Pre- and Post-Retirement Asset Portfolios Jinkook Lee Senior Economist, RAND Corporation
Arie Kapteyn Director, RAND Labor and Population ideas grow here
Erik Meijer
PO Box 2998
Economist, RAND Corporation
Madison, WI 53701-2998 Phone (608) 231-8550
www.filene.org
PUBLICATION #213 (6/10)
Jung-Seung Yang
ISBN 978-1-932795-92-9
PhD Candidate, Seoul National University
Pre- and Post-Retirement Asset Portfolios Jinkook Lee Senior Economist, RAND Corporation
Arie Kapteyn Director, RAND Labor and Population
Erik Meijer Economist, RAND Corporation
Jung-Seung Yang PhD Candidate, Seoul National University
Copyright Š 2010 by Filene Research Institute. All rights reserved. ISBN 978-1-932795-92-9 Printed in U.S.A.
Filene Research Institute
Deeply embedded in the credit union tradition is an ongoing search for better ways to understand and serve credit union members. Open inquiry, the free flow of ideas, and debate are essential parts of the true democratic process. The Filene Research Institute is a 501(c)(3) not-for-profit research organization dedicated to scientific and thoughtful analysis about issues affecting the future of consumer finance. Through independent research and innovation programs the Institute examines issues vital to the future of credit unions. Ideas grow through thoughtful and scientific analysis of toppriority consumer, public policy, and credit union competitive issues. Researchers are given considerable latitude in their exploration and studies of these high-priority issues.
Progress is the constant replacing of the best there is with something still better!
— Edward A. Filene
The Institute is governed by an Administrative Board made up of the credit union industry’s top leaders. Research topics and priorities are set by the Research Council, a select group of credit union CEOs, and the Filene Research Fellows, a blue ribbon panel of academic experts. Innovation programs are developed in part by Filene i3, an assembly of credit union executives screened for entrepreneurial competencies. The name of the Institute honors Edward A. Filene, the “father of the U.S. credit union movement.” Filene was an innovative leader who relied on insightful research and analysis when encouraging credit union development. Since its founding in 1989, the Institute has worked with over one hundred academic institutions and published hundreds of research studies. The entire research library is available online at www.filene.org.
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Acknowledgments
The authors would like to thank George Hofheimer and Ben Rogers at the Filene Research Institute for their thoughtful reviews and the Filene Research Council for the insightful comments on this project.
v
Table of Contents
List of Figures
ix
Executive Summary and Commentary
xi
About the Authors
xiii
Chapter 1
Introduction and Data
1
Chapter 2
Descriptive Findings
9
Chapter 3
Multivariate Analysis
21
Chapter 4
Conclusions
27
Appendix
Data and Figures
31
Endnotes
35
References
37
vii
List of Figures
1. HRS Cohorts and Years of Sampling 2. Direct Stock Holding in 2006 3. Direct Stock Holding over Time by Birth Cohort 4. Mean Share of Stock by Birth Cohort over Time 5. Change in Stock Market: Dow Jones Industrial Average Index 6. Direct Stock Holding before and after Retirement 7. Direct Stock Holding and Mean Share before and after Retirement by Household Composition 8. Direct Stock Holding and Mean Share before and after Retirement by Education 9. Direct Stock Holding and Mean Share before and after Retirement by Total Household Income (2006 real dollars) 10. Direct Stock Holding and Mean Share before and after Retirement by Total Financial Assets (2006 real dollars) 11. Direct Stock Holding and Mean Share before and after Retirement by Spouse’s Retirement Status 12. Direct Stock Holding and Mean Share before and after Retirement by Type of Private Pension 13. Direct Stock Holding and Mean Share before and after Retirement by Bequest Expectations 14. Stock Ownership over Time 15. Mean and Median Share of Stock by Birth Cohort over Time 16. Stock Holding before and after Retirement by Key Household Characteristics 17. Stock Holding before and after Retirement by Spouse’s Retirement Status and Private Pension Holdings 18. Bequest Expectation and Stock Holding before and after Retirement 19. Probit Regressions of Direct Stock Ownership Immediately after Retirement 20. Probit Regressions of Change in Direct Stock Value after Retirement
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Executive Summary and Commentary
By Ben Rogers, Research Director
If you own a home in Bakersfield, California; Henderson, Nevada; or Homestead, Florida, in 2010, you’re well acquainted with the effects of an asset bubble. Many get excited and some get greedy, leading almost all to buy in droves, but when the music stops, nobody is left to buy. Real estate values that once seemed normal appear in retrospect garishly inflated. With the depths of the American mortgage crisis fresh in the collective memory, it’s easy to see the devastation caused by a quick run up in value followed by a market suddenly devoid of demand. Value disappears. Consider, then, the prospect of a similar collapse in the equities market. Such a collapse might not be driven so much by speculation as by a quirk of demography: millions of baby boomers needing to sell stock holdings to fund retirement. This Filene report aims to quantify the risk of such a run and offer perspective for credit unions looking to serve the long-term asset-management needs of aging members.
What Is the Research About? Pre- and Post-Retirement Asset Portfolios draws on the RAND Corporation’s Health and Retirement Study (HRS) to track the asset-selling trends of previous generations. Those data offer the financial asset portfolios for a nationally representative sample of U.S. adults at least 50 years of age for more than two decades, enabling us to describe the changes in asset portfolios and trends in direct stock holding and to explore what influences direct stock holding. Stock holding has changed over time in the general population, but the holdings among older generations have generally moved in concert. So, it appears that the boomers’ retirement will be no more or less significant in the equities market than the retirements of previous generations. The researchers examine important individual variables, like education, age, and family situation, along with multivariate factors to elicit the most important factors affecting asset holdings before and after retirement.
What Did the Researchers Discover? A very real population wave of baby boomers is entering retirement. Yet, previous retirement waves indicate that no significant changes to direct stock holdings occur around retirement time. Economic theory may assume that people will sell off assets to compensate for lost wages and to smooth retirement expenses, but the actual data show that real-world behaviors are much more nuanced. For example, more than half of households did not hold stock before or xi
after retirement, but 1 in 10 acquired stock while 1 in 10 divested of stock. A quarter simply continued to hold stock. The research highlights some interesting findings relevant for anyone trying to understand investors’ asset holdings before and after retirement. For example: • Households with less than $50,000 in total financial assets hold about 11% of those assets in stock. Households with total financial assets of more than $150,000 have a lower stock-holding rate after retirement than before, indicating that they may sell assets to pay for retirement. • A high school education matters in stock ownership: Those with less than a high school education are less likely than those with a high school education to own stocks after retirement. • Households with higher mathematical ability and more wealth tend to hold rather than sell stocks after retirement. • Those with traditional pensions are more likely to directly hold stock than those with defined contribution plans, probably because annuitized retirement plans allow their holders to take more risks elsewhere.
What Are the Credit Union Implications? Credit unions that offer investment services should pay attention to the research findings that show consumers—especially higher-wealth consumers—maintaining direct stock holdings long after retirement. Although it’s tempting to think that members will unload direct stock holdings at retirement, it’s also far too simplistic a view. As credit union membership continues to age, it will be increasingly important to cater to members’ actual behavior. And while this research suggests that the baby boomer retirement surge will not depress the stock market in a significant way, it’s essential to ferret out the individual needs of retiring members. Just as the real estate market affected homeowners in Nevada, Michigan, and Texas differently, retirees’ needs are driven by individual circumstances.
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About the Authors
Jinkook Lee Jinkook Lee is a Filene research fellow and a senior economist at RAND Corporation. Before joining RAND, she was a professor at Ohio State University and held visiting positions at the Federal Reserve Board and the University of Wisconsin–Madison. Her research expertise covers family economics and consumer finance, and her recent work focuses on the economics of aging, with particular interest in the health, retirement, and well-being of the elderly. Lee received a career development award from the National Institute on Aging, National Institutes of Health, for investigating the linkage of health to wealth and wealth transfers. She developed the Korean Longitudinal Study of Aging, a large-scale, multidisciplinary study, and is currently developing the Longitudinal Aging Study in India as a co-principal investigator. With her colleagues at RAND, Lee is developing an information system that integrates all international surveys on aging, health, and retirement. She is also leading a biomarker collection initiative for the National Longitudinal Study of Youth. Her current research projects include mental health and its relationship to retirement and socioeconomic status, public and private wealth transfers, and the economic status of the elderly. Arie Kapteyn Arie Kapteyn is a senior economist at RAND, a fellow of the Econometric Society, past president of the European Society for Population Economics, and a corresponding member of the Netherlands Royal Academy of Arts and Sciences. Before joining RAND, Kapteyn held a chair in econometrics at Tilburg University, where he served in numerous capacities, including dean of the faculty of economics and business administration, founder and director of CentER (a research institute and graduate school) and CentERdata (a survey research institute), and director of CentER Applied Research (a contract research institute). He has held visiting positions at several universities, including Princeton University, the California Institute of Technology, Australian National University, the University of Canterbury (New Zealand), the University of Bristol, and the University of Southern California. Kapteyn’s research expertise covers microeconomics, public finance, and econometrics.
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Erik Meijer, PhD Erik Meijer is an economist at RAND. Before joining RAND, he was an assistant professor of econometrics at the University of Groningen, the Netherlands, and a researcher at the transportation research firm MuConsult in the Netherlands. He has a PhD in social sciences from Leiden University in the Netherlands. His primary expertise is econometric modeling, especially measurement issues. Recent work includes the measurement of earnings, health, and financial literacy, and estimating eligibility for a low-income subsidy for Medicare Part D. He has also worked on a variety of other topics, including preference heterogeneity and individual decision making, sample selectivity, and multilevel analysis. Much of his current work relates to the economics of aging. Jung-Seung Yang Jung-Seung Yang is a PhD candidate in economics at Seoul National University in South Korea. His dissertation is on income inequality and signaling. His research interests include income inequality, poverty, and population aging.
xiv
CHAPTER 1 Introduction and Data
The huge baby boomer generation is reaching retirement age. Their arrival has sparked fears of a market disruption if they all seek to sell investment assets at the same time. This research looks to solid historical data to address that fear.
The United States is facing a substantial demographic shift. In 2020, about 14% of Americans will be at least 65 years of age; by 2040, 20% will be (Kinsella and He 2009). In assessing the implications of such a shift for the financial market, a number of financial market analysts have suggested that security prices will decrease as the population of retirement age increases and more households try to sell their security assets (Parker 2001; Shambora 2006). How might individuals and households optimize their portfolio of assets over a lifetime, particularly before and after retirement? Previous research suggests several different approaches. Samuelson (1969) concluded that a rational investor should hold the same fraction of her portfolio in risky assets at all ages, if returns on such assets are uncorrelated over time. Recognizing that labor income can be considered as a substitute for riskless assets (albeit subject to job security), Cocco, Gomes, and Maenhout (2005) suggested that the optimal share of a financial portfolio invested in equities decreases in the absence of labor income. In practice, consistent with Cocco et al. (2005), financial advisors often suggest that investors reduce their equity exposures as they approach retirement. Such advice may include following the “110 – age” rule, in which In 2020, about 14% of Americans will be at least 65 years of the investor’s percentage of age; by 2040, 20% will be. risky assets does not exceed 110 minus the investor’s age (or, for example, 60% for a 50-year-old investor). To cater to the perceived desire of investors to reduce their equity exposures as they age, several financial institutions have created life-cycle funds, designed for an investor with a target retirement date. Such funds have become increasingly popular and a default option for many retirement funds. In this report, we examine household asset portfolios, particularly stock holding and its share of the total household assets before and after retirement. We use a nationally representative data set to investigate changes in stock holding and asset allocation before and after 2
retirement. Our goal is to describe changes in the household financial portfolio around retirement. Our descriptive model allows us to identify the extent to which household stock holding and portfolio allocation change before and after retirement and what influences household reallocation of the financial portfolio after retirement. We use the HRS to track over time the financial asset portfolios of a nationally representative sample of U.S. adults at least 50 years of age. These longitudinal data enable us to investigate the changes in asset portfolio over time and thereby to depict how older adults manage their financial portfolios in anticipation of and after retirement. In this chapter, we describe the HRS, the cohorts that compose it, its stock-holding measures, other variables that might influence household portfolios, and the sample we selected for analysis. In Chapter 2, we present descriptive findings, starting with an analysis of stock holding by age, followed by a longitudinal analysis of stock holding over time by birth cohort and changes in the stock market. We then compare stock holding before and after retirement for those who have retired, and explore potential variations by household composition, education, presence and retirement status of a spouse, private pension holding, and bequest expectations. In Chapter 3, we present findings from multivariate econometric models on household portfolio allocation decisions after retirement. To shed more light on the relative contributions of the variables we studied, we estimated multivariate econometric models that include all of these variables at the same time as explanatory variables. In this way, we take into account the correlations between the explanatory variables and are better able to attribute a certain change in stock ownership or stock wealth to specific explanatory variables. In Chapter 4, we present our conclusions and implications.
Data The data for this report are derived from RAND’s HRS, a multipurpose, longitudinal household survey representing the U.S. population at least 50 years of age. The HRS selects baseline respondents from the community-dwelling population and reinterviews them every two years (even if the respondent enters an institution) until death. The HRS conducted its first wave of interviews in 1992; its most recently available wave of data, Wave 8, was collected in 2006. In Wave 1, there were 12,652 respondents in 7,702 households. Wave 8 included 18,469 respondents in 12,605 households. We focus on the variables that indicate changes in financial portfolios— in particular, changes in stock holding around retirement—but present some cross-sectional results (primarily for 2006) as well. 3
Sampling History and Cohorts
The 2006 HRS sample consists of five cohorts. Each comprises people who were in the community-dwelling population at the time of the initial interview. Figure 1 summarizes the composition Figure 1: HRS Cohorts and Years of Sampling (Health and Retirement Study 2008). We also describe each Year of Ages at Cohort Birth years sampling sampling Ages in 2006 below. AHEAD
–1923
1993
70+
83+
CODA
1924–1930
1998
68–74
76–82
HRS
1931–1941
WB
1942–1947
EBB
1948–1953
The first, original HRS cohort consists of people born 1992 51–61 65–75 between 1931 and 1941 and 1998 51–56 59–64 represents the community2004 51–56 53–58 dwelling U.S. population in this age group at the time of the first interview in 1992, along with their spouse or partner (of any age) at the time of the first interview or of any subsequent interviews. The HRS cohort has been reinterviewed every two years since the initial 1992 interview. The AHEAD (Asset and Health Dynamics among the Oldest Old) cohort consists of people born before 1924 and their spouse or partner (of any age) at the time of the first interview in 1993–1994 or of any subsequent interviews. The AHEAD cohort was interviewed in 1993–1994, 1995–1996, 1998, and every two years thereafter. The War Baby (WB) cohort consists of people born between 1942 and 1947 who at the time of screening in 1992 (the same screening used for the HRS cohort) did not have a spouse or partner who was born before 1924 or between 1931 and 1941 (i.e., populations belonging to the first two cohorts), along with their spouse or partner at the time of the initial interview or subsequent interviews. The WB cohort was first interviewed in 1998 and has been reinterviewed every two years since. The Children of the Depression Age (CODA) cohort consists of people born between 1924 and 1930 who at the time of the first interview in 1998 did not have a spouse or partner who was born before 1924 or between 1931 and 1947 (i.e., populations belonging to the first three cohorts), along with their spouse or partner at the time of the initial interview or subsequent interviews. The CODA cohort has been reinterviewed every two years. The Early Baby Boomer (EBB) cohort consists of people born between 1948 and 1953 who at the time of the initial interview in 2004 did not have a spouse or partner who was born before 1948 (or from the populations belonging to the first four cohorts), along with their spouse or partner at the time of the initial interview or subsequent interviews. The EBB cohort was reinterviewed in 2006. 4
The inclusion of new cohorts over time means that the number of longitudinal observations differs across the cohorts. For the HRS cohort, data are available from 1992 to 2006. For the AHEAD cohort, data are available from 1993–1994 to 2006. For the CODA and WB cohorts, data are available from 1998. For the EBB cohort, only 2004 and 2006 data are available. Stock-Holding Measures in the HRS
The HRS collects household wealth data. In a household consisting of couples, the spouse or partner who is the most knowledgeable about household finances is the designated financial respondent and answers all questions about assets. The number of wealth categories in the HRS is quite large, yielding detailed information. The HRS distinguishes among stock holding in pension plans such as 401(k) accounts, in individual retirement accounts (IRAs), and in direct holdings, but it does not distinguish between stock holding in single companies and stock holding in mutual funds. The HRS wealth data are generally considered high quality (Juster and Smith 1997). One reason for this is the detailed disaggregation, which tends to minimize the amount of wealth underreported (“forgotten”) through amalgamation in a broad category. Another reason the HRS wealth data are considered high quality is the use of unfolding brackets. The survey first asks respondents whether the household owns a certain type of asset (or has a certain type of debt). If yes, then the survey asks the value (amount) of this asset. If the respondent does not know or refuses to provide the exact (or approximate) amount, the survey asks the respondent whether it is more or less than a certain amount, with subsequent interval questions to help researchers more accurately impute data. In this report, we focus primarily on direct stock holding, that is, stock holding outside pension plans and retirement accounts. We also assess the proportion of stocks that composes total household wealth.1 Because ambiguous wording of asset questions for the first wave of the AHEAD cohort (1993–1994) led to the underreporting of asset ownership (Rohwedder, Haider, and Hurd 2006), we do not use wealth data from it. Other Variables of Interest
We study relationships of stock holding and stock wealth with standard demographic variables (age, household composition, education) as well as other relevant variables such as income. The HRS measures income in great detail. Some components, such as earnings, are known at the individual level, while others, such as asset income, are measured at the household level. We only consider total household income. Income questions also include unfolding bracket sequences 5
for imputation of respondents who do not know or refuse to answer direct questions. The HRS asks individuals who are (still) working whether their employer offers a pension plan, and if so, whether it is a defined benefit (DB) or defined contribution (DC) plan and whether they are enrolled in it. (The HRS includes data on individuals enrolled in both a DB and a DC plan.) Individuals bear the market risk for DC plans, while DB plans provide guaranteed income (although they are still subject to inflation risk). Presence or absence of these plans can influence households’ direct stock holding, because they are part of the total wealth portfolio. One might assume that households with DB plans will typically have more risky assets in other parts of their portfolios (e.g., more direct stock holding), while households with DC plans may be more conservative in other parts of their portfolios.2 Upon an individual’s retirement, a DB plan typically takes the form of annuity payments, while a DC plan can be cashed to obtain a lump sum (which can be invested in stocks, annuities, or other assets). Sometimes, DC plans are not immediately cashed after retirement. Thus, current pension plan enrollment is likely to affect stock holding, both before and after retirement. The HRS indicates retirement status in two places (St. Clair et al. 2008, 850). One is the question, “At this time do you consider yourself completely retired, partly retired, or not retired at all?” The other is the labor force participation question. In identifying retirees, we give priority to the first question because we believe that this more accurately reflects how respondents view themselves and thus will be more closely linked to behaviors of interest regarding retirement. For respondents who did not answer this question, we used the labor force participation question to determine retirement status. For respondents who retired but later worked again, we used the initial retirement date in analyzing behavior before and after retirement. The HRS also asks a series of questions on bequests. In most waves, it first asks respondents the probability of leaving a bequest of $10,000 or more. Respondents who indicate a greater than zero probability of such a bequest are asked the probability of leaving a bequest of $100,000 or more; those who indicate a zero probability are asked the probability of leaving any bequest. However, in the first two AHEAD waves, the routing through these questions is different, and in the first HRS wave, a different bequest expectation question was asked. The HRS collects data on memory, computational ability, and basic cognitive ability. All waves of the HRS ask respondents to recall a list of 10 words immediately after reading (with the 1992 and 1994 waves asking 20 words) and to recall them again about 20 minutes 6
later. In waves after 1994, respondents were asked to subtract consecutive sevens from 100. Other cognitive questions include a series of Telephone Interview for Cognitive Status (TICS) questions, a version of the Mini-Mental State Exam modified for telephone interview (Brandt, Spencer, and Folstein 1988). HRS uses a reduced version of the TICS, including questions on backward counting, naming (objects, the president, and the vice president), and dates. Sample Selection
Because wealth is measured at the household level, our analyses use the household as the unit of observation. Our sample consists of all households that were ever in the sample, although due to mortality, missing data, or other causes (such as a respondent who has not yet retired in the most recent wave or who was already retired in the entry wave), we do not use all households in all analyses. A “household” consists of a respondent and his or her spouse or partner (hereafter simply called “spouse”), if any. Other household members and their income and wealth are not counted for the analysis. For each household, we randomly choose a representative individual and assess his or her stock ownership by individual characteristics such as retirement status and age. Focusing on one individual may prevent us from estimating such associations as sharply as possible, but attempting to account for the characteristics of both spouses has other drawbacks. These include making tables and figures much harder to comprehend, and making sample sizes for many cells (e.g., with large age differences or other unusual combinations) too small for useful analysis. Because household composition may change (due to mortality, divorce, or marriage), we randomly designate a representative individual for each household for each wave. We restrict longitudinal analyses—in particular, comparisons before and after retirement—to households in which no change in composition occurs in the relevant waves. In analyses of cohorts, we use the birth year of the representative individual to define the cohort, and not the entry cohort of the household (which may be the birth year cohort of the other spouse).3
7
CHAPTER 2 Descriptive Findings
The researchers examine stock-holding trends before and after retirement and break them down by key variables, including household composition, education, income, household assets, pension type, and bequest expectation.
Direct Stock Holding and Age Using the 2006 HRS, we first examine direct equity holding, that is, stocks held outside pension plans and retirement accounts, and the proportion of total wealth they compose.4 Figure 2 presents, by representative respondent age, the percentage of households directly holding stocks and, conditional upon holding stocks, the mean and median shares that stocks compose of portfolio values.
Figure 2: Direct Stock Holding in 2006
At first glance, these variables appear to be positively associ% Holding stock % Mean share % Median share ated with age. Among respon25.2 19.9 11.1 dents 50–54 years of age in 26.0 19.9 12.5 2006, only 25% directly held 30.3 22.6 13.0 stock, compared with 30% of 28.0 25.6 19.3 those at least 80 years of age. 28.2 23.9 15.9 Among those directly hold26.7 29.1 20.2 ing stocks, stocks composed a 30.1 35.9 29.4 mean 20% share and a median 11% share of portfolio value for those 50–54 years of age, but a mean 36% and a median 29% portfolio value for those at least 80 years of age. Conditional upon holding stock
Age
N
50–54
1,027
55–59
1,634
60–64
1,474
65–69
2,291
70–74
1,975
75–79
1,397
80+
2,503
Such patterns appear to contradict conventional investment advice. One explanation for them might be mortality bias: Because the poor are less likely to live to age 80, those respondents surviving to the time of the survey will tend to be more affluent and to hold stocks (Attanasio and Hoynes 2000). Another, less likely explanation is that this reflects a cohort effect, i.e., cohorts born in earlier years might be more likely to own stocks. We investigate these issues by using the longitudinal information in the HRS.
10
Changes in Stock Holding over Time To assess whether the pattern of stock holding by age found above is due to a cohort effect or mortality bias, we examine stock holding over time by cohort. Figure 3 presents the proportion of Figure 3: Direct Stock Holding over Time by Birth Cohort each cohort that owned stock by calendar year (see Fig40.0 ure 14 in the appendix). 35.0 % Holding stock
30.0 25.0 20.0 15.0 10.0 5.0 0.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 AHEAD
CODA
HRS
WB
EBB
Figure 4: Mean Share of Stock by Birth Cohort over Time 40.0 35.0 % Mean share
30.0 25.0 20.0 15.0 10.0 5.0 0.0 1992
1994 AHEAD
1996
1998 CODA
2000 HRS
2002
2004 WB
2006 EBB
There are a few notable trends in this figure. All cohorts, except the AHEAD cohort, move in parallel over time. This indicates that there is no significant difference in stock ownership by age across birth cohorts and that stock holding has changed over time in the general population. Figure 4 presents the mean share of stock in the total financial portfolios for each cohort, conditional upon stock ownership (also see Figure 15 in the appendix). For the HRS and WB cohorts, there is a slight curvilinear pattern with the proportion that stocks compose of portfolio value peaking in 2000 and decreasing afterward. Such a pattern is not evident for the AHEAD and CODA cohorts; for these, the mean share of stock in portfolio value increases over time. Because the EBB cohort entered the survey in 2004, we have only two points of time for it, in which mean share of stock in portfolios decreases.
There are two plausible explanations for the trends among the AHEAD and CODA cohorts. First, older investors (that is, those at least 70 years of age) may choose not to rebalance their portfolios 11
but instead let returns on stock investments accumulate over time. Second, among older cohorts, those who are less wealthy are more likely to die younger, creating mortality bias that results in relatively larger stock portfolios among surviving respondents. Figure 5, which shows the Dow Jones Industrial Average Index from 1992 to 2006, provides some context for these trends. There was a bull stock market from 1992 to 2000, but since then, a drop in 2002–2003 was followed by a recovery until 2006. Comparing this figure with earlier figures, it appears that stock holding among our samples increases during the time of the bull market, but has varied as the value of the market has fluctuated, with each age cohort reacting to the market differently.
Direct Stock Holding before and after Retirement Retirement may influence households’ direct stock holding. We examined direct stock-holding status in the last wave before retirement and the first wave after retirement for those who retired during the study period. Figure 6 shows the pattern of stock holding around retirement.
Figure 5: Change in Stock Market: Dow Jones Industrial Average Index 12K 11K 10K 9K 8K 7K 6K 5K 4K 1992
1994
1996
1998
2000
2002
2004
2006
Volume 228,960,000 2B 1B
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Figure 6: Direct Stock Holding before and after Retirement After retirement not having stock
After retirement having stock
Total
Before retirement not having stock
54.56%
8.98%
63.54%
Before retirement having stock
10.33%
26.13%
36.46%
Total
64.90%
35.10%
100.00%
Altogether, 64% of the households we analyzed did not own stock before retirement. Of those, 55% still did not hold stock after retirement, but 9% did own stock after retirement. Thirty-six percent of households owned stock before retirement. Of those, 26% also held stock after retirement and 10% did not. Put another way, a little more than half of households had no direct holdings of stock before or after retirement, but nearly 1 in 10 acquired stock after retirement, another 1 in 10 divested stock then, and more than 1 in 4 continued to hold stock. We next examine what influences household decisions to own stock after retirement.
Direct Stock Holding before and after Retirement by Key Household Characteristics As shown above, some households sell stock after retirement, while others purchase it. Below we examine how household direct stock holding changes with retirement by key household characteristics. (See also Figure 16 in the appendix.) Household Composition
A greater proportion of couples than singles holds stock both before and after retirement (Figure 7). The proportion of couples holding stock is nearly the same before and after retirement. Among singles before retirement, nearly equal proportions of males and females hold stock, but after retirement the proportion of males holding stock increases while that for females decreases. Among those holding stock, the mean share of stocks in portfolios is stable for couples before and after retirement but increases for single men and women after retirement.
13
Figure 7: Direct Stock Holding and Mean Share before and after Retirement by Household Composition 45.0 40.0 35.0 30.0
30.0
25.0
25.0
% Mean share
% Holding stock
35.0
20.0 15.0
20.0 15.0
10.0
10.0
5.0
5.0 0.0
0.0 Single male
Single female Have spouse/ partner
Before retirement
After retirement
Single male
Single female Have spouse/ partner
Before retirement
After retirement
Education
Stock holding and the share of stock in financial portfolios are positively associated with educational attainment (Figure 8). Before retirement, stock holding is highest among college graduates, at 58%, and lowest among those who did not graduate from high school, at 11%. This pattern persists after retirement, although stock holding drops more for those with at least some college education than those without. Before retirement, stock holding is highest among college Among stockholders, stocks graduates, at 58%, and lowest among those who did not graduaccount for a mean 28% of ate from high school, at 11%. portfolios for college graduates and about 21%–22% for high school graduates before retirement. The share of stocks in portfolios increases slightly after retirement for all educational groups except those with some college education. Income
Not surprisingly, stock ownership is highest among those with higher family income, which is correlated with educational attainment (Figure 9). The proportion of households with less than $50,000 in total annual income (as measured in 2006 dollars) that own stock is low before retirement but barely changes afterward. The proportion owning stock before retirement is higher but drops more for households 14
Figure 8: Direct Stock Holding and Mean Share before and after Retirement by Education 70.0 30.0 25.0
50.0 % Mean share
% Holding stock
60.0
40.0 30.0 20.0
20.0 15.0 10.0 5.0
10.0 0.0
0.0 Less than High school high school graduates Before retirement
Some college
College graduates
Less than High school high school graduates Before retirement
After retirement
Some college
College graduates
After retirement
Figure 9: Direct Stock Holding and Mean Share before and after Retirement by Total Household Income (2006 real dollars) 70.0 30.0 60.0 25.0
% Mean share
% Holding stock
50.0 40.0 30.0 20.0
20.0 15.0 10.0 5.0
10.0 0.0
0.0 Less than $50,000
$50,000– $74,999
Before retirement
$75,000– $99,999
$100,000 or more
After retirement
Less than $50,000
$50,000– $74,999
Before retirement
$75,000– $99,999
$100,000 or more
After retirement
with at least $75,000 in income. Among those owning stock after retirement, the mean share of stocks in the portfolio increases most for those with $75,000–$99,999 in income before retirement. Total Financial Assets
Wealthier households are more likely to own stock and, among stockholders, have stocks compose a higher share of their portfolios (Figure 10). 15
Figure 10: Direct Stock Holding and Mean Share before and after Retirement by Total Financial Assets (2006 real dollars) 70.0 30.0 60.0 25.0
% Mean share
% Holding stock
50.0 40.0 30.0 20.0
20.0 15.0 10.0 5.0
10.0
0.0
0.0 Less than $50,000
$50,000– $74,999
Before retirement
$75,000– $149,999
$150,000 or more
After retirement
Less than $50,000
$50,000– $74,999
Before retirement
$75,000– $149,999
$150,000 or more
After retirement
Among households with less than $50,000 in total financial assets, direct stock holding is about 11% both before and after retirement. Among households with total financial assets of $50,000– $149,999, the proportion of Wealthier households are more likely to own stock and, among stock holding remains stable, stockholders, have stocks compose a higher share of their while households with total portfolios. financial assets of more than $150,000 have a lower stockholding rate after retirement than before. Among the stockholders, the mean share of stock in portfolios increases after retirement for households with assets of less than $50,000 and among those with assets of $50,000–$149,999.
Direct Stock Holding and Riskless Assets Earlier research has suggested that labor income can substitute for riskless assets. While we have examined the relationship between retirement and stock holding, we have not accounted for labor income from a spouse. Figure 17 in the appendix presents stock holding before and after retirement conditional upon spouse’s work and income as well as by pension holding. We also consider stock ownership by pension type, differentiating among respondents who 16
have a DB plan, those who have only a DC plan, and those who have no plan. Spouse’s Retirement Status
Respondents with a spouse or partner are more likely to hold stocks than those without a spouse or partner (Figure 11). Among respondents with a spouse, stock holding is about 3% higher among those with a working spouse than those without. This descriptive finding may be related to higher household income rather than a working spouse per se. Regardless of having a spouse or spouse’s retirement status, stock holding drops after retirement for all categories. Among stockholders, those without a spouse increase the share of stocks in their portfolio after retirement, as we saw earlier for single males and single females. For couple households, the mean share of stocks remains stable before and after retirement, with households with a retired spouse having a higher mean share of stocks in portfolios than households with a working spouse. Private Pension
Private pension is an important source of old-age income. Households can hold multiple pensions. Because the market risk of pensions is borne by individuals for DC but not (or at least less so) for
Figure 11: Direct Stock Holding and Mean Share before and after Retirement by Spouse’s Retirement Status 50.0 45.0 40.0
35.0 30.0
30.0
25.0
25.0
% Mean share
% Holding stock
35.0
20.0 15.0
20.0 15.0
10.0
10.0
5.0
5.0
0.0
0.0 Spouse working Spouse retired Before retirement
No spouse
After retirement
17
Spouse working Spouse retired Before retirement
No spouse
After retirement
DB plans, we categorize households into three pension groups: those with a DB pension, those with a DC or an IRA but not a DB plan, and those without a private pension. Figure 12 shows stock holding before and after retireHouseholds with a pension are more likely to hold stocks than ment by type of private pension those without, but this may be an effect of household income holding. Households with a rather than pension holding. pension are more likely to hold stocks than those without, but the type of pension held does not appear to affect stock holding. This may be an effect of household income rather than pension holding. Conditional upon holding stocks, households with and without pensions have about a fourth of their portfolios in stocks.
Stock Holding and Bequest Expectation Another possible key influence on financial portfolios is bequest intention. Individuals planning to leave bequests may have a longer time horizon for investments and reduce their own lifetime consumption so as to leave more in stocks and similar assets for their heirs. As mentioned in the “Data� section in Chapter 1, the HRS asks about subjective probabilities for leaving bequests, which is a combination of (pure) bequest intentions and bequests resulting
Figure 12: Direct Stock Holding and Mean Share before and after Retirement by Type of Private Pension 50.0 45.0 30.0 25.0
35.0 30.0
% Mean share
% Holding stock
40.0
25.0 20.0 15.0 10.0
20.0 15.0 10.0 5.0
5.0
0.0
0.0 Having DB
Having DC Having no and/or IRA private pension but without DB
Before retirement
After retirement
18
Having DB
Having DC Having no and/or IRA private pension but without DB
Before retirement
After retirement
Figure 13: Direct Stock Holding and Mean Share before and after Retirement by Bequest Expectations 60.0
% Holding stock
50.0 40.0 30.0 20.0 10.0 0.0 Prob=0
0<Prob<=50
50<Prob<100
Prob=100
Probability of leaving a bequest of $100K+ Before retirement
After retirement
from other motives, such as precautionary savings. Hence, we will use the term bequest expectations rather than bequest intentions. Figure 13 presents stock-holding characteristics by bequest expecThose more likely to leave a bequest are more likely to hold tations (also see Figure 18 in the stocks, with those leaving larger bequests most likely to hold appendix). Those more likely to stocks. leave a bequest are more likely to hold stocks, with those leaving larger bequests most likely to hold stocks, although causality is not evident in these data, given that those with more assets have more stocks but also have a higher probability of leaving a bequest. The proportion holding stocks and the mean share of stocks in portfolios decrease most after retirement for those with the lowest bequest probabilities.
19
CHAPTER 3 Multivariate Analysis
Multivariate analysis of the retirement survey suggests a relatively smooth transition to retirement and that stockholders approaching retirement do not sell stock quickly enough to elicit serious concern.
In the previous sections, we have shown associations between several characteristics and stock holding, and differences associated with stock ownership before and after retirement. These bivariate associations can often be explained by joint associations with other variables of interest, or by differential mortality and other selection effects, preventing causal interpretations of them. To shed more light on the relative contributions of these variables to stock holding, we have estimated multivariate econometric models using all these variables. This better enables us to attribute a certain change in stock ownership or stock wealth to specific explanatory variables. As we did in the earlier descriptive analyses, we focus on the transition into retirement. In particular, our sample is the same as the one used in most figures in the previous sections (except for a small number of observations that were dropped for technical reasons), consisting of households in which the representative individual retired between waves. The explanatory variables are the descriptive ones we analyzed earlier, but using, when available, continuous rather than categorized variables.5 Because the bequest expectations questions (and especially the routing) differed across waves, we have combined these into four categories: the three dummies for “Expect to leave a large bequest” (at least 50% chance of leaving a bequest of $100K or more in Waves 2–8, or “Yes, definitely,” or “Yes, probably” in Wave 1), “Expect to leave a small bequest” (does not expect to leave a large bequest according to the definition above, but at least 50% chance of leaving a bequest of $10K or more or “any” bequest in Waves 2–8, or “Yes, possibly,” or “Probably not” in Wave 1), “Does not expect to leave a bequest” (everything else, provided that at least one valid nonmissing answer was given to any of the bequest questions; reference category), and “Missing bequest expectation.” We measure explanatory variables just before retirement, which reduces endogeneity problems, but the models should still be considered as multivariate descriptions and not as causal behavioral models. Also, we estimate separate models for households that directly 22
own stocks prior to retirement and those that do not, examining the probability for those households to own stocks after retirement. Our analysis allows us to examine (1) what influences households that did not own stock prior to retirement to buy stock afterward and (2) what influences households that own stock to sell it after retirement.
Stock Ownership Figure 19 in the appendix presents estimation results for the models in which direct stock ownership in the wave after retirement is the dependent variable. That is, the table indicates which variables most strongly influence stock ownership after retirement among those who did not own stock Households with more resources are more likely to buy stocks. before retirement (the “No” Similarly, households with a working spouse are more likely to column) and those who did own stocks after retirement. (the “Yes” column). Overall, a relatively small number of coefficients are statistically significant, confirming our earlier speculation that confounding factors (correlated covariates) account for many of the evident bivariate associations. We review these below. Households Not Holding Stocks before Retirement
Among households not holding stocks before retirement, we found completing high school does matter: Respondents with less than a high school education are less likely to own stocks after retirement than high school graduates, but college graduates are not more likely to buy stocks after retirement. Total household wealth, having a private pension (DB or DC), and expecting to leave a bequest all positively affect stock ownership after retirement among those not owning stock beforehand. This likely indicates that households with more resources are more likely Households with higher mathematical ability and more wealth to buy stocks. Similarly, housetend to continue holding rather than selling stocks after holds with a working spouse are retirement. more likely to own stocks after retirement. Finally, cognitive ability, particularly mathematical ability, is positively associated with stock holding after retirement among those not owning it beforehand. However, this effect is pretty modest once the model controls for education. Households Holding Stocks before Retirement
For households that already own stocks prior to retirement, the only variables that influence stock holding afterward are wealth and 23
mathematical ability. Households with higher mathematical ability and more wealth tend to continue holding rather than selling stocks after retirement. Education, private pension holding, spouseâ&#x20AC;&#x2122;s working status, and the expectation to leave a bequest do not affect stock ownership after retirement among those owning stock beforehand, even though, as noted above, they do influence households not owning stock before retirement to acquire it afterward.
Changes in Stock Wealth We also explore changes in the value of direct stock wealth using three alternative models (see Figure 20 in the appendix for results). This analysis can be related to economic theory, suggesting that the share of stocks in portfolios should diminish after retirement. The first model uses for its dependent variable an indicator for whether stock wealth (in real terms) increases. The second looks at whether stock wealth increases more than the Dow, indicating active investment or disinvestment in stocks as opposed to passive wealth accumulation or decumulation. (We used the Dow as a proxy indicator of the market: Although limited, it is strongly correlated with other indexes, such as the The peak in stock wealth in 2000 is mainly due to stock S&P.) The third, addressing market performance rather than any active stock market economic theory most directly, investment. studies whether stock wealth as a percentage of total household wealth decreases after retirement. These questions are only relevant for households that owned stocks before retirement, having examined earlier households that acquire stock only after retirement. We include households that owned stocks before retirement but not afterward, treating no stock ownership after retirement as a zero value. These analyses have largely similar outcomes. Most coefficients are not statistically significant, but their signs and magnitudes are often economically large. Some coefficients are just significant at the 5% level in one model and just miss significance in another, so separating those that are and are not significant might suggest less similarity than there actually is. Nevertheless, there are a few salient differences as well. One interesting finding is that in the models for real stock wealth and stock share, the coefficient for the year 2000 is large, positive, and statistically significant, yet practically zero in the model for stock wealth compared to the Dow. This confirms our earlier hypothesis that the peak in stock wealth in 2000 is mainly due to stock market performance rather than any active stock market investment. Note that this 24
is relative to the behavior of similar households in other time periods. The 2004 and 2006 dummy variables are significant only in the model for stock wealth compared to the Dow. We saw earlier that the stock market recovered in these years after the dip of the early 2000s, but that stock wealth among respondents did not increase accordingly; this analysis confirms that this cannot be attributed to other covariates. Regarding the other covariates, there is some evidence that single females do less well than single males and couples, and that mathematical ability has a positive effect on stock wealth. There is also some evidence that having a private DB pension is positively related to increases in stock wealth, but having a DC pension is not. This would support economic theory about portfolio risk. Because DC pension plans usually contain stocks but DB plans do not pose risk to enrollees, DB plan holders, controlling for other variables, should have more wealth in direct stock holding. A puzzling result is that total wealth prior to retirement has a significantly positive effect on increases in the share of stocks in the total portfolio but not on increases of absolute stock wealth and stock wealth compared to the Dow. That is, everything else being equal, households with higher Having a private pension influences those without stock before wealth see a larger rise (or retirement to acquire it afterward. Perhaps households that smaller fall) of the share of stock have a DC pension cash it and reinvest it in stocks. wealth in their portfolio but not a larger rise (or smaller fall) in the absolute level of stock wealth or the level of stock wealth compared to the Dow. This appears to suggest that for households with higher total wealth prior to retirement, the change in stock wealth is similar to that of households with lower total wealth, but other wealth components decrease faster (or increase slower).
Conclusion The multivariate analysis shows that many of the bivariate associations on stock holding are attributable to other, correlated covariates. Numerical ability and total wealth are most consistently associated with stock ownership. To a lesser extent, they are also associated with changes in stock wealth. Having a private pension (DB or DC) influences those without stock before retirement to acquire it afterward. Perhaps households that have a DC pension cash it and reinvest it in stocks. Those with a DB pension can make more risky investment decisions, given the guaranteed income through a DB plan. Among those already owning stocks, we find suggestive evidence that the peak of stock wealth in 2000 can be attributed to the peak of the stock market that year. 25
It should be noted, however, that we cannot draw strong causal inference from these results. The models must be viewed as descriptive, though they are still useful for taking all covariates jointly into account. A rigorous causal analysis should take the joint decision of stock ownership and retirement into account, as well as the potential endogeneity of several other explanatory variables (such as retirement and income) and unobserved heterogeneity. Such a model may have the structure of a dynamic multivariate probit model with random effects. Building such a model is beyond the scope of this study but would be a fruitful topic for future work.
26
CHAPTER 4 Conclusions
Several trends emerge from this study, such as trends in stock holding remain relatively constant, with little significant movement around retirement, and age is positively associated with stock holding.
In this work, we investigated changes in financial asset portfolios related to retirement. Using the HRS, we tracked over time the financial asset portfolios of a nationally representative sample of U.S. adults at least 50 years of age. Analyses of such nationally representative, longitudinal data enabled us to investigate the changes in asset portfolio over time, depicting how older adults manage their financial portfolios in anticipation of and after retirement. Through descriptive, longitudinal analyses, we found few notable trends in stock holding. We did not find any birth cohort effects: All cohorts move in parallel fashion over time, with the exception of the oldest cohort (the AHEAD cohort born in 1923 or earlier). We did find stock holding moving in parallel with market value indicated by the Dow Jones Industrial Average. The relationship between age and financial asset portfolios is complex. Whereas cross-sectional analyses show a positive relationship between age and stock holding, once we control for birth year we do not find any significant effect. The cross-sectional positive relationship can be partly explained by mortality bias, with the wealthy more likely to survive to older ages, creating a bias in the relationship between age and wealth holding. There are also varied patterns of stock holding before and after retirement. Some households that owned stocks before retirement sold them afterward, while others that did not own stocks before retirement purchased them afterward. Using multivariate analyses to investigate what influences households to purchase or sell stocks after retirement, we found that numeric ability and total financial wealth are positively associated with stock ownership after retirement among households that owned stocks before retirement and those that did not own stocks before retirement. We also found that private pension holding is positively associated with stock ownership after retirement among those who did not hold stock beforehand, but that it does not influence stock holding of the households that held stocks before retirement. Similarly, bequest 28
expectation is associated with stock holding after retirement among those who did not own stock beforehand, but it does not influence stock holding among those who already held stock before retirement. Finally, education is associated with stock holding after retirement among those who did not own it beforehand, but not among those divesting holdings after retirement. Credit unions that offer investment services should pay attention to the research findings that show consumers—especially higher-wealth consumers—maintaining direct stock holdings long after retirement. Although it’s tempting to think that members will unload direct stock holdings at retirement, it’s also far too simplistic a view. As credit union membership continues to age, it will be increasingly important to cater to members’ actual behavior. And while this research suggests that the baby boomer retirement surge will not depress the stock market in a significant way, it’s essential to ferret out the individual needs of retiring members.
29
Appendix
Data and Figures Figure 14: Stock Ownership over Time % Holding stock Birth year
1992
1993–1994
1995–1996
1998
2000
2002
2004
2006
AHEAD
Cohort
Prior to 1924
—
—*
29.8
30.1
31.6
30.0
30.9
31.1
CODA
1924–1930
—
—
—
29.2
31.5
29.9
30.0
26.7
HRS
1931–1941
28.9
32.5
32.3
32.5
33.0
31.9
31.8
28.3
WB
1942–1947
—
—
—
32.6
33.9
35.4
33.0
29.3
EBB
1948–1953
—
—
—
—
—
—
28.7
25.1
*Because ambiguous wording of asset questions for the first wave of the AHEAD cohort led to the underreporting of asset ownership, we do not use wealth data from it.
Figure 15: Mean and Median Share of Stock by Birth Cohort over Time % Mean share Cohort
Birth year
1992
1993–1994
1995–1996
1998
2000
2002
2004
2006
AHEAD
Prior to 1924
—
—
26.8
31.7
32.9
32.5
34.2
37.5
CODA
1924–1930
—
—
—
25.8
27.2
27.9
29.8
30.2
HRS
1931–1941
18.2
20.8
22.9
25.6
27.0
24.3
24.7
24.7
WB
1942–1947
—
—
—
24.7
25.8
23.6
24.7
22.1
EBB
1948–1953
—
—
—
—
—
—
23.9
19.1
Birth year
1992
1993–1994
1995–1996
1998
2000
2002
2004
2006
AHEAD
Prior to 1924
—
—
18.6
24.2
25.8
26.6
28.3
32.2
CODA
1924–1930
—
—
-—
19.0
21.1
19.4
22.7
20.2
HRS
1931–1941
11.4
14.3
16.4
19.2
20.5
17.9
17.8
17.8
WB
1942–1947
—
—
—
17.1
18.8
15.8
16.3
13.0
EBB
1948–1953
—
—
—
—
—
—
16.0
11.3
% Median share Cohort
31
Figure 16: Stock Holding before and after Retirement by Key Household Characteristics % Holding stock Household characteristic
N
Single male Household composition
Education
Total family income before retirement (2006 real dollars)
Total financial assets before retirement (2006 real dollars)
412
% Mean share
Before retirement
After retirement
Before retirement
After retirement
28.0
29.4
23.7
26.8
Single female
1,019
27.5
24.2
27.1
31.1
Couple
2,416
42.0
40.8
23.5
23.4
Less than high school
858
10.6
9.9
23.6
25.8
High school graduates
2,042
34.9
34.2
20.8
22.3
Some college
168
48.8
43.2
28.1
26.2
College graduates
770
57.9
55.0
27.5
28.3
Less than $50,000
1,797
17.8
17.6
22.7
24.2
$50,000–$74,999
713
36.3
38.2
22.3
21.9
$75,000–$99,999
494
48.6
43.0
22.6
27.2
$100,000 or more
843
62.5
59.1
26.1
26.1
Less than $50,000
1,751
10.9
10.9
7.7
15.6
$50,000–$74,999
304
30.2
29.6
13.6
14.7
$75,000–$149,999
514
42.1
43.1
17.8
22.4
1,278
64.4
60.4
28.0
27.4
$150,000 or more
Figure 17: Stock Holding before and after Retirement by Spouse’s Retirement Status and Private Pension Holdings % Holding stock % Holding stock before and after retirement; mean share of stock in financial portfolio
N
After retirement
Before retirement
After retirement
1,351
43.36
42.31
22.59
22.55
993
40.03
38.71
25.24
24.97
No spouse
1,431
27.63
25.91
25.81
29.51
DB only
1,288
46.09
44.65
24.30
25.81
DC and/or IRAs
1,265
45.30
44.17
24.17
24.44
No retirement accounts
1,270
15.67
14.19
23.45
25.40
Spouse working Spouse’s retirement status
Private pension
% Mean share
Before retirement
Spouse retired
Figure 18: Bequest Expectation and Stock Holding before and after Retirement % Holding stock Bequest expectation Probability of leaving a bequest of $10K+ Probability of leaving a bequest of $100K+
Prob=0
N 475
% Mean share
Before retirement
After retirement
Before retirement
After retirement
5.4
5.6
23.9
18.4
0<Prob<=50
487
23.1
21.8
23.3
30.1
50<Prob<100
683
43.5
40.3
23.3
23.3
Prob=100
1,320
47.9
45.7
25.1
26.1
Prob=0
1,197
14.8
14.0
22.9
24.3
0<Prob<=50
702
41.2
39.4
24.7
27.3
50<Prob<100
422
56.4
50.1
23.8
23.3
Prob=100
622
54.6
53.2
25.2
26.0
32
Figure 19: Probit Regressions of Direct Stock Ownership Immediately after Retirement Direct stock ownership just before retirement (t–1) Explanatory variable (at t–1 unless otherwise noted) Age (years/10) —, squared
No Coef.
Yes t-value
Coef.
t-value
1.85
1.1
1.41
0.8
–0.17
–1.2
–0.11
–0.7
t=1994
0.38
1.1
0.36
0.7
t=1996
–0.04
–0.1
0.25
0.6
t=1998
0.16
0.4
0.04
0.1
t=2000
0.17
1.0
0.26
1.6
t=2004
0.07
0.2
0.52
1.7
t=2006
0.18
0.9
0.10
0.5
Single female
0.11
0.8
–0.25
–1.3
Less than high school
–0.44
–3.9
-0.21
–1.3
Some college
–0.16
–0.8
0.20
1.0
College and above
1.5
–0.19
–1.6
0.14
Zero income
0.83
1.2
—
—
Log real income
0.08
1.3
0.08
1.0
Negative wealth
0.24
0.5
2.90*
3.0
Log real wealth
0.12
3.3
0.32
5.3
Spouse works
0.22
2.8
–0.13
–1.1
Missing spouse labor force participation
0.30
1.2
–0.62
–2.0
–0.02
–0.1
0.20
1.1
DB only
0.45
4.0
0.21
1.1
DC and/or IRAs
0.48
3.2
0.12
0.7
Expect to leave small bequest
0.22
1.8
0.21
1.1
Expect to leave large bequest
0.32
2.2
0.25
1.4
Missing bequest expectation
0.21
1.2
–0.31
–1.0
Word recall
0.01
1.3
–0.01
–0.8
Missing word recall
0.15
0.6
0.04
0.1
Serial 7s
0.12
2.9
0.12
2.7
Missing serial 7s
0.55
2.0
0.64
1.4
TICS
0.00
0.0
0.06
1.7
–0.7
0.22
No spouse/partner
Missing TICS N
–0.12 2460
1.0 1218
Note: Birth year (in five-year category dummies) and constant also included but not reported. All the birth year dummies are statistically insignificant. * The large positive coefficient for households with negative wealth prior to retirement may be the effect of bouncing back and investing the money in stocks, or it may be due to measurement error (incorrect omission of stock wealth in the wave prior to retirement). But since there are only six households with negative wealth, we should not attach strong conclusions to this.
33
Figure 20: Probit Regressions of Change in Direct Stock Value after Retirement Dependent variable (dummy for increase between t–1 and t) Explanatory variable (at t–1 unless otherwise noted) Age (years/10)
Real absolute stock wealth Coef.
t-value
Stock wealth compared to Dow Coef.
t-value
Stocks as share of portfolio Coef.
t-value
–0.84
–0.5
–0.76
–0.5
–0.87
–0.5
—, squared
0.10
0.7
0.09
0.6
0.11
0.8
t=1994
0.45
0.9
0.17
0.4
0.15
0.3
t=1996
0.04
0.1
–0.57
–1.5
0.03
0.1
t=1998
–0.22
–0.6
–0.58
–1.7
–0.11
–0.3
t=2000
0.41
2.5
0.03
0.2
0.49
3.0
t=2004
–0.17
–0.6
–0.61
–2.2
0.02
0.1
t=2006
–0.19
–0.7
–0.55
–2.3
–0.14
–0.6
Single female
–0.29
–1.8
–0.38
–2.0
–0.16
–1.1
Less than high school
–0.21
–1.3
–0.15
–0.8
–0.04
–0.2
Some college
0.01
0.0
0.12
0.6
0.13
0.6
College and above
0.12
1.2
0.14
1.5
0.02
0.2
Log real income
0.06
1.1
0.06
1.2
0.01
0.1
Negative wealth
0.28
0.3
0.45
0.5
a
—
Log real wealth
0.03
0.6
0.04
0.8
0.18
3.7
Spouse works
–0.07
–0.6
–0.08
–0.7
–0.08
–0.8
Missing spouse labor force participation
–0.66
–1.9
–0.69
–2.0
–0.12
–0.3
No spouse/partner
0.22
1.3
0.20
1.1
0.28
1.8
DB only
0.33
2.1
0.25
1.5
0.29
1.7
DC and/or IRAs
0.14
1.0
0.09
0.6
0.13
0.9
Expect to leave small bequest
–0.02
–0.1
0.00
0.0
0.00
0.0
Expect to leave large bequest
0.08
0.3
0.01
0.1
0.01
0.1
Missing bequest expectation
–0.51
–1.8
–0.46
–1.5
–0.66
–2.2
Word recall
–0.01
–0.9
–0.02
–1.0
–0.01
–0.7
Missing word recall
0.22
0.6
0.21
0.6
0.05
0.1
Series minus 7
0.07
1.5
0.03
0.7
0.10
2.1 2.9
Missing series minus 7
0.67
1.8
0.42
1.2
1.12
TICS
–0.01
–0.5
0.01
0.5
0.03
0.8
Missing TICS
–0.22
–1.0
–0.25
–1.1
–0.09
–0.4
N
1218
1218
Note: Birth year (in five-year category dummies) and constant also included but not reported. All the birth year dummies are statistically insignificant. a
Portfolio share is not meaningful if total wealth is negative, so this variable (and six observations) was removed.
34
1212
Endnotes
1. We define “total wealth” to include value of the primary residence, other real estate, vehicles, businesses and farms, stocks/ mutual funds/investment trusts, checking/savings/money market accounts, CDs/government savings bonds/T-bills, bonds/bond funds, and all other savings, minus mortgages, other home loans, and other debt. This definition excludes IRAs and Keogh accounts as well as the value of any second home, a question that was not asked in all waves. IRAs and Keogh accounts are excluded because they may include stocks, but they are, as noted, separately studied. We also implicitly exclude from our calculations of household wealth other pension wealth such as annuities, defined benefit plans, and defined contribution plans such as 401(k) accounts. 2. There may also exist selection bias that those who are more risk averse tend to choose DB plans or occupations that offer DB plans than those who are less risk averse. 3. In principle, this means we could obtain results for certain (birth year) cohorts before their (entry) cohort is officially sampled. For example, we could obtain average stock wealth of the CODA-aged respondents (birth years 1924–1930) in the first wave, which includes only the HRS entry cohort (birth years 1931–1941), because an HRS-aged respondent may be married to a CODA-aged respondent. Nevertheless, the number of such disparate cases is small and not representative of the birth year cohort, leading us to exclude them from presentation. We also exclude birth year cohorts from before 1924, or the years represented by the AHEAD cohort, from comparisons of wealth before and after retirement. This is because such respondents would have to retire well past 70 years of age to be in this sample. Such a small subsample would be highly unusual and not likely to yield much information for present purposes. 4. See the “Data” section in Chapter 1 for a detailed description of what constitutes total wealth. In particular, it excludes the second home, IRAs, and other pension wealth. 5. Several variables have large numbers of missing observations. To avoid dropping these observations, we use a standard method to deal with missing covariates: We impute an arbitrary value (typically 0, but for TICS we use 7) and then include an additional dummy variable to indicate missing values, thereby capturing the average effect of the missing variable.
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References
Attanasio, Orazio P., and Hilary W. Hoynes. 2000. “Differential Mortality and Wealth Accumulation.” Journal of Human Resources 35(1): 29. Brandt, J., M. Spencer, and M. Folstein. 1988. “The Telephone Interview for Cognitive Status.” Neuropsychiatry, Neuropsychology, and Behavioral Neurology 1: 111–17. Cocco, J. F., F. J. Gomes, and P. J. Maenhout. 2005. “Consumption and Portfolio Choice over the Life Cycle.” The Review of Financial Studies 18(2): 491–533. Health and Retirement Study. 2008. “Sample Sizes and Response Rates (2002 and beyond).” Retrieved December 7, 2009, from http://hrsonline.isr.umich.edu/sitedocs/sampleresponse.pdf. Juster, F. Thomas, and James P. Smith. 1997. “Improving the Quality of Economic Data: Lessons from the HRS and AHEAD.” Journal of the American Statistical Association 92(440): 1268–78. Kinsella, K., and W. He. 2009. U.S. Census Bureau, International Population Reports, P95/09-1. An Aging World: 2008. Washington, DC: U.S. Government Printing Office. Parker, T. 2001. “No Accounting for Gains.” Barron’s 81: 49. Rohwedder, Susann, Steven J. Haider, and Michael D. Hurd. 2006. “Increases in Wealth among the Elderly in the Early 1990s: How Much Is Due to Survey Design?” Review of Income and Wealth 52(4): 509–24. Samuelson, Paul A. 1969. “Lifetime Portfolio Selection by Dynamic Stochastic Programming.” The Review of Economics and Statistics 51(3): 239–46. Shambora, W. E. 2006. “Will Retiring Boomers Really Cause a Stock Market Meltdown?” Applied Financial Economics 16: 1239–50. St. Clair, Patricia, Darlene Blake, Delia Bugliari, Sandy Chien, Orla Hayden, Michael Hurd, Serhii Ilchuk, Fuan-Yue Kung, Angela Miu, Constantijn Panis, Philip Pantoja, Afshin Rastegar, Susann Rohwedder, Elizabeth Roth, Joanna Carroll, and Julie Zissimopoulos. 2008. RAND HRS Data Documentation, Version H. Santa Monica, CA: RAND Corporation, RAND Center for the Study of Aging.
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Pre- and Post-Retirement Asset Portfolios Jinkook Lee Senior Economist, RAND Corporation
Arie Kapteyn Director, RAND Labor and Population ideas grow here
Erik Meijer
PO Box 2998
Economist, RAND Corporation
Madison, WI 53701-2998 Phone (608) 231-8550
www.filene.org
PUBLICATION #213 (6/10)
Jung-Seung Yang
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PhD Candidate, Seoul National University