2012
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Journal Personal Finance
Volume 11
www.journalofpersonalfinance.com
Converting a Traditional IRA to a Roth IRA Ronnie J. Clayton, Jacksonville State University Lemuel W. Davis, CFP速 William Fielding, Jacksonville State University
When to Claim Social Security Benefits David Blanchett, CFA, CFP速, Morningstar Investment Management
Jean M. Lown, PhD. (corresponding author), Utah State University Devon Robb, M.S., Utah State University
Financial Advice: What About Low-Income Consumers? Journal of Personal Finance
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Ning Tang (corresponding author), San Diego State University Marie-Eve Lachance, San Diego State University
Tools, Techniques, Strategies & Research to Aid Consumers, Educators & Professional Advisors Volume 11 Issue 2
2012
Journal of Personal Finance
Volume 11, Issue 2 2012
The Official Journal of the International Association of Registered Financial Consultants
Volume 11, Issue 2
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CONTENTS Editor’s Notes...................................................................................................... 8 Converting a Traditional IRA to a Roth IRA ................................................ 10 Ronnie J. Clayton, Jacksonville State University Lemuel W. Davis, CFP® William Fielding, Jacksonville State University Congress eliminated the income limit required for converting a traditional IRA to a Roth IRA in 2010. The conversion must be recognized as income for tax purposes in the year of conversion. However, a traditional IRA may be partially converted each year, spreading the conversion over two or more years and effectively distributing the tax burden over the conversion period and potentially allowing the converter to remain in a lower marginal income tax bracket. This may make conversion from a traditional IRA to a Roth IRA extremely attractive. This analysis considers conversions, develops a mathematical model to determine the time to break even (in years) when converting a traditional IRA to a Roth IRA, and provides a Monte Carlo Simulation of the model based upon historic financial market data for bonds and equities. The simulation provides information that may serve to guide investors as the conversion decision is made, yet the decision remains an individual one and may be influenced by factors other than the time to break even. When to Claim Social Security Benefits ......................................................... 36 David Blanchett, CFA, CFP®, Morningstar Investment Management Social Security (SS) is the largest source of retirement income for most Americans. This paper provides the reader with an overview of the SS retirement system and offers insight into key factors that should be considered when determining when to begin receiving SS retirement benefits. Five separate tests are performed, each of which considers a component that is important to the optimal claiming decision, such as life expectancy, taxes, the cost of purchasing equivalent insurance, and the benefits of the surviving spouse. Three claiming scenarios are considered: receiving benefits early (e.g., at age 62 versus 66); delaying benefits past Full Retirement Age (e.g., age 66 versus 70); and the maximum realistic delay period (e.g., at age 62 versus 70). The results of this analysis suggest most retirees would be best served delaying SS benefits until at least Full Retirement Age (FRA) or later, and that delayed SS benefits are especially valuable for females, married couples, retirees who expect to invest in relatively conservative portfolios during retirement, and retirees who have longer life expectancies. The effective “return” achieved by a retiree from
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Journal of Personal Finance making the optimal SS decision can significantly exceed the return he or she could potentially earn by investing the monies received from starting benefits earlier and “investing the difference,” especially in today’s low interest rate environment. We find the optimal Social Security claiming decision can generate 9.15% more income for a hypothetical retired married couple, which creates an annual equivalent “financial planning alpha” (or Gamma) of +0.74% per year.
After The Global Financial Crisis: Attitudes Toward Immediate Annuities............................................................................................................ 88 Jean M. Lown, PhD. (corresponding author), Utah State University Devon Robb, M.S., Utah State University Based on the life cycle hypothesis, this study examined the attitudes toward immediate annuities of university employees 50 to 65 years old after the onset of the global financial crisis. Annuity attitudes were negatively correlated with risk tolerance and positively linked to life expectancy. Current income, expectation of receiving a pension, professed familiarity with annuities, and confidence that retirement assets would last were not related to annuity attitudes. Confirming the annuity puzzle, respondents claiming to be most familiar with immediate annuities expressed the least positive attitudes. Multiple regression analysis revealed that risk tolerance and familiarity with annuities were significantly negatively predictive of annuity attitudes. The results did not support the life cycle hypothesis. Future research should measure actual annuity knowledge and test for a curvilinear relationship between assets and annuity attitudes. Recommendations for financial advisors and educators are provided on how to frame annuities, plus suggestions for future research. Financial Advice: What About Low-Income Consumers?.......................... 121 Ning Tang (corresponding author), San Diego State University Marie-Eve Lachance, San Diego State University This paper uses data from the National Financial Capability Study (NFCS) to analyze the determinants and benefits of financial advice use, with a special emphasis on the low-income group. While, as expected, this group fares worse financially, we also find that they have different needs and priorities. For example, they use less investment advice than insurance advice. A discriminant analysis reveals that cost plays a lesser role in their decision to seek advice. Using an instrument variable strategy, we conclude that certain types of advice improve clients’ financial behaviors, with greater benefits for the low-income group.
©2012, IARFC. All rights of reproduction in any form reserved.
Volume 11, Issue 2
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CALL FOR PAPERS JOURNAL OF PERSONAL FINANCE (www.JournalofPersonalFinance.com) OVERVIEW The new Journal of Personal Finance is seeking high quality manuscripts in topics related to household financial decision making. The journal is committed to providing high quality article reviews in a single-reviewer format within 45 days of submission. JFP encourages submission of manuscripts that advance the emerging literature in personal finance on topics that include: -
Household portfolio choice Retirement planning and income distribution Individual financial decision making Household risk management Life cycle consumption and asset allocation Investment research relevant to individual portfolios Household credit use Professional financial advice and its regulation Behavioral factors related to financial decisions Financial education and literacy
EDITORIAL BOARD The journal is also seeking editorial board members. Please send a current CV and sample review to the editor. JPF is committed to providing timely, high quality reviews in a single reviewer format. CONTACT Michael Finke, Editor Email: jpfeditor@gmail.com www.JournalofPersonalFinance.com
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Journal of Personal Finance
JOURNAL OF PERSONAL FINANCE VOLUME 11, ISSUE 2 2012 EDITOR Michael S. Finke, Texas Tech University ASSOCIATE EDITOR Wade Pfau, National Graduate Institute for Policy Studies (GRIPS) EDITORIAL ASSISTANT Carey Yeary, Texas Tech University EDITORIAL BOARD Steve Bailey, HB Financial Resources Joyce Cantrell, Kansas State University Dale Domian, York University Monroe Friedman, Eastern Michigan University Joseph Goetz, University of Georgia Clinton Gudmunson, Iowa State University Sherman Hanna, The Ohio State University George Haynes, Montana State University Douglas Hershey, Oklahoma State University Karen Eilers Lahey, University of Akron Doug Lambin, University of Maryland, Baltimore County Rich Landsberg, Advanced Consulting Group Jean Lown, Utah State University
Mailing Address:
Angela Lyons, University of Illinois Ruth Lytton, Virginia Tech University Lewis Mandell, University of Washington and Aspen Institute Yoko Mimura, University of Georgia Robert Moreschi, Virginia Military Institute Edwin P. Morrow, Financial Planning Consultants David Nanigian, The American College Barbara O’Neill, Rutgers Cooperative Extension Cliff Rob, University of Alabama Jing Xiao, University of Rhode Island Rui Yao, University of Missouri Tansel Yilmazer, University of Missouri Yoonkyung Yuh, Ewha Womans University
IARFC Journal of Personal Finance The Financial Planning Building 2507 North Verity Parkway Middletown, OH 45042-0506
© Copyright 2012. International Association of Registered Financial Consultants. (ISSN 1540-6717) ©2012, IARFC. All rights of reproduction in any form reserved.
Volume 11, Issue 2
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Postmaster: Send address changes to IARFC, Journal of Personal Finance, The Financial Planning Building, 2507 North Verity Parkway, Middletown, OH 45042-0506 Permissions: Requests for permission to make copies or to obtain copyright permissions should be directed to the Editor. Certification Inquiries: Inquiries about or requests for information pertaining to the Registered Financial Consultant or Registered Financial Associate certifications should be made to IARFC, The Financial Planning Building, 2507 North Verity Parkway, Middletown, OH 45042-0506. Disclaimer: The Journal of Personal Finance is intended to present timely, accurate, and authoritative information. The editorial staff of the Journal is not engaged in providing investment, legal, accounting, financial, retirement, or other financial planning advice or service. Before implementing any recommendation presented in this Journal readers are encouraged to consult with a competent professional. While the information, data analysis methodology, and author recommendations have been reviewed through a peer evaluation process, some material presented in the Journal may be affected by changes in tax laws, court findings, or future interpretations of rules and regulations. As such, the accuracy and completeness of information, data, and opinions provided in the Journal are in no way guaranteed. The Editor, Editorial Advisory Board, the Institute of Personal Financial Planning, and the Board of the International Association of Registered Financial Consultants specifically disclaim any personal, joint, or corporate (profit or nonprofit) liability for loss or risk incurred as a consequence of the content of the Journal. General Editorial Policy: It is the editorial policy of this Journal to only publish content that is original, exclusive, and not previously copyrighted. Subscription Rates: Individual: Institution:
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Journal of Personal Finance
EDITOR’S NOTES
This issue of the Journal contains two normative and two empirical articles. Normative research estimates optimal financial choice given a set of assumptions. This type of research provides insight into how investors can make better decisions. The second two empirical articles help us understand how people actually behave by looking at samples of employees and lower-income households. Both types of research are valuable to both academics and practitioners. For example, few of us understand how investment portfolios and tax rates over time influence optimal conversion from a traditional to a Roth IRA. The article titled “Converting a Traditional to a Roth IRA” provides a number of insights into the moving parts of a household that affect optimal conversion. “When to Claim Social Security Benefits” is a complex and thorough treatment of an important decision all retirees will have to make. It is perhaps the most advanced overview in the existing academic literature of the factors that affect when a household should consider deferring benefits. In both instances, understanding when to recommend a Roth conversion, or the right time to begin taking social security, can have a significant impact on household welfare. For example, David Blanchett estimates that claiming social security retirement income later in life can add the equivalent of an investment alpha of 74 basis points to a households retirement income portfolio.
©2012, IARFC. All rights of reproduction in any form reserved.
Volume 11, Issue 2
9
One of the great mysteries of retirement income research is why so few household choose annuities. Since annuities provide both longevity protection and a mortality credit, they allow a retiree to consume more while also protecting against the risk of running out of money in old age. Research in this issue provides more evidence that the tepid demand for annuities can be traced to a lack of product knowledge. The lack of financial education on annuitization is a problem that both academics and practitioners can help solve. We also see more evidence that moderate income households in the United States often do not have the same access to professional financial advice as those with greater financial resources. This is to be expected since adviser compensation is often related to assets invested. In “Financial Advice: What about Low-Income Consumers,� we also see more evidence that financial needs differ by income level. Perhaps most interesting is the role insurance professionals play in the middle income financial advice market. In both retirement income planning and wealth protection, there exists an opportunity for the insurance industry to offer products that are often underused by average Americans. Academic research can help identify when and how financial products can improve the lives of consumers.
~Michael Finke
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CONVERTING A TRADITIONAL IRA TO A ROTH IRA: BREAK-EVEN ANALYSIS Ronnie J. Clayton1* Jacksonville State University Lemuel W. Davis, CFPÂŽ William Fielding Jacksonville State University Congress eliminated the income limit required for converting a traditional IRA to a Roth IRA in 2010. The conversion must be recognized as income for tax purposes in the year of conversion. However, a traditional IRA may be partially converted each year, spreading the conversion over two or more years and effectively distributing the tax burden over the conversion period and potentially allowing the converter to remain in a lower marginal income tax bracket. This may make conversion from a traditional IRA to a Roth IRA extremely attractive. This analysis considers conversions, develops a mathematical model to determine the time to break even (in years) when converting a traditional IRA to a Roth IRA, and provides a Monte Carlo Simulation of the model based upon historic financial market data for bonds and equities. The simulation provides information that may serve to guide investors as the conversion decision is made, yet the decision remains an individual one and may be influenced by factors other than the time to break even.

Contact Author: Ronnie J. Clayton, Glenn Huie Chair and Eminent Scholar, Professor of Finance, Jacksonville State University, Jacksonville, AL 36265; (256) 782-5715 10
Introduction In today’s environment, individual retirement planning is one of the more important activities for workers of all ages. Information gleaned from the popular press continuously discusses the likelihood that those approaching retirement age may find it necessary to remain in the workforce well past the age they had planned. Also prominent in the popular press are discussions of the probability that the Social Security trust fund in the United States will become insolvent in 25 to 30 years. Additionally, for many years businesses and city, state and local governments have been changing and adjusting the retirement plans offered employees. Many that offered defined benefit plans in the past now offer employees defined contribution plans structured as tax deferred 401(k) or 403(b) plans. For those workers who qualify, taxation and retirement acts passed by the United States Congress provide an opportunity to structure two types of tax-advantaged individual retirement plans: either a traditional Individual Retirement Account (IRA) or a Roth IRA. The traditional IRA provides retirement funding on a tax-deferred basis while the Roth IRA provides retirement funding that is tax free for qualified distributions. The earnings from Roth IRA investments are exempt from taxation and there is no required minimum distribution from the account.2 The long-term nature of retirement needs, the fact that an individual’s retirement lifespan may be one-third or more of 2
Additional information concerning the structure of IRAs can be found in IRS Publication 590. 11
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the individual’s total lifespan, and the uncertainties surrounding many of the vehicles designed to provide retirement income, require that individuals and households plan for a successful retirement. Successful planning requires many important decisions over the retirement wealth accumulation and expenditure periods. Many of the decisions involve the determination of the appropriate retirement program and financial instruments to provide a desirable level of certainty and return for an uncertain future. Financial education is important for those involved. Lusardi and Mitchell (2007) utilize the Rand American Life Panel data to examine the level of financial education in America. Their findings confirm that financial education is imperative in retirement planning and that those exposed to economics in school and to companysponsored financial education programs are more financially literate. Numerous authors, for example Binswanger and Grace (2012), Ameriks et al. (2003), Beshears et al. (2008), and Bernheim et al. (2001) have examined the factors that influence how households accumulate retirement wealth and considered the influence of variables such as age, sex, marital status, number of children, education and several additional variables on household wealth accumulation decisions. The class of instrument used by households and individuals in implementing a retirement plan will also influence the effectiveness of the plan. Brown (2007) examines the role of annuities in retirement planning and finds that some individuals may benefit from annuitization but many are reluctant to annuitize assets due to behavioral and psychological biases. Since Markowitz (1952), Sharpe (1964), Black and Scholes (1973) and others developed and applied foundational mathematical economics to financial market decisions, Wall Street and the financial planning community have embraced 12
many of the mathematical models that were developed. These quantitative financial models are increasingly important in making complicated financial decisions. Bodie, et.al. (2004) examine how quantitative models may be used to analyze optimization in consumption and portfolio choices and how individual issues and complications impact the retirement planning solutions. Congress provided an additional potential complication for retirement planning in 2010 when the income limitation placed on the conversion of a traditional IRA to a Roth IRA was removed, providing the opportunity for those with accumulated retirement wealth in a tax-deferred traditional IRA to convert that wealth to a non-tax-deferred non-taxable Roth IRA. The conversion requires that taxes be paid on the amount converted just as the taxes are paid on ordinary contributions to a Roth IRA; however, there are no future taxes paid on the principal amount invested nor the earnings on the investment. The merits of conversion have been discussed in the popular press yet the decision to convert remains confusing and complicated.3 The remainder of this paper is divided into several sections. In section 2, the traditional IRA is compared to the Roth IRA. The choice between a traditional and a Roth IRA is considered and discussed in section 3 while section 4 provides the development of a model to assist individuals as they choose between the two retirement wealth accumulation vehicles. Section 5 provides a Monte Carlo simulation that quantifies the break-even options available to the traditional IRA owner. Conclusions are provided in section 6. 3
See, for example, McCormally (2010), Greene (2009), Green and Tergesen (2009) and Saunders (2012) 13
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Traditional versus Roth IRA The traditional IRA was established under the Employee Retirement Income Security Act (ERISA) of 1974. Those without employee-sponsored pensions could use this facility to save for retirement. Contributions to the traditional IRA are fully tax-deferred until the funds are withdrawn. IRS code section 72t, as codified under ERISA, requires a 10percent penalty for early withdrawal of funds before age 59 ½. Workers with pensions were not eligible to participate in the original traditional IRAs. In 1981 Congress extended eligibility to establish an IRA for all workers, even those with pension coverage. A primary purpose of the extended eligibility was to encourage lower income workers to save for retirement; however, mainly high-income workers took advantage of their IRA eligibility due to the tax deferral. The Tax Reform Act of 1986 placed additional restrictions on IRA eligibility. IRA contributions by employees without coverage in an employersponsored pension plan were deemed fully deductible from taxable income while employees covered by an employersponsored pension plan could establish an IRA with contributions fully deductible from taxable income provided their income was below a certain level.4 The Roth IRA was established under the Taxpayer Relief Act enacted in 1997 and provides an alternative method for retirement funding. The Roth IRA removed some of the restrictions associated with the traditional IRA. A primary difference between the traditional 4
The original ceiling for full tax deferral was $35,000 for individuals and $50,000 for married couples filing a joint return. For 2011, the ceilings for full tax-deferral are $56,000 and $90,000 for individual and married couples filling joint returns, respectively. 14
and the Roth IRA is that contributions to a traditional IRA are tax deductible but contributions to a Roth IRA are not. However, no tax is levied upon qualified distributions of Roth IRA funds. In addition, the traditional IRA is subject to required minimum distributions beginning in the year the owner reaches age 70 ½. Roth IRA funds may remain invested indefinitely during the owner’s lifetime but funds left in the Roth IRA for a beneficiary must be withdrawn according to required minimum distribution rules and qualified distributions are not taxed. Congress once again changed the laws concerning IRAs in 2001. At that time, both traditional and Roth IRAs allowed a maximum contribution of $2,000. The Economic Growth and Tax Relief Reconciliation Act raised these limits to $3,000 in 2002, $4,000 in 2005, and $5,000 in 2008. The Act provided for the limit to be adjusted for inflation after 2008. As indicated above, the most recent changes to the rules are associated with converting traditional IRAs to Roth IRAs and were implemented for the 2010 tax year forward. The primary change is that the income limit required for converting traditional IRAs to Roth IRAs is eliminated. This change may seem to make conversion from traditional IRAs to Roth IRAs extremely attractive. There are detriments as well as benefits of conversion. Conversion requires that the taxpayer recognize amounts that have not previously been taxed as taxable income as required by the conversion rules. Taxpayers younger than 59 ½ may convert, but should have resources outside of the IRA sufficient to pay the required taxes so they may roll 100% of the traditional IRA amount into the Roth. Should these taxpayers choose to utilize funds within the IRA to pay taxes, they will be subject to the 10% tax penalty for early withdrawal 15
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of the funds. Taxpayers older than 59 ½ are not subject to the 10% early distribution penalty and may choose to roll 100% of the conversion distribution into a Roth IRA, paying the taxes due from another source, or to roll less than 100% and pay some, or all, of the taxes due from the distribution. The Choice5 An individual choosing to contribute funds to an IRA must choose between a Roth and a traditional IRA. The individual must also decide the amount to invest remembering that the traditional IRA reduces current taxable income (and taxes due) while the Roth IRA does not. Choosing a traditional IRA makes the funds relatively illiquid for most purposes until retirement age is reached because accessing the funds requires special use circumstances or the payment of penalties.6 Any individuals holding traditional IRAs may consider converting to a Roth IRA. Models or examples used to 5
The similarities and differences between traditional and Roth IRAs and the merits of conversion from traditional to Roth IRAs has been discussed extensively in the popular business press. For example, see the Wall Street Journal and other popular financial publications. 6 Lack of liquidity is a relative issue. While the intention of the 10% early distribution penalty is to encourage the IRA owner to remain invested in the IRA, other asset classes may suffer from a greater lack of liquidity. For example, equity investments that have declined by 25% or real estate investments that have declined in value by 40% may be considered less liquid than an IRA investment that will be penalized by 10% of the amount upon distribution. If the distribution from a Roth IRA is a qualified distribution (see IRS Publication 590 for discussion of qualified versus nonqualified) it is not subject to penalty. Nonqualified distributions of Roth IRA earnings may be taxable and may be subject to penalty, depending upon the purpose of the distribution. While this does impact Roth IRA liquidity, a Roth should be considered more liquid than a traditional IRA. 16
compare the two types of IRAs must have a common starting point. For a fair comparison the model must be careful to consider funds after accounting for the effects of taxation whether the decision is to establish an IRA or to convert an existing IRA. Anyone considering an IRA should evaluate the relative merits of the traditional IRA and the Roth IRA based upon the impact of upfront tax payment versus deferred tax payment, the expected time frame of the investment, and the expected rate of return on the investment. The traditional IRA owner has three options: 1. Keep the traditional IRA (don’t convert) and let the IRA and any taxable account continue to compound annually. Pay marginal income taxes on the traditional IRA accounts when it is liquidated and pay yearly taxes on capital gains and income from the taxable account. After taxes are paid, reinvest the remainder of the taxable income in the taxable account. 2. Convert to a Roth IRA and pay the conversion taxes out of an existing taxable account. Assume the “after tax value” of the taxable account is equal to the required funds for converting to a Roth. This zeroes out the taxable account, thus simplifying the analysis. 7 3. Pay the conversion taxes from the traditional IRA and convert the remainder of the traditional IRA to a Roth IRA and allow the Roth to 7
For options 1 and 2 (which include a taxable account during the investment period), taxes are paid on income (capital gains, dividends, and bond yields) from the taxable account yearly.
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accumulate tax-free. An IRA owner less than 59½ must also pay the 10% penalty on the distribution.
If the expected ordinary tax rate at final year (year of liquidation) is greater than the ordinary tax rate at year of conversion, conversion to a Roth should be seriously considered. In considering a conversion for modest/large IRAs, one should note that if large sums were converted in a single year the tax rate at the year of conversion would be driven higher. Thus it may be desirable to execute the conversion over a number of years so that in any one year the tax rate is not driven above the expected rate at the final year. If the expected ordinary tax rate at the final year is less than the ordinary tax rate at year of conversion, the decision to convert to a Roth is more complex since the benefit depends on the ratio of ordinary tax rate at distribution to the ordinary tax rate at conversion, asset appreciation rates, the future capital gains rate, the future marginal tax rate, and the time period assets remain invested. At some future time (“break-even point”), the Roth IRA will equal the traditional IRA plus the taxable account required to pay the taxes. The question is, how long to break even? If the expected break-even is short for someone in their early employment years, a conversion may make sense. For an older investor, an extended time to break even may render conversion an unattractive alternative unless a stretched Roth IRA is being considered. Model Development The model consists of three portfolios: A Roth IRA, a Traditional IRA, and a Taxable Account. All portfolios may include some combination of dividend-paying stocks, non18
dividend paying stocks, and bonds. Taxable income from the taxable account is reinvested proportionally after paying taxes on income and capital gains each year. Taxes on the traditional IRA are paid when the account is distributed at some future time n. Development of the model is based on the following assumptions: 1. Break-even occurs when the value of a converted Roth IRA is equal to the after tax sum of the traditional IRA plus the taxable account used to pay conversion taxes 2. Conversion to the Roth IRA is made at end of year using the ordinary tax rate t o . 3.
Transition to the new ordinary tax rate t n
occurs at the beginning of a tax year. 4. No withdrawals are made from the Roth IRA, traditional IRA, or taxable account after the conversion. 5. The taxable account may consist of any combination of stocks and bonds. Taxable income is paid annually and taxed annually at the marginal and capital gain tax rates. 6. To compare account performances, all accounts have proportionally the same assets to eliminate “riskadjusted-return” differences. 7. All portfolios are rebalanced annually. 8. Ibbotson data do not differentiate ordinary dividends from qualified dividends and long term capital gains from short-term capital gains. Ordinary dividends and long-term capital gains are assumed for this analysis. 19
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The model uses Monte Carlo techniques to build a histogram of the specified number of samples (break-even times) for a given set of input assumptions: the percent of stocks and bonds in the portfolios, the capital gains tax rate, the marginal income tax rate, and the ratio of ordinary income tax rates at conversion and at distribution. Each time to break even is computed using randomly-selected sequences of monthly returns. The sequence is long enough to provide 30 years of contiguous data for computing equation elements. The selected sequence of monthly returns is used by the Visual Basic Program to compute the yearly returns used to build equation elements for equation (8) until ď „ n changes sign and an approximate solution for time to break even is determined. The solution is refined using linear interpolation as explained below. After the specified number of samples is collected, the program sums the number of observations in each class interval until the sum is equal to or greater than 90% of the number of samples specified. The break-even time associated with the 90% class interval is placed in the solution matrix. A spillover class interval is defined for observations exceeding the 30-year limit. When the number of observations that exceed 30 years prevents a 90% solution, an error (-1000) is shown in the solution matrix. The Mathematical Model The symbols used in the development of the mathematical model are provided in Exhibit 1.
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Exhibit 1 Symbol
Definition
Pc
The traditional IRA converted to a Roth IRA
wk
The percent of equities in the portfolio (row k)
t0
The ordinary tax rate at conversion
tn
Assumed ordinary tax rate at distribution
tm
The marginal tax rate
tcg
The capital gains rate
t0Pc
After tax value of the initial Taxable Account
Ď„m
The horizontal Fixed Array. Where Ď„m = tn /t0
The values below are computed yearly ij
Weighted total portfolio appreciation rate
spir
S&P 500 taxable income rate
govir
Intermediate gov. bond taxable income rate
spca
S&P 500 capital gains rate
govca
Intermediate gov. bond capital gains rate
sptr
S&P total return rate
govtr
Intermediate government bond total return
Ij
Weighted percent of total income taxable at marginal tax rate
Gj
Weighted percent of total income taxable at capital gains rate
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Note that one must observe the following constraints: t m t n . Since m
tn or m t o t n this implies that t m m t o to
The total portfolio income is given by: i j w k ( sptr ) 1 w k govtr
(1)
and the percent of total income taxable at marginal tax rate is: I j w k spir 1 w k govir / i j
. (2)
When
wk spir 1 wk govir 0 ,
Ij 0
(3) and the net taxable ordinary income is zero. The percent of total income taxable at capital gains rate is: G j w k spca 1 w k govca / i j
. (4)
When
wk spca 1 wk govca 0 , G
j
0
(5) and the net taxable capital gains income is zero. The model developed below provides a realistic view of the complications involved in the decision to convert a 22
traditional IRA to a Roth IRA. As shown in equation 6, the model is expressed mathematically as: n n n Pc 1 i j Pc 1 i j 1 t n t o Pc 1 i j 1 I j t m G j t cg j 0 j 0 j 0 3
1
2
4
(6)
where the equation elements identified with numbers 1-4 in equation (6) denote the following: 1—the converted Roth IRA 2—the traditional IRA 3—the taxes paid on traditional IRA at distribution 4—the taxable account after taxes are paid. Equation 6 can be rewritten as: n 1 i j 1 I j t m j0 n 1 i j j0
n
G j t cg
tn t0
(7)
Define equation (7) elements as follows:
N j 1 i j 1 I j t m G j t cg
N 0 D0 1
D j 1 i j
i0 0 .
The programmed equation is then given as n
Nj Dj
m
(8)
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The Visual Basic Program builds elements Dj and Nj , multiplies these elements by the previous product, and increments the year counter “n” until n changes sign. When this occurs, break-even is between (n-1) ≤ ñ ≤ n. The estimate is then refined using linear interpolation as shown in equation (9) below:
n~ n
n n1 n
(9)
To operationalize the simulation requires the generation of two fixed arrays on the solution matrix. The first is a horizontal
array m of ordinary income tax ratios and the second is a vertical array that specifies the percentage (w) of equity in the portfolio. Monte Carlo Simulation The model presented in equation (8) uses historical data derived from financial markets. Five Monte Carlo simulations are conducted and presented in Table I, Panels A-E. The simulations are based on the five current marginal income tax rates spanning from highest current marginal income tax rate (Panel A) to the lowest marginal federal income tax rate (Panel E). Computations in the Visual Basic Program are based on the 2010 Ibbotson Monthly Market Data. The monthly data is used to compute the following yearly returns: S&P 500 Total Returns, S&P 500 Capital Gains Returns, Intermediate Government Bonds Total Returns and Intermediate Government Bonds Capital Gains Returns. S&P 500 Income Returns and Intermediate Government Bond Income Returns 24
are computed from these values. The results of the simulations presented in Table I provide insights into the importance of differential tax rates and will assist individuals in making the decision to convert or not to convert.8 The primary reason for converting to a Roth IRA is to maximize the Roth portfolio’s value relative to the traditional IRA portfolio immediately or at some future time consistent with financial objectives. This decision requires an assumption about the future marginal income tax rate relative to the marginal income tax rate at conversion. The prediction of a lower marginal income tax rate may suggest waiting to convert is a better strategy. Table I, Panel A, provides a matrix of break-even n for individuals in the highest current marginal income tax bracket of 35%. As would be expected, for relatively small differences between the marginal income tax rate at conversion and the marginal income tax rate at distribution, the time to break even is relatively short and depends on the portfolio’s rate of return. For example, when τm = 0.9714 and the portfolio consists only of bonds, the time to break even is approximately 5.6 years.9 Adding equities to the portfolio shortens the time to break even until the proportion of equities is approximately 0.6 (60%) of the portfolio and breakeven is approximately 1.9 years. Increasing equities more than 60% increases the portfolio risk without significantly shortening the break-even time. As τm declines (the assumed marginal income tax rate is lower when the traditional IRA is 8
Again, a Visual Basic program is used to find, by simulation, a matrix of years to break even when converting a tradition IRA to a Roth IRA.
For m = 0.9714, the assumed future marginal income tax rate, tn, is equal to 97.14% of the currently existing marginal income tax rate, t0. 9
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distributed ), the time to break even increases because the tax benefit of holding the traditional IRA (not converting) is greater and it takes longer to recover from the impact of the early payment of taxes from the taxable account. For example, for Ď„m = 0.8856 the all-bond portfolio requires approximately 17.8 years to break even while the all-equities portfolio requires only 5.9 years. While the actual numbers differ, the pattern holds throughout the simulations presented in Table I. As Panel E shows, for the marginal income tax rate equal to 16%, when Ď„m = 0.9714 the all-bond portfolio requires 9.4 years to reach break-even while an all-equity portfolio reaches break-even in approximately 2.8 years. Conclusions Retirement planning requires individuals (households) to enter into decisions involving an uncertain future. The ability to convert from a traditional IRA to a Roth IRA adds an additional decision that may impact the wealth of individuals and the availability of financial resources in retirement as well as resources remaining for estate purposes. If the future marginal income tax rate is assumed equal or greater than the marginal income tax rate at conversion, the decision to convert is clear. When the future marginal income tax rate is assumed lower than the marginal income tax rate at conversion, the decision is less clear. The simulations presented in this work provide useful information to the decision maker in choosing to convert to a Roth IRA or continue holding a traditional IRA. For those individuals with income sufficient to place them in the highest marginal income tax bracket, the time to break even is relatively short across a reasonable range of 26
future marginal income tax rates. If their marginal rate at distribution is only 87% of the current 35% rate (nte: this implies t0 = 0.4017 based on Ď„m) the times to break even are approximately 7.5 years when equities are at least 50% of the portfolio. For those in the lowest marginal income tax bracket, the same 87% of the current 16% rate requires approximately 11.9 years for the same portfolio mix of equities and bonds. This indicates, of course, that the tax benefits accruing to the Roth IRA are greater for those in the higher marginal income tax brackets. It is also important to note that the portfolio mix is important to the conversion decision and the time to break even. Except for the lowest marginal tax rate (16%), increasing equities more than 60% of portfolio does little to improve portfolio performance. The analysis and simulations provided are both valuable for those investors considering the conversion decision. When the marginal income tax rate is expected to be higher in the future than the current rate for the individual, conversion is the correct financial decision. When future marginal income tax rates are uncertain and may be lower than the current rate, the conversion decision is not as clear. The simulation provides information that may serve to guide investors as the conversion decision is made, yet the decision remains an individual one and may be influenced by factors other than the time to break even.
27
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Journal of Personal Finance
Table I: Time to Break Even Monte Carlo Simulation Panel A: Marginal Tax Rate = 35% TD
0.0% -2.9%
-4.3%
-5.7%
-7.2%
-8.6% -10.0% -11.4% -12.9% -14.3%
ď ´m
1.000
0.957
0.943
0.929
0.914
0.900
0.886
0.871
0.857
0.971
w
n
0.000
0.0
5.6
8.0
10.0
12.1
14.5
16.2
17.8
19.3
20.7
0.071
0.0
3.9
5.9
7.7
9.5
11.4
13.2
14.7
16.3
17.9
0.143
0.0
3.2
4.8
6.2
7.7
9.1
10.4
11.9
13.4
14.8
0.214
0.0
2.7
4.0
5.2
6.4
7.7
8.9
9.9
11.2
12.5
0.286
0.0
2.4
3.4
4.5
5.5
6.6
7.7
8.8
9.7
10.7
0.357
0.0
2.2
3.1
4.0
5.0
5.9
6.8
7.8
8.7
9.6
0.429
0.0
2.1
3.0
3.7
4.6
5.4
6.2
7.1
8.0
8.9
0.500
0.0
2.1
2.8
3.5
4.3
5.1
5.9
6.6
7.5
8.3
0.571
0.0
2.0
2.7
3.4
4.1
4.8
5.5
6.3
7.1
7.9
0.643
0.0
1.9
2.6
3.3
4.0
4.7
5.3
6.1
6.9
7.6
0.714
0.0
1.9
2.6
3.2
3.9
4.5
5.3
6.0
6.8
7.4
0.786
0.0
1.8
2.5
3.2
3.8
4.4
5.2
6.0
6.7
7.4
0.857
0.0
1.8
2.5
3.1
3.7
4.4
5.2
6.0
6.6
7.3
0.929
0.0
1.8
2.4
3.1
3.7
4.4
5.1
6.0
6.6
7.3
1.000
0.0
1.8
2.4
3.0
3.7
4.4
5.1
5.9
6.6
7.3
28
Panel B: Marginal Tax Rate = 33% TD
0.0% -2.9%
-4.3%
-5.7%
-7.2%
-8.6% -10.0% -11.4% -12.9% -14.3%
1.000 0.971 0.957 0.943 0.929 0.914 0.900 0.886 0.871 0.857
ď ´m w
n
0.000
0.0
5.8
8.3
10.3
12.6
15.0
16.6
18.5
19.9
21.2
0.071
0.0
4.1
6.2
8.1
10.0
11.9
13.5
15.4
16.8
18.6
0.143
0.0
3.3
4.9
6.5
8.0
9.3
10.9
12.6
13.8
15.4
0.214
0.0
2.8
4.1
5.4
6.7
7.9
9.2
10.3
11.6
12.8
0.286
0.0
2.5
3.6
4.7
5.7
6.9
8.1
9.1
10.0
11.1
0.357
0.0
2.3
3.2
4.2
5.2
6.1
7.0
8.1
9.0
10.0
0.429
0.0
2.2
3.1
3.9
4.7
5.6
6.4
7.4
8.3
9.1
0.500
0.0
2.1
2.9
3.6
4.5
5.3
6.1
6.9
7.7
8.6
0.571
0.0
2.1
2.8
3.5
4.3
5.0
5.8
6.6
7.4
8.1
0.643
0.0
2.0
2.7
3.4
4.1
4.8
5.6
6.4
7.2
7.9
0.714
0.0
2.0
2.7
3.3
4.0
4.6
5.4
6.2
7.0
7.7
0.786
0.0
2.0
2.6
3.3
3.9
4.6
5.4
6.2
6.9
7.6
0.857
0.0
1.9
2.6
3.2
3.9
4.6
5.3
6.2
6.8
7.6
0.929
0.0
1.8
2.5
3.2
3.9
4.5
5.5
6.1
6.8
7.5
1.000
0.0
1.8
2.5
3.1
3.8
4.5
5.3
6.1
6.8
7.5
29
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Journal of Personal Finance
Table I Continued Panel C: Marginal Tax Rate = 28% TD
ď ´m
0.0% -2.9% -4.3%
1.000 0.971 0.957
-5.7% -7.2% -8.6% -10.0% -11.4% -12.9% -14.3%
0.943 0.929 0.914 0.900 0.886
0.871 0.857
n w 0.000
0.0
6.6
9.2
11.3
14.2
16.4
18.2
19.8
21.4
23.0
0.071
0.0
4.6
6.9
9.0
11.1
13.2
15.1
16.9
18.6
20.2
0.143
0.0
3.7
5.4
7.2
8.7
10.5
12.2
13.6
15.2
17.0
0.214
0.0
3.0
4.6
6.0
7.4
8.7
10.0
11.4
12.8
14.2
0.286
0.0
2.7
4.0
5.3
6.4
7.6
8.8
9.9
11.2
12.4
0.357
0.0
2.6
3.5
4.6
5.6
6.7
7.8
8.8
9.9
10.8
0.429
0.0
2.4
3.4
4.3
5.2
6.1
7.2
8.2
9.1
10.1
0.500
0.0
2.3
3.2
4.1
5.0
5.8
6.7
7.6
8.5
9.6
0.571
0.0
2.3
3.1
3.9
4.7
5.5
6.3
7.2
8.1
9.1
0.643
0.0
2.2
3.0
3.8
4.5
5.3
6.2
7.1
7.8
8.7
0.714
0.0
2.2
3.0
3.7
4.4
5.2
6.0
6.9
7.6
8.4
0.786
0.0
2.2
2.9
3.6
4.3
5.1
6.0
6.7
7.5
8.2
0.857
0.0
2.1
2.9
3.5
4.2
5.1
6.0
6.6
7.4
8.3
0.929
0.0
2.1
2.8
3.4
4.2
5.1
5.9
6.6
7.4
8.2
1.000
0.0
2.1
2.8
3.5
4.2
5.1
5.9
6.6
7.3
8.2
30
Panel D: Marginal Tax Rate = 25% TD
ď ´m
0.0% -2.9% -4.3% -5.7% -7.2% -8.6% -10.0% -11.4% -12.9% -14.3%
1.000 0.971 0.957 0.943 0.929 0.914 0.900 w
0.886 0.871 0.857
n
0.000
0.0
7.1
9.9
12.3
15.1
17.1
19.2
20.7
22.4
24.4
0.071
0.0
5.0
7.4
9.6
11.8
14.2
16.0
18.0
19.7
21.2
0.143
0.0
4.1
5.8
7.7
9.4
11.1
12.9
14.7
16.2
18.0
0.214
0.0
3.4
4.9
6.4
7.9
9.3
10.7
12.3
13.5
15.0
0.286
0.0
3.0
4.2
5.4
6.7
8.1
9.3
10.4
11.8
13.0
0.357
0.0
2.8
3.8
4.9
6.0
7.2
8.3
9.4
10.5
11.7
0.429
0.0
2.6
3.5
4.6
5.6
6.5
7.7
8.7
9.7
10.8
0.500
0.0
2.5
3.4
4.3
5.3
6.2
7.1
8.2
9.2
10.2
0.571
0.0
2.4
3.3
4.2
5.0
5.9
6.9
7.7
8.7
9.8
0.643
0.0
2.4
3.2
4.0
4.8
5.6
6.6
7.5
8.3
9.3
0.714
0.0
2.3
3.2
4.0
4.6
5.5
6.4
7.3
8.1
9.1
0.786
0.0
2.3
3.1
3.8
4.6
5.5
6.4
7.1
8.0
8.9
0.857
0.0
2.3
3.1
3.8
4.5
5.4
6.3
7.0
7.9
8.8
0.929
0.0
2.2
3.0
3.7
4.4
5.3
6.3
6.9
7.8
8.6
1.000
0.0
2.2
3.0
3.6
4.4
5.2
6.2
6.9
7.7
8.5
31
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Journal of Personal Finance
Table I Continued Panel E: Marginal Tax Rate = 16% TD
m
0.0%
-2.9% -4.3%
1.000 0.971 0.957
-5.7%
-7.2%
-8.6% -10.0% -11.4% -12.9% -14.3%
0.943 0.929 0.914 0.900
w
0.886 0.871 0.857
n
0.000
0.0
9.4
12.5
15.9
18.6
20.6
23.2
25.4
27.8 -1000
0.071
0.0
6.6
9.8
12.6
15.3
17.8
19.9
22.3
24.5
26.7
0.143
0.0
5.2
7.4
9.7
12.0
14.1
16.3
18.4
20.3
22.7
0.214
0.0
4.2
6.1
8.0
9.7
11.7
13.6
15.2
17.3
19.1
0.286
0.0
3.8
5.3
7.0
8.5
9.9
11.6
13.2
14.8
16.5
0.357
0.0
3.4
4.9
6.2
7.7
9.1
10.5
12.0
13.5
14.8
0.429
0.0
3.3
4.5
5.8
7.0
8.3
9.7
11.1
12.4
13.8
0.500
0.0
3.2
4.3
5.4
6.6
7.8
9.0
10.4
11.9
13.1
0.571
0.0
3.1
4.1
5.1
6.3
7.5
8.5
9.9
11.2
12.4
0.643
0.0
3.1
4.0
4.9
6.1
7.2
8.1
9.5
10.6
11.8
0.714
0.0
3.1
3.8
4.7
5.9
7.0
7.9
9.1
10.2
11.3
0.786
0.0
3.0
3.7
4.6
5.8
6.7
7.7
8.8
10.0
11.1
0.857
0.0
2.9
3.8
4.5
5.6
6.6
7.6
8.7
9.7
10.7
0.929
0.0
2.9
3.6
4.4
5.5
6.5
7.3
8.3
9.5
10.4
1.000
0.0
2.8
3.6
4.3
5.4
6.4
7.2
8.2
9.3
10.2
Note that the variables on this table are defined in Exhibit 1 and that TD = % that tn is discounted from to [i.e. % = (τm – 1.0)], bold/italics indicates 100% bonds, bold indicates 100% equities, 1000 indicates time to break even exceeds 30 years. The number of simulations used to determine each value of n is 5,000. 32
References Ameriks, J. A. Caplin, and J. Leahy (2003). Wealth accumulation and the propensity to plan, Quarterly Journal of Economics, 118(3), 1007– 1047. Bernheim, B.D., J. Skinner, and S. Weinberg (2001). What Accounts For The Variation In Retirement Wealth Among U.S. Households? American Economic Review, 91(4), 832–857 Beshears, J., J. Choi, D. Laibson, and B. Madrian (2008) The importance of default options for retirement saving outcomes: Evidence from the United States, in S.J. Kay, T. Sinha (Eds.), Lessons from Pension Reform in the Americas, Oxford University Press, Oxford, 59–87. Binswanger, J. and K. G. Carman (2012). How real people make long-term decisions: The case of retirement preparation, Journal of Economic Behavior & Organization, 81(1), 39-60. Black, F. and M. Scholes (1974). The pricing of options and corporate liabilities, Journal of Political Economy, 81(3), 637-654. Bodie, Z., J.B. Detemple, S. Otruba and S. Walter (2004). Optimal consumption-portfolio choices and retirement planning, Journal of Economic Dynamics and Control, 28(6), 1115-1148. Brown, J.R. (2007). Rational and behavioral perspectives on the role of annuities in retirement planning, NBER Working Paper 135. Greene, K. (2009). The gift that keeps on giving: A Roth IRA, Wall Street Journal - Eastern Edition, 254(98), B2. Greene, K. and A. Tergesen (2009). Why it may pay to convert to a Roth IRA, Wall Street Journal - Eastern Edition, 254(139), B1-B2. Internal Revenue Service Publication 590, 2010 and 2011. Lusardi, A. and O. S. Mitchell (2007). Financial literacy and retirement planning: New evidence from the Rand American Life Panel, Working Paper 2007-157, Michigan Retirement Research Center, University of Michigan. Markowitz, H. (1952). Portfolio selection, The Journal of Finance, 7(1), 7791. McCormally, K. (2010). 7 Myths about IRA conversions, Kiplinger’s Personal Finance, November, 69-70. Saunders, L. (2012). IRAs get sexier,” Wall Street Journal - Eastern Edition, 259(51), B7-B10. Sharpe, W.F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance, 19(3), 425-42.
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APPENDIX A Brief Description of the Monte Carlo Simulation The simulation program is written in Visual Basic and requires the user to input three parameters: a marginal income tax rate, a capital gains tax rate, and the number of samples or simulations to run. In additional, two arrays are hard wired in the program: a horizontal array that contains ordinary marginal income tax ratios, Ď„m, and a vertical array that contains values that specify the percentage of equity in portfolios consisting of S&P 500 stocks and intermediate treasury bonds. One can view these array values as indices for the solution matrix. The data for the simulated financial market returns are based on the 2010 Ibbotson Yearly Market Data: S&P 500 Total Returns, S&P 500 Capital Gains Returns, Intermediate Government Bonds Total Returns and Intermediate Government Bonds Capital Gains Returns. S&P 500 Income Returns and Intermediate Government Bond Income Returns are computed from these values. Define a simulation set as one associated with a single histogram where break-even times are estimated from random financial data. Before each simulation set begins, a histogram array is cleared. The histogram array is made up of 300 class intervals of one-tenth year each. This limits break-even times to a maximum of 30 years. The Ibbotson data tables are randomly sampled to simulate yearly market performance. Using SBBI monthly data allows the generation of a large number of consecutive yearly intervals for computing each break-even time. Given 85 years of monthly data, one has 1020 monthly observations to work with. The simulation program limits break-even times to 30 years or 360 months so the number of overlapping consecutive intervals is 661 (1020+1-360). A random integer pointer is generated that points to the beginning of an array of continuous 34
twelve-month data sets. These data sets are used to compute yearly values that are used with input data and data from the built-in arrays to compute the breakeven time as described in the body of the manuscript.
~  , random When computing a single break-even time n data extracted from the Ibbotson tables are the only variables. Once break-even has been determined, it is rounded to the nearest one-tenth year, multiplied by ten, and converted to an integer. The integer is an index that points to a class interval in the histogram that is then incremented by one. This completes one simulation run. The process is repeated until the number of specified samples has been collected or the simulation set is complete. The last step before starting the next simulation set is to locate the sample in the histogram that is greater than or equal to 90% of all samples collected. Once determined, this value is placed in the solution matrix. If the sum of samples is less than 90% (there were too many samples over 30 years), an error code of -1000 is placed in the solution matrix. Next, the column index is incremented and a new ordinary income tax ratio is selected to begin the next simulation set. After finishing all the columns on the first row of the solution matrix a new value for percent of equities in the portfolio is selected and the process is repeated for all ordinary income tax ratios (τm). Processing continues until the solution matrix is complete.
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Journal of Personal Finance
WHEN TO CLAIM SOCIAL SECURITY RETIREMENT BENEFITS David Blanchett, CFA, CFP® Morningstar Investment Management Social Security (SS) is the largest source of retirement income for most Americans. This paper provides the reader with an overview of the SS retirement system and offers insight into key factors that should be considered when determining when to begin receiving SS retirement benefits. Five separate tests are performed, each of which considers a component that is important to the optimal claiming decision, such as life expectancy, taxes, the cost of purchasing equivalent insurance, and the benefits of the surviving spouse. Three claiming scenarios are considered: receiving benefits early (e.g., at age 62 versus 66); delaying benefits past Full Retirement Age (e.g., age 66 versus 70); and the maximum realistic delay period (e.g., at age 62 versus 70). The results of this analysis suggest most retirees would be best served delaying SS benefits until at least Full Retirement Age (FRA) or later, and that delayed SS benefits are especially valuable for females, married couples, retirees who expect to invest in relatively conservative portfolios during retirement, and retirees who have longer life expectancies. The effective “return” achieved by a retiree from making the optimal SS decision can significantly exceed the return he or she could potentially earn by investing the monies received from starting benefits earlier and “investing the difference,” especially in today’s low interest rate environment. We find the optimal Social Security claiming decision can generate 9.15% more income for a hypothetical retired married couple, which creates an annual equivalent “financial planning alpha” (or Gamma) of +0.74% per year. The author thanks Alexa Auerbach, Jim Daley, and Francisco Torralba for helpful comments.
36
Introduction Social Security (SS) retirement benefits are a big deal. Social Security is the largest income source for most retirees and determining when to claim benefits is an incredibly complex decision. This paper provides an overview of Social Security retirement benefits and offers insight into some of the key factors that should be considered when determining when to claim those benefits. Five separate tests are performed, each of which considers a component that is important to the SS decision process, such as life expectancy, taxes, the cost of purchasing equivalent insurance, and the benefits of the surviving spouse. Three scenarios are considered: receiving benefits early (e.g., at age 62 versus 66); delaying benefits past FRA (e.g., at age 66 versus 70); and the maximum realistic benefit delay period (e.g., at age 62 versus 70). This analysis suggests that benefits from delaying the receipt of SS benefits can be significant. We find that females, married couples, retirees who expect to invest in relatively conservative portfolios during retirement, and retirees who have longer life expectancies are likely to benefit most from delaying social security benefits. On the other hand, retirees who have shorter life expectancies or invest more aggressively and believe they can achieve a relatively high return on their retirement portfolios would likely be better off taking SS earlier. We also find that optimal social security claiming decisions can 9.15% more lifetime income for a married couple based on a hypothetical married couple. This 9.15% increase in retirement income is equivalent to “financial planning alpha� (or Gamma) of +.74% per year during the entire retirement period.
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Journal of Personal Finance
The Importance of Social Security Benefits Social Security retirement benefits are the largest single income source for retirees, representing between 40% and 51% of a retiree’s aggregate income (Butrica et al. (2004) and Rhee (2011), respectively). According to Rhee (2011), the bottom 25% of households (by income) will rely on SS for 87% of income, versus 74% for the middle 50%, and only 30% for the top 25%. Entmacher (2009) notes that without SS, nearly one in two seniors would be poor, whereas with SS less than one senior in ten is poor. Butrica et al. (2004) estimate that 91% of current retirees, 92% of near-term retirees, and 94% of babyboomer retirees will receive SS benefits10. The Social Security Administration (SSA) notes that 96% of workers are currently covered under SS. With respect to SS benefits, as of November 2007, the last month the SSA published its detailed monthly OASDI information11, 73.2% of eligible workers opted to take early SS benefits versus waiting until FRA. According to Reno and Lavery (2010), of those retirees turning 62 in 2006, 43% of men and 48% of women took benefits at the earliest opportunity. This marks a decline from 1985, when 51% of men and 62% of women turning 62 that year claimed benefits at the earliest opportunity. Determining Social Security Benefits Social Security retirement benefits are based on lifetime earnings. A worker’s actual earnings are increased (i.e., 10 11
Either their own or their spouse's benefit http://www.ssa.gov/policy/docs/statcomps/oasdi_monthly/2007-11/index.html
38
indexed) to account for changes in average wages since the year the earnings were received, based on the National Average Wage Index12 (NAWI). The NAWI is calculated annually by the SSA based on wages subject to federal income taxes. The highest 35 years of indexed earnings for the worker are used to compute the average historical earnings. The monthly average becomes the “Average Indexed Monthly Earnings” (AIME). The actual monthly retirement benefit, called the “Primary Insurance Amount” (PIA) is based on AIME. While the percentages of the PIA formula are set by law, the dollar amounts, or "bend points," change annually based on changes in the NAWI. The bend points for the year 2012 are $767 and $4,624. The benefit received is 90% of the AIME amount under the first bend point, 32% of the AIME amount between the first and second bend point, and 15% of the AIME amount over the third bend point. The maximum possible AIME value for 2012 is $9,175 which means the maximum SS benefit at FRA is $32,851 per year. Full retirement age (“FRA”) is the age when someone is eligible to receive “full” SS benefits. FRA for SS retirement benefits is age 66 for individuals born between 1943 and 1954. This group consists of individuals approximately between the ages of 58 and 69 as of 2012. This is the cohort of retirees currently facing the decision of when to take SS benefits13 and therefore is the focus group for this research. Someone can elect to receive SS retirement benefits as early as age 62. The impact on the benefit from claiming benefits at various ages is shown in Table 1, where the FRA is assumed to be age 66.
12 13
http://www.ssa.gov/oact/cola/AWI.html#Series Which is why age 66 is the assumed FRA for this analysis.
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Table 1: Social Security Benefits Assuming a Full Retirement Age of 66
Retirement Age 62 63 64 65 66 67 68 69 70
% of FRA Benefit 75.00% 80.00% 86.67% 93.33% 100.00% 108.00% 116.00% 124.00% 132.00%
% of Earliest Example Benefit Differences 100.00% $750 106.67% $800 115.56% $867 124.44% $933 133.33% $1,000 144.00% $1,080 154.67% $1,160 165.33% $1,240 176.00% $1,320
Full Retirement Age (FRA) Source: Social Security Adminstration
Someone who elects to receive SS at age 62 will receive a benefit that is 75% of the value he or she would have received if he or she waited to FRA (age 66). Postponing retirement past age 70 is never optimal from an income perspective, since SS benefits will not increase past that age. For an individual who is younger than FRA and continues to work after claiming SS benefits, the benefits may be reduced in proportion to the individual’s labor earnings. In particular if the worker is under FRA the SSA will deduct $1 from the benefit payments for every $2 earned above the annual limit. For 2012, the annual limit is $14,640. In the year the individual reaches FRA, the SSA will deduct $1 in benefits for every $3 earned in excess of a higher limit ($38,880 in 2012). Starting in the month the individual reaches FRA, SS benefits are no longer capped. 40
Social Security Benefit Taxation SS retirement benefits may be subject to taxation based on the total income of the retiree. Currently, no more than 85% of someone’s SS retirement benefits can be subject to federal taxation. The rules regarding the taxation of SS benefits are outlined in IRS Publication 915. The amount of SS benefits subject to taxation is based on the recipient’s “combined income”. Combined income is calculated by adding adjusted gross income to nontaxable interest plus half of the SS benefit. The tax thresholds to determine the amount subject to taxation were determined in 1983 with the idea that only the wealthy would pay taxes on their SS retirement benefits. SS benefits are not taxed up to the first threshold, but after the first threshold, up to 50% of total SS income is subject to federal taxation. This tax is applied to the entire amount of SS income, not just the incremental income above the threshold. After the second threshold, up to 85% of SS income is subject to taxation. The thresholds are not indexed for inflation, and are $32,000 and $44,000 for married couples, and $25,000 and $34,000 for single individuals (as of 2012). Slight changes in income can subject a SS benefit receipt to what is known as a “tax torpedo,” where the marginal tax rate on SS increases dramatically at the income thresholds. For example, a single worker with a combined income of $24,950 would be subject to no tax on his or her SS benefits. If the worker received an additional $50 in income, then 50% of the SS benefits would be subject to federal taxation. Therefore, it is very important to be aware of the total income of an
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individual and the respective tax thresholds. Taken to the extreme, if all of someone’s income (e.g., $30,000) were SS income, he or she would be subject to no tax. In contrast if this worker received the same $30,000 as IRA income, some tax would likely be due. Spousal Benefits Even if the SS recipient’s spouse has never worked, his or her spouse is eligible for SS retirement benefits, equal to one-half of the primary worker’s retirement amount at the spouse’s FRA. Whereas the spouse’s benefit is based on the primary worker’s benefit, the spousal benefit amount is not determined by when the primary worker claims SS, but rather by when the spouse begins claiming benefits. If the primary worker elects to receive benefits early, the spouse can still receive his or her full spousal benefits by waiting until full retirement age. If the spouse is eligible for retirement benefits based on his or her own work history, he or she will receive the greater of his or her own earned amount or the amount based on the spouse’s benefit. If the spouse is eligible to receive a pension for work not covered by SS, such as government or foreign employment, the amount of her SS benefits may be reduced. If a spouse has never worked under SS, she can begin collecting benefits as early as age 62. However, if the benefit begins early, the amount will be permanently reduced. A spousal benefit is reduced 25/36 of one percent for each month before normal retirement age, up to 36 months. If the number of months exceeds 36, then the benefit is further reduced 5/12 of one percent per month. This reduction factor is applied to the base spousal benefit, which is 50% of the worker's primary 42
insurance amount (PIA). The impact on the PIA amount varies with the primary worker’s PIA itself, whereby the reduction could potentially be greater than 25%14. If the primary worker elects to receive benefits early while the spouse waits until FRA to begin receiving benefits it is possible for the spouse to receive $0.67 for every $1.00 received by the primary worker, which is greater than the base 50% benefit. Spousal benefits do not include any delayed retirement credits the primary worker may receive; therefore, there is no advantage to waiting to begin collecting spousal benefits after the spouse reaches FRA. If the primary worker is at FRA, he or she can apply for retirement benefits and then request to have payments suspended. That way, the worker’s spouse can begin receiving a spouse's benefit and the primary worker can continue to earn delayed retirement credits until age 70. If the spouse has reached full retirement age and is eligible for a spouse's benefit and his or her own retirement benefit, he or she has a choice. The spouse can choose to receive only the spouse's benefit now and delay receiving his or her own retirement benefits until a later date. If retirement benefits are delayed, a higher benefit may be received at a later date based on the effect of delayed retirement credits. Survivor Spousal Benefits While the spousal benefit is not affected by when the primary worker claims SS, the spousal survivor benefit is affected, because the spousal survivor benefit is based on the respective benefit of the spouse. A widow or widower who is at 14 If the worker's primary insurance amount is $1,600 and the worker's spouse chooses to begin receiving benefits 36 months before his or her normal retirement age, we first take 50% of $1,600 to get an $800 base spousal benefit. Then we compute the reduction factor, which is 36 times 25/36 of one percent, or 25%. Applying a 25% reduction to the $800 amount gives a spousal benefit of $600, which is 37.5% of the primary insurance amount.
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his or FRA or older will receive the maximum of her own benefit and 100% of the deceased worker’s benefit amount, as long as the couple was married at least 10 years at the time of the primary worker’s death. According to the SSA website15, at present there are approximately five million widows and widowers receiving monthly SS benefits based on their deceased spouse's earnings record. SS provides 58% of the income for widows 65 and over, compared to 39% for all individuals and couples 65 and over (Entmacher 2009). Survivor spouses are eligible to receive benefits as early as age 60. For those spouses with an FRA of 66 (i.e., those born between 1945 and 1956) the benefit is reduced .396% for each month before FRA that the applicant takes the benefit. Therefore if the spouse elects to take spousal benefits at the earliest age possible (age 60), the benefit, based on primary worker’s PIA, will be reduced to 71.5% of the initial amount. Survivor spousal benefits are an important consideration for the primary worker when he or she claims for SS because they represent a “residual” benefit available should the spouse outlive the primary worker. Even if each member of a couple is receiving a benefit based on their own earnings the survivor spousal benefit would still be important for whichever spouse has the smaller benefit, especially if that spouse has a longer life expectancy. Literature Review There is significant disagreement in past research on when the “optimal” claiming age is for SS benefits. Determining the “optimal” age for SS requires complex calculations given the number of variables that must be
15
http://www.ssa.gov/survivorplan/onyourown2.htm
44
considered. Therefore, no single study can be expected to provide an answer for every case. A slight change in some of the assumptions (e.g., future tax rates, market returns, life expectancy, future benefit changes, etc.) can dramatically affect the result. This section includes an overview of past works to provide the reader with insights about what authors and experts have determined. Mahaney and Carlson (2007) examined a strategy in which retired individuals bought an annuity with personal retirement savings to bridge the period from age 62 to 70 and delayed claiming SS until age 70. This strategy out-performed a strategy of taking SS early while preserving funds in their other retirement savings. As reasons for this finding, they point to the robust increase in SS benefits due to delayed claiming; the expenses (such as commissions, management fees, and advisor charges) related to IRA investments; the tendency for retirees to invest more conservatively as they age (thus reducing investment returns); and the tax treatment of SS benefits versus other retirement income. Meyer and Reichenstein (2010) note that for single taxpayers with average life expectancies who will not be subject to an earnings test, the present value of SS benefits is approximately the same no matter when benefits begin. This supports the premise that SS benefits have generally been noted to be actuarially fair. They suggest that singles with short life expectancies should begin benefits early and those with longer life expectancies should delay. For an average couple, on the other hand, the decision revolves around spousal and survivor's benefits. The present value is usually maximized when the lower-earning spouse begins benefits as soon as possible (as long as those benefits would not be lost 45
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due to the earnings test), while the higher-earning spouse delays benefits until age 70. Meyer and Reichenstein (2012) review the implications of delayed benefits on a portfolio and find that delaying benefits can add more than ten years of longevity to a portfolio. They note that the additional longevity from delaying SS decreases for higher levels of wealth. Therefore, less-wealthy clients concerned with longevity risk should be especially interested in delaying SS benefits. They note that if early retirement is desired, one should wait until age 64, but if an individual does not retire at age 64, then he or she should retire no later than age 67. Ryan (2010) notes that the candidates who have the potential to gain the most from delaying (or resetting if benefits have already started) SS retirement benefits would likely have one or more of the following characteristics: first, sufficient confidence in the ability to survive the breakeven period; second, an aversion to market risk and a desire to trade riskier investment assets for an increased SS benefit; third, a wish to minimize the risk of outliving their portfolio; and finally, adequate non-tax-deferred assets from which to repay past benefits. With respect to the exact age at which to claim SS benefits, Rose and Larimore (2001) find age 62 is the optimal age for men and women. Munnell and Soto (2007) find the optimal age to be 62 for men and 68 for women. Sun and Webb (2009) find the preferred retirement age to be 62 or 69 for men and 67 or 70 for women depending on their risk aversion. McCormack and Perdue (2006) find age 66 to be the optimal retirement age. Cunningham and Erickson (2009) note age 62 is the optimal age for males with income less than 46
$30,000, or 66 if greater, and 66 for females (regardless of income). Docking, Fortin, and Michelson (2011) calculate that the optimal age is 64 for men and 67 for women. When to Start Taking Benefits For single workers the decision of when to claim for SS benefits is less complex than for married couples, since spousal survivor benefits are not a concern. A common approach used to indicate whether it is better to take benefits early or to delay benefits is a “breakeven analysis.� A breakeven analysis produces a value that equalizes the benefits of retiring to those of delaying retirement. The breakeven calculation can be solved in terms of age, return, or a combination of the two. If the breakeven is expressed as a life expectancy value, the worker should delay claiming SS benefits if he believes he will outlive the breakeven period. In contrast, if the breakeven is expressed as a return, the worker should take benefits early if he believes he can achieve a higher return by taking the payments earlier and investing those monies. Here is a list of important considerations that will affect each individual differently; each of these considerations will be reviewed in the following sections: 1. Ability to delay benefits. In order to delay SS benefits an individual or couple will need sufficient assets to provide income during the pre-SS years of retirement. 2. Life expectancy. This is important for both the primary worker and the spouse. Generally, the healthier someone is, the more beneficial it can be to delay receiving benefits, since the retiree is expected to live longer during retirement. Along these same lines, a 47
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younger healthy spouse also increases the potential value of delayed SS benefits due to spousal survivor benefits. 3. Tax considerations. As previously reviewed, SS benefits have a unique tax structure that make them superior (if only slightly at higher income levels) to other retirement income, such as a traditional IRA. Slight changes in income can subject a retiree to a “torpedo tax,” dramatically increasing the tax bill. 4. Benefit reduction considerations. The Senior Citizens’ Freedom to Work Act of 2000 allowed seniors to “file and suspend” their benefits upon reaching FRA, which enables the benefits of a worker to continue to accrue Delayed Retirement Credits (DRCs). 5. Spousal considerations: the age of the spouse relative to the primary worker is important as noted in number two above. This is perhaps one of the most complex considerations. While the election to receive benefits is not irrevocable, reversing the decision is allowed only during this first year of receiving benefits. In those instances the retiree must also pay back to the government what he or she has already received. A retiree could then start receiving benefits at some later date thereby increasing his or her SS benefit. An important consideration for retirees who want to delay retirement (something that is highlighted numerous times on the SSA website), is Medicare benefits. If one plans to delay receiving benefits and continue working, it is in the worker’s interest to sign up for Medicare three months before reaching 48
age 65, regardless of when he or she reaches full retirement age. Otherwise, Medicare medical insurance, as well as prescription drug coverage, could be delayed, and one could be charged higher premiums. Medicare is also an important consideration when thinking about the actual benefit a retiree will receive since the Part B monthly premium is automatically deducted from the SS benefit. These premiums range from $99.90 (the standard premium) to $319.70 in 2012, depending on the recipient’s Modified Adjusted Gross Income (MAGI). Note that this does not include the prescription drug monthly amount, which can be as high as an additional $66.40 per month (in 2012). While SS benefits are received monthly, research on benefit maximization typically assumes annual benefit payments, which will be the primary approach used in this analysis. This is both for simplicity purposes as well as because mortality tables only include annual survivorship rates. While Docking, Fortin, and Michelson (2011) make note of the “inaccuracy� of a number of past research studies on optimal SS retirement claiming ages that assume annual versus monthly benefits, they neglect to mention the insignificance of the difference. In a 62 vs 66 analysis (discussed next) the Internal Rate of Return (IRR) using annual SS benefits is 9.10% versus 9.14% using monthly values. We would contend a 4 bps difference is insignificant in light of the other material assumptions that need to be made (e.g., inflation, mortality rate, taxation, other forms of income, etc) when determining the optimal age to claim SS.
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Analysis There are a large number of potential scenarios that could be tested to determine the optimal age at which to claim SS benefits. For this analysis, though, there will be three primary test scenarios: retiring at age 62 versus age 66, 62 vs. 70, and 66 vs. 70. The first test (62 vs. 66) determines the potential benefit of taking benefits at the earliest possible age versus delaying to FRA. The second test (62 vs 70) determines the potential benefit of taking benefits at the earliest possible claiming age versus delaying as long as reasonably possible to create the largest possible difference (76% higher income than at age 62). The final test (66 vs 70) determines the potential benefit of taking benefits at FRA versus delaying as long as possible. These three scenarios cover the three most significant choices faced by a retiree. They assume that the retiree invests all benefits. Unlike a number of past studies, a standard (baseline) “scenario� is not created for the analysis for comparison purposes. Instead, five different tests are conducted, each of which seeks to provide insights about the variables that affect the optimal claiming age. The first test is a simple breakeven analysis that determines how much an investor would need to earn on his or her invested benefits to be indifferent between claiming ages. The second test is a breakeven analysis that incorporates taxes to find out how many years the retiree would need to survive in order to break even based on different aftertax income goals and levels of SS income. The third test incorporates mortality into the analysis and determines the breakeven returns necessary for a single worker (i.e., does not consider potential spousal survivor benefits). The fourth test 50
determines the return required in different scenarios in order to purchase an inflation-adjusted immediate annuity (i.e., to selffund the incremental difference lifetime income by investing the early SS benefits versus receiving the delayed benefit). Finally, the fifth test calculates the value of spousal survivorship benefits, which are an important consideration for married couples. It’s important to note that in the second test that incorporates taxes we need to make an assumption about the rate of return the SS recipient earns on invested benefits. This is significant because the higher the return the longer it takes to break even. One potential starting place to determine what is a reasonable return assumption is to look at how older Americans are actually allocating their financial assets (i.e., portfolios). Median equity allocations for US households from age 60 to age 90 (i.e., retirees), obtained from the 2010 Survey of Consumer Finances is included in Figure 1.
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Figure 1: Median Equity Allocations for Retirees
Median Equity Allocation
50% 40% 30%
y = -0.0075x + 0.8309 R² = 0.2801
20% 10% 0% 62
67
72
Source: 2010 Survey of Consumer Finances
77 Age
82
87
92
The average equity allocation for Americans from age 60 to 95 is approximately 24%, although the equity allocation tends to decrease with age. A portfolio with a 24% equity allocation (and 76% bond allocation) is expected to have a nominal (before inflation) return of approximately 5% based on Morningstar’s current 20 year market forecasts (as of June 2012), with long-term inflation estimated to be approximately 3%. For the second test we will assume a nominal return of 5% as the base return assumption (which equates to a “real” or inflation-adjusted return of approximately 2%). Some readers may question a 5% base return as too aggressive given the current interest rate environment (with government bonds yielding less than 2%), while others may contend it is too conservative. In either case, it is important to note that this 5% would be the after-fee total return of the portfolio. An investor who expects to achieve an annual return of 6% but pays 1% 52
total annual money management fees would achieve a net return of 5%, which is equivalent to the base assumption. Also, most of the tests include the breakeven returns that would need to be achieved, and are therefore not based on this 5% nominal return value. Breakeven Internal Rate of Return The primary metric used to demonstrate the potential benefit of delaying benefits is the “breakeven return.� The breakeven return is the annual compounded nominal after-fee return the investor would need to achieve on his or her invested benefits over a given time to be indifferent between claiming benefits early or delaying. For example, Figure 2 shows that in the 62 vs 66 analysis, the internal rate of return is -10.1% at year 10. This means that the investor who took benefits at 62 would have to have an average annual loss of 10.1% to be even with someone who delayed benefits until age 66 if they both died at age 72. The longer an investor lives, however, the more attractive it is to delay benefits. Had these investors lived to 92, the one who took benefits at 62 would have had to earn almost 10% per year on the invested benefits to be even with the investor who delayed benefits until age 66.
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Internal Rate of Return
Figure 2: Internal Rates of Return for Various Time Periods 15% 10% 5% 0% -5% -10% -15% -20% -25% -30% -35% 10
20 30 40 Years Survive After Receiving Initial Benefit 62 vs 70
62 vs 66
50
66 vs 70
One of the most noticeable points in Figure 2 is how much longer it takes for the 62 vs 70 scenario to break even. The reason for the longer time period has to do with the length of the delay from when benefits can be received initially (age 62) versus eventually (age 70). This eight-year delay requires a significant amount of time to overcome. The required returns for the 62 vs 66 and the 66 vs 70 are virtually identical, because they are both four-year periods and require a much a shorter breakeven point than the 62 vs 70 scenario. This can be attributed to the four-year gap from the early to delayed benefits, versus the eight year period for the 62 vs 70 scenario. While determining the breakeven period provides useful information, it is in many ways overly simplistic because it does not consider more complex considerations, such as taxes, life expectancy, or spousal survivor benefits. Life expectancy is especially important, though, because while 54
the retiree may be a little worse off if he or she passes away early, the implications of a lower SS benefit are significant for those retirees who end up living in retirement for a long time. Breakeven Analysis with Taxes Taxes are important because not all income, or types of income, are treated equally (or really taxed equally) during retirement. Social Security income has a more favorable tax treatment that other forms of income (such as income from a Traditional IRA), and the implications of this differential tax treatment also vary by retiree. For this section, we examine the impact of delaying benefits under the assumption that the retiree must pull money from an IRA, which has less favorable tax treatment than Social Security, to fund retirement during the delay years. All tax rates and tax assumptions (e.g., exemptions, standard deductions, etc.) are based on 2012 federal tax rates. State taxes are ignored. We assume that the individual or couple takes the standard deduction for the respective filing status (either single or married filing jointly) and has no additional income, dependents, etc., other than the base assumptions. Similar to the previous analysis, spousal survivor benefits are ignored. While in the previous breakeven analysis we solved for the return an investor would need to achieve to be indifferent about when to take benefits, in this analysis we solve for the number of years the retiree would need to survive to be indifferent. We assume a 5% nominal return on the IRA based on achieving a target level of inflation-adjusted after-tax income every year. The desired after-tax income amount is 55
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based on the initial income for the first year, which is then increased by inflation (3%) during retirement. The SS payment amounts are the other key base assumption, where different amounts of SS income are assumed for different after-tax income targets. The remaining variable, or “plug,� is the value that must be withdrawn from savings in order to fund the remaining target after-tax income need. For this analysis we assume all savings are in a Traditional (pre-tax) IRA. The amount required to be withdrawn from the IRA is the difference between the SS benefit and the target after-tax income goal. The three base scenarios are conducted for a single couple and married couple, since the tax implications differ between the two household types. The results from the analysis are included in Table 2.
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Table 2: Breakeven Social Security Years Incorporating Taxes Married Couple, 62 vs 66 Total Annual Social Security Benefit at Earliest Age (000s) $10 $20 $30 $40 $50 $60 Target $20 18.2 n/a n/a n/a n/a n/a After-Tax $40 19.3 21.9 20.6 n/a n/a n/a Income $60 18.1 18.1 18.1 13.6 18.3 n/a (000s) $80 18.1 18.1 18.1 18.1 18.1 18.1 Married Couple, 62 vs 70 Total Annual Social Security Benefit at Earliest Age (000s) $10 $20 $30 $40 $50 $60 Target $20 20.8 n/a n/a n/a n/a n/a After-Tax $40 21.8 24.0 22.9 n/a n/a n/a Income $60 20.7 20.7 17.9 18.1 22.1 n/a (000s) $80 20.7 20.7 20.7 20.7 20.7 17.8 Married Couple, 66 vs 70 Total Annual Social Security Benefit at Earliest Age (000s) $10 $20 $30 $40 $50 $60 Target $20 18.8 n/a n/a n/a n/a n/a After-Tax $40 20.0 22.6 21.3 n/a n/a n/a Income $60 18.8 18.8 18.8 13.9 18.9 n/a (000s) $80 18.8 18.8 18.8 18.8 18.8 18.8
Target After-Tax Income (000s)
Target After-Tax Income (000s)
Target After-Tax Income (000s)
$20 $40 $60 $80
Single, 62 vs 66 Total Annual Social Security Benefit at Earliest Age (000s) $5 $10 $15 $20 $25 $30 19.2 21.7 20.5 n/a n/a n/a 18.1 18.1 18.1 13.4 18.1 16.0 18.2 18.1 18.1 18.1 18.1 18.1 18.2 18.1 18.1 18.1 18.1 18.1
$20 $40 $60 $80
Single, 62 vs 70 Total Annual Social Security Benefit at Earliest Age (000s) $5 $10 $15 $20 $25 $30 21.5 23.7 22.7 n/a n/a n/a 20.7 20.7 17.8 17.8 20.0 20.9 20.7 20.7 20.7 20.7 20.7 20.7 20.7 20.7 20.7 20.7 20.7 20.7
$20 $40 $60 $80
Single, 66 vs 70 Total Annual Social Security Benefit at Earliest Age (000s) $5 $10 $15 $20 $25 $30 19.5 22.0 20.9 n/a n/a n/a 18.8 18.8 18.8 13.7 18.8 16.5 18.9 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8
As the reader can see from Table 2, the number of years required to breakeven can differ significantly within the same base scenario (e.g., 62 vs 66, 62 vs 70, and 66 vs 70) based on tax implications. For example, the minimum number of breakeven years in the 62 vs 66 scenario was 13.6 years for a couple targeting an after-tax income of $60,000 with $40,000 in SS benefits. In contrast, in that same scenario, the maximum number of breakeven years was 21.9. This difference (7.3 years) represents an increase of 53.7% more years from the minimum (13.6 years) to the maximum scenario. This is due to the tax torpedo that causes benefits to be taxed at higher rates at certain income levels. The disparity of years to breakeven was similar among the Single scenarios. 57
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Taxes had virtually no impact for the Single scenarios with after-tax incomes above $60,000 or the Married Filing Jointly with after-tax incomes above $80,000. However, taxes were material in some cases below those levels, because benefits would be subject to full taxation at this point. Introducing Mortality The previous two sections explored the potential benefits of delayed SS based on an IRR calculation and then the impact of taxes was reviewed. In this section the implications of life expectancy (i.e., mortality experienced) are reviewed given different rates of return on invested benefits. Life expectancy is an important consideration for anyone deciding whether to delay the receipt of SS benefits because he or she must live long enough after the potential initial beginning age (i.e., age 62) to make the delay worthwhile. For this analysis, life expectancies were based primarily on the 2007 SSA Periodic Life Table16 but we also consider the life expectancies from the Society of Actuaries Annuity 2000 Mortality Table. The SSA Periodic Life Table contains the life expectancies for the average American, while the Annuity 2000 Mortality Table includes life expectancies based on the longevity of “healthier� people who are more likely to purchase annuities. Kreuger (2011) has noted that the Annuity 2000 Mortality Table is often used as the basis for mortality projections for healthier, more affluent (middle to upper class) populations. The differences in the respective survival probabilities for the two mortality tables are noted in Appendix I.
16
http://www.ssa.gov/oact/STATS/table4c6.html
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While SS benefits are the same regardless of gender, this analysis will focus on males and females separately. Females have longer life expectancies than males, and therefore the potential benefits from delaying retirement should be higher for females. This analysis does not consider potential survivor benefits, since these are reviewed separately. For the analysis, the same three base scenarios are tested (62 vs 66, 62 vs 70, and 66 vs 70). Since inflation is assumed to be a constant 3% the only variable that changes within each simulation is how long the retiree is assumed to live. If we change the discount rate within a simulation and run a large number of simulations we can determine, for each discount rate, the probability of an individual being better off delaying SS retirement benefits or taking them earlier. These results are included in Figure 3.
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Figure 3: Probability of Being Better off Delaying Social Security Benefits for Various Returns Panel A: Male Panel B: Female
Probability of Being Better of Delaying Benefits
100% 75% 50% 25% 0% -10.0%-5.0%0.0% 5.0%10.0% 62 vs 66
62 vs 70
66 vs 70 Nominal Rate of Return
Probability of Being Better of Delaying Benefits
100% 75% 50% 25% 0% -10.0%-5.0% 0.0% 5.0%10.0% 62 vs 66
62 vs 70
66 vs 70 Nominal Rate of Return
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The higher the return the retiree expects to achieve on his or her invested benefits, the better off he or she is likely going to be taking SS benefits earlier versus later. The results from Figure 3 suggest that an investor must believe he or she will achieve returns around 7.0%-8.3% or higher in order to be better off receiving SS benefits early (at age 62) versus delaying benefits to FRA (age 66). In contrast, in the 66 vs 70 scenario, the investor would only need returns in the 4.6%6.6% range or higher to be better off delaying benefits. This is because despite the fact they are both four-year periods, the mortality experience changes for older individuals affecting the required returns. It should not surprise the reader that the required returns are higher for females, when compared to males, because females have longer life expectancies, on average, than males. We can use the values in Figure 3 to determine various “breakeven returns,� which is the required compounded nominal return on invested benefits where the retiree would be better off delaying benefits 50% of the time. The breakeven return for the 62 vs 66 scenario was 7.0% for males versus 8.3% for females. A more complete set of breakeven returns are included in Table 3. As a reminder, the base breakeven assumption is based on a 50% probability of being better off.
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Choice
Mortality Based on the Social Security Adminstration 2007 Perodic Life Table Male Female Probability of Being Better of Probability of Being Better of Delaying Benefits Delaying Benefits 75% 60% 50% 40% 25% 75% 60% 50% 40% 25% 62 vs 66 -0.7% 4.4% 7.0% 8.3% 8.7% 62 vs 66 2.8% 6.8% 8.3% 8.9% 9.1% 62 vs 70 -8.5% 2.0% 5.6% 7.7% 8.5% 62 vs 70 0.3% 5.5% 7.6% 8.5% 8.9% 66 vs 70 -6.0% 0.8% 4.6% 6.9% 7.9% 66 vs 70 -0.4% 4.1% 6.6% 7.9% 8.4% Mortality Based on the Society of Actuaries 2000 Annuity Table Male Female Probability of Being Better of Probability of Being Better of Delaying Benefits Delaying Benefits 75% 60% 50% 40% 25% 75% 60% 50% 40% 25% 62 vs 66 2.9% 6.8% 8.3% 8.9% 9.1% 62 vs 66 5.6% 8.4% 9.0% 9.3% 9.4% 62 vs 70 0.4% 5.6% 7.6% 8.6% 8.9% 62 vs 70 4.4% 7.4% 8.6% 9.0% 9.2% 66 vs 70 -0.4% 4.1% 6.7% 8.0% 8.4% 66 vs 70 2.8% 6.3% 7.9% 8.6% 8.8% Choice
Choice
Choice
Table 3: The Breakeven Return to be Better of Delaying Benefits for Various Life Expectancy Probabilites
Table 3 includes the required returns for various probabilities for both the SSA 2007 Periodic Life Table as well as the Society of Actuaries 2000 Annuity Table. As noted previously, the 2000 Annuity Table includes mortality rates for healthier Americans (i.e., those who would generally consider purchasing annuities). Since the life expectancies are longer, the required returns are higher (since the higher benefits from delaying retirement are going to be received for a longer time period, on average). Stated differently, the longer the retiree is expects to live, the higher the return he or she must be able to earn on the invested benefits in order to claim earlier. The breakeven rate serves as a “hurdle rate,� and if the retiree does not believe he or she can meet or exceed the breakeven rate, delaying benefits is going to be the best option. Focusing on the 50% probability, i.e., the return required for the retiree to be indifferent between delaying benefits, all but one of the three tests (66 vs 70) had a required 62
return less than the base target (5%). Since the required return for the 62 vs 66 is the highest this is the scenario where the retiree will benefit most from delaying benefits. These results suggest a retiree captures the greatest benefit from delaying benefits from 62 to 66, followed by delaying benefits from 62 to 70, and then 66 to 70. Insurance Perspective Social Security can be thought of as a form of insurance where inflation-adjusted income is guaranteed for life by the U.S. government. One potential option for a retiree who is interested in guaranteed lifetime income, but who would like to invest the monies for the short-term, is to claim benefits early, invest them for a time, and then use them to purchase an immediate annuity that has annual benefits that are adjusted for inflation. In this section we examine the return an investor would need to earn on benefits taken early to buy a large enough annuity to break even with a person who just delayed benefits. This analysis, and the resulting required returns, will differ from the previous test because the returns for this test are the annual compounded returns (after fees) the investor would need to achieve on invested benefits over just the period where the benefits are delayed. For example, in the 62 vs 66 scenario this would be a four-year period. In the previous analysis, the required return is the return that needs to be achieved on invested benefits over that individual’s entire retirement. Since SS benefits are increased annually for inflation, the immediate annuity benefits must also be increased for inflation. Automatic payment increases, or cost-of-living adjustment riders, are relatively rare in immediate annuities 63
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purchased in the United States. According to a report by LIMRA (2010), only 7% of immediate annuity contracts issued in 2008 and 2009 included any type of payment increase rider. One company that offers immediate annuities with inflation protection (tied to the Consumer Price Index, which is the inflation factor for SS) is Principal. The costs of these annuities are available online17 and can be used to determine the potential cost (or benefit) from delaying the immediate annuity purchase decision.
Initial Benefit as a Percentage of Cost
Figure 4: Income Rates for Inflation-Adjusted Immediate Annuities 8% 7% 6% 5% 4% 3% 2% 1% 0% 55
60 Male
65 70 Age 100% Joint and Survivor (J&S)
Female
75
Source: Principal
Figure 4 includes the cost of an immediate inflationadjusted annuity for a male, female, and joint couple (male and 17
http://www.principal.com/retirement/incomeannuity/elm/income.htm. It is worth noting the Principal rates are very completive. The author received quotes from Vanguard for different insurance companies offering inflation adjusted annuities and Principal generally offered the highest payout rate.
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female, the same age, with 100% survivor benefit) as of August 1, 2012. These rates reflect the income as a percentage of the cost of the annuity. Given the payout rates, it is possible to determine the annual compounded return (net of fees) an investor must achieve to purchase an annuity at the delayed date to provide the same amount of guaranteed income. Note, this analysis does not consider the tax implications of an annuity, which is going to be less advantageous than SS, but it does provide insight as to the return required to generate the same benefit time (i.e., guaranteed inflation-adjusted income for life). This is a concept similar to what has been explored by Sass (2012) in a piece titled “Should You Buy an Annuity from Social Security?� For example, the inflation-adjusted annuity income rate for a 66-year-old male is 4.83%. Therefore, every $1 of inflation-adjusted lifetime income will cost 20.69 times that amount at age 66 (1 / 4.83% = 20.69). If we assume a monthly benefit of $750 is available at age 62 versus $1,000 at age 66, we can determine how much an investor must earn on that $750 per month over the four years in order to purchase an annuity at age 66 to provide $250 ($1,000-$750) per month in inflation-adjusted income for life. Continuing with the previous example, if we assume an annual inflation of 3%, the $250 initial difference in monthly lifetime income at age 62 will increase to $281 in four years (at age 66), or to $3,368 on an annual basis ($281*12=$3,368). Given a purchase cost of 20.69 times (calculated in the previous paragraph), the retiree would need $69,686 to purchase an annuity at age 66 to create the same income he or she would have achieved by simply delaying benefits to full retirement age. The compounded annual return, net of fees, 65
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required for $750 to grow to $69,686 over four years is 31.73%. In other words, in order for an individual to be better off taking benefits early and purchasing an inflation-adjusted annuity versus delaying benefits until full retirement age, he or she would need to earn 31.73% per year on the invested early benefits to eventually purchase a large enough annuity given current annuity rates. It is worth noting that while a 3% assumed inflation rate is embedded in the primary analysis, if we remove inflation (i.e., assume the benefit payments stay constant) the required return, which would be the “real� or inflation-adjusted return, decreases to 28.02. This could be approximated linearly by just dividing the nominal return result by the inflation rate (((1+31.73%)/(1+3%))-1) = 27.89%, which is approximately the real return of 28.02%. Both nominal returns (assuming a 3% inflation rate) and real returns results are presented in Table 4. Nominal returns are the primary returns used for the analysis, though, because they are the way most people think about market returns and are less abstract than real returns. Unlike the previous analysis that assumes annual benefit payments, this analysis assumes monthly cash flows due to the limited time horizon to invest the monies. The current annuity rates are obviously very important because they determine the future cost of not delaying SS retirement benefits. Given the fact rates are currently near historic lows for annuities, the cost of this insurance is relatively high from a historical perspective. Should interest rates increase, the return required to purchase an annuity would decrease, ceteris paribus. In order to determine how the required return changes based on varying inflation-adjusted annuity income rates,
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different rates are tested, whereby the annuity rate is assumed to change. These results are included in Table 4. Table 4: Returns Required to Purchase and Inflation-Adjusted Annuity with Early Social Security Benefit Income
Change from Current Rates
Change from Current Rates
Annual Compounded Nominal Returns Required to Purchase an Equivalent InflationAdjusted Immediate Annuity at the Delayed Benefit Age (Inflation = 3%) Male Female 62 vs 66 62 vs 70 66 vs 70 62 vs 66 62 vs 70 66 vs 70 -0.5% 37.9% 18.0% 25.3% -0.5% 43.5% 20.7% 31.3% 0.0%* 31.7% 15.7% 20.3% 0.0%* 36.6% 18.1% 25.7% +0.5% 26.3% 13.6% 15.9% +0.5% 30.6% 15.9% 20.7% +1.0% 21.4% 11.7% 11.8% +1.0% 25.3% 13.8% 16.2% 9.9% 8.1% +1.5% 17.1% +1.5% 20.5% 11.8% 12.1% 8.2% 4.7% 8.4% +2.0% 13.1% +2.0% 16.2% 10.0%
Change from Current Rates
Change from Current Rates
Annual Compounded Real Returns Required to Purchase an Equivalent Inflation-Adjusted Immediate Annuity at the Delayed Benefit Age Male Female 62 vs 66 62 vs 70 66 vs 70 62 vs 66 62 vs 70 66 vs 70 -0.5% 34.0% 14.6% 21.7% -0.5% 39.4% 17.2% 27.6% 0.0%* 28.0% 12.4% 16.9% 0.0%* 32.8% 14.8% 22.1% +0.5% 22.7% 10.3% 12.6% +0.5% 26.9% 12.5% 17.3% 8.5% 8.7% +1.0% 18.0% +1.0% 21.8% 10.5% 12.9% 6.7% 5.1% 8.6% 9.0% +1.5% 13.8% +1.5% 17.2% 5.1% 1.8% 6.9% 5.4% +2.0% 9.9% +2.0% 13.0% 66 year old Male Rate* = 4.83% 70 year old Male Rate* = 5.72%
66 year old Female Rate* = 4.43% 70 year old Female Rate* = 5.18%
* Source: http://www.principal.com/retirement/incomeannuity/elm/income.htm
The results in Table 4 suggest that it is highly unlikely a retiree will be able to invest the early SS benefit monies and purchase an inflation-adjusted immediate annuity at the delayed benefit age given current annuity rates. The required annual nominal compounded returns are 31.7%, 15.7%, and 20.3% given current rates for the 62 vs 66, 62 vs 70, and 66 vs 70 scenarios, respectively. Even if rates increase by 1.0%, the 67
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required nominal returns to purchase an inflation-adjusted immediate annuity (ceteris paribus) would still be 21.4%, 11.7%, and 11.8%, respectively. Similar to previous tests, the highest required return (i.e., where the insurance is most valuable) is for the 62 vs 66 scenario. This suggests an individual interested in guaranteed lifetime income is better served delaying retirement, at least to FRA, in order to receive a higher eventual SS benefit versus trying to purchase an equivalent annuity. Interestingly, the scenario with the lowest required return was the 62 vs 70 scenario, which can partially be attributed to the decreasing impact of interest rates for older individuals. Modeling Spousal Survivor Benefits While spousal benefits are not directly affected by the primary worker’s claiming age, spousal survivor benefits are going to be affected because the surviving spouse is entitled to the greater of his or her benefit or his or her spouse’s benefit, assuming they have been married 10 years. Therefore, spousal benefits are an important consideration for the primary worker when determining at what age to claim SS benefits, because the potential benefit of the payments plus the potential residual benefit must be considered for a married individual (when his or her benefit is higher than his or her spouse’s) versus just the potential benefit of the payments for a single individual. In order to provide some insight as to the value of spousal survivor benefits a Monte Carlo simulation was performed. Similar to the previous single mortality analysis, the SSA 2007 Periodic Life Table is used as the primary table for mortality rates, although the Society of Actuaries Annuity 2000 Mortality Table is also considered because it better 68
approximates the life expectancies for healthier Americans. Life expectancies are an important consideration when modeling spousal survivor benefits because the spouse will only receive a spousal survivor benefit if he or she outlives his or her spouse. Therefore, the spousal survivor benefit is going to be more valuable for a primary worker with a spouse that is much younger (and female) versus a spouse that is older (and male). The first analysis to determine the potential value of spousal benefits is similar to the analysis conducted for Table 3, where the required nominal compounded return is determined based on various probabilities of being better off delaying benefits. However, unlike Table 3, this analysis incorporates a potential survivor spousal benefit when determining the breakeven return. The survivor benefit only has value, though, if the spouse lives longer than the primary worker. The first test assumes the spouse receives 100% of the delayed benefit. In this case, for example, for the spouse to receive 100% of the delayed benefit he or she would need to be receiving a SS benefit that is equal to or less than the benefit the primary worker would receive at the earlier retirement age considered for the scenario. For example, if the primary worker is eligible to receive a benefit of $750 per month at age 62 or $1,000 at age 66, the spouse would need to be receiving a benefit that is equal to or less than $750 for this test. For this test, spousal ages ranging between where the spouse is eight years younger and eight years older are tested. Within a given simulation, the spouse is assumed to begin receiving the spousal survivor benefit as early as possible 69
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following the death of the primary worker. This could be as early as age 60, which is the earliest a person can receive spousal survival benefits given an assumed FRA of 66. The age the survivor spousal benefits begin (if at all) is going to vary by simulation. The simulations are based on the idea that either the primary worker is male or female. If the primary worker is assumed to be male, the spouse is assumed to be female, and vice versa. This approach allows us to model the different potential spousal combinations that exist. Table 5 contains the results of this analysis.
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Table 5: Breakeven Return Required to be Better off Delaying Benefits for Various Simulation Probabilities Assuming Spouse Receives Full Incremental Increase of the Delayed Benefit
Spouse Age Differemce versus Early Claiming Age
Female is Primary Worker: 62 vs 70 Probability of Being Better off Delaying Benefits 75% 60% 50% 40% 25% (Younger Spouse) -8 7.1% 9.0% 9.2% 9.3% 9.3% -4 7.0% 8.9% 9.2% 9.2% 9.3% -2 6.6% 8.7% 9.1% 9.2% 9.2% 0 6.2% 8.4% 8.9% 9.1% 9.2% +2 5.8% 8.1% 8.8% 9.0% 9.1% +4 5.4% 7.9% 8.7% 8.9% 9.0% (Older Spouse) +8 4.5% 7.4% 8.4% 8.8% 8.9% Female is Primary Worker: 66 vs 70 Probability of Being Better off Delaying Benefits 75% 60% 50% 40% 25% (Younger Spouse) -8 5.4% 8.0% 8.8% 9.1% 9.2% -4 5.7% 8.2% 8.7% 8.9% 8.9% -2 5.2% 7.9% 8.5% 8.8% 8.8% 0 4.7% 7.5% 8.3% 8.6% 8.7% +2 4.1% 7.2% 8.1% 8.5% 8.7% +4 3.5% 6.8% 7.9% 8.4% 8.6% (Older Spouse) +8 2.4% 6.1% 7.6% 8.2% 8.4%
Spouse Age Differemce versus Early Claiming Age
Male is Primary Worker: 66 vs 70 Probability of Being Better off Delaying Benefits 75% 60% 50% 40% 25% (Younger Spouse) -8 5.0% 7.8% 8.7% 9.1% 9.2% -4 5.9% 8.3% 8.8% 9.0% 9.0% -2 5.4% 8.0% 8.6% 8.8% 8.9% 0 4.7% 7.6% 8.4% 8.7% 8.8% +2 4.1% 7.2% 8.1% 8.5% 8.6% +4 3.3% 6.7% 7.9% 8.3% 8.5% (Older Spouse) +8 1.9% 5.7% 7.3% 8.0% 8.2%
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Male is Primary Worker: 62 vs 70 Probability of Being Better off Delaying Benefits 75% 60% 50% 40% 25% (Younger Spouse) -8 7.0% 9.0% 9.2% 9.3% 9.3% -4 7.4% 9.1% 9.3% 9.3% 9.3% -2 6.9% 8.8% 9.1% 9.2% 9.3% 0 6.3% 8.4% 9.0% 9.1% 9.2% +2 5.7% 8.1% 8.8% 9.0% 9.1% +4 5.2% 7.8% 8.6% 8.9% 9.0% (Older Spouse) +8 4.1% 7.1% 8.2% 8.6% 8.8%
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Mortality Based on the Social Security Adminstration 2007 Perodic Life Table Male is Primary Worker: 62 vs 66 Female is Primary Worker: 62 vs 66 Probability of Being Better off Probability of Being Better off Delaying Benefits Delaying Benefits 75% 60% 50% 40% 25% 75% 60% 50% 40% 25% (Younger Spouse) (Younger Spouse) -8 6.1% 9.2% 9.5% 9.6% 9.6% -8 7.0% 9.5% 9.5% 9.5% 9.4% -4 6.9% 9.0% 9.3% 9.4% 9.4% -4 7.3% 9.2% 9.4% 9.3% 9.3% -2 7.1% 9.1% 9.2% 9.2% 9.2% -2 7.3% 9.2% 9.3% 9.3% 9.2% 0 7.3% 9.2% 9.3% 9.2% 9.2% 0 7.3% 9.2% 9.3% 9.2% 9.2% +2 6.8% 8.9% 9.1% 9.2% 9.2% +2 6.8% 8.9% 9.1% 9.2% 9.2% +4 6.4% 8.6% 8.9% 9.0% 9.1% +4 6.5% 8.7% 9.0% 9.1% 9.1% (Older Spouse) (Older Spouse) +8 5.3% 8.0% 8.6% 8.8% 8.8% +8 5.7% 8.2% 8.8% 8.9% 9.0%
Same Age Breakeven Return
The required returns in Table 5 are higher than those in Table 3, to varying degrees. We should expect this because these required returns (in Table 5) consider both the primary worker’s benefit as well as the spousal benefit versus just the 71
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primary worker’s benefit. For example, the breakeven (50% probability) required compounded nominal return for the 62 vs 66 scenario not considering spousal benefits (Table 3) was 7.0%. If we assume both spouses are the same age and the primary worker is a male, the breakeven return increases to 9.3% (Table 5). While an increase in the required return from 7.0% (single worker) to 9.3% (married worker) is notable, the increases for the other two scenarios (62 vs 70 and 66 vs 70) were more significant. For example, the breakeven (50% probability) nominal required compounded return for the 66 vs 70 scenario not considering spousal benefits (Table 3) was 4.6%, and was the lowest breakeven required return across the three scenarios. Given that this return was below our target return for the analysis (5%), it would likely be worthwhile for a single investor to not delay benefits past age 66. However, when spousal benefits are considered and the primary worker is male, the required compounded nominal return increases from 4.6% to 8.4%, which is 83% higher than the single breakeven return (4.6%). In fact, when looking at the probabilities, there is a 75% chance the primary worker would be better off delaying benefits if he can earn a return of 4.7% or more (assuming the spouse is the same age). Unlike Table 3, none of the returns at the 75% likelihood level are negative in Table 5. This means the required return is much higher for even the worst one-in-four scenarios. The breakeven returns increase even more when mortality is based on the Society of Actuaries Annuity 2000 Mortality Table. These results are included in Appendix 2, and are an abbreviated version of the results in Table 5, whereby only the breakeven returns (50% probability) are included. 72
The previous analysis contained information about the compounded annual nominal return required to break even when the surviving spouse receives the entire incremental benefit from the decision about whether the primary worker delays benefits. What if, though, the spouse expects to receive less than the full incremental increase because his or her own benefit is relatively high? In this case there would still be some advantage to delaying benefits, but less depending on the spouse’s own benefit. For example, assume a male worker can receive $750 per month at age 62 or $1,000 at age 66 while the spouse is eligible for $900 per month. If the primary worker takes benefits early, then the spouse will stick with his or her own $900 benefit. However, if the primary worker delays benefits, then the incremental increase to the spouse is only $100 more ($1,000-$900 =$100), not the full $250. So, the spouse captures 40% of the primary worker’s incremental benefit increase as calculated using equation 1: I∗
max
,
,0 (1)
Where: I ∗ = Incremental increase in spousal survivor benefit D = Primary worker’s delayed SS benefit S = Spouse’s SS benefit E = Primary worker’s early SS benefit Note the incremental increase in spousal survivor benefit (I ∗ ) cannot be negative. If the spousal SS benefit (S ) is greater than the primary worker’s delayed benefit (D ), then the incremental increase is zero (since it is bounded by the maximum function). Also, if the spousal benefit (S ) is less 73
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than the primary worker’s early SS benefit (E ), the primary worker’s early SS benefit should be considered for the incremental increase value (I ∗ ) since the primary worker’s early SS benefit (E ) is what the spouse would receive upon the death of the primary worker. The objective of this analysis is to determine how the required return changes based on varying levels of I ∗ . The breakeven required compounded nominal returns for various I ∗ values are included in Table 6. These are the values that correspond to a 50% probability of the primary worker being better off either delaying benefits or taking them early, i.e., the return that should make the primary worker indifferent between delaying benefits or taking them early.
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Table 6: Breakeven Return Required to be Better off Delaying Benefits for Various Simulation Probabilities Assuming Spouse Receives Only Some Percentage of the Incremental Delayed Benefit Increase
Spouse Age Differemce versus Early Claiming Age
Female is Primary Worker: 62 vs 70 Incremental Increase in Spousal Survivor Benefit (I*) 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 7.6% 8.2% 8.4% 8.7% 9.0% 9.2% -4 7.6% 8.0% 8.2% 8.5% 8.8% 9.2% -2 7.6% 7.9% 8.2% 8.5% 8.8% 9.1% 0 7.6% 7.9% 8.2% 8.5% 8.7% 8.9% +2 7.6% 7.9% 8.2% 8.4% 8.6% 8.8% +4 7.6% 7.9% 8.1% 8.3% 8.5% 8.7% (Older Spouse) +8 7.6% 7.8% 8.0% 8.1% 8.2% 8.4% Female is Primary Worker: 66 vs 70 Incremental Increase in Spousal Survivor Benefit (I*) 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 6.6% 7.4% 7.9% 8.3% 8.6% 8.8% -4 6.6% 7.3% 7.7% 8.0% 8.4% 8.7% -2 6.6% 7.3% 7.7% 8.0% 8.3% 8.5% 0 6.6% 7.2% 7.6% 7.9% 8.2% 8.3% +2 6.6% 7.2% 7.5% 7.7% 8.0% 8.1% +4 6.6% 7.1% 7.3% 7.6% 7.8% 7.9% (Older Spouse) +8 6.6% 7.0% 7.1% 7.3% 7.5% 7.6%
Spouse Age Differemce versus Early Claiming Age
Male is Primary Worker: 66 vs 70 Incremental Increase in Spousal Survivor Benefit (I*) 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 4.6% 6.2% 7.2% 7.9% 8.4% 8.7% -4 4.6% 6.3% 7.2% 7.8% 8.4% 8.8% -2 4.6% 6.3% 7.3% 7.9% 8.3% 8.6% 0 4.6% 6.3% 7.1% 7.6% 8.1% 8.4% +2 4.6% 6.2% 6.9% 7.4% 7.9% 8.1% +4 4.6% 6.1% 6.7% 7.2% 7.6% 7.9% (Older Spouse) +8 4.6% 5.7% 6.2% 6.7% 7.1% 7.3%
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Male is Primary Worker: 62 vs 70 Incremental Increase in Spousal Survivor Benefit (I*) 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 5.6% 7.1% 8.0% 8.4% 8.9% 9.2% -4 5.6% 7.0% 7.7% 8.3% 8.8% 9.3% -2 5.6% 6.9% 7.6% 8.3% 8.8% 9.1% 0 5.6% 6.8% 7.7% 8.3% 8.7% 9.0% +2 5.6% 7.0% 7.7% 8.1% 8.5% 8.8% +4 5.6% 6.9% 7.5% 8.0% 8.3% 8.6% (Older Spouse) +8 5.6% 6.7% 7.2% 7.6% 7.9% 8.2%
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Mortality Based on the Social Security Adminstration 2007 Perodic Life Table Female is Primary Worker: 62 vs 66 Male is Primary Worker: 62 vs 66 Incremental Increase in Spousal Incremental Increase in Spousal Survivor Benefit (I*) Survivor Benefit (I*) 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 7.0% 7.6% 8.0% 9.1% 9.4% 9.5% (Younger Spouse) -8 8.3% 8.4% 8.7% 9.3% 9.4% 9.5% -4 7.0% 7.8% 8.4% 8.8% 9.1% 9.3% -4 8.3% 8.6% 8.8% 9.0% 9.2% 9.4% -2 7.0% 7.8% 8.3% 8.6% 9.0% 9.2% -2 8.3% 8.6% 8.7% 8.9% 9.1% 9.3% 0 7.0% 7.7% 8.1% 8.5% 9.0% 9.3% 0 8.3% 8.5% 8.6% 8.8% 9.1% 9.3% +2 7.0% 7.7% 8.2% 8.5% 8.9% 9.1% +2 8.3% 8.4% 8.6% 8.8% 9.0% 9.1% +4 7.0% 7.7% 8.1% 8.4% 8.7% 8.9% +4 8.3% 8.4% 8.5% 8.7% 8.8% 9.0% (Older Spouse) (Older Spouse) +8 7.0% 7.5% 7.8% 8.1% 8.4% 8.6% +8 8.3% 8.4% 8.4% 8.6% 8.6% 8.8%
The reader should note that the 0% I* values in Table 6 are effectively the breakeven values for single workers since there is effectively no spousal survivor benefit. These are breakeven returns noted in Table 3. The 100% I* values in 75
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Table 6 correspond to the breakeven values where the spouse receives the entire incremental benefit from the primary worker delaying retirement, which is the same as the results in Table 5. The results for the Society of Actuaries Annuity 2000 Mortality Table are included in Appendix III. The simplest approach to determining the appropriate breakeven return for various I* values would be to weight the values in Table 3 and Table 5 respectively. While this would provide some indication of the additional value in the relationship, the change is not completely linear. Half of the increase in the breakeven return is achieved with I* values that are roughly 35%. In other words, even a small potential increase in survivor benefits has a meaningful impact on the required return. The “Gamma” Impact Blanchett and Kaplan (2012), BK herein, introduce a metric they call “Gamma” to measure the additional expected retirement income achieved by an individual investor from making intelligent financial planning decisions. In their initial research on the topic, BK focus on five fundamental financial planning techniques for retirees: a total wealth framework to determine the optimal asset allocation, a dynamic withdrawal strategy, incorporating guaranteed income products (i.e., annuities), tax-efficient decisions, and liability-relative asset allocation optimization. They find following a “Gamma optimized” approach can yield 29% more income for a retiree on a utility-adjusted basis, which is equivalent to achieving an annual return increase of 1.82%. This “Gamma equivalent alpha” of 1.82% represents the value a client could realize from working with a competent financial planner (or managed accounts provider). 76
In order to determine the potential “Gamma” available to retirees through optimal Social Security claiming an additional analysis is conducted using many of the base assumptions in BK, where an optimal approach (delaying SS benefits to FRA, or age 66) is compared to a “naïve” strategy, which is claiming benefits at age 62 (which is the most popular SS claiming age). For the Gamma analysis we assume the primary worker is a male and the spouse is a female, both of which are the same age. For the naïve (i.e., “non-optimal”) strategy the primary worker will receive a benefit that is the 25% less than the FRA benefit from claiming at age 62 and the spousal benefit (50% of the primary worker’s benefit) will be reduced by 30% from claiming at age 6518. The primary worker’s SS benefit amount is assumed to be $20,000 at FRA, and we apply the reductions to the actual benefit versus the PIA for simplicity purposes. Similar to BK, we use the Annuity 2000 mortality table for the analysis. In order to determine the benefit of delayed SS retirement benefits we calculate the median weighted net present value of expected benefits for the primary worker (including the spousal survivor benefit) and spouse for the two scenarios at age 62. Age 62 is used as a base age, because there is a “cost” associated with delaying SS benefits if the retiree dies during the period he or she could have already began commencing benefits. The analysis is conducted in real terms (i.e., today’s dollars) where the nominal return is 5% and inflation is 3%, thereby the real discount rate is 2%. These are the same general return assumptions for the original analysis and the return for the base portfolio in the original BK study. Table 7 contains the differences in the benefit values. 18
http://www.ssa.gov/oact/quickcalc/earlyretire.html 77
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Table 7: Net Present Value Benefit Differences
Claim Early Claim at FRA Difference
Primary $310,603 $337,983 $27,380
Spouse $128,045 $144,845 $16,799
Total $438,649 $482,828 $44,179
Delaying SS benefits to FRA would generate a median mortality weighted net present value in benefits that is $44,179 more than claiming early. In the original BK analysis the median mortality weighted net present value of SS benefits was equal to the value of the portfolio, therefore, following this line of reasoning the increase in the total SS benefits ($44,179), would represent an equivalent increase in assets to fund retirement (i.e., retirement income) of 9.15%. This 9.15% increase represents an example of the “Gamma� achievable to the client making an optimal, or at least more optimal, decision regarding when to claim SS benefits. In order to determine how much additional annual return, or alpha, that would need to be generated to create 9.15% more income, a Monte Carlo simulation is conducted. For the simulation, we compare the amount of income generated from a portfolio invested in 20% equities with a 4% initial withdrawal rate, where subsequent withdrawals are based on the original withdrawal amount but increased for inflation. We then change the average portfolio return by -2%, -1%, 0% (no change), +1%, and +2% to determine the impact the return differences have on the amount of income generated. These results are included in Figure 4.
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Figure 4: Relationship Between Change in Retirement Income and Changes in Returns for a 4% Initial Withdrawal 2.0% 1.5%
Change in Return
1.0% 0.5% 0.0% -0.5% -1.0% -1.5% -2.0% -20%
-10%
0%
10%
20%
30%
Median Change in Retirement Income
By fitting a third-order polynomial to the curve in Figure 4, we estimate the equivalent annual return impact of a +9.15% increase in retirement income to be +.74%. This .74% is “Gamma equivalent alpha” which represents the equivalent effective alpha an advisor would have to generate to create 9.14% more income for a client during retirement. An advisor able to generate +.74% of alpha each year during retirement, which could easily exceed 20 years, would widely be viewed to have added significant value for his client. In this case, the .74% of “alpha” can be achieved by simply helping a client make a more optimal SS claiming decision. Unlike traditional alpha, this “Gamma equivalent alpha” is not a zero-sum game.
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Additional Considerations With the decline of defined benefit plans and pension plans Americans are increasingly forced to manage their own retirement assets and create their own distribution strategies. While there are certainly benefits that can be achieved with this type of flexibility, research is starting to note significant potential costs with this “self-funding” approach. For example, Korniotis and Kumar (2011) note that while older investors tend to have more experience, the adverse effects of cognitive aging dominate the positive effects of experience. They conclude that older investors are likely to experience 3-5% lower annual returns on a risk-adjusted basis. This topic has also been explored by Finke, Howe, and Huston (2011) who note that financial decision making is a form of crystallized intelligence that requires both memory and problem-solving skills. Unfortunately, they find financial literacy scores decline by about 2% each year after age 60. This research suggests older retirees are likely better served having a retirement income strategy that is professionally managed and does not require on-going decision making. Delaying benefits and effectively “purchasing a SS annuity” is one way to remove the withdrawal/portfolio burden from a retiree since the income would be transferred from a portfolio (which would be used to fund the early years in retirement) to a form of guaranteed income (SS). The analysis conducted for this paper was rather robust, yet it still is only able to provide general guidance for an individual seeking to determine when to claim SS benefits. Most people would likely be best served engaging an independent financial planner to assist with this decision. 80
Additionally, there are online calculators that can help someone make the best decision possible. One example is http://maximizemysocialsecurity.com, which takes into account “SS's earnings test, reductions for early retirement, recomputation of benefits, delayed retirement credit, family benefit maximum, windfall elimination provision, government pension offset provision, and option to file and suspend.” Conclusions The gains that can be achieved from selecting the optimal age to commence Social Security (SS) benefits can be significant. This decision becomes even more important when considering the fact SS is the largest retirement income source for Americans. The factors affecting the SS claiming decision were explored in this paper, such as life expectancy, taxes, and the benefits available to a surviving spouse, primarily using a concept known as the “breakeven return.” The breakeven return is the annual nominal compounded rate of return a retiree would need to earn over his or her retirement, after fees, to be indifferent between claiming benefits early versus delaying benefits. We find the effective “return” achieved by a retiree from making the optimal SS claiming decision is likely to exceed the return the retire can make by taking benefits early and investing them, especially in today’s low interest rate environment. We find an investor would likely need to earn an annual nominal compounded rate of return, net of fees, of over 7% in order to be better off claiming benefits early. We also conduct an analysis to determine the “financial planning alpha,” or Gamma, generated by optimal SS claiming. We find that delaying benefits can generate 9.15% more income for a 81
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hypothetical married couple, which is equivalent to increasing returns (i.e., alpha) by +.74% per year in retirement. In summary, we find that most retirees are going to be better served delaying SS benefits until Full Retirement Age or later. Delayed SS benefits are especially valuable for females, married couples, and investors who expect to invest in relatively conservative portfolios during retirement. Delayed SS benefits can enable an investor to effectively achieve a rate of return that is much greater than he or she is likely to earn in the market. SS benefits can also provide a valuable hedge against longevity risk and offer a form of protection from the adverse effects of cognitive decline at older ages. References Blanchett, David and Paul Kaplan. 2012. “Alpha, Beta, and Now…Gamma.” Working Paper. http://corporate.morningstar.com/ib/documents/PublishedResearch/Alp haBetaandNowGamma.pdf Butrica, Barbara A., Howard M. Iams, and Karen E. Smith. 2004. “The Changing Impact of Social Security on Retirement Income in the United States.” Social Security Bulletin, vol. 65, no. 3. Cunningham, Donald F. and Erickson, Paul R. 2009. “The ‘Combined Income’ Tax Effect on Early versus Normal Social Security Benefits for Single Individuals.” Journal of Financial Service Professionals, March: 49-57. Docking, Diane, Richard Fortin, and Stuart Michelson. 2011. “The Influence of Gender and Race on the Social Security Early Retirement Decision for Single Individuals.” http://www.academyfinancial.org/11Conference/11Proceedings/(B4)% 20Docking,%20Fortin,%20Michelson.pdf Entmacher, Joan. 2009. “Strengthening Social Security Benefits for Widow(er)s: The 75 Percent Combined Worker Benefit Alternative.” January. http://www.nasi.org/sites/default/files/research/Strengthening_Social_S ecurity_for_Vulnerable_Groups.pdf Finke, Michael, John Howe, and Sandra Huston. 2011. “Old Age and the Decline in Financial Literacy.” Available at SSRN: http://ssrn.com/abstract=1948627 82
Jennings, William W., and William Reichenstein. 2001. "Estimating the Value of Social Security Retirement Benefits." Journal of Wealth Management, vol. 4 (Winter): 14–29. Korniotis, G.M. and Kumar, A., 2011. “Do Older Investors Make Better Investment Decisions?” The Review of Economics and Statistics, vol. 93, no. 1: 244-265. Kreuger, C. 2011. “Mortality Assumptions: Are Planners Getting it Right?” Journal of Financial Planning, vol. 24, 36-37. LIMRA. 2010. “Guaranteed Income Annuities Report.” http://www.limra.com/research/retirement.aspx Mahaney, James and Peter Carlson. 2007. “Rethinking Social Security Claiming in a 401(k) World.” Pension Research Council Working Paper. PRC WP 2007-18. McCormack, Joseph P. and Perdue, Grady. 2006. “Optimizing the Initiation of Social Security Benefits.” Financial Services Review, vol. 15: 335348. Meyer, William, and William Reichenstein. 2010. "Social Security: When to Start Benefits and How to Minimize Longevity Risk." Journal of Financial Planning, vol. 23 (March): 49–59. Meyer, William and William Reichenstein. 2012. “How the Social Security Claiming Decision Affects Portfolio Longevity.” Journal of Financial Planning, vol. 25, no. 4 (April): 53-60. Munnell, Alicia and Soto, Mauricio. 2007. “When Should Women Claim Social Security Benefits?” Journal of Financial Planning, vol. 20, no. 6: 58-65. Munnell, Alicia, Alex Golub-Sass, and Nadia Karamcheva. 2009. “Strange but True: Claim Social Security Now, Claim More Later.” April. Number 9-9. Rhee, Nari. 2011. “Meeting California’s Retirement Security Challenge.” http://laborcenter.berkeley.edu/research/CAretirement_challenge_1011. pdf Reno, Virginia and Joni Lavery .2010. “When to Take Social Security Benefits: Questions to Consider.” Social Security Brief, January, No 31. Rose, Clarence C. and Larimore, L. Keith. 2001. “Social Security Benefit Considerations in Early Retirement.” Journal of Financial Planning, vol. 14: 116-121. Ryan, Charles. 2010. “Social Security Reset: When Does It Make Sense?” Journal of Financial Planning, (June): 62-70. Sass, Steven A. 2012. “Should You Buy an Annuity from Social Security.” Number 12-10, May. Sun, Wei and Webb, Anthony. 2009. “How Much Do Households Really Lose by Claiming Social Security at Age 62?” Center for Retirement Research at Boston College, CRR WP 2009-11, March, 1-28. 83
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Important Disclosures Š2012 Morningstar. All rights reserved. This document includes proprietary material of Morningstar. Reproduction, transcription or other use, by any means, in whole or in part, without the prior written consent of Morningstar is prohibited. The Morningstar Investment Management division is a division of Morningstar and includes Morningstar Associates, Ibbotson Associates, and Morningstar Investment Services, which are registered investment advisors and wholly owned subsidiaries of Morningstar, Inc. The Morningstar name and logo are registered marks of Morningstar, Inc. The above commentary is for informational purposes only and should not be viewed as an offer to buy or sell a particular security. The data and/or information noted are from what we believe to be reliable sources, however Morningstar has no control over the means or methods used to collect the data/information and therefore cannot guarantee their accuracy or completeness. The opinions and estimates noted herein are accurate as of a certain date and are subject to change. The indexes referenced are unmanaged and cannot be invested in directly. Past performance is no guarantee of future results. This commentary may contain forward-looking statements, which reflect our current expectations or forecasts of future events. Forward-looking statements are inherently subject to, among other things, risks, uncertainties and assumptions which could cause actual events, results, performance or prospects to differ materiality from those expressed in, or implied by, these forward-looking statements. The forward-looking information contained in this commentary is as of the date of this report and subject to change. There should not be an expectation that such information will in all circumstances be updated, supplemented or revised whether as 84
a result of new information, changing circumstances, future events or otherwise. The results from the simulations described, while hypothetical in nature and not actual investment results or guarantees of future results, can provide an important look at strategies designed to help retirees reach their goals. Monte Carlo is an analytical method used to simulate random returns of uncertain variables to obtain a range of possible outcomes. Such probabilistic simulation does not analyze specific security holdings, but instead analyzes the identified asset classes. The simulation generated is not a guarantee or projection of future results, but rather, a tool to identify a range of potential outcomes that could potentially be realized. The Monte Carlo simulation is hypothetical in nature and for illustrative purposes only. Results noted may vary with each use and over time. This should not be considered tax or financial planning advice. Please consult a tax and/or financial professional for advice specific to your individual circumstances.
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Appendices
Male: SSA Periodic Life Table Current Age 60 65 70 75 80 65 93% 70 85% 90% 75 72% 77% 85% 80 56% 60% 67% 78% 85 37% 40% 44% 51% 66% 90 18% 20% 22% 26% 33% 95 6% 6% 7% 8% 10% 100 1% 1% 1% 1% 2% 105 0% 0% 0% 0% 0% 110 0% 0% 0% 0% 0%
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81% 55% 29% 11% 2% 0%
Female: SSA Periodic Life Table Current Age 60 65 70 75 80 65 96% 70 90% 94% 75 81% 84% 90% 80 68% 71% 76% 84% 85 51% 53% 56% 63% 74% 90 30% 31% 33% 37% 44% 95 12% 12% 13% 15% 17% 100 3% 3% 3% 3% 4% 105 0% 0% 0% 0% 0% 110 0% 0% 0% 0% 0%
Death Age (Neither Alive)
75% 47% 23% 8% 2% 0%
Female: Annuity 2000 Table Current Age 60 65 70 75 65 98% 70 94% 96% 75 88% 90% 94% 80 79% 81% 84% 89% 85 64% 65% 68% 72% 90 43% 44% 46% 49% 95 22% 23% 24% 25% 100 8% 9% 9% 9% 105 2% 2% 2% 2% 110 0% 0% 0% 0%
Death Age (Neither Alive)
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Death Age
Male: Annuity 2000 Table Current Age 60 65 70 75 65 96% 70 90% 94% 75 81% 84% 90% 80 68% 71% 75% 84% 85 51% 53% 56% 63% 90 32% 33% 36% 40% 95 16% 17% 18% 20% 100 6% 6% 6% 7% 105 1% 1% 1% 1% 110 0% 0% 0% 0%
Death Age
Death Age
Death Age
Appendix I: Survival Probabilities Joint: Annuity 2000 Table Joint Age 60 65 70 75 65 100% 70 99% 100% 75 98% 98% 99% 80 93% 94% 96% 98% 85 82% 84% 86% 90% 90 62% 63% 65% 69% 95 35% 36% 37% 40% 100 14% 14% 15% 16% 3% 3% 4% 105 3% 0% 0% 0% 110 0% Joint: SSA Periodic Life Table Joint Age 60 65 70 75 65 100% 70 98% 99% 75 95% 96% 99% 80 86% 88% 92% 96% 85 69% 72% 76% 82% 90 43% 45% 48% 53% 95 17% 18% 19% 21% 4% 4% 4% 100 4% 0% 0% 0% 105 0% 0% 0% 0% 110 0%
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95% 76% 45% 18% 4% 0%
80
91% 62% 26% 5% 1% 0%
Appendix 2: Breakeven Return Required to be Better off Delaying Benefits Assuming Spouse Receives Only Spousal Benefit
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Mortality Based on the Society of Actuaries 2000 Annuity Table Male is Primary Worker: 62 vs 66 Female is Primary Worker: 62 vs 66 Breakeven Return Breakeven Return 62 vs 66 62 vs 70 66 vs 70 62 vs 66 62 vs 70 66 vs 70 (Younger Spouse) (Younger Spouse) 10.3% 9.6% 9.4% 10.4% 9.5% 9.4% -8 -8 10.0% 9.5% 9.4% 9.9% 9.4% 9.4% -4 -4 9.7% 9.4% 9.3% 9.7% 9.4% 9.2% -2 -2 9.6% 9.5% 9.0% 9.6% 9.5% 9.0% 0 0 9.4% 9.4% 8.9% 9.4% 9.4% 8.9% +2 +2 9.4% 9.2% 8.6% 9.4% 9.3% 8.7% +4 +4 (Older Spouse) (Older Spouse) 9.2% 8.8% 8.2% 9.4% 9.0% 8.5% +8 +8
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Appendix III: Breakeven Return Required to be Better of Delaying Benefits for Various Simulation Probabilities Assuming Spouse Receives Only Some Percentage of the Incremental Delayed Benefit Increase
Spouse Age Differemce versus Early Claiming Age
Female is Primary Worker: 62 vs 70 % of Delayed Incremental Benefit Increase Received by the Spouse 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 8.6% 9.0% 9.2% 9.4% 9.4% 9.5% -4 8.6% 8.8% 9.0% 9.2% 9.6% 9.4% -2 8.6% 8.8% 8.9% 9.2% 9.5% 9.4% 0 8.6% 8.7% 8.9% 9.1% 9.4% 9.5% +2 8.6% 8.7% 8.9% 9.0% 9.2% 9.4% +4 8.6% 8.7% 8.8% 9.0% 9.1% 9.3% (Older Spouse) +8 8.6% 8.7% 8.7% 8.8% 8.9% 9.0% Female is Primary Worker: 66 vs 70 % of Delayed Incremental Benefit Increase Received by the Spouse 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 7.9% 8.5% 8.7% 9.0% 9.3% 9.4% -4 7.9% 8.3% 8.5% 8.8% 9.1% 9.4% -2 7.9% 8.3% 8.5% 8.7% 9.0% 9.2% 0 7.9% 8.2% 8.4% 8.7% 8.8% 9.0% +2 7.9% 8.2% 8.4% 8.5% 8.7% 8.9% +4 7.9% 8.1% 8.3% 8.4% 8.6% 8.7% (Older Spouse) +8 7.9% 8.1% 8.1% 8.3% 8.4% 8.5%
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Spouse Age Differemce versus Early Claiming Age
Male is Primary Worker: 66 vs 70 % of Delayed Incremental Benefit Increase Received by the Spouse 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 6.7% 7.7% 8.3% 8.8% 9.2% 9.4% -4 6.7% 7.7% 8.1% 8.6% 9.1% 9.4% -2 6.7% 7.6% 8.1% 8.6% 8.9% 9.3% 0 6.7% 7.6% 8.0% 8.4% 8.7% 9.0% +2 6.7% 7.5% 7.9% 8.3% 8.6% 8.9% +4 6.7% 7.3% 7.7% 8.1% 8.4% 8.6% (Older Spouse) +8 6.7% 7.2% 7.5% 7.8% 8.0% 8.2%
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Male is Primary Worker: 62 vs 70 % of Delayed Incremental Benefit Increase Received by the Spouse 0% 20% 40% 60% 80% 100% (Younger Spouse) -8 7.6% 8.5% 8.7% 9.2% 9.4% 9.6% -4 7.6% 8.2% 8.6% 8.9% 9.6% 9.5% -2 7.6% 8.2% 8.5% 8.9% 9.5% 9.4% 0 7.6% 8.1% 8.5% 8.9% 9.3% 9.5% +2 7.6% 8.1% 8.5% 8.8% 9.1% 9.4% +4 7.6% 8.1% 8.4% 8.7% 8.9% 9.2% (Older Spouse) +8 7.6% 8.0% 8.2% 8.4% 8.6% 8.8%
Spouse Age Differemce versus Early Claiming Age
Spouse Age Differemce versus Early Claiming Age
Mortality Based on the Society of Actuaries 2000 Annuity Table Male is Primary Worker: 62 vs 66 Female is Primary Worker: 62 vs 66 % of Delayed Incremental Benefit % of Delayed Incremental Benefit Increase Received by the Spouse Increase Received by the Spouse 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% (Younger Spouse) (Younger Spouse) -8 8.3% 8.5% 8.8% 9.5% 9.9% 10.3% -8 9.0% 9.2% 9.4% 9.7% 10.1% 10.4% -4 8.3% 8.8% 9.1% 9.4% 9.6% 10.0% -4 9.0% 9.3% 9.5% 9.4% 9.7% 9.9% -2 8.3% 8.7% 9.0% 9.3% 9.4% 9.7% -2 9.0% 9.2% 9.4% 9.4% 9.4% 9.7% 0 8.3% 8.7% 8.9% 9.2% 9.5% 9.6% 0 9.0% 9.2% 9.3% 9.4% 9.4% 9.6% +2 8.3% 8.6% 8.9% 9.1% 9.5% 9.4% +2 9.0% 9.1% 9.2% 9.4% 9.4% 9.4% +4 8.3% 8.6% 8.8% 9.0% 9.3% 9.4% +4 9.0% 9.1% 9.2% 9.3% 9.4% 9.4% (Older Spouse) (Older Spouse) +8 8.3% 8.5% 8.6% 8.8% 9.0% 9.2% +8 9.0% 9.1% 9.1% 9.2% 9.3% 9.4%
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AFTER THE GLOBAL FINANCIAL CRISIS: ATTITUDES TOWARD IMMEDIATE ANNUITIES Jean M. Lown, Ph.D. (corresponding author) Utah State University Devon Robb, M.S. Utah State University Based on the life cycle hypothesis, this study examined the attitudes toward immediate annuities of university employees 50 to 65 years old after the onset of the global financial crisis. Annuity attitudes were negatively correlated with risk tolerance and positively linked to life expectancy. Current income, expectation of receiving a pension, professed familiarity with annuities, and confidence that retirement assets would last were not related to annuity attitudes. Confirming the annuity puzzle, respondents claiming to be most familiar with immediate annuities expressed the least positive attitudes. Multiple regression analysis revealed that risk tolerance and familiarity with annuities were significantly negatively predictive of annuity attitudes. The results did not support the life cycle hypothesis. Future research should measure actual annuity knowledge and test for a curvilinear relationship between assets and annuity attitudes. Recommendations for financial advisors and educators are provided on how to frame annuities, plus suggestions for future research.
Introduction Retirement security for Americans is a critical domestic public policy issue. Because of growing longevity, many retirees can anticipate three decades of retirement. While immediate annuities are designed to ensure against outliving one’s assets, few retirees purchase this protection. Among both retirees and financial advisors, annuities elicit polarized responses. “Perhaps no other financial product generates the 88
passion and strong opinions for and against� according to Schulaka (2010, p. 3). Financial educators, planners, and policy makers are concerned about retirement asset adequacy (Helman, Copeland, & VanDerhai, 2010; Van Derhai, 2011). A plethora of studies confirm that Americans are not prepared for lengthy retirements (e. g., Lown, 2008; Munnell, Webb, & Golub-Sass, 2009; VanDerhai, 2011) at the same time that concerns are rising about the ability of Social Security and Medicare to continue paying benefits at the rate enjoyed by current retirees. The financial crisis which began in 2008 has been particularly severe for many older individuals (Pynoos & Liebig, 2009; Rosnick & Baker, 2009). In 2008, about one-fourth of retirees had incomes below $11,000 (Purcell, 2009). The National Retirement Risk Index, an estimate of the percent of households at risk of a lower level of living in retirement, increased from 44% in 2007 to 51% in 2009 (Munnell, et al., 2009). Retirement planning advice, education, and research has focused on asset accumulation. Such information is essential because, without wealth accumulation, many individuals would not be able to sustain their level of living in retirement (Brown, 2008). In response to the baby boom retirement wave, the focus of concern is shifting to asset decumulation strategies that extend beyond the ubiquitous 4% withdrawal recommendation (Robinson & Tahani, 2010; Stout, 2008). As defined benefit (DB) plans are replaced with defined contribution (DC) plans, finding sustainable ways to ensure that assets last a lifetime is a challenge for retirees, advisors, and educators.
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An immediate annuity offers retirees the opportunity to insure against the risk of outliving their resources by transforming assets into a lifelong stream of income. Retirees with pensions are more satisfied with retirement and report fewer signs of depression than those who lack pension income (Panis, 2004). In addition, satisfaction among retirees who do not have a DB plan or an annuity tends to decline during retirement (Panis, 2004). The alternative to defined benefit plans and annuities is a fixed or phased retirement withdrawal strategy, which offers the advantage of liquidity to meet large unanticipated expenses such as health care and funds for bequests. But this flexibility comes at the risk of outliving assets. Dus, Maurer, and Mitchell’s (2005) comparison of various phased withdrawal strategies, along with partial annuitization, illustrates the complexity of implementing this approach. Uncertainty about longevity carries the risk that retirees will outlive their money or will consume too little and live below their pre-retirement level of living (Brown, 2000; Brown, 2008; Yakoboski, 2009). Retirees need help negotiating a path between these two pitfalls. While immediate annuities address this concern, consumers are reluctant to embrace this product, a conundrum dubbed the “annuity puzzle.� According to Hurd and Panis (2003), only seven percent of the Health and Retirement Survey respondents who retired with a DC plan converted their balance into an annuity. One reason few retirees purchase annuities is because many 401(k) plans do not offer the option to convert (Turner, 2010). Only 27% of full-time employees had this option in 1997 (Mitchell, 2000). Other contributing factors include the complexity due to too many variables; more choice can result in decision paralysis. 90
Dushi and Webb (2004) point out that few current retirees purchase annuities because such a large portion of their wealth is already annuitized in the form of Social Security and DB pensions. However, future retirees are less likely to receive DB pensions and thus may be more amenable to annuitization. Turner (2010) identified another piece of the puzzle. Based on two hypothetical retirees, one of which was an attractive candidate for annnuitization, he evaluated the advice of 25 free internet retirement planning programs. Turner (2010) concluded that most programs ignore longevity risk and focus on systematic withdrawal strategies; only one program recommended annuities. The retirement system for faculty and professional employees at many universities is a defined contribution plan provided by TIAA-CREF. Until 1989, TIAA-CREF retirement savings could only be used to purchase an immediate life annuity; now numerous payout options are available. Ameriks (2002) explored the impact of this increased choice and discovered that many retirees postponed withdrawing funds from retirement savings and, among those taking an income, the life annuity significantly declined in popularity. Thus, while immediate annuities provide the security and dependability of a DB pension, few retirees choose this option. Partial annnuitization to cover routine fixed expenses allows remaining assets to continue to grow and be available for large unexpected expenses and bequests (Ameriks, Veres, & Warshawsky, 2001; Brown, 2008; Horneff, Mauer, Mitchell, & Dus, 2006). This strategy can include a laddered approach to purchasing annuities or the use of inflation indexed immediate annuities.
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A study of Financial Planning Association members (Schulaka, 2010) found that the global financial crisis resulted in a shift to more positive attitudes toward immediate annuities. Have attitudes of workers changed as well? The primary objective of this study is to determine attitudes toward immediate annuities of university employees who participate in a DC plan. A second objective is to examine the relationship between employee characteristics and their attitudes towards annuities. These attitudes are expected to differ based on risk tolerance, financial confidence, expected longevity, and familiarity with immediate annuities (hereinafter, annuities). Specifically, what are the attitudes toward annuities of employees of a large state university who participate in a DC plan? Literature Review Until recently, much of the research on optimal retirement withdrawal strategies focused on determining ideal asset allocations and sustainable withdrawal rates, typically using Monte Carlo simulation techniques. A large body of research on portfolio withdrawal rates concurs that a 4% withdrawal of the initial retirement portfolio, adjusted yearly for inflation, has a low probability of failure with a 30 year time horizon (e.g., Bengen, 1994; Cooley, Hubbard & Walz, 1999, 2003; Robinson & Tahani, 2010). However, due to unpredictable investment returns, unknown health and longterm care expenses, and longevity, the risk of outliving one’s assets remains a threat (Goodman & Heller, 2006; Reichenstein, 2003). Sophisticated asset allocation and withdrawal strategies described in academic journals (e. g., Cooley, Hubbard, and Waltz, 1999, 2003; Robinson & Tahani, 2010) require the 92
guidance of a professional financial planner. One post-crisis investment portfolio recommendation involves commodities, managed futures, junk bonds, and international investments to supplement a diverse domestic portfolio (Schulaka, 2010, p. 11). Arnott also proposed the inclusion of a “third pillar� composed of investments beyond the domestic stock/bond portfolios used in optimal withdrawal research (Stolz, 2011). Arnott (Stolz, 2011) and Reichenstein (2002) predict future stock market returns will likely be lower than historical US investment returns. Based on 109 years of historical returns from 17 developed countries, Pfau (2010) argues that the 4% guideline is overly optimistic because it is based on an unusually robust period of US investment returns that is unlikely to be replicated in the future. Combining Annuities and Percent Withdrawal Because of longevity risk, even with a prudent asset allocation and sustainable withdrawal rate, research suggests that the inclusion of immediate annuities can enhance retirement security. Ameriks et al. (2001) utilized a Monte Carlo simulation in their search for a sustainable withdrawal rate combined with an annuity, concluding that the larger the equity allocation, the higher the portfolio sustainability rate. To demonstrate the potential for annuities to generate higher income than the 4% withdrawal rate, Brown (2008) compared an annuity to three other decumulation strategies. In 2008 a single premium of $100,000 could provide $7,240 of lifetime annual income to a 65 year old. Withdrawing 4% yearly from $100,000 in a non-annuitized account, earning a market rate of interest, and consuming the same $7,240 93
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annually, would run out of money around age 85 (Brown, 2008). Spitzer (2009) used a bootstrap simulation to estimate the probability of outliving a retirement portfolio; five percent of hypothetical portfolios fail over a 30 year span. Purchasing an annuity reduced the risk of failure to zero. Spitzer’s (2009) inflation-adjusted annuity payout was 4.3%, providing more income than the 4% withdrawal alone. Purchasing an annuity is not an “all or nothing” or a “now or never” decision (Brown, 2008). Retirees can spread their annuity purchases over time, a strategy called laddering. The first step is ensuring a guaranteed “paycheck” with an annuity to meet basic income needs (Brown, 2008; Prudential Financial, 2006). Once basic needs are met, retirees can invest their remaining assets aggressively enough to keep up with inflation. Combining an immediate annuity with a moderately aggressive asset allocation improves outcomes more than either an annuity or a more aggressive investment strategy alone (Brown, 2008; Prudential Financial, 2006). While advisor attitudes toward immediate annuities range widely (Schulaka, 2010), the research based on historical rates of return in US markets, projections of investment returns for the 21st century, and recent global market volatility suggest that immediate annuities can provide considerable security. The “Annuity Puzzle” Research has demonstrated that annuities can eliminate income uncertainty related to longevity while boosting withdrawals above the 4% threshold. Yet the overall annuity market remains small compared to economic model predictions. Although annuities eliminate longevity risk,
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retirees are clearly reluctant to purchase them. Researchers label this conundrum “the annuity puzzle.” Results of the 2010 Retirement Confidence Survey confirm investor aversion to annuities (Helman, Copeland, & VanDerhei, 2010). Only14% of retirees purchased an annuity or chose a retirement plan option that pays guaranteed income for life, and just 11% of workers indicated they are very likely to make similar choices. The likelihood of choosing annuities decreases as age and assets increase (Helman et al., 2010). Until recently, studies addressing the annuity puzzle focused on identifying rational reasons why consumers avoid them. For example, some researchers suggest that private market annuities are too expensive either because of high costs or adverse selection, yet price loads are relatively low and declining (Brown, 2007; Mitchell, 2001). Another rational barrier that fails to explain the annuity puzzle is bequest motives. While guaranteeing against outliving one’s money, purchasing an annuity precludes leaving those funds to heirs, a commonly cited reason for reluctance to annuitize. However, annuities can be used strategically to ensure other assets, such as life insurance, are available to heirs (Schulaka, 2010). But self-directed investors may not be aware of these strategies. Brown (2007) suggests that psychological factors outweigh rational information in the annuity decision. Framing, or how annuities are presented, affects attitudes toward annuities (Agnew, Anderson, Gerlach, & Szykman, 2008). Positive framing presents an affirmative message emphasizing the beneficial outcomes for following a suggested behavior. For example, purchase of a life annuity guarantees income for the rest of one’s life. In contrast, 95
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negative framing focuses on losses resulting from not following a recommended behavior; i.e., if I don’t purchase an annuity, I may outlive my retirement savings. Agnew et al. (2008) tested the strength of negative framing on the annuity market using a retirement game where participants chose between an annuity and an investment split between an equity investment and a risk-free asset. Before choosing, participants were shown one of three slide shows about annuities. Based on actual marketing literature, one presentation utilized negative framing techniques, a second highlighted positive aspects of annuities, and the third slide show was neutral (Agnew et al., 2008). Women who viewed the investment presentation were less likely to choose an annuity than women who saw the neutral presentation. Men were less likely to select an annuity after watching the pro-investment presentation but more likely to choose an annuity after watching the pro-annuity slide show. Similarly, Hu and Scott (2007) explored the role of mental accounting in explaining the annuity puzzle. They demonstrated that immediate annuities are typically perceived as a gamble where retirees focus on the unlikely outcome that they will die prematurely. Brown, Kling, Mullainathan, and Wrobel (2008) also studied the importance of framing in retirement decisions, using various scenarios including annuities and other nonannuitized products. Some subjects were presented the scenarios using an “investment” framework, with words such as earnings, invest, and describing periods in terms of years, while other subjects viewed scenarios in a “consumer” framework using words like spend, payment, and references to the purchaser’s age. The consumer scenario shifts the frame; 96
instead of focusing on the investment returns, individuals were presented with consumption consequences of the investment (Brown et al., 2008). When options were presented in the consumption frame, the majority of individuals preferred the stream of income. In contrast, the majority of individuals given the same choices in the investment frame did not choose the annuity. Since the investment framework is the dominant reference point for retirement planning, this study helps explain why so few individuals purchase annuities (Brown et al., 2008). Framing clearly influences the annuity decision. A final, albeit small, piece of the annuity puzzle may be the role of financial advisors. Because purchasing an annuity means reducing the amount of assets under management, feebased advisors may hesitate to recommend annuities to clients. According to the 2010 Financial Planning Association survey of planners’ attitudes toward annuities, 90% of advisors have some confidence in their knowledge of annuity products. However only 18% are extremely confident and 9% lack confidence (Schulaka, 2010). Further, 81% of advisors believe their clients don’t understand annuities, which is likely a true assessment. Most annuity providers offer far too many complex choices that are hard for consumers to understand and weigh the differences, resulting in a reluctance to purchase. In sum, research suggests that if historical US investment returns continue into the future, retirees facing a 30year retirement can spend 4% of their nest egg each year with little risk of outliving their assets. However, Pfau (2010) questions the reliance on historical returns, demonstrating that they are unlikely to be repeated in the future. A joint study by AARP and the American Council on Life Insurance (2007), conducted prior to the global financial crisis, reports that only 97
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56% of male and 32% of female pre-retirees were confident of meeting the challenge of making their investments last for their lifetime (AARP/ACLI, 2007). Higher income respondents and married couples were most confident. However, the amount and sophistication of asset management required to accomplish this task is daunting and aggravated by volatile returns and unpredictable financial markets. Investors may be overconfident of their ability to manage their assets to last for a lifetime. Most retirees simply lack the knowledge (Lusardi, Mitchell, & Curto, 2009) and desire to acquire the necessary skills; further, these abilities are likely to diminish with age, especially after age 85. The life cycle savings hypothesis provides the conceptual framework for examining attitudes toward immediate annuities. The life cycle hypothesis posits that individuals make consumption decisions that balance their lifetime earnings against consumption needs to smooth out living standards (Ando & Modigliani, 1963). Young adults borrow against future earnings to obtain post-secondary education and invest in their human capital, buy a house, and start a family. During prime working years, they pay down debts and invest for retirement. Finally, retirees spend down their savings and live off their assets. Thus, an immediate annuity fits the life cycle hypothesis by ensuring no excess or shortfall at death. Hypotheses Based on previous research, the following directional hypotheses guided the study:
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1. The more risk averse an individual, the more positive their attitude will be toward immediate annuities (Agnew et al., 2008; Mitchell, 2001). 2. Individuals with longer than average life expectancies will have more positive attitudes toward immediate annuities (Brown, 2008; Drinkwater & Sondergeld, 2004). 3. The lower the confidence that their assets will last through retirement, the more positive their attitudes will be toward immediate annuities (AARP/ACLI, 2007). 4. Lower income earners will express more positive attitudes toward immediate annuities than higher earners (AARP/ACLI, 2007). 5. Individuals who rate themselves familiar with annuities will hold positive attitudes. 6. Workers who do not expect that they or their spouse will receive a DB pension will hold positive attitudes. Methods The variables included in this study were: (1) risk tolerance, (2) life expectancy, (3) confidence that assets will last for a lifetime, (4) income level, (5) familiarity with annuities, and (6) expectation of a DB pension. We compared these variables to workers’ attitudes toward immediate annuities and their annuitization plans. Risk tolerance was measured using a five-item scale with a Cronbach’s alpha of .80 (Grable & Joo, 2004). Participants estimated their own life expectancy which we divided into five categories. We measured confidence in assets lasting through retirement in four levels ranging from “very confident” to “not at all 99
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confident� plus a “not sure� category. Income was measured in five categories from less than $25,000 to over $100,000. Familiarity with immediate annuities was self-assessed and measured in four categories. Respondents indicated if they or their spouse/partner expected to receive a DB pension. We combined responses to three questions from the AARP/ACLI (2007) study to measure the dependent variable, attitude towards immediate annuities. One question asked the likelihood of purchasing an immediate annuity. A second question asked how much a guaranteed monthly income would add to their peace of mind. The third addressed reasons to purchase an annuity. The university human resources office provided addresses for all employees who participate in the DC retirement plan. We sent a letter through campus mail to all DC participants informing them of the study and encouraging participation. Data were collected via an internet survey sent to the university email addresses of 1,720 employees. Data were analyzed using the Statistical Package for the Social Sciences (SPSS). We used t-tests, the Pearson R coefficient, one way Analysis of Variance (ANOVA), and multiple regression to test the hypotheses. Results Description of the Sample Of the 1,720 employees we invited to participate, 744 completed the survey, resulting in a response rate of 43.2%. Because respondent ages ranged from 23 to 84, we limited our analysis to the 263 pre-retiree respondents aged 50-65, the age cohort we expected to be most interested in retirement security. 100
Of the 263 pre-retirees, males constituted 54.4% of the sample with females comprising 45.2%; 57.3% were faculty and 42.7% were professional staff. As shown in Table 1, most respondents were married (79.5%) and Caucasian (95.8%), reflecting staff composition. Despite being highly educated, almost two-thirds (56.3%) rated their investment knowledge as “simple” or “below average.” More than half (56.8%) expect to live to age 85 or beyond, a realistic expectation for this demographic. One-third of respondents anticipate that they or their spouse will be eligible for a DB pension. Almost half of employees have 50% or more of their retirement funds invested in stocks. At the bottom of the 2008-09 investment market, one-fourth of respondents estimated that they had lost over 30% of their retirement assets.
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Table 1 Financial Condition of Low- and High-Income Consumers Gender Males Females Missing
N
%
143 119 1
54.4 45.2
Marital Status Married Living Widowed Divorced Separated Never married
209 6 5 21 5 15
79.5 2.3 1.9 8.0 1.9 5.7
Age 50-54 55-59 60-65
86 106 71
32.7 40.3 27.0
Ethnic Group Asian/Pacific Black/AfricanHispanic/Latino White or Other
5 1 1 252 4
1.9 0.4 0.4 95.8 1.5
Degree Some Bachelor’s degree Master’s degree Ph.D./Professional
9 33 92 128
3.8 12.4 34.6 48.9
Employment Faculty Professional staff
149 111
57.3 42.7
102
Plan to Before 55 55 to 59 60 to 61 62 to 64 65 66 or later
N
%
1 7 20 57 36 144
0.4 2.6 7.5 21.4 13.5 54.1
Household Less than $50,000 to < $75,000 to < $100,000 or
22 53 61 124
8.3 19.9 22.9 46.6
Household Less than 100k-250k 250k-500k 500k-750k 750k-1 Million +
28 59 44 54 37 34
10.5 22.2 16.5 20.3 13.9 12.8
Investor Sophisticated Average Simple I know
17 16 112 36
6.5 36.5 42.6 13.7
Life Less than 80 80-84 85-89 90-94 95+
31 81 93 42 16
11.7 30.5 35.0 15.8 6.0
Attitudes Toward Immediate Annuities Responses to the three annuity attitude questions were summed, resulting in possible attitude scores ranging from 5 to 36; higher scores indicate more positive annuity attitudes. Actual scores ranged from 8 to 36 with a mean of 27.5 (SD = 5.74) and Cronbach’s alpha of .84. In response to the question on the likelihood of purchasing an annuity with a portion of their assets, approximately half of the respondents indicated they were “somewhat likely” to purchase an annuity for each of the four reasons suggested. Roughly one-fourth indicated they were “very likely” to purchase an annuity. When asked how much a guaranteed monthly retirement income would add to their peace of mind, 82.7% of respondents agreed that a guaranteed income would add a moderate amount or great deal to their peace of mind. Only 14.7% responded “not much” or “not at all,” with 2.6% unsure. Four reasons for purchasing an annuity generated positive responses, with half of respondents indicating the reasons as “somewhat convincing.” Three-quarters to 82.9% selected “very” or “somewhat” convincing as their response to each reason. See Table 2.
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Table 2 Reasons for Purchasing an Annuity
Very convincing
Somewhat convincing
Not too convincing
Not at all convincing
Larger amount than from withdrawing investment gains
28.1%
52.6%
13.7%
5.6%
Payments for as long as you live
34.4%
47.8%
13.0%
4.9%
Predictable monthly income
27.5%
47.1%
17.6%
7.8%
Help you remain independent
36.1%
46.8%
10.3%
6.7%
Risk Tolerance and Annuity Attitudes We expected that the more risk averse an individual, the more positive their attitude would be toward annuities. Risk tolerance was measured with five questions scored on a Likerttype scale (Grable & Joo, 2004). Scores range from 6 to 20 with a mean of 13.4 (SD=2.69), similar to results reported by Grable and Joo (2004). The Pearson R coefficient (-.295; p<.000) revealed a significant negative correlation between risk tolerance and attitudes toward immediate annuities. Thus, more risk adverse respondents had a more positive attitude towards immediate annuities, supporting hypothesis one.
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Life Expectancy and Annuity Attitudes Because annuities address the longevity dilemma, we expected individuals with longer than average life expectancy to hold positive attitudes toward annuities. Fully 56.8% of respondents expected to live to 85 or older, with 21.8% expecting to live at least to age 90. Respondents who expected to die before age 80 (11.7%) had the lowest mean attitude score of 25.2 (SD=7.10). The group with the highest anticipated life expectancy (95 years or more) was the most positive about annuities (mean = 31.1; SD=5.36). A one-way ANOVA testing the relationship between life expectancy and annuity attitudes was significant at the .05 level (p<0.046), supporting hypothesis two. Confidence in Assets Lasting and Annuity Attitudes We anticipated that lower confidence in assets lasting through retirement would be linked to more positive annuity attitudes. As shown in Table 3, the very confident expressed the least positive attitudes toward annuities while respondents who are not at all confident expressed the most positive attitude scores. However, a one-way ANOVA between confidence in retirement assets lasting and attitudes toward immediate annuities resulted in an F ratio of 2.141 (p< .078), indicating that the difference between the confidence groups is not significant at the .05 level. Thus, hypothesis three was not supported.
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Table 3 Annuity Attitudes and Confidence in Retirement Assets Lasting in Retirement
Annuity Attitude Confidence levels
N
%
Mean
SD
Very confident
19
12.2
24.7
7.56
Somewhat confident
75
48.4
28.3
4.72
Not too confident
38
24.5
26.5
5.99
Not at all confident
21
13.5
29.0
6.27
Not sure
2
1.3
27.5
3.54
Total
155
99.9
27.5
5.75
Income and Annuity Attitudes We expected lower income employees to hold more positive attitudes towards annuities than higher income earners. As shown in Table 4, the attitudes of the $50,000 to $75,000 income group were most positive (mean = 28.7). With little variation in attitude scores among the remaining income groups, the one-way ANOVA F ratio was .539 (p<.707) which was not statistically significant. Similarly, an ANOVA based on estimated retirement assets was not significant either. Thus hypothesis four was not confirmed.
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Table 4 Attitudes toward Annuities and Total Household Income Annuity Attitude Annual Income
N
%
Mean
SD
Less than $50,000
14
9.2
27.0
$50,000 to $75,000
32
21.0
28.7
4.56
$75,000 to $100,000
36
23.7
27.0
5.03
$100,000 plus
70
46.1
27.3
6.58
Total
152
100
27.5
5.78
Familiarity and Annuity Attitudes We hypothesized that the more familiar respondents claim to be with annuities, the more positive will be their attitudes. Most respondents indicated lack of familiarity with annuities; as shown in Table 5, 66 indicated that they are not at all familiar with annuities (46.6%); only 6 (3.9%) said they were very familiar. One quarter (25.2%) considered themselves not too familiar and 28.4% rated themselves as somewhat familiar. Employees who considered themselves not at all familiar expressed the most positive attitudes, while respondents who rated themselves most familiar held the least positive attitudes. The ANOVA for familiarity and attitudes towards immediate annuities (F= 2.544; p<.058) was not significant. Contrary to our expectations, individuals who were less familiar with annuities held more positive attitudes than 107
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respondents who considered themselves more familiar. Hypothesis five was not supported Table 5 Attitudes toward and Familiarity with Annuities Annuity Attitude Measure
N
%
Mean
SD
Not at all familiar
66
46.6
28.9
6.27
Not too familiar
39
25.2
27.0
4.57
Somewhat familiar
44
28.4
26.0
5.28
Very familiar
6
3.9
26.0
7.56
Total
155
100
27.5
5.74
Pension Expectation and Annuity Attitudes We hypothesized that respondents who did not anticipate that they or their spouse would receive a DB pension would hold more positive attitudes than those who anticipated a pension. More than half (58.1%) of respondents indicated that they or their spouse/partner would receive a DB pension. Respondents without a pension expectation had a mean annuity attitude score of 27.3 (SD=5.43), while those who anticipate a pension had a slightly higher mean score of 27.7 (SD=6.19). The t-test score of -.387 with p< .121 indicates no statistically 108
significant difference in attitudes towards annuities based on pension expectation, thus failing to support hypothesis six. Multiple Regression We conducted a multiple regression analysis to assess the relationship between the dependent variable, attitude toward annuities, and the independent variables risk tolerance, familiarity with annuities, income, DB pension expectation, and life expectancy. The significant predictor variables from this regression were risk tolerance and familiarity with annuities. Risk tolerance (Ă&#x; = -.300; p<.000) was negatively related to annuity attitudes, indicating that as risk tolerance increases, attitudes toward annuities become less positive. Self-assessed familiarity with annuities (Ă&#x; = -.154; p<.005) indicated that employees who claimed to be most familiar with annuities expressed the least positive attitudes. The R Square value of .14 indicated that this model accounted for 14% of the variance from the independent variables (see Table 6). In summary, the regression results confirmed that these university employees age 50 to 65 years old who expressed the most positive attitudes toward immediate annuities tended to be risk adverse and unfamiliar with immediate annuities.
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Table 6 Multiple Regression Analysis for Variables Predicting Attitudes Toward Immediate Annuities Variable
Beta
Standard Error of Beta
βeta
Risk tolerance
-.57
.12
-.28***
Age
-.01
.03
-.02
Gender
1.43
.61
.13*
Retirement class
.79
.63
.07
Selfefficacy
-.15
.07
-.12*
Life expectancy
1.51
1.85
.31
Note. R² = .15; *p < .05, ***p < .001
Discussion and Implications Retirement planning advice targeted toward individuals during their working years focuses on building wealth. Such information is essential; however, while accumulating assets is necessary, it is not sufficient to guarantee financial security during retirement. The need for a guaranteed lifetime income 110
for retirees continues to grow in significance as life expectancies increase and traditional sources of guaranteed pension income decline. Creating a sustainable strategy for withdrawing retirement assets to last for an uncertain life span raises new issues. Lusardi, Mitchell, and Curto (2009) report that Americans older than age 55 lack understanding of stocks and bonds, risk diversification, portfolio choice, and investment fees, suggesting that most retirees are not prepared to implement sophisticated withdrawal plans. Complicated withdrawal strategies require the services of a financial planner, an unaffordable luxury for many seniors. The respondents in this study rated themselves very low on investment knowledge (data available from authors), suggesting they lack the skills to manage their investments to last their lifetime. Despite their high educational level, few would have the skills, interest, and abilities to implement the sophisticated retirement withdrawal strategies that are prescribed in research studies. This study, examining attitudes towards the annuitization of retirement wealth, was conducted eight months after financial markets bottomed out in March 2009. At the time of data collection, equities had increased from their lowest values but were still considerably below their 2007 peak. Because most respondents had experienced losses in their retirement accounts, we expected a more positive attitude toward immediate annuities. More than half of respondents indicated they are not at all familiar with annuities; although the survey explained basic annuity concepts, lack of understanding of annuities likely affected respondent attitudes. Confirming the annuity puzzle, many individuals who could benefit from an immediate annuity did not plan to 111
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annuitize. We found no evidence that the respondents plan to conform to the life cycle savings hypothesis by annuitizing their wealth to ensure their funds last their lifetime. Appropriately, risk adverse individuals have a more positive attitude towards annuities than those with higher risk tolerance. However, our hypothesis that respondents who are least confident that their assets will last for their lifetime will hold positive views on annuities was not supported. Surprisingly, respondents who claimed to be most familiar with annuities expressed negative attitudes. Gardner and Wadsworth (2004) conducted a similar study in the United Kingdom exploring attitudes towards annuitization among pre-retirees. They found that individuals ages 50-64 with lower education, poorer health, and lower incomes are likely to oppose annuitization, while higher education and income respondents in good health are more willing to annuitize their wealth. In contrast, the AARP/ACLI (2007) study reports that lower income respondents hold more positive attitudes toward annuities. We found no statistically significant relationship between income and attitudes towards annuities. Because respondents with moderate incomes expressed positive attitudes, the true relationship with income may be curvilinear, with moderate income and asset individuals most amenable to purchasing annuities. This relationship should be explored in future research. Low income (and asset) retirees rely primarily on Social Security and may not have sufficient assets to purchase an annuity, while higher income (and higher asset) individuals may feel their assets are sufficient to last through retirement and may be more likely to have a bequest motive. Income and assets are positively correlated in this study; respondents with 112
higher incomes report more assets. The middle asset category is the most likely group to both be able to both afford and benefit from an annuity - having sufficient assets to purchase an immediate annuity - and yet not enough wealth to preclude the need for insurance against outliving assets. Future research should explore the possibility of a curvilinear relationship between income (and assets) and annuity attitudes. Limitations of this study include the lack of direct measurement of investment or annuity knowledge; we depended instead on subjects to rate their knowledge. The trade off in obtaining such data is making an already lengthy survey even longer. Also, we were unable to confirm the accuracy of respondentsâ&#x20AC;&#x2122; expectations of receiving a pension. The universityâ&#x20AC;&#x2122;s retirement plan is a defined contribution plan which does not provide a pension, although respondents and/or a spouse may be eligible for a pension from other employment. Future studies should directly measure annuity knowledge. Further, although the respondents were representative of the university employee population, very highly educated, high income, and almost entirely Caucasian, they are not representative of the US population. Nonetheless, these employees may represent the ideal potential clientele for annuities because of their longer than average life expectancy and higher than average assets. Because the typical annuity purchaser is a woman in her 70s (Andrus, 2010), some of the younger respondents (50-55 years old) are not yet at the stage to consider the purchase of an immediate annuity. Over half of respondents plan to work past age 65, so may not yet be concerned about ensuring their assets last a lifetime. Due to the timing of data collection, just six months after the financial markets hit bottom, respondents may have been focused on 113
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rebuilding assets rather than planning for the decumulation stage. Strengths in this study include a high response rate. Another contribution is the time period when the data were collected, following the global economic crisis of 2008-2009 and the resultant investment losses. Virtually all respondents had experienced loses in their retirement accounts. Much of the prior research on attitudes toward annuities was conducted when investments were increasing in value and rising stock market investments were more attractive than a guaranteed income. This study contributes a post-crisis perspective. The behavioral economics concept of framing suggests that choices are not only based on material consequences, but are filtered through lenses which an individual uses to interpret their choices (Agnew et al., 2008). For example, investors typically isolate one choice (how to invest) from others (how to consume) and focus on the details of one choice while neglecting to view the choice as part of a broader decision. In the presence of uncertainty, people spend more time processing information and pay more attention to information when framed negatively (Agnew et al., 2008). Based on the earlier framing research and the results of this study, educators and advisors should inform their clients about the role of both traditional immediate annuities and advanced life deferred annuities, within a framework promoting peace of mind in retirement through the guarantee of lifetime income. Since these respondents reacted very positively to the question on how much a guaranteed income would contribute to their peace of mind, advisors and educators should emphasize the guaranteed income aspect of immediate annuities.
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Recommendations for Future Research Although almost two-thirds of these highly educated university employees rate themselves as below average in investment knowledge, many are averse to the concept of annuitizing a portion of their wealth and apparently expect to manage their assets to produce a steady income in retirement. Future studies should test for actual annuity and investment knowledge rather than depend on self-report, and investigate what strategies pre-retirees plan to use to ensure that they do not outlive their assets. Since few online retirement planning tools even mention annuities (Turner, 2010), researchers should ask subjects about their use of retirement planning websites. Dependence on such tools may mean they have not even considered immediate annuities as a way to ensure financial security after their paycheck ends. A mixed methods study could combine a large scale survey with focus groups to delve into the reasons behind the attitudes. Future research should calculate an asset/salary ratio as described by Hammond and Richardson (2009). Results of this study further confirm the annuity puzzle. University employees who are least familiar with annuities express positive attitudes towards this product, while respondents who consider themselves most familiar are least favorable. However, the survey did not measure actual knowledge of immediate annuities; future studies should include such a measurement. With fewer DB pensions and lower Social Security replacement rates for future retirees, more research is needed on how to overcome consumer reluctance to annuitize a portion of retirement assets. Similar research could also be conducted 115
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at other universities, with large private employers, or a broader sample of the general public. Since this study focuses on attitudes of individuals, future research could focus on married or partnered couples and their attitudes towards immediate annuities to assess differences between couples. Research is needed to determine the factors that influence a coupleâ&#x20AC;&#x2122;s decision to annuitize retirement assets. What happens when a wife has a positive attitude towards annuities while her husband holds a less positive attitude? Another area for future research is to explore attitudes towards longevity insurance, also known as an advanced life deferred annuity. Longevity insurance allows retirees to purchase an annuity when they retire but delay receiving payments until 15 to 20 years into retirement, thus insuring against living too long. The initial cost of longevity insurance is less than for an immediate annuity providing the same income because the payments are delayed until about age 85. The lower cost and concentrated focus on providing for a longer than average life span could positively influence attitudes towards advanced life deferred annuities (Gong & Webb, 2007). In order to ensure that retirees do not outlive their assets, the United Kingdom and Germany recently initiated mandatory partial annuitization by age 75 and 85 respectively (Dus, Maurer, & Mitchell, 2005). The proposal by Gong and Webb (2007) for advanced life deferred annuities (longevity insurance) may prove more attractive to American workers than traditional immediate annuities, but would require extensive education and appropriate framing. The results of this study and previous studies on the annuity puzzle suggest that American workers would not be receptive to an annuitization mandate. 116
References AARP/ACLI. (2007). What now? How retirees manage money to make it last through retirement. Pg. 1-137. Retrieved from http://assets.aarp.org/rgcenter/econ/guaranteed_income_1.pdf Agnew, J. R., Anderson, L. A., Gerlach, J. R., & Szykman, L. R. (2008). The annuity puzzle and negative framing. Center for Retirement Research at Boston College, 1-6. Ameriks, J. (2002, December). Recent trends in the selection of retirement income streams among TIAA-CREF participants. TIAA-CREF Institute Research Dialog, 74, 1-19. Ameriks, J., Veres, R., & Warshawsky, M. J. (2001). Making retirement income last a lifetime. Journal of Financial Planning, 14(12), 60-76. Ando, A., & Modigliani, F. (1963). The life cycle hypothesis of savings: Aggregate implications and tests. The American Economic Review, 53(1), 55-84. Andrus, D. (2010, November 2). Who's buying immediate annuities and how to get their business. Advisor One. LIMRA International. Bengen, W. P. (1994). Determining withdrawal rates using historical data. Journal of Financial Planning, 14(12), 60-76. Brown, J. R. (2000). How should we insure longevity risk in pensions and social security? Center for Retirement at Boston College Issue Brief #4, 1-20. Brown, J. R. (2007). Rational and behavioral perspectives on the role of annuities in retirement planning. NBER Working Paper 13537. Cambridge, MA: National Bureau of Economic Research. Brown, J. R. (2008, June). A paycheck for life: The role of annuities in your retirement portfolio. TIAA-CREF Institute Trends and Issues, 1-11. Brown, J. R., Kling, J. R., Mullainathan, S., & Wrobel, M. V. (2008) Why donâ&#x20AC;&#x2122;t the people insure late life consumption? A framing explanation of the under-annuitization puzzle. American Economic Review Papers and Proceeding, 98:2. Cooley, P. L., Hubbard, C. M. & Walz, D. T. (1999). Sustainable withdrawal rates from your retirement portfolio. Financial Counseling and Planning, 10(1), 40-50. Cooley, P. L., Hubbard, C. M., & Walz, D. T. (2003). A comparative analysis of retirement portfolio success rates: Simulation versus overlapping periods. Financial Services Review, 12, 115-128. Drinkwater, M., & Sondergeld, E.T. (2004). Perceptions of mortality risk: implications for
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annuities. In Mitchell, O.S., Utkus, S.P. (Eds.), Pension Design and Structure: New Lessons from Behavior Finance (275-286). Oxford Press: New York. Dus, I., Maurer, R., & Mitchell, O. S. (2005). Betting on death and capital markets in retirement: A shortfall risk analysis of life annuities versus phased withdrawal plans. Financial Services Review, 14(3), 169-196. Dushi, I., & Webb, A. (2004). Annuitization: Keeping your options open. Center for Retirement Research at Boston College Working Paper 2004-04. Gardner, J., & Wadsworth, M. (2004). Who would buy an annuity? An empirical investigation. Watson Wyatt Technical paper, 1-27. Goodman, B., & Heller, M. (2006, October). Annuities: Now, later, never? TIAA-CREF Institute Trends and Issues, 1-18. Gong, G., & Webb, A. (2007, September). Evaluating the advanced life deferred annuity - An annuity people might actually buy. Center for Retirement Research at Boston College Working Paper #2007-15. Grable, J. E., & Joo, S-H. (2004). Environmental and biopsychosocial factors associated with financial risk tolerance. Financial Counseling and Planning, 15(1), 73-88. Hammond, P. B., & Richardson, D. P. (2009, December). Staying on the path to a secure retirement: Using the asset-salary ratio as a retirement compass. TIAA-CREF Institute Research Dialogue No. 95. Helman, R., Copeland, C. & VanDerhei, J. (2010, March). The 2010 Retirement Confidence Survey: Confidence stabilizing, but preparations continue to erode. Employee Benefit Research Institute Issue Brief No. 340. Horneff, W. J., Mauer, R., Mitchell, O. S., & Dus, I. (2006, July). Optimizing the retirement payout portfolio. Michigan Retirement Research Center Research Paper No. WP 2006-124. Hu, W-Y., & Scott, J. S. (2007, March). Behavioral obstacles to the annuity market. Working paper. Retrieved from: http://ssrn.com/abstract=978246 Hurd, M., & Panis, C. (2003). An analysis of the choice to cash out, maintain, or annuitize pension rights upon job change or retirement. RAND Working Paper, Santa Monica, CA. Lown, J. M. (2008). Retirement savings adequacy for the baby boom generation. Journal of Personal Finance, 7(1), 109-134. Lusardi, A., Mitchell, O., & Curto, V. (2009, September). Financial literacy and financial sophistication in the older population: Evidence from the 2008 HRS. Michigan Retirement Research Center, WP 2009-216. Mitchell, O. (2000). New trends in US Pensions. Pension Research Council WP 2000-1. Philadelphia, PA. Mitchell, O. S. (2001, June). Developments in decumulation: The role of annuity products in financing retirement. Discussion Paper PI-0110. The Pensions Institute. 118
Munnell, A. H., Webb, A., & Golub-Sass, F. (2009). The national retirement risk index: After the crash. Center for Retirement Research at Boston College, 1-10. Panis, C. W. A. (2004). Annuities and retirement well-being. In O.S. Mitchell & S.P. Utkus (Eds.), Pension design and structure: New lessons from behavior finance (pp. 259-274). New York, NY: Oxford Press. Pfau, W. (2010). An international perspective on safe withdrawal rates: The demise of the 4 percent rule? Journal of Financial Planning, 23(12), 5261. Prudential Financial. (2006, October). Learning the two-step: A new approach to asset allocation for the retiree. Prudentialâ&#x20AC;&#x2122;s Four Pillars of Retirement Series, 1-25. Purcell, P. (2009). Income and poverty of older Americans in 2008. Washington, D.C. Congressional Research Service Report for Congress, RL32697. Pynoos, J., & Liebig, P. (2009). Changing work, retirement, and housing patterns. Generations, 33(3), 20-26. Reichenstein, W. (2002). What do past stock market returns tell us about the future? Journal of Financial Planning, 15(7), 72-83. Reichenstein, W. (2003). Allocation during retirement: Adding annuities to the mix. American Association of Individual Investors Journal, 3-9. Robinson, C., & Tahani, N. (2010). Sustainable retirement income for the socialite, the gardener, and the uninsured. Financial Services Review, 19, 187-202. Rosnick, D., & Baker, D. (2009). The impact of the housing crash on the wealth of the baby boom cohort. Journal of Aging and Social Policy, 22(2), 117-128. Schulaka, C. (2010, April). Annuities in financial planning. Supplement to the Journal of Financial Planning. Retrieved from: http://www.fpanet.org/docs/assets/9BBBAE68-1D09-67A1AC4051D8F44C4C9A/Annuities0310_Web.pdf Spitzer, J. J. (2009). Managing a retirement portfolio: Do annuities provide more safety? Journal of Financial Counseling and Planning, 20(1), 5869. Stolz, R. F. (2011). Robert Arnott on the limitations of traditional market indexes and future equity returns. Journal of Financial Planning, 24(2), 14-16, 18. Stout, R. G. (2008). Stochastic optimization of retirement portfolio asset allocations and withdrawals. Financial Services Review, 17(1), 1-15. Turner, J. A. (2010). Why donâ&#x20AC;&#x2122;t people annuitize? The role of advice provided by retirement planning software. The Wharton School, University of Pennsylvania, Pension Research Council Working Paper 2010-07. 119
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VanDerhai, J. (2011). A post-crisis assessment of retirement income adequacy for baby boomers and gen xers. Employee Benefit Research Institute, Issue Brief No. 354. Yakoboski, P. J. (2009). Converting assets to income in retirement: What near-retirees are thinking. TIAA-CREF Institute Trends and Issues, 1-8.
Acknowledgements The authors thank Roxane Pfister for assistance in data analysis and the Utah State University Office of Human Resources for facilitating the data collection process.
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FINANCIAL ADVICE: WHAT ABOUT LOWINCOME CONSUMERS? Ning Tang (corresponding author) San Diego State University Marie-Eve Lachance San Diego State University This paper uses data from the National Financial Capability Study (NFCS) to analyze the determinants and benefits of financial advice use, with a special emphasis on the low-income group. While, as expected, this group fares worse financially, we also find that they have different needs and priorities. For example, they use less investment advice than insurance advice. A discriminant analysis reveals that cost plays a lesser role in their decision to seek advice. Using an instrument variable strategy, we conclude that certain types of advice improve clientsâ&#x20AC;&#x2122; financial behaviors, with greater benefits for the low-income group.
Introduction Historically, low-income communities have had limited access to financial services, affordable credit, and investment capital (Rubin, 2007). This is especially the case in the professional financial advice industry, which has targeted mainly the higher end of the market. From the supply perspective, pursuing clients with modest accounts is not profitable when compensation is based on a percentage of the assets under management. However, as Rubin (2007) points out, the rich and the poor both have the need and the desire to build financial assets to enable them to meet important life goals. Access to capital and basic financial services is a critical component of achieving such goals. Numerous products, programs, organizations, and policies have been designed to 121
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address the financial exclusion of low-income individuals and communities. It is more and more clear that, given incentives and provided with financial education, low-income individuals can change their financial behavior, and they need such changes (Schreiner, Clancy, & Sherraden, 2002; Mills, Patterson, Orr, & DeMarco, 2004). To better address the problem of low-income consumers' limited access to financial services and products, to serve them more efficiently, and to effectively advocate for a relatively new low-income financial service market, we need more insights into the financial behavior of low-income consumers. In particular, we need to know how the financial condition of low-income individuals compares to that of people with higher incomes, their financial advice-seeking behavior, and the effect of financial advice on their financial literacy and behavior. Unfortunately, the literature has not provided much information on these areas. Although there are a number of studies exploring who uses financial advice and what factors contribute to consumersâ&#x20AC;&#x2122; advice-seeking behavior, none of these examine the difference in advice-seeking behavior between low- and high-income groups. Considering that lowincome consumers have different financial exposures and needs, findings from the previous literature on wealthier individuals may not readily apply to them. In addition, due to the limitation of data and methodology, very little is known about the effects of various kinds of financial advice on individualsâ&#x20AC;&#x2122; financial literacy and behavior and whether these effects are the same among different income groups. The unique National Financial Capability Study (NFCS) dataset allows us to fill the gap in the literature by addressing the above issues. Specifically, the paper finds that 122
low-income individuals fare worse financially than highincome ones: they have lower levels of financial knowledge and ability, achieve fewer financial planning goals, and have less access to financial services and products. Meanwhile, they are using less financial advice and have less favorable attitudes towards the industry than their higher-income counterparts. We find that low-income consumers have different demand compositions of financial advice and consider different factors than high-income consumers when seeking such advice. Six percent more low-income consumers use insurance advice than use savings/investments advice, while the advice most demanded by high-income consumers is savings/investments advice. In addition, by incorporating demographic, socioeconomic, psychosocial factors and financial literacy and capability in one model, discriminant analysis shows that the cost of seeking financial advice is less important to low-income earners than to high-income earners. Last, we examine whether financial advice is beneficial in terms of improving financial knowledge (objective and selfassessed) and a broad set of financial behaviors. When performing this type of exercise, a common limitation is that reverse causality and selection may cause biased estimation. We use an instrument variable strategy to address the problem. We observe that those who received financial advice report higher levels of self-assessed financial knowledge, but they do not score higher on a set of objective financial literacy questions. In most cases, all types of advice help clients improve positive behaviors, but only savings/investments and tax advice help avoid negative behaviors. In a second set of regressions, we interact financial advice with a low-income
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dummy and find greater improvements in certain financial behaviors for the low-income group. The paper is structured as follows. Section 2 describes the data, and Section 3 compares the financial condition of low-income and high-income individuals. Section 4 analyzes the determinants of financial advice adoption and how they differ between low- and high-income groups. Section 5 uses the instrument variable strategy to examine the effects of financial advice on clients’ financial literacy and behavior, and Section 6 concludes. Data This paper uses the National Financial Capability Study (NFCS) dataset, which was commissioned by the Investor Education Foundation of the Financial Regulatory Authority (FINRA). Interviews were conducted between May and October 2009. The dataset collected information from about 500 respondents in each state (28,146 observations in total) regarding their demographic characteristics, use of financial advice, financial literacy and other aspects of financial planning. 19 In particular, the survey asks respondents “In the last 5 years, have you asked for any advice from a financial professional about any of the following: 1) debt counseling, 2) savings or investments, 3) taking out a mortgage or a loan, 4) insurance of any type, 5) tax planning?” 20 In addition, the survey records consumers’ attitudes towards financial advisors. In terms of financial literacy and capability, the survey features 19
Personal-planning topics include cash management, retirement planning, home and auto acquisition, credit management, and insurance planning. 20 We do not use the question for mortgage and loan advice in our analysis because mortgages and loans are very different in nature, which makes the interpretation of the results difficult. 124
five questions that measure financial literacy and asks the respondents to self-assess their financial knowledge and capability, including the ability to deal with day-to-day financial matters, their math skills, and whether or not they keep up with economic news (see Appendix for exact question wording). The rich dataset covering various aspects of financial well-being at the individual level allows us to explore the financial conditions and use of financial advice among lowincome and high-income consumers separately. The demographic composition of the sample is similar to that in the 2010 American Community Survey, with the exception of an underrepresentation of the group with less than a high-school degree. Financial condition of low- and high-income consumers To explore the use of financial advice and its effect on consumersâ&#x20AC;&#x2122; behavior among low- and high-income consumers, it is necessary to first investigate the different financial conditions within these two income groups. This is necessary because their financial condition and what they believe about financial services will largely determine their choice of professional financial advice and the effects of such advice. With the unique dataset, we can examine financial literacy and capability and financial conditions and attitudes towards professional financial advice among low- and high-income earners separately. We define low-income consumers as those with an annual household income lower than $25,000 (7,013 observations) and high-income consumers as those with an annual income higher than $75,000 (7,779 observations). These low- and high- income groups correspond, respectively, to the first and fourth quartiles of the income distribution in our sample. 125
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Table 1 Financial Condition of Low-Income and High-Income Consumers Financial literacy Financial literacy score Self-assessed financial knowledge Good at day-to-day financial matters Good at math Keep up with financial news Cash flow management Have emergency fund Spend more than income Overdraw checking account Savings and investments Save for children's college Have 529 plan Retirement planning Figured out need for retirement Use Soc. Sec. to decide retirement age Calculate withdraw amt. after ret. Contribute regularly to retirement Rebalance retirement account Hardship withdrawal from ret. account Credit management Checked credit score in past one year Compare auto loan providers Compare mortgage providers Consider mtg. pmt. as % of income Have high cost loans Late with mtg. pmt. in past 2 years Foreclosure process in past 2 years Credit card penalty Insurance planning Compare insurance providers Review insurance coverage General Drop in income in past year Savings and investments Have checking account Have savings account Investment outside ret. account Investment outside ret. account (in $) 126
Mean
Mean
0.49 0.59 0.71 0.71 0.55
0.75 0.73 0.82 0.82 0.72
19.4% 27.4% 31.5%
Difference -0.26 -0.14 -0.11 -0.11 -0.17
*** *** *** *** ***
59.9% 13.4% 20.1%
-40.5% 14.0% 11.4%
*** *** ***
15.5% 14.0%
55.3% 45.2%
-39.8% -31.2%
*** ***
23.8% 27.5% 44.3% 46.4% 24.9% 12.9%
64.9% 26.6% 74.5% 85.5% 57.9% 4.9%
-41.1% 0.9% -30.2% -39.1% -33.0% 8.0%
***
35.9% 39.0% 48.2% 80.5% 35.0% 33.1% 2.7% 34.9%
61.6% 50.6% 70.9% 81.7% 11.1% 12.9% 2.0% 21.9%
-25.7% -11.6% -22.7% -1.2% 23.9% 20.2% 0.7% 13.0%
*** *** ***
67.4% 53.8%
70.7% 76.0%
-3.3% -22.2%
*** ***
48.9%
28.3%
20.6%
***
82.2% 56.3% 14.9% 4,188
98.8% 93.1% 65.9% 69,271
-16.6% -36.8% -51.0% -65,083
*** *** *** ***
*** *** *** ***
*** *** *** ***
Retirement planning Have employer-sponsored ret. plan Non employer-sponsored ret. account Retirement account amount (in $) Stock in retirement account (yes=1) More than half Less than half None Invest in lifecycle fund Take loan from retirement account Retirement funding Social Security Pension plan payments Savings, investments, or retirement accounts Dividends or interest income Salary, wages, or self-emp. income Rental income or sale of real estate Reverse mortgage Family support Change in response to current economic conditions Real estate Homeowner Other real estate ownership Have a mortgage among homeowners Have a home equity loan among homeowners Credit Number of credit cards Credit card balance Have an auto loan Declare bankruptcy in past two years Insurance Have health insurance Have life insurance Have auto insurance Have homeowners or renters insurance Financial cost Mortgage interest rate Credit card interest rate Auto loan interest rate
127
18.2% 7.5% 24,956
84.8% 53.2% 125,560
-66.6% -45.7% -100,604
*** *** ***
38.7% 29.7% 31.6% 37.8% 9.3%
62.0% 33.4% 4.7% 28.2% 8.7%
-23.3% -3.7% 26.9% 9.6% 0.6%
*** * *** ***
88.9% 33.5%
69.8% 73.6%
19.1% -40.1%
*** ***
24.0%
37.8%
-13.8%
***
11.6% 7.5% 4.0% 1.6% 9.6%
42.4% 42.6% 14.9% 0.1% 1.4%
-30.8% -35.1% -10.9% 1.5% 8.2%
*** *** *** *** ***
52.2%
35.2%
17.0%
***
30.9% 8.2% 44.3%
86.7% 39.0% 77.7%
-55.8% -30.8% -33.4%
*** *** ***
12.1%
29.0%
-16.9%
***
1.55 3,513 17.9% 2.5%
4.40 5,569 45.3% 1.2%
-2.85 -2,056 -27.4% 1.3%
*** *** *** ***
62.6% 33.8% 70.8% 36.1%
95.8% 83.4% 97.7% 92.7%
-33.2% -49.6% -26.9% -56.6%
*** *** *** ***
7.3% 12.7% 9.9%
5.9% 11.9% 6.1%
1.4% 0.8% 3.8%
*** *** ***
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To give an overview of how the two income groups differ in their financial condition, Table 1 presents a variety of summary statistics for the low-income (column 1) and highincome (column 2) groups. The differences between the two groups are reported in the third column, and asteriske used to indicate whether these differences are significantly different when performing a t-test. The variables in Table 1 are divided into three panels: financial literacy and ability, financial management results, and financial status. Panel A shows the results on financial literacy and ability. We construct a scaled financial literacy score by adding up the number of correct answers for the five financial literacy questions and dividing the total score by 5.21 The self-assessed financial knowledge and capability scores are rescaled between 0 and 1 in the presentation of our results. The results show that low-income earners, on average, have lower levels of financial knowledge than high-income earners. Their self-assessed scores on financial knowledge and ability are also lower than those for the high-income group. However, it is worth noting that for the low-income group, average self-assessed knowledge is quite a bit higher than actual financial knowledge (0.59 vs. 0.49), while that is not the case for the high-income group (0.73 vs. 0.75). Possibly, low-income consumers are not as aware of their lack of financial knowledge, but we do not have sufficient data to test the hypothesis at this stage. It might also be that, because low-income workers are less exposed to investments, their perception of financial knowledge is less affected by investment knowledge than the financial literacy score is. Since self-assessed knowledge may capture some aspects of financial knowledge more related to day-to-day 21
Those who answered “don’t know” or “prefer not to say” to financial literacy questions are coded as zeros. 128
matters, in the later analysis we will use both self-assessed financial knowledge measures and the objective financial literacy scores. Panel B summarizes how individuals achieve personal financial planning goals in terms of cash flow management, savings and investments, retirement planning, credit management, and insurance planning by income groups. It shows that low-income consumers achieve less than highincome consumers. For example, under â&#x20AC;&#x153;cash flow managementâ&#x20AC;? there are fewer low-income earners setting up an emergency fund, and more of them are outspending their incomes or overdrawing on their checking accounts. The differences between the two income groups are statistically significant. However, it is worth noting that results here are simply summary statistics without controlling for individualsâ&#x20AC;&#x2122; characteristics and financial exposures, such as wealth, education, etc. Thus, differences may reflect not only behavior but also economic circumstances and financial needs. Panel C contrasts the current financial status of the lowand high-income groups. Low-income individuals have experienced an unexpected drop in income in the past year at a higher rate than their wealthier counterparts. They have less access to checking and saving accounts and have fewer investments both inside and outside their retirement accounts. They allocate less to equities in retirement accounts and rely more on social security and family support as retirement funding than high-income individuals do. More of the lowincome earners have changed their withdrawal amount or frequency from retirement accounts in response to the recent financial crisis, which reflects the fact that they are more vulnerable to economic shocks. In terms of real estate, fewer 129
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low-income consumers own their homes or other real estate properties. Low-income homeowners are less likely to have a mortgage or a home equity loan. Low-income consumers have fewer credit cards with lower card balances and are less likely to have auto loans. Low-income earners are less likely to be insured, especially for life insurance and homeowners or renters insurance: 34% of them have life insurance and 36% have homeowners or renters insurance, while the figures for their high-income counterparts are 83% and 93%, respectively. Last, the results indicate that low-income groups pay higher interest rates on mortgages, credit cards, and auto loans. Therefore, we conclude from the results in Panel C that lowincome individuals have less accumulated wealth through retirement and non-retirement savings and real estate, they face higher interest rates for borrowing, are less likely to have insurance, and are more vulnerable to financial shocks. Knowing that low-income consumers have lower levels of financial knowledge and ability, achieve fewer financial planning goals and have less access to financial products, more assistance is needed to improve their financial situation. Professional financial advice can be one solution. In the remainder of this section, we explore the prevalence of financial advice use among low- and high-income groups and their attitudes towards such professional services. Figure 1 shows the prevalence of financial advice among consumers. First, to give an overview of financial advice use, we find that 32% of respondents use savings/investments advice, 19% tax advice, 35% insurance advice, and 10% debt advice. Then, we investigate the use of different types of financial advice by income group. The use of financial advice increases with income, except for debt advice. 130
Figure 1. Use of Financial Advice by Advice Types 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
100%
Tax advice Average: 19%
90% 80% 70% 60% 50% 40% 30% 20% 10% 0% <15
15~25
25~35
35~50
131
50~75 75~100 100~150 150+ Income (in thousand dollars)
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100% 80% 60% 40% 20% 0%
100% 80% 60% 40% 20% 0%
132
Although low-income people need more help to improve their personal financial planning situation, we show that they are currently less likely to use financial advice than high-income people to solve the problem. The skewed supply of professional financial advice and products towards wealthy people and the less-favorable attitudes towards the industry among low-income communities could explain why lowincome consumers use less financial advice to improve their financial situation. We collect data on Certified Financial Planners (CFPs) and Certified Public Accountants (CPAs) with a Personal Financial Specialist designation, two major financial advice suppliers. In Figure 2, we show the average number of these suppliers by quartile of average income in the zip code. The results clearly indicate that CFPs and CPAs are located mainly in the top-quartile neighborhoods.22 To examine the role of attitudes towards financial advisers, Figure 3 compiles the answers to the three perception questions (trust financial professionals; financial professionals too expensive; hard to find the right financial professional) by income group. More low-income people believe that it is hard to find the right financial professionals and that advice is too expensive for them. Trust generally increases with income, except at the tail ends of the distribution. Overall, low-income consumers display less favorable attitudes towards the financial advice industry.
22
Of course, this is not to say that financial advisers work only for clients in their zip code. The result, however, is reflective of the skewed nature of supply in this industry. 133
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Figure 2. Supply of CFPs and CPAs by Income Quartile 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% First income Second income Third income quartile quartile quartile CFP supply
Fouth income quartile
CPA supply
Notes: Authorsâ&#x20AC;&#x2122; computation. The zip code income data comes from the annual file by IRS Statistics of Income Division for tax year 2008 (available at http://www.irs.gov/taxstats/indtaxstats/article/0,,id=242739,00.html). Number of CFPs by zip code (total 7,521) was derived from the Financial Planning Association directory http://www.fpanet.org/PLANNERSEARCH/PlannerSearch.aspx. Number of CPAs with a Personal Financial Specialist designation (total 4,688) by zip code was derived from the addresses in the American Institute of CPAs directory http://apps.aicpa.org/credentialsrefweb/PFSCredentialSearchPage.aspx.
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Figure 3. Attitudes towards Financial Professionals 0.53 0.51 0.49 0.47 0.45
Financial professionals too expensive
0.8 0.6 0.4 0.2 0 <15
15~25
25~35
35~50
0.6 0.58 0.56 0.54 0.52 0.5 0.48
135
50~75 75~100 100~150 150+ Income (in thousand
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Figure 4. Ranks of Financial Advice Use by Income Group Low income
1. Insurance advice
24%
2. Savings/investments advice
18%
3. Debt advice
10%
4. Tax advice
9% 0%
5%
10%
15%
20%
25%
High income
1. Savings/investments advice
47%
2. Insurance advice
44%
3. Tax advice
31%
4. Debt advice
8%
0%
10%
136
20%
30%
40%
50%
Before leaving this section, we further explore the demand compositions of financial advice among low- and high-income clients. Figure 4 ranks the prevalence of each type of financial advice among the two income groups separately. We find that low- and high-income individuals have very different demand compositions of financial advice. Insurance advice is the one that low-income consumers demand most (24%), followed by savings/investments (18%), debt (10%) and tax (9%) advice. For their part, high-income consumers use savings/investments advice most (47%), followed by insurance (44%), tax (31%) and debt (8%) advice. For policymakers, financial planners and educators to better fill the gap and serve the low-income community, there is a need to understand that group's special financial needs and exposures. For example, the low-income group needs insurance advice more than savings/investments advice. This makes sense given that they do not have as many assets to invest and are more exposed to economic insecurity. Determinants of financial advice adoption In the previous section, we found that the prevalence of using financial advice varies among income groups. In this section, we use discriminant analysis to explore the factors affecting consumersâ&#x20AC;&#x2122; decision to use financial advice and how these factors influence low- and high- income individuals differently. There is a rich literature on who seeks financial advice. The characteristics that have been found to affect individualsâ&#x20AC;&#x2122; financial advice-seeking behavior can be grouped into four categories: demographic, socioeconomic, psychosocial and financial literacy and capability. In the first category -
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demographic factors - older married females are found to be more likely to use financial advice (Bluethgen, Ginschel, Hackethal, & Müller, 2008; Finke, Huston, & Winchester, 2011; Gerhardt & Hackethal, 2009; Hackethal, Haliassos, & Jappelli, 2012; Hung & Yoong, 2010; Joo & Grable, 2001). As for the socioeconomic characteristics, education, homeownership, wealth and working status also affect financial advice-seeking behavior significantly (Bluethgen et al., 2008; Collins, 2010; Elmerick, Montalto, & Fox, 2002; Finke et al., 2011; Grable & Joo, 1999; Gerhardt & Hackethal, 2009; Hackethal et al., 2012; Haslem, 2010; Grable & Joo, 2001). Effects of psychosocial factors and financial literacy are relatively new topics in the literature. Risk tolerance, financial behaviors, attitudes towards retirement, financial stress and levels of financial satisfaction are found to affect financial advice adoption significantly (Grable & Joo, 1999 and 2001; Bluethgen et al., 2008; Gerhardt & Hackethal, 2009; Joo & Grable, 2001). Finke et al. (2011) shows that people with lower self-reported financial knowledge are more likely to pay for financial advice, and Collins (2010) finds that higher financial literacy increases the use of financial advice. We extend this literature by incorporating all these factors into one model and studying how they work together to affect individuals’ decision to pursue professional financial advice. Namely, we perform a discriminant analysis based on the following regression: Advice i 1 X i i
Advicei is a dummy variable indicating whether where individual i uses advice. Since Elmerick et al. (2002) point out that regression results vary by categories of advice, we run four regressions for savings/investments, tax, insurance and debt
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Xi advice separately. includes demographic characteristics such as gender, age, marital status, and race; it also has socioeconomic characteristics comprising education, homeownership, business ownership, income level, investment amount outside retirement accounts, working status and experiencing an unexpected income drop in past year. Xi , Additionally, psychosocial factors are incorporated in including being willing to take investment risk, being satisfied with current financial condition, and attitudes towards financial professionals (trust financial professionals, believe financial professionals are too expensive, and find it hard to find right Xi financial professionals). Last, considers the following financial literacy and capability variables: financial literacy score, self-assessed financial knowledge, good at dealing with day-to-day financial matters, good at math, and keeps up with economic financial news.23 We also control for other financial exposures and regional factors in the regression.24 We use discriminant analysis instead of traditional Probit regression because this approach can statistically test the relative importance of independent variables when explaining the decision to use financial advice. As mentioned by Hair, Anderson, Tatham and Black (1995), discriminant analysis can be used to determine which of the independent variables accounts the most for the differences in the average score 23
Satisfied with current financial condition, attitudes towards financial professionals, financial literacy and capability levels are scaled to a 0-1 range. 24 Financial exposure controls include dummy variables indicating if the individual claims it is difficult to pay bills, overdraws on checking account, has a defined-contribution plan, has been involved in foreclosure process in past two years, declared bankruptcy in past two years, has high-cost loans, and has health, homeowners or renters, life and auto insurance. 139
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profiles of two or more groups. Magnitudes of coefficients from discriminant regressions can show the relative explanatory power of each independent variable. Grable and Joo (1999) follow the same strategy to investigate the most important factors affecting individualsâ&#x20AC;&#x2122; decision to seek professional financial help.
140
141
18,792
0.21
Trust financial professionals N.
0.27
Income
Drop in income Financial professionals too expensive
Business owner
3,520
-0.21
0.23
0.33
0.34
Investment amount outside retirement accounts
-0.32 0.32
-0.42
Age
(2) Low-income Panel A. Savings/Investments Advice Investment amount outside 0.58 retirement accounts Financial professionals too -0.26 expensive Drop in income 0.25 Trust financial professionals 0.23 Have defined-contribution plan 0.21 3,522 Panel B. Tax Advice
0.34
0.29 0.17 0.16 18,813
-0.41
0.51
Business owner
Investment amount outside retirement accounts Financial professionals too expensive
Investment amount outside retirement accounts Financial professionals too expensive Trust financial professionals Drop in income Education N.
(1) All income groups
(3) High-income
Income
Investment amount outside retirement accounts Trust financial professionals
Business owner
Financial professionals too expensive
Financial professionals too expensive Investment amount outside retirement accounts Trust financial professionals Education Female
Table 2. Discriminant Analysis of Respondent Factors Using Standardized Coefficients
6,061
0.20
0.31
0.32
0.34
-0.43
0.40 0.17 0.15 6,068
0.44
-0.54
142
-0.20
Involved in foreclosure process in past 2 years
3,517
-0.18
0.18
Satisfied with current financial condition
6,072
-0.21
-0.26
-0.27
-0.53
0.27 -0.24 6,068
0.28
0.34
-0.38
Difficult to pay bills
Overdraw checking account
Declare bankruptcy in past two years Have high cost loan
Drop in income Age
Have life insurance
Financial professionals too expensive Trust financial professionals
Notes: The table shows the independent variables with the top five discriminant standardized coefficients in each regression. The full sample contains 28,146 observations, out of which 7,013 are classified in the low-income group and 7,779 in the high-income group. The numbers of observations in this table are smaller because of missing observations for some of the questions.
18,819
-0.17
Difficult to pay bills N.
-0.24
Education
-0.25
Have high cost loan
-0.34
0.25
-0.27
-0.61
-0.56
0.22 0.22 3,519
0.29
0.30
0.44
Panel D. Debt Advice Declare bankruptcy in past two years Overdraw checking account
Have high cost loan
Declare bankruptcy in past two years Overdraw checking account Satisfied with current financial condition
Have homeowner insurance Have auto insurance
Business owner
-0.24 0.24 0.23 18,815
Have life insurance
0.27
Business owner Financial professionals too expensive Have high cost loan Drop in income N.
Have high cost loan
0.29
Panel C. Insurance Advice
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Have life insurance
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Column (1) in Table 2 presents the top five canonical discriminant coefficients in each regression, which reflect the variables’ relative significance in contributing to the discriminant function. We find that the factors explaining the most variance in advice-seeking behavior among all income level respondents vary by type of financial advice. For example, wealth (investment amount outside retirement accounts) and believing that financial professionals are too expensive are the top two factors affecting consumers’ use of savings/investments and tax advice. Having life insurance and running a business are the top two determinants for insuranceadvice adoption. The two factors contributing the most to the use of debt advice are “declared bankruptcy in past two years” and “overdraw checking account.” In addition, factors that are shown to have significant effects and that have been the focus of much of the previous literature such as gender and homeownership, do not affect the use of financial advice so significantly after controlling for other factors. Factors such as trust in financial professionals and believing that financial advice is too expensive, not studied much in the previous literature, play very important roles in financial adviceadoption decisions. For example, trust in financial professionals is among the top five factors affecting savings/investments and tax-advice adoption. Lachance and Tang (2012) study the relationship between financial advice and trust and conclude the same. Next, we explore how these factors affect low- and high-income consumers’ decisions to use financial advice differently. We run the four regressions among low- and highincome individuals and show the results in columns (2) and (3) separately. We find that even for the same type of advice, low143
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and high-income consumers’ concerns are different. For example, for savings/investments, tax and insurance advice, believing that financial professionals are too expensive is the top concern among high-income consumers. However, lowincome consumers put other factors such as wealth, age, and having high-cost loans ahead of expense when considering using such advice. For example, in low-income consumers’ decisions to adopt savings/investments advice, investment outside retirement accounts with the standardized coefficient of 0.58 plays a more important role than believing that financial professionals are too expensive with a coefficient of -0.26. While it is commonly assumed that cost is the main concern when consumers decide whether to seek financial advice, our results indicate that this is only the case for high-income consumers. Effects of financial advice The effects of professional financial advice on individuals’ financial matters have been a very important topic in the literature since any positive effects that exist can justify the expense of seeking professional advice. To find an efficient way to improve society’s overall personal financial condition and financial literacy level, it is necessary to know if and how financial professionals, as one potential solution, can help improve consumers’ financial well-being. However, so far the literature has been limited in two ways. First, most studies focus only on the effects of investment advice on portfolio performance (Hackethal et al., 2012; Bluethgen et al., 2008; Jansen, Fischer, & Hackethal, 2008; Gerhardt & Hackethal, 2009) or credit management (Staten, Elliehausen, & Lundquist, 2003; Hirad & Zorn, 2001). Investment and credit advice is only a subset of professional financial advice offered in today's 144
market. In addition, financial advice not only affects individuals’ portfolio performance or credit management in a direct way, but can also affect consumers’ behavior and financial literacy level, which potentially have a bigger impact on consumers’ financial well-being. In the current “professionals” era of financial services, financial advisors do more than just suggest financial products or manage portfolios for their clients, they work “with” the clients. During the process, the education and coaching function can do more than just fix the current financial problem for which the client is seeking help (Warschauer, 2002). Very few studies have looked at the effects of professional advice on clients’ behavior and financial literacy level. Second, reverse causality and selection caused by unobserved characteristics can lead to biased estimations of causal effects of financial advice on individual financial behavior using linear regressions. Lyons (2005) concludes that, while the general consensus from the literature is that financial education positively affects financial outcomes, it is important to acknowledge that the findings are far from conclusive, especially with respect to the direction of causation. Well-designed experiments can be a solution to this problem. For example, to rule out the problem of reverse causality and selection bias, Hung and Yoong (2010) designs and implements a hypothetical choice experiment to study the effect of financial advice on defined-contribution plan holders’ financial behavior. However, the question that can be explored by one experiment is very limited. Currently, no such experiment allows us to investigate the effects of financial advice on consumers’ financial behavior from a broad perspective.
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Another possible solution is to use instrument variables. Hackethal et al. (2012) employ this strategy to study the impact of advice on advised accounts’ performance in Germany, and Yoong (2010) uses instrument variables to examine the effect of financial literacy on stock participation. However, very little has been done to study the effect of financial advice on individuals’ financial behavior in the United States due to the limitations of the data and unavailability of qualified instrument variables. The NFCS dataset offers new variables that can be used as instruments, and we consider the following six candidates: trust in financial professionals, believing that financial advice is too expensive, finding it hard to find the right professional, having defined contribution plans, and number of CFPs and CPAs in the zip code. These variables are expected to have effects on individuals’ decision to use financial advice but not on their financial literacy and behavior directly. To select the preferred instrument set for each regression, we follow Yoong’s (2010) criteria by checking the relevance, weakness and exogeneity of the instrument variables. In particular, we use Anderson canonical correlations test, a likelihood-ratio test of whether the excluded instruments are correlated with the endogenous regressors to check for relevance. The null hypothesis needs to be rejected to meet the relevance requirement. We test for weakness by using the Cragg-Donald Fstatistics from the first-stage regression, which must be higher than the Stock-Yogo critical values to pass the test. We also check for exogeneity with the Sargan-Hansen test of over-identifying restriction, which tests the joint null hypothesis that the additional instrument is uncorrelated with the error term and the excluded instruments are correctly excluded from the estimated equation. We need to fail the null to pass the exogeneity test. 146
We explore the effects of professional financial advice on individuals’ financial matters in three areas: financial literacy (both self-assessed and objectively measured); negative behaviors (including spending more than one's income over the past year, using high-cost loans,25 overdrawing on checking accounts, and being charged credit card penalties),26 and positive behaviors (including trying to figure out how much to save to retire, setting up emergency funds, and reviewing insurance coverage). We implement the instrument variable approach by running the following two-stage least squares regressions for each type of advice (savings/investments, tax, insurance, and debt):
Advice i 1 1 Instrument i 2 X i
Effect i 1 1 Advice i 2 X i i
Advicei is a In equation (2), the first-stage regression, dummy variable indicating if individual i uses the relevant Instrument i denotes the instrument variable type of advice. X used for each type of financial advice. Control variables i in (2) and (3) include gender, age, race, education, income, and working status. In addition, scaled financial literacy scores are Effecti used to test effects on positive and negative behaviors. 25
High-cost loans include auto title loans, short-term payday loans, refund anticipation loans, loans from pawn shops, and using rent-to-own stores. 26 One is considered to have been charged credit card penalties if he or she was assessed a fee for late payment or an over-the-limit fee for exceeding the credit line. 147
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in the second-stage regression (3) include individual i ’s selfassessed and objectively measured financial literacy scores, indicators of each of the negative and positive behaviors in the nine regressions, respectively. To explore the causal effect of Effecti , we use the instrumented variable financial advice on
Advice i estimated from the first-stage regression.
Table 3 shows the results among all income level respondents. All the regressions pass the relevance, weakidentification, and exogeneity tests.27 All four types of financial advice are associated with greater self-assessed knowledge. However, advice does not improve the objective measure of financial literacy. No significant effect is found for savings/investments or tax advice, and insurance and debt advice are associated with lower financial literacy scores.28 In most cases, all types of advice help clients improve positive behaviors, but only savings/investments and tax advice help avoid negative behaviors. For example, clients taking insurance 27
We use Anderson canonical correlations LM statistics to test for the relevance of instrument variables, Cragg-Donald Wald F-statistics for weakness test and Sargan-Hansen statistics are for exogeneity test. χ2(2) Pvalue for Anderson canonical correlations LM statistics needs to be lower than the significance level to meet the relevance requirement. Cragg-Donald Wald F-statistic must be higher than the Stock-Yogo critical values to pass the weakness test and χ2(1) P-value for Sargan-Hansen statistics should be higher than the significance level to pass exogeneity tests. Regressions of debt advice on “have emergency fund” pass the weak identification test at the 15% level instead of the 10% level, as in other regressions. 28 Financial literacy scores from the five questions are titled to investment knowledge, which may not truly reflect the influence of insurance/debt advice on clients’ financial knowledge related to insurance and debt matters, while self-assessed financial knowledge measures may capture some aspects of financial knowledge related to insurance and debt issues. Such disconnect between the two financial knowledge measures may explain the positive effects of insurance/debt advice on self-assessed financial knowledge, but negative effects on financial literacy scores. 148
149
Literacy score
0.01 24,913 365.1 185.11 0.19 0.02 24,876 145.48 73.10 0.27 -0.12
Selfassessed
0.19 *** 24,587 361.8 183.42 0.23 0.35 *** 24,549 145.97 73.36 0.12 0.20
Savings/investments advice
N. Instrument variables tests Anderson canon. corr. LM Cragg-Donald Wald F Ď&#x2021;2(1) P-value
Tax advice
N. Instrument variables tests Anderson canon. corr. LM Cragg-Donald Wald F Ď&#x2021;2(1) P-value
Insurance advice
0.65
139.70 70.19 0.23
24,302
-2.29 ***
358.9 181.94 0.39
24,326
-1.22 ***
Spend > income
0.19
146.81 73.78 0.17
24,578
-1.87 ***
369.6 187.46 0.33
24,608
-1.04 ***
High cost loan
0.79
145.43 73.11 0.11
22,914
-1.53 ***
375.69 190.78 0.20
22,938
-0.86 ***
Overdr. from checking
0.54
139.33 70.10 0.25
18,798
-1.51 ***
361.78 184.21 0.37
18,854
-0.80 ***
Credit card penalty
1.67
146.18 73.47 0.28
24,069
1.64 ***
363.92 184.57 0.38
24,105
0.88 ***
Calculate retire amt.
4.22
148.07 74.42 0.35
24,164
2.82 ***
140.84 70.73 0.23
26,774
2.30 ***
Emergency fund
0.79
143.71 72.23 0.28
23,083
0.91 ***
368.79 187.20 0.35
23,117
0.49 ***
Review insurance
Table 3. Effects of Financial Advice on Financial Literacy and Behavior among All Income Groups Financial Negative behavior Positive behavior literacy
150
-0.29 *** 26,141 75.47 37.81 0.28
0.47 *** 25,785 73.89 37.02 0.16
Debt advice
N. Instrument variables tests Anderson canon. corr. LM Cragg-Donald Wald F Ď&#x2021;2(1) P-value 68.17 34.15 0.91
25,502
1.49 **
176.32 88.69 0.68
25,493
***
75.30 37.73 0.64
25,811
0.34
182.11 91.62 0.67
25,805
62.57 31.34 0.61
24,033
1.79 ***
157.91 79.40 0.88
24,019
***
53.94 27.01 1.00
19,622
1.00
134.02 67.39 0.90
19,619
**
71.59 35.86 0.17
25,293
3.94 ***
178.89 90.00 0.37
25,291
***
35.60 17.81 0.46
24,201
-7.29 ***
60.58 30.34 0.25
24,203
***
69.33 34.73 0.70
24,199
1.87 ***
170.01 85.52 0.19
24,201
***
Notes: The table shows coefficients of financial advice on financial literacy, negative and positive financial behaviors in the second-stage regression. Control variables not shown in the table in the second-stage regression include gender, age, race, education, income and working status. In addition, scaled financial literacy scores are used as control variables to test effects on positive and negative behaviors. Anderson canonical correlations LM statistics are used to test for the relevance of instrument variables; Cragg-Donald Wald F statistics are for weakness test and Sargan-Hansen statistics are for exogeneity test. The sampl includes all the respondents who have complete set of information for the regression. *** indicates statistical significance at the 1% level, ** at the 5% level and * at the 10% level.
179.12 90.10 0.19
176.53 88.80 0.30
*** 26,133
***
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25,782
N. Instrument variables tests Anderson canon. corr. LM Cragg-Donald Wald F Ď&#x2021;2(1) P-value
150
e
advice will pursue more positive behaviors, but they are also more likely to spend more than income, overdraw from checking accounts and get credit card penalties. Therefore, we conclude from Table 3 that there are some positive effects from taking advice, but it depends on the type of advice. Savings/investments and tax advice serves better to correct clients’ behavior and promote positive behavior than does insurance and debt advice. The reason for such a phenomenon is worth investigating in further study. One explanation could be that savings/investments and tax advice is more likely to be provided in a comprehensive plan,29 with financial advisors working with clients to define the problem and work through the process. Insurance and debt advice, however, focuses more on solving one specific problem and referring products, which decreases the probability that advisors will help clients improve their overall financial behavior. In addition, none of the advice helps to improve clients' financial literacy, although it does make them perceive that they know more. Thus, our results indicate that, currently, professional financial advisors are not an effective channel to improve consumers’ financial literacy. Next, we examine whether our previous results are different for the low-income group. To do so, we modify the regressions in (2) and (3) as follows: Advice i 1 1 Instrument
i
2 X i i
Effect i 1 1 Advice i 2 Advice iLow 3 X
. 29
We find that those who use savings/investments or tax advice are more likely to use savings/investments, tax and insurance advice together than those who use insurance or debt advice. 151
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The first-stage estimation (4) is the same as in (2), but
AdviceiLow , in the second stage, we include a new variable which is the instrumented interaction between the low-income dummy and use of financial advice to test the additional effect
Effecti of Advice i on among low-income consumers.
As
AdviceiLow in the second there is one more endogenous variable stage, we need a new instrument. By following Wooldridge (2010), we create a new instrument variable by interacting the
low-income dummy with the estimated value of Advice i in the first stage. Table 4 shows the effects of financial advice on financial literacy and behavior and the additional effects among low-income consumers. All the regressions pass the three identification tests on instrument variables.30 As for financial literacy, low-income consumers are more likely to believe that their financial knowledge level is improved after receiving savings/investments, insurance, and debt advice, but their objective financial literacy level is not significantly higher than high-income clients after receiving the advice. Financial advice helps low-income clients more by avoiding most negative behaviors and improving some positive behaviors to a greater extent than the high-income clients. For example, receiving savings/investments advice lowers the chance to overdraw from checking accounts among all income-level respondents with a coefficient of -0.81, and it helps the low-income ones 30
Regressions of debt advice on “have emergency fund” pass the weak identification test at the 15% level instead of the 10% level, as in other regressions. 152
more with a coefficient of -1.27 (-0.81-0.46=-1.27). Considering the limited access to financial advice in lowincome communities, low-income consumersâ&#x20AC;&#x2122; lower financial literacy level and their worse financial condition, it is expected that, with the same input, the marginal benefit of financial advice to low-income consumers will be higher than it would be to higher-income groups. However, as we discussed in the introduction, the current professional financial service industry is skewed towards high-end consumers, mainly due to the compensation system. To improve the overall financial capability level of the society, policymakers, financial counselors, planners, and educators should find ways to promote more financial advice to low-income groups, as we have shown that the marginal benefit from providing such a service is higher for low-income clients. Conclusion Using a unique rich dataset from the NFCS, the paper first compares the financial condition of low- and high-income consumers. We find that low-income individuals have less financial knowledge and ability, achieve fewer financial planning goals, and have less access to financial services and products. Meanwhile, they are not using as much financial advice as wealthier individuals as a channel to solve the problem. The skewed supply of professional financial advice and products towards wealthy people and the less-favorable attitudes towards financial professionals among low-income communities could explain this. We provide insights to those who want to advocate financial advice services to low-income communities by showing that the unique demand compositions among low153
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income clients, and their various considerations, differ from those of high-income consumers when seeking financial advice. We find that low-income earners demand insurance advice most, followed by savings/investments advice, debt advice and tax advice, which is quite different from the demand compositions of high-income clients. Discriminant analysis shows that the top concern among low-income consumers seeking financial advice is not cost as among highincome consumers; rather, for low-income consumers, factors such as wealth, age and having high-cost loans appear of greater concern. Therefore, financial advice providers should focus on different sets of financial services and pay attention to the unique considerations among low-income consumers to serve them better and attract more potential clients. Using an instrument variable strategy to solve the problem of reverse causality and selection, we find that savings/investments and tax advice improves clientsâ&#x20AC;&#x2122; positive behaviors and corrects their negative behaviors. In addition, low-income consumersâ&#x20AC;&#x2122; financial behavior is improved more than that of high-income ones. Such findings might help justify the cost of subsidizing financial advice for low-income households with key stakeholders such as policymakers, financial planners and educators. In addition, our analysis indicates that financial advice has positive effects on clientsâ&#x20AC;&#x2122; financial behavior, which could potentially benefit the clients in the long term. The financial situation within the low-income community has gained a lot of attention as we learn more about increases in income and wealth gaps. As a group of consumers who need more resources to improve their financial well-being and who represent a large potential market to financial advice 154
suppliers, low-income consumers’ advice-seeking behavior has not yet been studied extensively. Today’s financial advice industry is more focused on wealthy clients, and the literature on advice-seeking behavior is biased towards higher-income individuals. This paper contributes to the literature by showing that financial exposures and advice-seeking considerations among low-income individuals differ from those of their higher-income counterparts. We also show that professional financial advice has a larger positive effect on the low-income group. However, we recognize that more research is needed to better understand the financial behavior of low-income consumers in order to serve them better. Appendix Financial Literacy Questions: The NFCS includes five standard questions on financial literacy developed by Lusardi and Mitchell (2009): Compound interest question. Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow? 1) more than $102, 2) exactly $102, 3) less than $102, 4) don’t know, and 5) prefer not to say. Inflation question. Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account? 1) more than today, 2) exactly the same, 3) less than today, 4) don’t know, and 5) prefer not to say.
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Bond question. If interest rates rise, what will typically happen to bond prices? 1) they will rise, 2) they will fall, 3) they will stay the same, 4) there is no relationship between bond prices and the interest rate, 5) don’t know, and 6) prefer not to say. Mortgage question. A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be less. 1) true, 2) false, 3) don’t know, and 4) prefer not to say. Diversification question. Buying a single company’s stock usually provides a safer return than a stock mutual fund. 1) true, 2) false, 3) don’t know, and 4) prefer not to say. Self-Assessed Financial Knowledge Question: On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall financial knowledge? Self-Assessed Ability Questions: How strongly do you agree or disagree with the following statements? Please give your answer on a scale of 1 to 7, where 1 = “Strongly Disagree,” 7 = “Strongly Agree,” and 4 = “Neither Agree nor Disagree”. You can use any number from 1 to 7. 1) I am good at dealing with day-to-day financial matters, such as checking accounts, credit and debit cards, and tracking expenses 2) I am pretty good at math 3) I regularly keep up with economic and financial news 156
References Bluethgen, R., Ginschel, A., Hackethal, A., & Müller, A. (2008). Financial advice and individual investors’ portfolios. Journal of Financial Transformation, 23, 77-87. Collins, M. (2010). A review of financial advice models and the take-up of financial advice. Center for Financial Security Working Paper 10-5. Elmerick, S. A., Montalto, C. P., & Fox, J. J. (2002). Use of financial planners by U.S. households. Financial Services Review, 11, 217-231. Finke, M. S., Huston, S. J., & Winchester, D. D. (2011). Financial advice: Who pays? Journal of Financial Counseling and Planning, 22, 18-26. Gerhardt, R., & Hackethal, A. (2009). The influence of financial advisors on household portfolios: A study on private investors switching to financial advice. Working Paper. Retrieved August 15, 2012, from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1343607. Grable, J. E., & Joo, S. H. (1999). Financial help-seeking behavior: Theory and implications. Financial Counseling and Planning, 10, 13-24. Grable, J. E., & Joo, S. H. (2001). A further examination of financial helpseeking behavior. Financial Counseling and Planning, 12(1), 55-74. Hackethal, A., Haliassos, M., & Jappelli, T. (2012). Financial advisors: A case of babysitter? Journal of Banking & Finance, 36 (2), 509–524. Hair, J. F. Jr., Anderson, R., Tatham, R., & Black, W. (1995). Multivariate data analysis with readings (4th ed.). Englewood Cliffs, NJ: Prentice Hall. Haslem, J. A. (2010). The new reality of financial advisors and investors. The Journal of Investing, 19, 23-30. Hirad, A., & Zorn, P. M. (2001). A little knowledge is a good thing: Empirical evidence of the effectiveness of pre-purchase homeownership counseling. Low-Income Homeownership Working Paper Series, LIHO-01.4. Cambridge, MA: Joint Center for Housing Studies, Harvard University. Retrieved August 15, 2012, from http://www.freddiemac.com/corporate/reports/pdf/homebuyers_study.p df. Hung, A., & Yoong, J. (2010). Asking for help: Survey and experimental evidence on financial advice and behavior change. RAND Working Paper Series WR-714-1. Jansen, C., Fischer, R., & Hackethal, A. (2008). The influence of financial advice on the asset allocation of individual investors. EFA 2008 Athens Meetings Paper. Joo, S. H., & Grable, J. E. (2001). Factors associated with seeking and using professional retirement-planning help. Family and Consumer Sciences Research Journal, 30, 37-63. Lachance, M., & Tang, N. (2012). Financial advice and trust. Financial Services Review, forthcoming. 157
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Lusardi, A., & Mitchell, O. S. (2009). How ordinary consumers make complex economic decisions: Financial literacy and retirement readiness. National Bureau of Economic Research Working Paper 15350. Lyons, A. C. (2005). Financial education and program evaluation: Challenges and potentials for financial professionals. Journal of Personal Finance, 4 (4), 56-68. Mills, G., Patterson, R., Orr, L., & DeMarco, D. (2004). Education of the American dream demonstration: Financial evaluation report. Cambridge, Mass.: Abt Associates. Rubin, J. (2007). Introduction in financing low-income communities: Models, obstacles, and future directions, Rubin, J. (Eds.). Russell Sage Foundation, New York. Schreiner, M., Clancy, M., & Sherraden, M. (2002). Saving performance in the American dream demonstration, a national demonstration of individual development accounts. St. Louis, Mo.: Center for Social Development, Washington University. Staten, M. E., Elliehausen, G., & Lundquist, E. C. (2003). The impact of credit counseling on subsequent borrower credit usage and payment behavior. Credit Research Center Monograph #36. Georgetown University. Retrieved August 15, 2012, from http://www.federalreserve.gov/communityaffairs/national/CA_Conf_S usCommDev/pdf/statenmichael.pdf. Warschauer, T. (2002). The role of universities in the development of the personal financial planning profession. Financial Services Review,11, 201-216. Wooldridge, J. (2010). Econometric analysis of cross selection and panel data. The MIT Express. Yoong, J. (2010). Financial illiteracy and stock market participation: Evidence from the RAND American life panel. Pension Research Council Working Paper 2010-29.
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Journal Personal Finance
Volume 11
www.journalofpersonalfinance.com
Converting a Traditional IRA to a Roth IRA Ronnie J. Clayton, Jacksonville State University Lemuel W. Davis, CFP速 William Fielding, Jacksonville State University
When to Claim Social Security Benefits David Blanchett, CFA, CFP速, Morningstar Investment Management
Jean M. Lown, PhD. (corresponding author), Utah State University Devon Robb, M.S., Utah State University
Financial Advice: What About Low-Income Consumers? Journal of Personal Finance
Find a Member at: www.IARFC.org International Association of Registered Financial Consultants
After The Global Financial Crisis: Attitudes Toward Immediate Annuities How can you be sure a financial consultant is among the finest? Just visit with a Registered Financial Consultant速. Those financial advisors who meet high standards of education, experience and integrity carry the RFC速 designation.
The Financial Planning Building 2507 North Verity Parkway Middletown, Ohio 45042
What the best financial consultants carry
INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS
IARFC
Issue 2
of
Ning Tang (corresponding author), San Diego State University Marie-Eve Lachance, San Diego State University
Tools, Techniques, Strategies & Research to Aid Consumers, Educators & Professional Advisors Volume 11 Issue 2
2012