ArticlePDF Available

Save More Tomorrow (TM): Using Behavioral Economics to Increase Employee Saving


Abstract and Figures

As firms switch from defined-benefit plans to defined-contribution plans, employees bear more responsibility for making decisions about how much to save. The employees who fail to join the plan or who participate at a very low level appear to be saving at less than the predicted life cycle savings rates. Behavioral explanations for this behavior stress bounded rationality and self-control and suggest that at least some of the low-saving households are making a mistake and would welcome aid in making decisions about their saving. In this paper, we propose such a prescriptive savings program, called Save More Tomorrow (hereafter, the SMarT program). The essence of the program is straightforward: people commit in advance to allocating a portion of their future salary increases toward retirement savings. We report evidence on the first three implementations of the SMarT program. Our key findings, from the first implementation, which has been in place for four annual raises, are as follows: (1) a high proportion (78 percent) of those offered the plan joined, (2) the vast majority of those enrolled in the SMarT plan (80 percent) remained in it through the fourth pay raise, and (3) the average saving rates for SMarT program participants increased from 3.5 percent to 13.6 percent over the course of 40 months. The results suggest that behavioral economics can be used to design effective prescriptive programs for important economic decisions.
Content may be subject to copyright.
[Journal of Political Economy, 2004, vol. 112, no. 1, pt. 2]
2004 by The University of Chicago. All rights reserved. 0022-3808/2004/11201S1-0018$10.00
Save More Tomorrow: Using Behavioral
Economics to Increase Employee Saving
Richard H. Thaler
University of Chicago
Shlomo Benartzi
University of California, Los Angeles
As firms switch from defined-benefit plans to defined-contribution
plans, employees bear more responsibility for making decisions about
how much to save. The employees who fail to join the plan or who
participate at a very low level appear to be saving at less than the
predicted life cycle savings rates. Behavioral explanations for this be-
havior stress bounded rationality and self-control and suggest that at
least some of the low-saving households are making a mistake and
would welcome aid in making decisions about their saving. In this
paper, we propose such a prescriptive savings program, called Save
More Tomorrow(hereafter, the SMarT program). The essence of
the program is straightforward: people commit in advance to allocat-
ing a portion of their future salary increases toward retirement savings.
We report evidence on the first three implementations of the SMarT
program. Our key findings, from the first implementation, which has
We are grateful to Brian Tarbox for implementing the Save More Tomorrowplan and
for sharing the data with us. We would also like to thank many people at the following
companies for their help: Financial Engines, Hewitt Associates, IspatInland, John Hancock,
Philips Electronics, and the Vanguard Group. Jodi Dicenzo, Bill Sharpe, and Steve Utkus
deserve special thanks. We are also grateful for comments from David Laibson, Brigitte
Madrian, Casey Mulligan, Ted O’Donoghue, and Cass Sunstein. Benartzi would like to
thank Reish Luftman McDaniel & Reicher for financial support. Save More Tomorrow is
a registered trademark of Benartzi and Thaler, but the plan is available at no charge to
any company that is willing to share data on the outcomes. This paper is dedicated to
Sherwin Rosen, Thaler’s thesis advisor. Thaler would not be an economist today if not
for Rosen’s help. The usual disclaimer, assigning none of the blame for errors to those
thanked above, applies in spades to Sherwin. He would not have liked this paper much,
but we sure would have enjoyed hearing him complain about it!
behavioral economics S165
been in place for four annual raises, are as follows: (1) a high pro-
portion (78 percent) of those offered the plan joined, (2) the vast
majority of those enrolled in the SMarT plan (80 percent) remained
in it through the fourth pay raise, and (3) the average saving rates
for SMarT program participants increased from 3.5 percent to 13.6
percent over the course of 40 months. The results suggest that be-
havioral economics can be used to design effective prescriptive pro-
grams for important economic decisions.
I. Introduction
Economic theory generally assumes that people solve important prob-
lems as economists would. The life cycle theory of saving is a good
example. Households are assumed to want to smooth consumption over
the life cycle and are expected to solve the relevant optimization prob-
lem in each period before deciding how much to consume and how
much to save. Actual household behavior might differ from this optimal
plan for at least two reasons. First, the problem is a hard one, even for
an economist, so households might fail to compute the correct savings
rate. Second, even if the correct savings rate were known, households
might lack the self-control to reduce current consumption in favor of
future consumption (Thaler and Shefrin 1981).
One fact that underscores the important role of self-control is that
the typical middle-class American household accumulates retirement
wealth primarily in three forms: social security, pensions, and home
equity. Neither social security nor defined-benefit pension plans require
willpower on the part of participants, and once a home is purchased,
the monthly mortgage bill provides a useful discipline in building up
Those Americans who have access to and make use of all three low-
willpower savings techniques appear to be doing a decent job of saving
for retirement. Gustman and Steinmeier (1998), using the 1992 Health
and Retirement Survey of households with heads of household born
between 1931 and 1941, find that households with pensions have what
appear to be adequate income replacement rates. A majority of the
pensions in their sample are of the defined-benefit variety, however, in
which self-control plays no role. Over the past decade, there has been
a rapid change toward defined-contribution plans that require employ-
ees to actively join and select their own savings rate. For those workers
who are eligible only for a defined-contribution plan and elect not to
join or to contribute a token amount, savings adequacy may be much
lower. One hint at this comes from Gustman and Steinmeier’s analysis
of workers who do not have pensions. The adequacy levels of their wealth
and savings are substantially lower than those with pensions. Indeed,
S166 journal of political economy
those workers with pensions are wealthier by approximately the value
of their pension.
For whatever reason, some employees at firms that offer only defined-
contribution plans contribute little or nothing to the plan. In this paper,
we take seriously the possibility that some of these low-saving workers
are making a mistake. By calling their low-saving behavior a mistake, we
mean that they might characterize the action the same way, just as
someone who is 100 pounds overweight might agree that he or she
weighs too much. We then use principles from psychology and behav-
ioral economics to devise a program to help people save more. The
program is called Save More Tomorrow(or SMarT), and the basic
idea is to give workers the option of committing themselves now to
increasing their savings rate later, each time they get a raise. We report
extensive data on one firm that implemented the program in 1998 and
preliminary data on two other firms that implemented it recently.
We note that the null hypothesis predicted by the standard economic
approach is that workers will have no interest in joining the SMarT plan.
If households are already choosing their optimal life cycle savings rate,
then they will not join a program that will commit them to periodic
changes. In contrast, the behavioral economics prediction is that work-
ers will find this program quite attractive and that it will significantly
increase the savings rates of those who join the plan.
II. A Prescriptive Approach to Increasing Savings Rates
Raiffa (1982) suggested that economists and other social scientists could
benefit from distinguishing three different kinds of analyses: normative,
descriptive, and prescriptive. Normative theories characterize rational
choice and are often derived by solving some kind of optimization prob-
lem. The life cycle hypothesis is an example of a normative theory of
saving since it is based on the solution to a lifetime consumption-smooth-
ing problem. Descriptive theories simply model how people actually
choose, often by stressing systematic departures from the normative
theory. In the realm of savings behavior, Shefrin and Thaler (1988) offer
the behavioral life cycle hypothesis as a descriptive model of household
It is sometimes argued that this fact can be explained by selection effects, i.e., that
those workers with a “taste for savings” go to work for companies with more attractive
pension benefits. But it is important not to push this argument too far. It is implausible
that pension benefits are so salient and important that workers sort themselves to firms
primarily on this basis. Many other features of a job determine its attractiveness, and
potential employees must make trade-offs. To give one example, one of the authors of
this paper is much more interested in collegiate athletics than the other, but he teaches
at the University of Chicago, not UCLA! Therefore, we should not expect underlying
preferences and employment characteristics to be perfectly correlated on any single
behavioral economics S167
savings in which self-control and mental accounting play key roles. Fi-
nally, prescriptive theories are attempts to offer advice on how people
can improve their decision making and get closer to the normative ideal.
Prescriptions often have a second-best quality. For a golfer who hits a
slice (in which the ball tails off to the right) when he would prefer to
hit the ball straight, simple prescriptive advice might be to aim to the
left. Better prescriptive advice would help the golfer hit the ball straight.
This paper is an attempt at good prescriptive savings advice.
Before writing a prescription, one must know the symptoms of the
disease being treated. Households may save less than the life cycle rate
for various reasons. First, determining the appropriate savings rate is
difficult, even for someone with economics training. Since the switch
from defined-benefit to defined-contribution savings plans is recent,
there are as yet no satisfactory heuristics that approximate a good so-
lution to the problem.
One obvious solution to this problem is financial
education (Bernheim, Garrett, and Maki 1997). Second, saving for re-
tirement requires self-control. When surveyed about their low savings
rates, many households report that they would like to save more but
lack the willpower. For example, Choi et al. (in press) report that two-
thirds of their sample of 401(k) participants think that their savings rate
is “too low.”
A third problem, closely related to self-control, is pro-
crastination, the familiar tendency to postpone unpleasant tasks. In Choi
et al.’s group of self-reported undersavers, 35 percent express an inten-
tion to increase their savings rate in the next few months, but 86 percent
of these well-intended savers have made no changes to their plan four
months later.
Self-control and procrastination used to be strange concepts to econ-
omists but are now topics of growing interest to behavioral economics
theorists (e.g., Laibson 1997; O’Donoghue and Rabin 1999). Modern
models of these problems use the concept of hyperbolic discounting
(see Ainslie 1975). Since Strotz’s (1955) early paper, economists have
known that intertemporal choices are time consistent only if agents
discount exponentially using a discount rate that is constant over time.
But there is considerable evidence that people display time-inconsistent
behavior, specifically, weighing current and near-term consumption es-
pecially heavily. Consider a choice between two rewards, a small one at
time t( ) and a big one at time ( ). When tis far off, agentsSt1B
prefer , since the difference in the value of the prizes exceeds theB
perceived costs of waiting. But as tapproaches zero, the ratio of dis-
The most common heuristics in place appear to be to save the maximum allowed by
law or to save the minimum necessary to receive the full “match” offered by the employer.
Neither of these amounts was computed to be a solution to the life cycle savings problem.
Similarly, a 1997 survey by Public Agenda finds that 76 percent of respondents think
that they should be saving more for retirement. See Farkus and Johnson (1997) for details.
S168 journal of political economy
counted values increases, causing people to switch their preferences.
Such present-biased preferences can be captured with models that em-
ploy hyperbolic discounting. These models come in two varieties: so-
phisticated and naive. Sophisticated agents (modeled by Laibson) re-
alize that they have hyperbolic preferences and take steps to deal with
the problem, whereas naive agents fail to appreciate at least the extent
of their problem (see O’Donoghue and Rabin 1999, 2001). Actual be-
havior is likely best described by something between naivete and
Hyperbolic agents procrastinate because they (wrongly) think that
whatever they will be doing later will not be as important as what they
are doing now. The more naive agents are, the more pronounced the
tendency to procrastinate. Procrastination, in turn, produces a strong
tendency toward inertia, or what Samuelson and Zeckhauser (1988)
have dubbed status quo bias. Status quo bias is prevalent in the retire-
ment savings domain. For example, Samuelson and Zeckhauser report
on the behavior of the 1987 participants of TIAA-CREF, the large re-
tirement plan that then catered to university employees. Their analysis
reveals that the median number of changes in the asset allocation over
the lifetime was zero! In other words, more than half the participants in
TIAA-CREF reached retirement with the same asset allocation as the
day they became eligible for the plan. Note that zero changes means
that participants were electing a constant flow into the two funds then
offered, TIAA, a fixed-income fund, and CREF, a stock fund, and en-
gaged in no rebalancing. Since stocks appreciated much more than
bonds over this period, participants with a constant flow (such as 50–
50, the most common allocation) ended up with a much larger share
in stocks over time. A recent study by Ameriks and Zeldes (2000), using
a 10-year panel of TIAA-CREF participants, finds a similar result. Nearly
half of the participants made no changes to their plan over the 10-year
The importance of procrastination and status quo bias in the design
of prescriptive savings plans is illustrated by the experience some firms
have had with so-called automatic enrollment plans. In such plans, when
employees first become eligible for the savings plan, they are automat-
ically enrolled unless they explicitly opt out. So, unlike the typical plan,
in which the default is not to join, here the default is to join. Employees
For evidence on hyperbolic discounting, see Thaler (1981) and the papers in Loew-
enstein and Elster (1992).
Choi, Laibson, and Mettrick (2000) find somewhat more frequent trading in a sample
of workers at two firms in 1998 and 1999, partly because of the ease of trading via the
Internet that was possible at both firms. But this increase in trading mayalso be attributable
to rapidly rising stock prices during this period and the resulting excitement among
individual investors.
behavioral economics S169
who take no action are typically enrolled at a modest saving rate (such
as 3 percent) and a conservative investment strategy. Standard economic
theory would predict that this change would have virtually no effect on
saving behavior. The costs of actively joining the plan (typically filling
out a short form) are trivial compared with the potential benefits of the
tax-free accumulation of wealth, and in some cases a “match” is provided
by the employer, in which the employer typically contributes 50 cents
to the plan for every dollar the employee contributes, up to some max-
imum. In contrast, if agents display procrastination and status quo bias,
then automatic enrollment could be useful in increasing participation
Consistent with the behavioral predictions, automatic enrollment
plans have proved to be remarkably successful in increasing enrollments.
In one plan studied by Madrian and Shea (1999), participation rates
for newly eligible workers increased from 49 percent to 86 percent.
Other plans have obtained participation rates of over 90 percent (Choi
et al., in press). But there is a downside to automatic enrollment. The
very inertia that explains why automatic enrollment increases partici-
pation rates can also lower the saving rates of those who do participate.
In the firm Madrian and Shea studied, the vast majority of new enrollees
elected the default saving rate (3 percent), and Madrian and Shea’s
analysis shows that many of these employees would have elected a higher
saving rate if left to their own devices (see Choi et al. [in press], who
explore these issues in depth). A goal of the SMarT plan is to obtain
some of the advantages of automatic enrollment while avoiding some
of the disadvantages.
On the basis of our analysis of undersaving households in the previous
section, some elements of a proposed solution are fairly obvious. The
presence of bounded rationality suggests that the program should be
simple and should help people approximate the life cycle saving rate
if they are unable to do so themselves. Hyperbolic discounting implies
that opportunities to save more in the future will be considered more
attractive than those in the present. Procrastination and inertia suggest
that once employees are enrolled in the program, they should remain
in until they opt out.
The final behavioral factor that should be considered in designing a
prescriptive savings plan is loss aversion, the empirically demonstrated
tendency for people to weigh losses significantly more heavily than gains.
Estimates of loss aversion are typically close to 2.0: losses hurt roughly
twice as much as gains yield pleasure. These estimates come both from
risky choice (Tversky and Kahneman 1992) and from riskless choice
(Kahneman, Knetsch, and Thaler 1990).
Loss aversion affects savings because once households get used to a
particular level of disposable income, they tend to view reductions in
S170 journal of political economy
that level as a loss. Thus households may be reluctant to increase their
contributions to the savings plan because they do not want to experience
this cut in take-home pay. Significantly, gains and losses appear to be
experienced in nominal dollars. For example, in a study of perceptions
of fairness (Kahneman et al. 1986), subjects were asked to judge the
fairness of pay cuts and pay increases in a company located in a com-
munity with substantial unemployment. One group of subjects was told
that there was no inflation in the community and was asked whether a
7 percent wage cut was “fair.” A majority, 62 percent, judged the action
to be unfair. Another group was told that there was 12 percent inflation
and was asked to judge the perceived fairness of a 5 percent raise. Here,
only 22 percent thought the action was unfair. Similar results suggesting
this money illusion are reported by Shafir, Diamond, and Tversky
(1997). The combination of loss aversion and money illusion suggests
that pay increases may provide a propitious time to try to get workers
to save more, since they are less likely to consider an increased contri-
bution to the plan as a loss than they would at other times of the year.
In summary, for households that appear to be saving too little, the
behavioral analysis stresses four factors that are important explanatory
factors: bounded rationality, self-control, procrastination (which pro-
duces inertia), and nominal loss aversion. These households are not
sure how much they should be saving, though they realize that it is
probably more than they are doing now; but they procrastinate about
saving more now, thinking that they will get to it later. Our program to
increase saving is aimed at these households.
III. The SMarT Program
Our goal was to design a program to help those employees who would
like to save more but lack the willpower to act on this desire. On the
basis of the principles discussed so far, we have proposed a program we
call Save More Tomorrow. The plan has four ingredients. First, em-
ployees are approached about increasing their contribution rates a con-
siderable time before their scheduled pay increase. Because of hyper-
bolic discounting, the lag between the sign-up and the start-up dates
should be as long as feasible.
Second, if employees join, their contri-
bution to the plan is increased beginning with the first paycheck after
a raise. This feature mitigates the perceived loss aversion of a cut in
take-home pay. Third, the contribution rate continues to increase on
each scheduled raise until the contribution rate reaches a preset max-
imum. In this way, inertia and status quo bias work toward keeping
The intuition here is the same as one in which requests to give a talk or write a chapter
meet with more success when they are received many months ahead of time.
behavioral economics S171
people in the plan. Fourth, the employee can opt out of the plan at
any time. Although we expect few employees to be unhappy with the
plan, it is important that they can always opt out. Knowledge of this
feature will also make employees more comfortable about joining.
The SMarT plan has many features that were included with the in-
tention of making it attractive to employees who want to save more. It
is not possible to say on theoretical grounds which features are most
important, nor can theory tell us the ideal levels to select for many of
the parameters that must be picked (e.g., the delay between the solic-
itation letter and the start of the program, the rate of increase, and the
methods of soliciting and educating potential participants). Similarly,
we cannot say a priori whether particular features, such as linking the
increases in the savings rate to pay increases, are just one of many
attractive components or are essential ingredients to success. We shall
learn more about these questions over time as firms adopt the plan and
provide data for analysis.
At this time we have three implementations on which we can report,
each done rather differently. The particular design features were gen-
erally not selected by us but, rather, reflect the preferences of the firms
that have adopted the plan. In this type of field research, we, the aca-
demic investigators, have quite limited control over many of the details,
especially if compared with a laboratory environment. Nevertheless, it
is not possible to study actual household savings behavior in a lab, so
we are grateful for the data we are able to report here.
A. The First Implementation of SMarT: Midsize Manufacturing Company
The first implementation of the SMarT plan took place in 1998 at a
midsize manufacturing company (which prefers to remain anonymous).
The company, with the help of an investment consultant, selected the
specific details of the implementation. Prior to the adoption of the
SMarT plan, the company suffered from low participation rates as well
as low saving rates. This was a concern for two reasons. First, since the
company did not have a defined-benefit plan, management was con-
cerned that some of the workers might not be saving enough to support
themselves when they retired. Second, the company was being con-
strained by U.S. Department of Labor nondiscrimination rules that re-
strict the proportion of benefits that can be paid to the higher-paid
employees in the firm. Since the lower-paid workers were the ones who
were typically saving little or nothing, the executives were not able to
contribute the maximum normally allowed to their own plan.
In an effort to increase the savings rates of the employees, the com-
pany hired an investment consultant and offered his services to every
employee eligible for the retirement savings plan. Of the 315 eligible
S172 journal of political economy
participants, all but 29 agreed to meet with the consultant and get his
advice. On the basis of information that the employee provided, the
consultant used commercial software to compute a desired saving rate,
which can be thought of as an estimate of the appropriate life cycle
savings rate. The consultant also discussed with each employee how
much of an increase in savings would be considered economically fea-
sible. If the employee seemed very reluctant to increase his or her saving
rate substantially, the consultant would constrain the program to in-
crease the saving contribution by no more than 5 percent.
The con-
sultant justified his decision not to go with the advice from the program
mechanically as follows:
In most cases with rank and file workers, the computer pro-
gram calculates that workers contribute the maximum [allowed
by the Internal Revenue Service (IRS) and the plan rules] and
makes that recommendation. As a practical matter, when the
average worker receives this recommendation from the com-
puter program or the “financial planner,” s/he shuts down and
does nothing. So in all cases, after we reviewed their current
plan but before I hit the “Get Advice” button, I would discuss
willingness to save with each participant. As you can imagine,
the majority of workers live paycheck to paycheck and can
barely make ends meet, and they tell you that immediately. …
If a participant indicated a willingness to immediately increase
their deferral level by more than 5 percent, I hit the “Get
Advice” button. Otherwise, I would constrain the advice pro-
posed to an increase of no more than 5 percent. [Personal
communication from Brian Tarbox, the investment consultant]
The participation data are reported in table 1. Of the 286 employees
who talked to the investment consultant, only 79 (28 percent) were
willing to accept his advice, even with the constraint that recommended
increases were usually no more than five percentage points. For the rest
of the participants, the planner offered a version of the SMarT plan as
an alternative, proposing that they increase their saving rates by three
percentage points each year, starting with the next pay increase. This
was quite aggressive advice, since pay increases were barely more than
this amount (approximately 3.25 percent for hourly employees and 3.50
percent for salaried employees). The pay increases were scheduled to
occur roughly three months from the time the advice was being given.
Here and elsewhere, when we refer to a five-percentage-point increase, we arereferring
to an increase of percentage points, e.g., from a 2 percent saving rate to a 7 percent saving
behavioral economics S173
Participation Data for the First Implementation of
Number of plan participants prior to the adop-
tion of the SMarT plan 315
Number of plan participants who elected to re-
ceive a recommendation from the consultant 286
Number of plan participants who implemented
the consultant’s recommended saving rate 79
Number of plan participants who were offered
the SMarT plan as an alternative 207
Number of plan participants who accepted the
SMarT plan 162
Number of plan participants who opted out of
the SMarT plan between the first and sec-
ond pay raises 3
Number of plan participants who opted out of
the SMarT plan between the second and
third pay raises 23
Number of plan participants who opted out of
the SMarT plan between the third and
fourth pay raises 6
Overall participation rate prior to the advice 64%
Overall participation rate shortly after the
advice 81%
With the 3 percent a year increases, employees would typically reach
the maximum tax-deferred contribution within four years.
Even with this aggressive strategy of increasing saving rates, the SMarT
plan proved to be extremely popular with the participants. Of the 207
participants who were unwilling to accept the saving rate proposed by
the investment consultant, 162 (78 percent) agreed to join the SMarT
plan. More important, the majority of these participants did not change
their mind once the savings increases took place. Only three participants
(2 percent) dropped out of the plan prior to the second pay raise, with
23 more (14 percent) dropping out between the second and third pay
raises and six more (4 percent) between the third and forth pay raises.
Hence, the vast majority of the participants (80 percent) have remained
in the plan through four pay raises. Furthermore, even those who with-
drew from the plan did not reduce their contribution rates to the orig-
inal levels; they merely stopped the future increases from taking place.
So, even these workers are saving significantly more than they were
before joining the plan.
The impact of the SMarT plan on saving is shown in table 2.
Interestingly, most of the employees who dropped out between the second and third
pay raises worked for a single supervisor who apparently disapproved of the SMarT plan.
The data for each year refer only to those workers who are still employed by the
company, so the sample shrinks over time from 315 to 229.
S174 journal of political economy
Average Saving Rates (%) for the First Implementation of SMarT
Who Did Not
Contact the
Who Accepted
the Consultant’s
Saving Rate
Who Joined
the SMarT
Who Declined
the SMarT
Plan All
option* 29 79 162 45 315
Pre-advice 6.6 4.4 3.5 6.1 4.4
First pay raise 6.5 9.1 6.5 6.3 7.1
Second pay
raise 6.8 8.9 9.4 6.2 8.6
Third pay raise 6.6 8.7 11.6 6.1 9.8
Fourth pay
raise 6.2 8.8 13.6 5.9 10.6
* There is attrition from each group over time. The number of employees who remain by the time of the fourth
pay raise is 229.
the investment consultant was brought into the company, the overall
savings rate in the plan was 4.4 percent. The employees who did not
want to talk to the consultant were saving more than the average, 6.6
percent. The group that accepted the advice of the consultant had been
saving at exactly the overall company average, 4.4 percent, and after
implementing the advice, they began saving 9.1 percent of their salary.
At the end of our data collection period, that rate had slipped slightly
to 8.8 percent. Those who were unwilling to accept the advice were, not
surprisingly, starting from a lower base of 3.5 percent and so would find
the advice harder to adopt. Once they got their first pay raise, however,
their saving rate jumped to 6.5 percent, and after three more raises, it
was up 13.6 percent. Those participating in the SMarT plan ended up
with a much higher saving rate than those who accepted the consultant’s
Of course, the implementation of the SMarT plan was not conducted
as an experiment with random assignment to conditions. Participants
selected themselves into the SMarT plan. In other circumstances, one
might worry that the observed increase in savings rates might be attrib-
utable to some unmeasured “taste for saving” in the households that
joined the SMarT plan; however, this worry seems unwarranted here on
two counts. First, the SMarT participants had been saving very little
before joining the plan, so one would have to believe that their taste
for saving was newly acquired. Second, recall that the SMarT plan was
offered only to those employees who were unwilling to increase their
savings rate immediately by 5 percent. So, if anything, the group that
behavioral economics S175
accepted the consultant’s advice would appear to have a greater taste
for saving than those in the SMarT plan.
The design of the study also rules out an information-based expla-
nation for our results. Since the employees met with the investment
consultant, they received useful information about proper savings rates,
and this information quite possibly could affect their savings rates. All
the employees who agreed to meet with the consultant received this
information, however, including those who accepted the consultant’s
advice to increase their savings rate immediately. We find it difficult to
construct an information-based explanation for the subsequent in-
creases in savings rates for those enrolled in the SMarT plan.
B. The Second Implementation of SMarT: Ispat Inland
The second implementation of the program took place in May 2001 at
Ispat Inland, a large midwestern steel company. Ispat had heard about
the SMarT plan and expressed to us an interest in increasing the saving
rates among its 5,000 unionized employees. Ispat employees have re-
ceived only one pay raise from the introduction of SMarT to date, so
we can report only on the initial results at this time.
The implementation at Ispat was quite different from our first ex-
perience in that it was implemented with quite minimal resources. Most
important, there was no financial consultant hired to meet one on one
with employees. Instead, employees received a letter sent jointly by the
human resources department and the union inviting them to join the
SMarT program. There were no follow-up letters, no financial education
seminars, and no other expenditures other than that single invitation
letter and a few posters displayed in the cafeteria. In this implemen-
tation, the annual increase to the savings contribution rate was set at
two percentage points every time they got a pay raise, with a cap on
contribution rates set at 18 percent of salary. The first pay raise was
scheduled for August 1, 2001, about two months after the solicitation
letter was sent. The pay raise was to be 50 cents per hour, which
amounted to roughly 2.5 percent of the average wage.
Participation in the program and the resulting saving rates are de-
scribed in table 3. Even with this very inexpensive solicitation strategy,
the program was popular with employees. Of the participants who were
already enrolled in the 401(k) plan and were not already saving the
maximum, 615 (18.1 percent) joined the SMarT plan. In addition, 165
employees joined SMarT who were not yet enrolled in the 401(k) plan;
this was 8.2 percent of those employees who were eligible to participate
in the 401(k) plan but had not yet enrolled. The lower take-up rate
among the employees who were not currently in the 401(k) plan might
be attributable to less interest in saving, but there is an additional con-
S176 journal of political economy
Average Saving Rates for Ispat Inland (%)
Employees Who Were
Already Saving on
May 31, 2001
Employees Who Were
Not Saving on May 31,
2001 All
Did Not
Join SMarT
Did Not
Join SMarT
(May 2001) 7.62 8.62 .00 .00 5.54
First pay raise
2001) 9.38 8.54 2.28 .26 5.83
Note.—The sample includes 5,817 employees who are eligible to participate in the 401(k) plan and have remained
with the company from May 2001 through October 2001. The sample includes 414 employees who were already saving
at the maximum rate of 18 percent, although they were not allowed to join the SMarT program. The reported saving
rates represent the equally weighted average of the individual saving rates.
tributing factor. Those who were not in the plan might have ignored
the letter altogether. The letter came with the heading “important in-
formation about your 401(k) account,” a teaser that would not be of
particular interest to employees who were not in the plan.
The immediate effect on savings was about what might be expected.
Those joining SMarT increased their saving rates by roughly 2 percent,
whereas those not joining the program did not change their saving rates
much. If the experience in the first implementation is repeated here
and few employees drop out of the SMarT plan, then saving rates will
continue to increase whenever the employees get raises.
C. The Third Implementation of SMarT: Philips Electronics
The third implementation of SMarT took place at two divisions (Divi-
sions A and O) of Philips Electronics in January 2002, with the first
saving increase taking place on April 1, 2002.
The remaining 28 di-
visions of Philips served as a control. Invitation letters were sent to 815
“non–highly compensated” employees whose saving rates were below 10
percent. Everything was done the same way at the two divisions except
for the following: Employees at Division A were given the option of
attending educational seminars devoted to retirement savings (includ-
ing a description of the new SMarT plan) but were not offered any one-
on-one meetings. For the employees at Division O, attendance at the
financial education seminar was strongly encouraged. The seminar was
described to the employees as “required,” although there was no penalty
for failing to meet the requirement. Whether because of the “require-
Additional details on the implementation at Philips are available at http://
behavioral economics S177
ment” or other reasons, 60 percent of the employees attended the sem-
inar, whereas only 40 percent did so in Division A. The employees in
Division O were also offered the opportunity to have a one-on-one meet-
ing with a certified financial planner. The average saving rates prior to
SMarT were quite similar at the two divisions, 3.12 percent and 3.74
percent for Divisions A and O, respectively, both rates slightly higher
than the saving rates in the rest of the Philips divisions (2.90 percent).
But the two divisions are different along many other dimensions, making
direct comparisons difficult. For example, Division A is in the technology
business, is located in the desert Southwest, and was suffering through
a severe recession at the time of the implementation, whereas Division
O focuses on consumer products, is located in the Pacific Northwest,
and has been doing well economically.
Thus the two divisions do not
represent a true controlled experiment in comparison with each other,
though they can reasonably be compared with the other control divi-
sions, at least in terms of saving rates.
There were two notable differences between the implementation at
Philips and the previous two trials described above. First, increases in
savings were not necessarily linked to pay raises. Instead, employees were
told that if they joined the plan, their saving rates would go up on April
1 of each year whether or not they received a pay raise. Pay raises do
tend to occur on April 1, but the employees could not be sure that the
extra contribution to the savings plan would come out of their raise.
Second, employees were allowed to pick the rate at which their savings
would increase: one, two, or three percentage points per year. Those
who joined the plan but did not choose a rate of increase were assigned
a 2 percent rate of increase.
Fifty-four percent of the SMarT enrollees
elected an annual increase of 1 percent, 35 percent elected the default
of 2 percent, and the remaining 11 percent elected 3 percent. Regardless
of the chosen annual increase, the annual increases will stop once the
participant reaches a saving rate of 10 percent.
The resulting saving rates are displayed in table 4. As expected, not
much is happening at the remaining 28 divisions of Philips Electronics
that served as our control group. In contrast, saving rates for those who
were already enrolled in the 401(k) plan and joined the SMarT plan
went up, as expected, by about 1.5 percent (the weighted-average pro-
Division A is now closing down, so the long-term results of the SMarT plan will not
be available.
There are pros and cons to offering this choice to participants, as opposed to just
picking a single rate of increase. The obvious advantage is that employees can select the
rate of increase they like best. The disadvantage is that simply being forced to make such
a choice adds another layer of complexity that could discourage some potential enrollees.
We included the default 2 percent rate of increase with the goal of mitigating this potential
impediment to enrolling. Only a controlled experiment will be able to determine whether
the pros of offering choice outweigh the cons.
S178 journal of political economy
Average Saving Rates (%) for Philips Electronics
Employees Who
Were Already
Saving in
December 2001
Employees Who
Were Not Saving
in December 2001
Did Not
Join SMarT
Did Not
Join SMarT
A. Control Group
Observations 7,405 7,053 14,458
Pre-SMarT (December
2001) 5.65 .00 2.90
Post-SMarT (March 2002) 5.76 .70 3.29
B. Test Group (Divisions A and O Combined)
Observations 180 339 36 260 815
Pre-SMarT (December
2001) 5.26 5.38 .00 .00 3.40
Post-SMarT (March 2002) 6.83 5.72 5.03 1.55 4.61
C. Division A
Observations 66 190 10 163 449
Pre-SMarT (December
2001) 5.47 5.48 .00 .00 3.12
Post-SMarT (March 2002) 7.32 5.97 6.80 1.54 4.38
D. Division O
Observations 114 149 26 77 366
Pre-SMarT (December
2001) 5.14 5.25 .00 .00 3.74
Post-SMarT (March 2002) 6.55 5.41 4.35 1.58 4.89
Note.—The “test” group consists of individuals at Divisions A and O.
grammed increase among those already saving). The savings rate went
up more dramatically for those employees who simultaneously enrolled
in the 401(k) and SMarT. Interestingly, there also seemed to be a spill-
over effect on those who did not join the SMarT plan. In the two ex-
perimental divisions in which SMarT was introduced, even those em-
ployees who did not join SMarT increased their savings rates more than
was observed in the control group.
At the time of this writing (summer 2003), the second raise has oc-
curred in Division O, and we have some preliminary data on attrition
rates from the SMarT program. Of those who originally joined the pro-
gram, 13.5 percent have left Philips because they either quit or were
terminated. Of those remaining in the plan, eight employees (5.4 per-
cent of the original participants, 6.2 percent of those still working at
Philips) dropped out before the second raise, but another five em-
This pattern is consistent with evidence by Duflo and Saez (2000) on peer effects.
behavioral economics S179
ployees joined the plan. This experience of low dropout rates is com-
parable to that in the first implementation and suggests that, over time,
savings rates will continue to rise.
In this implementation, we were given access to some demographic
information about the employees as well as information about how the
plan was administered in each division. The participation rates in SMarT
were quite different in the two divisions, as shown in table 5. In Division
A, only 16.9 percent of the division’s employees joined the program
( ), whereas Division O had a take-up rate of 38.3 percent76/449
( ). One potential explanation for this difference is that the140/366
employees at Division O had the opportunity to meet with a certified
financial planner. In fact, 41.8 percent of the employees at Division O
met with the financial planner, and 81 percent of those who attended
such a meeting actually joined SMarT. Of course, electing to meet with
the planner might by itself signal a desire to save, so it is not possible
to ascertain the incremental effect of the financial planner on either
saving rates or SMarT participation.
Table 5 also provides some basic information on who joins the SMarT
plan. Neither gender nor age appears to be an important determining
factor. Employees with four to five years of tenure working for Philips
were the most likely to join, as were those with annual incomes of less
than $50,000.
At this stage, there are some preliminary lessons that can be drawn
from the Philips experience. First, the SMarT design feature linking
savings increases to pay increases, while desirable, may not be essential.
This is important, since some firms find it difficult to coordinate the
savings plan with the salary increases. Second, one-on-one meetings with
a financial planner appear to be a very effective (though costly) re-
cruitment tool, though selection problems make it difficult to parse out
the precise value of this intervention.
IV. SMarT and Savings Adequacy
Obviously, the SMarT plan can produce dramatic increases in saving
rates. In the first implementation, those who joined the plan more than
tripled their saving rates in 28 months. This raises the question of what
effect SMarT has on savings adequacy. Is this increase enough to make
a substantial difference in the standard of living the participants will
have in retirement? If so, is it possible that we have been “too successful”
and have somehow duped the participants into saving too much? This
section offers some information on these important questions using
lessons from our first implementation. We focus on the first implemen-
tation since it has the longest track record.
We do not have demographic or financial information about the em-
Participation Rates in the SMarT Program at Philips Electronics
Explanatory Variable
Total Number
of Employees
Number of
Employees in
the Test Group
Number of
Joining SMarT
Rate (%)
Entire sample 15,273 815 216 26.5
Missing 9,355 430 92 21.4
Female 2,191 146 51 34.9
Male 3,727 239 73 30.5
Missing 3,598 134 33 24.6
20s 1,880 162 46 28.4
30s 3,990 264 66 25.0
40s 3,944 178 49 27.5
50s 1,861 77 22 28.6
Tenure (in years):
0–1 1,953 103 17 16.5
2–3 3,096 200 58 29.0
4–5 2,064 137 49 35.8
6–10 3,087 262 69 26.3
115,073 113 23 20.4
Missing 4,207 10 0 .0
!$25,000 1,786 155 56 36.1
$25,000–50,000 4,296 362 106 29.3
$50,000–75,000 2,386 134 27 20.1
$75,000678 53 11 20.8
Saving rate (prior to
0% 7,351 296 36 12.2
1–5% 1,914 162 62 38.2
6% 4,931 304 101 33.2
7–9% 1,079 53 17 32.1
A 449 449 76 16.9
Control 14,458 0 0
O 366 366 140 38.3
Participated in educa-
tion seminar:
No 389 389 20 5.6
Yes 426 426 196 46.0
Met with financial ad-
No 213 213 16 7.5
Yes 153 153 124 81.0
Registered Web user:
No 12,161 663 162 24.4
Yes 3,112 152 54 35.5
Note.—The initial sample included 46,873 individual-year observations (excluding highly compensated employees).
We first required that all the individuals be present before and after the implementation of the SMarT program, which
reduced the number to 20,122 individuals. Next, we eliminated those who switched between the test and control groups,
leaving us with 20,103 individuals. We also eliminated those saving more than 10 percent of their pay because they
were not allowed to join SMarT, resulting in 15,274 individuals. Of the remaining 15,274 individuals, most are in the
“control” group, and they were not offered the SMarT program. The “test” group consists of individuals at the A and
O Divisions.
behavioral economics S181
ployees in our study, so we need to make some assumptions about their
household financial situations in order to calculate the likely effects of
joining the SMarT plan. We make calculations for hypothetical workers
who join the plan at age 25, 35, 45, or 55 for three different annual
incomes: $25,000, $50,000, and $75,000. We estimate beginning 401(k)
account balances, using data from Hewitt Associates for some of the
larger 401(k) plans they administer. In particular, we calculate the ac-
count balances of people of a similar age, income, and savings rate. To
avoid the issue of multiple 401(k) accounts per individual, we select
only those who remained with the same employer through their career.
As to savings and investment choices, we assume that employees are
saving 4 percent in the 401(k) plan when they join the SMarT plan and
that saving rates are capped at 14 percent. We also assume that the
employer matches the employee’s contributions at a 50 percent rate on
the first 6 percent of employee contributions, as was true in the firm
we studied. For other financial assets, we assume that non-401(k) em-
ployee savings are half the existing balance in the 401(k) account, on
the basis of data from John Hancock Financial Services (the Sixth De-
fined Contribution Plan Survey [1999]). Finally, we assume that em-
ployees choose a portfolio mix of 60 percent stocks and 40 percent
bonds. The particular company in our study does not sponsor a defined-
benefit pension, so we assumed no pension benefits. Finally, we assumed
the statutory benefits from social security. We then use software provided
by Financial Engines to estimate the distribution of retirement income
that can be expected on the basis of these assumptions. The Financial
Engines software provides several points on a probability distribution
of retirement income. We use the fiftieth percentile of this distribution
to compute expected income replacement rates, that is, the ratio of
retirement income to preretirement income.
Table 6 reports retirement income replacement rates for various age
and income combinations. The issue of savings adequacy is well studied
by economists, but there is no agreement on a single number as the
appropriate replacement rate (see Boskin and Shoven [1987], Bernheim
[1993], and Gustman and Steinmeier [1998] for discussions of this is-
sue). Still, most economists writing on this issue consider replacement
rates near 100 percent adequate and judge replacement rates below 70
percent to be too low.
Panel A of table 6 shows the expected income replacement rates for
our employees before they join the SMarT plan, all of which are between
One might think that a 100 percent replacement rate would be too high, suggesting
that agents are very patient. However, survey evidence suggests that households desire an
increasing consumption profile. Laibson (1999) offers a cogent discussion of this issue
and reports that economists also prefer rising profiles for themselves. If agents want a
rising profile, then even a 100 percent replacement rate may be too low.
S182 journal of political economy
Median Income Replacement Ratios (%)
25 35 45 55
A. Pre-SMarT
$25,000 57 57 56 55
$50,000 51 51 51 54
$75,000 48 49 46 43
B. Post-SMarT
$25,000 108 90 75 63
$50,000 98 83 70 62
$75,000 90 77 63 50
Note.—The table displays the median income replacement ratios for different
age and income profiles, using investment advice software by Financial Engines. The
projections are based on the following assumptions: no defined-benefit pension,
statutory social security benefits, employee saving rate of 4 percent before SMarT
and 14 percent thereafter, employer match of 50 cents on the dollar up to6 percent,
portfolio mix of 60 percent stocks and 40 percent bonds, and retirement age of 65.
43 and 57 percent. Replacement rates are highest for the $25,000 in-
come category because social security offers substantial replacement at
that level. Panel B shows that replacement income rates are considerably
higher with the SMarT plan, especially for those joining the plan when
young. Obviously, increasing the savings rate is less effective when one
starts at 55 than at 25. Still, expected replacement rates exceed 100
percent in just one cell (108 percent replacement for those making
$25,000 per year who join the plan at age 25), so there does not appear
to be evidence that we have induced people to save too much. Fur-
thermore, if the stock market returns are exceptionally high, workers
nearing retirement can always reduce savings rates or plan an earlier
retirement if they have higher retirement benefits than they expected.
V. The Potential Effect of SMarT on the U.S. Personal Savings
The U.S. personal savings rate is currently close to zero. Some macro-
economists consider this rate too low and have advocated government
intervention to increase the savings rate. We do not take any stand on
whether such policies are good for the economy but, instead, ask a
different question. If it were desirable to increase the personal savings
rate, could widespread adoption of the SMarT plan make a substantial
contribution to meeting this goal?
To determine the potential impact of widespread adoption of SMarT,
we begin by characterizing how much employees are saving now in their
401(k) plans. To do so, we utilize a data set from Hewitt Associates that
includes demographic and account balance information on the partic-
behavioral economics S183
ipants in 15 large companies, covering a total of 539,516 employees.
On the basis of comparisons with data from Fidelity (a 2001 report on
corporate defined-contribution plans) and John Hancock Financial Ser-
vices (the 1999 Sixth Defined Contribution Plan Survey), two other large
401(k) service providers, we believe that our sample is representative
of employees who work for large companies. Consequently, we think
that it can serve as a basis for some rough estimates on the potential
contribution SMarT can make to increasing employee savings rates.
Starting from the baseline behavior we observe now, we make cal-
culations of changes in savings rates over a 10-year period for various
implementation strategies. Specifically, we consider three hypothetical
implementation strategies, each matched with increases in the savings
rate of 1, 2, or 3 percent per year, giving us nine configurations to
examine. We start each plan at a 5 percent savings rate, approximately
the average in the Hewitt data. We then simulate the impact of adding
specific implementations of SMarT. In all the simulations, we assume
that 5 percent of enrollees drop out of SMarT each year, leaving their
savings rate at the level they had obtained up to that point.
The first two implementation strategies we consider are based on the
experiences we have had in the implementations described above. Plan
A is based on the first implementation, which used one-on-one inter-
actions with a financial consultant. On the basis of the results in that
company, we assume that 80 percent of those who are currently partic-
ipating in the savings program will join the SMarT plan, and half of
those who are not enrolled will join. Plan B is based on the experience
at Ispat, where the SMarT plan was marketed to employees only with a
single direct-mail campaign rather than personal contact. This approach
is much less costly but is also less effective in reaching potential en-
rollees. In this scenario, we project 20 percent enrollment rates for those
currently in the savings plan and 10 percent for those who are not
currently saving anything.
Plan C is to combine the SMarT program with automatic enrollment.
Specifically, we assume that all employees would be enrolled in the
SMarT plan unless they opted out. Those who are not currently partic-
ipating in the 401(k) plan would be enrolled, and their initial saving
rate would be the savings incremental rate (i.e., 1, 2, or 3 percent). On
the basis of our experience and that of Madrian and Shea (1999) and
Choi et al. (in press), for plan C we estimate that 90 percent of the
employees would join the program in this design (i.e., only 10 percent
would opt out). The saving rates we report are weighted by income,
and they are averaged across all employees (whether or not they are
saving). Hence, the reported rates represent the average savings per
dollar of income.
For simplicity, our calculations exclude the effects of employer contributions and
S184 journal of political economy
Projected Saving Rates (%)
SMarT Annual
Increments (%)
Projected Saving Rates with SMarT in Year (%)
012 3 4 510
A. One-on-One Interaction with a Financial Consultant
1 5.0 5.6 6.2 6.7 7.2 7.6 9.2
2 5.0 6.2 7.3 8.2 9.0 9.7 11.9
3 5.0 6.8 8.3 9.5 10.6 11.4 12.9
B. One-Shot Mailing
1 5.0 5.2 5.3 5.4 5.5 5.6 6.0
2 5.0 5.3 5.6 5.8 6.0 6.1 6.7
3 5.0 5.4 5.8 6.1 6.3 6.5 6.9
C. Automatic Enrollment
1 5.0 5.8 6.4 7.1 7.7 8.2 10.2
2 5.0 6.5 7.8 8.9 10.0 10.9 13.7
3 5.0 7.2 9.0 10.6 11.9 13.0 15.0
The results of our projections are displayed in table 7. As of year end
2000, the saving rate in the Hewitt sample averaged 5.0 percent, less
than a third of the allowable IRS deferrals, which averaged 17.7 percent
for our sample.
This means that there is considerable opportunity for
the SMarT program to increase the saving rate. With plan A, which uses
one-on-one interaction with a financial consultant and the 2 percent
per year rate of increase, the SMarT program could boost the overall
saving rate from 5.0 percent to 9.7 percent within five years (see panel
A). When one switches to the cheaper method of one-shot mailing, the
effects are much smaller (see panel B). For instance, over the course
of five years, the saving rate would increase from 5.0 percent to 6.1
percent. But if employees were automatically enrolled in the program,
as in plan C, the average saving rate is projected to increase from 5.0
percent to 10.9 percent within five years (see panel C).
How large is the potential increase in saving rates? In terms of dollars,
we calculate that each one-percentage-point increase in the employee
saving rate would translate into $250 million of additional annual con-
tributions for the Hewitt sample. Extrapolating from our sample of half
a million individuals to the universe of 50–60 million individuals with
access to 401(k) plans, we estimate roughly $25 billion of additional
annual contributions for each 1 percent increase. So if a 5 percent
employee turnover. These omissions create biases in opposite directions. On one hand,
including employer contributions would increase the estimated effect of the SMarT pro-
gram because increased employee contributions will often trigger higher employer con-
tributions. On the other hand, employee turnover is likely to decrease the effect of the
SMarT program unless the employee moves to another firm with the SMarT plan in effect.
The IRS limit for the year 2000 was the lower of $10,500 or 25 percent of income.
behavioral economics S185
increase were obtained, this would increase personal saving by $125
billion per year. Percentage-wise, this would amount to 1.5 percent of
disposable personal income (data from the National Income and Prod-
uct Accounts: Personal Income and Its Disposition: http://www.bea Since the current personal savings
rate is hovering near zero, this is a substantial increase. Furthermore,
in contrast to other approaches to increasing the employee savings rate,
such as increasing the maximum allowable contribution, much of the
gains from the SMarT program come from those who are saving little
or nothing now. This means that the increase can be presumed to be
virtually all “new” savings, as opposed to substitution from other (pos-
sibly taxable) forms.
VI. Conclusions
The initial experience with the SMarT plan has been quite successful.
Many of the people who were offered the plan elected to use it, and a
majority of the people who joined the SMarT plan stuck with it. Con-
sequently, in the first implementation, for which we have data for four
annual raises, SMarT participants almost quadrupled their saving rates.
Of course, one reason why the SMarT plan works so well is that inertia
is so powerful. Once people enroll in the plan, few opt out. The SMarT
plan takes precisely the same behavioral tendency that induces people
to postpone saving indefinitely (i.e., procrastination and inertia) and
puts it to use. As the financial consultant involved in the first imple-
mentation has noted, in hindsight it would have been better to offer
the SMarT plan to all participants, even those who were willing to make
their initial savings increase more than the first step of the SMarT plan.
Very few of these eager savers ever got around to changing their savings
allocations again, whereas the SMarT plan participants were already
saving more than they were after just 16 months (see table 2)
Some economists have criticized practices such as automatic enroll-
ment and the SMarT plan on the grounds that they are paternalistic, a
term that is not meant to be complimentary. We agree that these plans
are paternalistic, but since no coercion is involved, they constitute what
Sunstein and Thaler (2003) call “libertarian paternalism.”
paternalism is a philosophy that advocates designing institutions that
help people make better decisions but do not impinge on their freedom
to choose. Automatic enrollment is a good example of libertarian pa-
ternalism. Notice that firms must decide what happens to employees
who take no action with respect to joining the savings plan. Traditionally,
employees who did nothing were presumed not to want to join the plan.
For a brief summary of this idea, see Thaler and Sunstein (2003).
S186 journal of political economy
Automatic enrollment simply changes that presumption. Neither ar-
rangement infringes on choice (so both are libertarian), but one pro-
duces higher savings rates and so might be considered paternalistic. The
SMarT plan is even less intrusive than automatic enrollment since par-
ticipants have to take some action to enroll, and it is even more suc-
cessful at getting people to save. So, we plead guilty to the charge of
trying to be paternalistic, but since we are striving for libertarian pa-
ternalism, we do not think that it should be considered objectionable.
Finally, we hope that this study serves as a valid reply to two frequent
critiques of behavioral economics: the reliance on laboratory studies
using modest stakes and the ex post explanation of anomalous facts,
drawing on what is alleged to be a limitless store of potential behavioral
explanations. Here, we have used behavioral principles to design a plan
to increase savings rates and tested the idea in the real world.
Ainslie, George. 1975. “Specious Reward: A Behavioral Theory of Impulsiveness
and Impulse Control.” Psychological Bull. 82 (4): 463–96.
Ameriks, John, and Stephen Zeldes. 2000. “How Do Household Portfolio Shares
Vary with Age?” Working paper. New York: Columbia Univ.
Bernheim, B. Douglas. 1993. Is the Baby Boom Generation Preparing Adequately for
Retirement? Plainsboro, N.J.: Merrill Lynch.
Bernheim B. Douglas, Daniel M. Garrett, and Dean M. Maki. 1997. “Education
and Saving: The Long-Term Effects of High School Financial Curriculum
Mandates.” Working Paper no. 6085 (July). Cambridge, Mass.: NBER.
Boskin, Michael J., and John B. Shoven. 1987. “Concepts and Measures of Earn-
ings Replacement during Retirement.” In Issues in Pension Economics, edited
by Zvi Bodie, John B. Shoven, and David A. Wise. Chicago: Univ. Chicago
Press (for NBER).
Choi, James J., David Laibson, Brigitte Madrian, and Andrew Metrick. In press.
“For Better or for Worse: Default Effects and 401(k) Savings Behavior.” In
Perspectives on the Economics of Aging, edited by David A. Wise. Chicago: Univ.
Chicago Press (for NBER).
Choi, James J., David Laibson, and Andrew Metrick. 2000. “Does the Internet
Increase Trading? Evidence from Investor Behavior in 401(k) Plans.” Working
Paper no. 7878 (September). Cambridge, Mass.: NBER.
Duflo, Esther, and Emmanuel Saez. 2000. “Participation and Investment Deci-
sions in a Retirement Plan: The Influence of Colleagues’ Choices.” Working
paper. Cambridge: Massachusetts Inst. Tech.
Farkus, Steve, and Jean Johnson. 1997. Miles to Go: A Status Report on Americans’
Plans for Retirement. New York: Public Agenda.
Gustman, Alan L., and Thomas L. Steinmeier. 1998. “Effects of Pensions on
Savings: Analysis with Data from the Health and Retirement Study.” Working
Paper no. 6681 (August). Cambridge, Mass.: NBER.
Kahneman, Daniel, Jack L. Knetsch, and Richard H. Thaler. 1986. “Fairness as
a Constraint on Profit Seeking: Entitlements in the Market.” A.E.R. 76 (Sep-
tember): 728–41.
behavioral economics S187
———. 1990. “Experimental Tests of the Endowment Effect and the Coase
Theorem.” J.P.E. 98 (December): 1325–48.
Laibson, David I. 1997. “Golden Eggs and Hyperbolic Discounting.” Q.J.E. 112
(May): 443–77.
———. 1999. Discussion of “The Adequacy of Household Saving” by Eric Engen,
William Gale, and Cori Uccello. Brookings Papers Econ. Activity, no. 2, pp. 174–
Loewenstein, George, and Jon Elster, eds. 1992. Choice over Time. New York: Sage.
Madrian, Brigitte C., and Dennis Shea. 1999. “The Power of Suggestion: An
Analysis of 401(k) Participation and Saving Behavior.” Working paper. Chi-
cago: Univ. Chicago, Grad. School Bus.
O’Donoghue, Ted, and Matthew Rabin. 1999. “Doing It Now or Later.” A.E.R.
89 (March): 103–24.
———. 2001. “Choice and Procrastination.” Q.J.E. 116 (February): 121–60.
Raiffa, Howard. 1982. The Art and Science of Negotiation. Cambridge Mass.: Har vard
Univ. Press.
Samuelson, William, and Richard J. Zeckhauser. 1988. “Status Quo Bias in De-
cision Making.” J. Risk and Uncertainty 1 (March): 7–59.
Shafir, Eldar, Peter Diamond, and Amos Tversky. 1997. “Money Illusion.” Q.J.E.
112 (May): 341–74.
Shefrin, Hersh M., and Richard H. Thaler. 1988. “The Behavioral Life-Cycle
Hypothesis.” Econ. Inquiry 26 (October): 609–43.
Sunstein, Cass R., and Richard H. Thaler. 2003. “Libertarian Paternalism Is Not
an Oxymoron.” Univ. Chicago Law Rev. 70 (Fall): 1159–99.
Strotz, Robert H. 1955. “Myopia and Inconsistency in Dynamic Utility Maximi-
zation.” Rev. Econ. Studies 23 (3): 165–80.
Thaler, Richard H. 1981. “Some Empirical Evidence on Dynamic Inconsistency.”
Econ. Letters 8 (3): 201–7.
Thaler, Richard H., and Hersh M. Shefrin. 1981. “An Economic Theory of Self-
Control.” J.P.E. 89 (April): 392–406.
Thaler, Richard H., and Cass R. Sunstein. 2003. “Libertarian Paternalism.” A.E.R.
Papers and Proc. 93 (May): 175–79.
Tversky, Amos, and Daniel Kahneman. 1992. “Advances in Prospect Theory:
Cumulative Representation of Uncertainty.” J. Risk and Uncertainty 5 (October):
... In the domain of employee savings, Thaler and Benartzi (2003) have proposed a method of increasing contributions to 401(k) plans that also meets the libertarian test. Under this plan, called Save More Tomorrow, employees are invited to sign up for a programme in which their contributions to the savings plan are increased annually whenever they get a raise. ...
A Economia Comportamental é um novo campo de estudo da Economia que busca fazer uma ponte entre conceitos econômicos e o comportamento do ser humano, numa visão psicológica, buscando desafiar o conceito tradicional da economia, uma vez que as pessoas nem sempre tomam decisões racionais e que suas escolhas são influenciadas por uma série de fatores, incluindo emoções, cognição e contexto social. Por meio de dados empíricos e experimentais, a economia comportamental apresenta cientificamente como o comportamento humano afeta diretamente o superávit econômico. Para a Economia Tradicional o comportamento humano apresenta uma maximização racional, ao passo que, nos estudos da Economia Comportamental verifica-se um viés psicológico nesse comportamento e seus reflexos nos resultados econômicos. Com esse estudo, ora apresentado, há de se evidenciar estratégias de intervenção baseadas em Economia Comportamental tais como os nudges, que têm demonstrado ser eficazes em influenciar o comportamento dos indivíduos e podem ser utilizadas para ajudar a corrigir algumas dessas distorções do mercado. A Economia Comportamental ainda cede um importante espaço para mais estudos, considerando a complexidade e profundidade de sua análise. Observa-se ainda que a interação entre os diversos vieses comportamentais levará a mais pesquisas a fim de observar como afetam as decisões econômicas e ao próprio superávit econômico.
The basic purpose of this article is to understand the impact of behavioural biases on the financial decisions of the rural women micro-entrepreneurs. The indicators were selected based on the literature review and exploratory interview, and in consultation with the experts of the funding agencies (mainly banks) to cover behavioural biases of the rural women micro-entrepreneurs. In order to design the scale, SEM AMOS 26 software along with SPSS 26 was used for data analysis. Reliability score and exploratory factor analysis were conducted with the help of SPSS 26 software. Overconfidence of the rural women micro-entrepreneurs negatively influenced their financial decisions. Rural women micro-entrepreneurs were found to be better financial decision-makers as past loss from their financial decisions did not deter them from taking risk. The loss aversion bias did not influence the financial decisions of the rural women micro-entrepreneur. However, the regret aversion bias and the anchoring bias positively influence their financial decisions, depicting the impact of behavioural biases on their financial decisions. The present study can be further extended to identify the impact of moderating and mediation variables. The impact of other emotional and cognitive behavioural biases on the financial decisions of the rural women micro-entrepreneurs can also be considered. The understanding of impact and the effect of behavioural biases on financial decisions of rural women micro-entrepreneurs will help to identify the rationale behind financial decision-making of the rural women micro-entrepreneurs. Most of rural women micro-entrepreneur has to deal with decisions related to finance. The confidence with which they handle their money is a reflection on how confidently they can handle their business. The present study attempts to design and validate a comprehensive behavioural bias scale influencing financial decisions of the rural women micro-entrepreneurs. To the best of the knowledge of the author, almost no studies have focused adequately to explore the influence of behavioural biases on the financial decisions of women micro-entrepreneurs. Thus, the aim of this research is to understand the influence of behavioural biases on the financial decisions of rural women micro-entrepreneurs and design a robust and comprehensive scale.
This chapter provides a synthesis of desirable management strategies for mission-driven organisations devoted to enhancing performance by leveraging micro-level behaviours. Specifically, the concluding remarks are dedicated to deriving common evidence-based practical implications regarding the following processes: performance management, information systems, investment strategies, people administration, and implementation of change and innovation. The chapter also pinpoints the main practical research that remains open for future work inspired by the theories and methodologies of behavioural management sciences. Lastly, it provides non-exhaustive examples of nudge units established across the globe and across institutional settings.
Management scholars worldwide increasingly capitalize on theoretical models and research designs from the behavioural sciences to close the implementation gap in mission-driven organisations. This book aims at synthesizsing the theoretical frameworks and evidence that has flourished over the past decades in order to advance the scholarly debate and the implications for practice in the domains of performance management, information systems, investments strategies, people administration, and change and innovation. It provides meaningful insights to tackle real-world challenges that organizations and their managers face on a daily basis.
Information management is the process of collecting, organising, storing, and disseminating information within an organisation to support its goals and objectives. The process involves the effective use of technology and methods to manage data and information assets. From an organisational perspective, the process of information management involves several key steps. The first step is to identify the information needs of the mission-driven organisation and define the data and evidence that need to be collected and stored. This involves analysing the organisation’s goals and objectives and determining the required information to support them. The next step is to design and implement information systems to collect, process, and store the data and information the organisation needs. This involves selecting appropriate hardware and software solutions, designing databases, and developing policies and procedures for managing the information. Information and communication technologies that craft the web platform and documents used within the organisation to make decisions almost by nature qualify as architectural structures, which will not be neutral. On the contrary, an intervention implemented through informatics has the potential to be extremely low cost and extremely high impact, for better or worse, as usual.
The behavioral literature suggests that minor frictions can elicit desirable behavior without obvious coercion. Using closures of ATMs in a densely populated city as an instrument for small frictions to physical banking access, we find that customers affected by ATM closures increase their usage of the bank’s digital platform. Other spillover effects of this adoption of financial technology include increases in point-of-sale transactions, electronic funds transfers, automatic bill payments, and savings, and a reduction in cash usage. Our results show that minor frictions can help overcome the status quo bias and facilitate significant behavior change. This paper was accepted by David Sraer, finance. Supplemental Material: Data and the online appendix are available at .
Full-text available
Contrary to theoretical expectations, measures of willingness-to-accept greatly exceed measures of willingness-to-pay. This paper reports several experiments that demonstrate that this "endowment effect" persists even in market settings with opportunities to learn. Consumption objects (e.g., coffee mugs) are randomly given to half the subjects in an experiment. Markets for the mugs are then conducted. The Coase theorem predicts that about half the mugs will trade, but observed volume is always significantly less. When markets for "induced-value" tokens are conducted, the predicted volume is observed, suggesting that transactions costs cannot explain the undertrading for consumption goods. Copyright 1990 by University of Chicago Press.
Full-text available
Although many economists, most notably Strotz, have discussed dynamic inconsistency and precommitment, none have dealt directly with the essence of the problem: self-control. This paper attempts to fill that gap by modeling man as an organization. The Strotz model is recast to include the control features missing in his formulation. The organizational analogy permits us to draw on the theory of agency. We thus relate the individual's control problems with those that exist in agency relationships.
The idea of libertarian paternalism might seem to be an oxymoron, but it is both possible and desirable for private and public institutions to influence behavior while also respecting freedom of choice. Often people's preferences are unclear and ill-formed, and their choices will inevitably be influenced by default rules, framing effects, and starting points. In these circumstances, a form of paternalism cannot be avoided. Equipped with an understanding of behavioral findings of bounded rationality and bounded self-control, libertarian paternalists should attempt to steer people's choices in welfare-promoting directions without eliminating freedom of choice. It is also possible to show how a libertarian paternalist might select among the possible options and to assess how much choice to offer Examples are given from many areas, including savings behavior, labor law, and consumer protection.
This paper presents a problem which I believe has not heretofore been analysed2 and provides a theory to explain, under different circumstances, three related phenomena: (1) spendthriftiness; (2) the deliberate regimenting of one’s future economic behaviour— even at a cost; and (3) thrift. The senses in which we deal with these topics can probably not be very well understood, however, until after the paper has been read; but a few sentences at this point may shed some light on what we are up to.
Using pooled cross-sectional data from the Surveys of Consumer Finances, and new panel data from TIAA-CREF, we examine the empirical relationship between age and portfolio choice, focusing on the observed relationship between age and the fraction of wealth held in the stock market. We illustrate and discuss the importance of the well-known identification problem that prevents unrestricted estimation of age, time and cohort effects in longitudinal data. We also document three important features of household portfolio behavior: significant non-stockownership, wide-ranging heterogeneity in allocation choices, and the infrequency of active portfolio allocation changes. Based on a specification including age effects and time effects (excluding cohort effects) we find that equity ownership has a hump-shape pattern with age, while equity shares conditional on ownership are nearly constant across age groups. Based on a specification that includes age effects and cohort effects (excluding time effects), we find that equity portfolio shares increase strongly with age. Following the same individuals over time, we find that almost half of the sample members made no active changes to their portfolio allocations over our nine-year sample period, while the vast majority of those who did make changes increased their allocations to equity as they aged.
Individual discount rates are estimated from survey evidence. For gains, they are found to vary inversely with the size of the reward and the length of time to be waited. Rates are found to be much smaller for losses that for gains.
This paper examines the composition and distribution of total wealth for a cohort of 51- to 61-year olds from the Health and Retirement Study (HRS), and the role of pensions in forming retirement wealth. Pension coverage is widespread, covering two-thirds of households and accounting for one-quarter of accumulated wealth. Social security benefits account for another quarter of total wealth.As calculated from earnings records, the present discounted value of social security benefits is less than the present value of taxes paid. Earlier than many expected, social security is already a poor investment on average for this cohort on the verge of retirement. When pensions and social security are included, wealth accumulated by the HRS population to date is substantial. At their expected retirement date, using only the wealth accumulated by their mid-fifties, the HRS household with median replacement rate could finance a fixed, nominal two-thirds joint and survivor annuity replacing 79 percent of last earnings, and a real annuity replacing 52 percent of last earnings. Replacement rates for median earners are higher. Additional savings made over the seven years remaining until retirement will raise those replacement rates by about a fifth. When measured against a standard of adequacy based on average yearly earnings over the worklife, with adjustments made for the absence of preretirement savings, children, taxes, work-related expenses and other factors, these replacement rates appear adequate.Lifetime earnings are measured for each individual in the HRS from social security earnings records augmented by self-reported earnings histories. When pensions and social security are counted in total wealth, the ratio of wealth to lifetime earnings declines from very high levels in the bottom ten percent of the earnings distribution, remains at roughly 40 percent from the 25th through 95th percentile of the lifetime earnings distribution, and then falls to 32 percent for those in the top five percent of the earnings distribution.This result is consistent with the predictions of a simple, stripped-down life-cycle model. Also consistent is a finding that the ratio of wealth to lifetime earnings is no higher for those with pensions than for those without pensions. However, heterogeneity is quite important. Real estate and business wealth are a larger share of total wealth for those without pensions, reflecting the importance of self-employment in wealth accumulation.Multivariate regressions relating total wealth to pension coverage and pension value, which standardize for sources of heterogeneity, suggest that pensions cause very limited displacement of other wealth, if any. Pensions add to total wealth by at least half the value of the pension, and in most estimates by a good deal more.These findings are not consistent with a simple life-cycle explanation for savings. They also raise questions about whether pensions are fundamentally a tax avoidance device, allowing substitution of pension for nonpension savings.