Family Health Histories and Their Impact on Retirement Confidence.

565330 research-article2015

JAHXXX10.1177/0898264314565330Journal of Aging and HealthZick et al.

Article

Family Health Histories and Their Impact on Retirement Confidence

Journal of Aging and Health 1–22 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264314565330 jah.sagepub.com

Cathleen D. Zick, PhD1, Robert N. Mayer, PhD1, and Ken R. Smith, PhD1

Abstract Objective: Retirement confidence is a key social barometer. In this article, we examine how personal and parental health histories relate to workingage adults’ feelings of optimism or pessimism about their overall retirement prospects. Method: This study links survey data on retirement planning with information on respondents’ own health histories and those of their parents. The multivariate models control for the respondents’ socio-demographic and economic characteristics along with past retirement planning activities when estimating the relationships between family health histories and retirement confidence. Results: Retirement confidence is inversely related to parental history of cancer and cardiovascular disease but not to personal health history. In contrast, retirement confidence is positively associated with both parents being deceased. Discussion: As members of the public become increasingly aware of how genetics and other family factors affect intergenerational transmission of chronic diseases, it is likely that the link between family health histories and retirement confidence will intensify. Keywords retirement confidence, family health history, retirement planning

1University

of Utah, Salt Lake City, USA

Corresponding Author: Cathleen D. Zick, Department of Family and Consumer Studies, University of Utah, 225 South 1400 East, Room 228, Salt Lake City, UT 84112, USA. Email: [email protected]

Downloaded from jah.sagepub.com at Bobst Library, New York University on May 31, 2015

2

Journal of Aging and Health

Introduction As members of the largest American birth cohort begin to retire, there is a growing interest in understanding the factors that affect retirement preparation behaviors and attitudes (Adams & Rau, 2011; Pruchno, 2012). Studies of retirement-related financial knowledge, financial advice seeking, retirement savings, retirement expectations, and retirement patterns abound (Agnew, Szykman, Utkus, & Young, 2007; Howlett, Kees, & Kemp, 2008; Lusardi & Mitchell, 2007, 2007a, 2007b, 2009; Mayer, Zick, & Marsden, 2011; Midanik, Soghikian, Ransom, & Polen, 1990; Morrin, Broniarczyk, Inman, & Broussard, 2008; van Rooij, Lusardi, & Alessie, 2012; Zick & Mayer, 2013). Lacking, however, are studies that focus on overall retirement confidence— that is, how sure working-age adults are that they will have sufficient resources to live comfortably throughout their retirement. Like consumer confidence, retirement confidence has the potential to be an important economic indicator. Since 1993, the Employee Benefit Research Institute (EBRI) has asked the following question on their annual employee survey: “Overall, how confident are you that you (and your spouse) will have enough money to live comfortably throughout your retirement years?” (EBRI and Mathew Greenwald & Associates, 2014). This survey item is meant to capture the degree to which working-age adults feel optimistic or pessimistic about their overall retirement prospects, and thus, it is an appealing summary measure of the range of factors that individuals consider when thinking about their retirement. For example, a survey respondent might contemplate how much she or he has saved to date, the safety of those savings, his or her debt obligations, what Social Security benefits are likely to be, and what economic needs may be after retirement. As such, retirement confidence has the potential to provide policymakers and researchers with an assessment of the “retirement pulse” of working-age adults and inform our understanding of subsequent choices about retirement savings, retirement timing, and paid work after an individual formally retires (i.e., “bridge employment”). The recession of 2008-2009 focused a spotlight on economic factors that affect American workers’ retirement confidence. Uncertain job markets and the precipitous decline in the value of most workers’ 401(k) plans certainly contributed to a substantial drop in Americans’ retirement confidence, leading some scholars to forecast that the recession would spur workers to engage in more retirement planning (Stone & Rainville, 2012). The annual retirement confidence survey of the EBRI shows, however, that over the past 20 years—through good economic times and bad—a sizable fraction of working-age Americans have been pessimistic about the likelihood that they will have enough money to live comfortably throughout their retirement (EBRI


3

Zick et al.

and Mathew Greenwald & Associates, 2014). In the aftermath of the most recent recession, in 2011, almost 50% of workers reported that they were either “not too confident” or “not at all confident” that they would have enough money to live comfortably throughout their retirement years, which was an all-time low since the survey began in 1993. However, even at the peak of the economic boom that preceded the recession, 29% of workers in 2007 reported they were either not too confident or not at all confident about their retirement prospects (EBRI and Mathew Greenwald & Associates, 2014). Thus, there must be other factors, beyond macroeconomic considerations, that influence retirement confidence. The consistent and sizable minority of American workers who report low levels of retirement confidence is less surprising if it is viewed within the context of the larger environment that working-age adults must navigate when making retirement planning decisions. Research shows that approximately one-third of baby boomers (i.e., those individuals born between 1946 and 1964) who are now on the cusp of retirement do not have an employerprovided retirement plan (Wright, 2012), and those who do are more likely than earlier generations to be in a defined contribution rather than a defined benefit plan (Copeland, 2013). These individuals must make choices among complex investment options that have varying financial risks, often based on low levels of financial literacy (Hung, Parker, & Yoong, 2009; Huston, 2010; Kozup & Hogarth, 2008; Lusardi & Mitchell, 2007, 2007a, 2008). Consequently, it is not surprising that workers’ retirement savings is typically far short of what is needed (Yuh, 2011), with the result that many individuals who are approaching retirement opt for phased retirement or a “bridge job” rather than a “clean” retirement (Cahill, Giandrea, & Quinn, 2006, 2013). To date, the correlates of retirement confidence have been examined in only two multivariate analyses. Joo and Pauwels (2002) investigate the reasons for the consistent finding that women express lower levels of retirement confidence than men. They conclude that age, education, income, and savings are linked to retirement confidence for both women and men but that the effects of financial attitudes (e.g., financial risk aversion) vary by gender (Joo & Pauwels, 2002). Kim, Kwon, and Anderson (2005) examine the relationship between workplace financial education and retirement confidence while controlling for income, savings, marital status, education, race, gender, and self-reported health status. They find that, along with various sociodemographics, workplace financial education is positively related to retirement confidence. More relevant to our purposes, they also find that self-reported good health is associated with higher retirement confidence (Kim et al., 2005).


4


While the focus of research to date has been on understanding the role of factors that are most likely affecting the income side of the retirement confidence “balance sheet,” anticipated expenditures during retirement may also influence working-age adults’ assessment of their retirement confidence. Yet, forecasting retirement expenditures is an exercise riddled with uncertainty due to the difficulty of predicting longevity and health care expenses. The costs—medical or otherwise—associated with average life expectancy have little relevance to the large number of retirees who experience either premature death or exceptional longevity. Like longevity, future health care expenses are a major source of uncertainty. One recent study found that only about 11% of individuals in their early 60s reporting having one or more limitations related to activities of daily living (Verbrugge & Liu, 2014), which suggests that most people who are entering the retirement years are healthy. Yet, another study found that two-thirds of baby boomers indicated that they expected to need long-term supports and services in the future, the expenses for which will only be partially covered by public programs such as Medicare and Medicaid (Robison, Shugrue, Fortinsky, & Gruman, 2013). Another study found that approximately half of all health care expenditures occurred after age 65. And, among those who are exceptionally long-lived, more than 33% of their total lifetime health expenditures occurred after age 85 (Alemayehu & Warner, 2004). Other research has found a positive relationship between subjective life expectancy and expected retirement age (Kahan, Rutledge, & Wu, 2014), suggesting that individuals elect to work longer and increase their retirement nest egg to ensure financial well-being during old age. Thus, we argue that both subjective assessments of life expectancy and forecasted health will affect individuals’ expectations about their financial expenditures after retirement and therefore they should help shape retirement confidence. To reduce the uncertainty regarding life expectancy and later-life health care expenditures, an individual may look for cues from her or his own health experiences as well as the health experiences of close relatives. In the case of own health experiences, the diagnosis of a serious, life-threatening disease should be inversely related to retirement confidence. Such a diagnosis may force an individual to reduce voluntary contributions to retirement plans to pay health care expenses and/or to prepare for significant health care expenses in the future. Both effects could lower retirement confidence. Fortunately, the diagnosis of a serious disease prior to retirement is not a common event; most individuals must look elsewhere for an indication of what their future health expenditures will be. Because both genes and environment play a role in chronic disease transmission and life expectancy (Jorde, Carey, & Bamshad, 2010; Rowe, 1994), an individual’s parents’


5

Zick et al.

disease experiences may foreshadow his or her own future health. Indeed, recently researchers have begun to examine how the interplay between genetics and environment affects physical and mental health (Bearman, 2013; Boardman, Barnes, Wilson, Evans, & de Leon, 2012; McClintock, Conzen, Gehlert, Masi, & Olopade, 2005; Nickels et al., 2013; Prabhakaran & Jeemon, 2012; Zuelsdorff et al., 2013). Studies typically note that the potential for sharing specific genes for serious conditions such as cancer and heart disease is high among first-degree relatives. In addition, some environmental exposures (e.g., second-hand smoke), social support, and patterns of exercise and diet are likely determined within the context of an individual’s family of origin. Because both genes and environmental factors affect health and longevity, we hypothesize that individuals will reflect on the contextual factors approximated by the health history and longevity of their parents when assessing retirement confidence. We posit that individuals who are aware of family health problems have lower retirement confidence for three reasons. These people might anticipate fewer working years of good health during which to save for retirement, lower ability to save before retirement due to health-related expenditures, and greater health-related expenditures during retirement. This negative association with retirement confidence might be attenuated to the extent that awareness of a family health problem motivates an individual to save more aggressively. It may also be that an individual with a negative family history might anticipate a shorter life expectancy, which would reduce the funds she or he needs to accumulate to insure adequate retirement income. Despite the potential for countervailing effects, it seems unlikely that a negative family health history would raise retirement confidence relative to someone with a more positive family health history. In one case, however, a negative family health history could boost retirement confidence—when both parents are deceased. To the extent that parental deaths result in an income transfer via a bequest or life insurance policy, expectations of income adequacy during retirement could be heightened relative to someone with one or more living parents. Researchers have observed positive links between inheritances and wealth historically (Mare & Tzeng, 1989; Semyonov & Lewin-Epstein, 2013), and a relatively recent study found that more than 50% of all baby boomers could expect to inherit funds from parents and/or grandparents and that the average inheritance, conditional on receiving any inheritance, would be approximately US$64,000 (Center for Retirement Research, 2010). While some scholars have forecast that inherited wealth will decline in the future (Lowrey, 2014; Wolff & Gittleman, 2011), past inheritance effects, as approximated by both parents being deceased, are posited to increase retirement confidence. Any such “inheritance effect”


6


would likely apply to people with and without a negative family health history. That is, an inheritance should boost retirement confidence regardless of whether a person’s parents died prematurely from disease or, say, a car accident. Reinforcing this argument is the possibility that the respondent may report a higher level of retirement confidence when both parents are deceased because she or he no longer has any financial obligations to help with parental care. Finally, it is important to remember that someone who may not expect to live a long life based on his or her parents’ age at death may in fact be more optimistic about having sufficient financial resources to live comfortably in retirement than an otherwise similar person who anticipates a long period of retirement based on his or her parental longevity. In sum, we offer two hypotheses. Own health and the health of parents capture key contextual elements of the life course that negatively influence retirement confidence. We also predict that the death of both parents will enhance retirement confidence. Despite increasing public awareness of the genetic and familial determinants of health and longevity, no study has assessed the relationship between family health history and retirement confidence. We begin to address this research gap. Our study includes several innovative features that enhance its contribution to the literature. First, we use clinical records to construct our health variables. Own medical histories and the medical histories of parents (e.g., disease diagnoses, vital status) are hypothesized to provide individuals with information they use to forecast their own future health and any associated health-related expenditures. Second, we link these clinical records to a survey measure of retirement confidence that mirrors the question asked in the EBRI survey. Third, our multivariate analyses are undertaken controlling for a wide array of possible covariates that have been linked to retirement confidence in prior research (Joo & Pauwels, 2002; Kim et al., 2005).

Method Unique data from two sources are linked to test the hypotheses posed in the present study. Information on retirement confidence comes from University of Utah University of Utah Retirement Planning Survey (UURPS). TheUURPS was designed to assess University of Utah employees’ retirement planning knowledge, priorities, perceptions, and behaviors in the aftermath of the economic recession of 2008-2009. All University of Utah benefits-eligible employees with valid email addresses (N = 9,747) were invited to participate online in theUURPS during October 2009. Publicity efforts and participation incentives resulted in 3,000 people submitting completed surveys for an overall cooperation rate of 32.1%. Sixty-five percent


7

Zick et al.

of the 3,000UURPS respondents were female, and the median respondent age was 44 years. As a point of comparison, as of October 2009, 58% of all university employees were female, and the median employee age was approximately 42. Detailed medical data on family health histories come from the Utah Population Database (UPDB). The UPDB is a shared research resource located at the University of Utah. For 35 years, researchers have used this resource to study health issues within a family context. The central component of UPDB is an extensive set of Utah genealogies, in which family members are linked to demographic (i.e., birth, death, marriage, and divorce records) and medical information. Relevant to the current investigation, the UPDB includes statewide medical information on cancer diagnoses, hospital inpatient discharges, and causes of death. Nearly all families living in Utah are represented in the UPDB, and individuals in the same family pedigrees are linked to one another with their familial relationship identified. Utah death certificates (1904-2009) and the U.S. Social Security Death Index are linked to the UPDB and provide the needed information on age and cause of death (if the death occurred in Utah) for the parents of the UURPS respondents. In addition, diagnoses of specific health conditions for both living and deceased UURPS family members come from two UPDB sources: (a) the Utah Statewide Inpatient Hospital Discharge Data (1996-2009) and (b) the Utah Cancer Registry (1966-2009). Information on diagnoses that occurred out of Utah is not available in the UPDB at this time. We do not have access to measures of the respondent’s awareness of her or his family health history as all of our health information regarding the parents is derived from administrative records. Nevertheless, we believe that the likelihood of the respondent knowing about the specific family health histories that we measure is quite high for several reasons. First, we limit our family health measures to parents because of their close genetic and social connection to the respondents. Individuals are more likely to be aware of the health limitations of parents than they are of more distant relatives (e.g., grandparents, aunts, uncles, cousins). Second, we capture significant health events as approximated by a hospitalization or a death. Thus, these should be salient events that are likely communicated to or known by the offspring. Admittedly, if the respondent is unaware of or has forgotten his or her parents’ health histories, then our variables will contain measurement error in approximating the respondent’s knowledge. This means that our resulting estimates are likely conservative to the extent that such measurement error is randomly distributed across the sample. In accordance with the university’s Institutional Review Board, consent for linkage was requested of the 2,795 respondents who provided contact


8


Figure 1. Data sources.

information when completing theUURPS survey. Of those, 81 declined. Of the 2,714 respondents who agreed to be part of the study, 2,669 respondents linked to one or more data sources in the UPDB, for a linkage rate of 98.3%. Linkage of the UURPS survey data to UPDB records was done by staff at the University of Utah’s Huntsman Cancer Institute, and a de-identified file was returned to the researchers for analysis. For the purposes of the current analyses, the sample is further restricted to those UURPS respondents who, at the time of the survey, were between the ages of 30 and 61, inclusive. We eliminate the very young employees as they have likely not given much thought to retirement. We also eliminate employees above age 61 because people are eligible for Social Security benefits beginning at age 62 and therefore they may think differently about retirement confidence than younger respondents. The final data set contained 2,059 respondents, all of whom answered the retirement confidence question, and 729 of whom have parental information for both their mothers and fathers. A detailed description of the data sources and sample construction is shown in Figure 1. We recognize that analyses done with the UURPS–UPDB linked data may not generalize to the larger population of employees working in institutions


9

Zick et al.

of higher education or to all employed individuals in the United States. The response rate for our survey was modest, and information regarding the position held by the respondent at the university was not asked. However, the distribution of educational attainment for the sample with parental data suggests that the survey captured a reasonable educational cross-section of employees with 5.5% having a high school education or less, 34% having some college, 35% having completed a bachelor’s degree, 19% having a master’s degree, and 6.5% holding a doctorate or advanced professional degree. In addition, by restricting one sample to those individuals where we also observe their parents in the health records, we are narrowing the scope to individuals who still live within relatively close proximity to their parents. We do not know the extent to which these sample selection criteria affect the generalizability of our results. Nevertheless, these data have the unusual ability to provide initial tests of our hypotheses. These tests should be subsequently validated with more representative samples. Given that there is virtually no literature examining family health histories and retirement confidence, we view the analyses done here to be an important first step. In our empirical work, the family health context is captured by variables that measure the respondent’s health experiences and those of her or his parents. In constructing these variables, we limit our measures to five leading causes of adult death in the United States (National Center for Health Statistics, 2010) that are also thought to have a genetic and/or an environmental component. These are cardiovascular disease, cancer, chronic obstructive pulmonary disease (COPD), diabetes, and nervous system diseases (e.g., Alzheimer’s disease, Parkinson’s disease). All these diseases likely require substantially and prolonged out-of-pocket expenditures. These diagnoses are obtained from the International Classification of Diseases (ICD)-9 and ICD10 codes available in the UPDB. Diagnoses and death record information are limited to those events reported through September 2009, the month before theUURPS survey was administered. Based on the five disease categories, we generate a series of dummy variables that indicate whether one or more of the respondent’s parents had been diagnosed with a specific disease. In addition, we include a dummy variable that measures whether both parents are deceased. Finally, we create two dummy variables that reflect the health history of the respondent and the respondent’s spouse (if present). Our average respondent is fairly well educated, middle-aged, and healthy enough to be employed. Thus, it is not surprising that specific disease diagnoses are somewhat rare for both the respondents and their spouses. Rather than treat each of these diagnoses separately, we group all five disease categories together and create a dummy


10


variable that captures whether the respondent (spouse) has had a disease diagnosis in one or more of the five categories. Economic resources that are likely to have an impact on the income side of an individual’s retirement confidence assessment are captured by annual household income (measured in US$10,000s) and the individual’s accumulated retirement savings (measured in categories). We also control for several aspects of the household’s retirement plans that could affect future income flows. Specifically, we include a dummy variable that measures whether the respondent is in the University of Utah’s defined benefit or defined contribution plan (the former of which guarantees a specified income for life after retirement), whether the respondent has separate retirement savings from past employment, and whether she or he has a spouse who has his or her own retirement plan. Home ownership and its associated wealth effects are measured by a dummy variable that takes on a value of “1” if the respondent owns or is in the process of buying a home. Prior research has found that basic retirement planning behaviors predict retirement confidence (Joo & Pauwels, 2002; Kim et al., 2005). Specifically, we include three dummy variables that reflect critical retirement planning activities. These are (a) whether the respondent has ever calculated her or his retirement needs, (b) whether the respondent has seen a financial advisor in the past two years, and (c) whether the respondent has a supplemental retirement savings account. Finally, we control for a somewhat standard set of socio-demographic covariates including gender, age, education, marital status, and number of children. We initially estimated models that included financial risk tolerance and self-assessed chances of living to age 85 because past research had found risk tolerance (Joo & Pauwels, 2002) and self-rated health (Kim et al., 2005) to predict retirement confidence. Tests for the endogeneity of these measures with retirement confidence (Bound, Jaeger, & Baker, 1995) revealed that they were indeed endogenous (statistically significant Durbin–Wu–Hausman F statistics of 14.71 and 7.60, respectively). In the absence of good instruments, we elected to omit them from our set of regressors. This means that our equations should be viewed as reduced form estimates. Our outcome measure of retirement confidence is based onUURPS respondents’ answers to the question included in the EBRI survey. Specifically, we asked, “Overall, how confident are you that you (and your spouse) will have enough money to live comfortably throughout your retirement years?” As in the EBRI survey, the response options were “very confident,” “somewhat confident,” “not too confident,” and “not at all confident.” This question was asked toward the end of our survey after respondents had been asked about their retirement planning activities.


11

Zick et al.

While the EBRI retirement confidence question has not been subjected to rigorous research-based validity and reliability assessments, it appears to have good construct and convergent validity. The two prior studies that have used this measure found that it was positively related to a respondent’s income, savings, and his or her confidence in government programs (Joo & Pauwels, 2002; Kim et al., 2005)—all of which would be expected. Moreover, in reviewing EBRI’s tracking of retirement confidence from 1993 to 2014 (EBRI and Mathew Greenwald & Associates, 2014), we observe that, over time, it tracks well with changes in other indicators of well-being including gross domestic product (GDP), unemployment, and disposable income. We estimate the retirement confidence equation using both tobit (PROC QLIM in SAS) and ordinary least squares (OLS) regression (PROC REG in SAS). The tobit estimation routine adjusts for the fact that our retirement confidence is censored at both the lower and upper ends (Greene, 1993), but the interpretation of the estimated tobit coefficients is complicated. In contrast, OLS regression provides easily interpretable coefficients but does not adjust for the censoring. Comparisons across the estimating routines reveal no differences in tests of statistical significance, and thus, we present the OLS estimates because of their ease of interpretation. (The tobit estimates are available on request.) Retirement confidence is estimated using the full sample (N = 2,059) and information about the respondent’s own health and the health of her or his spouse if married. For the sub-sample who have information on their mother’s and father’s health (n = 729), we re-estimate the retirement confidence regression and include our measures of parental health and vital status among the independent variables. Collinearity diagnostics for both models reveal no signs of pernicious multicollinearity as measured by condition indices and proportion of variation explained.

Results Description of the Sample Descriptive statistics appear in Table 1. The typical respondent in the full sample is female, age 45, married, has approximately two children, and is a college graduate. The respondent’s average annual household income is slightly more than US$90,000. Educational attainment, income, and marital status are all above the national average for 2009 (U.S. Census Bureau, 2013) as would be expected given the population from which respondents were drawn. Only 5% of the sample has had at least one serious/reportable


12


Table 1. Descriptive Statistics. Full sample (N = 2,059) Variable Gender (1 = female) Age (years) Education (years)a Married (1 = yes) Number of children Own/buying home (1 = yes) Household income in 2009 (US$10,000s)a Enrolled in the defined benefit retirement plan (1 = yes)a Retirement savings from past employment (1 = yes)a Spouse has retirement plan (1 = yes) Don’t Know/refused retirement savings question (1 = yes)b Retirement savings < US$100K (1 = yes)a,b Retirement savings US$100KUS$250K (1 = yes)a Has supplemental retirement account (1 = yes)a Has calculated retirement needs (1 = yes)a Has seen a financial advisor in past 2 years (1 = yes)a Respondent has had at least one diagnosis (1 = yes) Respondent’s spouse has had at least one diagnosis (1 = yes)a Both parents are deceased (1 = yes) At least one parent has had a cancer diagnosis (1 = yes) At least one parent has had a cardiovascular diagnosis (1 = yes) At least one parent has had a diabetes diagnosis (1 = yes) At least one parent has had a chronic obstructive pulmonary disease (COPD) diagnosis (1 = yes) At least one parent has had a nervous system disease diagnosis (1 = yes)

No parental data sub-sample (n = 1,330)

Parental data sub-sample (n = 729)

M

SD

M

SD

M

SD

0.65 45.31 16.51 0.70 1.88 0.86 9.23

0.48 9.23 2.30 0.46 1.78 0.35 5.05

0.65 45.51 16.94 0.70 1.78 0.86 9.86

0.48 9.01 2.33 0.46 1.75 0.35 5.34

0.66 44.95 15.74 0.69 2.08 0.86 8.09

0.47 9.60 2.02 0.46 1.82 0.34 4.23

0.21

0.41

0.18

0.38

0.28

0.45

0.33

0.47

0.34

0.48

0.29

0.45

0.39

0.49

0.42

0.49

0.34

0.48

0.19

0.39

0.18

0.38

0.21

0.41

0.49

0.50

0.47

0.50

0.53

0.50

0.16

0.37

0.18

0.38

0.14

0.34

0.71

0.45

0.73

0.45

0.68

0.47

0.46

0.50

0.48

0.50

0.41

0.49

0.41

0.49

0.42

0.49

0.37

0.48

0.05

0.22

0.05

0.23

0.04

0.20

0.04

0.18

0.03

0.18

0.04

0.20

— —

— —

— —

— —

0.17 0.34

0.38 0.48

—

—

—

—

0.42

0.49

—

—

—

—

0.26

0.44

—

—

—

—

0.10

0.30

—

—

—

—

0.04

0.20

aMeans

for the two sub-samples are statistically different from one another at p < .05. omitted group in this dummy variable sequence consists of respondents who reported more than US$250K in retirement savings.

bThe


13

Zick et al.

Figure 2. Retirement confidence frequency distribution.

diagnosis. Slightly fewer respondents have a spouse who has been diagnosed with one of the five health conditions. The sub-sample of respondents who have parental health history information in the last two columns of Table 1 differs from the no-parent sample in the middle two columns of Table 1 along several socio-demographic dimensions. On average, they have about one year less of schooling, and they have approximately US$18,000 per year less in total household income. In addition, they are more likely to be in the defined benefit retirement plan, their retirement savings balance is typically lower, and they are less likely to have engaged in basic retirement planning activities such as calculating retirement needs, opening a supplemental retirement account, or meeting with a professional financial advisor. In all these dimensions, the sub-sample of those who have parental health information is more like the general population of the United States. Among those respondents who had parental health data, a parental health history of cardiovascular disease was most common followed by cancer, diabetes, COPD, and nervous system diseases. In 17% of the cases, both parents were deceased at the time of the survey. Prior research has found that education, income, and marriage are all positively correlated with retirement confidence (Joo & Pauwels, 2002; Kim et al., 2005). Thus, theUURPS sample should have an overall level of retirement confidence that is higher than the general population. Figure 2 shows the distributions of the responses to the retirement confidence question for the full sample and the sample with parental health information along with comparative data from the 2009 EBRI survey where the same retirement confidence


14


question was asked of a nationally representative sample of working-age adults. Fully 40% of allUURPS respondents indicate that they are not at all confident or not too confident that they will have enough money to live comfortably throughout their retirement years. The percentage is slightly higher at 43% when the sample is restricted to those who do have parental health data. These percentages closely mirror those reported by EBRI (Helman, Copeland, & VanDerhei, 2009) where 44% of the respondents said they were either not at all confident or not too confident. In Figure 2, however, we see that individuals who participated in EBRI’s national survey were more likely to report they were at the extremes than were individuals in theUURPS survey. That is, they were more likely to report being not at all confident, less likely to report they were not too confident or somewhat confident, and slightly more likely to report that they were very confident compared with respondents in theUURPS survey.

Multivariate Results Table 2 contains the parameter estimates of the retirement confidence regressions. Focus first on the equation estimated with the full sample. Counter to our expectation, the respondent’s health (and his or her spouse’s health, if married) was not linked to retirement confidence. This result may be attributable to the small number of sample members who had experienced a serious health diagnosis. In the columns that follow, we present the estimates for the same model specification that was tested with the full sample but which makes use of only those respondents who have parental health data (i.e., Model 1). In the last set of columns, we use the sample with parental health data and include variables that capture parental vital status and parental health histories (i.e., Model 2). The associated F statistic for the change in R2 (Kamenta, 1986) is 2.18, which is slightly larger than the statistically significant threshold of 2.10 (p = .05), suggesting that the addition of the parental health information provides a modest but meaningful improvement in the overall explanatory power of the model. Contextual support for the role of family health history is reflected in the statistically significant coefficients associated with parental cancer history and parental cardiovascular history in Model 2. Consistent with our hypothesis, both estimated coefficients are negative and statistically significant. Certain types of cardiovascular disease and certain types of cancer have both been identified as having genetic and/or lifestyle components that may lead respondents with affected parents to anticipate that they are at risk of such a diagnosis as they get older. The absence of similar relationships for diabetes


15

Zick et al. Table 2. Parameter Estimates of Retirement Confidence Equations. Full sample model Independent variable Intercept Gender (1 = female) Age (years) Education (years) Married (1 = yes) Number of children Own/buying home (1 = yes) Household income in 2009 (US$10,000s) Enrolled in the DB retirement plan (1 = yes) Retirement savings from past employment (1 = yes) Spouse has retirement plan (1 = yes) DK/refused retirement savings question (1 = yes)a Retirement savings < US$100K (1 = yes)a Retirement savings US$100K-US$250K (1 = yes)a Has supplemental retirement account (1 = yes) Has calculated retirement needs (1 = yes) Has seen a financial advisor in past 2 years (1 = yes) Respondent has had at least one diagnosis (1 = yes) Respondent’s spouse has had at least one diagnosis (1 = yes) Both parents are deceased (1 = yes) At least one parent has had a cancer diagnosis (1 = yes) At least one parent has had a cardiovascular diagnosis (1 = yes) At least one parent has had a diabetes diagnosis (1 = yes) At least one parent has had a chronic obstructive pulmonary disease (COPD) diagnosis (1 = yes) At least one parent has had a dementia diagnosis (1 = yes) R2 F statistic

Parental data sample: Model 1

Parental data sample: Model 2

Coefficient

SE

Coefficient

SE

Coefficient

SE

2.19 −0.11 −0.02 0.04 0.10 −0.03 0.16 0.02

0.21* 0.04* 0.00* 0.01* 0.04* 0.01* 0.05* 0.00*

2.67 −0.21 −0.01 0.00 0.05 −0.04 0.07 0.04

0.35* 0.06* 0.00* 0.02 0.07 0.02* 0.09 0.01*

2.72 −0.22 −0.01 0.00 0.05 −0.04 0.06 0.04

0.35* 0.06* 0.00* 0.02 0.07 0.02* 0.09 0.01*

0.15

0.04*

0.09

0.07

0.09

0.07

0.03

0.04

0.01

0.06

0.01

0.06

0.15 −0.23

0.04* 0.06*

0.22 −0.19

0.06* 0.11

0.21 −0.16

0.07* 0.11

−0.39

0.06*

−0.34

0.10*

−0.32

0.10*

−0.15

0.06*

−0.11

0.10

−0.08

0.11

0.18

0.04*

0.17

0.06*

0.17

0.06*

0.32

0.04*

0.27

0.06*

0.26

0.06*

0.13

0.04*

0.28

0.06*

0.28

0.06*

0.03

0.07

−0.07

0.11

−0.07

0.11

−0.05

0.08

−0.09

0.11

−0.10

0.12

— —

— —

— —

— —

0.18 −0.13

0.08* 0.06*

—

—

—

—

−0.13

0.06*

—

—

—

—

−0.03

0.06

—

—

—

—

0.07

0.09

—

—

—

—

0.02

0.14

.284 33.60*

.288 15.93*

.301 12.61*

Note. SE = standard error. omitted group consists of respondents who reported more than US$250K in retirement savings. *p < .05. aThe


16


and COPD—but not nervous system diseases—may be a function of differences in perceptions of heritability or shared lifestyle influences. It is also possible that the lower incidence rates of parental diagnoses for these three diseases make it harder to detect their effects given our sample size. The estimated coefficient associated with both parents being deceased at the time of the survey is positive as was hypothesized. However, to the extent that any wealth transfers are channeled in to the respondent’s retirement savings, our analysis controls for such a wealth effect by including total retirement savings as a covariate. This suggests that the positive, statistically significant coefficient likely reflects the impact of wealth transfers that are used in ways that our analysis could not control for (e.g., to pay down a home mortgage, paying off student loans, investing in non-retirement focused assets). In addition to the highlighted parental health history variables, many of the more immediate individual and family context variables are also statistically significant, suggesting that individual characteristics and immediate family context are closely linked to retirement confidence. Turning first to the individual level characteristics, we find women are less confident than men, holding other factors constant. This gender difference may be a function of the fact that women typically spend more than men on health care later in life (Centers for Medicare and Medicaid Services, 2004). Age is negatively related to retirement confidence, which may reflect the fact that older respondents—relative to younger respondents—are closer to the typical retirement age and thus may be more concerned about their ability to recoup retirement losses experienced in the 2007-2008 recession. Both the age and gender differences we observe are consistent with past work (Joo & Pauwels, 2002; Kim et al., 2005). Turning to the family context, we consistently observe that respondents with more children are less confident than respondents with fewer children, all other factors held constant. The inverse relationship between number of children and retirement confidence may be attributable to a parent’s anticipation that she or he will need to divert resources away from retirement-related investments and toward adult children to fund their college education or to provide for other needs. The estimates based on the full sample also reveal that those respondents who are married and those respondents who have more education have higher levels of retirement confidence. Not surprisingly, we consistently find that economic resources matter. Those respondents who are in the university’s defined benefit retirement plan report higher retirement confidence than respondents who are enrolled in the university’s defined contribution plan—a finding that is consistent with the greater certainty associated with defined benefit plans. As expected,


17

Zick et al.

retirement confidence is also significantly higher for those respondents who report greater retirement savings and those who report having a supplemental retirement account. Retirement confidence also increases with an increase in household income and it is higher for individuals who are married and whose spouses have a retirement plan. Only 53% of married respondents have spouses who have retirement plans. This figure drops slightly to 47.5% for married respondents in the sample restricted to individuals who have parental health data. In both samples, the simple correlation between marital status and respondent retirement plan is about .42, which does not translate into any problematic collinearity as measured by standard multicollinearity diagnostics. Thus, the estimation suggests that being married increases retirement confidence. If one’s spouse has a retirement plan, that boosts retirement confidence even more. Finally, we find support for the importance of basic retirement planning activities in building retirement confidence. Specifically, reported retirement confidence is positively linked to having opened a supplemental retirement savings account, having calculated retirement needs, and having seen a professional financial advisor at some point during the previous two years.

Discussion Although our sample members vary in their job categories and retirement plans, the generalizability of our results is limited by the fact that all the respondents work at a single institution. Understanding of the relationship between family health history and retirement confidence would certainly benefit from future research that examined larger samples of individuals who work for a range of employers. It would also be desirable for future research to incorporate more detailed family health measures (e.g., different types of cancer). That said, our results represent an important first step in a potentially fruitful area of research for scholars interested in understanding the retirement planning process. Our findings provide mixed support for the roles of parental and personal health histories as they relate to retirement confidence. We observe that retirement confidence is inversely related to parental cancer history and parental cardiovascular disease history, but not to personal health histories or to parental health histories of other serious diseases. Most cancers and cardiovascular diseases are very costly illnesses that may have medical expenses associated with them for many years. Thus, it may be that individuals with one or more parents who were diagnosed with one of these diseases may already be using money that would otherwise be devoted to building a retirement nest egg to cover the health care costs of the afflicted parent.


18


Alternatively, these respondents may think that the health experiences of their parents foreshadow what their own health experiences—and associated costs—are likely to be during retirement. In contrast, we found no support for the contextual influences of the respondent’s own health or that of her or his spouse, if married, which was surprising. It may be that there were simply too few respondents who had had a serious diagnosis to detect an effect. Alternatively, it may be that a diagnosis has countervailing effects that ultimately cancel each other out—with some diagnosed individuals taking more actions to secure a comfortable retirement while for other respondents, a diagnosis diverts time and energy away from retirement planning to more immediately pressing health matters. Either way, the relationship between own health and retirement planning remains a domain that merits additional research. We also observed that if both parents of the respondent were deceased at the time of the survey, then retirement confidence was significantly higher. This is consistent with our hypothesis that parental death may signal an inheritance effect where the inherited funds may be used to relieve other financial obligations—such as paying off a mortgage or financing a child’s college education—that we were unable to control for in the present study. In sum, our research indicates that the relationship between family health history and retirement confidence is a worthwhile area for further investigation. As members of the public become increasingly aware of how genetics and other family factors affect the intergenerational transmission of chronic diseases, it is likely that the link between family health histories and retirement planning attitudes and behaviors will intensify. Turning to the potential macroeconomic implications of our findings, it is estimated that consumer spending accounts for almost 70% of Gross Domestic Product (GDP; Byun & Frey, 2012). Hence, economists view consumer confidence as an important barometer of future GDP growth. Retirement confidence may be another important indicator of the country’s future GDP, especially as more and more Americans anticipate moving to retirement. It is estimated that 10,000 baby boomers will turn 65 each day between now and 2030 (“Baby Boomers Retire,” 2010). Low levels of retirement confidence in this cohort may curb consumer spending, delay retirement, and/or motivate boomers to take bridge jobs (Cahill et al., 2006, 2013) that, in turn, would have consequences for the larger economy. This possibility coupled with our growing knowledge of the intergenerational transmission of health reinforces the importance of learning more about how working-age individuals assess their retirement confidence and what that portends for the actions they will or will not take in preparation for retirement.


19

Zick et al. Authors’ Note

Helpful comments were made on an earlier draft of this article by two anonymous reviewers and colleagues at the 2013 Population Association of America’s Conference.

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

Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for the financial support for this research project provided by National Institute on Aging Grant 1R21AG041246.

References Adams, G. A., & Rau, B. L. (2011). Putting off tomorrow to do what you want today planning for retirement. American Psychologist, 66, 180-192. doi:10.1037/ a0022131 Agnew, J. R., Szykman, L., Utkus, S. P., & Young, J. A. (2007, November). Do financial literacy and mistrust affect 401(k) participation? (Issue Brief No. 7-17). Boston, MA: Center for Retirement Research at Boston College. Retrieved from http://crr.bc.edu/wp-content/uploads/2007/11/IB_7-17_508.pdf Alemayehu, B., & Warner, K. E. (2004). The lifetime distribution of health care costs. Health Services Research, 39, 627-642. Baby Boomers Retire. (2010, December). Numbers, facts and trends shaping your world. Retrieved from http://www.pewresearch.org/daily-number/baby-boomers-retire/ Bearman, P. S. (2013). Genes can point to environments that matter to advance public health. American Journal of Public Health, 103(Suppl. 1), S11-S13. Boardman, J. D., Barnes, L. L., Wilson, R. S., Evans, D. A., & de Leon, C. F. M. (2012). Social disorder, APOE-E4 genotype, and change in cognitive function among older adults living in Chicago. Social Science & Medicine, 74, 1584-1590. doi:10.1016/j.socscimed.2012.02.012 Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90, 443-450. Byun, K. J., & Frey, C. (2012). The U.S. economy in 2020: Recovery in uncertain times. Monthly Labor Review, 135, 21-42. Cahill, K. E., Giandrea, M. D., & Quinn, J. F. (2006). Retirement patterns from career employment. The Gerontologist, 46, 514-523. Cahill, K. E., Giandrea, M. D., & Quinn, J. F. (2013). Retirement patterns and the macroeconomy, 1992–2010: The prevalence and determinants of bridge jobs,


20


phased retirement, and reentry among three recent cohorts of older Americans. The Gerontologist. doi:10.1093/geront/gnt146. Center for Retirement Research. (2010). The metlife study of inheritance and wealth transfer to baby boomers. New York, NY: The MetLife Mature Market Institute. Centers for Medicare and Medicaid Services. (2004). Total personal health care spending, by age groups, calendar years, 1987, 1996, 2002, 2004. Baltimore, MD: Health and Human Services. Copeland, C. (2013, November). Employment-based retirement plan participation: Geographic differences and trends, 2012 (EBRI Issue Brief No. 392). Retrieved from http://www.ebri.org/pdf/briefspdf/EBRI_IB_011-13.No392.Particip.pdf Employee Benefit Research Institute and Mathew Greenwald & Associates. (2014). Retirement confidence fact sheet #1. Retrieved from http://www.ebri.org/files/ Final-FS.RCS-13.FS_1.Conf.FINAL.pdf Greene, W. H. (1993). Econometric analysis (2nd ed.). New York, NY: Macmillan. Helman, H., Copeland, C., & VanDerhei, J. (2009, April). The 2009 Retirement Confidence Survey: Economy drives confidence to record lows; Many looking to work longer (EBRI Issue Brief No. 328). Employee Benefit Research Institute. Retrieved from http://www.ebri.org/pdf/briefspdf/EBRI_IB_4-2009_RCS2.pdf. Howlett, E., Kees, J., & Kemp, E. (2008). The role of self-regulation, future orientation, and financial knowledge in long-term financial decisions. Journal of Consumer Affairs, 42, 223-242. Hung, A., Parker, A. M., & Yoong, J. (2009, September). Defining and measuring financial literacy (RAND Working Paper Series No. 708). RAND. Retrieved from http://www.rand.org/content/dam/rand/pubs/working_papers/2009/ RAND_WR708.pdf Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs, 44, 296-316. Joo, S.-H., & Pauwels, V. W. (2002). Factors affecting workers’ retirement confidence: A gender perspective. Financial Counseling and Planning, 13(2), 1-10. Jorde, L. B., Carey, J. C., & Bamshad, M. J. (2010). Medical genetics (4th ed.). New York, NY: Elsevier. Kahan, M. R., Rutledge, M. S., & Wu, A. Y. (2014). How do subjective longevity expectations influence retirement plans? Boston, MA: Center for Retirement Research at Boston College. Retrieved from http://crr.bc.edu/wp-content/ uploads/2014/01/wp_2014-1.pdf Kamenta, J. (1986). Elements of econometrics (2nd ed.). New York, NY: Macmillan. Kim, J., Kwon, J., & Anderson, E. A. (2005). Factors related to retirement confidence: Retirement preparation and workplace financial education. Financial Counseling and Planning, 16(2), 77-89. Retrieved from http://www.afcpe.org/publications/ Kozup, J., & Hogarth, J. M. (2008). Financial literacy, public policy, and consumers’ self-protection—More questions, fewer answers. Journal of Consumer Affairs, 42, 127-136. Lowrey, A. (2014, March 16). Passing the bucks. The New York Times Magazine, pp. 14-16.


21

Zick et al.

Lusardi, A., & Mitchell, O. S. (2007). Financial literacy and retirement preparedness: Evidence and implications for financial education. Business Economics, 42(1), 35-44. Retrieved from http://www.nabe.com/pubs.htm Lusardi, A., & Mitchell, O. S. (2007a). Baby boomer retirement security: The roles of planning, financial literacy, and housing wealth. Journal of Monetary Economics, 54, 205-224. Retrieved from http://www.elsevier.com/locate/inca/505566/ Lusardi, A., & Mitchell, O. S. (2007b, October). Financial literacy and retirement planning: New evidence from the RAND American Life Panel (Working Paper No. 157). Michigan Retirement Research Center. Retrieved from http://ssrn.com/ paper=1095869 Lusardi, A., & Mitchell, O. S. (2008). Planning and financial literacy: How do women fare? American Economic Review, 98, 413-417. Lusardi, A., & Mitchell, O. S. (2009, March). Financial literacy, retirement planning, and retirement wellbeing: Lessons and research gaps. Paper presented at the Financial Literacy in Times of Turmoil and Retirement Insecurity, Washington, DC. Retrieved from http://www.retirementsecurityproject.org/pubs/File/Lusardi_ Mitchell_SSA-3-20-09.pdf Mare, R. D., & Tzeng, M.-S. (1989). Fathers’ ages and the social stratification of sons. American Journal of Sociology, 108-131. Mayer, R. N., Zick, C. D., & Marsden, M. (2011). Does calculating retirement needs boost retirement savings? Journal of Consumer Affairs, 45, 175-200. doi:10.1111/ j.1745-6606.2011.01199.x McClintock, M. K., Conzen, S. D., Gehlert, S., Masi, C., & Olopade, F. (2005). Mammary cancer and social interactions: Identifying multiple environments that regulate gene expression throughout the life span. Journals of Gerontology, Series B: Psychological Sciences & Social Sciences, 60B(1), 32-41. Midanik, L. T., Soghikian, K., Ransom, L. J., & Polen, M. R. (1990). Health status, retirement plans, and retirement. The Kaiser Permanente retirement study. Journal of Aging and Health, 2, 462-474. Morrin, M., Broniarczyk, S., Inman, J. J., & Broussard, J. (2008). Saving for retirement: The effects of fund assortment size and investor knowledge on asset allocation strategies. Journal of Consumer Affairs, 42, 206-222. National Center for Health Statistics. (2010). Leading causes of death. Retrieved from http://www.cdc.gov/nchs/fastats/lcod.htm Nickels, S., Truong, T., Hein, R., Stevens, K., Buck, K., Behrens, S., . . .ChangClaude, J. (2013). Evidence of gene–environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genetics, 9(3), 1-14. doi:10.1371/journal.pgen.1003284 Prabhakaran, D., & Jeemon, P. (2012). Should your family history of coronary heart disease scare you? The Mount Sinai Journal of Medicine, New York, 79, 721-732. doi:10.1002/msj.21348 Pruchno, R. (2012). Not your mother’s old age: Baby Boomers at age 65. The Gerontologist, 52, 149-152.


22


Robison, J., Shugrue, N., Fortinsky, R. H., & Gruman, C. (2014). Long-term supports and services planning for the future: Implications from a statewide survey of baby boomers and older adults. The Gerontologist, 54, 297-313. doi:10.1093/ geront/gnt094 Rowe, D. C. (1994). The limits of family influence—Genes, experience, and behavior. New York, NY: Guilford. Semyonov, M., & Lewin-Epstein, N. (2013). Ways to richness: Determination of household wealth in 16 countries. European Sociological Review, 29, 1134-1148. Stone, L., & Rainville, G. (2012). The fall in confidence among the pre-retired. In L. O. Stone (Ed.), Key demographics in retirement risk management (pp. 35-53). New York, NY: Springer. U.S. Census Bureau. (2013). American FactFinder. Retrieved from http://factfinder2. census.gov/faces/nav/jsf/pages/index.xhtml van Rooij, M. C. J., Lusardi, A., & Alessie, R. J. M. (2012). Financial literacy, retirement planning, and household wealth. Economic Journal, 122, 449-478. Retrieved from http://www.sciencedirect.com/science/journal/02649993 Verbrugge, L. M., & Liu, X. (2014). Midlife trends in activities and disability. Journal of Aging and Health, 26, 178-206. doi:10.1177/0898264313508189 Wolff, E. N., & Gittleman, M. (2011, February). Inheritances and the distribution of wealth or whatever happened to the great inheritance boom? (European Central Bank Working Paper Series No. 1300). European Central Bank. Retrieved from http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1300.pdf Wright, R. (2012). Paying for retirement: Sex differences in inclusion in employerprovided retirement plans. The Gerontologist, 52, 231-244. Yuh, Y. (2011). Assessing adequacy of retirement income for U.S. households: A replacement ratio approach. Geneva Papers on Risk and Insurance: Issues and Practice, 36, 304-323. Retrieved from http://www.palgrave-journals.com/gpp/ index.html Zick, C. D., & Mayer, R. N. (2013). Evaluating the impact of financial planners. In O. S. Mitchell & K. Smetters (Eds.), The market for retirement financial advice (pp. 153-181). Cambridge, MA: Oxford University Press. Zuelsdorff, M. L., Engelman, C. D., Friedman, E. M., Koscik, R. L., Jonaitis, E. M., Rue, A. L., & Sager, M. A. (2013). Stressful events, social support, and cognitive function in middle-aged adults with a family history of Alzheimer’s disease. Journal of Aging and Health, 25, 944-959. doi:10.1177/089826431349-8416


Missing Work After Retirement: The Role of Life Histories in the Retirement Adjustment Process.

Children's impact on the mental health of their older mothers and fathers: findings from the Survey of Health, Ageing and Retirement in Europe.

Misreported family histories and underestimation of risk.

Daily Eating Patterns and Their Impact on Health and Disease.

A multilevel dyadic study of the impact of retirement on self-rated health: does retirement predict worse health in married couples?

Social disparities among youth and the impact on their health.

Health, retirement, and migration from metro counties: evidence from the health and retirement study.

One health, many histories.

Digital family histories for data mining.

Impact of the severity of trauma on early retirement.

The impact of mid- and late-life loss on insomnia: findings from the health and retirement study, 2010 cohort.

Health and retirement: a complex relationship.

AIDS in classroom: a qualitative assessment.

Free Medicines Thanks to Retirement: Impact of Coinsurance Exemption on Pharmaceutical Expenditures and Hospitalization Offsets in a national health service.

Parents' Oral Health Literacy and its Impact on their Children's Dental Health Status.

Scandals in health-care: their impact on health policy and nursing.

Leaving the labour market: the impact of exit routes from employment to retirement on health and wellbeing in old age.

Planning for Retirement: Longitudinal Effect on Retirement Resources and Post-retirement Well-being.

Family violence: impact on children.

Inferring gene family histories in yeast identifies lineage specific expansions.

Children with disability and complex health needs: the impact on family life.

Providing patient education: impact on quantity and quality of family health history collection.

Family histories of school bullying: implications for parent-child psychotherapy.

Siblings' Perceptions of Their ADHD-Diagnosed Sibling's Impact on the Family System.