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3rd Christmas Conference of
German Expatriate Economists
20 – 22 December 2006
CESifo Conference Centre, Munich
Depression Babies:
Do Macroeconomic Experiences
Affect Risk-Taking?
Ulrike Malmendier & Stefan Nagel
CESifo
Poschingerstr. 5, 81679 Munich, Germany
Phone: +49 (0) 89 9224-1410 - Fax: +49 (0) 89 9224-1409
[email protected]
www.cesifo.de
Depression Babies:
Do Macroeconomic Experiences Affect Risk-Taking? *
Ulrike Malmendier
UC Berkeley and NBER
Stefan Nagel
Stanford University and NBER
December 2006
Preliminary and Incomplete
Abstract
We investigate whether individual experiences of macro-economic shocks affects long-term risk attitudes,
as often suggested for the generation that experienced the Great Depression in their early lifetime. Using
data from the Survey of Consumer Finances, we find that birth-cohorts that have experienced high stock
market returns throughout their life report lower aversion to risk, are more likely to be stock market
participants, and, if they participate, invest a higher fraction of liquid wealth in stocks. These results hold
controlling for age effects, year effects, and a broad set of household characteristics. Moreover, reported
risk aversion, stock market participation, and the proportion invested in stocks of young individuals are
significantly more sensitive to recent stock market returns than those of older individuals. Thus, lifecourse experience of risky asset payoffs affects risk-taking by shaping beliefs and/or preferences.
*
We thank Nelli Oster for excellent research assistance.
“I don’t know about you, but my parents were depression babies, and as a result, avoided the
stock market and all things risky like the plague.”
Source: moneytalks.org (“Investing: The Basics”)
I. Introduction
Does an individuals’ life-course experience of economic outcomes influence subsequent
decisions by shaping preferences and/or beliefs? For the generation of “Depression Babies” it has often
been suggested that their experience of a large macro-economic shock affected their long-term risk
attitude. In this paper, we ask whether people with different life-course macroeconomic histories make
different risky choices.
Standard models in economics typically assume that individuals are endowed with stable risk
preferences, unaltered by economic experiences. Standard economics also assumes that, when forming
beliefs about risky outcomes, individuals adhere to Bayesian updating and incorporate all available
historical data. The psychology literature, instead, makes a distinction between knowledge obtained from
“description” via statistical summary information (e.g., in books or via education) and knowledge
acquired by personal experience. Data learned from personal experience has a greater influence on
personal decisions than knowledge obtained from books or study. Moreover, when learning from personal
experience, recent events tend to get disproportionate weight. Low-probability events are under-weighted
until they occur, and over-weighted once they occur (Weber et al. 1993; Hertwig et al. 2004).
In this paper, we ask whether we can predict risk attitudes using experiences of risk due aggregate
macro-economic shocks. In particular, we relate stock market returns individuals experience over their
life time to their willingness to invest in the stock market and to their elicited risk attitude. We also ask
whether experiences early in life are particularly formative years for long-term risk attitudes. Our analysis
does not attempt to disentangle the channel, through which risk attitudes are affected (e.g., preferences
versus beliefs), nor to contrast cognitive and transaction cost explanations for the overweighing of
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experience. Rather, we aim at improving our model of risk-taking by exploring the predictive power of
life-time experiences.
To test our hypothesis, we use repeated cross-section data on household asset allocation from the
Survey of Consumer Finances from 1983-2004. For each birth-cohort, we calculate the arithmetic average
U.S. stock market return experienced each cohort since age 20 up to the interview date. We refer to it as
the life-time average stock return. Our basic finding is that there is indeed a strong positive relationship
between various measures of risk-taking and the life-time average stock return of the household head’s
cohort.
The first risk aversion measure is based on responses to a question that attempts to elicit
individuals risk aversion by asking about the willingness to take financial risk. This measure is likely to
capture a mixture between risk aversion of the Arrow-Pratt type and beliefs about future payoffs on risky
asset investments. This is not a problem for our purposes, since we do not attempt to disentangle risk
aversion and belief effects. Second, we look at stock market participation. To the extent that there are
some fixed costs of participating in the stock market (e.g., transaction costs or costs of attention), an
individual’s willingness to participate in the stock market depends on risk aversion and beliefs about
future payoffs from investing in stocks. Finally, for those households that participate in the stock market,
we look at the proportion of liquid assets (including bonds, cash, and cash equivalents) invested in stocks
or mutual funds, which should also be related to beliefs about future risky asset returns and risk aversion.
We relate each of these risk aversion measures to life-time average stock returns, controlling for
age, year fixed effects, and a number of socio-economic characteristics. We find an economically and
statistically highly significant relationship. The results are also robust to controlling for the standard
deviation of stock returns over the individual’s lifetime. While we cannot distinguish our results from
unrestricted cohort effects (the life-time average return does not vary within, only across cohorts), our
hypothesis relates risk-taking specifically to life-time average return, which provides a restriction on the
cohort effects that we can test.
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Consistent with the life-course experience hypothesis, we also find that young people’s
willingness to take risks according to our measures is more strongly positively related to recent stock
market returns (over the previous 10 years) than that of older people. Older investors appear to average
the return over the last 10 years with those they experienced earlier, while younger investors are
influenced only by the more limited history of returns they have experienced. .
Our findings suggest that individual investors’ willingness to bear financial risks is historydependent. Cohorts that have experienced higher stock market returns over the course of their life are
more willing to invest in risky assets. From a theoretical perspective, this kind of behavior could be
explained either with endogenous preferences, where risk aversion depends on the risky asset payoffs
experienced by the individual in the past, or with models of learning, where current beliefs depend on the
realizations experienced in the past. In the latter case, the learning from personal experience with
overlapping generations would lead to beliefs that never converge even in the long-run, and it would lead
to a source of belief heterogeneity.
Our paper connects to a few other papers. Most closely related is the paper by Greenwood and
Nagel (2006), which shows that young mutual fund managers, who had relatively more exposure to
technology stocks in the late 1990s, increased their holdings particularly after quarters with high tech
stock returns, consistent with our finding that young individuals’ allocation to stocks is more sensitive to
recent stock market returns than of older investors. In a similar vein, Vissing-Jorgensen (2002) shows that
at the end of the late 1990s young retail investors with little investment experience had the highest stock
market return expectations. Our paper shows that individuals’ willingness to hold stocks is sensitive to
long-run returns. Piazzesi and Schneider (2006), report evidence that in the late 1970s old households
expected lower inflation than young households. Young households apparently had a stronger tendency to
extrapolate from their recent experience of high inflation at the time, consistent with a strong weight
being placed on life-course experience. Graham and Narasimhan (2004) find that corporate managers that
have lived through the Great Depression in the 1930s choose a more conservative capital structure with
less leverage. Finally, Cogley and Sargent (2005) build a model that explains the equity premium based
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on the assumption that the Great Depression had a long-lasting effect on investors’ beliefs, along the lines
suggested by Friedman and Schwartz (1963). If individuals’ learn from personally experienced economic
events and asset payoffs, as our evidence suggests, a big disaster event like the Great Depression would
indeed have these kinds of effects. The personal-experience story would also explain why the effect on
beliefs slowly fades in the long-run, as more and more cohorts who have not personally experienced the
Great Depression reach the investing age.
II. Data and Summary Statistics
We use data from the Survey of Consumer Finances (SCF), which provides repeated crosssection observations on asset holdings and various household background characteristics every three years
from 1983 to 2004. The SCF oversamples high-income households (asset holdings are concentrated
among those high-income households) and is the most comprehensive source of data on U.S. households’
asset holdings.
The key variables for our analysis are thepast stock market returns that occurred during the
lifetime of the household head, and several measures of risk-taking. For each household we calculate the
real return on the S&P500 index1 from the time the household head turned 20 up to the end of the year
preceding the survey date. For example, for a household head that is 50 years old in 1983, we average the
real return on the S&P500 index from 1953 to 1982. We refer to it as the life-time average return.
The first risk-aversion measure is the risk aversion elicited in the SCF. The SCF includes
questions that asks to what extent the interviewee is willing to financial risks, which we code as an
ordinal variable with values from 1 to 4: (1) take substantial financial risks expecting to earn substantial
returns; (2) take above average financial risks expecting to earn above average financial returns; (3) take
average financial risks expecting to earn average returns; (4) not willing to take any financial risk.
1
We thank Bob Shiller for providing long-term real return data for the S&P Composite index (S&P500 index in the
recent period) on his website.
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This is clearly an imperfect measure of risk aversion. Different individuals may differ in their
interpretation of, say, “substantial” or “above average” risks and returns. For this reason, it would not
make sense to interpret the measure in a cardinal sense. Moreover, even if it were cardinal, a literal
interpretation as a (relative or absolute) risk aversion in the Arrow-Pratt sense, would not be warranted
once we recognize differences in beliefs about the future payoffs of risky assets. An individual who
believes that expected equity premium is high (expecting to earn a “substantial return”) would be willing
to put a large proportion of her portfolio into stocks (“take substantial risks”). Thus, the variable likely
represents the combined effect of Arrow-Pratt risk aversion and beliefs. At the same time, prior literature
documents that the SCF measure predicts individual willingness to take risks. For example, Faig and
Shum (2006) find that households that report higher risk aversion in these questions have a lower
allocation to risky assets. Shaw (1996) shows that the SCF measures of risk-taking help explain
differences in the willingness to make risky human capital investments and wage growth. For ease of
reference, we refer to the measure as “elicited risk aversion.”
Our second measure of risk taking is a binary variable for stock market participation. We define
stock holdings as the sum of directly held stocks (including through investment clubs) and the total
amount held in mutual funds, outside of retirement accounts. This measure can be constructed from the
survey questions of every SCF wave (1983-2004). The 1983 and 1986 surveys differ, however, from
those in 1989 and later in the level of detail at which data is reported. The key issues for our purposes are
that prior to 1989, no information on the composition of assets in retirement accounts (IRA, Keogh, and
Thrift plans) and no information on the type of mutual funds held is available. In our robustness checks,
we also re-run our tests on the 1989-2004 sample with an improved measure of stock holdings that
separates mutual funds that invest mostly in stocks from those that invest mostly in bonds, which is
available for the 1989-2004 subperiod. We exclude retirement account holdings from our analysis,
because prior to 2004 the SCF offers only very coarse information on the allocation of retirement assets
(mostly stocks, mostly interest bearing, or split), precluding any meaningful calculation of stock holdings
in retirement accounts.
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Our third measure is the proportion of liquid assets invested in stocks (directly held stocks plus
mutual funds). Liquid assets are defined as stock holdings (directly held, investment clubs, mutual funds)
plus bonds plus cash and cash equivalents (checking accounts, savings accounts, money market mutual
funds, certificates of deposit) plus the cash value of life insurance plus other liquid assets. Assets in
retirement accounts are excluded. Stock holdings divided by liquid assets yields the proportion invested in
stocks.
We also construct a control variable proxying for total wealth, which is the net worth of the
household, including liquid assets, debts, home equity, the value of vehicles, and retirement assets (IRA,
Keogh, 401k, etc., but not defined benefit plans or social security). As income measure we use total
family income. All income, wealth, and asset holdings variables are deflated into September 2004 dollars
using the consumer price index.
We impose some requirements for households to be included in our sample. For the portfolio
shares to be meaningful, we require that households have at least $10,000 of liquid assets and $10,000 of
total wealth. We also require that the household head is more than 30 years old, and less than 90 years
hold. We impose the lower bound so that we only have households whose head has had a meaningful
experience of stock market returns since age 20.
For our summary statistics, and the descriptive analyses based on plots of age-year cell means, we
weight the data using SCF sample weights. However, in our subsequent regression analyses we don’t,
since doing so would be inefficient use of the data (see, e.g., Deaton 1997, p. 70).
Table I provides some summary statistics on our sample. Panel A includes all households that
satisfy our sample requirements, including the minimum wealth thresholds. Panel B further restricts the
sample to include only stock market participants, i.e., households that have at least 1$ in stocks or mutual
funds. Comparing the two samples, it is apparent that stock market participants tend to be wealthier on
average. For example, median total wealth is $230,439 in the full sample, compared with $370,801 for
stock market participants. As Panel A shows, 45.1% of households participate in the stock market.
However, this number represents a sample-weights weighted mean, which represents an estimate for the
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participation rate in the U.S. population (subject to our sample requirements), but the actual proportion of
observations on stock holders in the SCF is higher than that, because high-income households are
oversampled. This is explains why the number of observations in Panel B is higher than 45.1% of the
number of observations in Panel A.
The two other risk aversion measures also show considerable dispersion across households. The
10th and 90th percentiles for elicited risk aversion in Panel A are 2.0 and 4.0, respectively. The proportion
of liquid assets invested in stocks (% Risky) in Panel B has 10th and 90th percentiles of 4.8% and 88.1%.
Our main question of interest is whether this variation in related to the life-time average stock return
experienced by household heads’ since age 20. The 10th and 90th percentile for the real life-time average
stock return among stock market participants in Panel B are 5.2% and 11.3%. Hence, there are
considerable differences in the life-time average returns experienced by different cohorts.
III. Results
A. Descriptive analysis
We start by describing some broad patterns in the data. We then examine to what extent these
patterns hold up once we control for various household characteristics and age and time effects.
Figure 1 presents some scatterplots of our risk-taking measures against the life-time average stock
return. We take all the data and average the risk-taking measure within age-year cells, weighted by SCF
sampling weights. We then plot these age-year cell averages against the life-time average stock return
(which is the same for each member of the same age-year cell). Figures (a), (b), and (c) reveal a positive
relationship between the willingness to take risk and the stock return a household head has experienced
over his or her adult life (note that in Panel (a) high values of the elicited risk aversion measure indicate
low willingness to take risks) and the effects appear to have big economic magnitudes.
These simple scatterplots don’t address the question whether age effects or time effects explain
this apparent positive relationship. For example, if there has been an upward trend in stock market
participation (perhaps because the costs of participating have decreased over time) and at the same time
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the average life-time stock return of households in the sample also has increased (as there are fewer
cohorts later in the sample that have experienced the Great Depression during their adult life), this could
explain the positive correlation between stock market participation and life-time average return.
We address this issue with our regression analyses, but first provides some intuition for the
limitations of this alternative explanation. In Figure 2, we look at how the three risk-taking measures vary
with age in different years. The simple time trend story would predict that there should be a parallel shift
in the age profiles of our risk-taking measures over time. In contrast, the personal experience story
implies that age profiles should change over time in the same way as the age profiles of life-time average
returns change.
To get the age profiles, we group households by age each year, and then calculate age-year cell
means of our risk taking measures and life-time average returns for different years in the sample (1983,
1992, and 2001). We estimate age profiles non-parametrically by running locally-weighted regressions of
life-time average return and our risk-taking measure cell means against age within a given year.
Figure (a) shows the age profile for the life-time average return. It is apparent that the profiles for
1983 and 2001 are very different. In 1983, the life-time average return of young households is dominated
by the low returns during the depression years of the 1970s. In contrast, in 2001, it is dominated by the
boom years of the 1990s. For older households, the differences are less extreme, and so we get a flipping
from a positively sloped to a negatively sloped age profile from 1983 to 2001.
Figure (b) presents the age profile for elicited risk aversion. It is clear that in general older
households report to have higher risk aversion. Looking at changes in the age profiles across time, it is
apparent that the age profile in 2001 is much steeper than in 1983, consistent with the idea that the high
returns of the stock market boom years in the 1990s made young investors less risk averse relative to
older investors in 2001 compared with 1983, when young investors had just experienced a decade of low
stock market returns.
The age-profile for stock market participation shows a much cleaner pattern. In 1983, the age
profile was upward sloping. By 2001, it has flipped to a downward sloping profile, just like the profile for
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life-time average returns. Similar to the returns profile, we also observe that most of the action happens
among young households, whereas the shifts for older households are much smaller. These kinds of shifts
cannot be explained by a time effect. A time effect can shift the age profile from one period to the next,
but it cannot lead to the tilts in age profiles that we see in Figure 2.
Figure (d) shows the profile for the proportion of liquid wealth invested in stocks. The pattern is
similar to that of Figure (c), but it is somewhat more dampened. We now turn to formal statistical
inference.
B. Elicited Risk Aversion
It is well known that it would not be possible to simultaneously identify age, time, and cohort
effects, if they are modeled as dummy variables without further restrictions, because of multicollinearity.
Things are different in our setting, because we have a specific measured cohort-level variable (life-time
average stock returns) that, according to our hypothesis, should explain risk-taking. In that case, the
identification problem is solved (Heckman and Robb 1985).
Another important identification issue is reverse causality. Our aim is to find out whether there is
a positive causal effect of past stock returns experienced by an individual on current risk aversion.
However, if variation in stock returns over time is caused, at least partly, by variations in risk aversion of
investors, then there could be reverse causality. For example, as risk aversion of investors goes down,
stock prices would rise, and so we would get a negative correlation between past stock market returns and
current levels of investors’ risk aversion, but with causality running from current risk aversion to past
stock returns. We address this issue by including time dummies in our regressions, which absorb variation
in average risk aversion of the households in our sample. This means that we focus on whether life-time
average stock returns explain cross-sectional differences in risk aversion in different time periods.
Denote SCF elicited risk aversion measure with J. It has 4 distinct categories, J  {1,…,4}. We
have a vector of explanatory variables x that also includes the life-time average stock return, our variable
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of main interest. We model the cumulative probability of the ordinal outcomes with a standard ordered
probit model
Pr >J d j | x @ ) D j xcE j 1,..., 4
(1)
where ) denotes the cumulate standard normal distribution function, Dj and the vector E denote model
parameters to be estimated, and D1 = 0 < D2 < D3 < D4 = ’. The coefficient vector E does not have a direct
economic interpretation. Therefore, to interpret the results of the ordered probit estimation, we focus on
the partial effects wPr[J = j| x ]/ wxi implied by the model, i.e., on how a marginal change in the regressor
xi changes the probability that J = j, where the partial effects are evaluated at the sample means x of the
regressors. We estimate the average partial effects as the sample average of the wPr[J = j| x ]/ wxi
evaluated at each sample observation, which is a consistent estimator of the average partial effect in
population.
Table II presents the results of the ordered probit model, estimated by maximum likelihood. In
addition to the variables shown in the table, all specifications also contain a number of control variables:
year dummies, log total wealth, log total wealth squared, log income, log income squared, a third-order
polynomial in age to allow for a non-linear age profile, and dummies for retirement, completed high
school education, completed college education, marital status, and race. To reduce clutter, the table
reports only report the coefficient estimate for the variables we are interested in, and the partial effects for
this variable. Bootstrap t-statistics for the average partial effects are almost identical to the t-statistic for
the coefficient estimate and are therefore omitted from the table.
In specification (i), we use our basic model with the life-time average stock return as the
regressor. The coefficient estimate is negative (-1.34) and significantly different from zero (t-statistic –
2.45). However, the coefficient estimate does not have a direct economic interpretation, and so we focus
on the estimated average partial effects. As the table shows, a marginal increase in the life-time average
return increases the probability that the household head reports risk aversion in the low categories (1 and
2) and it decreases the probability that the reported risk aversion is in the high categories (3 and 4). Recall
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from Table I that the difference between the 10th and 90th percentile of life-time average stock returns is
about 6%. Applied to the marginal effects in Table II and linearizing, specification (i), this means that a
change from the 10th to the 90th percentile implies about 0.41 u 6% = 2.46% increase in the probability of
being in the two low risk aversion categories. It is not a huge effect, but considering that the stock return
experienced by an individual is a crude measure of personal experience of stock market returns, and only
one of many possible factors that influence the willingness to take financial risks in general, it is not
negligible either.
Specification (ii) shows that the estimated effect of life-time average returns is somewhat stronger
if we control for the standard deviation of stock market returns since age 10 (life-time return standard
deviation). The sign of the effects of standard deviation are not positive, though. If higher risk
experienced in the past would lead to higher risk aversion, one would expect a positive effects. But the
effects are not statistically significant, so the data don’t clearly speak to the role of experienced variability
of past returns.
In Specification (iii), we add the return the household head has experienced since birth to age 20.
The motivation for adding this variable is to check whether events such as economic distress periods
experienced during childhood and youth also might have an impact on risk-taking, perhaps caused by
educational influences from parents and the environment in which an individual grows up. The negative
sign of the estimated effects on elicited risk aversion are consistent with this idea, but they are not
statistically significant. Adding this variable also increases the magnitude of the life-time average return
(age 20 to present). A change from the 10th to the 90th percentile implies about 0.63 u 6% = 3.78%
increase in the probability of being in the two low risk aversion categories.
If life-time average returns have an influence on the willingness to take financial risks and the
impact of life-time experience is much stronger than that of other historical data realized before an
individual reaches adulthood, this also implies that relatively recent stock market returns should have a
stronger effect on young individuals than on older individuals. For example, for an individual who is 30
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years old, the stock returns over the last decade are roughly the whole stock market experience the
individual has. An individual who is 60 years old, in contrast would average these recent 10-year returns
with the returns she has experienced over the 30 years over her adult life prior to that.
To test this, Specification (iv) includes an interaction term of age with the stock market returns
over the most recent 10-year period. Since the stock market return over the last 10 years is the same for
all households in a given cross-section of the data, and our regressions include time dummies, we cannot
identify the average effect of past 10-year returns on elicited risk aversion; we can only look at crosssectional differences between households of different ages (of the household head). The prediction is that
risk aversion should be more negatively related to recent 10-year returns for young people than for older
people, i.e., we expect to find positive marginal effects of the age and 10-year returns interaction variable
for high risk aversion and negative effects for the low risk aversion categories. As the table shows, the
estimated marginal effects are consistent with this prediction and statistically, they are significantly
different from zero. In terms of magnitudes, the differential effect between a 30-year old and a 60-year
old of a change in past 10-year returns of, say, 5%, on the probability of being in the high vs. low risk
aversion groups is roughly 0.02 u 30 u 5% = 3%, which is an economically significant difference.
C. Stock Market Participation
Survey-based measures of elicited risk aversion—very simple ones like the one in the SCF, in
particular—obviously have a variety of potential shortcomings. For example, it is not clear that
households actions actually conform to the attitudes expressed in the survey question on risk aversion
(although for the SCF there is some evidence that households do so. See, e.g., Faig and Shum (2006),
Shaw (1996)). For that reason, we now turn to examining households’ actual decisions.
Table III examines the effect of life-time average returns on the probability that a household
participates in the stocks market (i.e., that the household has some holdings of stocks or mutual funds).
All specifications contain the same set of control variables (not shown in the table) as the ordered probit
models in Table II. As we can see from Specification (i), the life-time average returns have a positive
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effect on stock market participation. The average partial effect of 1.11 (t-statistic 5.98) means that a
change from the 10th to the 90th percentile of life-time average returns (6%) leads to an increase of about
1.11 u 6% = 6.66% in the probability that a household participates in the stock market. This is a big
effect. It suggests that the personal stock market return experience of different cohorts is a major factor
that drives variation in stock market participation.
Specification (ii) adds the life-time return standard deviation. As in Table II, this risk measure
does not come in with a sign that one might have expected. It does not seem to be the case that high return
standard deviations in the past deter households from investing in the stock market.
In Specification (iii), we add the returns during the first 20 years of the life of the household head
and they come in positively, but the average partial effect is not significantly different from zero.
Finally, in Specification (iv) we look again at the interaction term of past 10-year returns with
age. We obtain an average partial effect of –0.04 (t-statistic 6.06). The differential effect between a 30year old and a 60-year old of a change in past 10-year returns of 5%, on the probability of being a stock
market participant is roughly 0.04 u 30 u 5% = 6%, which is an economically large difference.
D. Proportion Invested in Risky Assets
Table IV looks at how life-time average stock returns affect the proportion of liquid assets that
households invest in risky assets (i.e., stocks and mutual funds). This risky asset share should be a
somewhat cleaner measure of risk-taking than stock market participation. The stock market participation
decision is likely to be influenced not only by beliefs about future expected returns from investing in
stocks and by risk aversion, but also by costs of participating in the stock market. The risky asset share in
a households’ portfolio, in contrast, should depend primarily on the households’ views about future
expected return and relative risk aversion.
All specifications in Table IV contain the same set of control variables (not shown in the table) as
the ordered probit models in Table II. Instead of the age polynomial, however, we now include a full set
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of age dummies to absorb arbitrarily non-linear age effects. The sample now includes only households
that participate in the stock market. As Specification (i) shows, the life-time average return has a positive
and large effect on the proportion of liquid assets invested in risky assets. The point estimate of 0.92 (tstatistic 5.21) implies that a change from the 10th to the 90th percentile of life-time average returns (6%)
leads to an increase of about 0.92 u 6% = 5.52% in the proportion allocated to risky assets.
In the empirical literature on household portfolio choice it is a common finding that once one
restricts the sample to households that participate in the stock market, it is quite hard to find any
household characteristics that are correlated with the portfolio risky asset share (see, e.g., VissingJorgensen (2002), Curcuru, Heaton, Lucas, and Moore (2004), and Brunnermeier and Nagel (2006) for
recent evidence). In light of this evidence, it is quite remarkable that life-time average returns have such a
strong effect. Life-course experience of stock market returns seems to be one of the major factors that
influence a households’ willingness to bear stock market risk.
Specification (ii) adds the life-time return standard deviation. It again comes in positive, but the
point estimate is not significantly different from zero. So it seems that the variability of experienced stock
market return does not have a clear influence on households’ asset allocation decisions.
Returns experienced in the first two decades of the household head’s life also do not have a
statistically significant effect on the risk asset share, as is shown in the third column (Specification (iii)).
Finally, the interaction variable between age and past 10-year returns in Specification (iv) gets a
negative coefficient estimate (-0.02, t-statistic –4.23). This means that for younger households there is a
more strongly positive association of the current risky asset share with prior 10-year returns than for older
households, consistent with the idea that 10-year returns matter relatively more when the total return
history an individual has experienced is relatively short. The differential effect between a 30-year old and
a 60-year old of a change in past 10-year returns of 5% on the risky asset share is roughly 0.02 u 30 u 5%
= 3%, which not a small difference.
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E. Robustness Checks
We have conducted a variety of robustness checks, with largely similar results. As we explain in
Section II, the data definitions of the 1983 and 1986 surveys differ from those in the 1989 to 2004
surveys. In particular, the post-1989 data allow us to split mutual fund holdings into mutual funds that
hold mostly stocks and those that don’t (“combination” mutual funds that hold both stocks and bonds are
assumed to be split half-half between stocks and bonds), while the earlier data do not. For our purposes, it
is clearly useful to have a relatively long time-series, because to identify the effects of life-time average
returns on risk-taking, we need to separate it from age effects, which requires that the data have time
dimension. For that reason, we carried out our main tests with the long 1983-2004 sample. But to check
whether the results are robust, we re-ran all of our tests with the shorter 1989-2004 sample, using the
improved definition of risky asset shares, with mutual fund holdings split between stock funds and bond
funds.
IV. Conclusions
We show empirically that the stock returns experienced over the course of an individuals’ adult
life has a significant effect on the willingness to take financial risks. Individuals that have experienced
high stock market returns report lower aversion to financial risks, are more likely to participate in the
stock market, and allocate a higher proportion of their liquid asset portfolio to risky assets. Young
people’s risk-taking behavior is more sensitive to relatively recent stock market returns than that of older
people. Differences between cohorts in their life-course stock return experience appear to be one of the
major factors that influence heterogeneity in willingness to bear financial risks.
Our results are consistent with the view that personally experienced economic events have a more
significant impact on individuals’ beliefs and/or preferences than historical facts learned through study of
historical summary information in books and other sources. Such dependence of beliefs and/or
preferences on personally experienced data could have important implications for both explaining
16
heterogeneity between economic agents at the micro-level and for dynamics of asset prices at the macro
level.
17
References
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Aversion? Micro-Evidence on Individuals’ Asset Allocation, working paper, Stanford University.
Cogley, Timothy, and Thomas J. Sargent, 2005. The Market Price of Risk and the Equity Premium: A
Legacy of the Great Depression?, working paper, New York University.
Curcuru, Stephanie, John Heaton, Deborah Lucas, and Damien Moore, 2004. Heterogeneity and Portfolio
Choice: Theory and Evidence, Handbook of Financial Econometrics, forthcoming.
Deaton, Angus, 1997. The Analysis of Household Surveys: A Microeconometric Approach to
Development Policy, Johns Hopkins University Press, Baltimore MD.
Faig, Miquel, and Pauline Shum, 2006. What Explains Household Stock Holdings?, Journal of Banking
and Finance 30, 2579-2597.
Friedman, Milton, and Anna J. Schwartz, 1963. A Monetary History of the United States, 1867-1960,
Princeton University Press, Princeton, NJ.
Graham, John R., and Krishnamoorthy Narasimhan, 2004. Corporate Survival and Managerial
Experiences During the Great Depression, working paper, Duke University.
Greenwood, Robin, and Stefan Nagel, 2006. Inexperienced Investors and Bubbles, working paper,
Stanford University.
Hertwig, Ralph, Greg Barron, Elke U. Weber, and Ido Erev, 2004. Decisions from Experience and the
Effect of Rare Events, Psychological Science 15, 534-539.
Piazzesi, Monika, and Martin Schneider, 2006. Inflation and the Price of Real Assets, working paper,
University of Chicago.
Shaw, Kathryn L., 1996. An Empirical Analysis of Risk Aversion and Income Growth, Journal of Labor
Economics 14, 626-653.
Weber, Elke U., Ulf Böckenholt, Denis J. Hilton, and Brian Wallace, 1993. Determinants of Diagnostic
Hypothesis Generation: Effects of Information, Base Rates, and Experience, Journal of
Experimental Psychology: Learning, Memory, and Cognition, 19, 1151-1164.
Vissing-Jorgensen, Annette 2002. Towards and Explanation of Household Portfolio Choice
Heterogeneity: Nonfinancial Income and Participation Cost Structures, working paper,
Northwestern University.
18
Table I: Summary Statistics
10th pctile
90th pctile
Median
Mean
Stddev
N
Panel A: All households
Total wealth
55,221
230,439
1,116,960
632,426
2,713,839
17,594
Liquid assets
14,379
50,711
363,171
214,616
1,287,991
17,594
Income
16,459
51,000
148,606
84,700
293,067
17,594
Life-time avg. stock return
0.047
0.079
0.107
0.079
0.025
17,594
Elicited risk aversion
2.000
3.000
4.000
3.065
0.806
16,003
Stock mkt. participation
0.000
0.000
1.000
0.451
0.498
17,594
% Risky
0.000
0.000
0.722
0.192
0.294
17,594
Panel B: Stock market participants
Total wealth
95,549
370,801
1,795,426
987,191
3,697,309
10,483
Liquid assets
20,700
99,106
694,948
380,350
1,843,991
10,483
Income
23,848
69,363
205,375
118,333
416,516
10,483
Life-time avg. stock return
0.052
0.080
0.113
0.081
0.025
10,483
Elicited risk aversion
2.000
3.000
4.000
2.847
0.761
10,483
% Risky
0.048
0.387
0.881
0.427
0.302
10,483
Note: Sample period runs from 1983 to 2004. All wealth and income variables are deflated by the CPI
into 2001 dollars. Observations are weighted by SCF sample weights.
19
Table II: Elicited Risk Aversion and Life-time Average Stock Returns, Ordered Probit
Life-time average stock return
Coefficient estimate
Average partial effects:
Elicited risk aversion = 1
Elicited risk aversion = 2
Elicited risk aversion = 3
Elicited risk aversion = 4
(i)
(ii)
(iii)
-1.34
(-2.45)
-1.61
(-2.79)
-2.08
(-2.22)
0.15
0.26
-0.07
-0.34
0.18
0.31
-0.08
-0.41
0.23
0.40
-0.10
-0.53
Life-time return standard deviation
Coefficient estimate
-1.42
(-1.48)
Average partial effects:
Elicited risk aversion = 1
Elicited risk aversion = 2
Elicited risk aversion = 3
Elicited risk aversion = 4
0.16
0.27
-0.07
-0.36
Average returns from age 1 to 20
Coefficient estimate
-0.54
(-0.99)
Average partial effects:
Elicited risk aversion = 1
Elicited risk aversion = 2
Elicited risk aversion = 3
Elicited risk aversion = 4
0.06
0.10
-0.03
-0.14
Age u Average returns past 10 years
Coefficient estimate
0.06
(3.45)
Average partial effects:
Elicited risk aversion = 1
Elicited risk aversion = 2
Elicited risk aversion = 3
Elicited risk aversion = 4
#Obs.
Pseudo R2
(iv)
-0.01
-0.01
0.00
0.01
15,937
0.09
15,937
0.09
20
15,937
0.09
15,937
0.09
Table II continued:
Notes: Each specification contains a full set of controls (coefficient estimates not reported in the table),
including time dummies and a third-order polynomial in age, log income, log income squared, log total
wealth, log total wealth squared, and various other household characteristics (see text). Average partial
effects are the sample averages of the partial effects evaluated at each sample observation. t-statistics are
shown in parentheses. Bootstrap t-statistics of the average partial effects are virtually identical to the
coefficient t-statistics and are omitted.
21
Table III: Stock Market Participation and Life-time Average Stock Returns, Probit Model,
Average Partial Effects
Life-time average stock return
(i)
(ii)
(iii)
1.11
(5.98)
1.26
(6.30)
0.81
(2.45)
1.36
(4.10)
Life-time return standard deviation
Average returns from age 1 to 20
0.18
(0.90)
Age u Average returns past 10 years
#Obs.
Pseudo R2
(iv)
-0.03
(-10.95)
17,526
0.19
17,526
0.19
17,526
0.19
17,526
0.19
Notes: The table reports average partial effects, i.e., the sample averages of partial effects evaluated at
each sample observation. Each specification contains a full set of controls (average partial effects not
reported in the table), including time dummies and a third-order polynomial in age, log income, log
income squared, log total wealth, log total wealth squared, and various other household characteristics
(see text). Bootstrap t-statistics are shown in parentheses.
22
Table IV: Proportion of Liquid Wealth Invested in Risky Assets and Life-time Average
Stock Returns, OLS
Life-time average stock return
(i)
(ii)
(iii)
0.92
(5.21)
0.97
(5.31)
0.32
(1.02)
0.70
(2.26)
Life-time return standard deviation
Average returns from age 1 to 20
-0.16
(-0.88)
Age u Average returns past 10 years
#Obs.
R2
(iv)
-0.02
(-4.23)
10,449
0.08
10,449
0.08
10,449
0.08
10,449
0.08
Notes: Each specification contains a full set of controls (coefficient estimates not reported in the table),
including time dummies, age dummies, log income, log income squared, log total wealth, log total wealth
squared, and various other household characteristics (see text). Heteroskedasticity-consistent t-statistics
are shown in parentheses.
23
(a) Elicited risk aversion
(b) Stock market participation
Figure 1: Age-year cell means of risk-taking measures plotted against life-time average
stock market return
24
(c) Proportion of liquid wealth invested in risky assets
Figure 1 (continued): Age-year cell means of risk-taking measures plotted against life-time
average stock market return
25
(a) Life-time average stock return
(b) Elicited risk aversion
Figure 2: Age-Profiles of Life-time average stock returns and risk aversion measures in
1983, 1992, and 2001, locally weighted least squares estimates
26
(c) Stock market participation
(d) Proportion of liquid wealth invested in risky assets
Figure 2 (continued): Age-Profiles of Life-time average stock returns and risk aversion
measures in 1983, 1992, and 2001, locally weighted least squares estimates
27