PII: S1057-0810(99)00030-X


Gender differences in defined contribution
pension decisions

Vickie L. Bajtelsmita, Alexandra Bernasekb, Nancy A. Jianakoplosb

aDepartment of Finance and Real Estate, Colorado State University, Fort Collins, CO 80523, USA
bDepartment of Economics, Colorado State University, Fort Collins, CO 80523, USA

Abstract

This paper considers gender differences in allocation of household wealth to defined
contribution pensions. Using data from the 1989Survey of Consumer Finances, we estimate the
coefficient of relative risk aversion based on the allocation of wealth into defined contribution
pensions. Unlike previous studies, we consider the problem in the context of the household’s
overall portfolio. We find that women exhibit greater relative risk aversion in their allocation of
wealth into defined contribution pension assets. © 1999 Elsevier Science Inc. All rights reserved.

JEL classification: J16; G11; G23

Keywords:Risk aversion; Pension; Gender differences

1. Introduction

Trends in employer provision of private pensions show that defined contribution pension
plans are becoming increasingly prevalent (Gustman and Steinmeier, 1992). The United
States Department of Labor (1997) reports that the number of workers covered by defined
benefit plans decreased by 1.7 million between 1975 and 1993 whereas the number covered
by defined contribution plans increased nearly 300% over that same period to 36.4 million
in 1993. Bajtelsmit and VanDerhei (1997) summarize recent trends in pensions and conclude
that an increasing percentage of new plans also require participants to make their own

* Corresponding author. Tel.:11-970-491-0610; fax:11-970-491-7665.
E-mail address:vickieba@lamar.colostate.edu (V.L. Bajtelsmit)

Financial Services Review 8 (1999) 1–10

1057-0810/99/$ – see front matter © 1999 Elsevier Science Inc. All rights reserved.
PII: S1057-0810(99)00030-X



investment decisions. Although investment choice is arguably a source of empowerment,
increasing evidence that individuals tend to choose conservative investment strategies for
self-directed accounts raises questions of whether today’s workforce will retire with signif-
icantly lower pension benefits than those that would have resulted from investment by
professional pension managers. Thus this trend has potentially serious implications for the
retirement income adequacy of American workers.

A related problem that has received less attention is the impact of this trend on women in
retirement. Previous studies have found that women display greater risk aversion in a wide
variety of activities, including smoking, seat belt usage, and financial decision making
(Hersch, 1996; Jianakoplos and Bernasek, 1998; Bajtelsmit and Bernasek, 1996). There is
also evidence that women tend to exhibit greater risk aversion within their defined contri-
bution plans (DCPs) (Bajtelsmit and VanDerhei, 1997; Hinz, McCarthy and Turner, 1997;
Sunden and Surette, 1998). Women’s greater longevity implies that, even with the same
investment strategy and pension investment accumulation as men, retirement wealth must
support a longer period of retirement. Thus, all else, equal, consumption in retirement will
be lower for women. Furthermore, although the pension gap between women and men has
closed more rapidly than the earnings gap, women still tend to have lower coverage, lower
participation rates, and lower contribution rates (Magenheim, 1993).

This paper differs from previous studies of gender differences in pension decisions by
investigating pension allocations within the larger context of the household’s overall port-
folio balance. The studies by Bajtelsmit and VanDerhei (1997) and Hinz, et al. (1997) rely
on data from a single employer/provider. Many other studies of pension allocation, such as
those conducted by pension plan providers (e.g., Goodfellow and Schieber, 1997), are flawed
in that the researchers lack complete information on the household. For example, a conser-
vative pension portfolio might be part of an overall portfolio that includes investment in a
risky privately-held business or a small stock mutual fund. A similar problem arises when the
researcher considers individual investment portfolios in isolation because many households
may pool their resources. One spouse may have a conservative pension portfolio when the
other spouse has their pension invested in risky assets such that the overall household
portfolio is more diversified than it would appear by considering each in isolation. Jianako-
plos and Bernasek (1998), investigating gender differences in financial risk taking, find that
demographic and household variables are highly significant determinants of risky decision
making.

The paper proceeds as follows. Section 2 explains the theoretical models underlying our
estimation of relative risk aversion. In Section 3 we explain the empirical methodology and
provide the results of the estimations. Finally, conclusions and policy implications are given
in Section 4.

2. Measurement of risk aversion

Under expected utility theory, the dollar amount and proportion of risky assets in an
investor’s portfolio are a function of wealth and individual risk preferences. The relationship
between risk preferences and wealth was further developed by Pratt (1964) and Arrow

2 V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



(1971), who define measures of absolute risk aversion (change in dollar allocation to risky
assets as wealth increases) and relative risk aversion (change in portfolio allocation to risky
assets as wealth increases). Based on empirical and experimental studies of individual
decision-making under risk, there is now general consensus thatabsoluterisk aversion
declines with wealth. That is, individuals will invest a higher dollar amount in risky assets
as their wealth increases. The characteristics of individualrelative risk aversion are not as
clear and seem to exhibit systematic differences by some characteristics such as age and
income. The impact of gender on relative risk aversion was considered in Jianakoplos and
Bernasek (1998) who find evidence that single women exhibit greater relative risk aversion
than single men do.

The empirical estimation of risk aversion in pension allocations undertaken in this paper
follows the methodology developed by Friend and Blume (1975). This methodology for
measuring relative risk aversion is also employed by Siegal and Hoban (1982,1991), Morin
and Suarez (1983), Ballante and Saba (1986), Riley and Chow (1992), Jianakoplos and
Bernasek (1998), and Schooley and Worden (1996).

They define an individual’s division between risky and riskfree assets in their portfolio, in
the absence of taxes, according to the following:

ak 5
E~rm 2 r f!

sm
2 p

1

Ck (1)

whereak is the proportion of investork’s net worth that is placed in risky assets,E(rm 2 rf)
is the expected difference between the return on the market portfolio of risky assets (rm) and
the return on the risk free asset (rm), sm

2 the variance of the returns on the market portfolio
of risky assets,Ck is Pratt’s measure of relative risk aversion(Ck 5 [2U“(Wkt)/U’(Wkt)]Wkt)”
andWkt is investork’s wealth in period t. We ignore the effects of taxes in the estimation of
the model based on previous research which shows that taxes do not have a significantly
affect on the results (Bellante and Saba, 1986).

The model assumes that financial assets are infinitely divisible and can be traded with zero
transactions costs. These assumptions are problematic when applied to housing wealth and
human capital. For that reason, some studies estimate the value of wealth with and without
the value of housing (Friend and Blume, 1975). To include human capital in net wealth, a
reformulation of Eq. (1) is required takes into account the dependence ofak on the
covariance between the return on the market portfolio (rm) and the return on human wealth
(rh). This results in the following specification for allocation to risky assets:

ak 5
E~rm 2 r f!

sm
2 p

1

Ck~1 2 hk!
2

hk

~1 2 hk!
p bh,m (2)

wherehk is the ratio of investor k’s human wealth to net wealth, andbh,m is the ratio of the
covariance ofrm andrh to sm

2 . This equation can be simplified however, by making use of
the findings by Liberman (1980) and Fama and Schwert (1977) thatbh,m is zero. Then Eq.
(2) becomes:

3V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



ak 5
E~rm 2 r f!

sm
2 p

1

Ck~1 2 hk! (3)

For the analysis in this paper, Eq. (3) is rewritten to focus on the proportion of riskypension
assets in an individual’s portfolio as follows:

ak1 5 2ak2 1
E~rm 2 r f!

sm
2 p

1

Ck~1 2 hk! (4)

whereak1 is the proportion of net worth of investor k in riskypensionassets andak2 is the
proportion of net worth of investor k inother risky assets. Eq. (4) forms the basis for
estimating the coefficient of relative risk aversion when we consider an individual’s invest-
ment in risky pension assets.

3. Empirical model and estimation

3.1. Data

This study employs data from the 1989Survey of Consumer Finances(SCF89) to examine
whether defined contribution pension allocation decisions differ by gender. The SCF89
includes interview data collected from a sample of 2, 277 households, chosen to be repre-
sentative of households in the contiguous 48 states of the United States. Because one of the
principal goals of the survey was to obtain estimates of household wealth, which is a highly
skewed variable, an additional oversampling of 866 wealthy households was undertaken,
selected from tax records to represent wealthier households. The combined sample of 3,143
households reported on the composition of their balance sheets, employment status, income,
pensions, and other economic and demographic information. Kennickell and Shack–Marquez
(1992) provide more details on the survey methods. According to several studies (Curtin, et
al., 1989; Juster and Kuester, 1991; and Starr–McCluer, 1996) theSurveys of Consumer
Financesare the best available source of individual household wealth data collected in the
United States.

The SCF89 respondents reported balances in defined contribution pensions (DCPs) for
both themselves and spouse/partners, if any. Because the focus of this study is on pension
balances of individuals, rather than of households, data for respondents and spouses are
included separately in the sample. Consequently, pension allocation behavior is examined for
a sample of 5, 287 individuals—the 3, 143 respondents and an additional 2, 144 spouse/
partners. In the cases where there are two individuals from the same household, they have
the same household wealth, because there is no basis for dividing their joint assets. Because
of the oversampling of the wealthy households, all summary statistics reported in this paper
are sample weighted. In addition, the multiple imputation procedure employed on the
public-use data tapes by the Federal Reserve System to handle missing data is used here.

4 V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



3.2. Empirical methodology

Based on Eq. (4), that is the theoretical basis for the allocation of an individual’s portfolio
into risky pension assets, the estimating equation takes the following specification:

ALPHA5 b1 1 b2ln WEALTH1 b3AGE1 b4AGE2 1 b5AGE-S

1 b6AGE2-S1 b7EDUC-12 1 b8BLACK

1 b9KIDS 1 b10SINGLE1 b11HUMANj 1 b12HUMAN-S

1 b13RISKY1 b14HOMEOWNER1 b15OTHERPENS

1 b16OTHERPENS-S1 b17LAMBDA1 m, (5)

where ALPHA is the ratio of individual holdings of dollar balances in DCPs to total
householdwealth (WEALTH) that includes both riskfree and risky assets. Riskfree assets
include dollar balances in checking, savings, and money market accounts, certificates of
deposit, U.S. savings bonds, IRA balances invested in certificates of deposit or bank
accounts, and the cash value of life insurance less policy loans outstanding. Risky assets are
the sum of: balances in IRAs not invested in bank deposits, stock holdings less margin loans
outstanding, bonds, trust assets, the net value of real estate other than residential housing, the
net value of businesses owned, the net value of other miscellaneous assets (e.g., precious
metal, futures contracts, art work, etc.) reported by the household, and balances in DCPs.

To control for demographic differences, the specification includes:AGE andAGE2, the
individual’s age in years and it’s square;AGE-SandAGE2-S, age and age squared of the
spouse, if any;EDUC-12, a dummy variable that equals one if the individual has completed
more than 12 years of schooling;BLACK, a dummy variable that equals one if the individ-
ual’s race is reported as black;KIDS, the number of children living in the household; and
SINGLE, a dummy variable equal to one if the individual’s marital status is not reported to
be married or living with a partner.

A number of variables are included to capture other aspects of the individual’s portfolio.
RISKYis the ratio of other risky assets, excluding assets held in DCPs, toWEALTH(the
empirical estimate ofak) in Eq. (4). Other risky assets are all household risky assets except
those included inALPHAij . HUMAN andHUMAN-Sare the ratios of the individual’s and
spouse’s (if any) human capital to wealth, respectively. Human capital for each individual is
calculated as the present value of the stream of future earnings, assuming that current wages,
salaries, and/or self-employment earnings grow at a constant rate until retirement. Retirement
is assumed at age 65 for those 65 or younger. If the individual is still working and between
the ages 65 and 69, current earnings are assumed to continue for four more years; if between
70 and 74, to continue for three more years; if between 75 and 79, to continue for two more
years; and if over 79, for one more year, consistent with Friend and Blume (1975). The
discount rate is assumed to be 2%, which approximates the long run growth in real GDP.
Although this measure of human capital is obviously an approximation, analysis by Thorn-
ton, Rodgers, and Brookshire (1997) suggests that the assumption of a constant growth rate

5V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



of earnings, rather than the traditional inverted u-shaped pattern, is supported by longitudinal
data.

Given the ambiguity between investment and consumption aspects of residential housing,
rather than including it as a risky asset, we have included a dummy variable,HOMEOWNER
that equals one if the respondent owns a house.OTHERPENSand OTHERPENS-Sare
dummy variables that equal one if the individual or spouse, respectively, reports entitlement
to defined benefit pensions.

Following previous research, the sample is limited to those individuals withWEALTHj

greater than $1, 000. Furthermore, the nature of the question under consideration requires
that the sample be limited to those individuals with defined contribution pensions. If sample
selection were random, OLS would be an appropriate estimation technique. However, if the
sample selection is not random, OLS yields inconsistent results (Greene, 1993). To correct
for possible sample selection bias, Heckman’s (1979) two-step procedure is employed. A
probit regression of the factors that influence inclusion in the sample is estimated over all
observations jointly with Eq. (5). The specification of the probit equation is:

HASDC5 g1 1 g2AGE1 g3UNION 1 g4BIGFIRM 1 g5PROF1 g6SALES

1 g7CRAFTS1 g8LABOR1 g9FARM1 y, (6)

whereHASDC is a dummy variable that equals one if the individual has a defined contri-
bution pension and hasWEALTHgreater than $1, 000. The explanatory variables include:
AGE, UNION, a dummy variable that equals one if the individual belongs to a union,
BIGFIRM, a dummy variable that equals one if the individual works for a firm employing
over 500 people, and a set of five occupational dummies (PROF, SALES, CRAFTS, LABOR,
andFARM), where the excluded occupational category is service jobs. An estimate of the
inverse Mill’s ratio is calculated from this equation and included in Eq. (5) as the variable
LAMBDA.

3.3. Results

Because the sample used for analysis in this paper is comprised of only those survey
participants who report having DCPs and wealth greater than $1, 000, Table 1 provides a
comparison of variable means for the DCP subset and the rest of the SCF89 sample.
Individuals in this subset are, on average, more likely to be younger, to be union members,
to be employed by a large firm, to have higher education, and to have more children.
Comparison of male and female variable means shows differences that would be expected.
On average, women with DCPs allocate a smaller proportion of wealth to their pensions
(27%) compared to men (35%). The results of the first stage probit estimation, that are
available from the authors upon request, indicate that the survey subset is significantly
different from the overall SCF89 survey sample. The inclusion of theLAMBDAvariable in
the estimation procedure therefore serves to ensure that the other coefficients are consistent.

The results of the full information maximum likelihood estimations of Eq. (2) are shown
separately for men and women in Table 2. To test whether the factors determining the
proportion of wealth allocated to DCPs differ by gender, all of the variables in Eq. (2) were

6 V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



interacted with a dummy variableFEMALEand the equation was re-estimated for the sample
including both men and women. A likelihood ratio test of the null hypothesis that all the
female interaction terms equal zero is rejected, indicating that the factors determining
women’s allocation of wealth to DCPs are different from men’s. The test statistic was 220.66
distributedx2 with 16 degrees of freedom. The critical value at the 1% level of significance
is 31.99. Thus, the null hypothesis is rejected.

Coefficients that differ significantly between the men’s and women’s equations are
indicated in Table 2. Significant gender differences exist for age (both respondent’s and
spouse’s), education, number of children, marital status, human capital (both respondent’s
and spouse’s), allocation to other risky assets, and having claims on other pensions.

The coefficient oflnWEALTHis an estimate of relative risk aversion (Ck) up to a positive
multiplicative constant. A positive (negative) coefficient indicates decreasing (increasing)
relative risk aversion. For men, the coefficient is significantly positive, indicating decreasing
relative risk aversion. The estimated coefficient for women is negative, but not significantly
different from zero, indicating constant relative risk aversion. Thus women exhibit greater
relative risk aversion than men in the allocation of wealth to DCPs, holding other factors
constant.

To illustrate the impact on individual portfolios, consider the following application of
these estimation results. If initial wealth for both men and women is assumed to be
$116, 000, the mean wealth of the women in the sample, and all other variables are held

Table 1
Variables means

Defined contribution pension No defined contribution pension

Women Men Women Men

Alpha 0.27 0.35 — —
Wealth 116, 044 212, 933 134, 763 148, 497
Age 40.5 41.6 48.4 37.4
Age-Sa 41.1 39.8 48.5 46.5
Educ-12 0.58 0.68 0.35 0.41
Black 0.10 0.06 0.13 0.08
Kids 0.99 1.21 0.95 0.92
Single 0.35 0.15 0.33 0.20
Human 32.33 45.8 194.76 81.51
Human-Sa 32.76 12.30 192.90 99.36
Risky 0.36 0.34 0.41 0.42
Homeowner 0.74 0.78 0.62 0.63
Other pension 0.41 0.42 0.14 0.25
Other pension-Sa 0.43 0.20 0.27 0.17
Union 0.26 0.25 0.07 0.16
Big firm 0.66 0.60 0.16 0.23
Professional 0.39 0.46 0.13 0.18
Sales 0.49 0.18 0.20 0.13
Crafts 0.01 0.19 0.01 0.15
Labor 0.06 0.11 0.05 0.12
Farm — 0.01 0.01 0.04
N 319 573 2, 489 1, 906

a Means are calculates only for individuals with a spouse present.

7V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



constant at the sample means for men and women separately, the proportion of wealth held
in DCPs is predicted to be 27.1 and 35.5% for women and men, respectively. If wealth is now
increased to $213, 000, the mean wealth of men in the sample, the proportion of wealth held
in DCPs is predicted to decrease slightly to 26.3% for women and to increase only slightly
to 35.9% for men.

The gender difference in relative risk aversion explains only part of the difference in
allocation of wealth to DCPs. For example, an increase in number of children is estimated
to increase allocations to DCPs for both men and women, but by a small proportion for
women. Holding everything else constant, single men are estimated to allocate more to DCPs
compared to married men, but single women are estimated to allocate significantly less than
married women. When either the individual or spouse has access to pensions other than
DCPs, men are estimated to increase relative allocations to DCPs, but women are estimated
to reduce their DCP allocations.

One way to assess the relative importance of the factors that contribute to the lower
allocation of wealth to DCPs by women is to consider the following example. If the sample
mean characteristics for women are applied to the coefficients of the men’s equation, the
allocation to DCPs is predicted to be 33.8% of wealth compared to the sample average of
27%. This would bring the female allocation to DCPs very close to the mean for men which

Table 2
Dependent variable: Proportion of wealth held in defined-contribution pensions

Women Men

Independent variables COEF SE COEF SE

ln Wealth 20.004††† 0.004 0.007*** 0.002
Age 0.021***†† 0.006 0.005 0.003
Age2 20.000*** 0.000 20.000 0.000
Age-S 20.027***††† 0.006 0.006** 0.002
Age2-S 0.000***††† 0.000 20.000** 0.000
Educ-12 20.015††† 0.011 20.054*** 0.010
Black 20.004 0.018 0.002 0.018
Kids 0.007† 0.005 0.017*** 0.003
Single 20.634***††† 0.146 0.168*** 0.050
Human 20.000††† 0.000 0.000*** 0.000
Human-S 20.000 0.000 20.000 0.000
Risky 20.376***††† 0.018 20.561*** 0.015
Homeowner 20.027** 0.014 20.013 0.011
Other pension 20.014††† 0.011 0.024*** 0.008
Other pension-S 20.025**†† 0.013 0.011 0.010
Constant 0.650***††† 0.106 0.161** 0.072
Lambda 20.028** 0.013 0.022** 0.010
N:ProbituRegression 2, 808 319 2, 479 573
Log likelihood1 23140 24707

* ,** ,*** Significantly different from zero at the 10%, 5%, and 1% levels, respectively.
†,††,†††Significantly different from male coefficient at the 10%, 5%, and 1% level, respectively.
1 The likelihood ratio statistic for the women’s equation was 2, 929.96 distributed X2 with 23 degrees of

freedom. The equation is significant at the 1% level. The likelihood ratio statistic for the men’s equation was
2, 469.51 distributed X2 with 24 degrees of freedom. The equation is significant at the 1% level.

8 V.L. Bajtelsmit et al. / Financial Services Review 8 (1999) 1–10



is 35%. On the other hand, if women were assumed to have the same characteristics as the
sample average for men, and these mean values were applied to the estimated coefficients of
the women’s equation, the predicted proportion of wealth in DCPs would increase to only
27.9%. Thus, the gender differences in the allocation of wealth to DCPs are largely
attributable to differences in male versus female behavior. This is a reflection of the
differences in the estimated regression coefficients, rather than differences in the character-
istics of women versus men as summarized in the variable means.

4. Conclusions and policy implications

Although several previous studies have examined the issue of gender differences in
investing, this study specifically considers the allocation of wealth to pensions and improves
on earlier studies by including important socio-economic and demographic explanatory
variables. The results of this analysis demonstrate that there are significant gender differ-
ences in allocation of wealth into defined contribution pensions. This study improves on
earlier research by analyzing pension decisions within the broader context of the household
portfolio.

This conclusion has important implications for public policy, particularly in light of recent
demographic and legislative trends. Although pension coverage rates for women have
improved substantially in the last two decades as the number of women in the workforce has
increased, this study indicates that women allocate a smaller proportion of their total wealth
to these retirement vehicles. At the same time, Social Security replacement ratios are lower
than they have been in the past and most new pensions require self-direction of pension
account allocations. Given evidence that women tend to be very risk averse with respect to
the pension allocation decision, it is likely that women will retire with significantly lower
pension wealth than their male counterparts. Furthermore, this smaller wealth will have to be
spread over a longer retirement due to greater average longevity.

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