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Can Personality Help Explain Wealth Decumulation Behavior?*

Yilan Xu Jeffrey R. Brown

Brent W. Roberts

This version: 9/15/16

Abstract We explore the role of personality factors in explaining differences in wealth decumulation patterns among senior households. Using the Health and Retirement Survey (HRS) data, we find that respondents who were aged 51-61 in the first wave of the HRS (the HRS birth cohort) accumulate their non-pension wealth up to their middle 60s and then start to decumulate. However, a regression analysis suggests that such non-pension wealth trajectory is largely explained by personality traits, business cycle, and other time-variant personal characteristics. In particular, conscientiousness and openness are positively and agreeableness is negatively associated with the levels of wealth. Conscientiousness and agreeableness have consistent effects across a larger age range for World Babies and Early boomers. However, personality traits do not explain the pension wealth.

Introduction

According to the life-cycle model, households and individuals smooth lifetime consumption by saving at an early age (“wealth accumulation”) and spending down after retirement (“wealth decumulation”). Recently, more attention has focused on the wealth decumulation. Prior researchers have documented significant heterogeneity in wealth decumulation patterns, with some individuals showing extreme reluctance to draw down wealth and others drawing down at a rapid rate. Researchers have examined a range of factors, including economic status, demographics, precautionary saving (Carroll, Dynan, & Krane, 2003), financial literacy (Behrman, Mitchell, Soo, & Bravo, 2012; Eccles, Ward, Goldsmith, & Arsal, 2013), and bequest motives (De Nardi & Yang, 2014), to explain the variation in wealth. In this paper, we examine the role of personality factors in explaining the wealth decumulation pattern.

* Authors: Yilan Xu (yilanxu@illinois.edu), Department of Agricultural and Consumer Economics, University of Illinois; Jeffrey R. Brown (brownjr@illinois.edu), School of Business, University of Illinois; Brent W. Roberts (bwrobrts@illinois.edu), Department of Psychology, University of Illinois. This research was supported by the U.S. Social Security Administration through grant #RRC08098400-08 to the National Bureau of Economic Research as part of the SSA Retirement Research Consortium. The findings and conclusions expressed are solely those of the author(s) and do not represent the views of SSA, any agency of the Federal Government, or the NBER.

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Our study incorporates personality theories into the explanations of wealth decumulation patterns. Personality traits are the relatively enduring patterns of thoughts, feelings, and behaviors that develop with time and age (Roberts, 2009). There is a large literature on personality traits and how they affect major life outcomes and behaviors. In a similar spirit, we expect personality traits to predict wealth decumulation. Using data from the Health and Retirement Survey (HRS), we find that elderly typically accumulated their non-pension wealth up until in their late 60s and then started to decumulate. The decumulation disappears once the personality traits, business cycle, and personal characteristics are considered. We find that conscientiousness is positively associated with non-pension wealth in most of the lifetime after age 50, and openness is positively and agreeableness negatively associated with wealth at selected ages. We find no evidence that the effects of personality traits change over the lifecycle. When we examine the pension wealth, we find no evidence that personality traits predict the pension wealth. Our study contributes to the literature of wealth accumulation/decumulation at the old age. The empirical findings reveal the important mechanism of the wealth decumulation at senior ages, which is explained by business cycle, and personal characteristics such as marital status, employment status, education, race, gender, and health. Personality traits such conscientiousness, openness, and agreeableness are consistently associated with the level of non-pension wealth but do not predict the decumulation rate of non-pension wealth or the accumulation rate of pension wealth. Our study sheds lights on the financial preparedness of the elderly and highlights the role of personality traits in predicting retirement wealth.

Literature Review and Hypothesis Development

According to the Health and Retirement Study (HRS) data, 46.1% of the respondents in the Wealth and Health Dynamics Among the Oldest Old (AHEAD) cohort died with wealth less than $10,000 (Poterba, Venti, & Wise, 2012). A substantial share of near-retirement households did not accumulate enough wealth to purchase annuities, which in part explains the annuity puzzle (Poterba, Venti, & Wise, 2011). An enormous dispersion in the accumulated wealth of families approaching retirement exists at all levels of lifetime earnings (Venti, 2001). Most households and individuals relied on labor earnings for pre-retirement consumption and Social Security benefit for post-retirement consumption. In such circumstances, wealth allocation only had a minor impact on utility (Kopcke, Webb, Hurwitz, & Li, 2013). If consumption falls during retirement, it is called retirement consumption puzzle (Hurd & Rohwedder, 2008; Hurst, 2008). Even though households and individuals seldom withdraw from housing equity for consumption (Poterba et al., 2011), recent evidence from panel expenditure data suggests only moderate declines in consumption

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over retirement, which are attributable to the cessation of work-related expenses, unexpected retirement due to a health shock or by the substitution of time for spending (Hurd & Rohwedder, 2008). However, the low-wealth population has a higher consumption decline, mostly because of poor health (Hurd & Rohwedder, 2008). Moreover, evidence from the UK suggests that the greatest consumption decline during retirement is usually associated with involuntary retirement (S. Smith, 2006). There are distinct decumulation patterns across the population. Estrada-Mejia, de Vries, & Zeelenberg (2016) show that only people with low numeracy decumulate, but those with high numeracy do not. Poterba et al. (2011) find that singles decumulate while couples still accumulate after retirement, which could be explained by the motive to bequest to the surviving spouse, consistent with the findings of (Hurd & Rohwedder, 2013). Love, Palumbo, & Smith (2009) find similar decumulation rates across those aged 65 to 90 years in the HRS sample regardless of shortened life expectancy for the older. They explain the pattern by incorporating the precaution for medical expenses, longevity risk, and bequest motive. Yet Poterba et al. (2011) show larger decumulation rate for the older respondents of the AHEAD cohorts compared to WB/EBB birth cohorts. They explain the differences by the unexpected medical expenses for the older cohort. Many studies have been devoted to investigating the factors that contribute to the adequacy of retirement wealth. Although retirement wealth can also be subject to macroeconomic conditions such as the Great Recession (Gustman, Steinmeier, & Tabatabai, 2014a; Hurd & Rohwedder, 2010; Munnell & Rutledge, 2013), a study finds that the dispersion of retirement wealth is explained by the amounts that households choose to save rather than life circumstances (Venti, 2001). Individuals make investment decisions throughout different stages of life, leading to their retirement. Some of the decisions would change with age and life circumstances. For instance, investors are usually advised by financial planners to construct “life-stage” funds that automatically reduce the proportion of the portfolio held in equities as they age. A study finds a convex relationship between age and equity holdings as a share of financial asset for the elderly, with a predicted minimum at the age of 78 or 80 (Lai, 2008); yet another study finds that the fraction of wealth held in stock market does not change with age for elderly in the U.S. (Ameriks & Zeldes, 2004). Risk aversion is found to increase moderately among the elderly; however, personal characteristics such as race, education, health status, and the number of children significantly affect portfolio allocation (Bellante & Green, 2004). In addition, marital history affects elderly’s involvement in a particular asset and the fraction of wealth held in a particular asset (Ulker, 2008). Retirement saving decisions are related to economic, psychological, and health factors (Skinner, 2007). Regarding the psychological factors, Duckworth, Weir, Tsukayama, & Kwok (2012) find that conscientious and less neurotic HRS respondents made higher lifetime income and accumulated more wealth. A similar pattern was found among U.S. young adults: conscientious individuals accumulate more wealth (Letkiewicz & Fox, 2014). Recent evidence from the Irish Longitudinal Study on Aging suggests that conscientiousness was positively associated with wealth but only at the lower distribution

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of wealth, agreeableness and openness are negatively associated with wealth, but the relations are insignificant (Mosca & McCrory, 2016). Personality may explain wealth accumulation through multiple channels. First, personality traits are associated with financial planning and management behaviors. For instance, conscientious individuals had lower rates of debt (Nyhus & Webley, 2001). As an underlying facet of conscientiousness, the propensity to plan predicts budgeting and less spending, which could lead to higher wealth accumulation (Ameriks, Caplin, & Leahy, 2003). For instance, more conscientious and less neurotic young adults manage their financial matters better (Xu, Beller, Roberts, & Brown, 2015). In addition, personality traits have been shown to predict salary, unemployment, and household income. Conscientiousness, extraversion, and openness predicted higher salaries, and both neuroticism and agreeableness predicted lower salaries (Roberts, Jackson, Duckworth, & Culin, 2011). Moreover, conscientiousness was associated with finding a job faster after becoming unemployed and in experiencing a shorter duration of unemployment, whereas neuroticism predicted the opposite (Uysal & Pohlmeier, 2011). Only a few studies have examined the relationship between personality and retirement. Löckenhoff, Terracciano, & Costa (2009) find no evidence in the East Baltimore Epidemiologic Catchment Area Study that baseline personality explains future retirement decisions. Based on a Norwegian survey, (Blekesaune & Skirbekk, 2012) find that personality predicts only disability retirement but not non-disability retirement. Conscientiousness and neuroticism, however, can affect retirement preparedness through financial planning knowledge and future time perspective (Hershey & Mowen, 2000). The two traits were also found to be correlated with different perceptions of reasons for retirement: conscientious individual have positive while neurotic individuals have negative views of the circumstances leading to retirement (Robinson, Demetre, & Corney, 2010). Personality explains life satisfactions after retirement: Agreeableness, conscientiousness, extraversion and low neuroticism were found to be positively correlated with post-retirement life satisfaction (Löckenhoff et al., 2009; Robinson et al., 2010). In this study we examine the accumulation and decumulation of both non-pension wealth and pension wealth. Although non-pension wealth is a result of lifetime earnings and the active decisions to save, which could be largely affected by personality traits, pension wealth is related to the working history, wages, and occupation, and the accumulation process is subject to the plan participants’ choices to a smaller extent. Hence we expect the personality traits to have smaller effects on pension wealth than on non-pension wealth. We hypothesize that more conscientious individuals will accumulate more wealth by the time they retire and also spend it down more slowly. This is because conscientiousness is associated with deliberate financial planning and careful execution of financial plans, and therefore conscientious individuals are more likely to spend down their wealth more slowly. We also hypothesize that more neurotic individuals will spend their wealth more slowly out of possibly excessive fear about future uncertainties. The rationale is that neuroticism is associated with worries about financial inadequacy and uncertainty, and therefore neurotic individuals are also more likely to decumulate slowly.

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Method Data We use the RAND HRS data file from the Health and Retirement Study, a national representative survey of Americans aged 50 and above. We define two samples by the HRS sample cohorts and birth cohorts. The older birth cohort includes all HRS birth cohorts born between 1931 and 1941, i.e., aged between 51 and 61 in 1992. This sample includes 41,196 respondents. The younger birth cohort includes the World Babies (WB, born between 1942 and 1947) and Early Baby Boomers (EBB, born between 1948 and 1953). We only include the WB respondents who joined the HRS since 1998 and the EBB respondents who joined the HRS since 2004. This sample includes 20,668 respondents. Besides the usual demographic information of the respondents, we use two pieces of important information for this study. The first is the wealth measure of the respondent’s household; the second is the personality traits measures of a respondent. We use three measures of non-pension wealth from the RAND HRS dataset and an HRS user-constructed measure of pension wealth (Gustman, Steinmeier, & Tabatabai, 2014b). The trajectory based on wealth changes may suggest a different rate of decumulation than based on active savings (Hurd & Rohwedder, 2013). The baseline wealth measure we consider is the total non-pension wealth excluding secondary residence (thereafter “non-pension wealth”), which is the total assets net of total debt and secondary residence. We also consider the total non-pension wealth excluding IRA, which is the sum of all wealth components except the value of IRAs and Keogh plans less all debt. Finally, to minimize the influence of housing price fluctuation on the wealth, we consider total non-pension non-housing wealth, which is the sum of the appropriate wealth components less debt and the value of the primary and second residences, mortgages, and home loans. Approximately 3-5% observations in the samples have negative wealth values due to over debt or underwater real properties, and we explicitly deal with this issue in the model specification. The pension wealth includes the present value of both the Define Benefit and Defined Contribution plans from current and previous jobs (Gustman, Steinmeier, & Tabatabai, 2014b). More than 80% of the observations in our sample can be matched to the pension wealth. However, the pension wealth measure is positive for the HRS respondents who are still working and zero for the retirees. We therefore recode the zeros as missing values and restricted the regression analysis of the pension wealth for the HRS birth cohort to age 55 to 79 and that for WB/EBB birth cohort to age 54to67. The HRS measures the respondents’ Big Five personality traits in a self-administered questionnaire (the Leave Behind Survey) available in 2006, 2008, 2010, and 2012. Half of the original HRS sample took the questionnaire in 2006 and 2010, and the remaining sample took the questionnaire in 2008 and 2012. Response rates were 74% for 2006 and 71% for 2008. The MIDUS Big Five Adjectival Scale was used in the questionnaire to

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assess the “Big Five” personality traits (John, Naumann, & Soto, 2008), namely, conscientiousness, emotional stability/neuroticism, extraversion, agreeableness, and openness to experience. The respondents rated the extent to which each adjective describes them on a 4-point scale, from “not at all” (=1) to “a lot” (=4). For example, conscientiousness was assessed by “organized,” “responsible,” “hardworking,” “careless” (reverse-coded), and “thorough.” More items from the International Personality Item Pool (IPIP) were added starting from 2010 to cover sub-facets of conscientiousness, such as “reckless” (reverse-coded), “self-disciplined,” “impulsive” (reverse-coded), “cautious,” and “thrifty.” Model To investigate the wealth accumulation/decumulation pattern, we regress the wealth measures on a vector of personality traits, personal characteristics, wave dummies, and age dummies. Specifically, we test whether this fundamental part of personality has time-variant effects on wealth growth by including the interaction terms between personality traits and age dummies. The econometric model is the following:

𝑤𝑒𝑎𝑙𝑡ℎ!" = 𝛼!!×𝑎𝑔𝑒!"#

!"

!!!"

×𝑐𝑜𝑛𝑠! + 𝛽!×𝑐𝑜𝑛𝑠!

+ 𝛼!!×𝑎𝑔𝑒!"#

!"

!!!"

×𝑜𝑝𝑒𝑛! + 𝛽!×𝑜𝑝𝑒𝑛!

+ 𝛼!!×𝑎𝑔𝑒!"#

!"

!!!"

×𝑛𝑒𝑢𝑟𝑜! + 𝛽!×𝑛𝑒𝑢𝑟𝑜!

+ 𝛼!!×𝑎𝑔𝑒!"#

!"

!!!"

×𝑒𝑥𝑡𝑟! + 𝛽!×𝑒𝑥𝑡𝑟!

+ 𝛼!!×𝑎𝑔𝑒!"#

!"

!!!"

×𝑎𝑔𝑟𝑒𝑒! + 𝛽!×𝑎𝑔𝑟𝑒𝑒!

+ 𝛿!×𝑤𝑎𝑣𝑒!" +!!

!!!

𝛾!×𝑎𝑔𝑒!"#

!"

!!!"

+ 𝑋!"𝜇 + 𝜀!" ,

where𝑤𝑒𝑎𝑙𝑡ℎ!"is an inflation-adjustedmeasure of wealth. It could be one of thefollowing variables from RAND HRS dataset: total non-pension wealth excluding secondary residence, total non-pension wealth excluding IRA, and total non-pension non-housing wealth, and the pension wealth.Becauseapproximately 3-5% observations in the samples have negative non-pension wealth values due to over debt or underwater, we use a truncated model where all the negative values are dropped and the model explicitly accounts for the truncation and the positive values are natural log transformed. As a robustness check, we also run a Tobit model, treating the negative values as missing values and transforming the positive values by the inverse hyperbolic sine transformation.

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The Tobit results are similar to the truncated regression (results are available upon request). To explore the wealth accumulation and decumulation patterns over the lifetime, we include a set of age dummies, 𝑎𝑔𝑒!"#, which takes value one if individual 𝑖 is age 𝑘 in year 𝑡 and zero otherwise. The coefficient of an age dummy reflects the change of wealth level in the corresponding age relative to the baseline age. To examine the effects of the business cycle, we control for the wave effects by wave dummies, 𝑤𝑎𝑣𝑒!". The Big Five personality traits of the respondent of household 𝑖 are denoted by 𝑐𝑜𝑛𝑠!, 𝑜𝑝𝑒𝑛!, 𝑛𝑢𝑒𝑟𝑜!, 𝑒𝑥𝑡𝑟! , 𝑎𝑔𝑟𝑒𝑒! , as measured in the Leave Behind Survey of 2006/2008. Hence the coefficients 𝛼!

! measure the age-varying effects of a personality trait 𝑝 on wealth, and the coefficients 𝛽! measure the permanent effects of the trait. We consider a set of personal characteristics, 𝑋!" . Specifically, marital status is described by a vector of dummies indicating whether married; married, spouse absent; partnered; separated; divorced; separated/divorced; widowed; never married (omitted category). Employment status is denoted in work in full-time, work in part-time, unemployed, partly retired, retired, disabled, and not in labor force (omitted category). Health is measured by self-reported health, CESD score, ADLA score, BMI, dummies for high blood pressure, diabetes, cancer, lung problem, heart problem, stroke, psych problem, arthritis. Finally, demographics such as race, gender, age, and years of education are controlled. The respondents can be the household head or the spouse of the head for a household; hence the standard errors are clustered at the household level. The cross-sectional personal-level analysis weights of HRS are used to adjust for the sample bias. Because personality was measured only at one point of time at different ages for different respondents, we adjust the influence of age by standardizing the personality trait measures on a polynomial of age. As such, the standardized measure only captures the part of personality that is invariant with time. Specifically, we regress each personality trait on the polynomial forms of age at which the trait is measured.

𝑡𝑟𝑎𝑖𝑡! = 𝛾! + 𝛾!𝑚𝑒𝑎𝑠𝑢𝑟𝑒_𝑎𝑔𝑒! + 𝛾!𝑚𝑒𝑎𝑠𝑢𝑟𝑒_𝑎𝑔𝑒!! + 𝛾!𝑚𝑒𝑎𝑠𝑢𝑟𝑒_𝑎𝑔𝑒!! + 𝜁Then the residuals of the regression, 𝜁, are standardized by subtracting the mean and then dividing by the standard error using the following formula.

𝑡𝑟𝑎𝑖𝑡_𝑟! =𝜁 −𝑚𝑒𝑎𝑛(𝜁)

𝑠𝑑(𝜁)

Empirical Results

The Two Cohorts of HRS Respondents

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Table 1 reports the summary statistics for the two cohorts of HRS respondents from pooled waves of the HRS. For the HRS birth cohort, the average total non-pension wealth excluding secondary residence is $40,1578, the total non-pension wealth excluding IRA is $33,8574, the non-pension non-housing wealth is $28,7908, and the pension wealth is $181292. The personality traits are all on a four-point scale. The average level of conscientiousness is 3.382. The average neuroticism is 1.992, the average openness is 3.242, the average extroversion is 3.533, and the average agreeableness is 2.941. In terms of marital status, about 70% are married, 0.5% are married with an absent spouse in the sample, 1.9% are partnered, 1.1% are separated, 0.9% are divorced, and 1% are separated/divorced, 13.1% are widowed, and the remaining are never married. In terms of employment status, 26.4% are employed full-time, 5.7% are employed part-time, 1.3% are unemployed, 11.1% are partly retired, 46% are retired, 1.5% are disabled, with the remaining not in the labor force. The average age is 65. About 85% are white, 11.5% are black, and the remaining are other races. About 43% are male. The average education is 12.6 years. The average self-reported health is about 2.6 on a scale of five points, with 1 being excellent and 5 being poor. In terms of severe health conditions, the average ADL score is 0.05, the average BMI is 27.7, 47.8% report high blood pressure, 14.7% report diabetes, 11.7% report cancer, 8.5% report lung problems, 19% report heart problems, 3% report stoke, and 54.5% report arthritis. The WB&EBB birth cohort is on average 5 years younger than the HRS birth cohort, the reported age difference from the observations across waves is lower than the definitions by the birth cohort. This is because the respondents of the HRS birth cohort may die at later waves due to high age, leaving to a downward-biased average age. The WB/EBB birth cohort accumulated slightly lower non-pension wealth than the HRS birth cohort, yet their personality trait measures are similar. The marriage rate is two percent higher for the WB/EBB birth cohort. The employment status is dramatically different across the cohort: 45.6% of the WB/EBB birth cohort are employed full-time, and 8.7% are employed part-time, and only 8% are partly retirement and 26.6% are retired. The racial composition is similar, yet there are disproportionally fewer males in the WB/EBB birth cohort. The WB/EBB birth cohort is slightly more educated and healthier. The Raw Patterns of Lifecycle Wealth Growth We begin by analyzing the lifecycle wealth pattern in the raw data. The first panel of Figure 1 plots the unconditional lifecycle non-pension wealth trajectory. Each point represents the average (log) wealth observed at the corresponding age for the respondents in the sample. Only inflation is adjusted for the raw data of non-pension wealth but no other factors are considered. Overall, the average value of total non-pension wealth slightly increases from age 51 to middle and late 60s and then starts to decline. Specifically, the non-pension wealth at the peak age of 66 is about 60% higher than at age 50, and about 15% higher than at age 76.

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To visualize the potential effects of the Big Five personality traits on non-pension wealth trajectory, we also plot the lifecycle wealth growth patterns conditional on the quartiles of the five traits in the remaining panels of Figure 1. For instance, the second panel plots the non-pension wealth trajectory by the quartiles of conscientiousness. The non-pension wealth accumulation and decumulation patterns still exist for each subgroup, yet higher levels of conscientiousness are associated with higher levels of total non-pension wealth throughout most of the lifetime. Similarly, different levels of neuroticism and openness are associated with different levels of non-pension wealth throughout the lifetime, yet the wealth levels are less distinguishable by levels of agreeableness and extraversion. Low levels of neuroticism and high level of openness are associated with higher non-pension wealth. No Wealth Decumulation after Controlling for Characteristics The unconditional pattern in Figure 1 does not account for effects of time-variant and time-invariant factors on the non-pension wealth trajectory. Using Equation 1, we control for personality traits, business cycle, and personal characteristics. In the consequent sections, we report the findings from the regression analysis. The baseline results are derived from a truncated regression where the positive values of total non-pension wealth excluding secondary residence are log transformed and the negative values are dropped. The estimation results are reported in figures. Specifically, we present the conditional lifecycle of non-pension wealth growth, the effects of personality traits, business cycle, and personal characteristics. In Figure 2, we plot the coefficients for the age dummies estimated from Equation 1. These estimates describe the residual changes in the total non-pension wealth level after controlling for the impact of personality traits and the business cycle, and time-variant personal characteristics. Using wealth level at ages below 53 as the baseline, the wealth level increase steady for the rest of the life at positive and statistically significant rates. This is evidence that the lifecycle of non-pension wealth accumulation and decumlation can largely be explained by the personality traits, business cycles, and personal characteristics. After controlling these factors, the wealth at the peak age of 66 is only 50% higher than the level at ages below 53, as compared to 60% higher without controlling these factors. The following subsections describe the effects of personality traits, business cycle, and personal characteristics. The Effects of Personality Traits on Wealth Growth In Figures 3 – 7, we plot the effects of each of the Big Five personality traits on non-pension wealth growth. By estimating Equation 1, we allow the standardized personality traits to have different sizes of effects in different ages, and we assume the five traits have independent effects. As a robustness check, we examine the effect of each trait one by one, and we find similar effects, which bolster our confidence that the five traits have independent effects. Figure 3 plots the effects of conscientiousness on non-pension wealth at different ages with the 95% confidence intervals. The pattern suggests that

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conscientiousness has positive effects on total non-pension wealth throughout most of the post-53 ages. For instance, at age 62, a one-standard deviation increase in conscientiousness is associated with a 12% higher level of non-pension wealth. However, when we estimate Equation 1 with individual fixed effects, we don’t find statistically significant results, indicating that the sizes of effects are mostly indistinguishable from each other. This is evidence that the effects of conscientiousness do not change with age nor vary across the accumulation or decumulation phases. Figure 4 plots the effects of openness by age. The pattern suggests that openness has positive effects on total non-pension wealth in selected ages, mostly post 66. For instance, at age 73, a one-standard deviation increase in openness is associated with a 10% higher level of wealth. Figure 5 presents the effects of neuroticism by age. The patterns suggest that neuroticism does not have any statistically significant effects on total non-pension wealth after other traits, life cycle, business cycle, personal characteristics are considered. Figure 6 presents the effects of and extraversion and only shows negative effects of extraversion at selected ages mostly after age 70. Figure 7 presents the effects of agreeableness by age, it suggests that agreeableness has negative effects on total non-pension wealth, but such effects exist only at selective ages in the early 60s and late 60s. The Effects of Business Cycle on Wealth Growth The HRS respondents in our sample witness the economic expansions and contractions through the waves of the survey. The major economic contractions that overlapped with the HRS survey include the early 1990s recession associated with the oil price shock, the early 2000s recession resulting from the dot-com bubble, and the Great Recession in late 2000s as a consequence of the housing bubble. According to the NBER, the early 1990s recession lasted from July 1990 to March 1991, the early 2000s recession lasted from March 2001 to November 2001, and the Great Recession lasted from December 2007 to June 2009. All those events and their aftermaths affected the household wealth of the respondents through various channels. For instance, the asset value of stocks and real estate easily plunges during a recession, the housing value shrinks during a housing collapse, and the employment and income prospects become dismal during a recession. We control for the effects of business cycle by including the wave dummies. Figure 8 plots the coefficients of the wave dummies estimated from Equation 1. The pattern can be interpreted as the common economic shocks in a specific year/wave to all respondents. The figure shows positive and significant estimates for waves 7 (the year 2004) to 10 (the year 2010), with the point estimates increase and peak at wave 8 (the year 2006). Such pattern corresponds to the housing bubble between 2004 and 2006 and the housing collapse in the period that followed. This is evidence that the business cycle has sizable effects on the growth of senior households’ non-pension wealth. The Effects of Personal Characteristics

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In Table 2, we report the coefficients of the personal characteristics estimated from a truncated regression where the negative values of wealth are dropped and the positive values are log transformed. The sample size is smaller than in the sample sizes reported in Table 1 due to the truncation. The dependent variables are total non-pension wealth excluding secondary residence (Column 1), total non-pension wealth excluding IRA (Column 2), and non-pension non-housing wealth (Column 3). The results are comparable across all three dependent variables, indicating the robustness of the results. Compared to those who never married, the married respondents, those who are married but spouse is absent, or partnered, have higher non-pension wealth. Compared to respondents who are not in the labor force, those employed full-time or part-time, unemployed, partly retired, or disabled have lower non-pension wealth. Compared to other races, the white have higher non-pension wealth, and the black have lower wealth. There does not appear to be a gender difference in the non-pension wealth level after controlling for all other factors. One additional year of education increases the non-pension wealth by 16.6%. In general, poor health decreases wealth, which is consistent with the finding that health has causal effects on non-pension wealth (Michaud & van Soest, 2008). In particular, one unit increase toward bad self-reported health decreases non-pension wealth by 16.2%. One unit increase in BMI decreases wealth by 1.7%. ADLA, diabetes, lung disease, and stroke are all associated with lower non-pension wealth. It is interesting that cancer is associated with higher non-pension wealth, which is like because the wealthier are more likely to survive cancer. Cohort Comparison Studies have found cohort differences in retirement wealth patterns and other financial decisions at the senior age. Early boomers (born during 1946-57) have accumulated greater mean wealth than pre-boomers (born during 1934-45) at the same age, yet, the median wealth was about the same and the net worth among lower-middle wealth groups declined (Finke, Huston, & Sharpe, 2006). Anguelov & Tamborini (2009) examine the edge of baby boomers’ consumer and housing debt at near retirement age (50-61) in 2004, and find that they had higher debt than their near-retirement counterparts in ten years ago. In this section, we examine whether there exist a cohort difference between those who were born in 1931-1941 and those born in 1942-1953. The distinct early life experience of these two cohorts may contribute to particular money attitudes and lead to differences in wealth growth patterns. Figure 9 plots the unconditional lifecycle of non-pension wealth for the WB/EBB birth cohort. The age range is smaller as compared to that for the HRS birth cohort. Similar to the HRS birth cohort, the WB/EBB birth cohort keep accumulating wealth up to age 65 and then started to decumulate. Overall the wealth variation is flatter than for the HRS birth cohort. The wealth level at the peak of age 65 is about 30% higher than that at the early 50s. Figure 10 plots the residual lifecycle of non-pension wealth. Similar to the HRS birth cohort, there is no non-pension wealth decumulation in older ages when personality traits,

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life cycle, business cycles, and personal characteristics are considered. Moreover, the WB/EBB birth cohort accumulated wealth at three distinct stages: below 59, 59-64, and 65 and above. Figure 11 –15 plot the effects of the Big Five on the non-pension wealth. Conscientiousness is still positively associated with non-pension wealth since age 53 up to 61. Openness is positively associated with non-pension wealth at selected ages, and neuroticism is negatively associated with wealth in the late 50s. Agreeableness is negatively related with non-pension wealth in the majority of the post-53 ages. However, unlike for the HRS birth cohort, openness is negatively associated with wealth in selected waves. Figure 16 plots the business cycle effects. We only observe the WB/EBB birth cohort starting in wave 4 (the year 1998); hence we make it the baseline year. Contrary to the pattern for the HRS birth cohort, the WB/EBB birth cohort did not experience a boost to their non-pension wealth during the housing bubble (waves 7-8), yet their non-pension wealth declined significantly during and after the Great Recession. Table 3 reports the coefficients for the personal characteristics. The results are again consistent across three measures of non-pension wealth. The patterns of the effects of personal characteristics are similar to those for the HRS birth cohort. Being married, married with the spouse absent, and partnered are associated with higher non-pension wealth than for those who never married. Being employed full-time or part-time, or being disabled is associated with lower non-pension wealth than for those who are not in the labor force. White has higher non-pension wealth than other races while black has lower wealth. Year of education is positive and adverse health conditions are negatively associated with non-pension wealth.

Pension Wealth In this section, we examine the pension wealth. Unlike non-pension wealth, which is a result of lifetime earnings and the active decisions to save, pension wealth is related to the working history, wages, and occupation. There are two kinds of pension plans. The Defined Benefit retirement plans typically define the post-retirement benefits by a combination of working history and wage, subject to inflation adjustments and a vesting schedule. A plan participant gets vested values in the plan by working for an employer, and the employer is responsible for investing the retirement fund to meet the funding target. The Defined Contribution retirement plans only define the monthly contributions from the employee and the employer into the plan, and the employee is responsible for making the investment decisions with limited options of investment funds. Hence, the accumulation of pension wealth is less flexible than the accumulation of non-pension wealth, with limited freedom of choice available to the plan participants. For this reason, we expect the pension wealth to be affected by personality traits to a less extent than the non-pension wealth.

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We use the user-constructed HRS pension wealth data (Gustman, Steinmeier, & Tabatabai, 2014b), which reports the sum of the DB and DC plan wealth for both current and previous jobs if an HRS respondent was still working. Figures 17 and 18 reports the unconditional patterns of pension wealth for the HRS Cohort and WB/EBB cohort, respectively. The HRS cohort’s pension wealth accumulated until their early 70s at a rapid rate whereas the WB/EBB cohort’s pension wealth accumulated more slowly. This could be partially attributable to the trend for the US employers to switch from DB plans to DC plans in the last few decades. When the patterns of pension wealth are illustrated by the quartiles of the Big Five, there is no obvious pattern of associations between the personality traits and pension wealth levels. In results unreported here (available upon request), the effects of the Big Five are statistically insignificant in most ages for both cohorts except that extraversion has positive effects on pension wealth for the WB/EBB cohort between age 61 and 64. Business cycles do not affect the level of pension wealth, and personal characteristics have similar effects on pension wealth as they do on non-pension wealth. Conditional on personality, business cycle, and personal characteristics, pension wealth only slightly increases between age 57 and 64 for the HRS cohort and remains mostly the same for the WB/EBB cohort.

Conclusion and Discussion

We find that the HRS respondents accumulate their non-pension wealth up to their middle 60s and then start to decumulate. The accumulation and decumulation in wealth are more dramatic for the HRS birth cohort than for the WB/EBB cohort. Regardless, the non-pension wealth trajectory is largely attributable to personality, business cycle, and personal characteristics. After controlling for those factors, there is no much residual time variation in non-pension wealth. We find three personality traits are associated with the level of the non-pension wealth – conscientiousness and openness are protectors whereas agreeableness is a risk factor. We do not find evidence that the effects of those traits change in the accumulation phase as opposed to the decumulation phase. When we examine pension wealth, the accumulation of which depends less on the individual choices, personality traits do not explain the accumulation pattern. It is worthwhile to note that the personality traits used in our study was time-invariant and less of the effect of age. In fact, personality traits change with time, and the changes can be related to the development of personal characteristics. Although the static personality traits do not seem to have time-variant effects on wealth growth, it is possible that the evaluation of personality have different effects at different ages through its impact on the development of personal characteristic such as marital status, employment status, and health.

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Figure 1: Life-cycle Wealth Accumulation/Decumulation (HRS birth cohort)

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Figure 2: Residual Wealth Accumulation/Decumulation (HRS birth cohort)

Figure 3: Impact of Conscientiousness on Wealth Accumulation/Decumulation (HRS birth cohort)

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Figure 4: Impact of Openness on Wealth Accumulation/Decumulation (HRS birth cohort)

Figure 5: Impact of Neuroticism on Wealth Accumulation/Decumulation (HRS birth cohort)

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Figure 6: Impact of Extraversion on Wealth Accumulation/Decumulation (HRS birth cohort)

Figure 7: Impact of Agreeableness on Wealth Accumulation/Decumulation (HRS birth cohort)

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Figure 8: Business Cycle and Wealth Accumulation/Decumulation (HRS birth cohort)

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Figure 9: Life-cycle Wealth Accumulation/Decumulation (WB/EBB birth cohort)

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Figure 10: Residual Wealth Accumulation/Decumulation (WB/EBB birth cohort)

Figure 11: Impact of Conscientiousness on Wealth Accumulation/Decumulation (WB/EBB birth cohort)

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Figure 12: Impact of Openness on Wealth Accumulation/Decumulation (WB/EBB birth cohort)

Figure 13: Impact of Neuroticism on Wealth Accumulation/Decumulation (WB/EBB birth cohort)

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Figure 14: Impact of Agreeableness on Wealth Accumulation/Decumulation (WB/EBB birth cohort)

Figure 15: Impact of Extraversion on Wealth Accumulation/Decumulation (WB/EBB birth cohort)

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Figure 16: Business Cycle and Wealth Accumulation/Decumulation (WB/EBB birth cohort)

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Figure 17: Unconditional Pattern of Pension Wealth for the HRS Cohort

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Figure 18: Unconditional Pattern of Pension Wealth for the WB/EBB Cohort

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Table 1. Summary Statistics by Cohorts

HRS Cohorts WB and EBB Cohorts

Mean Std. Dev. Mean Std. Dev.

Non-pension wealth excl. 2nd home 401578 955771 383514 817501 Non-pension wealth Excluding IRA 338575 865919 315333 742102 Non-pension non-Housing Assets 287909 839398 268021 728894

Pension Wealth 181292 1051606 179124 297350

Conscientiousness 3.382 0.454 3.416 0.454 Neuroticism 1.992 0.590 2.097 0.622 Openness 3.242 0.533 3.205 0.551 Extroversion 3.533 0.459 3.537 0.473 Agreeableness 2.941 0.542 3.004 0.539 Married 0.706 0.456 0.722 0.448 Married, spouse absent 0.005 0.067 0.003 0.058 Partnered 0.019 0.137 0.036 0.188 Separated 0.011 0.104 0.016 0.127 Divorced 0.090 0.286 0.121 0.326 Separated/Divorced 0.010 0.101 0.000 0.016 Widowed 0.131 0.337 0.066 0.248 Works full-time 0.264 0.441 0.456 0.498 Works part-time 0.057 0.233 0.087 0.281 Unemployed 0.013 0.114 0.026 0.159 Partly retired 0.111 0.314 0.080 0.271 Retired 0.460 0.498 0.266 0.442 Disabled 0.015 0.123 0.028 0.164 Age 65.276 7.034 59.096 4.582 White/Caucasian 0.853 0.354 0.822 0.383 Black/African American 0.115 0.319 0.124 0.330 Male 0.431 0.495 0.390 0.488 Years of education 12.638 2.841 13.257 2.813 Self-reported health 2.596 1.056 2.601 1.081 ADL score 0.050 0.270 0.054 0.288 BMI index 27.729 5.143 28.903 6.109 Reports high blood pressure 0.478 0.500 0.473 0.499 Reports diabetes 0.147 0.354 0.166 0.372 Reports cancer 0.117 0.321 0.091 0.288 Reports lung problems 0.085 0.278 0.073 0.261 Reports heart problems 0.190 0.392 0.149 0.356

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Reports stroke 0.035 0.185 0.034 0.181 Reports arthritis 0.545 0.498 0.506 0.500 Observations

41,921

20,668

Table 2. HRS Cohorts, Log Transformation, Trucreg (1) (2) (3) VARIABLES Total non-

pension wealth excluding secondary residence

Total non-pension wealth excluding IRA

Non-housing wealth

Married 1.289*** 1.238*** 1.460*** (0.186) (0.184) (0.207) Married, spouse absent 1.028*** 0.991*** 1.283*** (0.286) (0.300) (0.330) Partnered 0.766*** 0.772*** 0.855*** (0.247) (0.248) (0.266) Separated -0.384 -0.424 -0.174 (0.311) (0.306) (0.308) Divorced -0.0615 -0.0751 0.0739 (0.199) (0.198) (0.218) Separated/Divorced 0.0127 0.000640 0.0856 (0.207) (0.205) (0.228) Widowed 0.484** 0.480** 0.519** (0.193) (0.191) (0.214) Works full-time -0.289*** -0.245*** -0.332*** (0.0711) (0.0708) (0.0882) Works part-time -0.259*** -0.211** -0.251** (0.0874) (0.0868) (0.104) Unemployed -0.617*** -0.610*** -0.711*** (0.103) (0.102) (0.123) Partly retired -0.128* -0.137* -0.128 (0.0747) (0.0744) (0.0931) Retired -0.0984 -0.131* -0.0994 (0.0674) (0.0670) (0.0840) Disabled -0.957*** -0.906*** -1.134*** (0.173) (0.171) (0.172) White/Caucasian 0.475*** 0.444*** 0.661*** (0.142) (0.140) (0.164) Black/African American -0.359** -0.321** -0.575*** (0.160) (0.157) (0.183) Male 0.0499 0.0232 0.104** (0.0409) (0.0401) (0.0470) Years of education 0.166*** 0.156*** 0.222*** (0.00942) (0.00925) (0.0111)

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Self-reported health -0.162*** -0.138*** -0.203*** (0.0194) (0.0193) (0.0227) ADL score -0.147** -0.140** -0.267*** (0.0620) (0.0636) (0.0693) BMI index -0.0170*** -0.0163*** -0.0186*** (0.00434) (0.00430) (0.00505) Reports high blood pressure

-0.0553 -0.0538 -0.0761*

(0.0395) (0.0391) (0.0461) Reports diabetes -0.342*** -0.327*** -0.373*** (0.0550) (0.0553) (0.0625) Reports cancer 0.160*** 0.146*** 0.162** (0.0539) (0.0537) (0.0646) Reports lung problems -0.405*** -0.363*** -0.395*** (0.0762) (0.0761) (0.0814) Reports heart problems -0.0504 -0.0628 -0.0632 (0.0450) (0.0453) (0.0541) Reports stroke -0.189** -0.171* -0.292*** (0.0917) (0.0903) (0.112) Reports arthritis -0.0292 -0.0233 0.00277 (0.0398) (0.0399) (0.0467) Constant 9.127*** 9.049*** 7.301*** (0.342) (0.338) (0.392) Observations 35,027 34,963 33,885

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 3. WB and EBB Cohorts, Log Transformation, Trucreg (1) (3) (5) VARIABLES Total non-

pension wealth excluding secondary residence

Total non-pension wealth excluding IRA

Non-housing wealth

Married 0.954*** 0.956*** 0.783*** (0.132) (0.136) (0.139) Married, spouse absent 0.958*** 1.080*** 0.623 (0.351) (0.351) (0.466) Partnered 0.609*** 0.637*** 0.494** (0.179) (0.181) (0.213) Separated -0.302 -0.169 -0.539* (0.260) (0.265) (0.279) Divorced -0.227 -0.171 -0.435*** (0.154) (0.157) (0.162) Separated/Divorced 0.241 0.483 -0.579 (0.578) (0.566) (0.939) Widowed 0.186 0.252 -0.101 (0.171) (0.172) (0.186) Works full-time -0.348*** -0.368*** -0.348*** (0.0971) (0.0959) (0.117) Works part-time -0.211* -0.241** -0.177 (0.109) (0.107) (0.131) Unemployed -0.399*** -0.462*** -0.452*** (0.121) (0.121) (0.144) Partly retired -0.0678 -0.145 0.0489 (0.108) (0.107) (0.130) Retired -0.0556 -0.161 0.0760 (0.105) (0.103) (0.124) Disabled -0.394** -0.449*** -0.709*** (0.154) (0.153) (0.197) White/Caucasian 0.484*** 0.415*** 0.579*** (0.147) (0.145) (0.160) Black/African American -0.354** -0.360** -0.409** (0.172) (0.170) (0.186) Male -0.110** -0.0780 -0.0755 (0.0486) (0.0476) (0.0551) Years of education 0.163*** 0.145*** 0.225*** (0.0109) (0.0109) (0.0127) Self-reported health -0.199*** -0.182*** -0.230*** (0.0251) (0.0250) (0.0282) ADL score -0.222** -0.223** -0.210* (0.0992) (0.0982) (0.114) BMI index -0.0224*** -0.0220*** -0.0243*** (0.00469) (0.00467) (0.00544) Reports high blood pressure -0.117** -0.121*** -0.122** (0.0464) (0.0463) (0.0549)

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Reports diabetes -0.0769 -0.0611 -0.142* (0.0632) (0.0624) (0.0751) Reports cancer -0.000824 -0.00847 0.0967 (0.0798) (0.0797) (0.0918) Reports lung problems -0.400*** -0.379*** -0.353*** (0.0960) (0.0928) (0.109) Reports heart problems -0.137** -0.131** -0.142* (0.0672) (0.0656) (0.0778) Reports stroke -0.101 -0.0903 -0.221 (0.145) (0.144) (0.167) Reports arthritis -0.161*** -0.151*** -0.183*** (0.0448) (0.0444) (0.0530) Constant 9.972*** 10.07*** 8.653*** (0.292) (0.292) (0.338) Observations 18,548 18,454 17,872

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1