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Volume 15 Issue 2 2016 www.journalofpersonalfinance.com

Journal of Personal Finance

Techniques, Strategies and Research for Consumers, Educators and Professional Financial Consultants

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS


National Financial Plan Competition

2016 April 20-21 Charlotte, NC

1st Place Bryant University Molly Funk Corporate Sponsors

Individual Sponsors Diamond:

Pete D'Arruda Barry Dayley Jon Rogers Angie Trandai

Platinum: Steve Bailey Bobbi Bailey Isabel Cooper

Ed Morrow Chris Roberts

Gold:

Silver:

Michelle Blair Ahmed Edris George Flack David King Eileen Kohler Noel Milner Jim Moss Thomas Price Thomas Stark

Theodore Aldershof Mitchell Beatty Lon Broske Tony Castillo David Dahl Kenneth Gallacher Bryan Hegarty Steve Hsin

Frederick Hoffman Norman Johnson Steven B. Katz Ed Ledford Anthoni Lightbourne Robert Love Patrick Lyman Susan Morrow

Fred Ostermeyer Jeff Reitzel Paul Schwabe Howard Sorkin Raymond Stiles Brian Walsh Daniel Yee John Young


Volume 15, Issue 2

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Journal of Personal Finance

Volume 15, Issue 2 2016 The Official Journal of the International Association of Registered Financial Consultants Š2016, IARFC. All rights of reproduction in any form reserved.


Journal of Personal Finance

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Contents Editors’ Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Evaluating Financial Planning Strategies and Quantifying Their Economic Impact . . . . . . . . . . . . . . . . . . . . . . 7 Michael E. Kitces, MSFS, MTAX, CFP®, CLU, ChFC, RHU, REBC, CASL, is a Partner and the Director of Research for Pinnacle Advisory Group (www.pinnacleadvisory.com), a private wealth management firm located in Columbia, Maryland. Most research on the value of financial advice has focused primarily on how advisors add value around a portfolio. Ultimately, though, financial advice can impact a wide range of areas from income and estate tax planning to insurance planning (where the expected value is typically negative in absolute terms, but an improvement in risk reduction), and more. In some cases, the “value” of financial planning is in the eye of the beholder—based on how the client values his/her own time relative to paying for the advisor. When comparing two financial planning strategies to each other, it’s feasible to compare the outcomes and decide which is best. Trying to assess the value of financial planning advice in the abstract, though, is much harder because of the “compared to what” problem. It’s not always clear exactly how any particular client would have behaved in the absence of the advice (since that future never actually happened), which makes it impossible to measure whether or how the advice actually changed the outcome. In a world where ultimately most financial planning strategies “could” be implemented by a consumer themselves—given sufficient education, time, and an inclination to get it done—arguably the greatest value a financial planner provides is the behavioral coaching and support to ensure the recommendations are actually implemented. Unfortunately, the financial impact of this is virtually impossible to measure, given the uncertainty of how a prospective client might have behaved in the future without an advisor. Nonetheless, it is essential to recognize that the economic impact of financial planning is not merely the strategy itself, but its implementation, too. This article projects the potential economic impacts of various financial planning strategies. Marriage and Taxes: Who Pays? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Allen J. Rubenfield, Lecturer of Accounting, Eugene W. Stetson School of Business and Economics, Mercer University Ganesh M. Pandit, Associate Professor of Accounting, Robert B. Willumstad School of Business, Adelphi University The Tax Policy Center states that a “marriage penalty” occurs in the tax system when a wife and husband pay more income tax filing jointly as a couple than they would if they had remained single and filed as individuals. Conversely, a “marriage bonus” occurs if a couple pays less tax filing jointly than they would if they were not married and filed single. (TPC) Competing Risks: Death and Ruin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Dirk Cotton, MBA, The Retirement Cafe Cary Cotton, MD, MPH, University of North Carolina Hospital Alex Mears, BS Early portfolio survival research in the field of retirement finance largely focused on the probability of outliving one’s retirement portfolio with constant spending over fixed time periods, such as 30 years. Seminal studies by William Bengen, for example, suggested that a retiree can invest his or her savings in a portfolio of stocks and bonds and spend around 4% of his or her initial savings balance annually from the portfolio with about a 5% probability of outliving those savings. Subsequent work by Stout and Mitchell and later by Milevsky and Robinson incorporated random lifetimes, but focused on the lifetime probability of ruin. Medical research uses methods of analyzing survival studies that are novel in retirement research. We use Kaplan-Meier estimates and competing risks analysis to explore the conditional probability of a retiree outliving her savings as age progresses, the relationship of the competing risks of death and ruin as age progresses, and the timing of portfolio failures due to poor market returns. We find that risk of ruin develops in three stages of a long retirement: a lowrisk period early in retirement with high sensitivity to market returns but few portfolio failures, a middle period in which portfolio failure peaks, and a late period in which death is much more likely than portfolio ruin. ©2016, IARFC. All rights of reproduction in any form reserved.


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Positive Health and Financial Behaviors: The Impact of Time Expenditure Behavior and Avoidance . . . . . 41 Barbara O’Neill, Ph.D., CFP®, Extension Specialist in Financial Resource Management, Rutgers Cooperative Extension Jing Jian Xiao, Ph.D., Professor of Consumer Finance, Department of Human Development and Family Studies (HDFS), University of Rhode Island Karen Ensle, Ed.D., RDN, Family and Community Health Sciences Educator, Rutgers Cooperative Extension of Union County This study explored relationships between positive personal health and financial practices that involve a routine time expenditure (e.g., 30 minutes of physical activity and eating two meals prepared at home) and those that involve avoidance of negative behaviors (e.g., avoiding sugar-sweetened beverages and high cost debts such as payday loans). Data came from an online quiz that provides a simultaneous assessment of individuals’ health and financial practices with 942 observations. Correlational and multivariate analyses indicated weak, but positive and statistically significant, relationships between health and financial behaviors that involve a time commitment and those that involve avoidance of certain negative practices. Findings of demographic subsamples indicated that older, White respondents and those with higher incomes and educational levels were more likely than their respective counterparts to perform recommended health and financial practices. The article includes literature about conscientiousness and health-wealth relationships and four implications for financial advisors. College Student Attitudes toward Retirement Planning: The Case of Mexico and the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Janet L. Koposko, Department of Psychology, Oklahoma State University, Stillwater, Oklahoma Martha Isabel Bojórquez, Faculty of Accounting and Administration, Universidad Autónoma de Yucatán, Calle 31 x 35 s/n, Boulevares de Chuburna, 97200 Mérida, YUC, Mexico Antonio Emmanuel Pérez, Faculty of Accounting and Administration, Universidad Autónoma de Yucatán, Calle 31 x 35 s/n, Boulevares de Chuburna, 97200 Mérida, YUC, Mexico Douglas A. Hershey, Department of Psychology, Oklahoma State University, Stillwater, Oklahoma College students are a population of particular interest when it comes to financial planning for retirement, because they will soon enter the workforce and be asked to make significant decisions that will set the stage for a lifetime of saving practices. In this investigation, college students in the United States (n = 346) and Mexico (n = 345) reported their attitudes, behaviors, and beliefs regarding an array of psychological variables related to financial planning for retirement. We cast the data into two theoretically-based path models—one for each country—and then compared the results. Both models accounted for appreciable variance in expectations of future financial planning. Although models for both groups were structurally similar, path coefficients revealed important cross-national differences in the psychological factors that underlie anticipated future saving practices. The discussion focuses on cultural differences in attitudes and beliefs likely to impact long-range financial planning and saving behaviors. IARFC National Financial Plan Competition: Case Solution by Bryant College . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Edited by Walt Woerheide, Ph.D., ChFC®, CFP®, RFC® The IARFC recently completed its 2016 National Financial Plan Competition at the Speedway Club in Charlotte, North Carolina. The winner was Molly Funk from Bryant University. Second Place went to Daniel Ingles and Grant Hulett from Central Michigan University, and Third Place to Cole Brownell and Anthony Pelaez from California State University Northridge. The competition began with students being given a fictional case study of a family with an overview of their financial picture. From that data, all of the participants produced a financial plan with recommendations for current and future actions. Selected teams advanced to the semi-finals and the top three teams ended up with in-person presentations in Charlotte. The case distributed to the teams is provided first, followed by the winning discussion. Although we would like to provide the entire solution by the winning team, the formal report was over 200 pages. This report included the analysis and recommendations, along with many tables, tutorials for the client family, and general financial planning information. What follows are the edited, salient points and recommendations from the report.

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Call for Papers Journal of Personal Finance (www.JournalofPersonalFinance.com) Overview The Journal of Personal Finance is seeking high quality submissions that add to the growing literature in personal finance. The editors are looking for original research that uncovers new insights—research that will have an impact on advice provided to individuals. The Journal of Personal Finance is committed to providing high quality article reviews in a single-reviewer format within 60 days of submission. Potential topics 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

Please check the “Submission Guidelines” on the Journal’s website (www. JournalofPersonalFinance.com) for more details about submitting manuscripts for consideration.

Contact Wade Pfau and Walt Woerheide, Co-Editors Email: jpfeditor@gmail.com www.JournalofPersonalFinance.com

©2016, IARFC. All rights of reproduction in any form reserved.


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Journal Of Personal Finance Volume 15, Issue 2 2016 Co-Editors Wade Pfau, Ph.D., The American College Walt Woerheide, Ph.D., ChFC, CFP™, RFC®, The American College

Editorial Board Benjamin F. Cummings, Ph.D., Saint Joseph’s University

Ruth Lytton, Ph.D., Virginia Tech

Dale L. Domian, Ph.D., CFA, CFP™, York University

Lew Mandell, Ph.D., University of Washington

Michael S. Finke, Ph.D., CFP™, RFC® Texas Tech

Carolyn McClanahan, MD, CFP™, Life Planning Partners

Joseph W. Goetz, Ph.D., University of Georgia

Yoko Mimura, Ph.D., California State University, Northridge

Michael A. Guillemette, Ph.D., University of Missouri

Robert W. Moreschi, Ph.D., RFC®, Virginia Military Institute

Clinton Gudmunson, Ph.D., Iowa State University

Ed Morrow, CLU, ChFC, RFC®, IARFC

Sherman Hanna, Ph.D., The Ohio State University

David Nanigian, Ph.D., Mihaylo College at Cal State Fullerton

George W. Haynes, Ph.D., Montana State University Douglas A. Hershey, Ph.D., Oklahoma State University

Barbara M. O’Neill, Ph.D., CFP™, CRPC, CHC, CFCS, AFCPE, Rutgers

Karen Eilers Lahey, Ph.D., The University of Akron

James Taggert, Ph.D., Taggert Consulting

Douglas Lamdin, Ph.D., University of Maryland Baltimore County

Jing Jian Xioa, Ph.D., University of Rhode Island

Jean M. Lown, Ph.D., Utah State University

Tansel Yilmazer, Ph.D., CFP™, The Ohio State University

Angela C. Lyons, Ph.D., University of Illinois

Yoonkyung Yuh, Ewha Womans University Seoul, Korea

Rui Yao, Ph.D., CFP™, University of Missouri

Mailing Address:

Disclaimer: The Journal of Personal Finance

IARFC Journal of Personal Finance 1046 Summit Drive Middletown, OH 45042-0506

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.

Postmaster: Send address changes to IARFC, Journal of Personal Finance, 1046 Summit Drive, Middletown, OH 45042-0506

Permissions: Requests for permission to make copies or to obtain copyright permissions should be directed to the Co-Editors. Certification Inquiries: Inquiries about or requests for information pertaining to the Registered Financial Consultant or Registered Financial Associate certifications should be made to IARFC, 1046 Summit Drive, Middletown, OH 45042-0506.

General Editorial Policy: It is the editorial policy of this Journal to only publish content that is original, exclusive, and not previously copyrighted. Subscription requests should be addressed to: IARFC Journal of Personal Finance 1046 Summit Drive Middletown, OH 45042 Info@iarfc.org 1-800-532-9060


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Editors’ Notes Welcome to the Fall 2016 issue of the Journal of Personal Finance. In this issue we have six articles covering topics including how to calculate the value of financial planning advice, the impact of marriage on taxes, the competing risks of death and portfolio depletion in retirement, the relationship between finances and health, the attitudes of college students about retirement planning, and the winning case solution from the 2016 IARFC National Financial Plan Competition. The issue begins with an article by Michael Kitces, which provides a framework to measure the economic benefits of financial planning strategies. Such work helps both to quantify the value of financial planning advice, and to help objectively evaluate what are “good” and “bad” planning strategies. Kitces discusses three recent research efforts to quantify the economic impact of financial advice and then further discusses some of the difficulties with quantifying these impacts in a world of counterfactuals: we do not necessarily know how a client would have behaved if not receiving advice. In the second article, Allan Rubenfield and Ganesh Pandit analyze the tax implications of marriage. A marriage penalty is said to exist when a married couple pays more taxes than they would have had they not been married and each spouse filed as an individual. A marriage bonus reflects the opposite situation. They find that dual-earner couples are often penalized through the tax code and suggest reforms to reduce the tax implications of marriage. For our third article, Dirk Cotton, Cary Cotton, and Alex Mears analyze the competing risks of death and ruin for retirees. They do this by drawing from medical research methods for survival studies. They use Kaplan-Meier estimates and competing risks analysis to explore the conditional probability of a retiree outliving her savings as age progresses, the relationship of the competing risks of death and ruin as age progresses, and the timing of portfolio failures due to poor market returns. They find that risk of ruin develops in three stages of a long retirement: a low risk period early in retirement, a middle period in which portfolio failure peaks, and a late period in which death is more likely than portfolio ruin.

Next, Barbara O’Neill, Jing Jian Xiao, and Karen Ensle use an online survey to explore the relationships between positive personal health and financial practices. They make a distinction between practices that require routine time expenditure, such as exercising and eating home cooked meals, and those that involve avoiding negative behaviors, such as avoiding unhealthy drinks or high cost debts. They find weak, though positive and statistically significant evidence for relationships between health and financial behaviors. In the fifth article, Janet L. Koposko, Martha Isabel Bojorquez, Antonio Emmanuel Perez, and Douglas A. Hershey explore the attitudes toward retirement planning for college students in the United States and Mexico. Their survey asked students to report their attitudes, behaviors, and beliefs around a variety of factors that affect financial planning for retirement. They find important cross-national differences for students in the two countries about the underlying psychological factors that underlie these students’ future savings practices. Finally, in a first for the journal, we conclude with an edited version of Bryant University student Molly Funk’s formal report of over 200 pages that provided her winning solution for the 2016 IARFC National Financial Plan Competition held at the Speedway Club in Charlotte, NC. The competition began with students being given a fictional case study of a family with an overview of their financial picture. From that data, all of the participants produced a financial plan with recommendations for current and future actions. Selected teams advanced to the semi-finals and the top three teams ended up with in-person presentations in Charlotte. We first provide the case, and interested readers may wish to think about their own solutions, followed by an edited version of Molly Funk’s winning entry. We hope you enjoy the current issue of the Journal of Personal Finance.

©2016, IARFC. All rights of reproduction in any form reserved.

— Wade Pfau — Walt Woerheide


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Evaluating Financial Planning Strategies and Quantifying Their Economic Impact

Michael E. Kitces1, MSFS, MTAX, CFP®, CLU, ChFC, RHU, REBC, CASL

Abstract Most research on the value of financial advice has focused primarily on how advisors add value around a portfolio. Ultimately, though, financial advice can impact a wide range of areas from income and estate tax planning to insurance planning (where the expected value is typically negative in absolute terms, but an improvement in risk reduction), and more. In some cases, the “value” of financial planning is in the eye of the beholder—based on how the client values his/her own time relative to paying for the advisor. When comparing two financial planning strategies to each other, it’s feasible to compare the outcomes and decide which is best. Trying to assess the value of financial planning advice in the abstract, though, is much harder because of the “compared to what” problem. It’s not always clear exactly how any particular client would have behaved in the absence of the advice (since that future never actually happened), which makes it impossible to measure whether or how the advice actually changed the outcome. In a world where ultimately most financial planning strategies “could” be implemented by a consumer themselves—given sufficient education, time, and an inclination to get it done—arguably the greatest value a financial planner provides is the behavioral coaching and support to ensure the recommendations are actually implemented. Unfortunately, the financial impact of this is virtually impossible to measure, given the uncertainty of how a prospective client might have behaved in the future without an advisor. Nonetheless, it is essential to recognize that the economic impact of financial planning is not merely the strategy itself, but its implementation, too. This article projects the potential economic impacts of various financial planning strategies.

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Michael E. Kitces is a Partner and the Director of Research for Pinnacle Advisory Group (www.pinnacleadvisory.com), a private wealth management firm located in Columbia, Maryland. In addition, he is an active writer and speaker, and publishes The Kitces Report and his blog “Nerd’s Eye View” through his website www.kitces.com.


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Introduction For any financial planner who charges for his/her advisory services, quantifying the value (or value-add) of good financial planning advice is crucial in order to justify the cost. At the most basic level, no service business is viable— financial planning or otherwise—if the value of what’s delivered fails to exceed its cost. Yet ultimately, the exercise of trying to determine the economic impact of financial planning strategies is more than just a self-serving exercise about the value of a financial planner. The ability to appropriately measure the economic consequences of a recommended strategy is crucial to assessing whether the supporting tactics are even appropriate to implement in the first place. After all, advice that has a negative value isn’t just “not worth its cost”—it’s a recommendation that perhaps shouldn’t be given at all. Viewed another way, demonstrating the value of a financial planning strategy is as much about validating the appropriateness of the strategy itself, as the value of the advisor who recommended it. We explore the issues to consider when trying to evaluate the benefits and economic impact of various financial planning strategies—from the importance of deciding how to measure the outcomes in the first place, to the challenging “compared to what” problem that makes it difficult to objectively assess the value of advice, and how for many financial planning strategies the economic impact is actually negative… but reduces risk enough that it’s probably good advice anyway.

Quantifying the Economic Benefits of (Good) Financial Advice In recent years, a growing base of research and white paper studies have begun to quantify the economic impact of popular financial planning strategies, particularly those related to portfolios (which can be measured in percentages or basis points relative to the value of the assets themselves). Such research is important again not only because it validates the value of the advisor providing those services, but also because it affirms that those services have positive economic impact and are worth trying to deliver or implement in the first place.

For instance, a 2013 study by David Blanchett and Paul Kaplan of Morningstar entitled “Alpha, Beta, and Now… Gamma” found that the benefits of financial advice for retirees improve their outcomes by the equivalent of a 1.59% per year increase in returns. Notably, these advisor-driven outcome improvements were not merely about delivering higher absolute investment returns or generating portfolio alpha, though. Instead, the advice pertained to areas like “tax alpha” through asset location and tax-savvy retirement liquidations (from a mixture of brokerage and retirement accounts), designing a ”more appropriate” holistic asset allocation that accounts for all of a household’s assets (including the asset value of Social Security and pensions), effective use of annuities and dynamic withdrawal strategies, and selecting investments in a manner that maximizes the stability and sustainability of inflation-adjusted retirement cash flows (as opposed to just picking investments that have the highest expected returns). Given that these value-adds were all outside of the portfolio itself, the authors dubbed the advisor’s contribution as “Gamma” to distinguish it from more traditional investment/portfolio metrics like alpha and beta. A similar 2014 study from Vanguard researchers Francis Kinniry, Colleen Jaconetti, Michael DiJoseph, and Yan Zilbering entitled “Putting a value on your value: Quantifying Vanguard Advisor’s Alpha” went a step further, estimating the economic benefits of a financial advisor’s advice to be as much as 3% per year. This included value-adds in areas from cost-effective investment selection and rebalancing to asset location, behavioral coaching (to avoid poorly timed portfolio changes), and the (tax-sensitive) withdrawal order of liquidation strategies. Again, the authors excluded any direct portfolio-related return enhancements like superior asset allocation or improved diversification, which ostensibly could just add further “portfolio” alpha on top of the “advisor alpha” (but aren’t necessary to justify the advisor’s cost). More recently, the Envestnet Quantitative Research Group also tackled the topic, in a white paper entitled “Capital Sigma: The Advisor Advantage” and, similar to Vanguard, suggested that financial advisors add value in a wide range of areas. These areas range from general financial planning strategies to systematic rebalancing, from portfolio tax management through tax loss harvesting, as well as more effective asset allocation diversification and choosing

©2016, IARFC. All rights of reproduction in any form reserved.


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Figure 1. Popular Studies Estimating the Economic Benefits of (Portfolio-Related) Financial Advice

lower cost investments. The researchers estimated these various advisor contributions cumulatively add up to as much as 3% per year of enhanced returns, which they dubbed “Capital Sigma” (the Greek symbol for summing up the parts).

the studies do not always even use the same framework to test and evaluate the advisor strategies, and how they’re being compared… which as it turns out, is crucial to properly understanding which advisor strategies really do or do not add value for any particular client situation.

The bottom line is that whether it is called Gamma, Advisor Alpha, or Capital Sigma, the research (as summarized in Figure 1) suggests significant potential value-added from a financial planner. In fact, arguably the total value of an advisor could be even greater than what any of the particular studies found, given that each cites and quantifies some unique value-adds that the others don’t (e.g., Capital Sigma estimates the value-add of an advisor at >3% per year without including asset location and tax-efficient withdrawal strategies cited in the Vanguard and Morningstar studies).

The Importance of Accurately Measuring Economic Impact

However, the notable caveat to this research is that the means by which an advisor’s “value” is measured varies in significant ways from one study to the next. Vanguard assesses the prospective increases in absolute wealth (compounded over time), while Envestnet largely looks at risk-adjusted return improvements, and Morningstar evaluates whether the advisor’s strategies improve the economic utility of the outcome (and equate it to what return enhancement would have been necessary to generate similar improvements in utility). More generally,

Yet in turn, the statement “you should determine in advance what the financial outcomes are likely to be, to evaluate which strategy is best” is actually a far greater challenge than it first appears, because of the trade-offs that any financial decision entails in the real world given most people’s limited resources. Saving more means spending less. Investing more aggressively can produce more upside potential but also more downside volatility. Spending more in retirement means leaving less to heirs.

While it might seem like an issue that is only relevant after the fact to measure an outcome, the reality is that establishing a proper methodology to evaluate the impact of a financial planning strategy is actually crucial in advance. After all, if you can’t determine ahead of time what the financial outcomes and economic impacts of various strategies are going to be, you can’t determine what an effective recommendation would be in the first place.


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In some cases, the trade-offs are so complex, and the outcomes so nuanced, that even determining what to measure to assess the economic consequences of a decision can be remarkably challenging.

Determining How to Measure What is “Best” Imagine for a moment that a 65-year-old couple is trying to decide how much to spend for a 30-year retirement from their $1 million portfolio, and how that portfolio should be invested. The seemingly simple trade-off choices might include: A) Spend an inflation-adjusting $30,000/year from the portfolio, by putting 90% of it into an immediate annuity and keeping the other 10% in cash reserves B) Spend an inflation-adjusting $45,000/year from the portfolio, and invest it 50/50 in stocks and bonds C) Spend an inflation-adjusting $60,000/year from the portfolio, and invest it 100% in stocks While many advisors might intuitively lean towards one strategy or another as likely to be the “best,” it turns out that accurately assessing which is really the best depends heavily on how the outcome is measured in the first place.

Measuring the Outcome: Projected Wealth

even though in general, long-term wealth would actually be maximized by spending the least (and allowing the most to compound for future growth). Yet in this case, the long-term compounding return of stocks is so dominant, it creates the most long-term wealth, even though that growth is also slowed by what are also the largest ongoing withdrawals.

Measuring the Outcome: Cumulative Spending Notwithstanding the fact that strategy C actually turned out to create the most wealth—despite taking the largest withdrawals—in practice, retirees who ultimately want to enjoy retirement should probably not measure outcomes based on final wealth alone. Otherwise, for any two strategies that have similar returns, the “better” one will always be the one with the least spending, which at the logical extreme would mean the “most successful” retirement strategy is the one where the clients never spend a dime of their retirement funds. An alternative approach would be to look at the cumulative amount of dollars actually spent, which more accurately represents the retiree’s opportunity to actually enjoy the retirement portfolio. In this context, the “best” strategy will not be the one that leaves the most money in the portfolio at the end, but the one that allows the most money to be consumed while the retiree is alive.

The first way these three strategies might be assessed— and what appears to have been the most common methodology for the first several decades of financial planning—is to project how wealth would accumulate and compound over the 30-year retirement time horizon.

In this case, evaluating outcomes based on cumulative spending once again supports strategy C as the “best.” As shown in Figure 3, strategy C produces by far the largest amount of cumulative retirement income spending, in addition to the fact that it also produces the greatest wealth accumulation over time (as shown earlier), thanks again to the long-term compounding return of equities.

For instance, Figure 2 graphs the remaining wealth in the portfolio across each of the three strategies, assuming inflation averages 3%, and that long-term 30-year investment returns are 3% for cash, 5% for (intermediate) bonds, and 10% for stocks. (The immediate annuity is assumed to have a principal refund feature if death occurs before the payments have been recovered, which winds down over time as the payments are made.)

Of course, the caveat to this methodology is that it doesn’t just show projected wealth and cumulative spending, per se. It shows the projected levels of wealth and spending if average returns are earned. Moreover, it’s based on having returns average out to their long-term target with no volatility along the way.

As the chart illustrates, on the basis of this analytical approach—which strategy accumulates the most wealth in the long run—strategy C is the best. Ironically, this is true

Yet a zero-volatility projection is not reflective of the real world. Thus, when those dynamics are considered—i.e., the “best” strategy is evaluated with a different measuring stick—suddenly the optimal approach changes.

©2016, IARFC. All rights of reproduction in any form reserved.


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Figure 2. Projected Wealth of Three Potential Retirement Spending Strategies

Figure 3. Projected Cumulative Spending of Three Potential Retirement Spending Strategies

Measuring the Outcome: Probability of Success Over the past 15 years, as computing power has continued to grow exponentially, it’s no longer necessary to project the financial outcome of a strategy by just measuring the economic impact based on average returns. Instead, we can now measure economic outcomes by modeling thousands of possible scenarios, each with randomized returns (based on the probability that they will occur), and instead quantify how often the results are “successful” (i.e., have money left at the end) or are not (i.e., run out of money before the end of the time horizon). When using this different methodology to quantify the outcomes, though, the relative benefits of each strategy begin to look very different as well. For instance, Figure 4 shows the financial outcomes of these strategies, and the range of possible outcomes based on a 95% confidence

interval (long-term returns that are plus or minus two standard deviations). When measured earlier based on (median) final wealth and cumulative spending dollars, the “best” scenario was the all-stock strategy C and the worst was the immediate-annuity-based strategy A (with the latter coming in last in terms of both spending and wealth accumulation). Yet now when we observe the range of results, Strategy C has the best average but also includes the worst failures, while Strategy A has an extremely narrow range of outcomes that are “mostly” well below the average of Strategy A… but none of them are failures. In other words, based instead upon probabilities of success, annuity-based strategy A is now the “best” (with no projected failures, presuming the annuity company is secure in the first place), and strategy C is the worst (the lowest probability of success and highest frequency of


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Figure 4. Probabilities of Success and Range of Outcomes (+/- 2 SDs) for Three Retirement Spending Strategies

Figure 5. Cumulative Spending of Adjusted Systematic Withdrawal Strategy vs. Annuity

depletions/failures). The entire sequence of which strategies are “best” changes completely when using a different measuring stick, as the “best” for accumulating wealth and spending on average is the all-stock portfolio but the “best” for avoiding any risk of depletion is to spend less and annuitize assets to secure that spending goal.

Measuring the Outcome: Magnitudes of Failure and Adjustment

year adjusting for inflation instead of $45,000/year, the approach would have been successful with a 99%+ probability of success. And to be fair, that is about the same as strategy A, which has a 100% probability of success when looking at the risk of market volatility, but is really only 99% (or perhaps 99.9% when considering the small-but-notzero default risk of the insurance company as well).

The charts in the prior section—based on probabilities of success— howed that strategy A was “best” and superior to both strategy B and strategy C.

Of course, if strategy B were adjusted to spend “only” $40,000/year and have a 99% probability of success similar to strategy A, now the only difference between the two is the spending level, which is 33% higher, for life, with strategy B over strategy A, as shown in Figure 5.

However, a more nuanced look reveals that the “superiority” of strategy A over strategy B was not by a large margin. For instance, if strategy B “only” spent $40,000/

Viewed another way, the key distinction here is that while the original strategy B had a 95% probability of success and a 5% probability of failure, the magnitude of that failure

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

wasn’t actually very severe, and it wouldn’t take much of an adjustment to stay on track. Cutting from $45,000/year to $40,000/year of spending is sufficient. And even with poor returns, there is only a 5% chance the portfolio runs out of money at all, and those scenarios don’t run out until almost 28 years into retirement. Which means realistically spending would likely only need to be adjusted later – if at all – to stay on track for those final years if returns had been especially poor along the way. Furthermore, for a 65-year-old couple, there’s a roughly 70% chance that both of them will have passed away by then anyway. Which means there’s a barely 30% probability that this 5%-failure risk is even relevant (i.e., the “joint probability” of both running out of money in their 90s and still being alive in their 90s is less than 2%). And again, if there’s still a fear that the bad returns are occurring or may occur soon, a “mere” 10% cut in spending is more than sufficient to ensure the plan stays on track, because the “failure” isn’t actually a very dramatic shortfall in the first place. Notably, even if the spending cut does have to occur, strategy B still produces more retirement spending cash flow than strategy A. On the other hand, strategy C still turns out to be vastly inferior under the “magnitude of failure” approach, as the “bad” outcome can be very bad (flat broke by the 23rd year), and the size of the adjustment necessary to get/stay on track would be far more than “just” a 10% spending reduction. In other words, when weighing the magnitudes of failure (and the small or large adjustments to stay on track) against the higher spending levels, strategy A turns out to be inferior to strategy B, but strategy C is still worse than all of them.

Measuring the Outcome: Utility Functions and Risk Aversion Notably, the conclusions of the prior section—which determined that strategy B was superior to strategy A because the likelihood of even needing a spending adjustment was “small”, and the magnitude of the adjustment required to get back on track was also “minor” —still presumes that the retirees are comfortable with those “small” and “minor” risks. In reality, not all retirees will be comfortable facing

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such trade-offs, even if the requisite spending adjustments in strategy B are likely “minor” and of remote likelihood. Conversely, the magnitude of potential adjustments for strategy C—which could fall 7 years short on a 30-year retirement goal and possibly need 20%–30% spending cuts to get back on track—were already deemed untenable, despite the materially higher initial spending amount. Yet again, in reality at least some retirees might be willing to risk such trade-offs, and are willing to face the possibility of a “big” spending cut in order to enjoy a “big” spending increase up front. In theory, these scenarios could be weighed against each other by trying to quantify how much “happiness” the retiree derives from greater spending, and weight it against the “unhappiness” of having a spending cut and how riskaverse the retiree is to the possibility such a cut would have to occur. And in point of fact, this is exactly what a “utility function” is meant to measure. A concept derived from economics, the purpose of a utility function is specifically to assign a measuring unit—“utils” —to potential outcomes. More positive outcomes (e.g., higher spending levels) have higher utils. Adverse outcomes (e.g., spending cuts necessitated by the depletion or near-depletion of assets) have negative utils. On this basis, we can then compare and contrast widely-differing strategies that have a complex range of outcomes by adding up the positive and negative “utils” over time to determine which creates the most satisfying net or cumulative outcome. Another key advantage of using a utility function is that it becomes possible to give different weights to positive versus negative outcomes – specifically, to assign greater negative weight to negative outcomes than positive weight to positive outcomes. In theory, this shouldn’t matter, because a “rational” human being should be equanimous in the face of gains or losses. In point of fact, though, the recognition that as human beings we have greater aversion to losses (more “negative utils”) than the enjoyment we gain from favorable results (relatively fewer “positive utils”) is the “Prospect Theory” first discovered by Daniel Kahneman and Amos Tversky, for which Kahneman won the Nobel Prize.


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If investors were indifferent to relative gains and losses, the utility function graphed in Figure 6 should be a straight diagonal line that goes from the bottom left to the top right. Instead, though, it is not. To the upper right, the line begins to flatten, revealing that we have a “diminishing marginal utility” for additional wealth. In practical terms, increasing your wealth by $1M if your prior net worth was $0 is a big deal (from poverty to being a millionaire). Increasing your net worth by $1M if you already had $99M is not such a big deal (it’s not as exciting for net worth to rise from $99M to $100M). Notably, both are a $1M increase in wealth, but we weight the latter one less favorably because its value is diminished by the prior millions already accumulated. On the other hand, as the Prospect Theory graphic shows (Fig. 6), when we lose money, we show a more “consistent” level of distress with both initial and extended losses (though the initial losses still appear to sting a little bit more). Given that behaviorally, we do not weight gains in the same manner as offsetting losses (and vice versa), this makes it even more important to give each its appropriate weighting in the first place. In the context of our three strategies, this means that the relative order of which is “best” or “worst” will depend heavily on how the retiree weights the positive utils of having more spending and wealth, versus the negative utils of being forced to cut spending in order to avoid running out of wealth altogether. Figure 6. Kahneman and Tversky’s Prospect Theory Utility Function

over spending gains, the “best” strategy is the all-annuity strategy A, which (if you believe in the security of the annuity company at least) has the smallest danger of any spending cuts, nor does it face any market volatility either (and thus no negative utils from bear markets along the way). For this retiree, anything that decreases wealth – temporarily with market volatility or permanently and necessitating spending cuts – will be inferior, and end out with a negative utility result. On the other hand, for the retiree who is far more sanguine about potential losses (or simply feels more flexible to accommodate them with spending adjustments) and places a greater weighting on upside potential and enjoying more money today, strategy C could actually still be the optimal result. While as noted earlier, this strategy has a “whopping” 25% probability of failure (or at least, a 25% probability of necessitating a spending adjustment), and could require a 25%+ spending cut to get back on track, for the retiree with flexible spending who doesn’t mind the downside risk if it means a better-than-50% chance of just getting to spend more, this may be an appealing trade-off. For this retiree, strategy A once again goes from being best to worst, and strategy C is superior. And for the retiree in the middle—who perhaps is “rather” negative about spending cuts but is willing/able to tolerate them as long as they’re “likely to be rare” and infrequent— strategy B turns out to be the “best” strategy after all, because it has the most appealing balance. For this retiree’s utility function, strategy A doesn’t bring enough upside happiness, strategy C exposes the retiree to too much downside unhappiness, and the ideal Goldilocks outcome (not too much risk, nor too little upside) is strategy B. The ultimate point: in order to determine which strategy is “best,” given both the potential for upside wealth, and downside spending cuts, and the trade-offs entailed in pursuing greater upside at the risk of more downside, it’s necessary to “score” both the upside and the downside to objectively find the best balance between the two. And how those upside and downside outcomes are weighted will in turn depend on the retiree, and his/her preferences for managing downside risk and enjoying upside return in the first place (i.e., his/her personal utility function).

For the highly risk-adverse retiree, who assigns an outsized negative weight (e.g., 5:1 or even 10:1) to spending cuts

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Volume 15, Issue 2

Determining the “Best” Strategy Depends on How It’s Measured As the examples in the preceding sections have shown, determining which option is the “best” financial planning strategy can be heavily reliant on the measuring stick used to quantify the outcomes in the first place. In our choice between three strategies—annuitizing most of a portfolio for guaranteed income, taking ”moderate” distributions from a moderate growth portfolio, or taking large distributions from an aggressive portfolio—each strategy’s outcomes were variously best, second, or worst, depending on how the outcome was measured. A summary of the results is shown in Figure 7. This means that careful thought about how a strategy will be evaluated is actually an essential aspect of the process in crafting financial planning recommendations. The issue is akin to what any scientist analyzing any problem has to consider: the research methodology used to analyze an issue can impact the conclusion about it, so it’s crucial to vet not just the results but the methodology itself. Otherwise, a flawed design to a research study can yield a flawed conclusion about its results. For instance, imagine a medical study analyzing a weightloss drug in the hopes that reducing obesity will cut down on deaths from diabetes and high blood pressure. The research focuses on whether the drug leads to weight reduction, and finds that it does, concluding it’s a good drug. However, in reality side effects of the drug itself

Figure 7. Summary of Which Strategies Are Best, Second, and Worst, Based on Means of Measuring Outcomes

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include diabetes and high blood pressure. As a result, the drug does “cure” obesity but actually increases the risk of the same bad health outcomes that losing weight was meant to help minimize. In this context, if you measure “impact on weight loss” the drug is a success, but when measured by “impact on overall health” it’s actually a failure. When it comes to financial planning, though, the situation is complicated by the fact most clients have multiple and complex goals and preferences. A Roth IRA may be a superior retirement savings vehicle over “just” keeping money in an annually taxable brokerage account, but if the client might leave his job to start a business in 5 years (and not necessarily use the funds for retirement) the “best” recommendation (Roth vs. brokerage account) becomes less clear. Similarly, a more aggressive portfolio with a higher growth rate can help a client retire much earlier, but can also cause a client to be forced to retire much later if returns are especially poor—a risky trade-off that not all prospective retirees may be want to pursue. And as we saw earlier, the relative appeal of an aggressive portfolio over a guaranteed annuity for retirement income depends a lot on the retiree’s desire for spending upside versus his/her tolerance for or aversion to the risk of future spending cuts instead. Of course, this is why the process of financial planning begins with the process of establishing goals and determining client preferences in the first place. It is not possible to determine the “best” strategies (or decide how to measure them) until it’s clear what the goal to be pursued and


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measured is to begin with. It’s only once the goal is set that it becomes possible to optimize the strategy to achieve it. Accordingly, then, it’s almost impossible to establish financial planning strategies that are “objectively” dominant and superior in all situations. At best, some products or solutions might be better than others for a particular goal, or subject to particular constraints and client preferences. For instance, an emergency savings fund invested in a money market that yields 1% is clearly better than one that only yields 0.1%, and for the “core” indexing portion of a retirement account an S&P 500 index with an expense ratio of 0.1% is better than one with an expense ratio of 1%. Nevertheless, whether the high-yield money market or the low-cost index fund are “best” in the first place depends on the goals to be pursued (accumulating for retirement versus saving for an emergency fund). Nonetheless, there are financial planning strategies and recommendations that are so common—or rather, are improvements to common goals (e.g., retirement) — that we can begin to assess the overall value of financial advice by looking at the favorable economic impact these common strategies can produce.

The “Compared to What” Problem An additional complication to be considered when trying to vet the quality of financial planning strategies or recommendations is that to evaluate the benefit, it is necessary to do a comparison—strategy A is not “best” in the abstract, but only compared to some alternative. When trying to compare whether known strategy A is superior

to known strategy B, the approach is relatively straightforward. We can quantify the economic impact and outcomes of strategy A and strategy B, determine which is superior (based on whatever form of measurement we choose to make that assessment), and conclude which is best. When it comes to abstractly measuring the value and benefit of financial advice, however, the problem is more complex. The issue is that while we can quantify the financial outcome of the recommended strategy, it’s not so clear how to quantify what would have happened in the absence of the financial planner’s recommendation. In other words, when we ask the question “did the financial planner’s advice improve the outcome” we can only answer it by measuring how the advice improved the situation compared to what would have happened without the advice. Except we don’t actually know (for certain) what would have happened without the advice or advisor. At best, we can only estimate what the “do-It-yourself” outcome might have been, as shown in Figure 8. For instance, consider the classic example of “financial planners add value by helping clients to close the ‘behavior gap.’” The behavior gap is the purported difference between the returns that investors earn with their portfolios, versus the return that the market provided. The difference between the two impliedly being the underperformance that investors bring upon themselves with “bad” investing behavior. To the extent the advisor can help the client minimize bad investment decisions and avoid underperformance, the advisor adds value—even if that “value” does not involve any alpha or excess market returns, but simply

Figure 8. Measuring Advisor Value-Add Over a Do-It-Yourself Baseline

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Volume 15, Issue 2

bringing the client up to the market return from an underperforming alternative. The problem with assessing the advisor’s value in closing the behavior gap is that we don’t really know, for any particular client, what that behavior gap would have been in the future. The future hasn’t happened, future market returns haven’t occurred, and future client behavior (or market-timing misbehavior) hasn’t actually manifested yet. Perhaps this client is actually already very good at staying the course and not selling out the portfolio during times of market volatility. Maybe this client won’t even have a bear market occur before it’s time to retire anyway. Alternatively, perhaps this client has already designed a well-diversified portfolio, such that a severe market decline won’t be as harmful to the portfolio anyway. Even worse, what if this client is not well diversified and holds a concentrated portfolio of company stock, but this stock really is the next Apple or Microsoft or Google or Facebook, and diversifying out of it will turn out, after the fact, to have dramatically reduced long-term wealth!? The fundamental issue is that when we try to assess the economic value of financial advice (or having a financial planner), we are stuck comparing the world that is to the world that “might have been,” without any way to know what might be in advance, nor even to know what might have been after the fact, either. Maybe the client who was prone to selling out in bear markets in the past learned his lesson and would have been able to stay the course next time on his own anyway. On the other hand, maybe the client who kept the concentrated portfolio of company stock that was “the next big one” wouldn’t have actually

Figure 9. Measuring Advisor Value-Add Over an Uncertain Range of Possible Do-It-Yourself Outcomes

17 managed to hold onto it for the long run anyway, and instead would have taken gains too early. When comparing the history that actually occurred to the one that might have been (but wasn’t), we just don’t know. Which means, as shown in Figure 9, the actual “do-It-yourself” outcome could have been quite close to what the advisor would have recommended anyway, or very far off, and there’s little way to tell (and therefore to understand how the advisor impacted the outcome). And of course, for a study whose goal is to “show value”, there’s a significant danger that the researchers deliberately pick a baseline scenario designed to show the best possible results and the most advisor value—even if it’s debatable whether that’s reflective of a real-world prospective client.

Evaluating Studies that Evaluate the Benefits of Financial Advice Comparing to Counterfactuals As discussed earlier, in recent years there have been a number of studies aiming to measure the (economic) benefits of working with a financial advisor. Yet all of them suffer, to varying degrees, from the “compared to what” problem discussed here—where it’s difficult to measure the economic impact of financial advice because there’s no clarity about what, exactly, to compare to. This doesn’t necessarily mean their conclusions are “wrong,” just that it’s difficult to validate whether the purported benefits would hold for any client in particular.


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For instance, in the Vanguard Advisor’s Alpha research, the authors attempted to quantify how many additional basis points a financial advisor can add to portfolio returns by better controlling investment costs. The starting point was to determine a baseline against which results would be measured; for the Vanguard paper, the decision was to use broad market metrics in the aggregate. Thus, for instance, Vanguard observed that for a 60/40 portfolio of stocks and bonds, the “average” investor pays an expense ratio of about 0.55% (based on the asset-weighted average expense ratio of the entire mutual fund and ETF industry) but by using the “lowest of the low” cost ETFs available, the investor could establish the same portfolio with an expense ratio of only 0.14%. Thus, through the advisor’s assistance in selecting low(er) cost funds, the advisor is bringing about 0.55%– 0.14% = 41bps of value to the table. (This analysis was before counting the advisor’s own fee.) The caveat to this outcome, though, is that the “value creation” of the advisor is based on the 0.55% expense ratio of a hypothetical portfolio that the client may or may not actually be holding. In fact, the Envestnet Capital Sigma research assumed that the average investor would be holding an actively managed mutual fund, while the advisor would recommend a low-cost ETF, and found an average expense ratio savings from the advisor of 82bps. Yet the advisor in the Envestnet study producing 82bps of value is recommending the same low-cost ETFs as the advisor in the Vanguard study producing only 41bps of value. The difference was the baseline investment they assumed the client would have held in the absence of the advisor. Furthermore, the reality is that some prospective clients may already be holding low-cost ETFs to varying degrees, for which this savings/advisor-benefit becomes moot altogether. On the other hand, some investors might be holding funds that are far more expensive, where the advisor could save the client enough to cover his/her entire fee on that basis alone. Ultimately, though, the problem remains that measuring the difference between an average cost and the lowest cost (or the average active mutual fund to the average passive ETF) may work on average—by definition—but not necessarily for any particular client. (And notably, as more investors buy low-cost ETFs and the dollar-weighted average expense ratio declines, the presumed advisor benefit from this strategy would also decline.)

In another example of this “compared to what” problem in the research on advisor value, the Vanguard paper also tries to evaluate the potential benefit of an advisor closing the “behavior gap.” To measure the impact of the behavior gap in the first place, the Vanguard study evaluated the performance of 58,168 self-directed IRA investors from 2008–2012, and compared it to the performance of a target-date fund over the same time period. The presumption is that the steady investment implementation and regular rebalancing of the target-date fund represents what the advisor “might have done” over that time period, as compared to what the IRA investors actually did. As the Vanguard results show, overall the “average” investor trailed the target date fund by 19bps (much of which may have simply been the difference in expense ratios between the investors’ other investments and the low-cost target date fund). But investors who made an exchange from one fund to another during the time period (and thus are presumed to have been engaged in some active investment decisions) underperformed by an average of 150bps. Thus, the conclusion is that for investors prone to the “behavior gap,” they could have gained another 150bps of annual performance with the help of an advisor to stay the course and not make those generally, poorly timed exchanges. In this scenario, the “actual” is what Vanguard investors really did hold, and the theoretical is what the advisor “might have” held (if the advisor mimicked the allocation of a target-date fund) supported by the assumption that any client who would have made a “bad” investment timing decision on his/her own will be successfully persuaded by the advisor not to (which may or may not turn out to be the case). In addition, while the Vanguard study notes that the average investor who made investment exchanges mistimed them for an average underperformance of 150bps, many investors did not make such changes. In fact, the Vanguard results suggest that only a minority of the accounts they analyzed had made such switches. Thus, the “average investor” actually underperformed by far less than 150bps (because most simply held their investments throughout), and investors who were able to hold their investments would not have experienced any “advisor value add” at all. Or viewed another way, the value of the advisor “closing the behavior gap” of bad market timing looks a lot better when the analysis is constrained to only those who engaged in market timing in the first place, but that means it’s a value the advisor at best can only provide to that particular subset of clients.

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Volume 15, Issue 2

Evaluating the Benefit of Risk Management and Risk-Adjusted Benefits Another important caveat to the studies about the value that advisors can potentially add is that not all of the potential benefits of an advisor are about enhanced returns and increased wealth. In fact, some of the greatest value that an advisor provides may actually be likely to reduce future wealth, albeit for the “benefit” of reducing risk even more. For instance, as discussed in Kitces (2015), the reality is that rebalancing—particularly between stocks and bonds—is generally a return-reducing strategy. After all, without any rebalancing, the fact that the long-term return of stocks is higher than the long-term return on bonds means an investor’s total allocation to stocks will naturally creep higher over time (simply due to the stocks compounding the bonds). And given an expectation that stocks have higher returns, that’s actually a good thing for long-term wealth, as it leads to accumulating more stocks with better returns. Systematically rebalancing from the higher-returning stocks back into the lower-returning bonds will simply reduce long-term returns for the overall portfolio. However, rebalancing is still appealing, because the process may reduce long-term returns by a little, but it can reduce long-term risk by a lot. Thus, for instance, the Vanguard Advisor Alpha study found that from 1960 to 2013, systematically rebalancing a 60/40 stock/bond portfolio reduces the average annual growth rate of the portfolio by about 0.24% (from 9.36% to 9.12%)... but at the same time, the risk of the portfolio (as measured by standard deviation) is reduced by a whopping 20% (from 14.15% to 11.41%). Which means on a risk-adjusted basis, this is an extremely appealing trade-off, with a very small reduction in return producing a much larger reduction in risk. In fact, Vanguard finds that the investor willing to tolerate the volatility of a 60/40 unrebalanced portfolio could also own an 80/20 rebalanced portfolio and come out with the same level of risk. Except the 80/20 portfolio has a higher return (by about 35bps). Which means, in essence, that annual rebalancing may reduce absolute returns by 0.24%/year, but it increases risk-adjusted returns by 0.35%/year. Similarly (albeit with a slightly different methodology), the Envestnet study finds that annual rebalancing increases risk-adjusted (but not absolute) returns by about 0.44%/year of alpha over just rebalancing every 3 years.

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Of course, enhancements to risk-adjusted returns aren’t just a benefit of systematic rebalancing. It’s also a benefit of diversification itself, which, again, generally isn’t about enhancing absolute returns but risk-adjusted returns. After all, for the investor who really just wants to maximize returns, the “optimal” portfolio is rather straightforward: just put 100% into the investment with the highest expected return, and hold on for the likely-to-be very bumpy ride. The caveat, of course, is that in practice not all investors have the tolerance to hold on for such a ride, and for those in retirement a too-volatile ride can expose the portfolio to sequence-of-return risk (where ongoing withdrawals deplete the portfolio so much during a period of bad returns that even if better returns show up later and average out in the long run, the unfavorable sequence causes the retiree to run out of money). Accordingly, it can be appealing to hold a more diversified portfolio, even if it gives up some long-term return (by owning other lower-returning investments) if it makes the overall portfolio less volatile. An added benefit of diversification, as illustrated with Markowitz’s Modern Portfolio Theory, is that in situations where the lower-returning investments have low (or negative) correlations, the reduction in risk may be far more than the reduction in return, which means risk-adjusted returns for the portfolio are enhanced by adding an allocation to those events (even as the portfolio’s absolute level of expected returns declines). Even in this scenario, though, the problem remains that determining the risk-adjusted return enhancements of a “more diversified” portfolio requires an assessment of what (less diversified) portfolio the investor would have owned, and what (more diversified) portfolio the advisor recommends instead. For instance, Envestnet estimates that the value of an advisor’s asset allocation and diversification guidance is 28bps of risk-adjusted return, based on the assumption that the “naïve” investor simply owns a 56/44 stock/bond portfolio (based on the world market cap of stocks and bonds), with the equities allocated into the Russell 3000 index and the fixed income invested into the Barclays U.S. Aggregate Bond index. By contrast, the advisor is assumed to invest into a more diversified portfolio (along the same 56/44 stock/bond split), including sub-equity asset classes like REITs and international stocks (both developed and emerging), and sub-bond asset classes like highyield, TIPS, emerging market fixed income, and bank loans. The end result over the past 18 years—that extra 28bps


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of alpha. This assumes, of course, that the investor really would have held nothing but 56% in the Russell 3000 and 44% in the Barclays Agg. It also assumes that the advisor really would have used all of those other asset classes. And that the additional asset classes that received allocations in the Envestnet study generate as much alpha in the future as they did for the past few decades. Alternatively, the Morningstar Gamma study took an entirely different approach to evaluating the benefit of an advisor’s diversification and investment allocation strategies. In the Morningstar study, the “measuring stick” for success was not about higher returns, nor risk-adjusted returns, but improvements in utility using a utility function as discussed earlier. In particular, the Morningstar study measures based on a utility function where losses and spending cuts to the downside are weighted more (in a negative manner) than gains to the upside are (in a positive manner). Furthermore, the Morningstar study’s utility function assumes that gains experience diminishing marginal utility over time. Based on this framework, the researchers then look at the strategies that produced higher utility (e.g., from better matching asset allocation to future spending needs), and calculated how much greater returns would needed to happen to produce a similar increase in utility. In other words, technically Morningstar didn’t show that advisors add return (or risk-adjusted return); the research showed that advisors improve utility by an amount equivalent to the additional utility you could get from higher returns. Of course, even with a utility measure, a comparison is still necessary—utility can’t be improved with the advisor’s strategies until we make an assumption about the utility the investor would have achieved with his/her own approach in the absence of the advisor. In this case, the Morningstar authors assume the retiree would have owned what an “average” retiree owns (based on the average equity exposure for investors aged 65 to 95 with at least $10,000 in financial assets in the 2010 Survey of Consumer Finances), which means the retiree is assumed to have “only” a 20% allocation to equities, with the other 80% in fixed income. The study further assumes this “naïve” investor would have had the equity allocation 100% in US Large-Cap stocks, with the fixed income split 80% to US bonds and 20% to cash. By contrast, the advisor’s asset allocation is assumed to have 45% in equities (with an allocation that includes Large Cap, Small Cap, International Developed, and Emerging Markets)

and 55% in fixed income (includes cash, US bonds, TIPS, and some international bonds). On this basis, the Morningstar study finds that this “more diversified” portfolio improves gamma by the equivalent of a 0.57% better return. Yet the result really only holds if we assume that the retiree was really going to invest in a US-only portfolio with “just” 20% in equities in the first place.

The Tax Alpha of Advisor Tax Strategies Another commonly cited area of value-add for advisors is the opportunity to engage in proactive tax strategies that generate tax savings, particularly in a portfolio (where the larger the portfolio, the larger the potential for tax benefits). For advisors managing portfolios, “tax alpha” generally comes in two primary forms: asset location (optimizing in which types of accounts the various investments will be held, such as putting annually taxable ordinary income investments inside of tax-deferred accounts); and tax-loss harvesting (capturing portfolio losses to offset taxable gains generated by other investments or triggered via rebalancing to minimize current tax obligations). The significance of tax alpha is not only that it presents an opportunity for the advisor to add value, but that unlike traditional investment alpha—which ultimately is a zero-sum game—leveraging “tax alpha” is simply about implementing portfolios in a manner that takes advantage of and maximizes the available tax rules. This means it’s a “pure” value-add that could be done for every investor (and is not zero-sum).

Asset Location As noted earlier, investors who hold a diversified portfolio of stocks and bonds (and sub-asset classes within those categories, and alternative investments) often have a choice about where to hold—or “locate” —those assets amongst taxable brokerage accounts, tax-deferred retirement accounts (e.g., IRA and 401(k) accounts), and tax-free Roth style accounts. Additionally, a growing base of research suggests that effective asset location—for instance, maximizing the tax-free Roth with the highest-returning investments overall, or sheltering annually-taxable-as-ordinary-income

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investments inside of tax-deferred accounts—can generate more long-term wealth over a less effective strategy.

loss, without necessarily changing investments any more than is necessary to claim the loss itself.)

For instance, the Morningstar paper suggests that asset location (combined with tax-efficient withdrawals) can provide a 3.2% enhancement in utility, equivalent to about a 0.23% enhancement in long-term wealth accumulation. The Vanguard research suggests that asset location benefits are about 0.30% with an ”optimal”’ allocation of indexed equities in taxable accounts versus bonds in a tax-deferred account (versus having investment locations reversed), or as much as 0.75%/year compared to the “worst possible” investor who puts active high-turnover equity strategies in the taxable account (which triggers the most severe adverse tax drag). These results are also generally consistent with research by Daryanani and Cordaro, who in 2005 wrote “Asset Location: A Generic Framework for Maximizing After-Tax Wealth” and estimated asset location benefits up to 20bps.

The caveat to tax loss harvesting is that, to limit potential abuses, Congress actually requires at least a temporary change in investments to claim the loss. Under the so-called “wash sale” rules, a tax loss can only be claimed if the investor does not replace the investment with a “substantially identical” investment within 30 days before or after the sale. In essence, this forces the investor to take at least 30 days of “risk” holding another investment—during which the new investment may potentially under-perform the original—in order to claim the loss.

Notably, though, as the Vanguard results highlight, the “value” of asset location is still prone to the “compared to what” problem, where asset location benefits are on the order of 20bps–30bps versus a “naïve” neutral strategy, but much higher if compared to a “deliberately inferior” (or maximally inefficient) portfolio. Of course, the value of asset location is also limited by the fact that it is only a benefit for investors who have multiple types of accounts across which allocations can be made in the first place. For the investor who already has most/all assets concentrated in one type of account or another (e.g., all brokerage accounts, or all IRAs), there is no benefit to asset location, because all investments will end out in the same type of account no matter what (since that’s where the only available dollars are).

Tax Loss Harvesting Tax loss harvesting is the strategy of selling an investment that has experienced a loss in order to capture a loss for tax purposes (to be offset against capital gains to generate tax savings), without permanently changing the underlying investment/portfolio. (Obviously investors can sell an investment at a loss, change to a new investment, and enjoy the tax benefits of claiming the loss, but that’s simply the consequence of any normal sale of an investment with a loss. The point of tax loss harvesting is to “just” harvest the

In addition, it’s important to recognize that with tax loss harvesting, there is a secondary effect to selling an investment (to claim the tax loss) and buying it back again—the investor’s cost basis is stepped down from the original basis (eligible for the loss) to the current value of the investment (the price at which it was bought back again). Thus, in practice a harvested loss triggers a future gain for an equivalent offsetting amount, which means tax loss harvesting is really just the value of tax deferral from the time the loss is originally claimed until the “recovery” loss is triggered in the future. For instance, imagine an investor who bought a stock for $20,000, and its value has now declined to $14,000. The investor can harvest the loss and claim the $6,000 tax loss, but doing so means the investment (after harvesting the loss) will have a cost basis of only $14,000. If in the future the investment appreciates and recovers back to its original $20,000 value, the investor will face a future gain of $6,000. The end result is that for an investment that started at $20,000 and ended at $20,000, the investor had a $6,000 loss and a $6,000 gain, which is exactly the same as if the investor had just held the $20,000 purchase for the full round trip ride to finish with $20,000 (and a net gain of $0), as shown in Figure 10. Notwithstanding these offsetting losses and future gains, the opportunity to defer taxes (saving on taxes with the loss now, and not owing the taxes on the gain until the future) still has economic value, equivalent to the growth that can be earned in the meantime. The Envestnet study estimates that tax loss harvesting may be worth as much as 60bps over a buy-and-hold portfolio (although it’s not clear that their analysis includes adjusting for the future gains created


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Journal of Personal Finance

Figure 10. How Tax Loss Harvesting Produces No Net Tax Savings

by initially harvesting losses and driving cost basis lower), and as much as 100bps over an especially tax-inefficient portfolio (that routinely triggers gains). An analysis by Kitces (2014) found that the value of tax loss harvesting may be closer to 20bps (at 15% capital gains tax rates) to 30bps (at 23.8% capital gains tax rates, including the 3.8% Medicare surtax) once the tax deferral and future capital gains implications are accounted for. Notably, transaction costs to implement tax loss harvesting (as well as the risk of tracking error during the wash sale period) could partially mitigate this value further. Nonetheless, the fundamental point remains that tax loss harvesting represents—similar to asset location—another form of “tax alpha” that is available to any/all investors proactive enough to take advantage of the opportunity themselves, or with an advisor doing so on their behalf. Though, again, to the extent that investors do so themselves, either on their own or with the assistance of available technology tools, the “value-add” of the advisor is diminished accordingly.

What Is the Value of Good Financial Planning? While the benefits of financial advice on a portfolio—from investment selection and better diversification to tax alpha opportunities—are hard enough to calculate due to the “compared to what” problem, the economic benefits of financial planning are even harder to analyze. The reasons are ultimately three-fold. First and foremost, there still remains little agreement on what exactly is and is not covered by “comprehensive

financial planning” in the first place, which leads to an inconsistent formulation of what should be measured for value. For instance, some advisors give guidance on property and casualty insurance policies (i.e., automobile and homeowners coverage) while others do not, so how can an estimate of value be formulated? The same is true for income tax advice (which is done to varying degrees from one advisor to the next), or budgeting and cash flow advice, etc. The second challenge to measuring the value of non-portfolio financial planning advice is yet another version of the “compared to what” problem—i.e., what would the individual have done in the absence of the advisor? Does the presence of an advisor have more impact for someone who lacks proper insurance, than someone who already has it (where the advisor cannot make the recommendation to buy coverage that’s already been purchased)? Is an advisor less valuable for someone who already has control of their spending, than someone who lacks a household budget? If the “average” consumer already claims most of their tax deductions, the “average” advisor relationship might not have a lot of impact… yet for the subset of households who are grossly failing to claim their deductions, and arguably are in the most need of an advisor, the potential financial benefit is many times greater. Yet that means the value of advice is not a generic “value of advice” but “the value of advice for a particular client with particular problems or failings that need to be remedied.” The third challenge in measuring the value of non-portfolio financial planning advice is determining the appropriate terms of measurement. As noted earlier, when evaluating the benefits of a financial planning strategy in general, the “value” will vary depending on the measuring stick used. But at least when the advice pertains to a portfolio, there is a natural way to calculate the benefits – relative to the value of the portfolio. For other financial planning strategies, however, it’s less clear how the value should be framed. For instance, the tax savings of contributing the maximum $5,500 (in 2015) to an IRA could be a material improvement in net worth for someone with very little in current savings, but worth less than 0.01% to a multi-millionaire whose net worth dwarfs a ”mere” $5,500 contribution (as the IRA limit has the same maximum contribution cap for both). Similarly, strategies like saving on insurance premiums, from life insurance to

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

automobile and homeowner’s coverage, may have a consistent absolute value—perhaps a few hundred or a couple thousand dollars a year—but that relative value could be 10%, 1%, or 0.01%, depending on overall income and net worth. Other strategies have even more significant scaling problems. The value of “estate planning” for a mass affluent household might be a few thousand dollars of probate expense savings by recommending a revocable living trust, but could be $10s of millions of dollars implementing a series of rolling 2-year GRAT strategies for a $100-millionaire. And of course, for some risk management strategies—e.g., buying insurance coverage—the expected value of the strategy is actually negative (as insurance premiums are a “known and certain” loss, albeit a small one)—but may be appealing for risk management purposes despite being an expected financial loss. Nonetheless, once again there is clearly some economic benefit to be measured for financial planning strategies, even if the relative benefits of particular recommendations will vary from one household to the next based on both their overall financial situation to begin with, and the extent to which their finances are already in good order (or not).

The Benefits of Good Income Tax Planning While financial planners are generally not in the business of tax preparation, there are tax planning implications for many strategies that a financial planner might recommend. Not to mention the outright tax planning strategies a good advisor can suggest. For some tax strategies, the “benefits” are simply limited to whatever the tax code itself permits as a benefit. For instance, the tax benefits of claiming the American Opportunity Tax Credit for college are limited to the $2,500 credit. The value of tax-free growth in a Roth IRA is limited to the $5,500 (in 2015) annual contribution limit and the growth that can accrue thereon. The $250 schoolteacher expense deduction is capped at the value of a $250 deduction. Although in some cases the size of the deduction is larger, such as with the Income in Respect of a Decedent (IRD) deduction for estate taxes that were paid on an inherited IRA (which can amount to a tax deduction of hundreds of thousands of dollars for a multi-million-dollar IRA).

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On the other hand, some tax strategies aren’t even outright tax savings at all, but just tax deferral instead. Most commonly, this includes contribution to pre-tax accounts (e.g., IRAs or 401(k)s), which are often characterized as “tax savings” but in truth just defer the taxes to the future (when the accounts are liquidated). The value is thus not the outright tax savings, but the growth on the taxes that were deferred. (Notably, tax loss harvesting falls into the same category.) Again, the value to these strategies is often limited by how much the tax code permits to be tax-deferred in the first place (e.g., contribution limits on retirement accounts). Fortunately, some tax strategies “scale” more effectively to the overall income and wealth of the client. For instance, systematic partial Roth conversions to fill up available lower tax brackets is limited “only” by the total amount of pre-tax dollars held in retirement accounts in the first place. The larger the IRA, the more the tax savings. Moreover, the higher the tax bracket, the more it matters. Of course, the reality for strategies like Roth conversions is that a pre-tax IRA is really only a tax deferral vehicle in the first place, and a Roth conversion is just an acceleration (hopefully at a more favorable tax rate) of a tax liability that would have eventually been due no matter what. Thus, it’s important to recognize that the tax savings of an IRA, or the benefits of a well-timed Roth conversion, aren’t just the outright tax savings or future tax-free growth, but the difference in tax rates between when the IRA deduction is claimed and when the income (as an IRA withdrawal or Roth conversion) is ultimately recognized. In essence, these strategies benefit from “tax rate arbitrage”—the opportunity to create tax deductions in higher-income years, and recognize that income later when rates are lower, benefitting from the tax rate differential. If a $250,000 IRA can be systematically partially converted at a 15% tax rate over time, instead of spent at a 25% tax rate in the future, the actual economic benefit is the 10% difference in tax rates x $250,000 = $25,000 of true tax savings. In fact, given today’s progressive tax system—where higher income levels are subject to higher tax rates—the tax bracket arbitrage opportunity to shift income from hightax-rate years to lower-tax-rate years, and generate tax savings for the difference, is often the biggest income tax planning opportunity available. Strategic retirement liquidation strategies that are sensitive to the withdrawal source—for instance, planning around the liquidation of taxable and


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tax-deferred accounts in a manner that leverages the benefits of tax bracket arbitrage—is another related opportunity.

The Benefits of Good Estate Planning When it comes to estate planning, the potential economic benefits can either be much larger than income tax planning, or much smaller, often depending on the size of the estate in the first place. For those estates “above the line” of $5.45M (in 2016, or $10.9M for a married couple with portability) which are potentially subject to Federal estate taxes (and possibly state estate taxes as well), the estate tax savings can be significant. After all, while income tax planning saves taxes only on the income produced by assets (and earnings), the estate tax is applied to the entire asset value, which means effective estate planning strategies can have a dramatically larger tax savings. The 39.6% income tax on a $10 million asset producing a 3% return is “just” $118,800, but an estate planning strategy to shift that $10 million out of an already taxable estate (e.g., because it’s the appreciation that shifted with a rolling GRAT or the growth of a business sold to an IDGT) at a top 40% federal estate tax rate is a whopping $4 million tax savings. If there is an up-to-16% state estate tax involved, that’s another $1.6 million of tax savings on top. On the other hand, for those whose estates are below the relevant federal (and state) estate tax thresholds (and/or live in a state without estate taxes altogether), estate planning is no longer an exercise in estate tax planning (although there may still be some opportunities to plan for step-up in basis from an income tax perspective). Still, though, the process of probating and estate administration after death can have a non-trivial financial impact as well. Many states permit a statutory probate fee as high as 2%–4% of the value of the estate, and some provide for another 2%–4% fee available to be paid to a personal representative/executor of the estate as well. By contrast, the use of a revocable living trust generally avoids the probate fees, and can stipulate lower (or no) fees to be paid to a personal representative. Given a “hard dollar” cost of a few thousand dollars to establish a revocable living trust in the first place, this effectively means that any estate worth at least about $250,000 can potentially save on estate administration costs by going through the process of setting up (and funding) revocable

living trusts. The greater the assets of the estate, the more the potential savings. Of course, arguably some of the biggest benefits of “good” estate planning are not financial in the first place. Estate planning is about the orderly disposition of assets after death, ideally utilized in a manner that helps to propagate the family’s values and the financial success of future generations. In some cases, the best estate planning may even “limit” assets to future generations, to shield against their potential creditors or their own fiscal irresponsibility. In other words, it’s hard to put a price on the value of simply ensuring that an inheritor doesn’t just blow his/her inheritance wastefully in the first place, even though that may be the greatest estate planning value of all.

The Benefits of Good Retirement Planning In the world of retirement planning, most of the economic benefits of a financial advisor are simply the application of various investment and tax strategies discussed earlier, applied in the retirement context. After all, retirement portfolio strategies like tax loss harvesting, asset location, and tax-sensitive and tax-efficient liquidation strategies are ultimately still “just” income tax planning strategies that happen to be applied to a “retirement” portfolio. Similarly, the benefits of good diversification, managing investment costs, etc., are relevant to any portfolio… including one for retirement. Nonetheless, some additional financial strategies emerge solely in retirement. One of the most substantive is the decision of when to begin claiming Social Security benefits, where optimal claiming strategies can bring in additional dollars (e.g., over $60,000 for a well-timed restricted application claim, while it is still available), or result in outright superior wealth accumulation (e.g., by spending down fixed income assets first while delaying Social Security for the long run with its much-higher internal rate of return). Other tax planning strategies in retirement have a unique angle simply because of the interplay between income for tax purposes and other retirement benefits—for instance, the phase-in of the taxability of Social Security, or the Medicare Part B and Part D premium surcharge for higher income individuals. For many prospective retirees, though, the biggest benefit is not about maximizing the investment and tax strategies in retirement, but simply figuring out whether the person

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

can retire, and/or what can be safely spent during retirement, and how to fit household expenses to that available budget (e.g., by moving/downsizing, adjusting spending habits, etc.). In other words, the opportunity for value creation is not about producing more dollars, but setting someone’s mind at ease about whether the accumulated assets and retirement spending plan laid out before them is sustainable, how to execute that plan, and if it’s not viable, what needs to be done to improve the situation. Notably, in this regard it means sometimes the “best” thing a financial planner can do to provide value is help someone accept that they may need to spend less than desired. While this may not necessarily be appealing to most—there are few who like to be told that they cannot spend what they wish—helping someone understand the risk and unsustainability of a current spending path can be a crucial value-add to prevent an even more adverse outcome in the future. Technically, this may still be a “wealth enhancement” in the long run—or at least, a wealth stabilizer—to spend a little less now to ensure that the retiree won’t have to spend a lot less later. But the fundamental point is that not everything about good planning is necessarily about creating more wealth. Often it’s about trying to help someone strategize about the best way to enjoy the (limited) wealth they do have.

The Benefits of Good Insurance Guidance While most financial planning strategies help to enhance wealth, good insurance planning is fundamentally different: it is normally expected to decrease wealth on average. After all, the simple mathematical reality of insurance itself is that from the insurance company’s perspective, total premiums collected (plus growth thereon, less insurance company expenses and profits) should exceed total claims paid. If the insurance company pays out more than it takes in, that means it’s an insurance company soon to go bankrupt. Yet the fact that the insurance company expects to pay out less on average than it takes in means from the consumer’s perspective, insurance premiums will cost more than the average claim is likely to be. It’s expected to be a financially losing proposition for the consumer (on average). This isn’t necessarily a bad strategy, though, because the advantage of insurance is turning an uncertain and potentially large (and possibly unmanageable) expense into

25

a certain but small and manageable one. For instance, if there’s a 0.1% probability that my $300,000 house will burn down, but I can insure the house for $350/year, it’s a “good trade-off ” for most. Technically, the mathematical expectation of loss on average is only 0.1% x $300,000 = $300 per year and paying $350/year increases the average loss. The difference is that for an insurance company, it can average out one house burning down against 999 that don’t; for the consumer, having a house burn down without homeowner’s insurance is a $300,000 loss most can’t afford. So paying a known $350/year is superior to an “average” loss of $300/ year that could be as high as a destructive $300,000 in any particular (albeit unlikely) year. Viewed another way, this is simply the recognition that good insurance planning is good risk management, turning potentially destructive and highly uncertain large expenses into certain expenses that are small and manageable. “On average” wealth will be decreased, but the (financial) danger of a catastrophe can be eliminated. Nonetheless, what this means from the perspective of the “economic benefits” of financial planning is that while obtaining proper insurance coverage may reduce the risk of disasters, it is not expected to increase wealth. Certainly, for the one person who buys homeowner’s insurance and actually has a homeowner’s claim, the coverage had a nearly 1,000,000% “return.” But that’s not an expected return (as if every buyer got that, the insurance company would quickly go broke), that’s a disaster averted. Stated more simply, good insurance guidance is truly all about risk management to avoid financial disasters, not enhancing (average) financial outcomes.

The True (Unmeasurable) Benefit of Financial Planning: Behavior Change Ultimately, perhaps the greatest financial impact of good financial planning is not directly financial at all, but behavioral—the fact that a financial planner may help ensure that everything gets done in the first place, that wouldn’t have been done without help. Of course, the caveat to this “benefit” is that virtually all of the actions necessary to implement a good financial plan are relatively straightforward, and things that an individual could do for themselves. At worst, it might take some time


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and self-education to be certain it’s done right, but most of financial planning is not “rocket science”; the tactics and implementation steps are able to be done directly by most consumers. Nonetheless, the clear reality is that not everyone actually does it all for themselves. Some don’t have the time to do so, given other demands of work and life. Some just don’t want to take the time or have the inclination and motivation to get it done. Others suffer from behavioral biases that keep them from recognizing the problems to be addressed, or taking the steps necessary to resolve it (e.g., failing to spot that a concentrated position in employer stock is a risk, not updating estate planning documents due to a discomfort in facing the issues of death and mortality, or just procrastinating to the point that insurance coverage is never actually bought). In other words, a behavioral benefit of working with a financial planner is the potential to “de-bias” the client away from behavioral finance mistakes, and in some cases a task that is delegated is simply more likely to be done than one the client “could” do themselves but realistically will just procrastinate about instead. Unfortunately, it’s especially challenging to measure the economic impact of “getting behavioral assistance,” due to the “compared to what” problem—in the end, there’s no way to know exactly how much an actual financial planning client might have done on their own (or not) in the absence of the planner. At best, we can see what has or hasn’t been done already, and project that behavior into the future (which may or may not have been how it was really going to turn out). Similarly, there’s no way to measure whether someone who doesn’t hire a financial planner really would have done more with the planner’s assistance (as not every planner is successful at getting every client to implement every recommendation). And of course, even if “some” or “many” or “most” people don’t do all their financial planning themselves, the next person may have been fully capable of doing it themselves. In practice, the “behavior change” benefit of financial advising may be most akin to those who seek out personal trainers for their physical fitness as well—it’s a service that most would benefit from, but everyone makes their own personal judgment about whether the cost is worthwhile relative to the incremental improvement in their behavior over just trying to motivate themselves to get it done.

Bringing It All Together Across all the different dimensions of financial planning, financial advisors have an opportunity to craft solutions that impact clients in a myriad of ways, as shown in Figure 11. Some strategies are about the outright enhancement of financial wealth and gain. Others are technically a reduction in expected wealth, but with an even greater reduction in risk (e.g., portfolio diversification, or buying insurance). Still other strategies express themselves primarily in improving a client’s overall mental state of happiness and well-being (from crafting a viable retirement spending strategy, to ensuring that heirs will not fight over assets after death). And in many cases, the primary value the advisor provides is helping clients actually implement the change that they hypothetically could have done themselves, but in practice had not and probably weren’t going to. Nonetheless, the fact that there is no way to know what the future might have been makes it remarkably difficult to effectively assess the economic impact of many financial planning strategies. Those planning recommendations are purely quantitative. For example, many tax-related strategies can be more reasonably assessed, both because the outcomes are easier to measure, and there is often a clearer baseline against which it can be measured. Others that are primarily behavioral—e.g., improving savings habits and helping someone to reduce their spending—are far more difficult to measure, likely to vary significantly in value from one client to the next, and arguably aren’t even “improvements” in many cases but simply trade-offs (for instance, saving more today does also mean spending less on things you enjoy today). Still, a proper assessment of the value of a strategy, including fully accounting for its costs, tax impacts (now and in the future), and with a good “measuring stick” for assessing the outcome, is vital to determine what strategies are even worthwhile to engage in to begin with. Otherwise, the advisor risks making recommendations that aren’t actually even an improvement in the first place. Of course, for those who go even deeper into various niches and specializations, the true value of financial planning for that particular clientele may encompass a wide range of benefits not discussed here. Advising younger clients could include career advice that has a significant financial impact in the long run. Consulting with executives about

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Figure 11. Potential Economic Impacts of Financial Planning Strategies

stock options and restricted stock has unique value creation opportunities of its own. And working with doctors selling their medical practice and maximizing the value may be the single greatest opportunity for wealth enhancement for those clients… but it is only for advisors working with doctors in the first place. Still, hopefully the list of prospective strategies that advisors can provide to add value, and an understanding of their economic benefits, is crucial both in deciding what strategies to implement and which may be most valuable for clients. Hopefully this discussion provides a helpful framework for making such assessments in the future.

References Blanchett, David, and Paul Kaplan. 2013. “Alpha, Beta, and Now… Gamma,” Journal of Retirement, (Fall) vol. 1, no. 2: 29-45. Daryanani, Gobind, and Chris Cordaro. 2005. “Asset Location: A Generic Framework for Maximizing After-Tax Wealth,” Journal of Financial Planning, (January) vol. 18, no. 1: 44–54. Envestnet. not dated. “Capital Sigma: The Return on Advice,” Envestnet Whitepaper. Kinniry, Francis M., Colleen M. Jaconetti, Michael A. DiJoseph, and Yan Zilbering. 2014. “Putting a Value on Your Value: Quantifying Vanguard Advisor’s Alpha,” Vanguard Research Paper.


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Kitces, Michael. 2015. “An In-Depth Look At Rebalancing Strategies,” The Kitces Report, (April) vol. 2. Kitces, Michael. 2014. “Evaluating the Tax Deferral and Tax Bracket Arbitrage Benefits of Tax Loss Harvesting,” The Nerd’s Eye View Blog, (December 3). Available at: https://www. kitces.com/blog/evaluating-the-tax-deferral-and-tax-bracket-arbitrage-benefits-of-tax-loss-harvesting/

Author Michael E. Kitces, MSFS, MTAX, CFP®, CLU, ChFC, RHU, REBC, CASL, is a Partner and the Director of Research for Pinnacle Advisory Group (www.pinnacleadvisory.com), a private wealth management firm located in Columbia, Maryland. In addition, he is an active writer and speaker, and publishes The Kitces Report and his blog “Nerd’s Eye View” through his website www.kitces.com.

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

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Marriage and Taxes: Who Pays?

Allen J. Rubenfield, Lecturer of Accounting, Eugene W. Stetson School of Business and Economics, Mercer University Ganesh M. Pandit, Associate Professor of Accounting, Robert B. Willumstad School of Business, Adelphi University

Abstract The Tax Policy Center states that a “marriage penalty” occurs in the tax system when a wife and husband pay more income tax filing jointly as a couple than they would if they had remained single and filed as individuals. Conversely, a “marriage bonus” occurs if a couple pays less tax filing jointly than they would if they were not married and filed single.1 (TPC)

1.

http://www.taxpolicycenter.org/briefing-book/what-are-marriage-penalties-and-bonuses


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Analysis2 Tax Brackets3 and the Medicare4 and Net Investment Taxes5 A marriage penalty or bonus kicks in after the decision to get married occurs. If the individuals choosing to get married have equal incomes they have a very good chance of paying this penalty. If only one individual is earning income or if the incomes are disparate, then they will most likely wind up paying less in taxes. The penalty and bonus are a result of the marriage and are compared to what they would have paid if they had remained single. Example 1: John and Mary are married on June 1, 2015. John has wages of $130,000 and net investment income of $20,000. Mary’s income and investments are identical. They have no children and $20,000 in itemized deductions. They are under 65 and not blind. Their taxable income is as follows:

Tax computation: $136,000 – 20,000 (investments taxed at lower rate) = $116,000. Tax on $116,000 from rate schedule: $25,551. The $20,000 is taxed at 15% which equals $3,000. The Medicare earned income additional tax is $130,000 – 200,000 = -0-, and the net investment tax is $150,000 – 200,000 (the AGI less the threshold amount) or the net investment income of $20,000—whichever is less—times 3.8%. In this case, because the threshold is not met, the additional tax is -0-. The total taxes are $28,551. If you multiply this by two you get $57,102 or $2,597 less in taxes for the couple by remaining single. Example 3: John and Mary are married but only one works and has income with all else remaining constant. Wages Investment income AGI (adjusted gross income) Itemized deductions Personal exemptions (2)

Wages

$260,000

Investment income Itemized deductions

$300,000 (20,000)

Personal exemptions (2)

(8,000)

Taxable income

$272,000

The tax computation is: $272,000 – 40,000 (investments taxed at lower rate) = $232,000. Tax on $232,000 from rate schedule: $52,089. The $40,000 is taxed at 15% which equals $6,000. The Medicare earned income additional tax is $260,000 – 250,000 = $10,000 x .9% = $90, and the net investment tax is $300,000 – 250,000 (the AGI less the threshold amount) or the net investment income of $40,000—whichever is less—times 3.8%. In this case, the lesser amount is $40,000 x 3.8% which equals $1,520. The total taxes are $59,699. Example 2: John and Mary are single. All else remains the same as Example 1. Wages

$130,000

Investment income

20,000

AGI (adjusted gross income) Personal exemptions (1) Taxable income 2. 3. 4. 5.

20,000 $150,000 (10,000) (4,000) $136,000

40,000

AGI (adjusted gross income)

Itemized deductions

Taxable income

$130,000

$150,000

Tax computation: $136,000 – 20,000 (investment income taxed at lower rate) = $116,000. Tax on $116,000 from rate schedule: $20,587. The $20,000 is taxed at 15% which equals $3,000. The Medicare earned income additional tax is $130,000 – 250,000 = -0-, and the net investment tax is 150,000 – 250,000 (the AGI less the threshold amount) or the net investment income of $20,000—whichever is less—times 3.8%. In this case because the threshold is not met, the additional tax is -0-. The total taxes are $23,587. This is $4,964 less in taxes than being single. In Example 1, John and Mary are married filing jointly. Since both work and are making equal incomes, they are penalized for their efforts. They are penalized because they fall into the 33% marginal tax bracket whereas if they were single as in Example 2 they would be in the 28% bracket. The 33% bracket begins at $189,300 for singles and $230,451 for married filing jointly. (IRS Tax Rates) As one can see, $239,451 is not two times $189,300. You multiply $189,300 by two and you get $378,600, $139,150 more than where the 33% bracket begins for married filing jointly.

(10,000) (4,000) $136,000

All brackets, phase-out limitations, etc. relate to the 2015 tax year. IRC § 1411. 26 CFR Part 1 and 31. IRC § 1411.

In Example 3 where only one member of the married couple is working and earning income, the bonus kicks in. They now fall into the 25% tax bracket as the 28% for married filing jointly doesn’t start until they hit the $151,200 taxable income mark. This results in lowering their taxes and providing the couple a tax bonus.

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Further, as can be noted from the above examples, another impact results from the additional taxes for the Medicare earned income tax and the net investment tax. For a single individual, the threshold before either of these taxes sets in is $200,000. For those who are married, the threshold does not double to $400,000, but only rises to $250,000, which is only $50,000 more than the threshold for singles. For married individuals who are working hard and trying to improve themselves and their incomes and who do invest, these penalties can be very harsh. This is especially true of the additional 3.8% on net investment income.

Social Security7

Example 4: A married couple earns $180,000 in wages and has an additional $120,000 in mutual fund capital gains and qualified dividends. While the .9% earned income tax for Medicare does not kick in because they are below the $250,000 threshold for the net investment tax, the lower of the amount over the $250,000 AGI threshold or the net investment income would still be taxed at 3.8%. In this case the $300,000 – 250,000 or the $50,000 would be less than the $120,000 net investment income. The additional tax would be $50,000 x 3.8% = $1,900. This is not an insignificant amount, especially when you consider that a single individual would not pay any additional tax if they earned half of these amounts, $90,000 and $60,000 or a total of $150,000 well below their threshold.

Example 5: Joe is single, 68, and has the following income: $20,000 from a part-time job and $10,000 from Social Security. His modified adjusted gross income for Social Security purposes is one-half Social Security of $10,000, which is $5,000, plus his wages for a total of $25,000. His taxable Social Security is equal to $0 ($25,000 MAGI less $25,000 threshold).

Capital Gains6 As already noted, there is a severe disparity between single and married filers when it comes to net investment income. The threshold for singles is $200,000 as compared to the threshold for married folks. The disparity grows even greater when more income is earned. An additional capital gains rate is added for those individuals who fall into the 39.6% bracket. Once the 39.6% bracket is reached, capital gains are taxed at 20%. The 39.6% bracket starts at $413,200 for those filing single and $464,850 for those married filing jointly. The difference is only $51,650 between single and married. Two individuals who have very good jobs and income from other sources including investments can reach that number fairly quickly. While two singles would be able to make $826,400, a much more significant amount before they would pay the additional 5% in capital gains tax. Remember, however that at this level of income the actual capital gains rate goes to 23.8%. Once again, you have to ask yourself at this level of income, does it pay to be married? 6.

IRC § 1(h).

There are some areas where you do not need to have a substantial income to be caught up in the net of additional taxes. Those on Social Security can pay dearly for being married. The taxable thresholds for Social Security are $25,000 for the 50% taxability and $34,000 for the 85% taxable for those who file single. For those who are married, 50% of Social Security becomes taxable at $32,000 and 85% at $44,000. Just by observation, the married numbers are nowhere near double the single amounts.

Example 6: Bill and Kate are both 69 and have the following income: $30,000 joint income from part-time employment and $10,000 each ($20,000) from Social Security. Their modified adjusted gross income for Social Security purposes is one-half Social Security of $20,000, which is $10,000, plus their wages for a total of $40,000. Their taxable Social Security is equal to $4,000, $40,000 MAGI less $32,000 threshold, which equals $8,000. Fifty percent of the excess above the threshold, $4,000 is compared to 50% of their Social Security which is $10,000, the lesser of which is added to taxable income. As can be seen, although overall income, including Social Security, was not twice Joe’s, $50,000 compared to $30,000, their taxable income was increased as a result of a portion of their Social Security being taxable. If the numbers were actually twice what Joe made, while his Social Security would still not be taxed, Bill and Kate’s would fall partially in the 85% taxability.

Earned Income Credit8 The earned income credit is a refundable tax credit for certain people who work and have earned income. The original purpose of the credit was to encourage low income individuals to find work and receive some assistance while they were employed. The earned income credit is available to those who are single or married and whether or not they 7. 8.

IRC § 86. IRC § 32.


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have children. Considering this is a credit to encourage the low income individuals to find work, one would think that it would not matter if a person were married or single and that the credit would be figured on a fair basis. However, if you look at where the credit phases out, whether an individual has one, two or three children, there is a tremendous disadvantage to being married. The maximum amount of income you can earn less than and still get the credit is: •

$14,820 ($20,330 if married filing jointly), don’t have a qualifying child, and are at least 25 years old and under 65,

$39,131 ($44,651 if married filing jointly), and you have one qualifying child,

$44,454 ($49,974 if married filing jointly), and you have two qualifying children, or

$47,747 ($53,267 if married filing jointly), and you have three or more qualifying children.

The phase-out is far quicker and at far less income for two as opposed to one when people are married. Therefore, it can be seen that the marriage penalty impacts both higher income earners and low income earners.

Phase-out Ranges and Thresholds The Tax Code is rampant with differentiations between married filers and those who file single. Additional areas where significant differences exist are the many phase-outs and the thresholds where they begin as related to deductions, exemptions, exclusions and credits. This, again, gives a tremendous tax advantage to those who are single.

Exclusions As an example of an exclusion we have the Series I and EE Bonds9, which are used for education. The phase-out for those who are single begins at $77,200 and is completely phased out at $92,200. If this was doubled the numbers would be $154,400 to $184,400, which would seem fair for those who are married. However, the phase-out for those who are married begins at $115,750 and is completely phased-out at $145,750—far less than doubled.

the adoption credit. If the Tax Code is used to incentivize certain types of behavior, one would think this would be an activity to be encouraged. In particular, public policy should try to encourage married folks to adopt to a greater extent than those who are single. Yet this is one of the most imbalanced thresholds and phase-out ranges simply because it is the same for all groups. The phase-out begins at $201,010 and phases out completely at $241,010. While this modified adjusted gross income is relatively high it seems that two incomes would get there a lot faster than one. In this case it seems to be self-defeating.

Child Tax Credit11 As for credits, the best example of favoring single over married is the child tax credit. The child tax credit is a credit that supposedly helps with the cost of raising children. A credit can be had for up to three children and in some cases can be partially refundable. The credit is $1,000 per child. Now it can be argued that it is more difficult for a single parent to raise a child but that does not lessen the need for joint parents of children to be treated equally as single parents. The child tax credit phase-out for singles begins at $75,000 and, for those who are married at $110,000 which is far less than double the single phase-out. Further, the phase-out applies across the board, so if there are three children the $3,000 is subject to being phased out, not $1,000 times three. Finally, a last advantage to a single parent with a dependent child is that he or she can file as head of household and be subject to even better rates than single rates.

Individual Retirement Accounts12 Retirement savings are to be encouraged. Social Security has problems and is running out of money. Social Security was created to be a supplemental retirement plan. Therefore, the government over the years has tried to encourage individuals to put away money for their own retirement. A significant number of people have not done this, however.

Another example of an exclusion which is even more unfair is the adoption exclusion10. The same phase-out applies to

To encourage people to save and have money to retire comfortably, the Traditional and Roth Individual Retirement Accounts were created and had tax benefits to accompany them. In the Traditional IRA you can deduct up to $5,500 up front if you qualify, and although the monies grow tax-free, withdrawals are fully taxable upon retirement. Monies put into a Roth IRA are after taxes, and

9. 10.

11. 12.

IRC § 135 IRC § 23 and § 137(a).

IRC § 24 IRC § 408

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Volume 15, Issue 2

the investments also grow tax free, but are not taxed when withdrawn. Contributions to traditional IRAs are now subject to phaseouts if one or both of the parties to the marriage are active participants in an employer’s pension plan. This has been true for many years and has always been weighted towards those who are unmarried. A single individual’s ability to contribute phases out between $61,000 and $71,000 while married individuals who are both participating in employer plans phase-out between $98,000 and $118,000. Once again, these amounts are far less than double what a single individual has. In this case, however, if one spouse is a nonparticipant, the phase-out range is $183,000 to $193,000, well more than double. But you have to ask yourself here why it should be limited at all since if one was single and not participating there would be no earnings limit. Roth IRA contributions are limited by a person’s modified adjusted gross income. For those who are single the adjusted gross income phase-out range begins at $116,000 and phases out completely at $131,000. For those who are married the range is $183,000 to $193,000. Once again there is a significant advantage to those who are single.

Alternative Minimum Tax13 The alternative minimum tax which was meant to capture a handful of very wealthy taxpayers by eliminating many of the tax advantages, preferences and loopholes they were accustomed to has caught many more taxpayers in recent years. Congress has continually tried to fix the problems with the alternative minimum tax, but one problem they have failed to address, once again, is the differences between those who are single and those who are married. If you are single, your alternative minimum tax exemption is $53,600 for 2015 compared to $83,600 for those who are married. These exemptions start to phase-out at $119,200 for singles and $158,900 for those who are married. Once again, there is a significant disparity between those who are single and those who are married.

Personal Exemptions14 and Itemized Deductions15 Finally, personal exemptions and itemized deductions need to be discussed because these are far more common 13. 14. 15.

IRC §§ 55-59. IRC § 151(d). IRC § 68(i).

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to the ordinary taxpayer. Personal exemptions are subject to being completely phased out based upon a formula, while itemized deductions are just reduced as a result of the Pease Amendment to the Code. The adjusted gross income thresholds again tilt significantly toward those who are single. The phase-out range is the same for both the personal exemption and the itemized deduction: $258,250 for single and $309,900 for married. As for personal exemptions, the phase-out applies equally across the board. Therefore, if you are married and have four children, your exemption would be 6 x $4,000 or $24,000. However, if your exemptions are totally phased out you get $0, not a per exemption phase-out. Thus, a single/married distortion once again exists.

Conclusion It is well known that the extensive U.S. Tax Code is quite complex and extremely difficult to deal with. It is also at times unfair. It is especially unfair to those who are married with two incomes. This is not an unusual situation in this day and time. Many married families have and need two incomes. However, the Tax Code offers these folks very little assistance and, in fact, often penalizes them. The more successful they are the more they incur the penalties. The incentive is really a disincentive. The alternatives are to make less money, divorce or just remain single. It is difficult to argue that the Tax Code has any real impact on the decision to marry, remain single or divorce, as these decisions are most likely based more upon emotions rather than taxes. It is also difficult to believe that taxes are not playing some role in these decisions today as more and more folks become at least somewhat tax savvy. It is time to stop talking and to actually start doing something in the way of tax reform. One of the easier ways would be to do away with the many classifications and have everyone file as single even on a joint return. At the same time any number of deductions, exemptions, exclusions and credits could either be done away with completely or re-evaluated and redone.

REFERENCES http://www.taxpolicycenter.org/briefing-book/ what-are-marriage-penalties-and-bonuses Internal Revenue Code


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Competing Risks: Death and Ruin

Dirk Cotton, MBA, The Retirement Cafe Cary Cotton, MD, MPH, University of North Carolina Hospital Alex Mears, BS

Abstract Early portfolio survival research in the field of retirement finance largely focused on the probability of outliving one’s retirement portfolio with constant spending over fixed time periods, such as 30 years. Seminal studies by William Bengen, for example, suggested that a retiree can invest his or her savings in a portfolio of stocks and bonds and spend around 4% of his or her initial savings balance annually from the portfolio with about a 5% probability of outliving those savings. Subsequent work by Stout and Mitchell and later by Milevsky and Robinson incorporated random lifetimes, but focused on the lifetime probability of ruin. Medical research uses methods of analyzing survival studies that are novel in retirement research. We use Kaplan-Meier estimates and competing risks analysis to explore the conditional probability of a retiree outliving her savings as age progresses, the relationship of the competing risks of death and ruin as age progresses, and the timing of portfolio failures due to poor market returns. We find that risk of ruin develops in three stages of a long retirement: a low-risk period early in retirement with high sensitivity to market returns but few portfolio failures, a middle period in which portfolio failure peaks, and a late period in which death is much more likely than portfolio ruin.

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Volume 15, Issue 2

Background Prior studies have used simulations based on historical market returns to estimate the percentage of a retiree’s initial portfolio value that could be spent annually without substantial portfolio failure (Bengen, 1994). The authors assumed past market performance would be repeated in the future, and estimated portfolio survival rates assuming fixed lifetimes (such as 30 years). For example, a retiree who spent 4% of initial portfolio balance each year for 30 years would have a 95% probability of portfolio survival. These findings have provided many important insights into the nature of portfolio survival with periodic spending, but depend on assumptions of fixed lifetimes and historical returns that are not realistic models of market or mortality risk. Later authors extended these findings with a closedform equation for estimating lifetime probability of ruin and incorporating stochastic (random) lifetimes (Milevsky & Robinson, 2005). Authors further expanded prior simulations by incorporating variable spending, such that the annual withdrawal by a retiree varies in response to portfolio value (Stout & Mitchell, 2006). Prior studies haven’t answered when portfolios fail or presented the conditional probability of failure (given survival) as a function of retiree age. When a portfolio fails may be important for several reasons. From the perspective of a retiree, an early portfolio failure has a greater consequence for retirement lifestyle than a late failure because it affects more years of life. The timing of portfolio failure due to market risk also may allow inference about why portfolios fail. Using techniques from medical research, we extend the findings of prior authors in two ways. First, we use KaplanMeier estimates to show the conditional probability of portfolio survival over time given that the retiree is alive at that time. Second, we use competing risks analysis to present absolute probabilities of portfolio failure before death and death before portfolio failure as a function of retiree age.

Methods Monte Carlo Simulation We performed Monte Carlo simulation using the programming language R 3.2.2 (R Core team, 2015). We simulated

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65-year sequences of market return scenarios using a log-normal distribution of returns from Marsenne-Twister pseudo-random number generator for a given arithmetic mean relative annual portfolio return and standard deviation (Matsumoto & Nishimura, 1998). We simulated random lifetimes based on United States Life Tables for 2011 for men aged 65, women aged 65, and at least one surviving member of a male and female couple. Times of death greater than 100 years were simulated using a gamma distribution with arithmetic mean equal to the life-expectancy at age 100 and shape parameter of 1. The random returns were used to calculate an annual portfolio balance given an annual real constant-dollar spending assumption. The annual balance was calculated as the previous year’s ending balance less the annual spending amount. If the balance was positive, we multiplied it by the random return, and if it was negative the year of portfolio failure was recorded. Portfolio returns are simulated as real and no adjustments for inflation were made. Scenarios in which the portfolio was depleted exactly at the year of death were considered to have survived retirement. We simulated scenarios for annual withdrawal rates of 2, 3, 4, and 5%; we simulated arithmetic mean relative annual market returns of 3, 4, 5, and 6% with standard deviation 11%. Models and data are available at GITHUB (https:// github.com/DirkCotton/Death-and-Ruin/).

Kaplan-Meier and Competing Risks Analysis The Kaplan-Meier estimator is a statistical tool developed to describe survival over time in the absence of complete follow-up. Such analysis is useful in a survival study in which some patients are still alive at the end of the study. These patients are no longer observed and thus “censored” because they are not at risk for the outcome (they could still die, but we wouldn’t observe it.) In effect, they are taken out of the denominator for risk calculations at future times. In our context, once retirees die, they are no longer at risk for portfolio failure and are thus censored. This estimate is plotted to yield a survival function over time, conditional upon being at risk at each time, i.e., the probability of portfolio failure given survival. Kaplan-Meier survival curves are plotted with 95% confidence limits of simulation standard error. Several reviews of the KaplanMeier estimator exist in the medical literature (see (Layton, 2013) and (Pepe & Mori, 1993), for example).


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In contrast to a Kaplan-Meier survival curve, competing risks analysis provides absolute, rather than conditional, probability of various outcomes. Whereas subjects who die are removed from the risk pool in the former analysis, they are plotted as an alternative outcome in competing risks. The curves show the cumulative probabilities of death and ruin occurring first. The ruin curve approaches the lifetime probability of ruin as would be shown by previous analytical approaches.

Results Personal Survival in Retirement Figure 2. Probability of Ruin by Three Different Estimating Methods.

Survival rates for men, women, and couples at age 65 differ substantially (Figure 1). The median age of death differs by three years between solo male retirees and couples. For all three cases, more than 80% of retirees would have died before the full 30-year fixed retirement timeframe elapsed. Median age of death was 84, 87, and 90 for solo men, solo women, and couples, respectively.

Portfolio Survival Censored for Death The lifetime probability of ruin as estimated by our model for a couple who retires at age 65 as a function of annual withdrawal rate is similar to both the Milevsky formula and Fixed Lifespan estimates (Figure 2). Due to higher life expectancies, the lifetime probability of ruin before death is greater for women than men and highest for couples. Kaplan-Meier survival curves (Figure 3) show the estimated conditional portfolio survival over time for a single man

Figure 3. Conditional Probability of Ruin Among Living Single Men by Age in Various Scenarios of Arithmetic Mean Market Returns and Annual Withdrawal Rate.

Figure 1. Simulated Personal Survival for Single Men, Single Women, and Joint Man-Woman Couple, based on 2011 United States Life Tables.

Figure 4. Absolute Probability of Ruin and Death among All Single Men by Age in a Scenario with 4% Arithmetic Mean Portfolio Returns and 4% Annual Withdrawal Rate.

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Volume 15, Issue 2

Figure 5. Comparing Estimates of Probability of Ruin by Age among All Single Men by Age in a Scenario with 4% Arithmetic Mean Portfolio Returns and 4% Annual Withdrawal Rate. Conditional estimates refer only to living retirees, while absolute risks also include dead retirees in the denominator. Probability of ruin “without death” assumes that all retirees live to at least 110 years of age; in other words, market volatility is the only determinant of ruin for this curve.

aged 65 and withdrawal rates of 3, 4, and 5%, and arithmetic mean market returns of 2, 3, 4, and 5%. Larger annual withdrawal rates and lower market returns increase the downward slope of the survival curve more than they move its inflection point. Portfolios fail at a faster rate with higher spending rates, even though the declines all begin around age 80.

Death and Ruin Presented as Competing Risks Kaplan-Meier curves show conditional probability of ruin, while competing risks curves show both absolute probability of ruin before death (dashed curve, Figure 4) and absolute probability of death before ruin. Comparing the absolute portfolio failure probability from the competing risks analysis to portfolio failure rate curve ignoring life expectancy (dashed curve, Figure 5), we can see that the absolute risk of outliving one’s savings after about age 85 is greatly reduced by the competing risk of death. The conditional probability of ruin given a retiree is alive (dotted curve, Figure 5) is also much greater than the absolute risk of portfolio failure before death (solid curve, Figure 5). Competing risks cumulative incidence plots for 3, 4, and 5% annual portfolio withdrawals among single men and assuming arithmetic mean market returns of 4% reveal the absolute risk for ruin before death is low before age 80 (Figure 6). Similar to findings in Kaplan-Meier analysis, the slope of the portfolio failure curve is dependent on the rate of withdrawals, but the timing of the first portfolio failures is similar. The portfolio failure curve (dashed line, Figure 6)

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Figure 6. Competing Risks Cumulative Incidence Plots of Ruin and Death by Age among All Single Men by Age in a Scenario with 4% Arithmetic Mean Portfolio Returns and Varied Annual Withdrawal Rate.

approaches the lifetime probability of ruin for male retirees aged 65 asymptotically. For 3% annual withdrawals, this probability was 0.6%. With 4% withdrawals, the probability of ruin was 3.6%, and with 5% withdrawals it was 11.4%.

Discussion We present the results of applying time-to-event analyses adapted from medical research to a simple simulation of portfolio failure and death. Assuming a reasonable regime of constant-dollar spending in the range of 3% to 5% of initial portfolio balance from a volatile portfolio of retirement savings, the probability that a retiree will outlive her savings is a function of both portfolio survival and the retiree’s survival. Examining the risk of portfolio failure as conditional on survival, as in Kaplan-Meier curves, or absolute, as in competing risks analysis, gives different impressions of the magnitude of risk. Both approaches to analysis of this model’s outcomes show three distinct stages of the risk of outliving savings. We find that only a small percentage of retirees would outlive their savings in early retirement because even a poor sequence of portfolio returns from the outset would take a decade or more to deplete a portfolio. In absolute terms, few retirees would deplete their savings late in a long retirement because the probability of living well into one’s nineties is relatively low, though the conditional probability of portfolio failure (i.e., that among those who still live) remains high. Most scenarios in which retiree’s would deplete their portfolios while still living occur in the retiree’s mid-eighties, about the midpoint of a maximum-length retirement, when the probability of portfolio


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Journal of Personal Finance

depletion begins to accelerate and the probability of the retiree’s survival has not yet precipitously declined. This work extends prior studies that quantify the risk of portfolio failure as a function of retiree withdrawal rate. Bengen (1994) researched maximum safe withdrawal rates from retirement portfolios and found that a 4% real withdrawal rate from a balanced portfolio of stocks and bonds would be historically sustainable for at least 33 years. The research assumed fixed-length retirements and historical market returns data. These assumptions might limit the usefulness of the Bengen (1994) findings for direct application to individual retirement planning in two ways. First, sustainable spending is highly sensitive to assumptions about future market returns, and these returns have been shown to be unpredictable. There is no reason to assume that we will not see worse periods of returns in the future than have been experienced in the past. Previous research has shown that probability of ruin research based on U.S. market returns was not reproducible in most developed market countries during the same time frame (Pfau, 2010). Second, it can be reasonably assumed that, in order to avoid ruin, many retirees would decrease spending in the face of eminent portfolio depletion. The assumption of 30-year retirements overstates the probability of ruin because few retirees will live that long. The assumption that retirees will continue spending constant-dollar amounts in the face of eminent ruin further overstates probability of ruin. Cooley, Hubbard, and Walz (1998) studied the importance of aggressive portfolio allocations in portfolio sustainability (the “Trinity Study”), also using historical stock data and fixed payout periods. Cooley et. al. introduced the concept of “probability of ruin” by counting the number of time periods in which a given withdrawal rate would have succeeded. Cooley (1998) concludes that “For stock-dominated portfolios, withdrawal rates of 3% and 4% represent exceedingly conservative behavior.” Cooley (1998) makes the same unrealistic assumptions of constant-dollar spending in the face of ruin, fixed lifetimes, and repeated twentieth century U.S. market performance as Bengen (1994) that tend to overstate a sustainable annual spending rate. Milevsky and Robinson (2005) provided a closed-form equation for stochastic present value that can be used for estimating the lifetime probability of ruin without simulation given median life expectancy, annual spending

rate and the arithmetic mean and standard deviation of expected portfolio returns. The authors suggest that “payout ratios should be lower than those many advisors recommend”. Milevsky (2005) incorporates stochastic (random) lifetimes into the retiree behavior model, but continues to assume constant-dollar spending even in the face of eminent portfolio depletion. The model incorporates expected market returns and volatility instead of relying on historic market data, but requires the financial planner to predict those future market returns. Stout and Mitchell (2006) introduced a dynamic retirement withdrawal model including periodic adjustments of retirement withdrawal rates based on both portfolio performance and remaining life expectancy. Stout (2006) asserted that “a first step in improving the accuracy of retirement withdrawal planning is the recognition of the uncertainty of the remaining life span” and introduced stochastic lifetimes into the withdrawal model. The study concluded that replacing fixed payout periods with stochastic life expectancies reduces the probability of ruin by almost 50%, and that dynamic withdrawals and stochastic lifetime assumptions reduce the probability of ruin by another 35–40%, while increasing average lifetime withdrawal rates by nearly half. Stout (2006) concluded that retirement is substantially lower than the probability of depleting that portfolio over a fixed 30-year time period and found, for example, that a fixed 4.5% real withdrawal rate has a 7.16% probability of ruin before death and a 13.44% probability of ruin before 30 years. Blanchett and Blanchett (Blanchett & Blanchett, 2008) explored the differences between using the joint life expectancy of a couple and a fixed payout period assumption when determining a sustainable real withdrawal from a retirement portfolio. Their research showed that assuming joint life expectancy tended to result in a sustainable real withdrawal rate that was between 1 percent and 2 percent greater than that of a portfolio based on a fixed payout period for the same probability of failure. Noting that the United States enjoyed particularly favorable asset returns for the twentieth century period most often used for previous portfolio survivability studies, Pfau (2010) researched sustainable withdrawal rates in 17 developed market countries, including the U.S. The research concluded that a 50% stock portfolio with a real 4% annual withdrawal rate would have failed in all 17 countries, suggesting that a withdrawal rate as high as 4% might be an anomaly of recent American asset returns.

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Volume 15, Issue 2

Finke, Pfau and Blanchett (2013) introduced a model that takes into account the then-present low-yield capital market and concluded that a 4% initial withdrawal rate was successful for only 50% of simulated scenarios and that a retiree who wants a 90% probability of achieving a retirement income goal with a 30-year time horizon and a 40% equity portfolio should currently (as of 2013) have a maximum initial withdrawal rate of only 2.8%. We extend this body of prior work by showing that KaplanMeier and competing risks curves demonstrate temporal characteristics of the portfolio ruin process. For withdrawal rates between about 3 and 5%, there is little probability of portfolio ruin for a 65-year old before about age 80. Bankruptcy, however, is most common before the age of 80 (Thorne, 2010) and our findings suggest that depletion of savings early in retirement is much more likely to result from large, unpredictable expenses than from a poor sequence of portfolio returns (Cotton, 2016). While probability of ruin with respect to age is low and nearly linear for 3% withdrawals, 5% annual portfolio returns, and 11% with standard deviations, the risk is moderate after about age 80 with 4% withdrawals and substantial with more conservative estimates of market returns. Though 3% withdrawals were safe for arithmetic mean portfolio returns as low as 4%, substantial portfolio failure was observed with 3% withdrawals in the setting of 2 or 3% arithmetic mean returns. While the conditional risk of portfolio failure is substantial among the minority of people who live to their 80s, the absolute risk of running out of money before death is much less. The resulting risk curves divide a long retirement into three stages. During the first stage, both the risk of death and risk of portfolio depletion are relatively low. Near age 80, retirement enters a high-risk stage for portfolio depletion. In the early to mid-nineties, that risk levels off, approaching the lifetime probability of ruin, as death before portfolio depletion becomes a much greater risk for those retirees surviving this long. We also note that increasing the annual withdrawal rate has significantly more impact on the slope of the increasing risk curve than it has on the inflection point at which the risk of ruin begins to accelerate. In other words, overspending rates decreases the remaining life of a doomed portfolio, but shows a significantly smaller impact on the age at which portfolio failure risk begins to increase. Both the Kaplan-Meier curves and the competing risks curves demonstrate that, while the cause of portfolio ruin may begin with poor returns early in retirement, portfolio depletion is experienced much later, roughly fifteen to twenty years later. Debate about whether the risk of

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portfolio ruin is greater in early retirement or constant throughout retirement overlooks the delay between a poor sequence of returns and portfolio failure, which often occurs many years later. We conclude that a portfolio is exposed to path-dependent risk throughout retirement, but the probability that this path-dependent risk will lead to portfolio ruin is greatest early in retirement when the probability of death before ruin is at a minimum. If outliving savings were a disease, we would say that we are exposed to the virus throughout retirement, but in the last half of a long retirement, we are more likely to die from something else. The authors recommend that retirement planning practitioners address the increasing probability of ruin with age shown in the Kaplan-Meier curves by reassessing withdrawal rates and risk of ruin periodically, perhaps annually, as recommended by Stout and Mitchell (2006). Our findings also suggest that spending-side issues are a greater risk than poor market returns in early retirement and should be a focus of advice. While constant-dollar withdrawal regimes provide a viable research mechanism, there is little evidence to suggest that any retiree can spend a constant amount annually throughout a highly unpredictable financial future.

References Bengen, W. P. (1994). Determining withdrawal rates using historical data. Journal of Financial Planning—Denver, 17(3), 64–73. Blanchett, D. M., & Blanchett, B. C. (2008). Joint Life Expectancy and the Retirement Distribution Period. Journal of Financial Planning, 21(12), 54–60. Cooley, P. L., Hubbard, C. M., & Walz, D. T. (1998). Retirement savings: Choosing a withdrawal rate that is sustainable. AAII Journal, 20(2), 16–21. Cotton, D. (2016, January 22). Death and Ruin. Retrieved from http://www.theretirementcafe.com/2016/01/deathand-ruin.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+TheRetirementCafe+%28The+Retirement+Cafe%29 Layton, D. M. (2013). Understanding Kaplan-Meier and survival statistics. The International Journal of Prosthodontics, 26(3), 218–226. Matsumoto, M., & Nishimura, T. (1998). Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random


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number generator. ACM Trans. Model. Comput. Simul., 8(1), 3–30. http://doi.org/10.1145/272991.272995 Milevsky, M. A., & Robinson, C. (2005). A sustainable spending rate without simulation. Financial Analysts Journal, 61(6), 89–100. Pepe, M. S., & Mori, M. (1993). Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine, 12(8), 737–751. Pfau, W. (2010). An International Perspective on Safe Withdrawal Rates from Retirement Savings: The Demise of the 4 Percent Rule? National Graduate Institute for Policy Studies, 10-12. Retrieved from http://papers.ssrn.com/sol3/papers. cfm?abstract_id=1699526 Pfau, W., Finke, M. S., & Blanchett, D. (2013, January 15). The 4 Percent Rule is Not Safe in a Low-Yield World. Retrieved from http://dx.doi.org/10.2139/ssrn.2201323 R Core team. (2015). R: A language and environment for statistical computing. Vienna, Austria. Retrieved from URL http://www.R-project.org/ Stout, R., & Mitchell, J. B. (2006). Dynamic retirement withdrawal planning. Financial Services Review, 15. Retrieved from http://papers.ssrn.com/sol3/papers. cfm?abstract_id=2542024 Thorne, D. (2010). The (Interconnected) Reasons Elder Americans File Consumer Bankruptcy. Journal of Aging & Social Policy, 22(2), 188–206. http://doi. org/10.1080/08959421003621093

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Positive Health and Financial Behaviors: The Impact of Daily Time Commitment and Avoidance Barbara O’Neill, Ph.D., CFP®, Extension Specialist in Financial Resource Management, Rutgers Cooperative Extension Jing Jian Xiao, Ph.D., Professor of Consumer Finance, Department of Human Development and Family Studies (HDFS), University of Rhode Island Karen Ensle, Ed.D., RDN, Family and Community Health Sciences Educator, Rutgers Cooperative Extension of Union County

Abstract This study explored relationships between positive personal health and financial practices that involve a routine time expenditure (e.g., 30 minutes of physical activity and eating two meals prepared at home) and those that involve avoidance of negative behaviors (e.g., avoiding sugar-sweetened beverages and high cost debts such as payday loans). Data came from an online quiz that provides a simultaneous assessment of individuals’ health and financial practices with 942 observations. Correlational and multivariate analyses indicated weak, but positive and statistically significant, relationships between health and financial behaviors that involve a time commitment and those that involve avoidance of certain negative practices. Findings of demographic subsamples indicated that older, White respondents and those with higher incomes and educational levels were more likely than their respective counterparts to perform recommended health and financial practices. The article includes literature about conscientiousness and health-wealth relationships and four implications for financial advisors.

Key Words Personal finance, health, health and wealth, finances, conscientiousness, time management


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Introduction

Review of Literature

Relationships between the health and finances of individuals have been explored in academic literature for decades (Smith, 1999). Recent research questions have included associations between the health and financial status of individuals (e.g., body mass index and income) and between specific health and financial practices (e.g., reading nutrition labels and saving for retirement). For a summary of research findings about health and personal finance relationships and personality traits that may be associated with the performance of recommended health and financial practices, see O’Neill (2015), O’Neill and Ensle (2014, 2013), and O’Neill, Xiao, & Ensle (2016). In O’Neill (2015), the Personal Health and Finance Quiz was first introduced to the financial advisor community. This online tool provides users with a self-assessment of their frequency of performance of recommended health and financial practices. This article describes a study that was conducted using data from this quiz. Specifically, it explored relationships between positive personal health and financial practices that involve a time expenditure (e.g., performing 30 minutes of physical activity and eating two meals prepared at home) and those that involve the avoidance of negative behaviors (e.g., avoiding sugar-sweetened beverages and the use of high cost debts such as payday and car title loans). The rationale for this study is that there are three major things that people manage in life—their health, finances, and time—and they are related (e.g., scheduling time for exercise and to develop and follow a spending plan). Thus this study is focused on select items from the Personal Health and Finance Quiz that involve time management and self-regulation. A complete analysis of quiz data can be found in O’Neill, Xiao, & Ensle (2016). Being organized, self-disciplined, persistent, achievement-oriented, and able to manage impulses are all characteristics of conscientiousness (Amen, 2012), a personality trait consistently associated with life success (Steenbarger, 2014). Conscientiousness may also be associated with individuals’ adherence to health and finance recommendations. The behaviors included in this study all require organization (e.g., time management) and self-control (e.g., avoiding practices that experts advise against); i.e., situations where conscientious individuals often excel.

Health and Personal Finance Relationships During the past decade, several studies have investigated relationships between specific personal health and financial behaviors. However, most of them have investigated just one aspect of health and personal finance such as 401(k) plan participation and nutrition label reading. For example, a 2014 study (Gubler & Pierce, 2014) found that contributing to a 401(k) retirement savings plan was associated with whether individuals acted to correct poor physical health indicators revealed during an employer-sponsored health examination. Existing retirement savings patterns and future health improvements were highly correlated, as indicated by findings that 401(k) plan contributors showed improvement in health behaviors about 27% more often than non-contributors despite having few health differences prior to program implementation. The researchers concluded that their findings were consistent with an underlying individual time-discounting trait that predicts long-term individual behaviors in multiple dimensions. Time discounting is the tendency of people to place less value on future rewards, and higher conscientiousness has been found to correlate with a greater preference for delayed rewards (Manning et al., 2014). Letkiewicz and Fox (2014) found that both conscientiousness and financial literacy are consistent predictors of asset accumulation among young Americans. Specifically, a one standard-deviation increase in conscientiousness was correlated with a 40% increase in net worth. Werstein (2013) studied personal health and finances through the “lens” of another key personality trait, self-control, which includes the ability to delay gratification. Health outcomes were measured by body mass index, and wealth outcomes by net worth. Results of this research indicated that self-control was significantly higher among healthier and higher net worth groups controlling for age, education, and income. In addition, self-control positively and significantly predicted exercise behavior and financial behavior. Another study (Carr et al., 2015) explored the connection between health information search behaviors, such as reading the details found on food nutrition labels (e.g., serving size, calories, nutrient values), and financial wellness, which was proxied by respondents’ answers to five questions about retirement planning behaviors (e.g., consulting a financial planner and calculating retirement savings need). Results of this study indicated that

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Volume 15, Issue 2

individuals who engage in health information search behaviors were more likely than those who do not to engage in financial planning activities related to planning for retirement. Interestingly, while cognitive health management processes were statistically significantly associated with planning for retirement, direct physical activities (e.g., exercise and eating well) were not. Rosen and Wu (2004) analyzed the role that health status plays in household investment decisions using data from the Health and Retirement Study. Their results indicated that health is a significant predictor of both the probability of owning different types of financial assets and the share of financial wealth held in each asset category. Households in poor health were less likely to hold risky financial assets, other things (including level of total wealth) being the same. Poor health was associated with a smaller share of wealth held in risky assets and a larger share in safe assets. Continuing the theme of risk preferences across different domains, Anderson and Mellor (2008) found that risk aversion was negatively and significantly associated with cigarette smoking, heavy drinking, being overweight/ obese, and seat belt non-use. O’Neill, Xiao, & Ensle (2016) used the same data set as the current study. Planning behavior was measured by responses to the statement, “I make written to-do lists or specific plans to organize my financial goals, spending, and/or daily activities.� Health behavior was measured by the total score for nine health behavior questions and financial behavior was measured by the total score for nine financial behavior questions. Correlation analysis was conducted between health and financial behavior indexes. The correlation was .463 at a significance level of 5%. This result suggests that desirable health and financial behaviors are moderately associated. Support was found for the three hypotheses in the study: respondents who reported frequent planning behavior had higher health behavior scores than others, respondents who reported frequent planning behavior had higher financial behavior scores than others, and respondents who had higher health behavior scores also had higher financial behavior scores than others.

Demographic Characteristics in Health and Financial Behaviors A key personal finance behavior is saving money for emergencies and short- and long-term financial goals.

43

Fisher (2010) investigated gender differences in personal saving behaviors and found that determinants of saving behavior differed by gender. Specifically, women were less likely than men to have saved over the previous year. Poor health status and low risk tolerance negatively affected the likelihood of women saving in the short term. Garrison and Gutter (2010) also found a significant gender difference in willingness to take financial risks, with females less likely than males to choose higher levels of financial risk, which were measured with a four-part scale ranging from no risk to substantial risk. Race and age are two additional demographic characteristics associated with personal finance behaviors. Gutter and Fontes (2006) found racial differences in investment asset ownership with Blacks less likely to own stocks than Whites. Shin and Hanna (2015) compared four racial/ethnic groups and found that Black and Hispanic households were less likely to hold high return investments than White households, but Asian/Other households were not different from White households. With respect to age, Xiao, Chen, and Sun (2015) found that four different measures of financial capability increased with age. Xiao and Yao (2014) found that younger households are more financially distressed than their older counterparts. Two final demographic variables reported in this study are income and education. Perry and Morris (2005) found a positive relationship between income and responsible financial management behavior. Xiao (1996) found that earned and unearned income usually have positive effects on financial asset ownership. Cole, Paulson, & Shastry (2012) found that education increases financial market participation, measured by investment income and equities ownership, while dramatically reducing the probability that an individual declares bankruptcy, experiences a foreclosure, or is delinquent on a loan. Grable (2000) found a positive association between higher levels of education and investment risk tolerance. With respect to demographic characteristics and health behaviors, it is well established that there is a positive association between income, education, and health. People who are wealthier and better educated live longer than poorer, less-educated people (Deaton, 2003; Woolf et al., 2015). Ross and Wu (1996) found that the gap in self-reported health and physical well-being among people with high and low educational attainment increases with age. The health advantage of the well-educated is larger in older age groups than younger. Drewnoski & Spector


Journal of Personal Finance

44

(2004) found that the highest rates of obesity occur among population groups with the highest poverty rates and the least education, and that poverty and food insecurity are associated with lower-quality diets and low fruit and vegetable consumption.

Conceptual Background This study extends the current interdisciplinary knowledge base about personal health and finances by exploring relationships between health and financial practices, specifically those that involve a time expenditure (i.e., routine daily activities) and avoidance of negative behaviors. It also examined findings of demographic subsamples to determine their likelihood of performing recommended health and financial practices. The conceptual base of this study is Self-Determination Theory (SDT), which focuses on conditions that facilitate, versus thwart, processes of self-motivation and healthy psychological development (Ryan & Deci, 2000). Specifically, the theory relates to self-motivated choices that people on their own accord make without external influence or interference. Using SDT as a framework, factors that enhance intrinsic motivation, self-regulation, and well-being have been studied, leading to the postulate of three innate psychological needs—competence, autonomy, and relatedness— which, when satisfied, yield enhanced self-motivation and, when thwarted, lead to diminished motivation and well-being (Ryan & Deci, 2000). SDT has been supported by empirical evidence as a reliable way to predict human behavior. It applies to this study because it stands to reason that people who are internally motivated to improve their health and personal finances will be more likely to make the time for positive practices (e.g., scheduling exercise time) and avoid negative practices (e.g., avoiding high-cost debts).

Hypotheses Based upon previous studies indicating positive associations between health and personal finance behaviors, and relationships between health, personal finances, and demographic characteristics, this study had four hypotheses: H1: Respondents who report frequent performance of health behaviors that involve a time expenditure will more frequently perform financial behaviors that involve a time expenditure.

H2: Respondents who report frequent performance of health behaviors that involve avoiding negative practices will more frequently perform a financial behavior that involves avoiding a negative practice. H3: Male, White, and older respondents and those with higher incomes and educational levels will more frequently perform health and financial behaviors that involve a time expenditure. H4: Male, White, and older respondents and those with higher incomes and educational levels will more frequently perform health and financial behaviors that involve avoiding a negative practice.

Methodology Data Source Data for this study came from a 20-question online quiz that provides a simultaneous assessment of individuals’ health and financial practices (http://njaes.rutgers.edu/ money/health-finance-quiz/). It is believed to be the only publicly available (versus proprietary tools developed by employee assistance programs and workplace wellness firms) online self-assessment tool of individuals’ health and financial practices combined. The online quiz is “advertised” primarily at professional conferences and is used in health and financial education classes taught by educators who become aware of it. It is also available via online searches that lead to the quiz web site and is mentioned in frequent social media messages. Respondents indicated one of four frequencies for their self-assessed performance of health behaviors and financial behaviors. The responses are 1= Never, 2= Sometimes, 3= Usually, and 4= Always. Upon completion of the quiz, respondents received a score for each section (i.e., Health Score and Finance Score), a Total Score, and links to online resources. A high quiz score means that respondents are doing many of the activities that health and financial experts recommend to improve health and build wealth, which increases their likelihood of success.

Sample Data for this study were pulled for analysis in July 2015 on the one-year anniversary of the initiation of the online survey instrument. The researchers chose to do this

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Volume 15, Issue 2

because they anticipate pulling data annually in one-year increments every July for future studies and comparative analyses. Excluding two cases with missing values, 942 observations were used in data analyses. The sample was primarily White (79%) and female (72%) and had a higher educational and income level than typical Americans, with 65% of respondents holding a bachelor’s degree or higher and 71% earning a household income of $50,000 or higher (versus a $51,939 median U.S. income in 2013). Educated White females were clearly over-represented. Only the age of respondents was more evenly distributed among various age ranges. While the online convenience sample of respondents had different characteristics than the general U.S. population, which is a limitation, some sample characteristics (e.g., ethnicity and higher income and educational level) are similar to demographics of financial planning clients (Elmerick, Montalto, & Fox, 2002), making results of this study of interest to advisors.

Variables The scores for Q1 (eat breakfast before starting my day) and Q9 (get at least 30 minutes of aerobic and/or muscle-strengthening physical activity at least 5 days per week) for health behaviors were combined, as were the scores of Q11 (follow a hand-written or computer-generated spending plan) and Q17 (eat at least two meals a day prepared at home instead of eating out) for financial behaviors. All of these items involve following a routine that requires an expenditure of time. While Question 17, on the surface, looks more like a health question than a financial one, it was included in the financial score because preparing meals at home, versus purchasing meals away from home, is a frequently recommended strategy to reduce household spending. For example, cutting $25 a week for 48 five-day work-week meals results in $1,200 of potential savings. Table 1 presents mean scores of health and financial activities. A similar type of analysis was conducted for so-called “avoidance behaviors” on the Personal Health and Finance Quiz. These are items where respondents indicated whether or not they avoided certain practices in accordance with expert recommendations. The mean scores of Q2 (avoid drinking sugar-sweetened beverages) and Q7 (avoid high-calorie salad dressings, gravies, spreads, and/or sauces) were used to measure health avoidance behaviors and Q15 (avoid payday loans, car title loans, pawn shop

45 loans, cash advances, tax refund loans, and other high-cost debt) was used to measure financial avoidance behavior.

Findings: Time Expenditure Behaviors Recall that quiz responses are 1= Never, 2= Sometimes, 3= Usually, and 4= Always. Thus for this part of the study, health and financial scores, for two questions apiece related to practices that require a time expenditure, ranged from 2 to 8. Table 1 presents group differences in health Table 1: Means of Health and Financial Activities Health (Q1 & Q9) Total sample Gender

5.64 (.154)

Financial (Q11 & Q17) 5.30 (1.48)

p=.015

p=.070

Male

5.84(1.56)

5.16(1.57)

Female

5.57(1.52)

5.35(1.45)

Race

p<.001

p<.047

White

5.75(1.52)

5.35(1.47)

Nonwhite

5.24(1.51)

5.11(1.57)

Age

p<.001

p<.001

18–24

5.43(1.68)a

4.86(1.42) a

25–34

5.30(1.50)a

5.38(1.63) b, c

35–44

5.38(1.46)a

5.15(1.47) a, b

45–54

5.91(1.55)b

5.36(1.39) b, c

55 or older

6.00(1.37)b

5.64(1.43) c

P=.003

p<.001

5.32(1.59) a

4.75(1.53) a

$25,000–$49,000

5.45(1.58) a, b

5.14(1.46) a, b

$50,000–$74,999

5.56(1.47) a, b

5.39(1.53) b, c

$75,000–$99,999

5.76(1.59) a, b

5.62(1.43) c

5.88(1.47) b

5.37(1.42) b, c

P=.017

p<.001

5.53(1.60) a, b

4.90(1.45) a

Some college, vocational or associate degree

5.27(1.61) a

5.06(1.47) a, b

Bachelor’s degree

5.68(1.50) b

5.34(1.50) b, c

Graduate or professional degree

5.84(1.46) b

5.56(1.48) c

Income Under $25,000

$100,000 or higher Education High school or lower

Note: Except for places indicating p values, numbers in the table refer to means and numbers in parentheses refer to standard deviations. ANOVA were conducted to examine group differences. For variables with three or more subcategories, superscripts were used to distinguish homogenous subsets. For example, for the daily financial behavior variable, among age groups, scores of three younger subgroups indicated by a were different from those of two older groups indicated by b.


46

Journal of Personal Finance

and wealth behaviors. For the total sample, the health score (5.64) was slightly higher than that of the financial score (5.30). Men had a higher score on the two health activities than women but no gender difference was found in the score on the two financial activities. Whites had higher scores than nonwhites for both health and financial activities. Respondents aged 44 or younger scored lower than those aged 45 or older on health activities and older respondents (55 or older) had higher scores on financial activities than their younger counterparts. The lowest income group had a lower score than the highest income group regarding health behavior. The lowest income group had a lower score on financial behavior than groups with income of $50,000 or higher. For health behavior, the group “some college, vocational, or associate degrees” had a lower score than other education groups. Regarding financial behavior, the lowest education group had a lower score than that of respondents with a bachelor’s degree or higher educational level.

Table 2: Correlations between Health and Financial Behaviors r Total sample .297 Gender Male .245 Female .328 Race White .294 Nonwhite .284 Age 18-24 .279 25-34 .471 35-44 .282 45-54 .241 55 or older .197 Income Under $25,000 .417 $25,000-$49,000 .396 $50,000-$74,999 .281 $75,000-$99,999 .279 $100,000 or higher .179 Education High school or lower .286 Some college, vocational or associate .409 degree Bachelor’s degree .253 Graduate or professional degree .249

p <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 .006 <.001 <.001 <.001 .001 .002 <.001 <.001 <.001 <.001

As shown in Table 2, all of the correlations between the health and financial behaviors that involve time expenditures were positive and statistically significant, but small. Women’s correlation score was higher than men’s. Correlation scores of white and nonwhite respondents are similar. The young adult group (25-34) had the highest correlation while the age group 55 or older had the lowest correlation. Income seemed to have a linear negative association with correlation scores; the lowest income group had the highest correlation and the highest income group had the lowest correlation. Regarding education groups, people having some college, vocational, or associate degrees had the highest correlation, while people with graduate and professional degrees had the lowest correlation. To better understand relationships between health and financial practices and control for various individual characteristics, regression analyses were performed. Without controlling for these factors, researchers cannot draw any conclusions, even for correlations. Table 3 presents results of two-step regression analyses. After controlling for age, family income, and education factors, the score of health behaviors was still positively associated with the financial behavior score at a .001 level of significance with a R2 change of .073, suggesting an additional 7.3% of variance was explained by the health behavior variable. Findings shown in Table 2 and Table 3 provide support for H1: Respondents who report frequent performance of health behaviors that involve a time expenditure will more frequently perform financial behaviors that involve a time expenditure.

Findings: Avoidance Behaviors Table 4 presents mean scores of health and financial avoidance behaviors; i.e., frequency of non-performance of negative practices that experts recommend avoiding. Again, quiz scores ranged from 1 to 4. The health score, based on two variables, was the mean for the two quiz questions. This was done for consistency with the financial variable, which is based on one quiz question. For the total sample, the health score was lower than that of the financial score. Perhaps this is not surprising as 28.3% of Americans are unbanked or underbanked, therefore over 70% have banking relationships, especially those with higher education and income like this sample (Federal Deposit Insurance Corporation, 2012).

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Volume 15, Issue 2

47

Table 3: Results of Multiple Linear Regression on Financial Behavior Activities Model 1 Model 2 B Beta p B (Constant) 4.68 <.001 3.32 Male -0.10 -0.03 0.39 -0.18 White 0.06 0.02 0.61 -0.04 Age: 25–34 0.22 0.06 0.33 0.24 Age: 35–44 -0.06 -0.01 0.81 -0.06 Age: 45–54 0.14 0.04 0.52 0.01 Age: 55 or older 0.41 0.12 0.06 0.27 Income: $25,000–$49,999 0.20 0.05 0.31 0.16 Income: $50,000–$74,999 0.36 0.10 0.06 0.35 Income: $75,000–$99,999 0.59 0.14 <.001 0.54 Income: $100,000 or higher 0.29 0.09 0.12 0.24 Education: some college or associate degree -0.12 -0.03 0.59 0.01 Education: bachelor degree 0.13 0.04 0.52 0.15 Education: graduate or professional degree 0.30 0.10 0.16 0.30 Daily health behavior 0.27

Beta -0.05 -0.01 0.06 -0.01 0.00 0.08 0.04 0.10 0.13 0.07 0.00 0.05 0.10 0.28

p <.001 0.10 0.73 0.27 0.80 0.96 0.20 0.39 0.06 0.01 0.20 0.96 0.46 0.13 <.001

.055 .073 R2 change p <.001 <.001 Note: reference categories: Female, nonwhite, age under 25, income under $25,000, education of high school or lower Table 4: Means of Avoiding Health and Financial Activities (On a Scale of 1–4) Health Financial (Q2 & Q7) (Q15) Total sample 2.80(.77) 3.66(.82) Gender p<.001 p=.029 Male 2.64(.82) 3.57(.90) Female 2.86(.75) 3.70(.78) Race p<.001 p<.001 White 2.87(.75) 3.77(.664) Nonwhite 2.64(.80) 3.25(1.17) Age p<.001 p<.001 18–24 2.36(.75)a 3.15(1.18)a 25–34 2.75(.72)b 3.68(.77)b b 35–44 2.68(.75) 3.77(.65) b 45–54 2.96(.74)c 3.80(.62) b 55 or older 3.12(.67)c 3.87(.55) b Income p<.001 p<.001 3.12(1.21)a Under $25,000 2.36(.78)a $25,000–49,000 2.71(.80)b 3.60(.82)b $50,000–$74,999 2.81(.76)b,c 3.67(.83)b,c $75,000–$99,999 2.79(,73)b,c 3.71(.77)b,c c $100,000 or higher 3.01(.71) 3.86(.52) c Education p<.001 p<.001 High school or lower 2.37(.78)a 3.13(1.18)a Some college, vocational or asso2.81(.78)b 3.51(.98)b ciate degree Bachelor’s degree 2.83(.70)b 3.82(.58)c Graduate or professional degree 2.98(.74)b 3.86(.51)c Note: Except for places indicating p values, numbers in the table refer to means and numbers in parentheses refer to standard deviations. ANOVA were conducted to examine group differences. For variables with three or more subcategories, superscripts were used to distinguish homogenous subsets. For example, for the avoiding financial behavior variable, among age groups, scores of three subsets are different from each other, which are group aged 18–24, 25–34 and 35–44, and 45–54 and 55 or older.

Table 5: Correlations between Health and Financial Avoidance Behavior r Total sample

p .257

<.001

Gender Male

.189

.002

Female

.281

<.001

White

.236

.001

Nonwhite

.227

<.001

18–24

.177

.015

25–34

.147

.064

35–44

.184

.024

45–54

.120

.086

55 or older

.297

<.001

Under $25,000

.240

.010

$25,000–$49,000

.244

.002

$50,000–$74,999

.278

<.001

$75,000–$99,999

.109

.197

$100,000 or higher

.135

.018

High school or lower

.155

.044

Some college, vocational or associate degree

.332

<.001

Bachelor’s degree

.045

.463

Graduate or professional degree

.242

<.001

Race

Age

Income

Education


Journal of Personal Finance

48

correlation score was lower than women’s. The correlation score of Whites was higher than that of nonwhites. Three age groups’ correlations were significant at the 5% level. Among these age groups, the oldest age group had the highest correlation while the next to youngest age group had the lowest correlation.

Women had higher scores than men on the average of the two avoidance health behaviors and the financial avoidance behavior. Whites had higher scores than nonwhites for both health and financial avoidance behaviors. Age-associated health activity scores showed three subsets, the youngest group had the lowest score and two older subgroups (45-54, and 55 or older) had the highest scores. For financial behavior, the youngest group had a lower score than other older groups.

All income groups’ correlations were significant except for the second highest income group. Among the four income groups with significant correlations, the correlation scores showed a hump shape with the middle-income group having the largest correlation score. Three out of four education groups’ correlations were significant. Among the three education groups, people having some college, vocational, or associate degrees had the highest correlation, while people with a high school degree or lower education level had the lowest correlation.

Income-associated health and financial behaviors also showed a linear upward pattern, in which the lowest income group had the lowest scores and the highest income group had the highest scores in both behaviors. Education associated health and financial behavior scores also showed an upward pattern, in which the lowest education group had the lowest score than the other higher educational groups in health behavior. For health behavior, the lowest education group had the lowest score and the two highest education groups had the highest scores.

Again, to control for various individual characteristics, regression analyses were performed. Table 6 presents results of two-step regression analyses. After controlling for age, family income, and education factors, the score of daily health behavior was still positively associated with the daily financial behavior score at a .001 level of significance, with a R2 change of .016, suggesting an additional

Correlations between the health and financial avoidance behaviors are reported in Table 5. For the whole sample, the correlation between health and financial avoidance behaviors was positive and significant, but small. Men’s

Table 6: Results of Multiple Linear Regression on Avoiding Financial Behavior Model 1

Model 2

(Constant)

2.71

Male

0.02

0.01

0.74

2.39

White

0.38

0.18

<.001

0.04

0.02

0.54

Age: 25–34

0.12

0.06

0.28

0.35

0.17

<.001

Age: 35–44

0.22

0.10

0.07

0.11

0.05

0.35

Age: 45–54

0.21

0.10

0.08

0.21

0.09

0.07

Age: 55 or older

0.26

0.14

0.02

0.17

0.08

0.15

Income: $25,000–$49,999

0.23

0.11

0.02

0.20

0.10

0.08

Income: $50,000–$74,999

0.20

0.10

0.05

0.21

0.10

0.03

Income: $75,000–$99,999

0.18

0.08

0.09

0.18

0.09

0.07

Income: $100,000 or higher

0.29

0.17

<.001

0.16

0.07

0.12

<.001 <.001

Education: some college or associate degree

0.11

0.05

0.32

0.25

0.14

0.01

Education: bachelor degree

0.37

0.21

<.001

0.09

0.04

0.42

Education: graduate or professional degree

0.39

0.23

<.001

0.35

0.19

<.001

0.35

0.21

<.001

Health avoiding behavior R2 change p

.174

.016

<.001

<.001

Note: reference categories: Female, nonwhite, age under 25, income under $25,000, education of high school or lower.

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Volume 15, Issue 2

1.6% of variance were explained by the health behavior variable. Findings shown in Tables 5 and 6 provide support for H2: Respondents who report frequent performance of health behaviors that involve avoiding negative practices will more frequently perform a financial behavior that involves avoiding a negative practice. With respect to H3 and H4 regarding relationships between five key demographic characteristics (gender, age, race, income, and education) and the performance of health and financial practices that involve a routine time expenditure and avoidance of negative behaviors, the hypotheses were generally, but not always, supported. For example, there was no gender difference in the score for financial activities that involved a time commitment and women had higher scores than men on the scores for two avoidance health behaviors and the financial behavior. Findings for race, age, education, and income were generally as hypothesized with the latter three categories showing an upward linear pattern in quiz scores.

49

certain negative practices. The first two hypotheses were supported, which is consistent with previous studies that found associations between the health and financial practices of individuals (Gubler & Pierce, 2014; Carr et al., 2015; O’Neill, Xiao, & Ensle, 2016). Findings of demographic subsamples generally indicated that older, White respondents and those with higher incomes and educational levels were more likely to both perform recommended health and financial practices and avoid negative practices than their respective counterparts. Correlation findings, while generally indicating weak to moderate relationships between variables, were all positive, indicating some association between health behaviors and financial practices that involve routine activities and avoidance of negative behaviors. Initial results from correlational analyses were confirmed with multivariate analyses. Women had higher health and finance correlations than men, perhaps indicating greater attention paid to improving both aspects of their lives.

Limitations

Following are four specific implications of this study for financial advisors:

This study used data from an online convenience sample of respondents with different characteristics than the general U.S. population but more similar characteristics to clients of financial advisors. Sample bias toward affluent White females was present. Self-selection bias is also possible if more conscientious individuals who are more interested in personal health and finances than the average person elected to take the online quiz. In addition, survey responses relied on respondents’ self-assessment of the frequency of their performance of certain health and financial management practices. These self-assessments could be biased and differ from an objective assessment made by a neutral third party such as a financial planner. Nevertheless, the findings of this study offer unique insights into relationships between personal health and finance behaviors with data that are not available elsewhere.

Pay Attention to Clients’ Health Habits—This study, as well as others cited in the literature review, provides interesting insights into personal health and finance relationships. For example, clients who are motivated to exercise regularly and carve out at least 30 minutes per day to do so may be more likely than others to have more time for routine financial maintenance activities and actions (e.g., budgeting and preparing lunch at home to reduce household spending). Advisors should also pay attention to clients’ personality traits, such as conscientiousness, which have been found to be stable over time and across various aspects of people’s lives. Clients who indicate by their comments and actions that they are thorough and diligent in performing health-related activities may be easier clients for financial planners to work with than others.

Summary and Implications This study explored relationships between personal health and financial practices that involve time expenditures and those that involve avoidance of negative behaviors. Results indicated weak, but statistically significant, relationships between health and financial behaviors that involve a time commitment and those that involve avoidance of

Provide Helpful Tools and Resources—Financial planners add value to their clients’ lives when they provide useful resources. An example related to the time-intensive, money-saving practice of preparing lunch at home, a quiz item that was part of this study, is the Bankrate.com Lunch Savings Calculator (http://www.bankrate.com/calculators/savings/bring-lunch-savings-calculator.aspx). The calculator illustrates how eating a bag lunch can increase the size of someone’s savings by asking five personalized questions:


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“cost of bagged lunch,” “eating out lunch price,” “number of bagged lunches per month,” “expected rate of return,” and “years to save.” Foster Conscientiousness—It’s no surprise that activities that require effort and attention to detail (i.e., conscientiousness) are associated with successful health and financial outcomes. What is interesting, however, is how transferrable they can become across life domains. Bergland (2011) explained it this way: “Staying committed to a regular exercise regimen and the process of beginning and finishing a prescribed workout rewires your mind to be more conscientious in everything you do. The ‘good habits’ that you become skilled at and reinforce consistently create a mindset. This mindset becomes the way that you approach everything in life.” Automating good habits is a good way to build conscientiousness. By making a positive action routine, it does not require ongoing conscious control. One initial disciplined act (e.g., enrolling in a 401(k) plan) can result in several decades of subsequent financial achievement. Pay Attention to Demographic Differences in Health and Financial Practices—This study found that scores for race, age, education, and income were generally as hypothesized. Thus, nonwhites, younger persons, and those with less education and income had lower scores on the quiz items that were studied and may face additional challenges in practicing positive health and financial behaviors. Financial planners may want to use the Personal Health and Finance Quiz with these clients to increase awareness of recommended practices and provide feedback about their individual health and financial practices.

References

curiosity-and-conscientiousness-more-important-intelligence. Carr, N.A., Sages, R.A., Fernatt, F.R., Nabeshima, G.G., & Grable, J.E. (2015). Health information search and retirement planning. Journal of Financial Counseling and Planning, 26(1), 3-16. Cole, S.A., Paulson, A.L., & Shastry, G.K. (2012). Smart Money: The effect of education on financial behavior. Harvard Business School Working Paper. Retrieved from http:// academics.wellesley.edu/Economics/gshastry/cole-paulson-shastry-financial%20behavior.pdf. Deaton, A. (2003). Health, income, and inequality. National Bureau of Economic Research (NBER) Reporter. Retrieved from http://www.nber.org/reporter/spring03/health.html. Drewnoski, A. & Spector, S.E. (2004). Poverty and obesity: The role of energy density and energy costs. American Journal of Clinical Nutrition, 79, 6-16. Elmerick, S.A,, Montalto, C.P., & Fox, J.J. (2002). Use of financial planners by U.S. households. Financial Services Review, 11, 217-231. Federal Deposit Insurance Corporation (2012). 2011 FDIC national survey of unbanked and underbanked households. Washington, DC: Federal Deposit Insurance Corporation. Retrieved from https://www.fdic.gov/householdsurvey/2012_unbankedreport.pdf. Fisher, P. J. (2010). Gender differences in personal saving behaviors. Journal of Financial Counseling and Planning, 21(1), 14-24. Retrieved from http://afcpe.org/assets/pdf/ volume_21_issue_1/pattiejfisher.pdf.

Amen, D.G. (2012). The secret to longevity is conscientiousness. Next Avenue. Retrieved from http://www.nextavenue. org/daniel-amen-secret-longevity-conscientiousness/.

Garrison, S.T. and Gutter, M.S. (2010). Gender differences in financial socialization and willingness to take financial risks. Journal of Financial Counseling and Planning, 21(2), 60-72. Retrieved from http://afcpe.org/assets/pdf/vol_21_ issue_2_garrison_gutter.pdf.

Anderson, L.R. & Mellor (2008). Predicting health behaviors with an experimental measure of risk preference. Journal of Health Economics, 27(5), 1260-1274. Retrieved from http://www.sciencedirect.com/science/article/pii/ S0167629608000714.

Grable, J.E. (2000). Financial risk tolerance and additional factors that affect risk taking in everyday money matters. Journal of Business and Psychology, 14(4), 625-630.

Bergland, C. (2011). Curiosity and conscientiousness more important than intelligence. Psychology Today. Retrieved from https://www.psychologytoday.com/blog/the-athletes-way/201112/

Gubler, T. & Pierce, T. (2014). Healthy, wealthy, and wise: Retirement planning predicts employee health improvements. Psychological Science, 13(3), 219-224. Retrieved from http://pss.sagepub.com/content/early/2014/06/25/095679 7614540467.abstract.

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Gutter, M.S. & Fontes, (2006). A. Racial differences in risky asset ownership: A two-stage model of the investment decision-making process. Financial Counseling and Planning, 17(2), 64-78. Retrieved from http://afcpe.org/assets/pdf/vol-1726gutter.pdf. Letkiewicz, J.C. & Fox, J.J. (2014). Conscientiousness, financial literacy, and asset accumulation of young adults. Journal of Consumer Affairs, 48(2), 274-300. Manning, J., Hedden, T., Wickens, N., Whitfield-Gabrieli, S., Prelec, D., & Gabrieli, J.D. (2014). Personality influences temporal discounting preferences: Behavioral and brain evidence. Neuroimage, 98, 42-49. Retrieved from http://www. ncbi.nlm.nih.gov/pmc/articles/PMC4142702/. O’Neill, B. (2015). The greatest wealth is health: Relationships between health and financial behaviors. The Journal of Personal Finance, 14(1), 38-47. O’Neill, B., Xiao, J.J., & Ensle, K. (2016). Propensity to plan: A key to health and wealth? Journal of Financial Planning, 29(3), 46-54. O’Neill, B. & Ensle, K. (2014). Small steps to health and wealth: Program update and research insights. The Forum for Family and Consumer Issues, 19(1). Retrieved from https:// ncsu.edu/ffci/publications/2014/v19-n1-2014-spring/oneilensle.php. O’Neill, B. & Ensle, K. (2013). Small steps to health and wealth. Ithaca, NY: PALS Publishing. (Available online at http://njaes. rutgers.edu/sshw/). Perry, V.G. & Morris, M.D. (2005). Who is in control? The role of self-perception, knowledge, and income in explaining consumer financial behavior. Journal of Consumer Affairs, 39(2), 299-313. Rosen, H.S. & Wu, S. (2004). Portfolio choice and health status. Journal of Financial Economics, 72(3), 457-484. Ross, C.E. and Wu, C. (1996). Education, age, and the cumulative advantage in health. Journal of Health and Social Behavior, 37(1), 104-120. Retrieved from http://www.ncbi. nlm.nih.gov/pubmed/8820314. Ryan, R.M. & Deci, E.L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. Retrieved from http://psycnet.apa.org/journals/ amp/55/1/68/.

Shin, S.H. & Hanna, S.D. (2015). Decomposition analyses of racial/ethnic differences in high return investment ownership after the great recession. Journal of Financial Counseling and Planning, 26(1), 43-62. Retrieved from http://afcpe.org/ assets/pdf/volume_26_1/pages_43-62.pdf. Smith, J.P. (1999). Healthy bodies and thick wallets: The dual relation between health and economic status. Journal of Economic Perspectives, 13(2), 144-166. Retrieved from http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC3697076/. Steenbarger, B. (2014). How to build discipline and conscientiousness. TraderFeed. Retrieved from http://traderfeed. blogspot.com/2014/11/how-to-build-discipline-and.html. Werstein, K. M. (2013). An examination of the role of self-control in the health and wealth connection. Unpublished doctoral dissertation, Iowa State University. Retrieved from http://lib.dr.iastate.edu/etd/13116/. Woolf, S.H., Aron, L., Dubay, L., Simon, S.M., Zimmerman, E., & Luk, K.X. (2015). How are income and wealth linked to health and longevity? Washington, DC: Urban Institute and Virginia Commonwealth University. Retrieved from http:// www.urban.org/sites/default/files/alfresco/publicationpdfs/2000178-How-are-Income-and-Wealth-Linked-toHealth-and-Longevity.pdf. Xiao, J.J. (1996). Effects of family income and life cycle stages on household financial asset ownership. Financial Counseling and Planning, 7, 21-30. Retrieved from http:// afcpe.org/assets/pdf/vol-73.pdf. Xiao, J.J. Chen, C., & Sun, L. (2015). Age differences in consumer financial capability. International Journal of Consumer Studies, 39(4), 387-395. Xiao, J.J. & Yao, R. (2014). Consumer debt delinquency by family life cycle categories. International Journal of Bank Marketing, 32(1), 43-59.


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College Student Attitudes toward Retirement Planning: The Case of Mexico and the United States

Janet L. Koposko, Department of Psychology, Oklahoma State University, Stillwater, Oklahoma Martha Isabel Bojórquez, Faculty of Accounting and Administration, Universidad Autónoma de Yucatán, Calle 31 x 35 s/n, Boulevares de Chuburna, 97200 Mérida, YUC, Mexico Antonio Emmanuel Pérez, Faculty of Accounting and Administration, Universidad Autónoma de Yucatán, Calle 31 x 35 s/n, Boulevares de Chuburna, 97200 Mérida, YUC, Mexico Douglas A. Hershey,1 Department of Psychology, Oklahoma State University, Stillwater, Oklahoma

Abstract College students are a population of particular interest when it comes to financial planning for retirement, because they will soon enter the workforce and be asked to make significant decisions that will set the stage for a lifetime of saving practices. In this investigation, college students in the United States (n = 346) and Mexico (n = 345) reported their attitudes, behaviors, and beliefs regarding an array of psychological variables related to financial planning for retirement. We cast the data into two theoretically-based path models—one for each country—and then compared the results. Both models accounted for appreciable variance in expectations of future financial planning. Although models for both groups were structurally similar, path coefficients revealed important cross-national differences in the psychological factors that underlie anticipated future saving practices. The discussion focuses on cultural differences in attitudes and beliefs likely to impact long-range financial planning and saving behaviors.

Keywords retirement, financial planning, cross-cultural, Mexico, United States, saving 1.

The fourth author is indebted to the Netherlands Interdisciplinary Demographic Institute (NIDI) in The Hague and the Netherlands Institute for Advanced Studies (NIAS), both of which provided sabbatical support during the development of this paper.

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Volume 15, Issue 2

Introduction A variety of factors, including economic, demographic, social, psychological, and country-specific structural dimensions, influence how individuals go about the process of financial planning for retirement. Some of these factors have been shown to be more important than others, and the influence these dimensions have on planning practices can vary not only from situation to situation, but also from one individual to the next. One thing, however, seems to remain constant, and that is that most individuals do not adequately plan and save for the post-employment period (Adams & Rau, 2011; VanDerhei & Copeland, 2010; Wiener & Doescher, 2008). A review of the literature suggests that retirement saving challenges in both developed and emerging economies are not at all uncommon. Moreover, researchers have observed an inadequate preparation for retirement (financial and otherwise) in a number of countries around the world—even those with well-developed pension systems (Litwin & Sapir, 2009; Lusardi & Mitchell, 2011a). This state of affairs has led to widespread economic concerns across the globe, one of which is how financially secure individuals will be once they leave the workforce (Lusardi & Mitchell, 2014). Although investigators have published a number of studies on cross-cultural comparisons regarding retirement habits (Alavinia & Burdorf, 2008; Coe & Zamarro, 2011; Hershey, Henkens, & Van Dalen, 2010; Wahrendorf, Dragano, & Siegrist, 2012), no studies to date examine cross-national attitudes toward retirement planning among college students. In the present study, we explore the anticipated retirement planning practices of students in the United States and Mexico, as well as the psychological dimensions believed to underlie individuals’ financial planning expectations. This study stands to make two unique contributions to the literature on financial planning for retirement. The first involves identifying the psychological predispositions to planning among members of this understudied population. By doing so, we seek to determine whether students’ predispositions are consistent with those of already working (non-student) adults. Indeed, a large proportion of college students will enter the workplace soon after matriculating, and shortly thereafter, they will be asked to make thoughtfully-considered decisions about their involvement in employer-sponsored retirement saving

53

programs. By understanding how students are likely to think about their involvement in such programs, it should be possible to construct age-appropriate interventions designed to stimulate proactive planning and saving practices. A second contribution involves what can be learned from the cross-national comparison we carry out. It is altogether possible that culturally-based attitudes toward retirement and differences in the retirement financing systems in Mexico and the United States will differentially shape students’ perceptions of the importance of planning for the future. Thus we carefully consider not only cross-national differences in the strength of the psychological predispositions that underlie the planning process, but also the extent to which those predispositions differentially predict the anticipated likelihood of planning.

Retirement Preparation: A CrossCultural Perspective Previous investigations have revealed cultural differences in planning for later life, demonstrating the need to explore retirement as a specific area of interest with regard to culture. For example, Hershey, Henkens, and Van Dalen (2007) compared the retirement attitudes of employees living in two different countries, the United States and The Netherlands. They found that individuals in The Netherlands perceived their retirement savings as more adequate compared to individuals in United States. Surprisingly, however, the results also revealed that employees in The Netherlands had lower levels of retirement goal clarity and engaged in fewer retirement planning activities compared to working adults in the United States. Along similar lines, Imamoglu, Kuller, Imamoglu, and Kuller (1993) carried out a study on attitudes toward retirement and aging using two samples of respondents, one drawn from Sweden and one drawn from Turkey. The authors found that relative to individuals from Sweden, members of the Turkish sample established larger social networks and engaged in more frequent social interactions as they approached retirement. Despite this fact, Turkish respondents were more likely to report negative attitudes toward aging, higher levels of loneliness, and lower levels of life satisfaction in retirement compared to Swedish respondents. The fact that previous cross-cultural studies of retirement have led to some unanticipated findings suggests that retirement planning practices among different cultures is not yet well understood and should be subject to further inquiry. One finding


54

Journal of Personal Finance

that has remained consistent, however, is that individuals in many countries around the world fail to adequately plan and save for retirement (Aegon, 2014). Nearly 25 million Americans over the age of 60 are economically insecure—living at or below 250% of the federal poverty level (National Council on Aging, 2015). Consistent with this finding, the Social Security Administration (2012) found that 36% of single retirees over the age of 65 depend on Social Security for all or most of their monthly income. In a study conducted by the Employee Benefit Research Institute (Helman, Copeland, & VanDerhi, 2015), only 22% of adults in the United States felt they would have enough money to live comfortably in retirement. Over half of respondents reported their net worth (not including housing equity) was under $25,000. Although housing equity is a principle asset for a large fraction of Americans, it seems that it is not typically viewed by homeowners as a general source of wealth that will support consumption during retirement. Based on data from the Health and Retirement Study, Venti and Wise (2002) concluded that home equity is typically used as a retirement asset in only a small fraction of cases, and then, primarily late in retirement following a serious “family shock” (i.e., death of a spouse or serious illness). For cases in which housing equity is converted to a liquid asset to support retirement consumption, this typically happens quite late in life (Poterba, Venti, & Wise, 2011) and usually in the form of a reverse mortgage (Sanai & Souleles, 2007). Retirees in Mexico face a similarly troubling retirement income situation. The Organisation for Economic Co-operation and Development (OECD) reports that Mexico has the highest old-age poverty rate among the countries they track, with 26% of adults over 65 living below the poverty level (OECD, 2013). In fact, an industry study revealed that 67% of Mexican workers are concerned they will have to work full or part-time during retirement in order to make ends meet, and 63% have concerns about outliving their retirement funds (MetLife, 2013). Indeed, an appreciable number of Mexican adults expect to rely on the support of family members in old age instead of saving for retirement (Rodriguez-Flores & DeVaney, 2006). Unfortunately, there is little to suggest that working adults in the United States and Mexico are actively involved in the planning process as a way of staving off retirement income insecurity. An investigation by Brucker and Leppel

(2008) queried American adults over the age of 43 about their plans for retirement. Fewer than half of those surveyed reported they either had a plan for managing their finances in retirement or had set a specific savings goal. Along similar lines, Lusardi and Mitchell (2011b), using data from the Health and Retirement Study, found that only about one-third of households (31%) had ever attempted to calculate how much they would need to finance their retirement. A comparable lack of financial planning for retirement has been seen in Mexico, where fewer than one in four working Mexican adults have taken any steps to calculate their future retirement needs (MetLife, 2007). Yet, that same study revealed that just over half of Mexican employees indicated that they planned to retire between the ages of 51 and 60. This is surprising given that only 12% of respondents reported they are “on track” or “have achieved” their retirement savings goal. Troublingly, some 30% of Mexican citizens do not feel responsible for ensuring a financially comfortable retirement (Principal Financial Group, 2005), choosing instead to defer that responsibility to the state. It goes beyond the scope of this paper to provide a detailed synopsis of the retirement support systems in Mexico and the United States, but suffice it to say that retirees in both countries rely on the three-pillar system of retirement income support (World Bank, 1994). Within this system, the first pillar consists of publicly or privately managed state funds (i.e., social security), the second consists of employer-sponsored occupational pensions, and the third consists of personal savings (Whitehouse, 2007). For better or worse, shifts in pension financing over the past two decades have created a situation in which “retirees are now left to balance on a one-legged stool [of personal savings] as decreasing public and employer benefits shift the greater share of the responsibility onto the individual” (Natixis, 2015, p. 8). For that reason, it will be critically important for current (and future) working adults to play an active role in not only saving for the post-employment period, but also managing their own resources in the years prior to their departure from the workforce. The following section of the paper outlines a number of psychological determinants of retirement saving practices among working adults, with an eye toward illuminating how those same factors might shape college students’ expectations of planning once they enter the workforce.

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Volume 15, Issue 2

Psychological Predispositions Toward Planning We tested a path model as part of this investigation in which four different determinants of students’ expectations of financial planning are examined: financial knowledge, retirement goal clarity, future time perspective, and the extent to which one’s parents have encouraged saving for the future. In previous research, each of these constructs has been shown to predict planning and saving tendencies among working adults. Each of the four is briefly described below. Financial knowledge. Comparative cross-national data on financial knowledge are sparse in the primary research literature. However, findings from the Visa International Financial Literacy Barometer Survey (Visa, 2012) suggest Mexican children and young adults are relatively financially well informed. One question in the survey asked: “To what extent would you say that teenagers and young adults in (Country) understand money management basics and are adequately prepared to manage their own money?” Mexican respondents earned a score of 47.8 out of 100, ranking fifth in a field of 28 countries surveyed. Respondents in the United States earned a score of 18.5, leading to a ranking of 27th among the countries studied. Perhaps the important take-away message is that there is room for improvement in both countries, given that scores in all countries were relatively low. Consistent with this assertion, a study by Hastings and Tejeda-Ashton (2008) found basic financial knowledge to be seriously lacking among Mexican adults, which parallels a result reported by Lusardi and Mitchell (2014) who found knowledge levels in the United States to also be suboptimal. However, a recently released cross-national examination of financial literacy involving 140 countries (Klapper, Lusardi, & Van Oudheusden, 2015) contradicts these findings, showing that the percentage of financially literate adults in the United States is 57%, compared to 32% in Mexico. Retirement goals. Our review of the literature failed to identify any studies that have compared levels of retirement goal clarity between individuals living in the United States and Mexico. However, the literature on cultural differences in goals suggests that individuals in the United States may score higher on this dimension than individuals in Mexico. In one review (Oettingen, Sevincer, & Gollwitzer, 2008), the authors discuss differences in goals between

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individualist (i.e., the United States) and collectivist (i.e., Mexico) cultures. Individuals who live in individualist cultures are more likely to have goals related to social independence and personal success. We conclude from that finding that workers in the United States would be more likely to have stronger goals related to financial independence (social independence), and accordingly, set goals to achieve financial freedom. Work by Briley and Aaker (2006) is suggestive of another reason why individuals in the United States might be likely to rank higher along the retirement goal clarity dimension than individuals in Mexico. In countries with developing economic systems (e.g., Mexico), personal safety and preventing danger from occurring to oneself and family members is a top goal, whereas in economically developed nations (e.g., the United States), personal safety needs have largely been satisfied and individuals are more likely to devote time and effort to goals related to future achievements. Future time perspective. In the literature, investigators have characterized future time perspective as the extent to which one enjoys thinking about the future (Hershey & Mowen, 2000). Research suggests that individuals in the United States tend to have an orientation toward the future, whereas individuals in Mexico tend to be oriented toward the present (Kluckhohn & Strodtbeck, 1961; Spears, Lin, & Mowen, 2000). These differences in time orientation may be due to cultural conceptualizations of time as being linear in the United States and circular in Mexico (Graham, 1981; Jones, 1988). Recent published findings from Earl, Bednall & Muratore (2015; see also Yang & Devaney, 2011) suggest that one’s orientation to time is predictive of retirement planning tendencies. However, an investigation by Petkoska & Earl (2009) failed to demonstrate a relationship between time perspective and planning activities. Early family influences. Individuals who work in the area of family processes have suggested that the way children and young adults are socialized by family members (and particularly parents) has an effect on an offspring’s financial decisions (Gudmunson & Danes, 2011; Payne, Yorgason, & Dew, 2014). Parents not only serve as role models, but they are in a position to cultivate forward-thinking attitudes when it comes to financial behavior. One question on the Visa Financial Barometer Survey (Visa, 2012) asked individuals how often they talk with their children about money management issues. On a scale of 1 to 100, Mexico ranked first on this dimension among the 28 countries studied,


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with a score of 80.2. The United States, in contrast, ranked 6th with a score of 49.7. Researchers determined that Mexicans talk to their children about money roughly 41.7 days out of the year (a comparable value was unreported for the United States). A similar question on the survey queried respondents about their financial literacy training in schools, and again, Mexico outranked the United States.

Present Investigation One purpose of the present study is to examine the extent to which college students in the United States and Mexico differ in their retirement planning expectations. We anticipate that cultural factors will lead to mean differences in retirement expectations, and perhaps the psychological dimensions that underlie those expectations. The second purpose of this study is to test a hierarchically-structured path model for students that is formulated based on findings from existing studies of the retirement planning practices of working adults. Toward this end, we will estimate the model shown in Figure 1 separately for students from Mexico and the United States. As seen in the figure, expectations of financial planning for retirement will serve as the dependent variable, and the four psychological variables described above (financial knowledge, goal clarity, future time perspective, and parental influences on saving) will be cast as predictors. Hypotheses. Culture can be thought of as representing collectively held values and beliefs among a group of people (Hofstede, 1980/1981). Therefore, to the extent that individuals from Mexico and the United States form their beliefs on the basis of different values, it is likely that the

magnitude of scores for the different variables in the study will differ cross-nationally. For that reason, the first empirical goal was to probe for mean differences in expectations of financial planning for retirement, as well as the psychological dimensions that underlie anticipated planning practices. Given the particularly strong cultural emphasis on retirement preparation in the United States (Ekerdt, 2004), we predicted that American students’ scores for the expected financial planning construct would be larger than those of Mexicans. In contrast, based on findings from the Visa Financial Barometer Survey (Visa, 2012) described above, we anticipated that mean scores for the financial knowledge and parental influences on saving dimensions would be larger for students from Mexico. Furthermore, given cultural values surrounding individuals’ retirement goals in the United States and in Mexico, we posited scores on the retirement goal clarity dimension would be larger for students from the United States. Finally, the strong future orientation of individuals in the United States relative to the present orientation of individuals in Mexico (Spears, et al., 2000) suggests that Mexican students’ scores would be lower on this dimension than those of students in the United States. The second empirical goal was to test the path analysis model depicted in Figure 1. Findings from existing studies reveal that a number of the constructs in the model are related to one another; importantly however, investigators have not studied these relationships among college students, nor have they comparatively studied these relationships between individuals from Mexico and the United

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Volume 15, Issue 2

States. Consistent with previous findings, we anticipated financial knowledge would be predictive of expectations of future financial planning activities (H1; Koposko & Hershey, 2014; Noone, O’Loughlin, & Kendig, 2012; Van Rooij, Lusardi, & Alessie, 2011; Van Rooij, Lusardi, & Alessie, 2012). The second hypothesis suggests retirement goal clarity will account for variability in financial knowledge (H2). This relationship has already been demonstrated among a sample of American adults (Hershey, Jacobs-Lawson, McArdle, & Hamagami, 2007), and a different investigation found that teenagers with strong financial goals actively sought out financial information relative to those with weak financial goals (Koonce, Mimura, Mauldin, Rupured, & Jordan, 2008). We also expected, on the basis of previous findings, that future time perspective would be related to financial knowledge (H3; Gutierrez & Hershey, 2014; Hershey et al., 2010; Hershey et al., 2007; Noone et al., 2012). Furthermore, we hypothesized that future time perspective would be related to goal clarity (H4; Hershey et al., 2007; Koposko & Hershey, 2014) and parental influences on saving would be significantly linked to future time perspective (H5; Gutierrez & Hershey, 2014; Koposko & Hershey, 2014; Wilkins, 2010). Finally, we anticipated that parental influences on saving would significantly predict financial knowledge (H6; Akben-Selcuk & Altiok-Yilmaz, 2014; Gutierrez & Hershey, 2014; Koposko & Hershey, 2014; Payne et al., 2014). We expected that all beta coefficients in the path models would carry positive valences.

Method Participants Study participants (N = 691) were college students from Mexico (n = 345) and the United States (n = 346). The average age of participants was 20.61 years with a standard deviation of 2.89 years, with students from Mexico being 1.72 years older than those from the United States (MMEX = 21.47; SD = 1.84; MUS = 19.75; SD = 3.44; t(689) = 8.19, p = .04). Some 56.3% of the sample was female, with somewhat more female students in the U.S. sample (United States: 61.3%; Mexico: 51.3%; χ2 = 6.98, p < .01). Marital status demonstrated little variability as is characteristic of college student samples; overall, 94.9% of participants reported being single (United States: 93.6%; Mexico: 96.5%; χ2 = 0.43, ns).

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Procedure We obtained the sample from the United States from a larger, more comprehensive study that focused on college students’ perceptions of and attitudes toward retirement (N = 722; Koposko & Hershey, 2014). At the time of testing, all American students—who represented a wide range of majors—were enrolled in a course in psychology or communication sciences at a large Midwestern university. All individuals received partial course credit for completing the retirement questionnaire online. Research assistants in Mexico collected data from students enrolled at a large university located on the Yucatan Peninsula. They too represented a wide range of majors; most were taking classes in business administration, accounting, and finance. Each Mexican respondent completed a paper-and-pencil version of the questionnaire. Both the questionnaire and procedures for this investigation were subject to appropriate Institutional Review Board scrutiny. In both countries, the questionnaire took about seven minutes to complete. Prior to conducting the data analysis, we recognized the possibility that differences in sample size across the two groups could lead to significant effects in the path model for one group (with the larger N) but not the other, given equivalent parallel beta coefficients. Our concern was that readers might interpret comparable effects for the two groups as being differentially important. To remedy this situation, prior to analysis we removed cases from the larger American sample using the SPSS random case selector until the sample was reduced to 346 individuals—which is roughly the same size of the Mexican sample. By doing so, we were able to equate statistical power levels across the Mexican and American models, and thus, the ability to detect significant parametric effects.2

2.

To verify that this sample size reduction procedure did not result in biased beta coefficient values, we also estimated a path model for the full American sample (N = 722). We then compared model parameters from that analysis to those from the model using the reduced (N = 346) set of respondents. We observed negligible differences in beta weights for all parallel path coefficients across the two American models, which suggests that the sample reduction procedure did not result in biased parameter values for the reduced sample set.


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Table 1 Descriptive Statistics for Members of the Two Groups. All Bivariate Correlations are Positively Related to One Another at the .01 level. 1

American Students 1. Expectations of Financial Planning

2

3

4

5

1.00

2. Financial Knowledge

.38

1.00

3. Retirement Goal Clarity

.35

.77

1.00

4. Future Time Perspective

.53

.26

.29

1.00

5. Parental Influences on Saving

.35

.32

.25

.43

1.00

Mean

5.51

3.65

3.82

5.69

5.52

(SD)

(1.12)

(1.54)

(1.50)

(1.09)

(1.34)

1

Mexican Students 1. Expectations of Financial Planning

2

3

4

5

1.00

2. Financial Knowledge

.22

1.00

3. Retirement Goal Clarity

.35

.60

1.00

4. Future Time Perspective

.47

.31

.49

1.00

5. Parental Influences on Saving

.53

.30

.33

.41

1.00

Mean

5.53

3.73

4.31

5.10

5.21

(SD)

(1.07)

(1.48)

(1.67)

(1.15)

(1.32)

Measures The questionnaire contained the five multiple item psychological scales described below. All questions employed a 7-point (1 = strongly disagree; 7 = strongly agree) Likert-type response format. The psychometric properties we report below are for all study participants; separate means and standard deviations for members of the two groups are shown in Table 1. Expectations of financial planning for retirement. This 2-item measure (M = 5.52; SD = 1.09) developed by Koposko and Hershey (2014) was specifically designed to be used with high school and college students. The measure assesses expectations of how challenging individuals will find the task of retirement planning once they enter the workforce. We eliminated one item from the original 3-item version of the scale in order to improve internal consistency reliability and the fit of the measurement model. A sample item from this scale is, “Success at financial planning for retirement will be something that will come easily to me.” We observed a unitary factor structure for the scale; the

Spearman-Brown reliability coefficient (i.e., the optimal coefficient for a 2-item scale; Eisinga, Grotenhuis & Pelzer, 2013) was adequate at 0.77. Higher mean scores on this measure indicate the expectation of minimal difficulties in carrying out future financial planning tasks. Self-reported financial knowledge. This 3-item scale (M = 3.69; SD = 1.52), designed by Hershey et al. (2010), is a perceptual measure of financially-oriented retirement planning knowledge. A sample item is, “I know more than most people about retirement planning.” Psychometric evaluation of the measure revealed a single factor structure and a coefficient alpha level of 0.88. Higher mean scores on the measure indicate higher levels of self-rated financial knowledge. Retirement goal clarity. This 5-item scale (M = 4.07; SD = 1.36), developed by Stawski, Hershey, and Jacobs-Lawson (2007), is designed to measure the extent to which individuals report thinking about and setting specific goals for retirement. A sample item is, “I have a clear vision of how life will be in retirement.” Psychometric evaluation of the

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Volume 15, Issue 2

measure revealed a single factor structure and a coefficient alpha level of 0.88. We associate higher mean scores on this item with a greater degree of retirement goal clarity. Future time perspective. This 5-item scale (M = 5.40; SD = 1.08), developed by Hershey et al. (2007; see also Koposko & Hershey, 2014), was designed to measure the extent to which individuals are prone to and enjoy thinking about the future. A sample item is, “I enjoy thinking about how I will live years from now in the future.” Psychometric evaluation of the measure revealed a single factor structure and a coefficient alpha level of 0.84. Higher mean scores indicate a greater tendency toward future-oriented thinking. Parental influences on saving. This 3-item scale (M = 5.37; SD = 1.34), developed by Koposko and Hershey (2014), was designed to assess the effect one’s parents had on money management and saving practices. We eliminated one item from the original 4-item scale in order to improve the fit of the measurement model. A sample item is, “My parents had a strong influence on my current opinions about saving.” Psychometric evaluation of the measure revealed a single factor structure and a coefficient alpha level of 0.80. Higher mean scores indicate a stronger positive parental influence on saving. In addition to the measures described above, each participant also reported his or her age, gender, and marital status. Individual items for all scales are shown in the Appendix.

Measurement Model We computed a full measurement model using AMOS v.21 (IBM, 2012) to ensure that the factor structure of the scales were as hypothesized and there were no substantial item cross-loadings. The initial model fit was adequate, χ2(160) = 608.65, p < .01, GFI = .91, AGFI = .88, NFI = .92, TLI = .93, CFI = .94, RMSEA = .06. Modification indices suggested the fit could be improved by deleting two items: “Financial planning for retirement is something that will come easily to me” (Expectations of Financial Planning for Retirement Scale) and “My parents made sure I understood that money was a limited resource” (Parental Influences on Saving Scale). Deletion of these two items appreciably improved overall model fit, χ2(123) = 339.53, p < .01, GFI = .95, AGFI = .93, NFI = .95, TLI = .96, CFI = .97, RMSEA = .05. There were no substantial cross-loadings in the revised measurement model.

59

Results Mean Differences Between Nationalities Prior to testing our a priori hypotheses, we cleaned the data and examined distributions for skew, kurtosis, outliers, and any other possible distorting conditions that might violate the assumptions of general linear model analyses. We found no distributional aberrations in this regard. We then compared mean scores across nationalities for each of the five scales in the study. Planned comparisons revealed no significant mean differences between students from Mexico and the United States for two variables: expectations of financial planning for retirement and self-reported financial knowledge. Means revealed that students from the United States and Mexico both had high expectations of the likelihood of future planning (values of 5.51 and 5.53, respectively, on the 7-point scale) and moderate levels of financial knowledge (3.65 and 3.73, respectively). Contrary to expectations, however, Mexican students had significantly higher levels of goal clarity on average, compared students from the United States, t(689) = 4.88, p < .01, d = 0.31. Consistent with predictions, relative to Mexican students, respondents from the United States had significantly higher scores for future time perspective, t(689) = 7.53, p < .01, d = 0.53. Finally, in contrast to what we had hypothesized, parental influences on saving scores were higher for individuals from the United States, t(689) = 3.18, p < .01, d = 0.23. We report mean scores and standard deviations for all scales as a function of nationality in Table 1. As part of our analysis examining group differences in mean scores, we probed for effects within nationalities as a function of gender.3 Gender differences were evident along two dimensions for members of the sample drawn from the United States: future time perspective (t[344] = 4.56, p < .01, d = 0.50), and financial knowledge (t[344] = 3.31, p < .01, d = 0.37). For members of the Mexican sample, only a gender difference in future time perspective was significant, t(343) = 2.20, p < .05, d = 0.24. For members of both nationalities, females had a significantly longer future time

3.

We did not carry out mean score comparisons for other sociodemographic dimensions—notably, age and marital status—because of the limited range of values for these two variables.


Journal of Personal Finance

60

perspectives than males; in the U.S. sample, males had a higher level of financial knowledge than females. Path Analysis Comparison of the Models For the next step in the analysis process, we tested the hypothesized partial mediation model outlined in the introduction, using conventional path analysis techniques. Accordingly, we calculated the model shown in Figure 1 separately for members of each group, which involved the computation of four hierarchical regression equations for each path model. In the first of the four regressions, we regressed expectations of financial planning for retirement scores on financial knowledge (level one), retirement goal clarity (level two), future time perspective (level three), and parental influences on saving (level four). For the remaining regression equations, we changed the dependent variable according to the order displayed in Figure 1, such that for the second equation financial knowledge served as the criterion, for the third goal clarity was the criterion, and for the last (flat) regression future time perspective was the dependent variable. To distinguish between statistically significant and empirically meaningful paths (cf., Kirk, 1996), if an observed path coefficient carried a standardized beta weight between -0.15 and +0.15, we omitted it from the path diagram. In the path model for college students from the United States (see Fig. 2), the overall hierarchical regression for expectations of financial planning for retirement was

statistically significant, F(4, 341) = 46.69, p < .01, R2 = .35. In support of H1, financial knowledge was found to be positively related to expectations of financial planning for retirement (p < .01). Contrary to our predictions, however, future time perspective also emerged as a significant indicator of planning expectations at the .01 level and the relationship between retirement goal clarity and expectations of future planning demonstrated a trend (p < .10). The second hierarchical model predicting financial knowledge was also significant (F[3, 342] = 176.98, p < .01), accounting for 61% of the variance in the criterion. In support of H2 and H6, both retirement goal clarity and parental influences on saving exhibited significant effects at the .01 level. Contrary to expectations, however, the effect we hypothesized for future time perspective predicting financial knowledge (H3) failed to exceed the significance threshold. The third regression model captured far less variability in retirement goal clarity, yet the overall model was significant, F(2, 343) = 19.99, p < .01, R2 = .10. Hypothesis four was supported at the .01 level, which was the path from future time perspective to the retirement goal clarity indicator. An unexpected contribution to the goal clarity criterion involved a significant path that emanated from parental influences on saving. The fourth and final regression model for members of the sample from the United States was also statistically significant, F(1, 344) = 79.48, p < .01. In support of H5, we found parental influences on saving to have a moderately strong effect on future time

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Volume 15, Issue 2

perspective, accounting for 19% of the variability in the criterion. The path model for students from Mexico (see Fig. 3) was nearly structurally identical to the model for students from the United States. Among Mexicans, the overall hierarchical regression for expectations of financial planning for retirement was statistically significant, F(4, 340) = 49.35, p < .01, with 36% of the variability captured in the criterion. The impact of financial knowledge on planning expectations (H1) was supported, in the company of three other non-hypothesized paths. Against predictions, goal clarity, future time perspective, and parental influences on saving all exhibited moderately strong influences on future retirement planning expectations (all three paths p < .01). The subsequent model predicting financial knowledge also captured appreciable variance, F(3, 341) = 66.27, p < .01, R2 = .36. Hypothesis two and H5 found support in this model (both p < .01), but H3—the link between future time perspective and financial knowledge—failed to cross the significance threshold for Mexican students, as was the case in the model for individuals from the United States. The overall hierarchical regression for retirement goal clarity captured 26% of the variability in the criterion, F(2, 342) = 60.94, p < .01. The model supported H4 (future time perspective to goal clarity; p < .01) and it also showed a weak unexpected path from parental influences on saving to retirement goal clarity. The final bivariate regression of future time perspective on parental influences on saving

61

also exceeded the significance threshold, F(1, 343) = 70.26, p < .01, accounting for 17% of the variance in the time perspective construct. In addition to computing the path models reported above, we calculated cross-national slope comparisons (z-tests) to determine which parallel paths statistically differed from one another. In Figures 2 and 3, dashed pathways are those in which there was a significant difference in slope coefficients across countries. Solid paths, in contrast, indicate slope coefficients that failed to differ from one another. As seen in the figures, six of the nine paths revealed cross-national effects at the .05 level. In descending order of magnitude, parallel paths that revealed significant effects were: parental influences on saving to expectations of financial planning for retirement (βdiff = 0.31), future time perspective to retirement goal clarity (βdiff = 0.20), goal clarity to expectations of financial planning (βdiff = 0.20), goal clarity to financial knowledge (βdiff = 0.17), financial knowledge to expectations of financial planning (βdiff = 0.16), and future time perspective to expectations of financial planning (βdiff = 0.07).

Discussion The goal of this study was to compare expectations of future financial planning for retirement between American and Mexican college students. To inform the theoretical basis of the study, we consulted the literature on the planning practices of working adults, which has identified a number of psycho-motivational dimensions associated


62

Journal of Personal Finance

with the retirement planning process. Students in both countries were highly (and equally) confident that they would engage in financial planning practices once they entered the workforce; however, mean differences were observed for three of the four constructs believed to underlie planning. Furthermore, based on the path model analyses we were able to account for an appreciable amount of variance in future planning expectations among students in both Mexico and the United States. These findings are worth noting, as no previous investigations have directly explored students’ perceptions of this issue. We identified significant mean differences between samples for three dimensions—future time perspective, retirement goal clarity, and parental influences on saving. As predicted, students from the United States had higher levels of future time perspective than students from Mexico (Graham, 1981; Jones, 1988; Spears et al., 2000). This reflects the strong cultural bias toward a future orientation to time in the United States, and a more circular (present) orientation to time in Mexico. Contrary to expectations, however, we found that students from Mexico had higher levels of retirement goal clarity, and students from the United States had higher levels of parental influences on saving. A possible explanation for the former finding is that for individuals living in Mexico, higher levels of goal clarity are needed as a way of dealing with the lack of clear and successful economic goals experienced by most members of the population (MetLife, 2007). Our finding that Mexican college students reported lower levels of parental influences on saving ran counter to what we expected based on findings from the 2012 Visa Financial Barometer survey. One should not dismiss the outcome of the present investigation regarding parental influences, however, as it is based directly on students’ perceptions of parental support. In contrast, parental encouragement for saving in the Visa survey was based on Mexicans’ perceptions of the frequency of parents’ savings discussions with their children. This difference in outcomes is informative as it suggests a disconnect between Mexican adults’ perceptions of general parent-child interactions (i.e., the 2012 Visa Barometer Survey) and the first-person reports of interactions adult students had as children with their own parents (i.e., the present study). The lack of cross-national mean differences with respect to expectations of financial planning and financial knowledge are also intriguing. High mean scores for the former dimension suggests that students in both countries are

acutely aware of the need for them to engage in planning activities as a way of adequately supporting themselves in old age. Whether today’s college students actually end up planning and saving for the future once they enter the workforce will be an important issue to explore in future investigations. The lack of a hypothesized cross-national mean difference in self-rated financial knowledge is also interesting, but it needs to be viewed in the context of scores that were in the mid-range of the self-reported financial knowledge scale. That is, students in both countries were only moderately confident in the quality of their financially-based knowledge structures, which is a finding that is consistent with the work of Lusardi and Mitchell (2014) and Hastings and Tejeda-Ashton (2008). Indeed, there is room for improvement when it comes to the need for financial education and financial literacy training in both the United States and Mexico. It was surprising to see that few gender differences emerged among study variables, in light of the fact that males tend to outperform females in matters related to financial planning and financial literacy in most advanced and developing economies (Klapper et al., 2015; Lusardi & Mitchell, 2011c; OECD, 2014). The general lack of gender differences in this study provides some hope that gender parity is increasing among young adults, at least, when it comes to individuals who reside in Mexico and the United States. The path model analyses were also theoretically informative, as they help to explain the drivers of students’ perceptions of future financial planning practices. Structurally, the models we observed for students in the two countries were comparable. That is, for the most part, the paths that emerged as statistically significant in one country were also significant in the other. In the four paths that make up the core of the path model (Fig. 1) we posited parental influences on saving would predict future time perspective, future time perspective would predict retirement goal clarity, retirement goal clarity would predict financial knowledge, and financial knowledge would predict financial planning expectations. Each of these paths emerged as statistically significant in both student models. Moreover, in both models, additional non-hypothesized paths needed to be added between retirement goal clarity and expectations of financial planning for retirement, and future time perspective and financial planning expectations. These findings are potentially

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Volume 15, Issue 2

important inasmuch as they signal psychomotivational influences on study variables not previously identified in comparable research among working adults. Despite comparable model structures, we identified parametric differences (i.e., differences in slope parameters) between the models for students from Mexico and the United States. In fact, six of the nine pathways in the models revealed differences in slopes. These differences in beta weights translated into appreciable differences in explained variance for financial knowledge and retirement goal clarity. The cross-national difference in R-squared values for financial knowledge was primarily driven by a differential impact of goal clarity on knowledge, a pathway that had a stronger influence on the criterion among college students in the United States. Thus, although Mexican students had higher mean retirement goal clarity scores, the goal clarity scores for students from the United States were more closely linked to their financial knowledge assessment. The other endogenous indicator that revealed a noteworthy difference in R-squared values was retirement goal clarity, where we observed a 16% difference in explained variance. This differential effect was due to the moderately strong impact future time perspective had on goal clarity among members of the Mexican sample, not witnessed among students from the United States. Also worth noting is the direct effect parental influences on saving had on expectations of financial planning for retirement among Mexican students, not evident among students in the United States. This differential effect is intriguing inasmuch as it suggeststhat in the United States, parental lessons learned regarding saving are mediated by a series of intervening psychological constructs. However, in Mexico, the nature of parental lessons learned are such that they capture unique variance in future planning expectations, not otherwise explained by the other psychological predictors. From a public policy perspective, it is encouraging to see findings that indicate strong expectations of future financial planning among students in the two countries. It suggests young adults in the United States and Mexico may have been swayed by informational campaigns designed to increase public awareness of the need for individual involvement in the financial planning process (Hogarth, 2012), such as campaigns carried out by the Jump$tart Coalition for Personal Financial Literacy (McElrath, 2015), the National Endowment for Financial Education (NEFE, 2015) (both in the United States), and a major informational and financial training campaign in Mexico sponsored by

63

the World Bank (Bruhn, Ibarra, & McKenzie, 2014). Caution is warranted, however, in assuming that studentsâ&#x20AC;&#x2122; expectations will actually translate into adaptive planning and saving behaviors. Yet, to the extent that intentions have been found to be one of the best predictors of future behavior (Ajzen, 1991; Quellette & Wood, 1998), we are cautiously optimistic that the next generation of workers in the two countries will be more involved in voluntary saving practices than their predecessors. We hasten to acknowledge certain limitations associated with this study. One is that due to the paucity of literature on college students and retirement, we based our initial hypotheses on the studies of adults in an older age range. Additional future investigations that focus on pre-employment populations would serve to remedy this gap in the literature. A second limitation is that our data were exclusively correlational in nature, that is, we introduced no experimental manipulations. The findings from this investigation could serve to inform future experimentally-based intervention studies designed to increase financial knowledge, goal clarity, and future time perspective. Individuals who complete an intervention program could then be compared to individuals who did not, to determine whether the general pattern of effects observed in this study are similarly witnessed in an experimental setting. One other possible future direction would be to examine the retirement planning practices of students in other nations with developing economies, where the burden of old age financial security is in part carried by family members and members of the community (the so-called â&#x20AC;&#x153;fourth pillarâ&#x20AC;? of retirement support; World Bank, 2008). A study such as this could lead to a more comprehensive theoretical understanding of how individuals from around the globe envision alternative pillars of financial support in retirement.

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planning. Psychology and Aging, 24, 245-251. doi: 10.1037/ a0014096 Poterba, J., Venti, S., & Wise, D. (2011). The composition and drawdown of wealth in retirement. Journal of Economic Perspectives, 25(4), 95-118. doi: http://dx.doi.org.argo. library. okstate.edu/10.1257/jep.25.4.95 Principal Financial Group (2005). The global financial well-being study. In C. A. Crabbe (Ed.), A quarter century of pension reform in Latin America and the Caribbean: Lessons learned and next steps (pp. 375-404). New York: Inter-American Development Bank. Quellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54-74. doi:10.1037/0033-2909.124.1.54 Rodriguez-Flores, A., & DeVaney, S. A. (2006). Amount and sources of income of older households in Mexico. Financial Counseling and Planning, 17, 64-72. Retrieved from: http:// papers.ssrn.com/sol3/papers.cfm?abstract_id=2239385 Sanai, T., & Souleles, N. S. (2007). Net worth and housing equity in retirement. NBER Working Paper Series No. 13693. Washington, D.C.: National Bureau for Economic Research. Retrieved from: http://www.nber.org.argo.library.okstate. edu/papers/w13693.pdf Social Security Administration (2012). Income of the population 55 or older, 2012. Table 9, A1. Retrieved from: http:// www.ssa.gov/policy/docs/statcomps/income_pop55/2012/ sect09.pdf Spears, N., Lin, X., & Mowen, J. C. (2000). Time orientation in the United States, China, and Mexico: Measurement and insights for promotional strategy. Journal of International Consumer Marketing, 13, 57-75. doi: 10.1300/J046v13n01_05 Stawski, R. S., Hershey, D. A., & Jacobs-Lawson, J. M. (2007). Goal clarity and financial planning activities as determinants of retirement savings contributions. International Journal of Aging and Human Development, 64, 13-32. doi: 10.2190/13GK-5H72-H324-16P2 VanDerhei, J., & Copeland, C. (2010). The EBRI retirement readiness rating: Retirement income preparation and future prospects. Employee Benefits Research Institute (Issue Brief No. 344). Washington, D. C.: EBRI. Retrieved from http://ebri.org/publications/ib/index.cfm? fa=ib Disp&content_id=4593

Van Rooij, M. J., Lusardi, A., & Alessie, R. M. (2011). Financial literacy and retirement planning in the Netherlands. Journal of Economic Psychology, 32, 593-608. doi: 10.1016/j. joep.2011.02.004 Van Rooij, M. J., Lusardi, A., & Alessie, R. M. (2012). Financial literacy, retirement planning and household wealth. Economic Journal, 122, 449-478. doi: 10.1111/j.1468-0297.2012.02501.x Venti, S. F., & Wise, D. A. (2002). Aging and Housing Equity. In O. S. Mitchell (Ed.), Innovations in retirement financing (pp. 254-281). Pension Research Council Publications. Visa (2012). Visaâ&#x20AC;&#x2122;s International Financial Literacy Barometer. Retrieved from: http://www. practicalmoneyskills.com/ resources/pdfs/FL_Barometer_Final.pdf Wahrendorf, M., Dragano, N., & Siegrist, J. (2012). Social position, work stress, and retirement intentions: A study with older employees from 11 European countries. European Sociological Review, 29, 792-802. doi: 10.1093/esr/jcs058 Whitehouse, E. (2007). Pension panorama: Retirement-income systems in 53 countries. Washington, D. C.: The World Bank. Wiener, J., & Doescher, T. (2008). A framework for promoting retirement savings. The Journal of Consumer Affairs, 42, 137164. doi: 10.1111/j.1745-6606.2008.00102.x Wilkins, N. J. (2010). Family processes promoting achievement motivation and perceived school competence among Latino youth: A cultural ecological-transactional perspective. Retrieved from http://search.proquest.com.argo.library. okstate.edu/docview/822366951? accountid=4117 World Bank (1994). Averting the old age crisis: Policies to protect the old and promote growth. Washington, D.C.: The World Bank. World Bank. (2008). The World Bank pension conceptual framework. Washington, D.C.: The World Bank. Retrieved from: http://siteresources.worldbank.org/INTPENSIONS/ Resources/ 395443-1121194657824/PRPNoteConcept_ Sept2008.pdf Yang, T., & Devaney, S. A. (2011). Intrinsic rewards of work, future time perspective, the economy in the future and retirement planning. Journal of Consumer Affairs, 45, 419444. doi: 10.1111/j.1745-6606.2011.01211.x

Š2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

Appendix: Items from the Five Scales in the Investigation Expectations of Financial Planning for Retirement 1. I expect to meet my financial goals in terms of planning and saving for the future. 2. I think I will do a good job of planning and saving for retirement. 3. Financial planning for retirement is something that will come easily to me.* Self-Reported Financial Knowledge 1. I know a great deal about financial planning for retirement. 2. I have informed myself about financial preparation for retirement. 3. I know more than most people about retirement planning. Retirement Goal Clarity 1. 2. 3. 4. 5.

I have set clear goals for gaining information about retirement. I have thought a great deal about my quality of life in retirement. I set specific goals for how much will need to be saved for retirement. I have a clear vision of how life will be in retirement. I have discussed retirement plans with a spouse, friend, or significant other.

Future Time Perspective 1. 2. 3. 4. 5.

I enjoy thinking about how I will live years from now in the future. I like to reflect on what the future will hold. I look forward to life in the distant future. It is important to take a long-term perspective on life. My close friends would describe me as future oriented.

Parental Influences on Saving 1. 2. 3. 4.

Growing up, my parents helped me to imagine situations when I might need extra money to fall back on. Saving money for the future was an important lesson I learned as a child. My parents suggested to me concrete ways to save money on my own. My parents made sure I understood that money was a limited resource.*

* Item removed from scale to improve model fit.

67


Journal of Personal Finance

68

IARFC National Financial Plan Competition: Case Solution by Bryant College

Edited by Walt Woerheide, Ph.D., ChFC®, CFP®, RFC®

Editor’s Note The IARFC recently completed its 2016 National Financial Plan Competition at the Speedway Club in Charlotte, North Carolina. The winner was Molly Funk from Bryant University. Second Place went to Daniel Ingles and Grant Hulett from Central Michigan University, and Third Place to Cole Brownell and Anthony Pelaez from California State University Northridge. The competition began with students being given a fictional case study of a family with an overview of their financial picture. From that data, all of the participants produced a financial plan with recommendations for current and future actions. Selected teams advanced to the semi-finals and the top three teams ended up with in-person presentations in Charlotte. The case distributed to the teams is provided first, followed by the winning discussion. Although we would like to provide the entire solution by the winning team, the formal report was over 200 pages. This report included the analysis and recommendations, along with many tables, tutorials for the client family, and general financial planning information. What follows are the edited, salient points and recommendations from the report. A copy of the full report is available at www.iarfc. org/2016CaseStudy. Finally, we are thinking that if enough people are interested in commenting on the case, we would be happy to include reader comments in the next issue as to how the recommended solution could be improved. Please send any comments to walt. woerheide@theamericancollege.edu. Walt Woerheide, Co-Editor

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

69

Comprehensive Case Facts Jamie and Claire Jackson 1234 Oakwood Circle Homewood Heights, OH 45044 Family Information Jamie is a local store manager at Homewood Groceries, Inc. with an annual gross earned income of $94,400 and net after-tax income of $64,600. Claire earns an annual gross earned income of $59,400 and net after- tax income of $41,800 from her job as an executive assistant at Wildwood Residence, a local nursing home. Family Member

Age

DOB

Relationship

Health

Jamie

40

6/8/1975

Husband

Excellent – Nonsmoker

Claire

38

7/12/1977

Wife

Excellent – Nonsmoker

Brianna

14

9/10/2001

Daughter

Excellent – Nonsmoker

Riley

12

10/13/2003

Son

Excellent – Nonsmoker

Earned income and tax information as furnished by the Jackson’s federal income tax calculation is based on tax year 2015. Wage Earner

Gross Income

FICA

State

Federal

401(k)

Net Income

Jamie

$94,400

$7,220

$4,250

$10,780

$7,550

$64,600

Claire

$59,400

$4,540

$2,670

$6,830

$3,560

$41,800

Total

$153,800

$11,760

$6,920

$17,610

$11,110

$106,400

The Jackson’s presume that their after-tax income (net cash flow) for 2015 will be as indicated above. However, they have not been projecting their cash flow accurately. Are the assumptions above realistic? What do you believe will be their net income for calendar year 2015? Even with both salaries totaling $153,800, the Jackson’s budget has been running an average expense of $522 per month in the hole. Their credit card debt is now $25,000, incurred primarily to pay taxes.

Goals and Objectives In order of priority, the specific goals and objectives of Jamie and Claire are as follows: 1.

Budgeting and debt management.

2.

Education funding for the children.

3.

Retire with an income of $7,000 per month in today’s dollars when Jamie turns 65, with funding for a projected 30-year life expectancy.

4.

Wedding for Brianna - save $15,000 by Brianna’s age 25, assumed 6/20/2027. The wedding fund savings could be invested 50% in conservative stocks and 50% in corporate bonds if this is suitable for their objective.


Journal of Personal Finance

70

Insurance Information Life Insurance

Insured

Owner

Beneficiary

Jamie

Jamie

Claire

Jamie

Jamie

Claire

Claire

Type

Purchase Date

Amount

20-year Level Term

$350,000 10/1/2005

Claire

Variable Universal Life

$250,000 10/1/2012

Jamie

Group Life

$100,000

Cash Value None $11,080 None

Annual Premium $263 $3,145 None

Disability Insurance

Insured

Type

Benefit

AnnualPremium

Payor

Jamie

LTD

60% of income to age 65; maximum $4,200/mo. benefit

Jamie

$1,560

Claire

STD LTD

60% of Salary 40% of Salary

Wildwood Wildwood

-

Notes Includes residual disability benefit, partial disability benefit, and return-to-work benefit; 90-day wait. Plan uses split definition of disability 1 – 6 months Max Benefit $26,000 until age 65

Medical Insurance Owner Jamie

Insured

Type

Jackson Family

High Deductible Plan

Benefit

Payor

Premium

$3,000 Family Deductible Homewood Groceries 75/25 Co-Insurance

$0

Investment Portfolio Information Retirement Plans Jamie has a 401(k) through his employer. Jamie contributes 8% of his salary each year and the company contributes 3% each year. Claire contributes 6% of salary each year to her 401(k) at Wildwood Nursing Home, and her employer matches 100% of the first 3% and 50% of the next 2% that she contributes. Both Jamie and Claire are 100% vested in the employer contributions.

Owner

Allocation

Market Value

Beneficiary

Annual Growth Contribution Rate

Jamie – 401(k)

Aggressive Growth Fund

$47,800

Claire

$ 2,776

6%

Jamie – 401(k)

International High-Yield Bond Fund

$22,300

Claire

$1,295

3%

Jamie – 401(k)

Emerging Markets Fund

$30,900

Claire

$1,795

4%

Jamie – IRA

Growth Fund

$29,000

Brianna & Riley

$1,684

4%

Claire – 401(k)

Wildwood Nursing Home Stock*

$32,400

Jamie

$1,424

7%

Claire – 401(k)

Aggressive Growth Fund

$20,250

Jamie

$890

6%

Claire – 401(k)

International Growth Fund

$20,250

Jamie

$890

4%

Claire – 401(k)

Growth Fund

$8,100

Jamie

$356

4%

Total

$211,000

*Cost basis in the Wildwood Nursing Home stock is $15,000.

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

71

Personal Investments

Name

# of Shares

Price per share

Current Value

Cost Basis

Growth Rate

American Electric Corp.

250

$21.50

$5,375

$2,437

3%

First Southern Bank Corp.

150

$46.78

$7,017

$2,456

4%

whatsnext.com

1,000

$24.00

$24,000

$20,500

2%

NewTech Video

900

$2.00

$1,800

$2,500

7%

Equity Growth Mutual Fund

400

$18.75

$7,500

$4,500

6%

Total

$45,692

Name NewTech (Bond)) (five bonds)

Face Amount

Current Value

Interest Rate

Payable

When Matures

$5,000

$4,327

4%

Annually

3 years

**Comparable bonds are yielding 4.7%.

Risk Tolerance Expressed by Each Jamie – Aggressive

Claire – Moderate

Estate and Income Tax Information Jamie and Claire each have a basic will, written after their first child, Brianna, was born. They have done no other estate planning. They would like their assets to be split equally between their children after the death of the surviving spouse. They are in the 25% federal marginal income tax bracket and 4.5% state income tax bracket.

Education Planning Jamie and Claire want their children to go to their alma mater, Eastern State University. In-state tuition for each child is currently $12,000 per year. Their current residence is 30 miles from Eastern State, and they believe it may be beneficial for the children to reside on campus, as opposed to commuting. The current residence cost is $5,000 per year. They want to fund 100% of the cost of a 4-year education, ensuring that the cost is fully funded when the child starts school. Brianna has $20,000 in a CD. She is the only child with any money currently saved for college. It is set up as an UTMA account, with Claire as the owner. They expressed a desire to provide a Master’s Degree for each child; Tuition is $32,000, housing is $7,000 totaling $39,000 in today’s dollars.


Journal of Personal Finance

72

Investment and Planning Assumptions Client Assumption

Rate of Return/Increase

Safe Retirement Account

5%

Personal Residence

2%

Aggressive Stocks

8%

Conservative Stocks

6%

Corporate Bonds

3%

5-year CDs

5.5%

General Inflation

3%

College Inflation Rate

7%

Risk-Free Rate

3%

15-Year Mortgage Current Rate

5.25%

30-Year Mortgage Current Rate

6.75%

Refinance Costs (points and fees)

2%

Auto Loan

5.75%

Credit Card

12%

Claire’s Expected Salary Increase

3%

Jamie’s Expected Salary Increase

2%

General Cost of Living Increase

3%

Cost of Living After Retirement

2%

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

73

JAMIE AND CLAIRE JACKSON STATEMENT OF FINANCIAL POSITION AS OF DECEMBER 31, 2014 ASSETS

CURRENT LIABILITIES Visa General Credit Card Debt2 (12%)

Cash/Cash Equivalents Checking (JTWROS)

$ 800

Savings (JTWROS)

$7,000

Money Markets (JTWROS) Total

TOTAL CURRENT LIABILITIES

$25,000

$21,800 $29,600

Invested Assets Stock Portfolio (JTWROS)

$ 38,192

Equity Growth Mutual Funds (JTWROS)1

$ 7,500

Bonds (JTWROS)

$ 5,169

Total

$25,000

LONG-TERM LIABILITIES Mortgage (Residence)3 (7.75%)

$175,446

Vehicle Loan

#14

(6.25%)

$ 12,877

Vehicle Loan

#25

(7.25%)

$ 18,857

TOTAL LONG-TERM LIABILITIES

$207,180

TOTAL LIABILITIES

$232,180

NET WORTH

$370,461

TOTAL LIABILITIES AND NET WORTH

$602,641

$ 50,861

Life Insurance Life Insurance Cash Value (H)

$ 11,080

Total

$ 11,080

Retirement Plan Assets 401(k)1 (H)

$101,000

401(k)1 (W)

$ 81,000

IRA1

$ 29,000

(H)

Total

$211,000

Use Assets Residence (JTWROS)

$260,000

Vehicle #1 (JTWROS)

$ 18,300

Vehicle #2 (JTWROS)

$ 21,800

Total

$300,100

TOTAL ASSETS

$602,641

H = Husband W = Wife JTWROS = Joint Tenants with Right of Survivorship 1 See

Investment Portfolio Information. Assets presented at fair market value (FMV).

2

Average credit card interest rate is 12%. Credit card balances have been increasing.

3

Purchase price: $220,000, four years ago. Original Mortgage: $210,000, 15-Year at 7.75%.

4

Car #1 Loan information: Original balance $19,750, 5-year note at 6.25%, taken out two years ago.

5

Car #2 Loan information: Original balance $22,400, 5-year note at 7.25%, taken out last year.


Journal of Personal Finance

74

Jamie and Claire Jackson Monthly and Yearly Budget 2015

Monthly

Yearly

Income Gross Earned Income (Net after taxes and FICA)

$8,866

$106,400

$8,866

$106,400

Mortgage payment

$1,976

$23,712

Real estate taxes

$ 185

$ 2,220

Automobile loan #1

$ 384

$ 4,608

Automobile loan #2

$ 446

$ 5,352

Charge accounts (interest is $3,000 per year)

$ 408

$ 4,896

Total Liabilities

$3,399

$40,788

Transportation

$300

$3,600

Life insurance

$ 284

$ 3,408

Disability income

$ 130

$ 1,560

TOTAL Liabilities

Insurance:

Auto insurance

$ 117

$ 1,404

Homeowners insurance

$ 175

$ 2,100

Total Insurance

$ 706

$ 8,472

Charitable Contributions

$ 100

$ 1,200

Household Utilities Expenses

$600

$7,200

Household Furniture and Repairs

$120

$1,440

Household Food Expenses

$800

$9,600

Medical and Dental

$425

$5,100

Children - Fees, School, Allowances

$325

$3,900

Clothing/Grooming

$425

$5,100

$1,558

$18,696

Gifts

$250

$3,000

Miscellaneous

$380

$4,560

$9,388

$112,656

Recreation, Vacation and Entertainment

TOTAL

Š2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

75

Case Solution by Bryant College The winning submission starts with the following cover letter to the clients: December 10, 2015 Dear Jamie and Claire: I am pleased to have this opportunity to review your financial analysis with you. During this session, we will discuss the following topics: 1.

your present situation

2.

details of your financial analysis

3.

a recommended financial strategy

4.

implementation of your strategy

Before we discuss the details of your analysis, it is helpful to review some key money management ideas so you can keep them in mind as we progress through your reports. My objective is to provide you with an analysis that is easy-to-understand and a clearly-defined course of action for implementing your strategy. Please feel free to ask as many questions as you like. Sincerely, The report then provided discussions on the key financial planning topic areas, and each of those is reproduced below.

Budget and Debt Management Planning Strengths As we can see from your family profile, you both have a grasp on your current state of finances. You have both a monthly and annual budget in place that gives you an idea of where your income is being channeled. You have listed budgeting and debt management as your top priority going forward, which is a great step to plan for your future goals. Opportunities Your family, however, has many opportunities for improvement. I see from your financial statements that you have $25,000 in credit card debt, primarily due to charging your

taxes to the account, which we should work to reduce. I believe that you can reduce the rates on your mortgage through refinancing. Your car loans may also be eligible for refinancing. By reducing some of your debt balances and insurance rates, you would be able to free up cash flow that could be applied to your other financial objectives including retirement and college funding. 1)

Credit Card Debt

In regards to your familyâ&#x20AC;&#x2122;s credit card debt, there exists an opportunity to refinance this debt. You currently pay 12%, which is consistent with market rates. However, depending on your credit score, you may be able to refinance this debt with another credit card to achieve a lower rate for a refinancing fee, with the hope of accelerated repayment. The second option in regard to credit card debt would be to roll over your current balance into your mortgage. The advantages of this option are that you would be paying the low rate of your mortgage, and would not have to pay another bill towards the card. By rolling over your credit card debt, the principle amount of your mortgage would increase, although it would be offset by rate reductions in monthly payments. 2)

Mortgage Refinancing

Currently, your family is paying a mortgage rate that is higher than typical 15-year rates in Ohio. The current market rate is approximately 3.8%, significantly lower than the 7.75% you now pay. While there are fees involved with refinancing, it would be worthwhile to reduce your interest rate by almost 4%. Refinancing fees are typically 1-2% of the principal debt amount, which in this case would be about $3,000. By refinancing your mortgage, you could reduce your monthly payment by approximately $800. This means that within four months, you would recoup the money spent on refinancing, and going forward would continue to save the extra $800 a month. 3)

Automobile Loans

As with your mortgage, you are currently paying rates that are substantially higher than current market rates, which are listed at 2.75%. For your first automobile, you are paying 6.25% interest on a $19,750 loan. If you were to refinance at the current rate, you would save $100 a month in payments


Journal of Personal Finance

76

and a total of $1,000 in interest over the remainder of the loan’s life. For your second automobile, you are paying 7.25% on a $22,400 loan. If you were to refinance, you would save an additional $100 a month in payments and an overall $1,600 in interest.

your credit card debt into your mortgage principle and take advantage of the lower interest rates. Your family already has an extensive monthly and yearly budget. I have adjusted some of your budgeted expenses in the sample budget below. If you make minor spending changes, along with refinancing some of your debt, you can use the additional unallocated cash to apply to both your credit card debt and savings for your retirement and children’s college.

Suggestions and Recommendations By refinancing, your family can free up cash flow that can be applied to your other goals. I suggest you do roll over

Jamie and Claire Jackson Monthly and Yearly Budget 2015 Current Monthly

Revised Monthly

Current Yearly

Revised Yearly

Income: Gross Earned Income

$ 8,867

$ 8,867

$ 106,400

$ 106,400

$ 8,867

$ 8,867

$ 106,400

$ 106,400

$ 1,976

$ 1,100

$ 23,712

$ 13,200

Real estate taxes

185

185

2,220

2,220

Automobile loan #1

384

284

4,608

3,408

Automobile loan #2

446

346

5,352

4,152

Charge accounts

408

4,896

TOTAL Liabilities: Mortgage payment

Total Liabilities

$ 3,399

$ 1,915

$ 40,788

$ 22,980

$ 300

$ 250

$

3,600

$

3,000

$ 284

$ 378

$

3,408

$

4,541

Disability Income

130

130

1,560

1,560

Auto Insurance

117

117

1,404

1,404

Homeowner’s Insurance

175

175

2,100

2,100

$ 706

$ 800

$

8,472

$

9,605

Charitable Contributions

$ 100

$

50

$

1,200

$

600

Household Utility Expenses

$ 600

$ 600

$

7,200

$

7,200

Household Furniture and Repairs

$ 120

$ 120

$

1,440

$

1,440

Household Food Expenses

$ 800

$ 800

$

9,600

$

9,600

Medical and Dental

$ 425

$ 425

$

5,100

$

5,100

Transportation Insurance: Life Insurance

Total insurance

Children: Fees, School, Allowances

$ 325

$ 325

$

3,900

$

3,900

Clothing/Grooming

$ 425

$ 325

$

5,100

$

3,900

Recreation, Vacation, and Entertainment

$ 1,558

$ 900

$ 18,696

$ 10,800

Gifts

$ 250

$ 150

$

3,000

$

1,800

4,560

$

3,000

Miscellaneous

$ 380

$ 250

$

TOTAL

$ 9,388

$ 6,910

$ 112,656

$ 82,925

$ (521)

$ 1,956

$ (6,256)

$ 23,475

Net Cash Flow

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

Retirement Funding Strengths You have already begun a healthy savings plan for retirement; you are currently holding a combined market value of $211,000 in your appropriate accounts. Jamie has increased the value of his 401(k) accounts; contributing 8% of his salary each year with a 3% match by his employer, Homewood Groceries, Inc. Jamieâ&#x20AC;&#x2122;s 401(k) is invested in three different assets: Aggressive Growth Fund, International High-Yield Bond Fund, and Emerging Markets Fund with estimated growth rates of 6%, 3%, and 4%. All three of these are fairly high risk funds, and as you approach retirement you may be wise to transfer to more moderate risk assets. As per regulations, you can contribute $17,500 to a 401(k) account annually and you are currently contributing $5,866, which indicates it may be time to begin increasing annual contributions. Jamie also has $29,000 invested in Growth Fund in his IRA account, which has an estimated growth rate of 4% annually. He contributes $1,684 each year to this account. Due to the tax-free growth nature of this account, it could make sense to increase his dollar contributions. Claire currently has a balance of $81,000 in her 401(k). She contributes 6% of her annual salary and her company, Wildwood Nursing Home, contributes 100% of the first 3% and 50% of the next 2% that she contributes. Claireâ&#x20AC;&#x2122;s 401(k) is invested in four different assets, Wildwood Nursing Home stock, Aggressive Growth Fund, International Growth Fund, and Growth Fund, where the projected growth rates are 7%, 6%, 4%, and 4%. Claire currently contributes $3,560 into her 401(k) each year. At this stage of her life, she could also increase her contributions. Opportunities Jamie is currently saving funds in both a 401(k) and an IRA, both of which allow for higher contributions. Claire is saving via a 401(k) account. If Jamie continues to save in the same fashion that he currently is, he will have saved $988,938 by age 65. The maximum contribution to a 401(k) account is $18,000 annually. This cap is expected to increase by $500 each year, following the trend over the past ten years. There is an opportunity to increase the amount that both Jamie and Claire are contributing to

77

their respective 401(k) accounts. The maximum contribution to an IRA account is $5,500 for those under the age of 50, and $6,500 for those over 50 years old. There is an opportunity to increase the amount of money that Jamie is contributing to his IRA each year. Suggestions & Recommendation In the figure below, you can see that if your family continues to save exactly the same way you currently are, you will have saved $782,715 for retirement at age 65. Current Plans

Age

Retirement Value

Modified Retirement Value

65

$782,715

$816,002

67

$845,544

$882,173

69

$911,446

$951,552

You expressed wanting to have enough money for retirement to live until age 95 while withdrawing $7000 a month in present dollars. To achieve this goal, you would have to save $2,991,687. This would equate to saving approximately $51,000 a year towards retirement alone. As we discussed in the Budget and Debt Management portion of this report, you currently do not have enough free cash each month to reach all of your financial goals. If you continue to save in your current fashion, and withdraw the equivalent of $7,000 each month, you would deplete your retirement savings by age 73. I think it is important to adjust your retirement goals. Keep in mind that as your family ages, your expenses such as your mortgage, automobile loans, and the costs of children will decline. That being said, at retirement your cost of living will not be as high. Industry standard suggests that you may safely withdraw 4% of your retirement savings each year. I recommend that in our next discussion, we reevaluate your monthly income needs after retirement. Furthermore, after reviewing your familyâ&#x20AC;&#x2122;s revised budget, you will have an extra $1,000 a month to allocate toward retirement savings. I suggest Jamie increases his contribution to his 401(k) account from 8% to 9% to distribute these funds until your other debts have been paid off.


Journal of Personal Finance

78

RETIREMENT TIME TABLE:

1 YEAR BEFORE RETIREMENT

CONTINUOUSLY BEGINNING AT LEAST 10 YEARS IN ADVANCE •

Audit Social Security records.

Study all existing retirement plans.

Analyze assets.

Evaluate liabilities—plan to have these paid off by your retirement date.

Determine if there will be an income deficiency.

Develop a plan.

Review will(s) and trust(s).

Confirm beneficiary designations on insurance and retirement plans.

5 YEARS BEFORE RETIREMENT

Start shopping for the medical insurance you will need after retirement.

Initiate housing changes if planned.

Set up or renew Home Equity Line of Credit to have available for emergency.

Liquidate liabilities.

Review your plan.

Consolidate IRAs for simplicity.

Obtain needed health care, dental, operations, etc.

Check for available property tax relief.

Tryout “second/third” career on weekends if planning to work.

Check with previous employers for possible retirement benefits.

Re-evaluate projected retirement income level.

Collect all documents necessary for Social Security.

Audit Social Security records.

Social Security card

Review status of company retirement programs— get projections.

proof of age, birth certificate

copy of latest W-2(s)

Maximize contributions to all retirement plans.

spouse’s Social Security card (if married)

Confirm company retirement provided medical benefits.

marriage license (if married)

Re-evaluate liabilities—plan to have paid off by retirement date.

“Practice” living on anticipated retirement income for at least 3 months.

Review will(s) and trust(s).

Confirm beneficiary arrangements.

Make house repairs now (will help whether staying or moving).

• •

If planning to move away, visit potential sites now during different times of year.

6 MONTHS BEFORE RETIREMENT •

Begin shopping for Medigap insurance.

Review life insurance needs.

Review personal investments.

If selling your home, put it on the market.

Emergency fund and home equity line of credit should equal 1 year’s expenses.

Finish paying off debts, if they are not already liquidated.

Consider lifestyle issues: –

attitude adjustments

role adjustments

meaningful use of time

housing changes

health requirements

Will you choose to work during retirement?

3 MONTHS BEFORE RETIREMENT •

Apply for Social Security benefits (3 months prior to retirement).

Inquire about Veteran’s benefits (if applicable).

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

79

Complete paperwork for company retirement benefits.

Apply for needed medical insurance (2 months prior to retirement).

Confirm direct deposit arrangements.

However, these tax credits can only be applied if your family invests in the Ohio 529 plan. If the account is owned by the parent and is reported as a parental asset, the reduction in potential financial-aid is much less than if owned outside a 529 plan. Another positive effect of the 529 plan is that when money is withdrawn for college, it is tax-free. Savings from a 529 can also be applied to graduate school, a benefit to your children who plan to attend a graduate program.

RETIREMENT •

Take advantage of all benefits of retirement and age. –

state and federal tax credits and increased deductions

property tax credits

senior discounts

Fine tune your budget on an annual basis.

Review your financial plan regularly with your advisor.

Enjoy the fruits of your planning.

There are two options for this plan, a direct-sold plan and a plan sold through a registered investment advisor. The direct-sold plans are managed directly by the investor and generally have lower fees, averaging 0.52%. Advisor-sold plans are bought through a registered investment advisor or broker dealer who helps parents manage investments. Advisor-sold plans average fees at approximately 1.07%. 2)

This plan is more flexible than the 529 plan as it can be used to pay for educational expenses from kindergarten through graduate school, which could be beneficial to your family as you would like to fund both Brianna and Riley’s graduate school as well. As with the 529 plan, a Coverdell is registered in a parent’s name and is considered to be a parental asset on the federal financial-aid application. Contributions above $2,000 a year are taxed with a 6% excise tax and contributions are no longer allowed after the beneficiary reaches age eighteen. Coverdells are sold through brokerage and investment firms and allow investors to choose investments which can include stocks, bonds, or mutual funds.

College Funding Strengths You have already begun saving for your children’s education, which based on your client profile is substantial, and this is one of your main financial goals. To summarize the progress you have already made: You have set up a $20,000 CD for Brianna’s education, which you set up as a Uniform Transfer to Minors Account (UTMA) with Claire as the owner. Opportunities There are plenty of different methods for saving for your children’s education. Options include 1)

529 College-Savings Accounts These are popular accounts among parents due to their tax benefits and their relatively small impact on the amount of financial aid their child could be eligible to receive. Parents contribute after-tax cash to the fund, and the earnings on the 529 are free from income tax. Ohio offers tax credits for annual contributions, which means that your state income taxes will be reduced dollar for dollar.

Coverdell Education Savings Accounts

3)

Certificate of Deposit (CD) This option is not geared directly towards college savings; it works similar to a regular savings account in which you make deposits and let the interest accrue. Currently your family has a CD in place for Brianna’s education, valued at $20,000. This type of account is funded by after-tax cash with no tax benefit. Furthermore, any of the interest earned on this account is included in your annual taxable income.


Journal of Personal Finance

80

4)

Life Insurance In this option, you would use the cash value of your life insurance policy to help pay for your children’s college expenses. At this point in time, your life insurance cash value is insufficient to cover the costs of college. However, as it continues to grow and as your finances become more complex, we can revisit this option if necessary.

Recommendation It is great that you have started saving money for Brianna, but it is estimated that tuition at Eastern State University will continue to rise at a rate of 7% annually. This means that additional funds will need to be invested to meet the goal of paying for both Brianna and Riley’s education. I would suggest you plan to use Brianna’s certificate of deposit and a 529 savings plan to fund both children’s education. The current worth of the CD ($20,000) will cover almost all of Brianna’s first year of her undergraduate program, thus giving you more time for saving.

expenses are eliminated, specifically credit card debt and automobile loans, you will have more money available to deposit into these accounts. In order to fully fund each child’s undergraduate education in full, your family would have to contribute $1,770 a month ($21,240 annually) toward the 529 plan. Currently, with your other financial goals, I do not recommend attempting to save this amount right away. However, in a few years, as your financial situation changes, I would suggest to have another conversation regarding your college funding efforts.

Year

Tuition Cost

Withdraw Amount

529 Beg. Bal.

529 End. Bal.*

2019

$ 22,284

$ 22,284

$ 80,037

$ 61,219

2020

$ 23,843

$ 23,843

$ 77,612

$ 56,995

2021

$ 25,512

$ 51,025

$ 73,134

$ 23,436

2022

$ 27,298

$ 54,597

$ 37,562

$ (18,056)

2023

$ 29,209

$ 29,209

$ (6,420)

$ (37,767)

2024

$ 31,254

$ 31,254

$ (27,313)

$ (62,080)

*remaining ending balance expected to continue to earn 6%

Tax Ramification Furthermore, I suggest you deposit the certificate of deposit into Brianna’s 529 plan now in order to take advantage of future returns. This will give you an accelerated start into saving for the next six years to fund the rest of your children’s education. After looking over your monthly budget, I have reallocated funds that would allow you to put away an extra $1,000 a month for your children’s education until 2024, when both children are projected to complete graduate school. I would like to avoid overfunding college planning accounts in order to avoid negative tax consequences. Therefore, by contributing only $1,000 each month to this plan, there is additional cash available that can be allocated to other expenses such as retirement planning, life insurance policies, and paying off credit card debt. It is also important to keep in mind that the average financial aid package for Eastern State, including merit scholarships is $12,500. Below is an illustration of future undergraduate college tuition as well as a demonstration of how much money your family could accumulate in your children’s 529 plans if you were to contribute $12,000 to the account each year. As you can see, there is an established negative balance towards the end of Riley’s undergraduate experience, however, it is important to keep in mind that as your other

The balance of a 529 plan grows tax-free, and payouts are also tax-free as long as they are utilized for educational purposes. There is also no penalty for moving funds from Brianna to Riley since they are siblings. The State of Ohio allows you deduct up to $2,000 of contributions per beneficiary per year from your State of Ohio taxable income with unlimited carryforward. Taking this into consideration, I think investing in the 529 plan is the best option for your family. Time Horizon Short Term (0−5 years): I would suggest you begin contributing $1,000 per month for the next ten years, which will help fund Brianna and Riley’s educations. Once Brianna is accepted into a college and receives her financial aid and scholarship information, we can revisit how much funding will need to stay allocated to her account and how much we can roll over to Riley’s 529 plan. Intermediate Term (5−10 years): I would like to have another discussion about the return on the 529 plans that you set up in the next five to ten years to ensure that both educations will be fully funded.

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

Long-Term (10+ years): It is estimated that your youngest child Riley should be finishing up his graduate degree in approximately ten years. We will of course meet prior to this to discuss if your children’s educations are moving along as planned. Once both children are out of school we will discuss how to reallocate the money you had been setting aside for education to other uses, such as retirement.

81

Life Insurance Evaluation: Family Needs Method: Jamie Lump Sum Needs at Death Funeral expenses

$ 12,000

Mortgage

$ 175,446

Credit Card Debt

$ 25,000

Car Loan

$ 12,877

College Funding:

Life Insurance Funding Strengths Currently you both hold life insurance policies, which is a great step toward managing your family’s risk. Jamie currently holds a total of $600,000 of life insurance spread between two policies, both owned by himself as an individual. The first, a 20-year level term policy is worth $350,000, with an annual premium of $263 until October 2025. This policy will only cover Jamie until he is fifty years old; the children will be twenty-four, and twenty-two, potentially still in graduate school. The second policy, a variable universal life policy is worth $250,000 with an annual premium of $3,145 until retirement. This policy holds a current cash value of $11,080, and both policies list Claire as the beneficiary. Claire holds a group life insurance policy through her employer worth $100,000, and it has no annual premium. This means that it is not a portable policy if she were to leave Wildwood. Opportunities There are several opportunities for your family to increase its risk management planning. Using the family needs method to evaluate your life insurance needs, I’ve created a benchmark need for both Jamie and Claire. This method provides an estimation of lump sum and income needs at the time of death. Income needs take into consideration maintaining the standard of living your family currently is used to. The lump sum total would be available to pay funeral expenses, outstanding debt, and college funding. Recommendation Based on the family needs evaluation, your family is not adequately insured under your current life insurance policies. As Jamie is responsible for over 60% of your family’s monetary income, I suggest your family focuses on increasing his life insurance coverage before Claire’s coverage.

Brianna

$ 126,090

Riley

$ 146,000

Total Lump Sum Needs

$ 497,323

Income Needs Annual Income

$ 94,400

Years

15

Estimated Inflation

3%

Present Value of Annual Income

$1,126,841

Lump Sum Needs

$ 497,323

Total Life Insurance Needs

$ l,624,164

Less Current Assets

$ 29,600 .

Less Existing Life Insurance

$ 600,000

Need for Additional Life Insurance

$ 994,564

I suggest he purchase a Supplemental Group Term policy through his employer, Homewood Groceries, Inc. The benefits to buying the policy through his employer include the ability to bypass the health exam requirement that he would have to undergo through a private insurer, as well as lower premiums. Furthermore, by purchasing only the amount your family needs to supplement the coverage he already has, Jamie can decrease the amount he would pay in premiums. Jamie can purchase up to five times his salary, $472,000. The monthly premium would be twenty cents for every thousand, or $94.40. By purchasing this additional policy, it would leave the family with a need for another $500,000 in life insurance coverage, which could be obtained privately. In the future, I would like to discuss Claire’s potential to increase her own life insurance policy as she contributes 40% of your household income. Furthermore, as your family’s financial situation becomes more complex and developed it would be beneficial to revisit both Claire’s and Jamie’s policies.


Journal of Personal Finance

82

Tax Ramification

Tax Ramifications

There are no tax benefits or penalties for purchasing additional life insurance.

Creating an estate plan that includes a will can avoid a large amount of taxes upon death.

Time Horizon

Time Horizon

Short Term (0-5 years): I recommend Jamie purchase additional life insurance through his employer if possible. This will allow Claire and the children to continue living a comfortable lifestyle if Jamie were to pass away.

Short Term (0-5 years): Update your wills to incorporate guardianship of your children and a health care proxy into your planning.

Intermediate Term (5-10 years): I would like us to have another conversation regarding life insurance policies. In this conversation, we could discuss Claire’s life insurance, and whether we see a need to increase her coverage. Long Term (10+ years): I would like to have a conversation at retirement to discuss all life insurance policies, estate plans, and retirement income.

Intermediate Term (5-10 years): I would like to see you update your will once Brianna and Riley turn 18 to remove guardianship and as you begin to include valuable assets into your estate. Long Term (10+ years): I would like another conversation regarding your estate planning. At this point, it may be advisable to set up a trust for your family. It would also be wise to consider planning things such as funeral costs and life insurance plans as retirement approaches.

Estate Planning Risk Management Strengths Medical Insurance Your family currently has little estate planning practices in place; you are limited to a basic will that is in need of updating. This analysis serves as a good foundation in order to revamp your will and begin your estate planning process. Opportunities & Suggestions The minimum that your family should do to establish a more concrete estate plan would be to update your will. Because your wills have not been updated since Brianna’s birth, they should be updated to include Riley. You should also consider establishing guardianship plans and a health care proxy. You might consider setting up a trust. At this point in time, I would not suggest setting up a trust. However, I would like to revisit this option as your finances grow and become more complex in the future.

Strengths Jamie currently pays for a group family health plan through his employer, Homewood Groceries, Inc. The policy is a high deductible plan ($3,000) co-insurance policy where the health insurer pays 75% of costs after the deductible is reached, and Jamie’s family pays the other 25% of the cost. Opportunities 1)

Purchase a preferred-provider organization policy with a lower deductible but with the cost of a greater premium.

2)

Purchase individual medical insurance policies for family members. This option is extremely expensive, and holds no benefit over a family plan.

Recommendations Recommendation I suggest that both Jamie and Claire update their wills immediately to incorporate guardianship of both Brianna and Riley. This is especially important for Riley who is only 12 years old. I would be sure to name a health care proxy in the event that either of you become incapable of making medical decisions.

Without much information, it is difficult to evaluate your family’s health insurance plan. Your current policy is cost-beneficial as it covers your entire family in one policy as opposed to multiple individual policies. Due to the fact that your family members are all currently healthy, the expense risk is very low, especially as your policy has no

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

83

premium costs to you. As your family ages, medical insurance becomes more essential to your financial planning process and we will revisit this topic.

me to suggest your family should set up an emergency savings fund that is equal to the sum of six months of expenses.

Disability Insurance

Claire’s policies are both owned by her employer, meaning that if she were to leave Wildwood, the policies are not portable and she would not have disability coverage. Her long-term coverage is also insufficient to satisfy her current income.

Strengths Jamie currently has a long-term disability policy that would pay him 60% of his income each month until age 65 if he were to become disabled (to a maximum of $4,200/ month). Currently, Jamie’s monthly salary after taxes is $4,400 and is projected to increase 2% annually. This policy uses the split definition of disability which is beneficial to Jamie, as in the first few years disability is defined as not being able to do his own job, and then after a few years, it switches to a modified definition of disability. Jamie’s policy does include a 90-day wait period, in which he would not be able to access his disability payments. Jamie currently pays a $1,560 annual premium for this long-term policy. Claire has two different disability insurance policies, both paid for by her employer. The first is a short-term policy that pays 60% of her salary during a one- to six-month period. The second is a long-term policy that agrees to pay 40% of her salary with a maximum benefit of $26,000 each year until age 65. Claire’s current annual salary is $59,400, and is expected to grow by 3% each year. Opportunities There are several ways to supplement the disability policies currently held by Jamie and Claire.

I would advise Jamie to look to his employer and find out if he can buy supplemental disability insurance which would cover the discrepancy between his current monthly income and the maximum payout his policy provides. I suggest Claire also buy an additional disability policy to cover the $33,000 of her salary that is currently not included in her employer’s policy. I recommend she look to an outside provider for this additional policy. Auto Insurance Strengths Your family has combined the cost of automotive insurance for both cars into one payment, which is a good strategy as bundling insurance reduces costs. Overall you pay a premium of $117 a month or $1,404 a year. Opportunities & Recommendations Without knowing more about your insurance policy it is difficult to offer advice on a better policy option. I would like to table the discussion on automotive insurance and revisit this option at our next appointment.

Purchase additional long-term care insurance to supplement the insufficient policies currently held.

Homeowners Insurance

2)

Purchase additional long-term care insurance to cover both Jamie and Claire beyond age 65.

3)

Additionally, Claire should look to diversify her insurance coverage by buying an outside policy as an alternative to her employer sponsored coverage.

Your family has a homeowner’s policy with a monthly premium of $175, which totals to $2,100 annually. Your home is currently worth $260,000, which you purchased four years ago for $220,000.

1)

Suggestions and Recommendations Jamie’s current long-term disability policy leaves him short $200 of his monthly salary if he is to become disabled. This deficit will only grow as his salary increases year over year. The policy also includes a 90-day wait period, which leads

Strengths

Opportunities & Recommendations There is an opportunity to have your home appraised as you must insure your house at its market value, or replacement cost. If you decide to refinance your mortgage as I suggested, your home will have to be appraised by the bank conducting the refinance contract. The price of your home is increasing at a rate of 2% annually, and your


Journal of Personal Finance

84

insurance coverage should match that annually. I also suggest that you begin to protect some of the valuable assets within your home including any antique furniture, artwork, or jewelry. For coverage, you would need to purchase a valuable personal property insurance policy to cover all of your valuable, in-home assets. Finally, I would suggest we revisit homeowners insurance at another conversation. Without more information about your current policy, I do not want to make assumptions with a different suggestion.

Time Horizon Short Term (0-5 years): I would like to see you looking into long-term care insurance and start thinking about your potential medical needs. Medium Term (5-10 years): I would suggest you purchase a policy in ten years, once Riley has finished with school. Long Term (10+ years): This policy can be reviewed once you both retire, and adjustments can be made if necessary.

Long-Term Care Insurance

Investment Planning

Opportunities

Strengths

You currently do not have any long-term care insurance. As you grow older, this type of insurance becomes more important as your potential need for long-term care increases. Long-term care insurance helps cover the costs of an assisted living facility, or paying for live-in assistance. Long-term care insurance can help with some of the challenges of growing older, including medication monitoring, and every day activities including bathing or using the restroom. Assisted living facilities and live-in nursing are expensive, and long-term care insurance is essential to making these services affordable.

It is great that you have long-term investments in asset classes such as stocks and bonds. You own American Electric Corp. stock that is worth $5,375 and has a growth rate of 3%. You also own 150 shares of First Southern Bank stock worth $7,017, which has a growth rate of 4%. You own one thousand shares of stock in whatsnext.com, worth $24,000 and which has a projected 2% growth rate. Also in your portfolio are 900 shares of New Tech Video stock worth $1,800 and which has a 7% growth rate. In addition, you own 400 shares of Equity Growth Mutual Fund that is worth $7,500 and has a 6% growth rate. Finally, you also have five New Tech bonds worth $4,327 with a coupon rate of 4%, which matures in 3 years (2018).

According to the US Department of Health and Human Services, the average annual cost of an assisted living facility in Ohio is $47,000. The average annual cost of a nursing home is $60,000 for a private room. Costs for assisted living and nursing homes are increasing each year, which is why acquiring a long-term care plan is important. Recommendations I recommend you wait until Riley is finished with college to purchase a long-term care insurance policy. At the age of 52, Jamie would still be ahead of most individuals purchasing long-term care insurance as the average purchase age is 59 years old. It is my recommendation that we revisit the topic of long-term care insurance once your family is finished funding both children’s education.

Opportunities There is an opportunity to begin a “fallback” investment fund that could potentially cover your expenses for about six months in case of emergency. This would be necessary if Jamie suddenly became disabled as his insurance policy has a three-month waiting period. This would need to be an extremely safe investment and could be placed in a savings account at a local bank. The goal of this investment is for it to be extremely liquid and accessible. You also have a goal of funding Brianna’s wedding when she turns twenty-five years old. There is an opportunity to begin a savings fund for this goal as well. Recommendations

Tax Ramifications The premium for long-term care insurance can be an itemized tax deduction as a medical expense, which will be helpful as you manage your finances.

I recommend the following two options for your family to take in order to meet financial goals: 1)

Create an additional savings account at your local bank that can act as an emergency fund. This

©2016, IARFC. All rights of reproduction in any form reserved.


Volume 15, Issue 2

would need to hold six months of expenses, which at $7,104 a month would be a total of $42,624. 2)

Hold off on funding Brianna’s wedding fund until she has graduated from college. This will allow you to focus on her education funding first, and then allow a few years to save for her wedding if that is still a goal. We can approach this topic again as Brianna nears going to college.

I would not rely on your investment assets as sources of long-term funding. Because of the variable nature of highrisk assets, they should not be factored into long-term planning for goals such as retirement or college funding. Tax Ramifications Creating a savings account with a local bank will not have any tax benefits. The need for liquid assets trumps any tax-advantaged investments for this type of account. Time Horizon Short Term (0-5 years): I suggest you create a savings account as an emergency fund with a local bank. Intermediate Term (5-10 years): I would like to meet again to discuss funding Brianna’s wedding as well as checking in to see how the emergency fund is progressing. Long Term (10+ years): I would suggest that after our second conversation you begin saving for Brianna’s wedding once she finishes with her education.

85


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No

IF THE ANSWER TO ANY OF THE ABOVE QUESTIONS IS “YES” PLEASE ATTACH A WRITTEN EXPLANATION

The following should be read carefully by the applicant:   1. I hereby certify that I have read and understand the foregoing statements and that my responses are true and complete to the best of my knowledge.   2. I hereby apply for IARFC registration and, in consideration of my application, I submit myself to the jurisdiction of the organization and hereby verify that I agree to abide by all the provisions of the bylaws and regulations of the organization as they are and may be amended; and I agree to comply with all such requirements, subject to right of appeal as provided by law, and I agree that any decision as to the result of any examination(s) that I may be required to pass or annual CE requirements will be accepted by me as final.   3. I further agree that neither the association nor its officers or employees shall be liable to me for action taken or omitted in official capacity or in the scope of employment, except as otherwise provided in the statutes, bylaws, or the association’s rules and regulations.   4. I authorize the organization to make available to any federal, state or municipal agency, or any securities or commodities industry self-regulatory organization, any information they may have concerning me or to request confirmation of my status, and I release those organizations, employees and agents, from any and all liability of whatever nature by reason of furnishing such information.   5. I further agree that any part of the information contained in this application and any subsequent documents in my IARFC registration file may be divulged to interested parties as part of the referral system for the benefit of members and the public.   6. I hereby certify that I have a sound record of business integrity with no suspension or revocation of any professional licenses, and I hereby subscribe to the IARFC Code of Ethics, a copy of which I have read and understand.   7. It is agreed and understood that any material misrepresentation of facts or information given in this or subsequent application or renewal may be cause for immediate revocation of membership and all its privileges, without refund of any dues or fees paid.   8. I understand that failure to disclose any regulatory event, including suspensions or revocations, may disqualify me.   9. I agree to maintain proficiency in my work by completing continuing education in the field of financial planning and counseling — which can include subjects relating to practice management, delivery of professional services, portfolio management or financial product application and service. 10. As an applicant for registration, I understand and agree that my RFC designation with the IARFC will not become effective until submission of all required documentation in proper order and upon written acceptance by the IARFC. 11. I understand that all IARFC Certificates of Registration remain the property of the Association and must be destroyed or returned to the Association should my membership or the right to display the designation certificate be suspended or terminated. 12. I understand that continuation of the RFC designation requires 40 hours of CE per year, which commences January of the year following acceptance. 13. I understand that if I do not meet the required professional experience of 4 years, the IARFC will qualify and award me the RFA designation. _________________________________________________ SIGNATURE OF THE APPLICANT (required) How did you learn about the RFC? Direct Mail

E-mail

Exhibit

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__________________ DATE Article

Association_______________

Insurance Co.__________________

Presentation by______________________________

_________________________________________________ SIGNATURE OF A WITNESS (required) Broker/Dealer_____________

  Partnership

Referral by ______________________________

  IARFC website

RFC class

Other__________________________________________________________________________________________________________________ Please recommend an associate or colleague for IARFC Membership. Name_____________________________________________________ Firm_______________________________________________________ Address__________________________________________________________________________________________________________________ City/State/Zip______________________________________________

Phone__________________ Email______________________________

IMPORTANT: Evidence of license, diploma or similar documents may be requested. However, you need not submit evidence with the application. The Association is compensation neutral regarding plan or portfolio fees, insurance, securities or real estate commissions, salary or bonus. The application fee is nonrefundable.

Please Mail this Application — or Fax to: 513.345.9479


Ethics Approved

IARFC Code of Ethics Brand Your Ethics Approved Status Set yourself apart from other consultants • Affix your Ethics Approved Seal to your framed RFC® Certificate • Send an IARFC Ethics Approved media release to your contacts • Order business cards with the Ethics Approved Seal • Place digitized Seal on your website in a prominent position • Mention this program in client newsletters • Order additional Ethics Approved Seals as a visual reminder • Display the IARFC Code of Ethics plaque in office • Put a link to the IARFC Code of Ethics on your website Visit the IARFC store for these valuable branding tools www.IARFC.org/Store or contact 800.532.9060, info@iarfc.org The Register | September-October 2016

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

Page 15


Journal of Personal Finance International Association of Registered Financial Consultants - IARFC 1046 Summit Drive P.O. Box 42506 Middletown, Ohio 45042

www.journalofpersonalfinance.com

Join the IARFC Membership Door Opens for Life Insurance Professionals

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

www.iarfc.org 800.532.9060

Journal of Personal Finance Vol 15 issue 2  

Welcome to the Fall 2016 issue of the Journal of Personal Finance. In this issue we have six articles covering topics including how to calcu...