2010), the results show little or no association between health and the holding of risky assets, especially after taking into consideration the selected channels. Wealth, net income, and risk aversion are significant determinants of the relationship of interest. Health deterioration could translate into depletion of wealth and income, consequently, affecting portfolio allocation. The findings of this work suggest that a decrease in financial assets, which could be potentially induced by health problems, lowers the probability of owning risky assets and their share in the portfolio. Nonfinancial assets positively affect the likelihood of risky asset ownership but negatively affect the ratio of risky assets. Higher net income seems to negatively affect financial risk-taking. Furthermore, the existing studies argue that health shocks might also have an impact on the holding of risky assets by altering the degree of risk aversion. The paper finds no association between health status and risk aversion; nonetheless, risk preferences substantially affect portfolio composition. Amongst demographic characteristics, age, education, and gender display statistical significance across most specifications. When investigating younger and older generations separately, the results for the two groups are somewhat similar.
This paper, nevertheless, is subject to several limitations. Firstly, since I investigate a relatively large time interval, serial correlation might be a problem. The informal test, proposed by Love and Smith (2010), shows that only Age squared and Education modify their significance; however, the accuracy of the approach is questionable. Secondly, several potential mechanisms are missing from the analysis. In particular, some variables are not included due to alterations that occurred across waves (e.g. health insurance) or the absence of relevant measures (e.g. out-of-pocket medical expenses). Finally, the selected psychological measures might not be good proxies for such determinants as risk aversion and time horizon.
21 Furthermore, future research can investigate more extensively the proposed channels to get a better understanding of the relationship between health and portfolio choice. One can also explore other alternative mechanisms, such as advancements in medical technology. The technological development in the medical sector could have a substantial impact on health status and, in turn, alter portfolio composition.
Acknowledgement
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25 8. Appendix
Table A1. Summary of the existing literature.
Study Goal Data
Sources (Country) Channels Empirical approach Key findings Rosen & Wu, 2004 Assess whether differences in health status determine portfolio allocation HRS (The US) Risk aversion, planning horizon, bequest motives, insurance Probit, Tobit (random effects)
Poor health is associated with a higher probability of holding less risky assets;
People with poor health tend to have safer portfolio;
No evidence for the presence of a third variable that drives the effect.
Berkowitz & Qiu, 2006 Test the difference in the effect of health status on financial and nonfinancial wealth; Explain the channel; HRS
(The US) Indirectly via financial wealth
Probit, Tobit (random effects)
The impact of health status on single households’ financial wealth is larger and more significant than on nonfinancial wealth;
Health status indirectly affects household portfolio allocation; Health stock reduces financial wealth, consequently determining households to alter their portfolio.
Edwards, 2008 Evaluate how self-perceived health status is associated with holding risky financial assets after retirement AHEAD, HRS (The US) Background risk (out-of-pocket medical expenses), the change in marginal utility Tobit (random effects, instrumental variables)
Presence of a spouse and bequest motives hedge against health risk; Number of surviving children is negatively associated with risk-taking;
Additional health insurance
increases risk-taking;
Risky health could explain 20% of the decrease (associated with age) in financial risk-taking.
Cardak & Wilkins, 2009 Investigate what factors determine households to hold risky financial assets. HILDA Survey (Australia) Background risk, time, risk preferences
Tobit Labour income risk: negative
effect;
Committed expenditures: positive effect;
Poor health status: negative effect (for employed households); Background risk: no impact; Homeownership: positive effect.
Fan & Zhao, 2009 Evaluate how unobserved heterogeneity affects the health-portfolio relationship. NBS (The US) Background risk OLS, Fixed-effects and random-effects, Tobit, Probit,
There is a strong correlation between health status and total assets, financial assets, and
nonfinancial assets, which
disappears in the fixed-effects model;
Health remains a significant determinant of the share of risky assets in the case of two out of four indices. Love & Smith, 2010 Test the effect of health on the HRS
(The US) Insurance, risk aversion, planning
Logit,
26
Study Goal Data
Sources (Country) Channels Empirical approach Key findings allocation of financial wealth between risky and safe assets, taking into account unobserved heterogeneity horizon, financial and nonfinancial wealth, income, out-of-pocket medical expenses, expected bequests (fixed effects, correlated random effects, random effects)
accounting for unobserved
heterogeneity;
For married households, there is a small negative effect.
Atella, Brunetti & Maestas, 2012 Assess the association between current and future health status, and financial portfolio composition across ten countries SHARE (10 European countries) Background risk (medical expenditure risk)
Probit Perceived rather than objective
health status drives the decision to hold risky assets;
Households consider future health when creating their portfolios; Health status determines portfolio composition in countries with less protective health care systems; Forward-looking behaviour is only applicable to middle-aged and highly educated households.
Bogan & Fertig, 2013 Investigate whether mental health can explain portfolio choice. HRS (The US) Out-of-pocket medical expenses, mood, cognitive abilities Tobit, Logit (fixed effects)
Mental health problems decrease the probability of owning risky assets by up to 19%.;
Single females with psychological disorders are more likely to hold safe assets;
Presence of cognitive functioning difficulties leads to an increase in financial assets allocated to retirement accounts. Bressan, Pace & Pelizzon, 2014 Understand the link between health and portfolio choice. SHARE (11 European countries) Precautionary saving motive, the change in marginal utility, life span, planning horizon
Tobit, Probit There is a significant negative
relationship between
self-perceived health and portfolio choice.
27 Table A2. Overview of the variables incorporated into the analysis.
Variable Composition Description
Share of risky assets b2b, b3b, b4b, b6b, b7b, b8b, b12b-b25b
The ratio of risky assets (b12b, b14b) to total assets. Ownership of risky
assets
b12b, b14b Dummy variable, taking the value of 1 if the respondent
owns stocks (b12b) or mutual funds (b14b).
Self-perceived gez3 Self-reported health condition. The scale from 1 to 5,
where 1 – “excellent” and 5 – “poor”.
Long illnesses gez5 Dummy variable, taking the value of 1 if the respondent
suffers from a long illness, disorder, or handicap.
Smoking gez7 Dummy variable, taking the value of 1 if the respondent
smokes.
Drinking gez9 Dummy variable, taking the value of 1 if the respondent
drinks more than four alcoholic drinks a day.
Age gebjaar The age of the respondents computed as the year of the
questionnaire minus the birth year.
Age2 – Age squared.
Male geslaacht Dummy variable, taking the value of 1 if the respondent
is male.
Number of children aantalki Number of children in the household.
Couple burgst Dummy variable, taking the value of 1 if the respondent
is married or live with a partner.
Education scholing, oplmet Level of education completed. The scale from 1 to 7,
where 1 – “(continued) special education” and 7 –
“university”.
Risk preference beschryf Investment risk taken over the past few years. The scale
from 1 to 5, where 1 – “I have taken no risk at all” and 5 – “I have often taken great risks”.
Time horizon periode1 Time horizon considered when planning expenditures
and savings. The scale from 1 to 5, where 1 – “the next
couple of months” and 5 – “more than 10 years from now”.
Log net income ntot Natural logarithm of the net income.
Log financial assets b2b, b3b, b4b, b6b, b7b, b8b, b12b-b18b, b24b, b25b
Natural logarithm of the financial assets computed as the sum of the corresponding asset categories.
Log nonfinancial
assets
b19hyb, b19ogb, b20b, b21b, b22b, b23b
Natural logarithm of the nonfinancial assets computed as the sum of the corresponding asset categories.
Liabilities s1, s2, s3, s4, s5, s6 Total liabilities computed as the sum of the
corresponding debt categories.
Liabilities2 – Liabilities squared.
28 Table A3. Correlation matrix of the independent variables.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (1) Self-perceived 1.000 (2) Long illnesses 0.459 1.000 (3) Smoking 0.036 -0.009 1.000 (4) Drinking 0.017 -0.010 0.144 1.000 (5) Age 0.178 0.186 -0.140 0.038 1.000 (6) Age2 0.175 0.184 -0.153 0.025 0.985 1.000 (7) Male -0.050 -0.033 -0.009 0.121 0.123 0.125 1.000 (8) Number of children -0.129 -0.140 0.030 -0.046 -0.456 -0.461 -0.038 1.000 (9) Couple -0.058 -0.044 -0.080 -0.014 0.153 0.104 0.068 0.121 1.000 (10) Education -0.085 -0.039 -0.054 0.021 -0.090 -0.107 0.120 -0.021 0.021 1.000 (11) Risk preference -0.022 -0.002 -0.043 0.033 0.006 -0.003 0.192 0.004 -0.002 0.122 1.000 (12) Time horizon -0.032 -0.021 -0.048 -0.007 0.033 0.020 0.034 -0.012 0.048 0.091 0.138 1.000
(13) Log net income 0.007 0.015 -0.050 0.052 0.192 0.168 0.343 -0.208 -0.001 0.287 0.155 0.094 1.000
(14) Log fin asset -0.026 -0.002 -0.107 0.018 0.148 0.139 0.197 -0.139 -0.002 0.227 0.218 0.150 0.373 1.000
(15) Log nonfinancial asset -0.020 0.003 -0.068 0.029 0.201 0.172 0.236 -0.083 0.154 0.148 0.123 0.084 0.311 0.402 1.000
(16) Liabilities -0.004 -0.006 0.006 0.016 -0.048 -0.049 0.025 0.002 -0.005 0.042 0.025 -0.013 0.041 0.013 0.021 1.000
29 Table A4. Multicollinearity diagnostics, Variance inflation factor (VIF).
Variables VIF SQRT VIF Tolerance R-Squared
Self-perceived 1.24 1.12 0.8039 0.1961 Long illnesses 1.25 1.12 0.8030 0.197 Smoking 1.08 1.04 0.93 0.07 Drinking 1.05 1.03 0.9516 0.0484 Age 48.36 6.95 0.0207 0.9793 Age2 48.59 6.97 0.0206 0.9794 Male 1.29 1.14 0.7741 0.2259 Number of children 1.34 1.16 0.7471 0.2529 Couple 1.12 1.06 0.8916 0.1084 Education 1.13 1.06 0.8886 0.1114 Risk preference 1.1 1.1 0.9065 0.0935 Time horizon 1.05 1.02 0.9535 0.0465
Log net income 1.35 1.16 0.7432 0.2568
Log financial assets 1.38 1.17 0.7249 0.2751
Log nonfinancial assets 1.27 1.13 0.7875 0.2125
Liabilities 3.06 1.75 0.3269 0.6731
Liabilities2 3.04 1.74 0.3289 0.6711
Mean VIF 6.98
Table A5. The impact of health status on Risk preference, Time horizon, Log net income, Log financial assets and Log nonfinancial assets (Pooled OLS).
Variables Risk preference Time horizon Log net income Log financial assets Log nonfinancial assets
