Download Planning for Retirement Terrance K Martin Jr.* and Michael Finke

Planning for Retirement
Terrance K Martin Jr.* and Michael Finke
Department of Personal Financial Planning, Texas Tech University, Lubbock, TX 79409
Abstract
Most Americans have not estimated how much they need to save for retirement. This lack of
awareness may contribute to low levels of retirement savings. We investigate the relation
between planning and retirement wealth. Those who have estimated how much they need to save
for retirement have significantly higher retirement wealth. Respondents who rely on an advisor
to help plan for retirement save more than those who do it themselves and are more likely to own
tax advantaged accounts. Advising services that did not involve estimation of retirement needs
had no measurable value. Results suggest that planning, particularly with a comprehensive
approach, improves retirement outcomes.
Key words: financial planner, comprehensive approach, retirement savings, 2008 NLSY 79
Corresponding author. Tel.: 806-742-5050, ext. 239; fax: 806-742-5033.
Email address: [email protected]
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Introduction
Consumption and savings decisions made in the earlier stages of the life-cycle can have a
disproportionately large impact on retirement preparedness. These saving decisions have had an
even greater impact on U.S. household welfare over the last two decades as the responsibility for
funding retirement shifted from employers to employees (Ibbotson, Milvesky, Chen & Zhu,
2007). According to the Employee Benefit Research Institute (EBRI), only 38% of all private
workers participate in employer-sponsored defined contribution plan and just 14% of Americans
are confident in their ability to retire comfortably (Helman, Copeland, and VanDerhei, 2012 ).
Greater employee responsibility for funding retirement means that individuals, rather
pension professionals, must estimate how much saving is needed to provide an adequate
retirement income (Poterba, Rauh, Venti, and Wise., 2007). A lack of financial knowledge and
sophistication among many American workers contributes to inefficient retirement savings.
Most American households could not maintain a constant level of consumption in retirement
with their current retirement savings (Bernheim, 1992, Yuh, Montalto, & Hanna, 1998, Mitchell
& Moore, 1998), and one-third of retirees obtain 90% of their income from social security (SSA,
2012).
The shift in retirement funding responsibility, coupled with the complexity of the
investment process, should increase demand for professional financial advice (Ibbotson,
Milevsky, Chen, and Zhu, 2007). Professional financial advisors can help a household
accurately estimate the amount of retirement income needed to fund their retirement goals.
Additionally, a financial advisor can provide a financial plan that involves establishing the steps
needed to meet a retirement goal and regular interaction to evaluate progress. A financial advisor
may also increase a worker’s awareness of the consequences of low savings, reduce the psychic
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costs of making complex choices, and help improve investment performance and tax efficiency
within household portfolios.
However, there is little evidence in the current literature that the use of financial advisors
improves household financial outcomes. We review these articles and emphasize the importance
of differentiating comprehensive advisors from professionals who are primarily brokers of
financial products. We also question the use of gross investment performance in evaluating the
value of professional advice.
This paper contributes to the literature on the value of financial advice by evaluating the
impact of financial advice on retirement outcomes including retirement wealth accumulation. We
use both the 2004 and the 2008 waves of the National Longitudinal Survey of Youth (NLSY79)
to estimate the impact of financial advice on retirement savings and the change in accumulated
retirement wealth between 2004-2008. We identify respondents who do, and do not, use a
comprehensive approach to retirement planning and compare the effectiveness of creating your
own retirement plan versus using a professional advisor only.
The remainder of the paper is separated into four sections. Section I present a brief
review of literature. Section II describes the conceptual framework and presents the hypotheses.
Section III describes the data and the method selection. Section IV summarizes the results and
Section V concludes and comments on the implications of the research.
I Review of literature
Household retirement saving is motivated by a desire to smooth consumption beyond the
working years. Many are not successful, leading to a welfare loss from reduced consumption in
old age (Bernheim, Skinner and Weinberg, 2001). Bernheim (1992) concludes that actual
savings rates are 9 to 19% lower annually than optimal levels for young household’s aged 35 to
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451. Bernheim, Forni, Gokhale, and Kotlikoff (1999) find that the median household would need
to increase their savings rate by as much as 20% to replace pre-retirement levels of consumption.
Studies of employee participation in employer-sponsored retirement plans shows that
many do not participate, those who participate often do so at a default savings rate that is too
low, and retirement savings decisions change little over time due to an unwillingness to actively
change savings amounts (Choi, Laibson, Madrian and Metrick, 2006).
Individuals are bounded by their own rationality due to information asymmetry, time, and
their cognitive abilities - which may result in sub-optimal choices (Simon, 1955). As such,
financial knowledge is critical to the process of accumulating retirement wealth (Jacobs-Lawson
& Hershey, 2005). Complex retirement decisions can be challenging for households that lack
financial knowledge (Finke, Huston, & Winchester, 2011) and may also result in a household
deciding to not participate in financial markets (Campbell, 2006).
Behavioral biases also contribute to inadequate retirement savings and investment
decisions (Bailey, Kumar, & Ng, 2011). Investors with strong behavioral biases are more likely
to select investments based solely on past performance (Siri and Tufano, 1998; Barber and
Odean, 2008), to use funds with higher expenses (Gil-Bazo et al., 2009), and to have higher
transaction costs and tax inefficiencies due to high turnover (Barber and Odean, 2001). Further,
investors with less financial sophistication select high expense mutual funds, trade frequently,
chase returns, avoid index funds, and time the market (Bailey, et al., 2011).
Professional Advice
Households faced with the responsibility of funding their own retirement can rent the
services of a financial expert to make better choices. The decision to delegate assistance involves
1
See Bernheim (1992, 1993, and 1994) for more thorough discussion of his assumptions and calculations used to
estimate savings rates.
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a tradeoff between the time and transaction costs of acquiring financial knowledge and the costs
(including direct, indirect and agency costs) of hiring an advisor. A professional financial
advisor makes an investment in specific human capital related to individual financial planning.
Households should only hire a professional financial advisor if the expected discounted lifetime
utility from using an advisor exceeds the expected utility of self-directed planning.
A more comprehensive financial professional’s recommendations may incorporate any
one or more of the six main planning areas: cash management, risk management, investments,
retirement, tax planning, and estate planning (Warschauer, 2008). As an example, a discussion
on retirement planning will include an analysis of the assets needed to generate a sufficient
income replacement, a recommended pre- and post-retirement portfolio, and a regular savings
plan to meet the retirement accumulation goal.
Unfortunately, there is little evidence that financial advisors improve welfare outcomes.
Using Dutch data, Kramer observes differences in advised and self-directed accounts but finds
no evidence of increases in risk-adjusted performance (Kramer, 2012). Dahlquist et al. (2011)
provide evidence that advised accounts experience lower net returns. The authors posit that
trading costs contribute to outcomes, as advised accounts feature high turnover consistent with
commissions being the main source of advisor income. Another study finds evidence of financial
advisors being incentivize to promote equity concentrated asset allocation and conclude that
there might be a need for enhanced regulative investor protection (Jansen, Fishcher, &
Hackethal, 2008). Empirical evidence suggests a positive correlation between moral hazard and
mutual fund usage by financial advisors (Kramer & Lensink, 2012). Zhao (2003) concludes that
in the presence of moral hazard, financial advisors tend to encourage higher load funds. Further,
broker-sold funds show persistent evidence of return chasing and no superior market timing
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(Bergstresser et al., 2009). Since brokers are not required to place the interests of their clients
above those of their employer, their primary objective is to maximize commissions from sales of
financial products within the constraints of product suitability. Commission compensation
decreases the incentive to hold investments for long periods of time in order to generate greater
sales commissions and to recommend products with higher loads - which are often actively
managed mutual funds with higher expense ratios and lower historical net returns. This
compensation structure encourages some financial intermediaries to recommend
underperforming funds (Bergstresser, et al., 2009; Stoughton, et al., 2010).
Two shortfalls of the existing empirical literature include the inability to distinguish
between the type of financial advisors23 and the use of investment alpha to quantify the value of
advice. Professional financial advisors may improve retirement savings behavior by increasing a
worker’s awareness of the consequences of low savings, by reducing search and psychic costs of
making complex choices, and by improving investment performance and tax efficiency of
household portfolios. In the United States, advisors can be split between two groups regulated by
two different entities with a different set of compensation incentives and legal standard of care
toward clients (Macey, 2002). Brokers are self-regulated by the Financial Industry Regulatory
Authority according to the 1934 Securities Exchange Act. The primary service provided by
registered representatives of brokers is to facilitate the transactions of financial products, and
they owe their allegiance to their employer and are not required to make product
recommendations that are in the best interest of clients. In fact, brokers are required to only
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“the fiduciary duties and legal requirements imposed on particular financial intermediaries may differ substantially
across jurisdiction and across the profession. For instance, broker –dealers are excluded from regulation aimed at
investment advisers if they offer advice that is ‘solely incidental” to their business and receive “no special
compensation” for the service.” (Inderst and Ottaviani, 2012)
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In an SEC sponsored report, investors were asked their beliefs about the services offered by financial services
professional, 91% of respondents associated financial planners with retirement planning and 88% felt that financial
planners provide more general advice (Hung, et al., 2008). See Hung, et al., 2008 for further discussion.
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make financial recommendations that are incidental to the sale of products. Registered
Investment Advisors are regulated by the Securities and Exchange Commission according to the
1940 Investment Advisers Act. Nearly all RIAs are compensated based on asset fees and are
required to provide a fiduciary standard of care toward their client. Many who provide
comprehensive financial planning services are regulated by the SEC as investment advisers and
hold themselves out as financial planners. These differences in legal standard of care and in
compensation may account for predictable differences in household financial outcomes among
those served by comprehensive and non-comprehensive advisors.
Households that use professional financial advice execute investment strategies more
consistent with the economic theory (Bodie & Crane, 1997) and show evidence of improved
investment performance among households who actively seek professional advice (Hung &
Yoong, 2010). Financial advisors also benefit from financial information economies of scale and
are able to increase investor utility by knowing and aligning preferences (Bluethgen et al., 2008).
Lusardi & Mitchell (2005) sort their sample into simple planners, serious planners, and
successful planners. Forty-nine percent of successful planners (created a retirement plan)
consulted a financial planner compared to 39% of simple planners (did some form of retirement
calculations). Notably, those that consulted a financial professional are found to have more
accurate information relative to those that spoke to family and friends. Further, financial
advisors also provide access to direct distribution channels of high performing mutual funds
(Stoughton, Wu, & Zechner, 2010).
Shapira & Venezia (2001) provide evidence that professional financial advice can curtail
some cognitive errors and behavioral biases. The authors notice that the disposition effect is
lower among financial professionals. Evidence of psychic cost reduction is found in Engelmann,
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Capra, Noussair, and Berns (2009) who use MRI scans to conclude less cognitive strain when
individuals making financial decision are receiving advice.
In a study at Oregon State University, Chalmers and Reuters conclude that individuals
who used brokerage services are less likely to remain in the default option in retirement plans
and also more likely to allocate their retirement portfolios across multiple asset classes rather
than the default fund (Chalmers and Reuters, 2012). Using German bank data, Gerhardt and
Hackethal find less risky and speculative trading in household portfolios (Gerhardt & Hackethal,
2009). This result is consistent with another European study which provides evidence of reduced
trading activity based on a dataset of Dutch equity investors (Kramer & Lensik, 2012). Some
financial advisors actively attempt to send quality signals to their clients and potential clients by
increasing their bonding cost by embracing the fiduciary standard (see Spence, 1973) and by
initiating contracts that increase their bonding cost and outlines a long-term client planner
relationship (Finke, Huston, & Waller, 2009).
II Conceptual Framework and hypotheses
This paper uses the life cycle hypothesis and draws on human capital theory to evaluate
the motivations of households planning for retirement. Less than 50% of US households report
estimating their retirement needs. This study examines the value of financial advice by seeking
to explain the impact of financial advice on retirement decisions and retirement wealth
accumulation. Households that lack the financial sophistication to navigate complex financial
markets and to make informed financial decision may seek out financial advisors to substitute for
their human capital (Collins, 2012). The shift to defined contributions plans has placed
retirement planning decisions in the hands of workers who may or may not possess the skill set
and financial knowledge to make optimal decisions. Campbell (2006) identifies areas of some
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groups that may be more prone to make investment mistakes. To avoid discontinuity of
consumption at retirement, households must be willing to forego some current consumption in
exchange for increased consumption during retirement years4.
Being able to quantify the amount that one will need to replace income in retirement can
send a strong signal of over or under-preparedness and force individuals in the retirement
planning process to act. Mayer, Zick, and Marsden (2011) examine the relationship between
retirement income calculations and retirement wealth accumulation. The study shows that
individuals that estimated income needs in retirement self-reported increases in retirement
savings. As an example, a modest 10% increase in the likelihood of calculating retirement need
is related to $16,600 in additional retirement savings for respondents over 40 (Mayer, et.al,
2011).
In this study, a logistic regression model is used to estimate the likelihood of calculating
retirement need by controlling for the use of financial advice, the respondents own human
capital, the respondents economic status, and possible cultural influences, see equation 1.
Calculating retirement need = ƒ (financial advice, human capital, economic status,
cultural influences.) (1.0)
In another logistic regression, calculating retirement need is included as an exogenous variable to
determine the relative impact on using a tax deferred saving accounts such as IRA, Keoghs etc.
In this model, the financial planner and calculating retirement needed are interacted to obtain the
household groups discussed below.
Own IRA = ƒ (retirement motivations, human capital, economic status, cultural
influences.) (2.0)
4
See Appendix for graphical representation using a simple lifecycle estimation.
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The sample is separated into four households groups:1) households that report consulting
a financial planner for retirement decision and calculating retirement need (comprehensive), 2)
households that use only a financial planner for retirement decisions (planner), 3) households
that only report calculating retirement need (self-directed), and 4) households that do neither (No
Plan). These groups are then included as the main hypothesis variables in the study.
By means of this framework, the impact of the retirement motivations is compared to
assess the difference that each approach has on accumulation of retirement wealth up to 2008
and the most recent change in retirement wealth between 2004 and 2008
Accumulated retirement wealth = ƒ (retirement motivations, human capital, economic
status, cultural influences.) (3.0)
∆ accumulated retirement wealth= ƒ (retirement motivations, human capital, economic
status, cultural influences.) (4.0)
We also include four logistic regression models estimating the likelihood of a household
choosing one approach to retirement over another.
III. Data and the variable selection
Data
This study uses data from the 2004 and 2008 administration of the National Longitudinal
Survey of Youth (NLSY79). The survey, which was started in 1979, consists of a random sample
of 12,686 people and is representative of U.S. men and women who were born between 1957 and
1965. During the 2008 survey, respondents were between 45 and 52 years old. Annual interviews
were conducted by phone or in-person and were continued every second year from 1994 onward.
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Sample
A special module on retirement appeared in the 2008 administration of the NLSY79
which includes the use of professional advice. The module saw a soft introduction in the 2006
wave. To obtain the final sample, the data censored only to households that are asked whether
they consulted a financial planner for retirement planning. This condition on the data reduces the
sample size to 7616 respondents. Descriptive and univariate analysis focuses on the four
household approaches to retirement planning and wealth accumulation:1) comprehensive (836
respondents), 2) planner (478 respondents), 3) self-directed (1006 respondents), and 4) no plan
(5296 respondents).
Dependent Variables
The hypotheses are tested using logistic regression models and quantile regression
models. The study incorporates four dependant variables to assess overall retirement
preparedness. We use several questions from the NLSY79 to derive the dependant variables. To
proxy whether respondents had calculated retirement need and/or had owned other tax
advantaged accounts, we used the following to questions:
I.
“Have you [or] [Spouse/partner's name] ever calculated how much
retirement income you would need at retirement?"
II.
“Other than the accounts you have through your workplace,) Do you [or]
[Spouse/partner's name] have any money in IRAs, Keoghs, variable
annuities, 529 plans, or other tax-advantaged accounts?”
To derive the third dependent variable, accumulated retirement wealth as of 2008, the
value of the respodnent and his/her spouse/partner’s qualified retirement accounts and tax-
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advantaged accounts for 2008 are totaled. Likewise, the fourth dependent variable in the same
manner for 2004 and 2008, with the difference (2008-2004) taken.
Independent Variables
Hypothesis variables (retirement planning motivators)
In a special module on retirement in the 2008 administration of the NLSY79, respondents
are asked if they consulted a financial planner for retirement and if they have calculated
retirement wealth. These questions are used to create the household groups specified earlier in
the paper. The first household group includes respondents that answered ‘yes’ to consulting a
financial planner and ‘yes’ to calculating retirement need (comprehensive). The second group
consists of those households that only consult a financial planner (planner); third, households
that only calculated retirement need (self-directed), and fourth households that answered no to
both questions (no plan). The expectation is high values of retirement savings among households
that consult and a financial planner for retirement purposes and calculates retirement need.
Other predictor variables
Marital status was included to account for increased saving ability due to shared
household resources. Education and IQ are included as measures of human capital and as a proxy
for earnings path. Research suggests the attainment of higher education would result in a better
understanding of investments (Coleman, 2003) and increase the chances of holding a risky asset
(Haliassos & Bertaut, 1995). Health limitation is included as a control variable due to the
potential shock to life cycle consumption and portfolio allocations (Rosen & Wu, 2004).
Respondents with poorer health are less likely to save for retirement because of heightened
liquidity needs. Empirical evidence suggests minority households show a preference for holding
tangible assets and less likely to invest in risky assets (Coleman, 2003). Therefore, we control for
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race to assess the effect preference of retirement savings. Gender is included to to account for
possible differences in preferences on retirement savings. Age is included as control variable to
account for differences in attitude toward savings within the cohort. Family size is included
since larger families may reduce savings in order to meet current consumption needs relative to
households with smaller families.
The amount of household income affects life cycle behavior. If households forecast an
increase in income, they may postpone saving while households who anticipate income to
decrease may save more in order to smooth consumption. Households with higher expected
income growth may save less early in life and increase savings later during the accumulation
stage of the life cycle (Hanna, et al., 1995). Net worth is included to capture the effect of wealth
on retirement savings. Households with a higher level of net worth are able to smooth
consumption and are more likely to take financial risks. Home ownership is included due to the
impact on households’ life-cycle behavior. Homeownership reduces saving needs if mortgage
payments create home equity that can provide a stream of consumption during retirement.
Likewise, business ownership may alter life cycle consumption and savings due to the liquidity
needs associated with running a business. Business owners may view the business as their
retirement plan and may be less likely to save for retirement. Ownership of tax advantage
accounts is included to capture the effect of non-employer sponsored retirement accounts on
retirement savings. Respondent risk tolerance preference is included to control for the effect on
life cycle consumption. Highly risk tolerant households may forego current consumption today
for higher future consumption and are generally less concerned about retirement, while less risk
tolerant households prefer constant consumption with minimal consumption shocks. Finally,
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region is included to capture possible regional differences on retirement savings and retirement
planning.
Hypotheses
This study intends to test and answer the following hypotheses:
H1: Households using professional financial advice are not likely to have calculated retirement
need.
Similarly, respondents are probed on their use of tax advantage accounts outside of
employer specified accounts. If a private tax advantage account is used respondents are coded as
1 and 0 if they did not use them. The responses are used to test the following hypothesis:
H2: Households using professional financial advice are not likely to have additional nonemployer sponsored retirement tax deferred accounts.
Finally, the dataset makes it possible to calculate accumulated retirement wealth in 2008
and the change in retirement savings between 2004 and 2008. To arrive at the retirement values,
the value of respondent’s and their spouse or their partners value of qualified retirement accounts
and the value of other tax-advantaged accounts for both 2004 and 2008 and took the difference.
The hypotheses tested are:
H3: Accumulated retirement wealth is not affected by the use of a financial planner or the
calculation of retirement need.
H4: Change in retirement saving is not affected by the use of a financial planner or the
calculation of retirement need.
Quantile model justification
We use quantile regression (QR) to model accumulated retirement wealth and the change
in accumulated retirement wealth between 2004 and 2008. QR allows the analysis to go beyond
the estimation of conditional means. A priori knowledge of the groups that tend to use
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professional financial advice suggests that evaluating the impact of the hypothesis variables
using OLS may limit the scope of the analysis. Therefore, conditional quantiles are desired to
best explain the outcome variable, i.e. accumulated retirement wealth and the ∆ in accumulated
retirement wealth. Also, from the previous discussion, accumulated retirement wealth variable is
laden with outliers. To avoid using a variable transformation that can lead to difficult
interpretation of the results, a QR can be used.
The regression estimates calculated by QR are more robust against outliers in the
response to measurement. Relative to an OLS technique, QR provides are more comprehensive
view of the relationship between the groups of hypothesis variables, the predictor variables and
accumulated retirement wealth. Last, QR allows to compares outcomes across quantiles. For
example, the impact of a households comprehensive approach to retirement planning
(comprehensive) at the median can be compared to higher quantiles groups that maybe more
consistent with using financial advice .
QR estimates the linear conditional quantile function
eq. 5.0,
By solving
eq. 6.0,
For any quantile τ ϵ (0,1).
IV. Empirical Results
Univariate Analysis
All univariate analyses are weighted using the specified sample weightings provided by
the NLSY79 for 2008. Among all households, figure 1 illustrates sample classification by
households. In this study, 13% of the sample uses a comprehensive approach to retirement
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planning, 7% use a planner only approach, while the self-directed group accounted for 14% of
the sample. The vast majority of the sample, 66%, had no plan in saving for retirement.
Table 1 (also see figure 2), compares mean retirement wealth (ARW) of all households.
The mean retirement wealth for all households is $103,978.66. Comprehensive approach
households’ ARW is higher than all other households groups. Specifically, comprehensive
households report ARW of $246,797.33, while planner households report less than half,
$112,668.24. The self-directed households record ARW nearly 50% higher relative to planner
households, $163, 659. The no plan households have substantially less ARW, $62,087. Similar
patterns are observed in the ∆ in retirement wealth between 2004 and 2008, comprehensive
households record a mean changes 100% higher than all households, ∆ARW = $83,500, while
∆ARW among planner households over the four year period of $14,851, which is 50% less than
the households using no plan, $28,448.
Table 1 also provides results for median retirement wealth and the change in retirement
wealth. Median retirement wealth for all households was a mere $1200 in 2008 with a median
change of $0. Comprehensive households record higher mean retirement wealth changes
compared to fin planner households, $77,568 to $30,000 respectively. At the median, planner
households realized higher retirement wealth than self-directed households ($30000 to $20000,
respectively). Only comprehensive households report retirement wealth changes at the median,
$12,000 relative to all other households that report zero change at the median.
Noteworthy, if average retirement wealth is examined closer by survey year 1994-2008,
households with a comprehensive approach to retirement planning consistently record higher
mean values of accumulated retirement wealth. During 1994-2004, households using the planner
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approach are a modest second; however, between 2004 and 2008 self-directed households show
a mean change of $70,000 accumulated retirement wealth (see figure 3).
A more complete list of frequency data is outlined in table 2. Minority groups, black and
Hispanics, only accounted for approximately 20% of the entire sample. Of the 14.11%, of black
households, 8.84% took a comprehensive approach to planning for retirement, 4.87% turned only
to a financial professional, and 11.75% employ a self-directed strategy, while 74.07% chose
neither option. Results are similar among Hispanic households. However, Hispanics show a
higher propensity to adapt a self-directed approach to retirement planning. Non black and non
Hispanic households were less likely to do neither activity. Additionally, married households
appear more likely to do both activities or simply calculate retirement needs on their own.
Eighty-five percent of separated households in the sample did not consult a professional or took
the time to calculate retirement need.
As it relates to education, there are observed monotonic increases in partaking in the
three retirement planning activities. Specifically, respondents that earned at least a bachelors
degree are more likely to consult a financial planner and estimate their retirement need. Notably,
about 62% of the sample had only a high school or some high school education and within this
group in action the vast majority chose to do neither consult a financial planner nor calculate
retirement need. Respondents in the top 20% of cognitive ability chose to do one or both of the
retirement planning activities, 22.46% opted to consult a professional and calculate need. Among
households in the second highest IQ quintile, estimate retirement need only seem a more likely
option.
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Multivariate Analysis
Logistic regression models
Table 3 shows results of four logistic regression models that estimate the likelihood of a
household choosing one approach to retirement planning over another. Education is the strongest
predictor of choosing a comprehensive retirement planning strategy. Among all four household
groups, the more educated a respondent the more likely the respondent will actively plan for
retirement. As an example, households with at least a Bachelors degree are 196% more likely to
report using a comprehensive approach compared to households with less than a high school
education, and having a graduate degree increases the likelihood of using a comprehensive
approach to 273%. IQ has a stronger impact on the likelihood of receiving non-comprehensive
professional financial advice, while education is a positive but weaker predictor. Greater wealth
increases the likelihood of using either type of professional advisor, and of estimating retirement
needs without an advisor. More educated, higher IQ, and wealthier respondents were more
likely to estimate their own retirement needs. Black households are 56% more likely to choose a
comprehensive approach to retirement planning relative to non-black and non-Hispanics. Black
households are also 30% less likely to approach retirement without a plan. Married households
are 36% more likely to choose a comprehensive approach compared to never married
households, while separated households are 54% less likely to use a planner only approach
compared to the same reference category. Households who report owning a tax advantage
account are 165% more likely to choose a comprehensive approach and 52% more likely to
implement a planner only strategy compared to households who report not owning such
accounts. Investor attitude towards risk is important to self-directed households. Aggressive
investors are 25% more likely to have a self-directed approach to retirement planning relative to
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conservative households. Northern Central households are 28% more likely to use a planner only
approach when compared to households in the South.
Table 4 provides empirical results for two regression models i.e. the likelihood of
calculating retirement need and the likelihood of owning and funding a tax advantage account
(Own IRA). Recall, the hypothesis variables i.e. retirement planning motivators are derived from
interactions of the raw financial planner and the calculating retirement need variables. Among
households that work with a financial professional for advice, there is a 504% increase in the
likelihood of calculating retirement need relative to households not using professional financial
advice. Blacks are 29%more likely than non-black and non-Hispanics to attempt to calculate
their retirement need.
Being exposed to higher levels of education also contributes monotonically to the
likelihood of estimating retirement income need. Bachelor degree holders are 86% more likely to
calculate retirement need compared to households with less than high school education, while
graduate degree holders are 101% more likely than the less than high school group to make the
same calculations. Considering IQ, observe increases in the likely are recorded above the 40th
percentile. Compared to the lowest IQ quintile group, the highest IQ households are 48% more
likely to calculate retirement need. The older and wealthier a respondent is makes them more
likely to calculate retirement need. Homeowners are 35% more likely than non-homeowners to
make the calculation. In addition to this, owners of tax advantage accounts are 60% more likely
than those not owning and funding to go through the process of calculating their retirement
needs. North eastern residents appear to be less likely compared to southerners to calculate their
retirement need.
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The results of a second logistic model are also presented in table 3. First, households
with a comprehensive approach to retirement planning are 232% more likely to use a tax
advantage account than households with no plan. Planner households are 124% more likely to
use a tax advantage account relative to No Plan households. Hispanics are 35% less likely to own
a tax advantage vehicle compared to non-blacks and non-Hispanics. Blacks, on the other hand,
are 48% less likely to own a tax deferred vehicle when compared to non-blacks and nonHispanics. College graduates are a 117% more likely to tax advantage of tax deferred account
compares to less than high school groups. Likewise, households with highest cognitive ability
are 118% more likely to use such accounts as an IRA compared to less than high school.
Business owners are 72% more likely than those not owning a business to own a tax advantage
account.
Quantile regression models
Tables 5 and 6 provide quantile regression results that estimates the relationship of
predictor variables on accumulated retirement wealth (table 5) and ∆ in retirement wealth (table
6). First, across all reported quantiles, households that adapt a comprehensive approach to
planning retirement show evidence of significantly higher retirement wealth accumulation across
all quantiles relative to households with no plan. As an example, within the 90th quantile of
accumulated retirement wealth, households using a comprehensive approach report $189,558
more in retirement wealth than households reporting not having a plan. At the median, doing
both activities result in $17,162 more in retirement wealth accumulation relatively to not doing
either. By implementing a self-directed approach, households appear to be strongly motivated to
save for retirement. Among self-directed households at the 80th quantile, accumulated retirement
wealth is $34,998 more than when compared to households with no plan. Surprisingly, statistical
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significance only emerges at the 70Th quantile for households only using financial advice;
however, in all other quantile regressions presented there is no statistical significance. Moreover,
at an alpha level of 10% results suggests that Hispanics have less retirement wealth than nonHispanics and non-blacks. For example, at the 70th quantile Hispanics have $2,910 less than the
reference groups. Married couples show statistical significance at the investigated quantiles. At
the median, married households report $2,557 more in accumulated retirement wealth than never
married households. Health limitations negatively affect reported retirement wealth at the
median, 60th, and the 70th quantile. Level of education, cognitive ability, and home ownership are
also important contributors to accumulated retirement wealth.
Table 6, shows a similar pattern of results as described in the above paragraph. At the
90th quantile, comprehensive households realized an increase in retirement wealth of $142,553
relative to households doing neither activity. In the same way, at the 80th quantile ($70,525), at
the 60th quantile ($17,718.70) increases in retirement wealth than between 2004 and 2008
compared to households actively not to seek financial advice or calculate retirement need. Like
the results in table 5, simply adapting a planner only strategy proves to have no measurable
impact on retirement wealth.
Case-control study sample: Robustness check for endogeneity
We use a case and control study as a further test for the potential endogenity in
the study. We classify households that are using a comprehensive advisor as cases and
derive controls from the no planning group. To do this, we use a SAS program (see
Mounib and Satchi, 2000). The program developed by Mounib and Satchi (2000)
allows us to matches multiple variables. We match the controls to the cases according
to net worth and IQ. The program randomizes the selection of the controls. We match
21
one control per case. The initial number of cases n=836. From a sample of 5029 (no
plan) we randomly select controls based on a net worth range of $25000 and a
difference in IQ quintile of 1. After adjusting for some missing values the number of
cases was reduced to 785. From the analysis we want to determine whether the mean
of retirement wealth is different between the plan approaches, comprehensive vs.
noplan. We test the mean difference using the Wilcoxon-Mann-Whitney test because
of the non-normality of retirement wealth. The mean retirement wealth for
respondents with comprehensive advisor is approximately $$61,655.00 higher than
those with no plan who had similar net worth ±25000 and relative similar IQ.
V. Conclusions
Prior research has shown that the lack of sufficient retirement savings can negatively
affect consumption in retirement (Hamermesh, 1984, Bernheim et a., 2001, Skinner, 2007). The
transition to defined contribution plans has transferred the financial responsibility of retirement
funding and investment management to households. However, not all households possess the
financial sophistication needed to efficiently plan for retirement. Financially unsophisticated
households and households with other behavioral biases can benefit from the use of a financial
intermediary such as a comprehensive financial planner. Although there is little evidence in the
prior literature that financial advisors provide value, most of these studies focus on investment
performance within accounts of clients who use non-comprehensive advisors.
Our results show important outcome differences between households who are more likely
to use a comprehensive financial advisor to create a retirement savings plan and clients who use
22
an advisor but do not calculate retirement needs. Even though both groups include wealthier,
more educated, and higher IQ households, there are consistent and economically significant
differences in retirement savings. Using a quantile regression to estimate the marginal
differences in accumulation at various levels of retirement savings, we find that using a
comprehensive advisor increases retirement savings by from $190,000 at the 90th percentile to
$17,000 for the median household. Consistent with findings from previous studies, we find no
significant increase in retirement saving among those who use a non-comprehensive advisor.
These results demonstrate the importance of differentiating between advisors whose primary
objective is to sell a (often underperforming) financial product versus advisors who create a
comprehenive plan.
We also attempt to distinguish between households approaches to planning for
retirement. We do this by comparing households that a comprehensive approach, planner only
approach, self-direct approach, and a no plan approach. From the results of the logistic
regression models, race, education, IQ, business ownership, and risk tolerance are important to a
households decision to select a retirement planning strategy. Notbaly, being Black5, being
married, being highly educated, and being willing to take financial risk are some of the key
determinants of a households’ decision to choose a comprehensive approach to planning
retirement. Among planner households, education and IQ are also important; however, a
respodnent’s race and risk profile are of no measurable value. Education and being in the highest
quintile of IQ are the main characteristic that drive a households decision to take a self-directed
do it yourself approach to planning for retirement.
5
Results are similar to Hanna (2011) who found that Black households are more likely to demand the services of a
financial planner.
23
The use of financial professional overwhelmingly increases the likelihood that
households will go through the process of calculating retirement need. The process forces
households to truly sit and and evaluate where they see themselves in the future, as well as to
prioritise and quantify their retirement goals and objectives. Furthermore, our results show that
adapting a comprehenive, planner, or self-directed strategy may increase the likelihood of
owning a tax advantage account. By taking advantage of the tax benefits from participating in
these accounts, households are better able to maximise accumulation of retirement wealth. .
From the quantile regression estimate, there is observe retirement wealth gains among
households that own and fund tax advantage vehicles across reported quantiles.
One potential criticism of the use of accumulated wealth as an outcome variable is the
possibility that planners target higher-wealth clients after they have already become wealthy.
We address this first by focusing on savings in sheltered retirement accounts which are less
likely to be affected by sudden exogenous increases in wealth such as an inheritance. We also
investigate the change in retirement retirement wealth between 2004 and 2008 in order to
measure active saving during this time period. Our results are the same. We find that the use of
a comprehensive approach is associated with significant increases in retirement savings
accumulated during this four year period, while there is no signicant increase associated with the
use of a planner only strategy.
We also find that a self-directed approach (only calculating retirement wealth needs) falls
short of the monetary gains of incorporating the use of a financial professional. The use of a
comprehensive approach is found to consistently result in higher retirement wealth across all
quantiles, but the use of a comprehensive approach in conjunction with a professional financial
planner has nearly twice the magnitude of effect at each quintile of retirement savings. For
24
example, at the 90th percentile households who used a comprehensive advisor saved an
additional $142,000 more for retirement all else equal, while those who had estimated their
retirement needs but did not use a professional had accumulated $70,000 more in retirement
savings. These results relate to the literature on the importance of planning in achieving longterm goals. For example, there is evidence that goal specificity increases likelihood of success
(Locke & Latham, 1990) and that making a process more concrete (less abstract) increases
likelihood you will follow through (McCrea et al., 2008). The process of estimating retirement
needs necessarily involves the consideration of a future amount needed to fund retirement
spending. Estimation of this amount can increase a household’s awareness of the consequences
of failing to save and provides a more defined path to meet retirement goals.
25
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29
Figure 1 percent of household within each household group in the study.
Source National Longitudinal Survey of Youth 1979, 2008 Administration
30
Graph showing Mean Accumulated Retirement
wealth and mean ∆ in accumulated reitrement
wealth by housheold groups.
$250,000.00
$200,000.00
$150,000.00
$100,000.00
$50,000.00
$All HHs
Comprehensive
HHs
Planner HHs
Retirement Wealth 2008
Self-directed
HHs
No Plan HHs
∆ in Retirement Wealth
Figure 2 showing mean accumulated retirement wealth and mean change in retirement
wealth by household groups.
Source National Longitudinal Survey of Youth 1979, 2008 Administration
31
Figure 3 showing average retirement wealth by survey year and by household groups.
Source National Longitudinal Survey of Youth 1979, 2008 Administration
32
Table 1 shows mean and median values of household’s economic data by group.
N
Income
Net Worth
Retirement Wealth 2008
∆ in Retirement Wealth
Income
Net Worth
Retirement Wealth
∆ in Retirement Wealth
Full Sample
7616
Planner HHs
Comprehensive HHs
836
Self-Directed HHs
1006
478
No Plan
5296
$
75,183.96
$
MEAN
125,043.41
***
$
101,677.28
***
$
86,577.02
***
$
60,118.67
$
347,417.27
$
798,630.15
***
$
578,582.34
***
$ 400,752.68
***
$
221,893.33
$
103,978.66
$
246,797.33
***
$
112,668.24
**
$ 163,659.69
***
$
62,087.12
$
41,493.27
$
83,500.34
***
$
14,851.58
$
77,597.58
***
$
28,448.60
$
58,000.00
$
MEDIAN
96,700.00
$
84,400.00
$
$
142,000.00
$
472,500.00
$
267,525.00
$
1,400.00
$
77,568.00
$
$
12,000.00
$
-
***,**,* indicates significance at the 0.01, 0.05, and 0.1 levels, respectively
Source National Longitudinal Survey of Youth 1979, 2008 Administration
33
$
71,600.00
$
45,200.00
$ 210,000.00
$
82,750.00
30,000.00
$
$
-
-
$
$
-
20,000.00
-
Table 2 provides a list of frequencies of several households’ characteristics.
Full
Sample
Comprehensive HHs
Planner
HHs
Self=Directed
HHs
No
Plan
836
478
1006
5296
%
%
%
%
14.11
6.51
79.38
8.84
7.24
14.31
4.87
4.36
7.94
11.75
13.22
14.41
74.07
74.82
63.01
12.51
63.06
3.9
19.01
1.51
8.13
15.82
4.03
9.08
10.64
6.25
8.6
1.79
4.85
5.24
11.22
15.36
8.32
12
17.37
73.96
59.85
85.67
73.62
66.51
9.92
51.64
12.01
17.04
8.38
2.76
7.89
14.94
24.84
29.67
2.78
6.25
8.11
9.95
12.39
8.84
12.61
16.6
16.98
18.32
84.91
72.88
59.95
48.02
39.42
11.66
14.88
18.39
23.57
27.41
3.59
5.71
10.3
14.26
22.46
2
4.61
6.49
9.01
10.57
8.44
11.55
12.17
15.06
17.65
85.01
77.91
70.67
61.61
48.89
50.8
49.2
13.04
13.04
7.56
6.98
14.82
13.07
64.31
66.4
72.77
6.67
23.35
15.69
27.14
28.53
8.71
13.53
11.77
15.96
13.3
17.92
59.36
45.25
41.29
South
West
16.29
28.13
37.24
17.44
11.89
14.28
12.01
14.6
8.23
8.5
6.03
7.15
14.33
13.33
13.77
14.92
65.02
63.46
67.89
62.99
Health
10.4
7.3
3.23
11.9
76.61
N
VARIABLE
7616
%
RACE
Black
Hispanic
Non Black, Hispanic
Marital Status (ref: )
Never Married
Married
Separated
Divorce
Widowed
Education
Less than high school
High School
Some College
College
Graduate School
IQ
Lowest IQ
IQQ2
IQQ3
IQQ4
Highest IQ
Gender
Male
Female
Asset ownership
Homeowner
Business Owner
Own Ira
Region
North East
North Central
Other
***,**,* indicates significance at the 0.01, 0.05, and 0.1 levels, respectively
Source National Longitudinal Survey of Youth 1979, 2008 Administration
34
Table 3 Likelihood of choosing each retirement planning approach.
Comprehensive
Households
7616
VARIABLE
N
Education (Less than High School ref.)
High School
1.42 *
Some College
2.21 ***
College
2.955 ***
Graduate School
3.734 ***
IQ (ref IQ lowest)
IQQ2
1.213
IQQ3
1.878 ***
IQQ4
1.852 ***
Highest IQ
2.051 ***
1.005
Log_Income
1.064 **
Log net worth
Health
0.813
Male
1.008
Age
1.038 **
Family size
0.999
Homeowner
1.218 *
Business Owner
1.767 ***
Own IRA
2.651 ***
RISK PROFILE (ref Conservative)
Aggressive
1.268 **
Moderate
1.311 **
REGION (ref. South)
NE
0.789
NC
1.126
W
1.068
RACE (ref Non Black, Hispanic)
BLACK
1.557 **
HISPANIC
0.936
Marital Status (ref: never married)
Married
1.361 **
Separated
0.742
Divorce
1.226
Widowed
1.124
Pseudo R-Square
19.75%
***,**,* indicates significance at the 0.01, 0.05, and 0.1 levels, respectively
Source National Longitudinal Survey of Youth 1979, 2008 Administration
Planner
Households
7616
1.409
1.597
1.665
2.029
1.715
2.013
2.294
2.217
1.011
1.053
1.051
1.081
0.976
1.025
1.254
1.578
1.528
*
**
**
**
**
**
**
**
*
**
**
0.918
0.878
1.28
1.282
1.078
Self-directed
Households
7616
1.231
1.537
1.463
1.444
1.098
1.176
1.265
1.407
1.006
1.03
1.051
1.081
1.025
0.963
1.411
0.845
1.147
1.247
1.104
*
**
**
**
*
**
**
**
0.764
0.541
0.437
0.336
**
***
***
***
0.826
0.645
0.594
0.51
0.994
0.95
1.087
0.95
0.977
1.013
0.74
0.614
0.423
**
***
***
***
0.784
0.851
**
**
***
*
***
***
***
0.918
0.878
1.099
1.076
0.927
0.892
1.196
0.882
1.16
1.119
0.697
0.967
***
0.959
0.464 **
0.763
1.017
8%
1.095
0.922
1.047
1.308
4%
0.829
1.318
0.962
0.774
21%
**
*
35
*
**
No Plan
Households
7616
Table 4 shows logistic regression results of the likelihood to calculate retirement need and owing tax advantage accounts.
VARIABLE
Financial Planner
Motivation (No Plan)
Comprehensive HHs
Planner HHs
Self-Directed HHs
N
Calculate
Retirement
Need
7616
6.04 ***
-
Own IRA
7616
3.32
2.24
1.72
***
***
***
0.52
0.65
***
***
1.12
0.73
0.98
1.27
*
RACE (ref Non Black, Hispanic)
Black
Hispanic
Marital Status (ref: never married)
Married
Separated
Divorce
Widowed
Education (Less than High School ref.)
High School
Some College
College
Graduate School
IQ (ref IQ lowest)
IQQ2
IQQ3
IQQ4
Highest IQ
Log Income
Log net worth
Health
Male
Age
Family size
Homeowner
Business owner
Own IRA
RISK PROFILE (ref Conservative)
Aggressive
Moderate
1.29
1.11
**
1.19
0.95
1.14
1.31
1.23
1.67
1.86
2.01
1.07
1.26
1.30
1.48
1.01
1.04
1.03
1.07
1.04
0.97
1.35
1.06
1.60
1.32
1.23
*
**
***
***
**
**
**
**
**
**
1.22
1.50
2.17
2.19
1.19
1.35
1.94
2.18
0.99
1.11
0.65
0.81
1.06
0.97
1.60
1.72
***
**
**
1.35
1.42
**
***
***
**
***
***
***
**
**
**
*
***
***
**
**
REGION (ref. South)
NE
0.83 **
1.53 ***
NC
0.92
1.26 ***
W
1.10
1.23 **
Pseudo R-Square
18.21%
18.51%
***,**,* indicates significance at the 0.01, 0.05, and 0.1 levels, respectively
Source National Longitudinal Survey of Youth 1979, 2008 Administration
36
Table 5 provides quantile regression beta estimates for retirement wealth at the 90th, 80th, 70th, 60th, and median quantiles.
VARIABLE
Motivation (ref. No Plan)
Comprehensive HHs
Planner HHs
Self-Directed HHs
RACE (ref Non Black, Hispanic)
Black
Hispanic
Marital Status (ref: never married)
Married
Separated
Divorce
Widowed
Education (Less than High School ref.)
High School
Some College
College
Graduate School
IQ (ref IQ lowest)
IQQ2
IQQ3
IQQ4
Highest IQ
Log Income
Log net worth
Health
Male
Age
Family size
Homeowner
Business owner
Own IRA
N
Quantile Regressions; Accumulated Retirement Wealth
0.8
0.7
0.6
7589
7589
7589
0.9
7589
189558
2087.24
61049.6
***
**
-3030.77
-2637.86
64480.6
-208.5
-1973
461.698
510.002
3242.07
125115
141567
-427.499
1501.56
36770.8
141049
247.331
5063.86
-945.19
2438.89
517.653
664.863
22968
-36120.2
73335.6
107809
11675.3
34998.3
-3205.1
-4800.7
***
***
***
31250.2
-260.81
-2406.6
-5363.7
-257.11
2862.94
98265.4
107082
**
***
**
-15.588
3281.86
7752.18
86348.1
463.163
2101.94
-1397.9
4128.05
746.849
1557.4
12869.3
-17439
50445.5
**
*
**
***
***
***
**
66283.3
13924
19655.1
***
*
**
44331.5
7585.25
7930.25
*
-2496.3
-2910.9
*
-512.6
-706.91
***
***
***
15791.8
-851.07
-3094.1
-5076.2
158.919
3139.37
54735.2
70451.2
***
**
*
6945.35
-519.86
-1582.9
-4116.6
***
***
522.864
1901.01
35156
51172.1
***
**
-16.149
-103.12
***
**
**
2557.51
-247.68
-823.9
-1507.3
***
***
110.784
574.216
16390
39369.4
**
***
**
**
**
**
***
***
34717.4
16284.5
**
**
11481.1
8665.33
**
**
4122.23
4924.39
**
**
1464.72
1905.32
**
**
5461.22
2730.29
-1538
**
5081.12
2850.01
-1131.5
**
*
3914.12
2205.79
-985.55
***
**
1326.13
1083.42
-439.48
**
**
*
***
***
***
**
-91.392
-42.198
1706.54
18954.2
196.062
237.473
-554.09
-286.26
56.8444
243.659
1065.61
-3169.7
10030.9
***
**
**
**
**
**
**
**
***
*
**
**
17162.1
4585.28
2218.01
-1390.5
1327.54
6066.85
53353.6
597.61
1121.71
-2693.6
1710.26
435.488
1129.3
8276.87
-11385
27699.9
***
**
***
-307.72
183.966
5948.12
35062.2
379.207
661.504
-1511
-106.91
178.43
665.848
4189.82
-7461.5
13969.6
***
Median
7589
**
***
***
***
**
**
***
***
***
**
**
**
**
RISK PROFILE (ref Conservative)
Aggressive
Moderate
60049.9
21574.7
REGION (ref. South)
NE
2589.59
NC
886.293
W
-183.262
***,**,* indicates significance at the 0.01, 0.05, and 0.1 levels, respectively
Source National Longitudinal Survey of Youth 1979, 2008 Administration
37
Table 6 provides quantile regression beta estimates for change retirement wealth at the 90th, 80th, 70th, 60th, and median quantiles.
VARIABLE
Motivation (ref. No Plan)
Comprehensive HHs
Planner HHs
N
Quantile Regressions; ∆ Accumulated Retirement Wealth
0.8
0.7
0.6
0.9
7062
7062
7062
7062
142553
776.258
70137.5
***
Self-Directed HHs
**
RACE (ref Non Black, Hispanic)
Black
-2746.22
Hispanic
-4060.72
Marital Status (ref: never married)
Married
36816.4
***
Separated
969.662
Divorce
-624.261
Widowed
-757.877
Education (Less than High School ref.)
High School
-200.596
Some College
1030.88
College
112482
***
Graduate School
126175
***
IQ (ref IQ lowest)
IQQ2
-932.537
IQQ3
1567.33
IQQ4
13185
Highest IQ
75574
***
Log Income
234.71
Log net worth
3224.98
***
Health
-867.206
Male
3154.54
Age
533.049
Family size
1233.18
Homeowner
13920.3
**
Business owner
-23139.6
Own IRA
35776.9
**
RISK PROFILE (ref Conservative)
Aggressive
51349.5
Moderate
15303.6
REGION (ref. South)
NE
4297.91
NC
2420.89
W
-656.806
***,**,* indicates significance at the 0.01, 0.05, and 0.1 levels, respectively
Source National Longitudinal Survey of Youth 1979, 2008 Administration
70525
5339.59
24388
***
**
-473.16
-1492.8
17903.4
-5.3956
-1381.5
-2064.6
-457.98
988.471
59804.2
77086.7
281.648
1670.97
4677.99
39468.5
315.068
1385
-1477
2496.26
398.451
795.124
5607.84
-7240.3
29652.7
34444.6
2490.13
9330.86
***
**
252.464
-363.04
***
***
***
***
***
**
**
8312.79
-606.75
-959.29
-1455
179.956
1054.15
39608.7
47197.8
-891.12
427.17
2954.21
25203
328.741
533.508
-1246.3
1084.35
95.0878
345.513
2642.23
-7060.1
11541.5
17718.7
3737.26
1992.89
***
**
-153
-131.93
***
***
***
***
***
***
**
1921.11
-321.03
-368.06
-929.48
-21.255
369.114
20509.8
27824.3
-180.55
3.0087
976.634
14517
137.303
140.925
-249.65
103.51
2.7609
130.865
399.717
-2731.7
2421.56
17009.1
3153.58
3607.51
2548.57
648.213
843.063
2506.24
1517.77
-438.37
1041.17
1487.94
-524.35
483.778
826.102
-391.28
38
Median
7062
6000
0
0
***
**
0
0
***
***
***
***
***
***
**
**
**
**
0
0
0
0
0
0
100
5100
0
0
0
3900
0
0
0
0
0
0
0
0
0
0
0
0
0
0
***
***
***
***
**
Table 7 showing results of the case control study.
Analysis of Variance for Variable RW
Classified by Variable COMP
COMP
N
Mean
1
785
215309.233
0
785
157644.092
Source
DF
Sum of Squares Mean Square
Among
1
1295958888425
1.295959E12
Within
1557
2.452497906E14
1.575143E11
39
F Value
Pr > F
8.2276
0.0042