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Financial Decisions, Winter 2010, Article 5
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Retirement Planning of Younger Baby-boomers: Who Wants Financial Advice?
Swarn Chatterjee and Velma Zahirovic-Herbert
University of Georgia
Abstract
This paper uses a recent wave of a nationally representative survey to determine the predictors
of financial planning services utilization among younger baby boomers. The results suggest that
cognitive factors and factors related to human capital, such as IQ and educational attainment,
are positively associated with use of financial planning services in this group. The study also
shows that participation in a tax-advantaged account and higher net worth increase the
probability that a person will seek professional financial advice. The paper provides useful
information for financial planning practitioners, economists, and policy makers.
Financial Decisions, Winter 2010, Article 5
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Retirement Planning of Younger Baby-boomers: Who Wants Financial Advice?
1. Introduction
The number of individuals approaching retirement age is increasing rapidly as younger baby
boomers, born between 1956 and 1964, form a sizable cohort (Wellner, 2003). Over the next 20
years, more than 78 million Americans will turn 65 (Paul, 2001). Historically, retirees have
received retirement benefits that paid out as an annuity for their lifetime through a defined
benefit plan. However, because of the escalating costs of offering such plans, most employers
have replaced them with participant-directed, defined contribution plans such as 401(k) plans,
wherein the responsibility for wealth accumulation and contribution rests on participants, rather
than their employers (Bassett & Rodrigues, 1998). The growing market of personal financial
advice helps to fill the gap between the skills of most individuals and the skills required to
maximize financial security in retirement. The availability of these services enables even the
average household with limited financial skills to plan effectively for retirement. Past research on
household use of financial advice has found that the portfolio allocations of individuals who
access investment advice are more rational and more consistent with economic theory than the
allocations of those who manage their own wealth (Bodie & Crane, 1997). Bae and Sandager
(1997) used the CFP Board Survey of Trends in Financial Planning to find that, among available
resources, households primarily used the services of financial planners for retirement planning,
investment planning, and tax planning. In addition, complex economic situations, changes in tax
laws, and new investment alternatives were associated with households’ decisions to hire
financial planners. Using the Survey of Consumer Finances, Chang (2005) found that, while
wealthier households were more likely to seek professional financial advice, lower income
households consulted their social networks, including friends and family, for investment and
wealth-management decisions. Using a data set of German investors, Bluethgen, Gintschel,
Hackethal, and Muller (2008) found that wealthier and older individuals and women were more
likely to seek financial advice than were others.
The purposes of this study are to examine the determinants of financial and retirement planning
services use among younger baby boomers, to discuss possible opportunities for the financial
services industry, and to make recommendations for the industry and public policy makers to
improve both the means of and access to resources for effective financial planning.
2. Data and Methodology
This paper uses data from the latest wave (2006) of the National Longitudinal Survey of Youth
(NLSY79). NLSY79, a comprehensive, nationally representative data set comprising 12,686
respondents residing in the United States, is managed by the Center for Human Resource
Research at Ohio State University (Zagorsky, 2007). The NLSY79 contains information on
socioeconomic, demographic, and health-related factors of respondents, including a special
section with a random sample of 1,000 respondents born between 1957 and 1964 who answered
questions on the retirement expectations of younger baby boomers. We use these data to
examine the respondents’ willingness to plan for retirement and their use of various kinds of
financial planning services as they approach retirement. The data enable us to study the effect of
Financial Decisions, Winter 2010, Article 5
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cognitive abilities and human capital attainment on the willingness to plan for retirement and to
understand the market for financial advice among younger baby boomers.
Model
Our primary analysis estimates the marginal effects of the determinants of financial planning
service use. Because the participation outcome is binary, we use probit methodology to estimate
the coefficients and compute their test statistics. In the first specification, we test for utilization
of any financial planning services—a financial planner, financial planning software, retirement
seminars, or books on retirement. The second specification determines the unique predictors of
separately utilizing the services of a financial planner. The final specification examines the
predictors of retirement plan preparation through self-study only. For example, the variable is
coded as 1 if a household prepared for retirement by using computer software, by attending
retirement seminars, or by reading books on retirement, and zero otherwise. In addition, we also
examine whether households with children are less likely to plan for their retirement, as the
presence of children might place constraints on their planning horizon and financial resources
that they may have otherwise devoted to retirement savings.
Independent variables are factors related to income and resources, behavior and cognition, and
demographics. For the income- and resource-related factors, the model controls for income (the
log form of income is used in the analysis) and net worth (NLSY’s calculated net worth from
2004 is inflated to reflect 2006 dollars). Participation in defined contribution plans is also
included among the income and resource variables. Defined contribution plans are tax-
advantaged retirement accounts for which individuals are responsible for the contribution and
allocation of assets in their portfolios. The behavioral and cognitive variables comprise IQ,
educational attainment, risk tolerance, and self-reported health. IQ is calculated from the Armed
Forces Qualification Test scores included in the NLSY79 data set using the method suggested by
Zagorsky (2007). Risk tolerance is measured from the income gamble-related questions included
in the data set, using the method suggested by Barsky, Juster, Kimball, and Shapiro (1997). The
responses to the following set of questions developed by Barsky et al. (1997) are provided in the
data set for estimation of the self-reported risk tolerance of respondents:
“Suppose that you are the only income earner in the family, and you have
a good job guaranteed to give you your current income every year for life. You
are given the opportunity to take a new and equally good job, with a 50-50
chance it will double your income and a 50-50 chance that it will cut your income
by (1) 33% (2) 50%, or (3) 20%. Would you take the new job?”
If the respondent answers “yes” to the first question, then the second follow-up question (50%
income cut) is asked. If the respondent says “no” to the first question, then the third follow-up
question (20% income cut) is asked. The risk tolerance measure in NLSY coincides with the risk
tolerance measure created by Lusardi (1998) for the Health and Retirement Study (HRS) data
set. Educational attainment variables compare attainment of high school, some college, college,
or graduate school to the reference group of individuals who did not complete 12 years of
education. The health status of the respondents is included as a control variable in the model. The
health status measure is based on a question in the NLSY that asks the respondents to self-report
an assessment of their general health on a scale of 1-5 with excellent health=1 and poor health=5.
Financial Decisions, Winter 2010, Article 5
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This variable was reverse coded for estimation in our model. Finally, marital status, family size
and children, and age are controlled as demographic variables in the model. Age squared is also
included to control for the quadratic effect of age (Wooldridge, 2006). The inclusion of the age
squared variable helps estimate whether there exists a significant difference between older and
younger respondents in the cohort.
The binary dependent variables used in our study are analyzed using probit models, summarized
as follows:
Pi*= ατ + βτCi + δτYi + γτ Wi,+ Φτ Ti + ε ,
where Pi=1 if P*i>0
and Pi=0 if otherwise for i= {1,2,…,Ι} (1).
In the first model, Pi is a discrete dependent variable equal to 1 for the ith participant using a
financial planning service, and zero otherwise. Pi is determined, in this case, by Pi*, which is a
latent continuous variable indicating whether the marginal benefit of using a financial planning
service is greater than the marginal cost of doing so. The error term ε is distributed normally
with mean zero and variance 1. Ci is the vector of the income- and resource-related variables, Yi
is the vector for the cognitive and behavioral variables, and Ti is the vector for the demographic
variables controlled in our model.
Similarly, in the next part of this study, two more probit models are used. One model estimates
the predictors of consulting a financial planner. A second model estimates the predictors of
financial planning on one’s own by reading books, attending seminars, or using financial
planning software. Each model controls for the same set of variables as the first model.
3. Results
3A. Descriptive Statistics
The descriptive statistics from Table 1 show that the average age of the respondents in the 2006
survey was 45, the average family income was $73,628, and the average net worth in 2006
dollars was $224,850. The 2006 wave of NLSY79 does not contain information on individual net
worth; the net worth from 2004 was inflated, assuming a normal inflation rate of 3.5%, to
approximate the individual net worth value for 2006. The inflation rate of 3.5% was calculated
from the consumer price index (CPI) data provided by the Bureau of Labor Statistics.1 The
average IQ of the respondents was calculated at 96, whereas the highest percentage of
respondents had graduated from high school (46%). The largest percentage of respondents in the
sample was in the most risk-averse category of the risk tolerance scale (54%). While 10% of
respondents had not planned or calculated their retirement, the highest percentage who claimed
to have planned for their retirement had used the “do-it-yourself” approach of reading books on
retirement planning (38%), and 21% had consulted a financial planner. Only 13% of the
respondents had utilized software programs on their computers or programs available online to
prepare their financial plans. Nineteen percent of the respondents had attended retirement
seminars.
1 U.S. Department of Labor (2009), BLS Handbook of Methods, Bulletin 2490, U.S. Government Printing Office.
Financial Decisions, Winter 2010, Article 5
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The test results in Table 2 show the differences between respondents who used the services of a
financial planner and those who self-prepared their retirement plans by reading books, attending
seminars, and using software programs. The results show the importance of cognitive ability and
human capital attainment on an individual’s decision to access the services of a financial planner.
Furthermore, respondents who used the services of a financial planner had significantly higher
income, net worth, and educational attainment. They also had higher IQs and better health status.
Determinants of planning for retirement
The results of the probit analysis (Table 3) show that income, net worth, and having a defined
contribution plan are positively associated with planning for retirement. Among cognitive and
behavioral factors, IQ, educational attainment, and health are positively associated with planning
for retirement. These findings are consistent with the findings of previous studies, which have
shown a positive relationship between human capital and financial services utilization (Miller &
Montalto, 2001; Soberon-Ferrer & Dardis, 1991). Among the demographic variables, women are
more likely than men to plan for their retirement, and white households are more likely than
others to plan for retirement. Having children is negatively associated with seeking financial
advice and retirement planning. These findings are similar to those of the Miller and Montalto
(2001) study on utilization of financial planners. It is likely that the presence of dependent
children reduces the availability of financial resources and time for households to plan
effectively for their retirement. In a previous study, Zagorsky (2005) found that having children
is a negative predictor of net worth because the presence of children increases the current
consumption of households while reducing the availability of resources for savings and future
consumption.
3B. Determinants of utilizing financial planning services
Table 4 shows the probit models that examine the predictors of the younger baby boomers’
utilization of the services of a financial planner (columns 1, 2, 3, 4) and self-preparation of
retirement plans (columns 5, 6, 7, 8). Household income and net worth are positively associated
with both consulting a financial planner and self-preparation of retirement plans. In addition,
those who have a defined contribution plan are more likely to use the services of a financial
planner. Among the cognitive factors, IQ and attainment of a college degree or higher are
positively associated with utilization of a financial planner and self-preparation of retirement
plans. Good health is also positively associated with consulting a financial planner. In addition,
the results show that age squared is positively associated with seeking the services of a financial
planner. Women are less likely than men to self-prepare their retirement plans and instead are
more likely to consult a financial planner for their retirement planning needs. Respondents who
are white are more likely than others to consult a financial planner, while married respondents
are more likely than the control group to consult a financial planner and self-prepare their
retirement plans.
4. Conclusion
This research empirically tests the determinants of utilizing financial planning services among
younger baby boomers. The results provide a particularly interesting perspective on the role of
Financial Decisions, Winter 2010, Article 5
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cognitive ability on utilization of financial planning services within this group. The association
between factors related to human capital and utilization of financial advice to plan for retirement
is worth further examination. The results suggest that a very high percentage of young boomers
have not yet planned for their retirement or have self-prepared their retirement without
necessarily having the sophisticated financial skills required for such tasks. Therefore, it is clear
that, as members of this cohort approach retirement, their attitudes about retirement planning will
pose both a challenge and an opportunity for the financial services industry and public policy
makers; these groups will need to act fast to develop products, services, and policies to increase
participation by this cohort in saving and preparing for their retirement.
The broader implications of our research suggest that those best capable of making financial
planning decisions choose to access financial planning services. Related studies indicate that
portfolio allocations of individuals who access investment advice are more rational and more
consistent with economic theory than the allocations of those who manage their own wealth
(Bodie & Crane, 1997). American workers are increasingly responsible for securing their own
retirement savings as employers continue to transfer the risk and responsibility of saving for
retirement to their employees. However, only a minority of American households feels
“confident” about the adequacy of their retirement savings, and one third of adults in their 50s
say they have failed to develop any kind of retirement saving plan at all (Lusardi 1999, 2003;
Yakoboski & Dickemper, 1997).
One reason people fail to plan for retirement, or do so unsuccessfully, is because very few are
financially well informed (Lusardi, 2003). Many fail to appreciate the role of (or may not be
competent at solving problems with) compound interest, inflation, and risk. A clear way to
address this issue is through the use of the professional services of a financial planner. Our
findings indicate that currently the wealthier, more educated households are the biggest users of
these professional services, though they perhaps have fewer barriers to independent investment.
Those who need professional financial planning advice the most are least able to access these
services because of the challenges of paying for them and locating credible professional
providers.
Throughout the 1990s, there was an explosion of products and programs in financial services,
along with several government programs and workplace financial education seminars, geared
toward employees (Lusardi, 2004). Some researchers, including Lusardi (2004), contend that
these programs have only minimal effects on savings. We suggest that a more efficient and
effective approach is to provide incentives for consumers, particularly less educated and lower
income consumers, that will defray their costs and increase their access to financial planning
services. Specifically, this research can inform public policy decisions regarding programs that
make credible, professional financial planning services available to those who need them most.
In addition, the financial services industry must introduce additional retirement savings products
that help reduce the complexity of investment decisions for households, provide some degree of
protection against market volatility, and mitigate some of the individual longevity risk for the
long run. Increasing the availability of low-cost, annuity-type products might serve such a
purpose. Making these products available to the retail investor through tax-advantaged accounts
and regular channels might go a long way in providing an opportunity for asset building and
financial security for households that currently do not have the means to access the services of a
Financial Decisions, Winter 2010, Article 5
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financial planner. Public investment in educating consumers directly may be more appropriately
channeled to another model that provides greater access to consumers who do not currently avail
themselves of financial planning services. In this paper, we identify the characteristics of soon-
retiring baby boomers who do use financial planning services. These results and further research
can help financial planners and policy makers target potential customers based on age,
occupation, income, marital status, and similar factors to determine consumers who are not
currently using these services. Departing from the current strategy of focusing only on increasing
the financial literacy of consumers, both the public policy makers and the professional services
industry need to direct more effort toward developing a streamlined environment for increasing
access to and growing the utilization of financial planning services among those who currently
cannot access such services but need them the most.
Financial Decisions, Winter 2010, Article 5
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REFERENCES
Bae, S.C. and Sandager, J.P. (1997). Why consumers look for financial planners. Journal of
Financial Counseling and Planning, 8(2), 9-16.
Barsky, R. B., Juster, T.F., Kimball, M.S., & Shapiro. M.D. (1997). Preference parameters and
behavioral heterogeneity: An experimental approach in the health and retirement study.
Quarterly Journal of Economics, 112(2), 537-79.
Bassett, W. F., and Rodrigues, M. J. (1998). How workers use 401 (k) Plans: The participation,
contribution and withdrawals. National Tax Journal, 51(2), 263-289.
Bluethgen, R., Gintschel, A., Hackethal, A., and Mueller, A. (2008). Financial advice and
individual investors’ portfolios. Working paper 2008. Available at SSRN:
http://ssrn.com/abstract=968917.
Bodie, Z. and Crane, D.B. (1997). Personal investing: Advice, theory and evidence. Financial
Analyst Journal, 53(6), 13-23.
Chang, M.L. (2005). With a little help from my friends (and my financial planner). Social
Forces, 83(4), 1469-1498.
Lusardi, A. M. (1998). On the importance of the precautionary saving motive. American
Economic Review, 88 (2), 449-54.
Lusardi, A.M. (1999). Information, Expectations, and Savings for Retirement. In
Behavioral Dimensions of Retirement Economics, edited by Henry Aaron. Washington,
D.C.: Brookings Institution Press and Russell Sage Foundation.
Lusardi, A.M.(2002). Preparing for Retirement: The Importance of Planning Costs.
National Tax Association Proceedings 2002: 148-154.
Miller, S.A. and Montalto, C.P. (2001). Who uses financial planners? Evidence from the 1998
Survey of Consumer Finances. Consumer Interests Annual, 47, 1-9.
Paul, P. (2001). Getting inside generation Y. American Demographics, 23(9), 42-49.
Soberon-Ferrer, H. and Dardis, R. (1991). Determinants of household expenditure services.
Journal of Consumer Research, 17(4), 385-397.
Wellner, A. S., (2000). Generational divide: are traditional methods of classifying a generation
still meaningful in a diverse and changing nation? American Demographics, 22(10), 52-
58.
Wooldridge, J. M. (2006). Introductory Econometrics: A Modern Approach. South-
Western Thomson Learning, Mason, OH.
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Yakoboski, P. & Dickemper, J. (1997). Increased saving but little planning. Results of the 1997
Retirement Confidence Survey. EBRI Issue Brief 191.
Zagorsky, J.L. (2005). Marriage and divorce’s impact on wealth. Journal of Sociology, 41(4),
406.
Zagorsky, J.L. (2007). Do you have to be smart to be rich? The impact of IQ on wealth, income
and financial distress. Intelligence, 35(5), 489-501.
Financial Decisions, Winter 2010, Article 5
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Table 1: Descriptive Statistics
Variables
All
N=1000
Demographic
Age
Continuous
45
Female
Equal to 1 if yes; 0 otherwise
54%
Married
Equal to 1 if yes; 0 otherwise
64%
Family Size
Continuous
3.1
Have Children
Equal to 1 if yes; 0 otherwise
83.00%
Race
White
Equal to 1 if yes; 0 otherwise
48%
Income & Resources
Family Income
Continuous
$73,628
Net worth
$224,850
Pension Plan
Defined Benefit
Equal to 1 if yes; 0 otherwise
35%
Defined Contribution
Equal to 1 if yes; 0 otherwise
65%
Cognitive & Behavioral
IQ
Continuous
96.00
Education
< 12 years
Equal to 1 if yes; 0 otherwise
6%
12 years
Equal to 1 if yes; 0 otherwise
46%
13-15 years
Equal to 1 if yes; 0 otherwise
24%
16 years
Equal to 1 if yes; 0 otherwise
13%
>16 years
Equal to 1 if yes; 0 otherwise
11%
Risk Tolerance
1=Most Risk averse
54%
2
12%
3
18%
4= Most Risk taking
16%
Retirement Preparedness
Planned for retirement
Use Financial Planner
Equal to 1 if yes; 0 otherwise
21%
Attend Seminar
Equal to 1 if yes; 0 otherwise
18%
Read Books
Equal to 1 if yes; 0 otherwise
38%
Use Software
Equal to 1 if yes; 0 otherwise
13%
Not planned for retirement
Equal to 1 if yes; 0 otherwise
10%
Financial Decisions, Winter 2010, Article 5
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Table 2: Demand for Financial Planning services
Retirement Planning
Use Financial Planner
Self preparation
Income
$125,165***
$77,689
Net worth
$578296***
$186,786
Years of education
15*
14
IQ
103*
101
Health status
4.06***
3.03
*p < .10, **p < .05, ***p < .01.
Table 3: Probit Analysis of Demand for Retirement Planning
Variable Type
Variables
Coef.
St. Error
Marginal effects
Income & Resources
Log Income
0.816***
0.152
0.296
Log Networth
0.695***
0.093
0.219
Defined Contribution
0.738*
0.350
0.249
Cognitive & Behavioral
IQ
0.004***
0.000
0.008
Education
High School
0.197
0.101
0.154
Some college
0.295
0.131
0.143
College
0.542**
0.121
0.171
Grad school
0.544**
0.141
0.194
Risk Tolerance
0.085
0.063
0.026
Health
0.122***
0.000
0.001
Demographic
Age
-1.594
2.061
-0.253
Age square
0.017
0.022
0.002
Female
0.324*
0.151
0.094
Married
0.282
0.289
0.056
Family size
0.043
0.089
0.018
Children
-0.817**
0.221
-0.111
White
0.119***
0.014
0.235
Intercept
0.471
0.083
*p < .10, **p < .05, ***p < .01.
Financial Decisions, Winter 2010, Article 5
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Table 4: Probit analysis of predictors of financial planning services utilization
Consult Financial Planner
Self preparation of retirement plans
N=1000
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Variable type
Variables
Coeff
St.
Error
Marginal
effect
Sig
Coeff
St. Error
Marginal
effect
Sig
Income &
Resources
Log Income
0.154
0.027
0.033
**
0.152
0.021
0.031
**
Log Net worth
0.243
0.053
0.057
***
0.107
0.049
0.039
**
DC plan
0.349
0.143
0.096
**
0.191
0.169
0.068
Cognitive &
Behavioral
IQ
0.006
0.003
0.001
*
0.021
0.004
0.011
***
High school
0.232
0.357
0.056
0.342
0.360
0.113
Some college
0.510
0.367
0.135
0.440
0.355
0.172
College
0.579
0.098
0.164
***
0.786
0.364
0.306
**
Grad. School
0.549
0.143
0.156
***
0.936
0.455
0.356
**
Risk tolerance
0.069
0.053
0.018
0.266
0.217
0.081
Health
0.396
0.065
0.504
***
-0.223
0.186
-0.072
Demographic
Age
-2.364
1.633
-0.671
-0.410
1.525
-0.101
Age square
0.032
0.016
0.007
**
0.006
0.014
-0.001
Female
0.143
0.023
0.031
***
-0.068
0.022
-0.026
*
Married
0.151
0.067
0.035
**
0.555
0.151
0.131
***
Family size
0.231
0.210
0.052
-0.088
0.283
-0.036
Children
-0.306
0.226
-0.064
-0.113
0.214
-0.040
White
0.110
0.018
0.025
**
0.117
0.152
0.049
Intercept
69.422
3.290
***
22.978
4.333
***
Pseudo R2
0.234
0.247
*p < .10, **p < .05, ***p < .01.
