By
Department
of Finance and Real Estate
March 1, 1999
THIS IS A PRELIMINARY DRAFT AND SHOULD NOT BE QUOTED OR OTHERWISE DISTRIBUTED WITHOUT THE AUTHOR’S CONSENT.
ABSTRACT
This study reviews the literature on individual risk aversion and investment allocation. Relative risk aversion is estimated using the 1994 wave of the Health and Retirement Study, a large nationally representative sample of households nearing retirement. After controlling for age, income, dependents, and other demographic characteristics, the results confirm earlier findings of decreasing relative risk aversion. Single women are found to be relatively more risk averse than married couples. Risky portfolio allocation is significantly lower for older households, for those with lower educational levels, and for black households, when housing is not included in the definition of wealth. Examination of the wealth accumulation in this sample of households indicates excessive levels of debt and insufficient savings are common. A smaller sample of individuals completed an experimental component of the survey designed to measure risk aversion with respect to gain or loss of income. The respondents’ self-professed risk aversion hasa positive impact on risky allocation but the significance level is low.
The prevalence of self-directed pensions and the recent performance of the stock market have increased public awareness of the impact of asset allocation on portfolio accumulations. While investment professionals have long been aware of the importance of taking risk for long term growth, the “common wisdom” is that individuals, on average, are more risk averse investors. The social security reform debate has put the spotlight on this issue since the essence of the argument for privatization is that retirement income goals can be more easily achieved when returns on payroll taxes are invested in higher return (and higher risk) assets. If certain groups of individuals tend to be more risk averse than others, there may be adverse and unintended consequences. For example, if women and minority groups are more risk averse than men, the current disparities in working income will be exacerbated in retirement. Women’s greater average longevity implies that they actually need to have a greater accumulation at retirement to support the same level of retirement income as men.
The purpose of this study is to examine household wealth allocations by individual characteristics, to estimate relative risk aversion, and to test for differences by gender and other characteristics. The Health and Retirement Study, a unique nationally representative sample of households on the verge of retirement, provides the necessary financial and demographic information. A thorough literature review of the previous research related to risk aversion and individual asset allocation is provided in Section II. Section III presents the theoretical and empirical models as well as the results of the various estimations. Conclusions and policy implications are given in Section IV.
A. Studies of Risk Aversion and Wealth
There have been a number of studies that have attempted to empirically estimate the relationship between relative risk aversion and wealth within the framework of expected utility theory. One of the earliest studies of risk aversion and wealth is by Friend and Blume [1975]. Their measure of risk aversion depends on the individual investor’s portfolio allocation between risky and risk-free assets, according to the following:
ak = [E(rm - rf )/s 2m] (1/Ck)
where ak is the proportion of net worth that investor k places in risky assets, E(rm - rf) is the expected difference between the return on the market portfolio of risky assets (rm) and the return on the risk free asset (rf), s 2m is the variance of the return on the market portfolio, and Ck is the Pratt-Arrow measure of relative risk aversion.[1]
Friend and Blume estimate the relationship between ak and wealth with cross sectional data from the 1962 and 1963 Federal Reserve Board Surveys of the Financial Characteristics of Consumers and Changes in Family Finances. Their results are sensitive to the way that wealth is defined. The narrowest definition of wealth excludes the value of houses, cars, and human capital, since these asset categories violate the assumptions of divisibility and liquidity which are inherent in the model. On the basis of this definition of wealth, they find evidence of decreasing relative risk aversion (DRRA), ie. individuals invest a larger proportion of their wealth in risky assets as wealth increases. When wealth is defined to include the value of houses, cars and human capital, they find evidence of constant relative risk aversion (CRRA).
Following the methodology of Friend and Blume, several studies find evidence of DRRA. For example, Morin and Suarez [1983] use data from the 1970 Canadian Survey of Consumer Finances and find DRRA when wealth is defined exclusive of housing. Bellante and Saba [1986], building on the work of Morin and Suarez and using data from the U.S. Department of Labor’s Consumer Expenditure Survey for 1972-73, find evidence of DRRA when wealth is defined to include the value of housing but not the value of human capital. When the definition of wealth includes human capital as well, they find that the result of DRRA still holds but is significantly weaker. Confining their sample to less wealthy households they find evidence of increasing relative risk aversion (IRRA), ie. individuals invest a smaller proportion of their wealth in risky assets as wealth increases.
Other studies find similar results. Siegel and Hoban [1982] find evidence of DRRA among wealthy households and IRRA among less wealthy households, when wealth is defined exclusive of housing. Riley and Chow’s [1992] study of the 1984 panel of the Survey of Income and Program Participants finds evidence of DRRA when wealth is defined inclusive of houses but exclusive of human capital.
Most of these studies estimate the relationship between an individual’s investment in risky assets and wealth in a simple equation that excludes the effects of individual and household characteristics. They then estimate the relationship using a more complex equation which includes a wide range of control variables at the individual and household level that have been hypothesized to influence an individual’s degree of risk aversion. These characteristics are not included to test the theory but rather to fill in gaps in the theory in terms of variables in addition to wealth that seem to explain risk aversion.
B. Studies of Risk Aversion and Individual Characteristics
Researchers have only recently explored the issue of differences in risk aversion by gender. Anecdotal evidence suggests that women are more risk averse than men. A number of studies have confirmed this finding even when controlling for the effects of other individual characteristics such as age, education, and wealth. Jianakoplos and Bernasek [1998] use the framework of Friend and Blume [1975] to look for evidence of gender differences in financial risk taking. They use data from the Federal Reserve’s Survey of Consumer Finances (1989) and estimate relative risk aversion by gender. They find that single women were relatively more risk averse than single men and married couples. The proportion held in risky assets was found to increase with wealth(DRRA) but for single women the effect was significantly smaller than for single men and married couples.
Palsson’s [1996] study of Swedish households also finds evidence that women are more risk averse than men when she examines the effects of a wide range of household variables on financial risk taking. Riley and Chow [1992] also look at the effects of a broad range of individual and household variables on risk aversion and find a small but significant gender difference in risk taking with women being more risk averse than men. In their study, never married women were less risk averse than married women, who were less risk averse than widowed and separated women.
Other studies have explored gender differences in risk aversion in the context of non-financial decisions. These studies also find evidence of women’s greater risk aversion. Hersch [1996] finds that, on average, women made safer choices than men in a number of risky consumer decisions such as smoking, seat belt use, preventative dental care and having regular blood pressure checks. Hersch [forthcoming 1998] looks at gender- specific estimates of illness and injury incidence rates for different industries and occupations and concludes that women are more risk averse than men based on the observation that women are paid a higher compensating wage differential for accepting a given job-injury risk. Brinig [1994], analyzing data on speeding convictions, finds that women appear to be less willing than men to be caught and convicted of speeding.
Despite the focus on the existence of gender differences, none of these studies addresses the question of why such differences exist. Bajtelsmit and Bernasek [1996] review the literature on gender differences in risk taking, paying particular attention to risk taking and investing for retirement. They suggest a conceptual framework for understanding why it is that women invest differently than men. They work backwards from differences in wealth and income, to labor market experience, to discrimination, human capital, and finally to the effects of socialization and biological difference.
If women are systematically more risk averse than men, the implication is that women will earn lower rates of return on their investments than men at the same wealth level. This implies that they will accumulate less wealth than men over time, a conclusion that is compounded by the fact that women, on average, have lower income and wealth than men and are less likely to have private pensions. Looking at this from the perspective of retirement income adequacy, women will have less accumulated wealth at retirement with which to support a retirement period which is generally greater than that of their male counterparts (due to greater average longevity). Ensuring that women are not poor in their retirement years thus requires that we better understand why they are more risk averse than men.
Age is a demographic characteristic that has long been hypothesized to affect an individual’s degree of risk aversion. The lifecycle risk aversion hypothesis predicts that risk aversion will increase over the lifecycle – the older a person gets, the more risk averse they become. The underlying explanation for this lies in the relative importance of labor income and asset income over the lifecycle. It is believed that the further a person is from retirement the more risk they are willing to accept in their investments since the number of paychecks they expect to get is large and labor income can offset any adverse investment outcomes. The closer to retirement a person gets, the fewer paychecks they have to cover any such adverse investment outcomes.
Several studies that have considered the effects of age on risk aversion claim to test the lifecycle risk aversion hypothesis but do not. Most studies have used cross sectional rather than longitudinal data and therefore can only draw inferences about the effects of age across a cross section of the population – at any given time younger people may be more or less risk averse than older people. For example, Morin and Suarez [1983] conclude that risk aversion increases with age such that older people are more risk averse than younger people. Palsson [1996] finds the same results. Riley and Chow [1992] find that risk aversion decreases with age up to 65 years, then increases significantly. Bellante and Saba [1986] attempt to distinguish between the effects of human capital and age on risk aversion and find evidence of increasing relative risk aversion with human capital but decreasing relative risk aversion with age. Although they interpret their results as evidence of a pure lifecycle effect of age that is independent of the human capital effect, the cross sectional nature of their study cautions against such a conclusion.
A rare study using time series data, Bakshi and Chen [1994] find evidence to support the lifecycle risk aversion hypothesis. Focusing on the effects of demographic changes on capital markets, they find an increase in the risk premium associated with an increase in the average age of investors.
The effects of age on risk aversion are further complicated by the possibility of cohort effects. There has been some suggestion that young people today may be less risk averse than young people a decade ago, for example. A study by Brown [1990] examines the effect of the distribution of wealth across age cohorts on security prices taking into account the non-marketability of human capital earnings. He finds that middle age investors were less risk averse than young investors and that older investors were more risk averse than middle age investors.
Jianakoplos and Bernasek [1998] attempt to disentangle cross sectional, lifecycle and cohort effects of age on women’s risk aversion. They find that younger women in 1983 and in 1995 are less risk averse than older women in the same years, 30-41 year old women were less risk averse in 1995 than in 1983, and that in 3 out of 4 stages in the lifecycle, relative risk aversion decreases with age.
Very little research has been done on the effects of race/ethnicity on risk aversion. In her study of risky consumer decisions, Hersch [1996] finds that, overall, whites make safer choices than blacks but that the racial gap closes considerably when education and wealth are controlled for. Within racial categories women are found to exhibit safer behavior than men. The conclusion Hersch comes to is that race is not as important a determinant of risk taking as other individual characteristics such as age, education, and wealth.
Jianakoplos and Bernasek [1998] in their study of financial risk taking find that black single women are significantly less risk averse than white single women, and are less risk averse than black single men and married couples. This is in contrast to their finding that white single women are more risk averse than white single men and married couples.
Comparing MBA students at the University of Houston and the Madrid School of Business, Zinkhan and Karande (1991) find that the Spanish students were less risk averse as a whole than the American Students. (This study also finds significant gender differences, an indication that the gender effect exists cross-culturally as well).
A number of studies have examined the effects of formal education on risk aversion. A common concern in interpreting the results of these studies is that education, income and wealth tend to be highly correlated. The results on the effects of education on risk taking are mixed. Riley and Chow [1992] find that financial risk aversion decreases with education. Jianakoplos and Bernasek [1998] find the opposite – that risk aversion increases with education, without any significant difference between women and men. Hersch [1996] finds that risk aversion increases with education when considering risky consumer choices.
In the context of financial risk taking, it would seem that a more relevant effect to measure would be access to financial knowledge rather than education in general. A study by Bayer, Bernheim and Scholz [1996] examines the effects of financial education in the workplace on participation in and contributions to voluntary savings plans. They find that measures of savings activity are significantly higher when employers offer retirement seminars and the effects are greater for lower paid employees than for higher paid employees.
C. Experimental Studies of Risk Aversion
Another avenue for investigating risk taking behavior has been through the study of games and the use of games as experiments. Examples of this are: Gertner’s [1993] study of risk taking behavior in the context of the television game show “Card Sharks”; Metrick’s [1995] study of people’s attitudes to risk and their ability to play best-responses in the game show “Jeopardy”; Altaf’s [1993] experiment to test the extent to which risk preferences are context dependent; and Levy’s [1994] experiment in which students had to choose between investing in risky and riskfree assets in a series of ten decision-making stages. The results of these experiments are mixed. Most find evidence that people’s behavior is inconsistent with the predictions of expected utility theory.
Focusing specifically on risk taking by gender, Brinig [1994] conducts an experiment to compare the behavior of women and men in a game that does not involve any possible loss. Her experiment involves drawing a winning ball from one of three jars which represent different risk-return combinations. She finds no difference in the risk taking behavior of women and men when gender alone is considered. When gender is combined with age however, she finds that women are more risk averse than men before age forty, then they are less risk averse until age forty-five, and beyond age forty-five women and men are found to have the same tendencies for risk taking. The fact that participants do not face any risk of loss in the experiment cautions against drawing any strong conclusions from Brinig’s results.
Jianakoplos and Bernasek undertake an experiment to test for differences in risk taking by gender, which, like Brinig’s does not have any possibility of loss. They do not find any statistically significant difference in risk taking by gender. Using the same game as Altaf [1993] they invite students to participate in an experiment where they played a game which pays $25 to the winner of the game. Individuals have to choose between rolling a die and tossing a coin, each of which involves certain payoffs in the form of points. The objective is to accumulate the greatest number of points in the fewest possible plays of the game. The expected payoffs to a coin toss and a roll of the die are the same, but the variance is different -- rolling the die is the riskier option. Their measure of willingness to take risks is given by the proportion of plays of the game that a person chose to roll the die.
One of the issues that comes up
with experiments is whether or not there is any down-side risk. If there is not then the results derived
from such experiments may not be generalizable to people’s behavior in the real
world. Another issue is the small
sample size of most of these experiments.
The Jianakoplos and Bernasek experiment, for example, had only 37
participants. With such small sample
sizes it is difficult to draw general conclusions.
Theoretically, the optimal portfolio for all individuals is a combination of the market portfolio and the risk-free asset. (Markowitz, 1952) Differences in risk aversion are manifested in differing proportionate allocations between these two investment choices. However, in practice, we know that individuals do not all “hold the market”.
Several studies have been conducted looking at individual investment allocation. However, most of these are flawed in that they look at a particular aspect of the individual’s portfolio without considering the whole. This is typical of studies that have been conducted by private pension providers since these firms do not have access to very detailed information on household income and wealth. The literature reviewed in this section therefore comes with the caveat that the conclusions are largely descriptive and comparative rather than statistically validated.
1.
Pension Surveys
Observation of plan level data has generally yielded fairly consistent conclusions. Individuals tend to pick fairly conservative pension portfolios. A 1993 study by Hewitt Associates found that when guaranteed investment contracts (GICs) were offered, they accounted for almost half of all employee contributions. Equities and balanced funds accounted for only 21 percent and 13 percent respectively. A Fidelity Investments study including over 1500 plans and 2 million participants in 1994, found that when employer stock was offered, it accounted for about 16 percent of plan assets. Nearly half of the funds were allocated to non-employer stock and 28.7% in GICs. When employer stock was not available, the percentage in both equities and GICs increased.
Goodfellow and Schieber (1997) tabulated the investment allocations for a sample of more than 36,000 participants in 24 defined contribution plans holding nearly $1.4 billion in total assets. They find that fixed income investments are about 58 percent of total funds and that stocks represent approximately 28 percent. The percent in fixed income increases with age and the percent in stocks declines. Higher income individuals are more inclined to invest in stocks, as are men.
A study by Bajtelsmit and VanderHei (1997) used individual plan data on 20,000 employees of a single U.S. firm. Account allocations in that study were 41 percent employer stock, 14.2 percent equity, and 44.8 percent GICs for the men in the sample. Regression analysis indicated that women were less inclined to invest in employer stock and equities.
The largest pension fund in the world, TIAA-CREF has provided participants with investment choices since 1952. In the early years, there were only two choices, the TIAA traditional guaranteed annuity and the CREF equity account. Internal studies over many years indicated that most participants allocated their premiums 50-50 and did not regularly reallocate over time, even when the actual account balances differed substantially from the original 50-50 mix. In more recent years, TIAA-CREF has expanded the asset choice set to include equity accounts of differing risk and return characteristics as well as money market, bond, and real estate choices. At the same time, the firm has attempted to increase participant knowledge through educational programs. The end result is that in 1996, the proportion of participants allocating some portion of their premiums to equities has steadily increased and 22.2% are 100% in equity. The 50-50 allocation strategy is still popular (24.6%) and the proportion investing entirely in the guaranteed fund (9.0%) is half what it was in 1986. (TIAA-CREF, 1997). The allocation patterns by age have also changed over the last decade. In 1986, nearly 30% of participants over age 55 were invested entirely in the TIAA guaranteed annuity account, whereas in 1996, that percentage had dropped to only16.1%. The younger aged participants are on average less conservative than in 1986 as well, with more than half of the under-35 participants having at least 50% of their premiums allocated to equities (TIAA-CREF, 1997) compared to only 11% in 1986.
Hinz, McCarthy, and Turner (1997) examined the 1990 allocation patterns of federal government workers in the Thrift Savings Plan. Although their primary research question was related to gender differences, it is interesting to note that the observed patterns of allocations is very similar to that observed for other types of pensions. These workers were allowed to allocate up to 60% of their contributions to common stock and fixed income funds (the remainder to be in a fund of Treasury securities). Only 28% of women compared to 45% of men participated in the common equity fund. Overall, 13.4% of funds were allocated to the fund (average 8.9% for women and 15.3% for men.
2.
Special issues
Empirical analysis of investment allocation presents several special problems given the limitations of currently available data. The studies surveyed above are all based on in-house pension fund data and, as such, lack valuable explanatory information on the participants’ overall financial condition. Some specific issues that deserve mention here are the household versus individual observation unit, gender, age and cohort patterns, and differences between what people say and what they do.
a. Household vs. individual
As previously discussed, the data typically collected by plan sponsors are limited to plan specific information such as allocation percentages, account balances, and loans for individual participants. Although the Pension and Welfare Benefits Administration of the Department of Labor collects and disseminates information on pension plans, these data are limited to aggregate plan information, most notably from the IRS Form 5500 annual report. Two large cross-sectional data sets that have been used to examine financial decision-making are the Surveys of Consumer Finances (SCF) and the Current Population Surveys (CPS), which both include weights to make them representative of the U.S. population. The most recent new survey is the Health and Retirement Study (HRS) which follows a representative sample of individuals who were age 51-61 in the first year of the survey (1992) and their spouses.
There is some question as to whether studies of financial decision-making should have the individual or the household as the unit of measurement. While in many households, spouses or partners keep all finances completely separate, there are many in which the finances are combined. Therefore, examination of one spouse’s pension allocation may show a very conservative allocation of funds, but the other spouse’s pension may be in stocks. Consideration of these individuals separately would thus yield incorrect conclusions regarding the risk-preferences of the household. The SCF, CPS, and HRS surveys do not include any information on who makes financial decisions for the household[2].
b. Gender differences
Although several studies have purported to find gender differences in investing patterns between men and women (Bajtelsmit and VanDerhei, 1997; Hinz, et al., 1997; Goodfellow and Schieber, 1997), the problems discussed above make it difficult to make definitive conclusions. When this issue is examined with controls for income, wealth, and demographic characteristics, Bajtelsmit, Bernasek, and Jianakoplos (1997) find significant gender differences remain. In order to avoid the issue of household decision-making, the study considered single households of men and women separately and compared them to married couples’ investments. The main problem with the study however is that the SCF survey includes limited pension information. Although pension balance information is provided on each of their three largest pensions, allocation information for defined contribution plans is more limited. For each pension, respondents were asked to indicate whether they allocated their pension to (1) mostly stocks, (2) mostly interest bearing investments, or (3) mixed. For participants in the 1989 SCF who had defined contribution plans and wealth in excess of $1000, Bajtelsmit et al. (1997) find that women are relatively more risk averse than men and that women’s percentage wealth allocations to risky pensions decrease with wealth (increasing relative risk aversion). The conclusions of this study is limited by the fact that the survey does not indicate whether the respondent or the respondent’s employer had allocation decision-making authority for their account. Although self-directed plans are increasingly common, there are still many defined contribution plans for which participants do not make allocation decisions. Lastly, as in most studies, the household decision-maker is not identified.
Another gender related issue is that women and men may differ in their tendency to say one thing and do another (see below). Jianakoplos and Bernasek (1998) consider actual risk-taking compared to self-professed risk-taking and find that women say they are more risk averse than their investment allocations would indicate. Women who said they were “unwilling to take any risk at all” in their investments were still invested in risky assets.
c. Investment patterns over the life cycle
Financial planners often recommend a life-cycle approach to investment allocation. The so-called “Rule of 70” suggests that investors should subtract their age from 70 and invest that portion of their portfolio in equities with the remainder in fixed income securities. There is actually no theoretical basis for such a rule and, in fact, research on time diversification suggests that the investor’s age is less important than his or her investment time horizon (Siegal, 1996). Neither the private pension surveys nor the SCF and CPS data sets are particularly useful for examining investment patterns over the life cycle. Although each includes information on age of participants, this tells us only what individuals of different ages are doing at a particular point in time. There is no longitudinal survey that follows the same set of individuals over a long period of time and includes sufficient financial information. However, many studies have attempted to draw life cycle conclusions or inferences based on cross-sectional data. Comparison of cross-sections of the SCF, CPS, or Survey of Income and Program Participation (SIPP) have shown that cohorts do seem to exhibit patterns of wealth accumulation and decumulation.[3] Limitations in the financial data make it difficult to make strong conclusions regarding the asset mix. Even if there were a good source of longitudinal data on individual financial decision-making, a potential problem is the bias introduced by the researcher. By answering financial questions every few years, the participants in the survey are probably more aware of the issues than the average individual and may tend to make different financial decisions as a result. The psychometric literature shows that once an individual is made aware of a risk, they are more likely to take it into consideration.
It is difficult to separate the age effects from cohort effects. The term cohort refers to a group of people in a certain age group at a particular point in time. For example, the observation that a baby boomer couple carries a higher risk portfolio than their parents at the same age, may be due to their age or it may be to the greater conservatism of people in a cohort that has lived in the post-depression era. Several studies examine cohort differences in investing and seem to find patterns that support the financial planner models that recommend lower risk at higher ages. Poterba (1997) tabulates 401(k) eligibility and participation data for different cohorts and finds, not surprisingly, that younger cohorts are more likely to be eligible for 401(k) pension plans, with highest eligibility for the 35-45 year old group. However, the participation rates, given eligibility, do not differ substantially across cohorts and average about 70%.
Bajtelsmit and VanDerhei (1997) find significant cohort effects in the allocations of plan participants as do Hinz et al. (1997), with participants in older age cohorts having more conservative portfolios. However, since private pensions were less common for earlier cohorts, direct comparison of asset portfolios in more comprehensive data sets does not necessarily make sense. In fact, the lack of detail in the SCF survey regarding private pension assets mix can be attributed in part to the fact that, until recently, participants usually had no choices.
The HRS study, which has more complete financial information, essentially follows a single cohort through time, although only three waves of the study are complete at this time. To the extent that there are some survey participants who are older or younger spouses of the target survey group, there may be some evidence of cohort effects, but the respondent pool does not have a representative sample of those age cohorts.
d. What people say is not always what they do
Survey data is the primary source of data for most studies of risk aversion, investment and preparation for retirement. Some surveys ask for information on their wealth and their assets and liabilities. Some surveys however, ask for answers to hypothetical questions. For example, in the Survey of Consumer Finances (1989) respondents were asked how much risk they would be willing to take for certain returns on a hypothetical investment. Jianakoplos and Bernasek [1998] find that what people said in answer to that question was not consistent with how they actually allocated their wealth among risky and risk free assets. Caution is warranted in drawing conclusions from studies based on what people say rather than what they do.
Despite the growing body of knowledge related to individual risk-taking and investment allocation, there is still much that we do not understand. As public policy makers consider changes to public and private pension regulation, it is increasingly important that there be a clear understanding of the factors that influence individual investment choices.
III. Data and Empirical
Methodology
A. The Health and Retirement Study
1. General information
The Health and Retirement Study (HRS) is a nationally representative longitudinal data collection effort begun in 1992 focusing on Americans who are close to retirement (aged 51-61 in 1992) and their spouses. The survey investigates aspects of these households’ finances, health, and retirement decisions. The intent is to follow the same group of respondents through their retirement years. For the purposes of this report, the second wave of the survey, conducted in 1994, will be used. Future research on these topics might benefit by making use of the longitudinal nature of the survey although there is some risk that the behavior of the households surveyed might be influenced by the survey itself.
This survey is particularly well-suited to the study
of retirement savings since it includes information on all aspects of household
wealth and income as well as key demographic information that researchers need
for control variables. The
investigation of investment risk-taking in many previous studies has been
limited to examination of investment portfolios without consideration of the
rest of the household portfolio.
2.
Asset information in
the HRS
The HRS survey includes extensive asset and debt information for each of 6,979 households. Respondents provide information on their primary residence and second homes as well as investment real estate. They also give estimated values for personal property, self-owned businesses and farms, IRA and Keogh accounts, stock and bond investments (outside of tax deferred pensions), checking and savings accounts, money market funds, CDs, and government securities. Household debt, both secured and unsecured, is also summarized.
In addition to asset and debt information, the HRS includes substantial employment and pension information (e.g. type of pension plan for current and previous employers as well as characteristics of the plan and the expected benefits). As with the Survey of Consumer Finances, the survey is somewhat deficient in its information on defined contribution[4] pension investment allocations. The respondent provides information on each of his or her three “most important” defined contribution plans. In addition to an estimate of the pension balance, the respondents are asked to indicate whether the funds in these plans are invested in “mostly or all stocks”, “mostly or all interest earning (or bonds)”, or “split”. This is a bit too vague to allow inferences about pension investment risk-taking and asset allocation.
For investment allocation questions, an important consideration is whether the participants in a plan can direct the investment allocation. Of the 11,596 respondents, only 350 had an employer-provided defined contribution pension. For the pension designated their most important, 185 indicate that they can choose the way the money in the account is invested and 129 said that they invested the pension in either a mostly stock portfolio (48) or split between stocks and interest earning investments (81).
Table 2 provides a summary of wealth allocations to particular asset categories for households in the survey. The households are separated by marital status. These allocations can exceed 100% because many households have negative housing equity. [5] The standard deviations are also included in the table to illustrate the wide variation in allocations across households in the sample.
(Note: Denominator is total assets including housing equity, investments, and pension accumulations.)[6]
Asset Category |
Single Men |
Single Women |
Married Couples |
|||
|
|
Mean |
Stand. Dev. |
Mean |
Stand. Dev. |
Mean |
Stand. Dev. |
|
Residences |
.295 |
.368 |
.414 |
.405 |
.414 |
.549 |
|
Stock (Non-pension) |
1.207 |
17.258 |
4.942 |
89.706 |
.760 |
31.468 |
|
Bonds |
.064 |
.704 |
3.381 |
149.29 |
.027 |
.664 |
|
CDs, Govt Securities |
1.018 |
24.157 |
1.123 |
20.715 |
.115 |
2.760 |
|
IRA, Keoghs |
1.163 |
7.675 |
15.735 |
457.793 |
.606 |
21.126 |
|
Investment Real Estate |
24.441 |
837.126 |
11.211 |
345.541 |
1.052 |
22.578 |
|
DC Pension Accum. |
.128 |
.307 |
.126 |
.303 |
.111 |
.279 |
|
Checking/Savings |
2.965 |
43.689 |
8.394 |
160.717 |
.532 |
9.744 |
|
Automobiles |
.175 |
.287 |
.140 |
.269 |
.109 |
.233 |
Source:
Author’s tabulations of the Health and Retirement Study Wave 2.
Note:
Columns will not sum to 100%.
The wide va