Abstract
We examine the effect of financial asset allocation and asset liquidity on individuals’ health. Earlier literature finds empirical evidence and provides theoretical justification of the impact of health on financial decisions but speculates that reverse causality is unlikely. Through panel data methods and an instrumental variable approach, we refute this claim and establish a causal effect of financial choices on physical and mental health outcomes. Our findings suggest that accounting for endogeneity changes the results from a basic specification. Stock holdings no longer significantly affect health while ownership of time accounts and retirement accounts have a strong positive effect on health outcomes. An exploration of the channels driving these effects provides confidence that the potential stress caused by the risk level of financial assets as categorized by the literature is not the primary driver of health outcomes. However, the findings support the time preference channel, i.e. willingness to forego financial satisfactions today in return for greater financial well-being in the future causes beneficial physical and mental health outcomes. There is also some support for the allostatic load hypothesis as well as a dopamine substitutability hypothesis.
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Notes
Males, younger and taller people are more risk loving.
They do not claim causality.
Rosen et al. (2004) study how health status affects the likelihood of owning different types of assets in an individual’s portfolio and the share of financial wealth allocated to each asset category using 4 waves of the Health and Retirement Study (HRS). For single individuals, they find that being in poor health has a significant, negative effect on the likelihood of owning a retirement account, bonds and risky assets. The respective reductions are 2.1, 0.2 and 1.7 percentage points.
Guiso et al. (1996) find that (Italian) investors in worse health hold fewer risky assets.
However, Coile and Milligan (2006) do not find a significant effect of health on the share of risky assets.
Much earlier, Arrow (1965) first made the DARA (decreasing absolute risk aversion) assumption that risk aversion decreases with wealth.
Using the Survey of Health, Aging, and Retirement in Europe (SHARE), Courbage et al. (2018) examine the effect of a change in individual’s wealth and health and the presence of a financial and health risk on financial risk aversion.
The presence of background financial or health risk is related to an increase in risk aversion i.e., individuals exhibit risk vulnerability and cross-risk vulnerability. When financial wealth is instrumented, the effect of wealth on financial risk aversion increases (in absolute value) from −0.047 to −0.057 (and is still significant). When financial background risk is instrumented, its effect on risk aversion increases from 0.024 to 0.157. The IV approach confirms the DARA hypothesis and the convexity of risk aversion with respect to wealth (Courbage et al. 2018).
Less relevant articles examine the effect of health on risk preferences but use a self-reported risk tolerance measure, or alternatively study the effect of other factors (different from health status on asset allocation. An example of the former is the study conducted by Kumar (2006) while an example of the latter is the research of Hong et al. (2004).
Authors often use the share of financial wealth invested in risky assets (stocks) with financial risk preferences.
In addition, for married couples, a health shock to the wife reduces financial assets nearly 3 times more than it reduces non-financial assets (Berkowitz and Qui 2006).
Examples of unobserved factors which might be correlated with both health and portfolio choice include risk aversion, information networks, impatience, expected longevity, family values regarding health and finances, etc. (Smith and Love 2007).
In contrast, his OLS results confirm the positive significant cross-sectional correlation between health (x) and total assets (y), non-financial assets (y), share of financial assets (y) and share of risky assets (y) based on the US New Beneficiary Survey (NBS) conducted in 1982 and 1991 (Fan and Zhao 2009).
Focusing only on elderly individuals, Sahm (2012) uses a correlated RE model and HRS data, and also finds no conclusive effect of health shocks (measured by the onset of a severe condition) on variations in risk attitudes among those people.
Coile and Milligan (2009) find evidence from the HRS that health shocks explain part of the changes in households’ portfolios over time.
Bressan et al. (2014) do not find evidence of the life span channel, but health continues to have a negative effect on investments.
Shorter horizons reduce the present value of the expected annuity income and thus incentivize households to make safer portfolio choices.
People fear potential health shocks which increase their medical spending, and thus lead to lower financial wealth. For instance, Guiso et al. (1996) use data from the Survey of Household Income and Wealth, and find that individuals who spend more days sick tend to hold safer assets.
Bressan et al. (2014) find a significant positive effect of owning a health insurance on portfolio choice.
Health shocks affect out-of-pocket medical expenses which in turn might affect economic decisions (Smith and Love 2007).
Finkelstein et al. (2008) use data from HRS and find that health deterioration is associated with a significant decline in the MU of consumption. Cardak and Wilkins (2009) and Rosen and Wu (2004) both find evidence that poor health has a significant, negative effect on the share of risky assets in financial portfolios.
Bressan et al. (2014) do not find evidence of the MU of consumption channel.
Health shocks can change the marginal utility of consumption and therefore households’ risk tolerance. Literature finds that risk aversion increases with age (Dohmen et al. 2018; Schurer 2015), childbirth (Görlitz et al. 2015), openness, agreeableness and health shocks (Jones et al. 2018), and decreases with cognitive ability (Dohmen et al. 2018).
They use movements in stock market prices as an exogenous variation in the economic environment.
Financial literacy is likely to affect financial outcomes. Even though not directly related to our choice of variables and financial outcomes, Brown et al. (2016) find that mathematics (quantitative) and financial literacy training decreases the likelihood of holding non-student debt and improve debt repayment behavior of young individuals between 19 and 29 years old, but economics education increases the likelihood of holding debt and repayment difficulties.
Other authors have also used other measures of individuals’ health condition. For instance, Smith and Love (2007) have used the number of diagnosed conditions; Edwards (2008) and Fan and Zhao (2009) have used a binary variable indicating whether an individual can perform daily activities (likelihood of health limiting the individual’s work activity) to measure health risk; Bressan et al. (2014) have used the number of limitations in activities of daily living. As a measure of a new health shock, Berkowitz and Qui (2006) use information on new severe health conditions (heart problem, stroke, cancer, malignant tumor, lung disease, diabetes) reported between 2 waves. Coile and Milligan (2006) use chronic health shocks to measure health status. Jones et al. (2018) separate acute health shocks (cancer, stroke, heart problems) (a dummy equal to 1 in all waves following the shock, and 0 before) and chronic health shocks (lung problems, diabetes, high blood pressure, arthritis, psychological problem) (a dummy equal to 1 in all waves following the shock). One of the measures of health which Courbage et al. (2018) use is based on the questions asking individuals whether they have ever been diagnosed with a series of diseases.
Bressan et al. (2014) distinguish between direct stock ownership (equals 1 if a household has invested any amount in stocks) and total stock ownership (equals 1 if the household has invested into stocks, or mutual funds, or individual investment accounts).
Slightly differently, Rosen et al. (2004) and Berkowitz and Qui (2006) split bonds and retirement accounts, and thus collapse assets into four categories: safe assets (checking and saving accounts, money market funds, T-bills, government savings bonds, CDs), bonds (corporate, bond funds, municipal and foreign bonds), risky assets (stocks, mutual bonds) and retirement accounts (IRAs, Keoghs). Fan and Zhao (2009) separate financial assets into 4 categories: safe assets (checking and saving accounts, money market funds, CDs, government savings bonds and T-bills), bonds (corporate and municipal and foreign bonds and bond funds), risky assets (stocks and mutual funds), and retirement accounts (individual retirement account and Keogh accounts). However, they use a different dataset (NBS).
Two alternative measures of portfolio allocation are an indicator for holding any asset (equal to 1 if the household owns any asset regardless of its risk level, and zero otherwise), and the share of total financial assets allocated to stocks i.e., risky portfolio share, or share of risky asset holdings in the portfolio.
Literature typically counts bonds and retirement accounts as medium risk assets while checking, saving and money market accounts, and time accounts and T-bills belong to the category of safe assets. While time accounts are arguably safe, the degree of risk presented by bonds is less clear. In addition, while individuals have to personally open a CD (and thus will definitely be aware of its existence), some people might hold bonds without knowing it. The latter possibility is very likely given that only 6% of the respondents have reported ownership of a bond as compared to percentages in the range from 20% to almost 80% for the other types of assets.
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Milovanska-Farrington, S., Farrington, S. Discipline, risk, and the endogeneity between financial decisionmaking and health. J Econ Finan 45, 596–636 (2021). https://doi.org/10.1007/s12197-021-09542-y
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DOI: https://doi.org/10.1007/s12197-021-09542-y