Precautionary savings against health risks: evidence from the Health and Retirement Study.

Article

Precautionary Savings Against Health Risks: Evidence From the Health and Retirement Study

Research on Aging 2014, Vol. 36(2) 180-206 ª The Author(s) 2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0164027512473487 roa.sagepub.com

Tansel Yilmazer1 and Robert L. Scharff1

Abstract The precautionary savings model predicts that households accumulate wealth to self-insure against unexpected declines in future income and unforeseen expenditures. The goals of this study are twofold. First, we investigate whether the near-elderly who face higher health risks save more. Second, we examine the factors that contribute to health risks that the nearelderly face. We use data from the Health and Retirement Study to construct two measures of health risks. Our results do not support the hypothesis that household savings increase with the health risks that they face. Individuals who confront higher health risks in the future are those who are already in fair or poor health status or those who have a health condition such as diabetes or lung disease. Lower earnings and high medical expenditures caused by current poor health status prevent households from accumulating savings for future health adversities. Keywords health, wealth, aging

1

Ohio State University, Columbus, OH, USA

Corresponding Author: Tansel Yilmazer, The Ohio State University, 1787 Neil Avenue, 265E Campbell Hall, Columbus, OH 43210, USA. Email: [email protected]

Downloaded from roa.sagepub.com at Freie Universitaet Berlin on May 3, 2015

Yilmazer and Scharff

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Introduction The life-cycle framework predicts that individuals smooth the marginal utility of consumption over time though their savings (Browning & Crossley, 2001). Individuals accumulate wealth during their working life and then dissave their wealth after retirement. As an extension to the traditional life-cycle framework, the theory of precautionary saving posits that individuals save in advance for anticipated intertemporal income changes, but also to insure consumption against unanticipated expenses or income variations. Theoretical models of saving have recognized the role of precautionary motives on household savings (Browning & Lusardi, 1996; Carroll, 1997; Kennickell & Lusardi, 2006; Kimball, 1990; Kotlikoff, 1989; Palumbo, 1999; Skinner, 1988; Zeldes, 1989, among others). The underlying premise of precautionary saving models is that a combination of a positive third derivative of the utility function1 and uncertainty about future income or expenses causes households to reduce consumption and raise savings (Leland, 1968; Sandmo, 1970). Empirical studies designed to test the theory of precautionary saving often focus on savings made to blunt the impact of income shocks. While it has been recognized that health adversities and high medical spending affect household resources, empirical studies examining the relationship between health risks and precautionary savings are not complete. Understanding the size of savings accumulated to self-insure against the risk of future health expenditures is an important step toward improving the welfare of the near-elderly and the elderly, and it has important public policy and macroeconomic implications. First, near-elderly and elderly individuals are faced with the risk of high medical spending. While Medicare provides nearly universal coverage to those older than age 65, it also requires substantial cost-sharing from most beneficiaries.2 In 2010, 16% of individuals between ages of 50 and 64 were uninsured (Schoen, Doty, Robertson, & Collins, 2011). In addition, 17% of this group was underinsured, meaning that they spent at least 10% of their income on out-of-pocket expenses. If these uninsured and underinsured households do not have enough savings, they are most likely to go without needed medical care or incur medical debt when they become ill. High medical expenses are shown to be the contributing factor to half of the personal bankruptcies in 2007 (Himmelstein, Thorne, Warren, & Woolhandler, 2009). Households with lower savings are the most vulnerable against high medical bills. From a public policy perspective, savings caused by other motives, such as saving for retirement and bequests, respond differently to the changes in tax and other transfer polices than savings caused by precautionary motives.


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Research on Aging 36(2)

In addition, the continuous decline in savings rates in the United States has been suggested to be caused by the increases in the coverage of various types of social insurance such as Medicare and disability insurance. Therefore, from a macroeconomic perspective, it is important to understand whether the increase in the coverage of various types of insurance has diminished the need for accumulating savings. The goals of this study are twofold. First, we investigate whether households with higher health risks accumulate wealth to create a self-insurance cushion against future health adversities. Second, we aim to understand the factors affecting the likelihood that near-elderly individuals experience a health problem. Using data from the Health and Retirement Study (HRS), we construct two measures of health risk based on experienced health problems and respondents’ self-reported predictions of having health problems.3 Our first measure exploits the longitudinal nature of the HRS and uses information from five survey waves (1994–2002) to assess whether the respondents had experienced a health problem. We define the near-elderly as individuals between age 51 and 61. We follow the health status of husbands and wives in households headed by a near-elderly individual for 10 years and measure whether the members of the household actually experienced a health problem. Our second measure uses the self-reported probability of having future health problems. The 1992 wave of the HRS includes a question specifically inquiring about the likelihood of health problems in the next 10 years. In the HRS, most data are collected for both spouses, allowing us to calculate health risks for husbands and wives separately. Our findings provide no evidence that savings increase with health risks. This result is consistent for health risks affecting both husbands and wives. Our results also show that individuals who have high health risks are those who are already in fair or poor health status or those who already suffer from a health condition such as diabetes or lung disease. Individuals are good at the predicting whether they will actually experience a health problem in the future. Among husbands who predict a 50% probability of having health problems in the next 10 years, 40% had actual health problems between 1994 and 2002. Our explanation for our findings is that households who are expected to experience future health problems have lower levels of savings because of current lower earnings and high medical expenditures due to existing health conditions. The actual observations of experienced health problems show that future health problems are highly correlated with the current health status and health conditions. Our goal is to understand whether an individual’s own perception of health risks affects the saving behavior. Our analysis



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shows that individuals are consistently aware of the health risks that they face, but still do not or cannot accumulate savings. We acknowledge that unobserved characteristics that increase health risks may be correlated with savings. For example, a risk-averse person may invest in her health by abstaining from risky health behaviors (such as smoking), by having annual checkups, and also by choosing to self-insure for unexpected medical expenditures by accumulating savings. After we discuss the potential issues related to endogeneity and sample selection below, we present our findings from a two-stage least squares estimation. The results of the twostage estimation are consistent with our main findings. The remainder of this article is structured as follows. The second section reviews literature on the relationship between health risks and precautionary savings. The third section constructs the empirical framework, and the fourth section describes the data. We present descriptive statistics in the fifth section. Sixth section presents the results of the empirical analysis. Following, the seventh section investigates the factors that increase the likelihood of experiencing health problems. The issues related endogeneity, sample selection, and justification bias are discussed in the eighth section. Finally, concluding remarks and policy implications are provided in the ninth section.

Related Literature Empirical studies examining the effects of future medical expenses on savings provide mixed evidence of precautionary savings. The premise behind each of these studies, however, is the same. A reduction in the variation of future health expenses should be associated with lower household savings. The principal difference between existing studies is the measure of risk that they use. Measures of risk used so far include private medical insurance (Starr-McCluer, 1996), Medicaid eligibility (Gruber & Yelowitz, 1999), exogenous changes in health care coverage due to reforms (Chou, Liu, & Hammit, 2003), and variations in service quality within a given system (Atella, Rosati, & Rossi, 2006; Guariglia & Rossi, 2004; Jappelli, Pistaferri & Weber, 2007).4 Contrary to expectations, Starr-McCluer (1996) finds a positive effect of private health insurance coverage on wealth holdings, even after controlling for potential selection effects which results from highly risk-averse individuals’ elevated propensity to purchase private health insurance and to accumulate wealth to self-insure. On the other hand, studies that examine precautionary savings in the context of health care reforms (in Italy and Taiwan) find a significant precautionary saving motive as a response to


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exogenously imposed health risks. For example, using a natural experiment created by Taiwan’s implementation of National Health Insurance, Chou, Liu, and Hammit (2003) find a reduction in household savings as a response to reduction in risk (increase in insurance coverage). Similarly, Gruber and Yelowitz (1999) find support for the precautionary savings motive using the variation in Medicaid eligibility provided by the program’s expansion over the 1984–1993 period. Their findings show that a negative relationship between the generosity of Medicaid entitlement and household asset holdings. Kennickell and Lusardi (2006) use the state-specific level of out-ofpocket health costs to proxy for health care expenses (health risk), and they also use more individual-specific information regarding whether respondents foresee expenses for health care in the next 5–10 years. In their study, Kennickell and Lusardi investigate the importance of precautionary saving motive using a direct question on the desired amount of precautionary wealth. Their findings show that while the state-specific level of out-ofpocket health costs is not significant, foreseen future health expenditures increase the desired amount of precautionary wealth. In addition, relative to saving for other types of risk, saving for health risks results in the largest amount of precautionary savings. Studies examining precautionary savings have questioned the validity of the risk measures used in previous research. The most common concern is that the measures of risk used in previous studies do not account for the knowledge that only the individual has about his or her own health condition, which cannot be observed by the researcher. Our study contributes to the empirical literature using two measures of health risk that are less susceptible to this type of criticism. Our first measure allows us to control for the observed health condition following the saving decisions made by the household. Our second measure uses the self-reported probability of having future health problems. This minimizes the measurement error by relying on probabilities provided by the respondents. Finally, most research on precautionary saving focuses exclusively on income shocks. Our study extends the current literature by exploring whether households accumulate wealth to insure against the risk of large medical expenditures.

Empirical Model The precautionary savings model is an extension of the basic intertemporal optimization model. The basic intuition of the life-cycle/permanent income framework is that household savings depend on permanent income. Saving



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is assumed to be high (low) when income is high (low), relative to its average. The precautionary saving model extends the life-cycle framework by introducing uncertainty about future resources. We test the predictions of the model using the following reduced-form equation: Ai 0 ¼ b0 þ b1 Ri þ Xi b2 þ ei ; Yip

ð1Þ

where Ai is the household wealth, Ri is the health risk, Yip is the permanent income, and Xi is a set of control variables for wealth including the demographics for the husband and wife (age, race, education), the number of children, household size, bequest motive (expectation to leave a large bequest), the length of financial planning horizon, the level of risk aversion (reported by the head of the household), and the couple’s health and employment status. By dividing household wealth (Ai ) by permanent income (Yip ), we estimate the interaction between control variables and permanent income. For example, households headed by more educated individuals might save a higher proportion of their permanent income. This reduced-form equation with similar control variables are utilized in Jappelli, Pistaferri, and Weber (2007), King and Dicks-Mireaux (1982), and Starr-McCluer (1996). The coefficient of Ri (b1 ) measures the effect of health risk on the ratio of household wealth to permanent income. We hypothesize that the coefficient of Ri in Equation 1 is positive. Note that Equation 1 does not make any specific assumptions about liquidity constraints. However, it has been shown that the introduction of a liquidity constraint increases the precautionary saving motive (Carroll & Kimball, 2001). We create measures of wealth, permanent income and health risk, and estimate the equation above by the Ordinary Least Squares (OLS) and median regressions.

Data We use data from the HRS to conduct the empirical analysis. The HRS is a national longitudinal study of older Americans. It includes comprehensive information on the health status of individuals and the financial status of households including assets, liabilities, and income. The HRS began in 1992 and interviewed a nationally representative sample of more than 12,600 persons in 7,600 households with a member between ages 51 and 61. The HRS has been repeated every 2 years since 1992, and we use data from the 1992, 1994, 1996, 1998, 2000, and 2002 HRS. A description of the


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HRS data is provided in Heeringa and Connor (1995) and Juster and Suzman (1995).5 Our sample includes couples who were initially interviewed in 1992 and remained together from 1992 to 2002. This excludes couples who were in the 1992 HRS but divorced or separated between 1992 and 2002, and it also excludes households where one of the spouses died in the interim period.6 We do not distinguish between married or cohabitating couples. The number of couples in our sample is 2,703. We use two measures of health risk. First, we measure health risk as being in a health status that limits work activities. We utilize the panel feature of the HRS to calculate our first health risk measure. One of the questions on current health within the HRS asks each individual to describe whether his or her health limits work activities: ‘‘Now I want to ask how your health affects paid work activities. Do you have any impairment or health problem that limits the kind or amount of paid work you can do?’’ Regardless of the work status, respondents replied to this question. The respondents’ answer at each survey wave from 1994 to 2002 is converted to a variable from h ¼ 1 (yes) to h ¼ 0 (no). We convert the experienced health problems during the 10 years into a probability. We create an average by summing up the indicator variables for five surveys and dividing the sum by 5. We assume that the average equals the probability of being in a health status that limits work activities is Prðh ¼ 1Þ: We check the robustness for our results by creating different versions of this measure. For example, we assumed Prðh ¼ 1Þ ¼ 1 if the respondent experienced a health problem in any of the five surveys and 0 otherwise. In another version, we created a weighted average by assigning decreasing weights to the survey waves from 1994 to 2002. Here, the assumption is that a health problem experienced later would allow the household be financially prepared for the catastrophic event than a health problem experienced earlier. Our findings are robust to the use different versions of the first measure. For our second measure, we utilize the self-reported probability of having a health problem that limits work activities. The 1992 HRS asks, ‘‘What are the chances that your health will limit your work activity during the next 10 years?’’ measured on a 0–10 scale, where 0 equals absolutely no chance and 10 equals absolutely certain. This question is answered by respondents who were working at the time of the 1992 survey. After scaling from 0 to 1, responses are interpreted as the subjective probability of the event. We describe Prðs ¼ 1Þ as the self-reported probability of having a health problem. Both measures are utilized as measures of health risk (Ri ) in our empirical analysis. We calculate both measures of health risks for husbands and wives.



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We use two measures of wealth (financial net worth and total net worth). Financial net worth is defined as the sum of checking, saving accounts, bonds, stocks, Individual Retirement Accounts (IRA), Keoghs, and other assets, minus short-term debt. Total net worth is obtained by adding financial net worth to the sum of home equity, business equity, and net value of vehicles. Finally, we construct a measure of permanent income by regressing household income in 1992 on demographic characteristics including age, education, and race of spouses, all of which are interacted with age, and occupation dummies and employment status. The predictions from this regression are used as a proxy for permanent income. Similar measures of permanent income are used by Lusardi (1998) and King and Dicks-Mireaux (1982).

Descriptive Statistics Table 1 provides the description and summary statistics of health-related variables for husbands and wives. First, the percentage of husbands and wives who report that they have health problems are approximately same in 1992, around 16%. Second, the percentages of those who report a health problem increase for both husbands and wives by approximately 2 percentage points each wave of the survey, from 1992 to 2002. Between 1994 and 2002, almost 37 and 32% of husbands and wives experienced a health problem, respectively. Interestingly, the average self-reported probabilities of having health problems for husbands and wives are 39.3 and 35.9%, respectively.7 There is a strong correlation between the respondent’s prediction of health problems and actual health problems that he or she experienced during 1994–2002 (not shown in Table 1). Among husbands who predicted 50% probability of having health problems, 40% had actual health problems between 1994 and 2002. At the same time, 70% of husbands who predicted 100% probability of having health problems had actual health problems. This ratio drops to 25% among husbands who predicted zero probability of having health problems. In our sample, 24.2% of husbands and 37.9% of wives did not report a probability of health problems because they were not working at the time of the interview. Current health status is measured by current health conditions including high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, psychiatric problems, and arthritis. The respondent’s answer to the question, ‘‘Has the doctor ever told you whether you have the following condition?’’ is turned into a dummy variable that equals one for affirmative answers. In 1992, the most common health conditions for husbands are high blood pressure (32.1%) and arthritis (27.0%), and the least common health conditions


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Table 1. Definitions of Health-Related Variables and Descriptive Statistics. Variable

Definition

Husband

Wife

Health problems that limit work activities N ¼ 2,703 N ¼ 2,703 Health Problems_92 ¼1 if health problems limit work in 0.159 0.153 1992; otherwise ¼ 0 Health Problems_94 ¼1 if health problems limit work in 0.189 0.181 1994; otherwise ¼0 Health Problems_96 ¼1 if health problems limit work in 0.215 0.203 1996; otherwise ¼0 Health Problems_98 ¼1 if health problems limit work in 0.224 0.211 1998; otherwise ¼0 Health Problems_00 ¼1 if health problems limit work in 0.229 0.221 2000; otherwise ¼0 Health Problems_02 ¼1 if health problems limit work in 0.259 0.237 2002; otherwise ¼0 0.223 0.211 Average of the indicator variables First measure: measuring health problems Experienced health from 1994 to 2002 problems from 1994 to 2002. 0.386 0.354 Self-reported probability of Second measure: Selfhealth problems that limit reported probability of work activities in the next 10 health problems year in 1992 Health conditions in 1992 High blood pressure ¼1 if the respondent ever had high 0.317 0.245 blood pressure; ¼0 otherwise Diabetes ¼1 if the respondent ever had 0.069 0.049 diabetes; ¼0 otherwise Cancer ¼1 if the respondent ever had 0.025 0.057 cancer; ¼0 otherwise Lung disease ¼1 if the respondent ever had lung 0.034 0.032 disease; ¼0 otherwise 0.115 0.049 Heart disease ¼1 if the respondent ever had a heart disease; ¼0 otherwise Stroke ¼1 if the respondent ever had a 0.022 0.011 stroke; ¼0 otherwise 0.029 0.066 Psychiatric problem ¼1 if the respondent ever had a psychiatric problem; ¼0 otherwise Arthritis ¼1 if the respondent ever had 0.277 0.336 arthritis; ¼0 otherwise (continued)



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Table 1. (continued) Variable

Definition

Self-reported health status in 1992 Excellent health ¼1 if the respondent reports excellent health; ¼0 otherwise Very good health ¼1 if the respondent reports very good health; ¼0 otherwise Good Health ¼1 if the respondent reports good health; ¼0 otherwise Fair health ¼1 if the respondent reports fair health; ¼0 otherwise Poor health ¼1 if the respondent reports poor health; ¼0 otherwise Hospital stays the number of nights stayed in hospital during 1992 Doctor visits the number of doctor visits during 1992 Smoker ¼1 if the respondent is a smoker; ¼0 otherwise Obese ¼1 if the respondent’s BMI is greater than 30; ¼0 otherwise

Husband

Wife

0.266

0.288

0.321

0.342

0.281

0.244

0.094

0.095

0.039

0.031

0.74

0.53

3.31

4.26

0.211

0.209

0.202

0.201

Note. HRS ¼ Health and Retirement Study. Data are drawn from the 1992, 1994, 1998, 2000, and 2002 HRS. The summary statistics are weighted using household weights.

for husbands are stroke (2.1%), cancer (2.7%), and psychiatric problems (2.8%). Among wives, high blood pressure and heart disease are less common (25.8 and 5.4%, respectively), and arthritis and psychiatric problems are more common (34.0 and 6.8%, respectively). We also measure the self-reported health status variable using the answer to the following question: ‘‘Would you say your health in general is excellent, very good, good, fair, or poor?’’ The HRS codes the answers to this question on a 1–5 scale, with 1 representing excellent health and 5 representing poor health. Again, the distribution of self-reported health status among husbands and wives are similar. Among husbands (wives), while 25.2% (27.0) respond that they are in excellent health in 1992, 4.3% (3.7) respond that they are in poor health. Wives visit the doctor’s office more frequently than their husbands (4.25 vs. 3.28 times in the previous 12 months). The definitions and summary statistics of the nonhealth-related variables are reported in Table 2.


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Results Table 3 presents the distribution of two measures of wealth (financial net worth and total net worth) that are used in the empirical analysis. Table 3 also presents the ratios of financial net worth to permanent income and total net worth to permanent income. According to our calculations, the average permanent income is $46,479.8 Both financial net worth and total net worth have skewed distributions, where the medians are well below the means. The median of the financial net worth and total net worth are $23,000 and $128,095 in 1992, while the means are $76,150 and $227,245, respectively. Similarly, the median of the ratios of the financial net worth to permanent income and total net worth to permanent income show that the household in the median of the distribution has half of its permanent income saved in financial assets and 3 times of their permanent income saved in total net worth. Table 4 presents the OLS regression results for the ratios of wealth measures to permanent income. For each ratio of wealth measure (financial net worth and total net worth) to permanent income, we estimate two regressions, one for each of the health risks (Models I–IV). Sample sizes vary across estimations because the self-reported probability of having health problems used in Models II and IV is only reported by the respondents who were working at the time of the interview. For both husbands and wives, Models I and III include 2,599 observations since there is some missing information on health problems across years. In these 2,599 households, 2,048 husbands and 1,676 wives were working at the time of survey in 1992. We include the measures for health risks for husbands and wives in Models I and III. We estimate Models II and IV separately for husbands and wives because the sample size reduces significantly when we include both health risks for husbands and wives. We present the estimation results for only husbands in Table 4 for Models II and IV, but the results are robust to using health risks for only wives. The sample size in Models II and IV in Table 4 is 2,048. The coefficients for both measures of health risks in Models I–IV are mostly negative but insignificant, showing that neither of the ratios of financial net worth to permanent income and total net worth to permanent income increase with health risks. In terms of other control variables, the ratios of both financial net worth to permanent income and total net worth to permanent income increase with husband’s age and couple’s education levels, while the ratios decrease by the number of children. Households where the husbands are Black and Hispanic, as well as households that have a shorter planning horizon and no bequest motive, have lower



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Table 2. Definitions of Nonhealth-Related Control Variables and Descriptive Statistics. Mean Husband age Wife age Husband race White Black Other Wife race White Black Other Husband Hispanic Wife Hispanic Education Husband no high school Husband high school Husband college graduate Husband graduate degree Wife no high school Wife high school Wife college graduate Wife graduate degree Children Household size Bequest motive Plan 5 years Least risk averse Employment status Husband working full time Husband unemployed Husband retired Husband other Wife working full time Wife unemployed Wife retired Wife other

Age of the husband Age of the wife

56.8 53.0

¼1 if the husband is White; ¼0 otherwise ¼1 if the husband is Black; ¼0 otherwise ¼1 if the husband is other race; ¼0 otherwise

0.919 0.052 0.029

¼1 if ¼1 if ¼1 if ¼1 if ¼1 if

0.921 0.051 0.028 0.048 0.049

the the the the the

wife is White; ¼0 otherwise wife is Black; ¼0 otherwise wife is other race; ¼0 otherwise husband is Hispanic; ¼0 otherwise wife is Hispanic; ¼0 otherwise

¼1 if the husband did not complete high school; ¼0 otherwise

0.198

¼1 if the husband completed high school; ¼0 otherwise

0.532

¼1 if the husband completed college; ¼0 otherwise

0.145

¼1 if the husband has a graduate degree; ¼0 otherwise

0.125

¼1 if the wife did not complete high school; ¼0 otherwise

0.165

¼1 if the wife completed high school; ¼0 otherwise ¼1 if the wife completed college; ¼0 otherwise

0.663 0.108

¼1 if the wife has a graduate degree; ¼0 otherwise

0.064

Number of children Number of people in the household ¼1 if the husband’s expectation to leave a large bequest is definitely or probably; ¼0 otherwise ¼1 if the household has a financial planning horizon of five years or longer; ¼0 otherwise ¼1 if the respondent is in the least risk-averse category (based on income gamble questions); ¼ 0 otherwise

3.4 2.9 0.311

¼ if the husband works full-time; ¼0 otherwise

0.689

¼ if the husband is unemployed; ¼0 otherwise

0.022

¼ if the husband is retired; ¼0 otherwise ¼if the husband works part-time, is disabled, or not in labor force ¼ if the wife works full-time; ¼0 otherwise

0.226 0.063 0.427

¼ if the wife is unemployed; ¼0 otherwise ¼ if the wife is retired; ¼0 otherwise ¼ if the wife works part-time, is disabled, or not in labor force

0.018 0.139 0.416

Note. HRS ¼ Health and Retirement Study. Data are taken from the 1992 HRS. The summary statistics are weighted using household weights.


0.403 0.120

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Table 3. Distribution of Financial and Total Net Worth. Financial Total Percentile net worth net worth

Financial net worth/ permanent income

Total net worth/ permanent income

4,800 1,000 1,800 23,000 84,000 213,036 352,000

3,100 16,800 55,000 128,095 270,500 535,000 816,000

0.156 0.036 0.048 0.495 1.727 4.722 7.381

0.115 0.452 1.344 2.921 5.902 12.381 19.589

76,150

227,245

1.628

5.417

5 10 25 50 75 90 95 Average

Note. HRS ¼ Health and Retirement Study. Data are drawn from the 1992 HRS. Permanent income measure is constructed by regressing log of household income on household characteristics including age, education and race of spouses, all of which are interacted with age and occupation dummies and employment status. The percentiles are calculated using unweighted data.

assets compared to their permanent income. Compared to households where the husband and wife are in excellent health, those in good, fair, and poor health have lower ratios of financial and total wealth to permanent income. The negative significant coefficient for permanent income shows that the ratios of financial net worth and total net worth to permanent income decrease as permanent income increases. Finally, households with retired husbands and wives have higher financial assets compared to their permanent income. The estimated permanent income is higher for households where the husband is in labor force or retired compared to households where the husband is out of the labor force. Therefore, the financial ratio for total net worth is lower for the households where the husband is in labor force or retired. We also estimate the models in Table 4 with a median regression. Median regression reduces the sensitivity of the results to outliers that have high holdings of financial net worth and total net worth. Using median regression, we avoid having to throw out outlier observations on the basis of our subjective judgment about the validity of the data. The results are not reported for brevity. The coefficient estimates of median regressions are smaller than OLS, but their significance levels are similar to OLS estimates in Table 4. We find that the ratios of financial and total net worth to permanent income do not increase with an increase in health risks of husbands and wives. We also investigate whether the initial health status might play a major role on precautionary saving against health risks.9 We create a sample of


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First measure: Husband health risk Second measure: Wife health risk Second measure: Husband self-reported probability of having health problem Husband age Wife age Husband race (reference group: White) Black Other Husband Hispanic Education (reference group: no high school) Husband high school Husband college graduate Husband graduate degree Wife high school Wife college graduate Wife graduate degree Children Household size Permanent income/10,000 Bequest motive Plan 5 years

Model II

Model III

Model IV

SE

SE

0.075 0.016*** 0.036 0.013***

0.163 0.231

Coeff.

SE

0.149 0.039*** 0.056 0.033*

0.091 0.574 0.836 0.582

Coeff.

SE

0.132 0.042*** 0.054 0.033

0.699 0.608

Coeff.

0.247 0.881 0.808 0.513 0.664 1.116 0.096 0.027 0.211 1.042 0.494

(continued)

0.171 0.153 0.182 0.092 0.441 0.330 0.480 0.273*** 0.597 0.284** 1.209 0.704* 0.987 0.748 0.337** 0.779 0.347** 1.530 0.869* 1.593 0.914* 0.188*** 0.266 0.200 1.006 0.485** 0.778 0.527 0.294** 0.518 0.305* 1.318 0.757* 1.552 0.802* 0.378*** 0.525 0.391 3.641 0.973*** 3.227 1.030*** 0.032*** 0.113 0.033*** 0.242 0.081*** 0.230 0.087*** 0.055 0.009 0.055 0.160 0.141 0.121 0.145 0.063*** 0.120 0.064* 1.144 0.162*** 1.017 0.168*** 0.130*** 0.923 0.133*** 3.687 0.335*** 3.371 0.349*** 0.124*** 0.425 0.127*** 0.613 0.320* 0.271 0.333

1.034 0.218*** 0.665 0.233*** 3.515 0.562*** 2.812 0.612*** 0.348 0.359 0.506 0.371 1.465 0.925 1.760 0.977* 0.541 0.271** 0.274 0.284 1.947 0.696*** 1.768 0.748**

0.044 0.015*** 0.037 0.013***

0.277 0.223 0.512 0.226**

Coeff.

Financial wealth/ Financial wealth/ Total net worth/ Total net worth/ permanent income permanent income permanent income permanent income

Model I

Table 4. The Impact of Health Risks on the Household Wealth (OLS Estimates).

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Model II

Model III

Model IV

Coeff.

SE

0.045 0.503 1.054 1.568 0.011 1.447 2.183 1.373

0.409 0.444 0.659 1.529 0.411 0.451*** 0.630*** 1.082 0.719*** 1.296 0.554** 1.134** 0.714** 0.415 0.967 0.418** 1.166* 0.506** 2.412 1.451 2.564 0.145 2,048

0.411 0.438*** 0.630*** 0.920*** 0.404 0.454*** 0.647*** 0.998**

SE

0.589 0.499

Coeff.

Note: HRS ¼ Health and Retirement Study; OLS ¼ ordinary least squares. Coeff. ¼ Coefficient estimates; SE ¼ Standard errors. Data are drawn from the 1992 HRS. The first measure of health risk uses data from 1994, 1996, 1998, 2000 and 2002 HRS. ***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.

2.120 2.926 1.762 0.701 0.159*** 0.681 1.967 1.202 3.413 0.974*** 1.586 0.143 0.166 2,048 2,599

0.538 0.210**

0.155 0.169** 0.250** 0.581 0.156 0.171* 0.239*** 0.411**

0.627 1.314 1.793 3.196 0.452 1.396 2.320 2.559

SE

0.124 0.393 0.579 0.660 0.159 0.304 0.820 0.933

Coeff. 0.300 0.473

SE

0.060 0.189

Coeff.

Financial wealth/ Financial wealth/ Total net worth/ Total net worth/ permanent income permanent income permanent income permanent income

Model I

Low risk averse 0.077 0.184 Self-reported health status in 1992 (reference group: excellent health) Husband very good health 0.120 0.160 Husband good health 0.359 0.170** Husband fair health 0.631 0.245** Husband Poor health 1.232 0.357*** Wife very good health 0.012 0.157 Wife good health 0.272 0.176 Wife fair health 0.688 0.251*** Wife poor health 0.952 0.388** Employment status (reference group: other) Husband working full time 0.100 0.279 Husband unemployed 0.126 0.440 Husband retired 1.028 0.277*** Wife working full time 0.563 0.161*** Wife unemployed 0.855 0.453* Wife retired 0.735 0.197*** Constant 1.940 0.937** R2 0.190 Number of observations 2,599

Table 4. (continued)


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households where the husbands and wives were in excellent or very good health. The sample size is 1,035 for Models I and III and 874 for Models II and IV. Magnitudes of the coefficient estimates of health risks are larger than the coefficient estimates in Table 4 since the probability of having a health problem is smaller for healthy households. However, the coefficients are still insignificant. We also investigate whether households that are already in poor health have higher savings against future health risks. Of the 2,703 households, 598 have husbands and wives in good, fair, or poor health status. The probability of having a health problem is higher for these households. However, the estimated coefficients in Models I–IV are still insignificant. Our results provide no evidence that savings increase with health risks among healthy or nonhealthy households. Since health problems cause both expenditures and loss of income, we also investigate whether our results are robust for those who do not have health insurance and also for those households that have only one income earner. Both of these groups are the most vulnerable to an adverse health event and high medical spending. Uninsured couples face the risk of high out-of-pocket medical expenditures and having one income earner prevents the household from self-insuring against a major income loss caused by a health problem. We expect savings of both of these household groups to increase with health risk. To the contrary, our findings provide no evidence of precautionary savings.10

Determinants of Health Problems The second goal of this article is to understand the factors that affect the likelihood that near-elderly individuals experience health problems. Are the current health status and health conditions good predictors of future health problems? To answer these questions, we regress the respondent’s responses to whether they experienced a health problem in five survey waves from 1994 to 2002 on age, marital status, race, education, being a smoker, being obese, existing health conditions (high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, psychiatric problems, and arthritis), current health status (excellent, very good, good, fair, and poor health) in 1992, the number of nights in the hospital in 1992, the number visits to the doctor in 1992, and year dummies. We use information from the 1992 HRS to create the control variables because this is information that is available to the respondent when the probability of future health problems is predicted and the amount of savings are decided. We use the random effects probit model and random effects model to estimate these models.


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Table 5 presents the coefficients of the random effects models for husbands and wives since marginal effects are easier to illustrate with random effects coefficients. Marginal effects of the random effects probit regression are very similar to the coefficient estimates of the random effects regression. First, we discuss the determinants of having health problems for the husbands and later briefly explain the differences in estimates for wives. The health status in 1992 is a strong predictor of whether or not the respondent would experience health problems in the subsequent waves. For example, compared to those who are in excellent health status in 1992, husbands who are in fair health are 29.0 percentage points more likely to have health problems, while husbands who are in poor health are 52.3 percentage points more likely to have future health problems. Also, those who have health conditions in 1992 are more likely to have health problems in the subsequent waves. Those who had a stroke and lung disease are 16.9 and 10.3 percentage points more likely to have health problems. Being a smoker and being obese increase the likelihood of health problems by 5.9 and 4.7 percentage points, respectively. Also, the likelihood of health problems increases by the age of the respondent. These patterns observed for husbands are similar for wives, with the following exceptions. For wives, being a smoker does not have a significant effect on the likelihood of having health problems. Education level, however, is a more significant indicator of having health problems for wives than husbands. Finally, stroke and heart disease are not significantly related to future health problems for wives. As shown in Table 1, both stroke and heart disease were less common among the wives. We are also interested in factors that influence the respondent’s selfreported probability of having a health problem. Our goal here is to understand the relationship between higher probabilities of future health problems as reported by the respondents and current health status and health conditions. The estimates of the OLS regression for husbands and wives are presented in Table 6. This regression analyzes whether self-reported probabilities depend on the current health status and other observed characteristics. Because the probabilities are originally reported on a 0–10 scale and then are scaled to 0–1, we also utilized an ordered probit regression. The marginal effects of the ordered probit model are very similar to the OLS regression, but are not reported here. First, we discuss the factors that are correlated with higher probability of health problems for husbands. Similar to the coefficient estimates in Table 5, there seems to be high correlation between health status in 1992 and the respondent’s prediction of health problems. Compared to those who are in



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Table 5. Determinants of Having Health Problems between 1994 and 2002. Husband

Wife

Random effects

Random effects

Coeff.

SE

Coeff.

Age 0.004 0.001*** 0.001 Race (reference group: White) Black 0.010 0.017 0.025 Other 0.033 0.029 0.009 Hispanic 0.016 0.020 0.080 Education (reference group: no high school) High school 0.004 0.013 0.028 College graduate 0.022 0.019 0.027 Graduate degree 0.024 0.020 0.069 Smoker 0.059 0.012*** 0.018 Obese 0.047 0.013*** 0.053 Health conditions in 1992 High blood pressure 0.009 0.011 0.014 Diabetes 0.056 0.020*** 0.055 Cancer 0.018 0.033 0.007 Lung disease 0.103 0.029*** 0.058 Heart disease 0.071 0.017*** 0.013 Stroke 0.169 0.035*** 0.054 Psychiatric problem 0.107 0.031*** 0.125 Arthritis 0.081 0.012*** 0.118 Self-reported health status in 1992 (Reference group: excellent health) Very good health 0.029 0.013** 0.039 Good health 0.111 0.014*** 0.141 Fair health 0.290 0.020*** 0.348 Poor health 0.523 0.030*** 0.546 Hospital stays 0.001 0.001 0.005 Doctor visits 0.008 0.001*** 0.003 Income/10,000 0.004 0.001*** 0.003 Constant 0.225 0.064*** 0.042 R2 Within 0.008 0.005 Between 0.370 0.390 Overall 0.227 0.248

SE 0.001* 0.017 0.029 0.021*** 0.014** 0.021 0.025*** 0.013 0.013*** 0.012 0.022** 0.022 0.029** 0.023 0.046 0.020*** 0.011*** 0.013*** 0.015*** 0.021*** 0.032*** 0.001*** 0.001*** 0.001** 0.052

Note. HRS ¼ Health and Retirement Study; Coeff. ¼ Coefficient estimates; SE ¼ standard error. Data are drawn from the 1992, 1994, 1996, 1998, 2000 and 2002 HRS. The regression includes year dummies. The respondent characteristics (age, education, smoker, obese, health conditions, self-reported health status, hospital stays, and doctor visits) are utilized in the regression. The only variable measured at the household level is income, which is defined as total household income. ***Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.


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Table 6. Determinants of Self-Reported Probability of Poor Health Limiting Work in the Next 10 Years. Husband Coeff.

Wife SE

Coeff.

Age 0.005 0.001*** 0.006 Race (reference group: White) Black 0.009 0.021 0.009 Other 0.007 0.035 0.050 Hispanic 0.012 0.025 0.010 Education (reference group: no high school) High school 0.008 0.016 0.043 College graduate 0.015 0.022 0.033 Graduate degree 0.008 0.024 0.025 Smoker 0.045 0.015*** 0.009 Obese 0.017 0.015 0.046 Health conditions in 1992 High blood pressure 0.010 0.013 0.000 Diabetes 0.003 0.025 0.046 Cancer 0.044 0.040 0.022 Lung disease 0.026 0.039 0.035 Heart disease 0.052 0.021** 0.003 Stroke 0.036 0.054 0.018 Psychiatric problem 0.034 0.043 0.019 Arthritis 0.028 0.014** 0.016 Self-reported health status in 1992 (Reference group: excellent health) Very good health 0.058 0.015*** 0.087 Good health 0.106 0.017*** 0.136 Fair health 0.168 0.025*** 0.235 Poor health 0.384 0.056*** 0.219 Hospital stays 0.004 0.002* 0.002 Doctor visits 0.002 0.001 0.002 Income/10,000 0.002 0.001 0.001 Constant 0.029 0.077 0.043 Number of observations 2048 1676 R2 0.103 0.130

SE 0.001*** 0.022 0.041 0.030 0.020** 0.027 0.031 0.016 0.017*** 0.016 0.033 0.029 0.039 0.033 0.080 0.029 0.014 0.016*** 0.018*** 0.030*** 0.081*** 0.002 0.001* 0.002 0.066

Note. HRS ¼ Health and Retirement Study; Coeff. ¼ Coefficient estimates; SE ¼ standard error. Data are drawn from the 1992 HRS. The respondent characteristics (age, education, smoker, obese, health conditions, self-reported health status, hospital stays, and doctor visits) are utilized in the regression. The only variable measured at the household level is income, which is defined as total household income. ***Significant at the 1% level. **Significant at the 5% level *Significant at the 10% level.



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excellent health, husbands in poor (fair) health report that they are 38.4 (16.8) percentage points more likely to have health problems. Also, the respondent’s prediction of having health problems increases with age. After controlling for health status, the respondents who have health conditions generally did not predict higher probabilities of having future health problems. The exceptions are the husbands who have heart disease and arthritis. Those who have heart disease and arthritis report that they are 5.2 and 2.8 percentage points, respectively, more likely to have health problems in the future. These patterns observed for husbands are similar for wives with the exception of smoking and being obese. For husbands, those who smoke predict that they are 4.5 percentage points more likely to be in a poor health condition. While smoking does not affect wives’ prediction of being in a poor health condition, wives who are obese predict that they are 4.6 percentage points more likely to be in a poor health condition.

Endogeneity and Sample Selection As our findings illustrate, individuals who are already in fair or poor health or who have a current health condition are more likely to experience health problems in the future. If unobserved factors influencing health problems are uncorrelated with the unobserved factors influencing the saving behavior, our estimates should not suffer from a sample selection problem. Given the literature that showed the role of discount rates in risky health-related behaviors (such as drinking and exercising) and in consumption and saving, this assumption might not be particularly realistic. At the same time, the impact of selection problems on our estimates would be minimal if every variable influencing health risks is controlled for in the savings equation. Our regressions were composed of a comprehensive list of variables that included demographics, preferences, and health status. In order to account for endogeneity in the presence of unobserved heterogeneity and sample selection, we also utilize a two-stage least squares estimation. As instruments for future health status, we use both the husband’s and the wife’s parents’ mortality (father’s and mother’s current age or age at death, and whether both the spouses’ parents are alive or not). We use these variables to control for the husband’s and wife’s health endowment. We assume that if parent’s current age or age at death is high, this reflects good health endowment for the respondent. We include the dummy variables showing whether the parents are alive or not to differentiate between current age and the age at death. While parents’ current age or age at death should not have an effect on the saving behavior, we acknowledge that the parents’


200


mortality would have considerable impact on inheritance or expectation of inheritance. Our results are robust to excluding the indicator variables that measure whether the parents are alive or not. The estimates of the first stage where we regress health risks on parents’ current age or age at death, and whether the parents are alive or not show that health risks are negatively and significantly correlated with parents’ current age or age at the death. In the second stage, we regress the financial ratios on the predicted values of health risks that we obtained from the first stage. The second-stage estimates show that accumulated savings do not increase with health risks.11 Finally, our first measure of health risk is based on the question ‘‘Do you have any impairment or health problem that limits the kind or amount of work you can do?’’ A possible problem with measuring health risks using this question is the probability that different groups of respondents may use different response scales (Kapteyn, Smith, & van Soest 2009). Our measure could suffer from the justification bias such that respondents who are not working might report a health problem as a more serious work limitation than the respondents who are in labor force. A comparison of the means for the respondents working full time to the remaining participants show large differences: 0.37 versus 0.15 for husbands and 0.27 versus 0.13 for wives. However, this does not certainly identify the justification bias since there is a high correlation between experiencing health problems and exiting from labor force. We acknowledge that this bias might influence our results if the respondents who are not working systematically classify their health problem worse than it is and also have lower savings than the respondents who are working fulltime. In order to reduce the severity of this problem, we include variables that measure employment status of the husbands and wives in our regressions.

Conclusion This study has two goals: To understand the extent of the role that medical spending risk plays on saving decisions of near-elderly and to investigate the factors affecting future health problems. We construct two measures of health risks utilizing the actual observation of having health problems and the self-reported probability of having health conditions. To the contrary of our expectations, we find that those who have higher health risks do not save more. We show that those already in poor and fair health and those who already have serious health conditions are the ones who face higher health risks. One explanation for our findings of no effect of health risks on precautionary savings is that higher prior health expenditures and lower earnings



201

due to health limitations cause low levels of savings. In addition, we find that households who are in very good health condition are not engaged in precautionary savings. This is due to the fact that the health risk that they are faced with is very small. While our findings are contrary to the predictions of precautionary savings model, they are consistent with previous studies that find the relationship between income risk and savings is relatively small (Dynan, 1993; Skinner, 1988) and studies that find the relationship between health insurance and savings is positive (Guariglia & Ross, 2004; Starr-McCluer, 1996). Our results are also consistent with studies that find poor health affects socioeconomic status and debt holdings, such that individuals who are in poor health work fewer hours or are unemployed, and are therefore limited in their ability to accumulate income and wealth (Babiarz, Widdows, & Yilmazer, 2012; Ettner, 1996; Lyons &Yilmazer, 2005; Smith, 1998, 1999; Wu, 2003). There are a number of policy implications that may be drawn from our findings. Lower labor income and higher out-of-pocket medical expenditures are two important costs of major health problems. The potential impact of poor health on household resources and consumption is mediated by private insurance, social programs, and accumulated savings. Households show no evidence of engaging in precautionary savings against health risks. Therefore, the possibility that there is less than full insurance suggests a major illness can cause a potential large shock to household finances. The age group studied in this article will have more complete coverage in the near future as they age into Medicare. However, the intermediate risk that they face seems to be substantial.12 The Patient Protection and Affordable Care Act is a major step forward expanding health insurance coverage for Americans, especially those who are underinsured or uninsured. Three provisions of the ACA will have a major impact on the expansion of insurance coverage.13 First, individuals with income under 133% of the Federal poverty level will be eligible for Medicaid. Second, state health insurance exchanges will allow individuals without employer-sponsored health insurance, Medicare or Medicaid to obtain subsided insurance via the exchanges. The exchanges also will verify that the insurance plans meet benchmarks for quality and affordability, which will help the individuals who are currently underinsured. A similar health care reform in Massachusetts has already been shown to reduce consumer bankruptcies (Miller, 2011). In addition, the insurers will not be able decline coverage to individuals with preexisting conditions after 2014. These policy changes will help unhealthy individuals with their medical expenditures. However, the coverage of disability insurance remains to be


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limited. In case of a disability, the most widespread income protection is from Social Security Disability Insurance (SSDI). However, the definition of disability under SSDI is restrictive. In order to qualify for SSDI, the disability must have lasted or be expected to last for at least a year or result in death, and the disability must prevent the individual from working at a substantial level, which means not being able to earn more than $1,010 a month (in 2011).14 Besides SSDI, only 36% of employees have access to group short-term disability insurance, and 33% are offered group long-term disability insurance (Bureau of Labor Statistics, 2011). Disability insurance can be costly due to moral hazard problems. But expanding the coverage seems to be necessary to improve the financial well-being of households struggling with health problems. Acknowledgements We thank Patryk Babiarz and three anonymous referees for their valuable suggestions.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes 1. Constant absolute risk aversion (CARA) and constant relative risk aversion (CRRA) are two utility functions that have a positive third derivative. 2. The risk of incurring substantial health expenditures generally increases with age. As a result, elderly spend a larger share of their budget on health care expenses. Median health spending for the elderly was approximately 12% of their budget in 2007, while median spending for non-elderly was only 3% (Gruber & Levy, 2009). 3. Both risk and uncertainty relate to randomness. In this study, we define risk as randomness in which events have measurable probabilities. We assume that households have information to predict the probability of having health problems. Knight (1921) distinguished risk from uncertainty as risk being quantifiable randomness and uncertainty being not. Our definition of risk and uncertainty follow the definitions in Knight’s seminal book, Risk, Uncertainty, and Profit. 4. Some theoretical studies have investigated the effects of future health expenses on savings. Based on a simulation analysis of a life-cycle model with uncertainty



5. 6.

7.

8. 9. 10. 11. 12.

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about medical expenditures, Kotlikoff (1989) shows that asset accumulation is considerably lower in an economy with Medicaid-type program and actuarially fair insurance than in an economy in which individuals must self-insure their medical expenses with their savings. Palumbo (1999) incorporates uncertain future medical expenses into a life-cycle model of consumption to investigate whether random shocks to pension income provide incentives for the elderly households to engage in precautionary behavior. Palumbo’s model demonstrates that slow rates of dissaving among retired households can be explained with precautionary saving behaviors to offset potentially large future out-of-pocket medical expenses. Finally, the simulation model in Hubbard, Skinner, and Zeldes (1995) investigates whether asset-based, means-tested social insurance programs create a significant disincentive for saving. Their findings show that low wealth accumulation can be explained as a response to these types of social insurance programs that create two types of disincentives for saving: (1) Insurance reduces the uncertainty of medical expenses and weakens the precautionary-saving motive and (2) some households spend their wealth in order to qualify for the asset-tested social health insurance. Also, see Hurd, Juster, and Smith (2003) and Juster and Smith (1997) for an extensive examination of the quality of the HRS. Our first measure of health risk uses the information on health problems that each spouse experienced during 1994–2002. Therefore, the exclusion of couples who did not survive as a couple (due to death or divorce) was necessary to be able to use a balanced panel. We acknowledge that this restriction omits households who are more likely to be subject to income shocks. The risks faced by these households who did not survive as a couple are more intricate than health risks. For example, the death of a spouse and divorce might cause permanent shocks to income. The response distribution to the question on the self-reported probability of having health problems is very similar for husbands and wives (not shown in Table 1). Among husbands, 17.4% predict that there is zero probability of experiencing health problems, while 4.0% predict that there is 100% probability, and 30.9% predict that there is 50% probability. Also, 30% of the husbands predicted between 10 and 40% probability of health problems, and 20% of the husbands predicted between 60 and 90% probability of health problems. The regression estimates are available from the authors upon request. The regression estimates are available from the authors upon request. The results are available from the authors upon request. The results are available from the authors upon request. According to findings of the survey conducted by Schoen, Doty, Robertson, and Collins (2011), underinsured and uninsured adults have high rates of financial


204


stress related to medical bills. Despite having coverage all year, Schoen et al. (2011) report that 52% of underinsured adults have difficulty paying bills, have being contacted by collection agencies for unpaid bills, or are going into medical debt. In addition, 32% of the uninsured or underinsured with medical expenses took on a loan, a home mortgage, or credit. 13. The official U.S. government site (http://www.healthcare.gov/) provides ongoing news and information about health care reform, including the key features of the law and timeline. The act consists of both the Patient Protection and Affordable Care Act (PPACA) and Public Law (P.L.) 111-148, as amended by §1003 of the Health Care and Education Reconciliation Act of 2010 (P.L. 111-152). 14. See http://www.ssa.gov/dibplan/index.htm.

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Author Biographies Tansel Yilmazer is an assistant professor in the Department of Human Sciences at the Ohio State University. Her research focuses on issues related to household economics, family finance, and applied health economics. Robert L. Scharff is an associate professor in the Department of Human Sciences at the Ohio State University. His research focuses on issues in applied health economics and risk analysis.


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