JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS
COPYRIGHT 2006, SCHOOL OF BUSINESS ADMINISTRATION, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195
VOL. 41, NO. 4, DECEMBER 2006
Do Behavioral Biases Vary across Individuals?
Evidence from Individual Level 401(k) Data
Julie R. Agnew∗
This paper investigates whether some individuals are prone to behavioral biases in their
401(k) investments. Using demographic data and allocation information for over 73,000
employees, I examine two allocation biases and a participation bias. The findings suggest
that higher salaried employees tend to make significantly better choices. Participants who
earn $100,000 hold 12.7% less in company stock, are 3% less likely to follow the framing
1/n heuristic, and are37.7% more likely to participate than those earning $46,000. Women
make better choices in two of the three cases and I find evidence of mental accounting.
There is a growing literature that suggests an individual’s investment deci-
sions are affected by behavioral biases. Researchers explain financial decisions
based on behavioral theories such as excessive extrapolation, loyalty, and famil-
iarity (Benartzi (2001), Cohen (2004), and Huberman (2001)). Data from 401(k)
plans provide a fertile ground for examining these behavioral biases because par-
ticipants, who represent a diverse population of working individuals, are faced
with the same choice environment. At the 401(k) plan level, ample evidence ex-
ists that behavioral biases can be overcome or made worse by 401(k) plan design
∗Agnew, email@example.com, The College of William and Mary, Mason School of Busi-
ness Administration, PO Box 8795, Williamsburg, VA 23187. I thank an anonymous large benefits
provider for providing the 401(k) plan data and Stefan Bokor for his immense help in organizing the
data. I thank Pierluigi Balduzzi, Hendrik Bessembinder (the editor), John Boschen, Alicia Munnell,
Eric Jacquier, Peter Gottschalk, Shlomo Benartzi, and Jeff Pontiff (associate editor and referee) for
their careful comments and insight. In addition, Jim Poterba, Josh Rauh, Henry Richardson and Steve
Utkus were very helpful. I thank conference participants from the Retirement Research Consortium
Fourth Annual Conference, the 2002 Financial Management Association Conference, and the Frank
Batten Young Scholars Conference. In addition, I thank participants in seminars held at Boston Col-
lege, Drexel University, the Federal Reserve Board of Governors, George Washington University,
Georgia Tech, The College of William and Mary, the University of Georgia, and the University of
Richmond. I gratefully acknowledge financial support from a dissertation fellowship from the Center
for Retirement Research. Any errors are my own. The research reported herein was performed pur-
suant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement
Research Consortium. The opinions and conclusions are solely mine and should not be construed
as representing the opinions or policy of the SSA or any agency of the Federal Government. This
paper was previously circulated under the title “Inefficient Choices in 401(k) Plans: Evidence from
Individual Level Data.”
940Journal of Financial and Quantitative Analysis
(Benartzi (2001), Choi, Laibson, Madrian, and Metrick (2001)). Markedly less
attention has been paid to these biases at the individual level. The outstanding
questions are: does the propensity to follow behavioral biases vary across indi-
vidualsand are there commoncharacteristicsthat matter? Previousliterature does
not adequately address these questions, and this gap provides the motivation for
This paper takes a deeper look into three behavioral biases by providingnew
estimates of their severity at the individual level and by examining whether cer-
taintypesofindividualsarepronetothesebiases. I examinetwoallocationbiases,
following na¨ ıve diversification strategies and investing in company stock, and a
participationbias, optingnot toparticipatein the companysponsored401(k)plan.
Using a new dataset representing over 73,000 eligible participants, this is the first
paper to jointly model these three biases as functions of individual characteristics
and to providethe opportunityto assess the relative influence of these characteris-
tics on these choices. This paperimproveson and complementspreviousanalyses
of the two allocation biases mainly because the majority of past studies use plan
level data that can lead to aggregation bias in the results and the studies do not
address individual level heterogeneity in decisions.1This study takes advantage
of individual level allocation data. These data overcome the aggregate data prob-
lems and provide the opportunity to more strongly test new behavioral theories,
such as loyalty and its influence on company stock investment.
The principal finding suggests that higher salaried employees tend to make
significantly better choices in all three cases. Specifically, I find participants who
earn $100,000 hold 12.7% less in company stock, are 3% less likely to follow
the 1/n heuristic, and are 37.7% more likely to participate than those earning the
average wage of $46,000. Women also appear to make better choices in two of
the three cases, viz., 401(k) participation and investment in company stock. This
suggests that behavioral biases do vary across individuals and highlights that plan
level analysis will suffer from an omitted variable bias. The paper also finds more
direct evidence of mental accounting related to company stock holdings. Until
now, this theory has only been studied at the plan level.
These findings suggest that empirical research should control for individual
level heterogeneity and that more research into why salary and gender matter is
needed. Onapracticallevel,theseresults canhelpplansponsorsidentifyhighrisk
individuals with a view toward improving plan design. The results may also be
helpful in the current Social Security debate over personal accounts by providing
demographic insights into investor behavior. Finally, these results have relevancy
beyond the 401(k) literature and the broader implications are discussed in the
The paper begins with a consideration of the allocation topics because com-
pared to the participation choice, there has been relatively little research devoted
1Huberman and Jiang (2006) use individual level data in their analysis of how the number of
investment funds offered affects equity investment. They analyze how many people follow the 1/n
heuristic, but do not focus their discussion on the influence of individual characteristics. The study
also does not investigate how the mental accounting of company stock affected the 1/n strategy. Choi,
Laibson, Madrian, and Metrick’s (2004) company stock allocation study uses individual level demo-
graphic data with past company stock returns. They focus their discussion on whether participants
practice “feedback” investing and not on the role of individual characteristics on this decision.
to the allocation biases at the individual level. The first topic studied is the na¨ ıve
diversification bias. Na¨ ıve diversification strategies can result when individuals
are faced with complicated decisions that cause them to fall back on simple rules
of thumb. This paper investigates one na¨ ıve strategy called the “framing 1/n
heuristic.” This strategy is considered irrational because investors divide their
contributions evenly among the number (n) of investment options offered. They
do so regardless of the menu of investment options presented and thus are influ-
enced by the fund choices available. My paper finds that salary and employment
tenure are negatively related to this practice. I address a similar heuristic that is
considered rational, “the conditional 1/n heuristic,” and find highly compensated
individuals are 7.4% more likely to follow this rule.
Turning to the second allocation bias, company stock investment, evidence
pany stock. In fact, Hewitt Associates LLC (2003) reports from a sample of 1.5
million 401(k) plan participants in 2003 that the average company stock balance
was 41%, and the NASD recently issued a company stock warning to investors
This paper examines the links between individual characteristics, past com-
pany stock returns, and company stock allocations. The influence of past com-
pany stock performance and plan design on company stock holding is already
well documented in the literature (Benartzi (2001), Choi, Laibson, Madrian, and
Metrick (2004), Liang and Weisbenner (2002), and Sengmuller (2002)). This
study contributes to the literature by analyzing the additional link between in-
dividual characteristics and company stock holdings while controlling for past
performance. The individual level data I use offer more demographic detail than
in past research, and the returns are more precisely calculated than those used
in plan level studies. I find a one standard deviation increase in short-term re-
turns increases company stock holdings by 8% and that short-term returns matter
more than long-term returns. In addition, I find that company stock allocations
are greater for males, decrease with salary, and are higher for employees in non-
Finally, since lack of participation stands out as one of the most obvious in-
vestment mistakes an individual can make, I include a brief study of participation
choice for completeness. My findings are consistent with previous studies, and I
find that the probabilityof participatingincreases with age, job tenure, and salary.
The paper is organized as follows. Section II summarizes the dataset. Sec-
tion III describes the plan design and the asset allocation choices. Section IV
summarizes the demographic and employment characteristics of all the partici-
pants eligible to participate in the plan. Section V and VI present the empirical
results associated with na¨ ıve diversification and company stock holdings, respec-
tively. Section VII discusses the participation level in this plan, and Section VIII
presents the Conclusion.
This paper uses a detailed database supplied by an anonymous large bene-
fits provider. The cross-sectional data are from one large 401(k) plan with over
942Journal of Financial and Quantitative Analysis
73,000 eligible employees.2The plan is sponsored by a global consumer product
company and the entire sample is used to investigate the participation decision.
Thestudyoftheallocationbiases is basedonasmallersubsampleof“active”
participants. This paper defines an active participant as a plan eligible participant
who made a contribution to the plan during the first two weeks of August 1998.
The dataset includes each active participant’s contributionallocation and for most
of these individuals the actual date that this allocation was chosen. For this allo-
cation date, company stock returns over various prior periods are calculated. A
total of 28,793 participants are considered active participants.
Of these active participants, the allocation date is missing for 5,814 indi-
viduals who were enrolled in the plan prior to 1992 and did not change their
contribution allocations after this date. The data are missing because the current
ing the design of the plan prior to this date. Thus, it is possible that a different
set of fund choices may have been available prior to 1992 or that an employer
match may have been offered. Since features like these can influence choice, this
group might behave differently than participants who make allocation decisions
after 1992 (Benartzi (2001), Benartzi and Thaler (2001)).
The missing allocation decision date means that estimating company stock
returns prior to the allocation decision is not possible, nor is it possible to esti-
mate the individuals’ ages and the number of years employed at the time of the
allocation decision. Therefore, these individuals are eliminated from the sample
leaving 22,979 active participants for the analysis.3
One of the most importantfeatures of the data is detailed demographicinfor-
mation. For each eligible participant, the individual’s participation status, salary,
birth date, date of employment, compensation status, and gender are available.
Finally, three additional features of these data deserve mention. First, the
asset allocations of the contributions are broken down at the individual level. Ag-
gregatecontributionplan data can blur the results if high contributingparticipants
invest differently from low contributing participants. The effect of large contri-
bution levels is analogous to the influence of large market capitalization stocks
on a value-weighted index. Furthermore, aggregation can exaggerate very weak
relations at the individual level. This is called aggregation bias. Huberman and
Jiang (2006) use a simulation to demonstrate how aggregation bias can amplify
individual level findings in 401(k) plans. Second, these data are from one plan.
While multiple plan data are appropriate for studying across-plan variation, they
can create a potential for omitted variable bias related to plan design or plan ed-
ucational efforts. Analyzing one plan eliminates this concern. A final advantage
of the data is that allocations are based on contributions not asset balances. Asset
performance can move asset allocations based on asset balances away from the
participant’s intended allocation. Contribution allocations do not suffer from this
A disadvantage of this dataset is that information regarding participants’ as-
sets outside of the plan is not available. Another drawback of the dataset is that
2An eligible employee is an employee who may participate in the 401(k) plan if he chooses.
3Separate analyses of the allocation biases including and controlling for this subgroup were com-
pleted. These analyses are not reported here, but the findings are qualitatively the same.
to participate than high wage earners due to the greater liquidity constraints they
bear, the reduced tax breaks they earn due to their lower tax brackets, and the
higher replacement rates they earn from Social Security. The positive relation
with age may be because people grow more interested in their retirement savings
as time goes by. Finally, the positive relation between participation and time
employed may be a result of individuals’ vesting schedules increasing over time
and their growing familiarity with the plan. For further discussion of why these
characteristics might matter, see Munnell, Sunden, and Taylor (2001/2002).
To test whether a common factor exists that relates to the efficiency of all
three choices and to examine whether participants in this study’s plan behave
similarly to participants in other plans, a probit regression of the participant’s
decision is modeled. Table 8 presents the findings. The findings are consistent
with previous work and, most importantly, one characteristic, salary, is related
to the efficiency of all three decisions (Tables 5, 7, and 8). In the participation
regression, salary plays a large role. For example, compared to an average partic-
ipant earning $46,000, an average participant earning $100,000 would be 37.7%
more likely to participate. This same individualwould be expected to hold 12.7%
less in company stock and would be 3.0% less likely to follow the framing 1/n
Marginal Effects from a Probit Regression: 401(k) Participation Decision
Table 8 presents the marginal effects calculated from the results of a probit regression. The dependent variable equals
one if the participant is an active participant in the plan and zero if not. Male is a dummy variable equal to one if the
participant is male, zero otherwise. Salary is the annual 1997 salary (unit: $10,000). Age is the age of the participant as of
August 1998 (unit: years). The marginal effect takes into account a nonlinear effect of age. Time Employed equals the time
the participant has been employed as of August 1998 (unit: years). Division # and Other are dummy variables that equal
one if the participant is in the division. The Corporate Division is the omitted dummy. Robust standard errors, reported in
parentheses, are adjusted for heteroskedasticity. The pseudo R2is the log-likelihood value on a scale from zero to one,
where zero corresponds to the constant only model and one corresponds to perfect prediction (a log-likelihood of zero).
**,* indicate significance at the 1% and 5% levels, respectively.
The model : Prob(Y = 1)
=Φ(β0+ β1Male + β2Age + β3Age2+ β4Time Employed + β5Salary
+ β6Division 1 + β7Division 2 + β8Other)
No. of Observations
adF/dx is for a discrete change of the dummy variable from zero to one.
960Journal of Financial and Quantitative Analysis
In addition, gender plays a statistically significant role in two of the biases—
participation and company stock allocations. In both cases, women make better
choices, with women being 4% more likely to participate and expected to hold
2% or 3% less in company stock than men.
This paperexamines the influence of individualcharacteristics on behavioral
biases in 401(k) plan allocation decisions. With over 73,000 eligible participants,
the database pertains to a single plan that consists of a diverse set of individuals
who are presentedwith similar investmentchoices. The investmentbiases studied
are the na¨ ıve 1/n heuristic and its variations, investment in company stock, and
the decision not to participate in the plan.
The goal of this paper is to determine whether the propensities to follow
biases vary across individualsand whether a commoncharacteristic can be found.
The 401(k) literature has already documented the importance of plan design, and
this papercontributestothe literaturebyhighlightingtheimportanceofindividual
The principal finding suggests that higher salaried employees tend to make
significantly better choices in all three cases. Women also appear to make bet-
ter choices in two of the three cases, viz., 401(k) participation and investment in
companystock. These findingshighlightthe needto controlfor individualhetero-
geneityin empiricalwork,andtheyindicate somedirectionsforfutureresearchas
well. For example, althoughseveral theories are presented in this paper to explain
why gender and salary matter, additional work is needed to study these theories
in detail. In particular, data on individual level financial literacy would be useful
to understand the salary results. On a practical level, these results can help plan
sponsors identify high risk individualswith a view toward improvingplan design.
Theresults may also be helpfulin the currentSocial Securitydebateoverpersonal
accounts by providing demographic insights into investor behavior.
Finally, this research has implications beyond the 401(k) literature. For ex-
ample, studies suggest that the empirical failures of the C-CAPM are a result
of including non-market participants in the sample studied (see Brav, Constan-
tinides, and Geczy (2002), Mankiw and Zeldes (1991), and Vissing-Jorgensen
(2002)). This is because the C-CAPM, just like the I-CAPM, assumes that in-
dividuals participate in the markets. The theories also assume that individuals
hold well-diversified portfolios. My study suggests that low salaried market par-
ticipants are less likely to hold well-diversified portfolios because they tend to
concentrate their assets in one security, namely, company stock. In addition, they
are more likely to follow na¨ ıve diversification strategies, which generally will not
result in a well-diversified portfolio. As a result, my findings suggest that a sam-
ple of higher income market participants is more likely to meet the diversification
conditions established by the underlyingC-CAPM and, thus, should perform bet-
ter in an empirical analysis of the model.
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