Journal of Personal Finance

Volume 18 Issue 1 2019 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

Journal of Personal Finance

Volume 18, Issue 1 2019 The Official Journal of the International Association of Registered Financial Consultants Š2019, IARFC. All rights of reproduction in any form reserved.

Journal of Personal Finance Volume 18, Issue 1 2019 Editor Benjamin Cummings, Ph.D., CFP®, RFC® The American College of Financial Services

Editorial Assistant Amber Lemmon Texas Tech University

Editorial Board Sarah D. Asebedo, Ph.D., CFP® Texas Tech University H. Stephen Bailey, MRFC® HB Financial Resources, Ltd./IARFC David Blanchett, Ph.D., CFA®, CFP® Morningstar Investment Management, LLC Dale L. Domian, Ph.D., CFA®, CFP® York University Ric Edelman, RFC® Edelman Financial Services Michael S. Finke, Ph.D., CFP® The American College of Financial Services Joseph W. Goetz, Ph.D. University of Georgia Michael A. Guillemette, Ph.D., CFP® Texas Tech University Tao Guo, Ph.D., CFP® William Paterson University

Sherman D. Hanna, Ph.D. The Ohio State University Douglas A. Hershey, Ph.D. Oklahoma State University Karen Eilers Lahey, Ph.D. University of Akron Douglas J. Lamdin, Ph.D. University of Maryland Baltimore County Jean M. Lown, Ph.D. Utah State University Lew Mandell, Ph.D. University of Washington Carolyn McClanahan, M.D., CFP® Life Planning Partners, Inc. Yoko Mimura, Ph.D. California State University, Northridge Robert W. Moreschi, Ph.D., RFC® Virginia Military Institute

David Nanigian, Ph.D., CFP® California State University, Fullerton Barbara O'Neill, Ph.D., CFP®, CRPC, AFC, CHC Rutgers University Wade D. Pfau, Ph.D., CFA® The American College for Financial Services Sandra Timmermann, Ed.D. The American College for Financial Services Walt Woerheide, Ph.D. Jing Jian Xiao, Ph.D. University of Rhode Island Rui Yao, Ph.D., CFP® University of Missouri Yoonkyung Yuh, Ph.D. Ewha Womans University

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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 Board, the Institute of Personal Financial Planning, and the Board of the International Association of Registered Financial Consultants specifically disclaim any personal, joint, or corporate (profit or nonprofit) liability for loss or risk incurred as a consequence of the content of the Journal.

General Editorial Policy: It is the editorial policy of this Journal to only publish content that is original, exclusive, and not previously copyrighted.

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ISSN 1540-6717 (Print); 2638-3217 (Online)

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Call for Papers Journal of Personal Finance The Journal of Personal Finance is seeking high-quality manuscripts that add to the growing literature in personal finance and household financial decision making. The editor is looking for original research that uncovers new insights – research that will have an impact on professional financial advice provided to individuals. Potential topics include: •

Individual financial decision making

•

Household portfolio choice

•

Retirement planning and income distribution

•

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. The Journal of Personal Finance is committed to providing high-quality article reviews in a blind, single-reviewer format within 60 days of submission.

Editorial Board The Journal is also seeking qualified members for the Editorial Board. If you are interested in joining the Editorial Board, please send your current CV to the editor at the email address below.

Contact Benjamin F. Cummings, PhD, CFP®, RFC® Editor jpfeditor@gmail.com www.journalofpersonalfinance.com

Volume 18, Issue 1

Contents Financial Planning Client Interaction Theory (FPCIT) ��������������������������������������������������������������������������������������������������� 9 Sarah D. Asebedo, Ph.D., CFP®, Texas Tech University The personal financial planning (PFP) profession has grown substantially since its inception in 1969. While PFP is widely practiced, the recognition of PFP as a formal profession is not widespread. Experts have noted that this is largely due to a lack of PFP-specific theory that illuminates how the PFP profession is unique from its peers. This paper seeks to strengthen the theoretical foundation of PFP by introducing Financial Planning Client Interaction Theory (FPCIT). FPCIT is a theory derived from the very thing that makes PFP special—the financial planner/client relationship. FPCIT explains the utility derived from the interaction between a financial planner and client. FPCIT provides theoretical grounding for quantifying the value of PFP as a professional area of research and practice, which informs PFP stakeholders—financial planners, consumers, academics, regulators, and policy makers— about the benefits and uniqueness of the PFP profession.

A Mechanistic Model of Personal Finance ����������������������������������������������������������������������������������������������������������������������� 25 Joseph L. Galatowitsch, Bachelor of Science, Biomedical Engineering, MBA Widespread personal finance education and advice have not been proven to materially impact the financial health and well-being of most households in the U.S. Ironically, a clear and comprehensive understanding of the operating model of how income, expenses, spending and asset accumulation are interconnected has not been seriously explored or defined. Without an adequate understanding of this operational subsystem, the impact of spending and asset accumulation decisions cannot be objectively assessed. This model provides a robust and actionable tool to help improve understanding of the operating implications and consequences of these decisions.

Broad adoption and use of a standard model of income, expenses, spending, and asset accumulation in financial literacy education may have a positive and sustainable impact on individuals and personal finance professionals alike.

“As Soon As…” Finances: A Study of Financial Decision-Making ����������������������������������������������������������������������������� 37 Barbara O'Neill, Ph.D., CFP, CRPC, AFC, CHC, CFEd, CFCS, Extension Specialist in Financial Resource Management and Distinguished Professor Yilan Xu, Ph.D., Assistant Professor, University of Illinois at Urbana-Champaign Carrie Johnson, Ph.D., AFC, Assistant Professor and Extension Specialist in Personal & Family Finance D. Elizabeth Kiss, Ph.D., Associate Professor and Extension Specialist, Kansas State University Steven Buyske, Ph.D., Associate Professor, Rutgers University This article reports findings from a study of financial decision-making featuring analyses of responses to open-ended questions. The target audience was young adults with 69% of the sample under age 45. Four key financial decisions were explored: financial goals, homeownership, retirement planning, and student loans. Results indicated that many respondents were sequencing financial priorities instead of funding them simultaneously, and they were delaying homeownership and retirement savings. Three-word phrases like “once I have…,” “after I [action],” and “as soon as…” were noted frequently, indicating a hesitancy to fund certain financial goals until achieving others (i.e., sequential goal pursuit). This article also provides implications for financial practice. 37 Credit Card Use of College Students: A Broad Review ������������������������������������������������������������������������������������������������� 55 Alex Yue Feng Zhu, Ph.D., Visiting Research Assistant Professor, Lingnan University (Hong Kong) Using credit cards is an effective way for college students to learn about credit and shape their credit behavior in preparation for financial independence. However, negative outcomes are associated with unhealthy credit card use. By reviewing articles in the past 15 years, this article summarizes the factors influencing credit card use among college students, and identifies the research gaps in previous studies. The purpose of this article is to direct future studies in promoting healthy and responsible credit card use, which promotes financial wellbeing of college students.

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Using donor images in marketing complex charitable financial planning instruments: An experimental test with charitable gift annuities ��������������������������������������������������������������������������������������������������� 65 Russell N. James III, Ph.D, JD, CFP®, Professor, Texas Tech University Previous experimental research has demonstrated simple charitable giving decisions can be influenced by examples of another’s donation. This article uses a series of experiments to explore how the example of another’s complex donation influences interest in a complex charitable financial planning arrangement (a charitable gift annuity). The influence of an example donor varied depending upon the presence and age of the example donor image. A key factor in determining the effectiveness of an example donor image was matching the age of the participant with the age of the example donor image. When the age of the donor image was similar to (differed substantially from) the participant’s age, including the donor image generated higher (lower) interest in the complex gift as compared with the overall interest generated when including a non-donor image or no image. This effect from age matching arose through the impact on perceived similarity and identification with the example donor. The value of a donor image for increasing interest in a complex charitable financial planning instrument depends upon its ability to advance the idea that “people like me do things like this.”

Issues with the Transition Mechanism in the Actuarial Approach to Retirement Spending ���������������������������� 75 Ken Johnston PhD CFA, Associate Professor of Finance, Berry College John Hatem PhD, Professor of Finance, Georgia Southern University Thomas Carnes PhD, Professor of Accounting, Berry College Arman Kosedag PhD, Associate Professor of Finance, Berry College This article highlights some issues with the actuarial retirement withdrawal strategy. There are potential problems with the transition mechanism (present value of an annuity calculation, PVAN) as the retiree ages. With poor initial returns, the actuarial approach does not cut spending quickly enough, due to the mathematics of the annuity formula. Additionally, spending rates can initially be too high or too low depending on the assumed discount rate, which can result in the inefficient spending down of wealth. If a future value is specified as a bequest/safety net, the minimum annual spending over the 30-year period decreases as the future value increases.

The effect of a desired future value on annual spending volatility depends on whether the actual subsequent compounding rate is high or low. An increase in the desired future value results in smaller initial withdrawals, with the portfolio’s recovery dependent on future returns. As remaining longevity declines with a constant future value, large (small) positive returns as the retiree ages force the transition mechanism (PVAN) to significantly increase (not significantly increase) the annual withdrawal, thus increasing (decreasing) the standard deviation.

This effect can possibly turn annual spending negative. Therefore, while the actuarial approach provides solutions to some issues, it also creates new ones.

©2019, IARFC. All rights of reproduction in any form reserved.

Volume 18, Issue 1

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Editor's Notes

I’m excited to share with you the Spring 2019 issue of the Journal of Personal Finance. This issue includes some thought-provoking theoretical pieces and impressive research in personal finance that moves us forward in our understanding of how people interact with their finances.

In the first article, Sarah Asebedo, PhD, CFP®, introduces the Financial Planning Client Interaction Theory (FPCIT). This theory is intended to strengthen the theoretical foundation for the financial planning profession and can be useful for practitioners in communicating their value to clients. By focusing on the uniqueness of the client-planner relationship, the FPCIT provides a helpful framework to connect the technical and non-technical aspects of financial planning.

Joseph L. Galatowitsch, MBA, provides an intriguing model of personal finance in the second article. In his model, he explores the interconnectedness of one’s financial affairs, including how income and expenses interrelate with spending and asset accumulation decisions. He is optimistic that a better understanding of these concepts and how they interact can help improve financial literacy education, and ultimately, financial outcomes.

In the third articles, Barbara O’Neill, PhD, CFP®, CRPC, AFC, CHC, CFEd, CFCS, and her colleagues, Yilan Xu, PhD, Carrie Johnson, PhD, AFC, D. Elizabeth Kiss, PhD, and Steven Buyske, PhD, share their results of research exploring the approach individuals – particularly young adults – take to accomplishing their financial goals. They analyzed responses to open-ended questions about common financial goals to find that people often pursue financial goals sequentially rather than simultaneously. This article is an informative read for advisors working with clients who are pursuing multiple financial goals to better understand how clients may be approaching these goals.

Alex Yue Feng Zhu, PhD, provides an extensive literature review in the fourth article, summarizing what has been studied about the use of credits cards among college students. This overview also provides guidance for future studies on optimal uses of credit cards among young adults. Practitioners will also find the article informative for working with clients who have children in or approaching college, and how to help them use credit cards appropriately.

In the fifth article, Russell N. James III, PhD, JD, CFP®, studied whether showing images of donors influenced interest in complex charitable planning tools. He found that interest was higher when the age of the person in the image was similar to the age of

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the participant. This research has interesting implications that suggest that individuals may look to their peers for guidance about complex financial decisions.

Lastly, Ken Johnson, PhD, CFA, and John Hatem, PhD, explore potential issues with the actuarial approach to designing a retirement withdrawal strategy. They raise concern about the recommended level of initial spending as well as the impact of poor initial returns and subsequent recommended adjustments to spending. Practitioners may find this article interesting as they evaluate their own approach to helping clients establish retirement withdrawal strategies.

We hope you enjoy the latest research and thinking found in this issue of the Journal of Personal Finance.

Sincerely, Benjamin F. Cummings, PhD, CFP®, RFC® Editor

©2019, IARFC. All rights of reproduction in any form reserved.

Volume 18, Issue 1

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Financial Planning Client Interaction Theory (FPCIT) Sarah D. Asebedo, Ph.D., CFP®, Texas Tech University

Abstract The personal financial planning (PFP) profession has grown substantially since its inception in 1969. While PFP is widely practiced, the recognition of PFP as a formal profession is not widespread. Experts have noted that this is largely due to a lack of PFP-specific theory that illuminates how the PFP profession is unique from its peers. This paper seeks to strengthen the theoretical foundation of PFP by introducing Financial Planning Client Interaction Theory (FPCIT). FPCIT is a theory derived from the very thing that makes PFP special—the financial planner/client relationship. FPCIT explains the utility derived from the interaction between a financial planner and client. FPCIT provides theoretical grounding for quantifying the value of PFP as a professional area of research and practice, which informs PFP stakeholders—financial planners, consumers, academics, regulators, and policy makers—about the benefits and uniqueness of the PFP profession.

Key Words Client relationship; Interaction; Personal financial planning; Theory; Utility.

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The practice of personal financial planning (PFP) emerged from the financial services industry in 1969 and is now thriving with almost 80,000 CFP® Certificants across the United States (CFP Board, 2017a). Despite this growth, scholars and practitioners have noted that PFP is not yet widely recognized as a distinct profession amongst its peers (Altfest, 2004; Black, Ciccotello, & Skipper, 2002; Warschauer, 2002). Today, experts continue to discuss PFP as an emerging field that has yet to be recognized and respected alongside other formal professions such as law, medicine, finance, and economics (Kitces, 2017). This is, in part, because PFP is a multidisciplinary practice that incorporates theory, technical knowledge, and strategies from a variety of areas such as investments, income tax, law, communication, and others. (Altfest, 2004; Overton, 2008). It is also due to the lack of formal theory grounding PFP research and practice (Altfest, 2004; Black et al., 2002; Warschauer, 2002). While the body of PFP research has grown substantially since the turn of the century, a formal PFP-specific theory still does not exist today.

Yeske (2010) observed that this theoretical gap has persisted, and Grable (2017) renewed the call for PFP theory at the inaugural CFP Board Academic Research Colloquium for Financial Planning and Related Disciplines. Grable’s call amplifies Altfest’s (2004) assertion that academia must have a direct and concentrated focus on PFP with theory development at its core.

The purpose of this paper is to stimulate PFP theory development and discussion amongst academics and practitioners by proposing a theory that is born out of the very thing that makes Personal Financial Planning (PFP) unique—the financial planner/client relationship. This relationship is a logical place to develop a PFP-specific theory because the financial planner/ client relationship is what sets PFP apart from the professions from which it emerged. The client relationship is a necessary component of the professional practice of PFP. Without a beneficial interaction between a client and financial planner that is built upon trust and commitment, PFP would primarily be a technical and transaction-based commodity, thereby weakening the argument that it is a distinct profession. A theory focused on the financial planner/client relationship provides a foundation for building the PFP profession and expanding the scope of PFP research and practice.

A theory is a set of interconnected ideas that explain a particular phenomenon (Doherty, Boss, LaRossa, Schumm, & Steinmetz, 2009). This paper introduces Financial Planning Client Interaction Theory (FPCIT), which presents a set of interconnected ideas that describe the financial planner/client relationship phenomenon. FPCIT explains the utility derived from the

interaction between a financial planner and client and provides a foundation for quantifying the value of PFP as a professional area of research and practice. FPCIT informs PFP stakeholders—financial planners, consumers, academics, regulators, and policy makers—about the benefits and uniqueness of the PFP profession.

FPCIT Concepts The following sections introduce the theoretical concepts and assumptions relevant to FPCIT. This paper then discusses the connection between FPCIT concepts and applies those concepts to the financial planner/client relationship.

Commodities Commodities are defined as the wants and needs of an individual, household, or firm (Becker, 1976). Commodities often represent tangible goods, such as food, but they can also represent intangible wants and needs like reputation, distinction, and benevolence (Becker, 1976).

Utility Utility is the overall level of satisfaction derived from a combination of commodities. Within FPCIT, utility is conceptualized as a non-domain-specific outcome, such as well-being, happiness, or life satisfaction (Bryant & Zick, 2006); domain-specific utility (e.g., financial satisfaction) is a commodity that contributes to overall utility.

Personal Financial Planning (PFP) FPCIT’s definition of PFP follows the CFP Board (2017b): “‘Personal financial planning’ or ‘financial planning’ denotes the process of determining whether and how an individual can meet life goals through the proper management of financial resources” (p. 1). While scholars have proposed alternative definitions of PFP (e.g., see Warschauer, 2002), the CFP Board’s definition captures the generally accepted meaning and function of PFP.

Client Relationship Within FPCIT, the client can consist of an individual or a couple; the financial planner can consist of an individual professional or an aggregate firm. FPCIT conceptualizes the PFP client relationship as a helping relationship, “in which at least one of the parties has the intent of promoting the growth, development,

©2019, IARFC. All rights of reproduction in any form reserved.

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maturity, improved functioning, [and] improved coping with life of the other” (Rogers, 1958, p. 6). Brammer and MacDonald (2003) further define helping as facilitating growth toward personal goals and strengthening life coping skills. The helping process entails building a relationship and facilitating positive action (Brammer & MacDonald, 2003). FPCIT posits that these helping definitions describe the fundamental role of the financial planner in the client relationship, which is consistent with and complementary to the CFP Board’s (2017b) conceptualization of PFP.

Client Relationship Utility A client and financial planner will enter into a client relationship when both parties expect to experience gains that facilitate utility maximization. Client relationship utility results from realized gains the financial planner and client experience as a result of the client relationship.

Inputs Human capital, resources, and social environmental characteristics represent the inputs the financial planner and client conjointly bring to the relationship. These inputs range from the basic inputs required to provide (and receive, from the client’s perspective) financial planning recommendations, to advanced technical and relational competencies and/or needs. Inputs provide the necessary ingredients for a client relationship to exist and function. Given the central role inputs play within FPCIT, they are described in further detail below.

Scope of Functioning The financial planner’s and client’s unique set of inputs create a commodity production and utility maximization framework that defines the scope of functioning possibility for each party.

The opportunity for gain (or no gain) in the client relationship results from the interaction between the financial planner’s and client’s scope of functioning; client relationship utility is highly dependent upon this interaction.

FPCIT Assumptions FPCIT makes several assumptions about the client, financial planner, and client relationship. First, FPCIT assumes the client’s financial resources are not the outcome or focus of utility; rather, they are a means for producing a general desired utility outcome, such as life satisfaction or well-being. Second, PFP is

conducted within the context of a helping client relationship focused on growth, development, and optimal life functioning through the effective management of the client’s financial resources. Consequently, the client relationship is inclusive of, but not centered upon, financial transactions, technical advice, and/or financial products. Third, FPCIT assumes that both the financial planner’s and client’s scope of functioning are dynamic and subject to intertemporal influences; however, the financial planner’s scope of functioning expands and contracts at a faster rate than the client’s. The financial planner can quickly expand their scope of functioning by leveraging technology, and/or increasing human capital in a cost-effective manner by hiring new talent or leveraging human capital within their firm or business social network. While the client’s scope of functioning can expand, it will do so at a slower rate than that of the financial planner due to monetary constraints and access barriers (i.e., finding the appropriate professionals who can add value, access to technology, etc.). The financial planner faces an inherent risk of losing human capital in the context of a PFP firm where multiple professionals contribute to the client relationship, whereas the client has a greater chance of retaining their scope of functioning through the retention of hired professionals and personal human capital acquired over time. Fourth, any interaction between the financial planner and client can have a neutral, positive, or negative effect on the client’s scope of functioning. For example, the simple act of setting an appointment might cause the client to procure additional financial knowledge and change their financial behavior prior to meeting with a financial planner. Alternately, the client may have a negative experience with a financial planner that harms their belief that they can effectively manage their money or that increases their resistance to change, thereby diminishing the client’s scope of functioning. For example, the financial planner may cause increased client resistance to advice and behavior change through poor communication skills and illtimed techniques that do not match the client’s readiness for change (Horwitz & Klontz, 2013; Klontz, Kahler, & Klontz, 2016).

FPCIT assumes the financial planner’s scope of functioning is less susceptible to change as a result of the financial planner/ client interaction.

FPCIT Inputs A variety of PFP client relationship inputs exist. This section presents the most fundamental inputs applicable to FPCIT.

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Human capital

financial environment.

FPCIT defines human capital as person-specific assets that contribute to future income production and optimal functioning (Becker, 1962), such as physical abilities, mental abilities, and psychological well-being. Essentially, human capital refers to personal resources that are either innate or acquired through education and experience that allow a person to operate effectively in their environment (Becker, 1962). According to Becker (1962), investments in human capital often include education, on-the-job training, healthcare (physical and mental), and knowledge acquisition. FPCIT categorizes human capital as tangible and intangible.

Intangible human capital. FPCIT defines intangible human capital as person-specific characteristics that translate tangible human capital (such as education, experience, and financial knowledge) into a quantifiable outcome—such as financial behavior and goal achievement. Intangible human capital includes physical health, mental health, personality, psychological characteristics, biases, values, beliefs, and communication ability, for example. The role of intangible human capital was illuminated in Huston’s (2010) financial literacy model, which suggested that an individual’s ability to transform their financial knowledge into desired financial behavior is dependent upon contextual variables and psychological characteristics such as cultural/familial circumstances, economic conditions, time preference, and behavioral biases. Thus, tangible human capital is accompanied by intangible human capital; these forms of human capital interact to produce financial behavior.

For the financial planner, intangible human capital enables the effective use of their tangible human capital within the client relationship. For example, the financial planner’s communication skills, empathy, and personality combine to affect how their technical knowledge and recommendations are delivered to the client. This is consistent with Brammer and MacDonald’s (2003) conceptualization of the helping process where the helper’s (i.e., the financial planner’s) personality (traits, values, attitudes) combine with their specific skills to produce a growth-facilitating (or debilitating) condition where specific client outcomes become possible (or diminished). The notion that a helper’s personal characteristics affect the client relationship is well-established and recognized within the helping profession research literature. For example, Ackerman and Hilsenroth (2001, 2003) conducted a comprehensive review of research studies that provided evidence for certain therapist attributes, personality characteristics, and techniques that contributed both positively (such as confidence, warmth, openness, reflection, noting past therapy success, facilitating expression of affect) and negatively (such as uncertainty, inappropriate use of silence, inappropriate self-disclosure, rigidity) to the psychotherapy process and therapeutic client relationship.

Tangible human capital. Education and experience are examples of tangible human capital that are easily measurable and quantifiable. According to Bryant and Zick (2006), on-the-job training and learning by doing are forms of experience acquired over time outside of traditional education. These human capital investments have a positive effect on the individual by increasing their marginal productivity and, consequently, wages (Bryant & Zick, 2006). One example of a basic, necessary investment in human capital for the financial planner is an undergraduate degree with the CFP® certification. Additional investments in education, specialized certifications or designations (e.g., CFA®, CPA, EA, etc.), on-the-job training (e.g., a communication training workshop), and years of experience generate advanced human capital inputs (e.g., knowledge, skills, and abilities) that are available for the client relationship.

These advanced human capital inputs, in turn, increase the financial planner’s scope of functioning and client relationship utility, thereby allowing the financial planner to work with a wide array of clients with a high level of client service efficiency.

For example, a financial planner with advanced communication skills can discover client goals and objectives more effectively than a financial planner with a more basic communication skill set.

For the client, tangible human capital consists primarily of financial knowledge and personal financial management experience (i.e., “learning by doing”). The client can invest in financial human capital in many ways, such as through financial literacy education programs, reading technical financial literature, and obtaining expert opinions and advice. The client can procure a broader scope of functioning by increasing their financial knowledge and by obtaining years of experience managing their financial situation. A client with less financial human capital will have a more limited scope of functioning within their

Resources The client relationship inherently involves an exchange of resources between the financial planner and client. Because the client relationship is defined as a helping relationship, FPCIT posits that both financial and non-financial resources are

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essential inputs. The notion that resources are both financial and non-financial is supported by Foa (1971), who recognized that “because people depend on one another for the material and psychological resources necessary to their well-being, they associate to exchange these resources through interpersonal behavior” (p. 345). Foa (1971) presented an integrated framework that asserts six economic and psychological resources form the foundation for interpersonal relationships: goods, services, money, status, information, and love. Time is an additional resource used to obtain commodities. While Foa (1971) did not directly incorporate time as a resource, Becker (1976) suggested that the cost of time is just as important as the cost of goods. FPCIT posits that goods, services, money, status, information, love, and time are fundamental inputs to the financial planner/client relationship and are categorized according to Foa’s concrete/symbolic and particular/universal continuums (see Figure 1). The concrete/symbolic continuum refers to the tangible (goods and services) vs. intangible (status) nature of the resource (Foa, 1971). The particular/universal dimension refers to the significance of the individual providing the resource, with greater particularity positively correlated with the significance of the resource provider. Figure 1 summarizes the various resource types according to the symbolic/concrete and particularistic/universal continuums.

Figure 1. Resource type continuums, adapted from Foa (1971) Symbolic

Concrete

Status and Information

Love, Money, and Time

Particularistic

Love

Services and Status

Goods and Services Universal

Goods and Information

Money and Time

These resource continuums are relevant to FPCIT because symbolic and particular resources have a more significant impact on the financial planner’s scope of functioning than concrete and universal resources. Concrete and universal resources— goods, services, information, time, and money—represent basic resource inputs to the client relationship. At a fundamental level, PFP involves the exchange of goods (e.g., a comprehensive financial plan, investment or insurance products, etc.), services (e.g., portfolio rebalancing, account paperwork, etc.), information (e.g., advice, recommendations, opinions, financial education, etc.), time (e.g., analysis preparation and meeting time), and money (e.g., compensation, human capital investments, purchase of financial analytical software programs,

assets under management, etc.).

Advanced resource inputs expand the financial planner’s scope of functioning; these advanced resources fall predominantly within the symbolic and particularistic paradigms because they are dependent upon who (particularistic) is providing them and how (symbolic) they are provided; status, information, love, and services are examples of resources that possess these qualities.

Status is an important input within FPCIT since it enhances the financial planner’s social capital, thereby expanding their scope of functioning as a professional within the community. Clients also hire a financial planner for status reasons; a client that simply communicates that they have a financial planner suggests the presence of financial resources or knowledge that can raise their status amongst their peers.

Information can also fall within the advanced realm since it is more symbolic; effective delivery of financial information is highly dependent upon the financial planner’s interpersonal attributes. Similarly, while services are fairly concrete and represent a basic input to the client relationship, they can be particularistic and, thus, highly dependent upon the tangible and intangible human capital of the person delivering the service. Information and services tailored to specific client needs and skillfully delivered become more symbolic and particularistic and consequently, more valuable to the client relationship.

An additional advanced input within FPCIT is love: affectionate regard, warmth, or comfort (Foa, 1971). This notion of love is typically portrayed to the client in a symbolic manner, using verbal and nonverbal communication such as language, body posture, facial expressions, emotions, time, energy, etc. Love might also be portrayed in concrete ways, such as a hug, pat on the back, a kind note, or a gift.

Clients contribute a combination of these resources to the client relationship. For example, the client’s available time and money significantly affect the type of goods and services delivered. The client’s needs also determine the resources contributed by the financial planner. For example, clients vary in the amount of information, warmth, or comfort they need. Overall, the client brings a certain level of resources and needs to the client relationship; FPCIT posits the financial planner’s resources must complement the client’s resources and needs to realize client relationship utility.

Social Environment Becker’s (1976) Social Interaction Theory suggests that social environmental characteristics have a significant effect

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Table 1 Examples of basic to advanced financial planner inputs to the client relationship according to FPCIT Human capital

Basic

Advanced

Tangible: Undergraduate degree, CFP® Certification, and 0-5 years of experience

Tangible: Additional education (e.g., Master’s degree, specialized certificate programs) and additional specialized designations certifications (e.g., CFA®, CPA, JD, etc.), 5+ years of experience.

Intangible: Basic communication skills (e.g., open vs. closed ended questions, active listening, non-verbal body language awareness, etc.).

Resources1

Concrete and universal: goods, services, money, time, and information.

Intangible: Relationship self-efficacy, business development acumen, emotional intelligence, advanced communication and conflict resolution skills, and behavioral bias awareness.

Symbolic and particularistic: love and status.

Symbolic and universal: information.

Particularistic and concrete: services.

Business Social Environment

Allied professional referral network Allied professional referral network conconsisting of accountants, attorneys, and sisting of accountants, attorneys, insurinsurance experts. ance experts, psychologists, therapists, researchers, coaches, etc.

1. Primarily resource inputs (e.g., input a certain level of goods, services, information, love, time, money, and status). Time is typically the amount of time spent on client work and relationship management; money may represent business investments (e.g., software, human capital of staff, employee benefits, etc.) that directly or indirectly impact the Client.

Table 2 Examples of basic to advanced client inputs to the client relationship according to FPCIT Basic

Advanced

Human capital

Tangible: Financial knowledge Intangible: Basic communication skills (e.g., open vs. closed ended questions, active listening, non-verbal body language awareness, etc.).

Tangible: Financial knowledge Intangible: Financial self-efficacy, emotional intelligence, advanced communication and conflict resolution skills, awareness of own behavioral biases, etc.

Resources1

Concrete and universal: goods, services, money, time, and information.

Symbolic and particularistic: love and status.

Symbolic and universal: information.

Particularistic and concrete: services.

Financial Social Environment

A financial social environment consisting of 1 to 2 people (e.g., self and a spouse).

A financial social environment consisting of multiple people, groups, and networks (e.g., spouse, children, parents, friends, charities, other advisors, etc.)

1. Primarily resource needs (e.g., need a certain level of goods, services, information, love, and status). Time and money are the exception, these Client resources are primarily used as currency to obtain the desired commodity.

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on commodity production. In FPCIT, the social environment consists of the social context surrounding the client’s financial environment and the financial planner’s business environment.

The client’s financial social environment encompasses their interpersonal relationships such as with a spouse or significant other, children, parents, grandchildren, siblings, friends, etc. The financial social environment also encompasses broader societal groups that affect how clients interact with their money, such as the media, local community groups, an existing network of professionals, co-workers, etc. The composition of the client’s financial social environment can have a positive, negative, or neutral effect on their ability to produce commodities, thereby affecting the client’s scope of functioning. For the financial planner, the business social environment might include their network of allied professionals (e.g., accountants, attorneys, insurance product experts, mortgage brokers, bankers, etc.) and their access to markets (e.g., investment, credit, etc.). This business social environment affects the financial planner’s ability to deliver client services and expertise, thereby affecting their scope of functioning.

Financial planner and client inputs relevant to FPCIT are summarized in Tables 1 and 2, respectively. The items listed under the basic and advanced columns are not comprehensive—they serve as examples of items that could fall within the human capital, resource, and social environment categories.

These categories provide a foundation for the key ingredients that contribute to client relationship utility. Advanced inputs assume the basic inputs have already been acquired. Overall, inputs create a scope functioning for the client and financial planner that interact together within the context of a client relationship.

Financial Planning Client Interaction Theory (FPCIT) FPCIT posits that the financial planner and client each have a utility function that they seek to maximize as individual units.

The financial planner and the client will enter into a client relationship if each expects the relationship to result in respective utility gains that would not be realized if they were to reject the relationship. Upon entering into a client relationship, the interaction of the financial planner’s and client’s scope of functioning determines overall client relationship utility. This section explains the formation and maintenance of a PFP client relationship, defines the financial planner and client utility functions, and describes the financial planner/client relation-

ship interaction effects.

The Client Relationship The financial planner and client will enter into a client relationship because the total commodity output of the client relationship (ZCR) is greater than or equal to the sum of the single aggregate commodity outputs of the financial planner (ZFP) and client (ZC) (Bryant & Zick, 2006): ZCR ≥ ZFP+ ZC. (1.0) So long as inequality 1.0 holds, the client and financial planner will maximize utility as a result of the client relationship.

Inequality 1.1 draws from Britt and Huston’s (2012) depiction of the expected utility of marriage. From a utility perspective, the financial planner and client will enter into a client relationship when expected client relationship utility (EUCR) is greater than or equal to the sum of the expected utility of the client managing their finances independently (EUC manage own), and the expected utility of the financial planner without the client (EUwithout client), as depicted in inequality 1.1: EUCR ≥ EUC manage own + EUwithout client (1.1) Actual client relationship utility (AUCR) is a function of the variance in actual net utility gains compared to the initial expected utility gains, as depicted in equation 1.2 (Bryant & Zick, 2006):

AUCR = f[EG,VarAG] (1.2) Greater expected net gains and/or lower variance in actual net gains result in positive relationship outcomes (e.g., trust and commitment), whereas lower expected net gains and/or higher variance in actual net gains result in negative relationship outcomes (e.g., dissatisfaction, disengagement, client attrition, etc.). Consequently, the client will want to maintain the client relationship so long as the client receives actual utility gains greater than what they expect to receive from managing their own financial situation or firing the financial planner and seeking a new PFP client relationship. The financial planner will want to maintain the client relationship if the actual utility gains are greater than expected utility gains resulting from firing the client and seeking a client relationship with a new client, as depicted in inequality 1.3: AUCR > EUC manage own/seek new + EUFP fire client (1.3)

The Client’s Utility Function This section defines the financial planner and client utility functions within FPCIT according to Becker’s (1976) Social Interac-

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tion Theory. FPCIT assumes that a client aims to obtain financial stability, achieve their financial goals, and feel financially satisfied. A combination of these commodities will maximize client utility (e.g., well-being, life satisfaction, etc.). Financial stability means that the client is functioning well financially: a growing balance sheet, adequate asset protection, appropriate portfolio allocation, etc. (Asebedo & Seay, 2015). Financial goal achievement refers to the client’s life goals that require ongoing management of financial resources (CFP Board, 2017b).

Financial satisfaction is defined as “satisfaction with one’s present financial situation” (Joo & Grable, 2004, p. 25). Each of these commodities can incorporate both objective and subjective factors, as subjective perceptions are often more powerful than objective economic circumstances (Prawitz et al., 2006).

Function 1.4 states that the client’s ability to maximize utility is dependent upon their production capacity for each commodity (financial stability, goal achievement, and financial satisfaction), where C refers to the client: UC = UC (Zfinancial stability(FS), Zgoal achievement(GA), Zfinancial satisfaction(S)).

(1.4) A specific production function for each commodity determines the quantity and quality of each commodity derived from a given combination of inputs. We will use the financial stability (FS) commodity as an example to illustrate the production function inputs, which correspond with the FPCIT inputs defined above: ZFS = fCFS (xFS,tFS,EC,RlFS, . . . , RpFS ).

(1.5)

Function 1.5 suggests the client can achieve financial stability through a combination of goods and services (xFS), such as a life insurance policy, financial plan, or budgeting program; and allocating more time towards improving their financial situation (tFS), such as tracking expenses, reviewing financial data, and obtaining more advanced or specific financial knowledge. The client’s personal environmental characteristics (EC) affect their ability to achieve financial stability, including tangible human capital (e.g. education and experience) and intangible human capital (e.g. psychological factors, communication skills, mental health status, etc.). It is important to note that the client can make time and/or monetary investments in human capital to achieve greater financial stability levels. Moreover, psychological composition is a component of EC. The client’s basic personality traits (like openness, conscientiousness, extroversion, agreeableness, and neuroticism) and other psychological characteristics (such as financial self-efficacy, future time perspective, etc.) can affect their ability to achieve financial stability.

The client’s social environment

l , (RFS

...,

p) RFS

can also affect their

capacity to achieve financial stability. Following Becker’s (1976) definition of the social environment, FPCIT assumes that the client’s social environment surrounding their financial situation (R) is a combination of their natural environment without influence (the status quo, DC), and the financial social environment as a result of the client’s effort (h): R = DC + h.

(1.6)

Equation 1.6 states that the client’s natural financial social environment (DC) affects their ability to produce financial stability, but the client’s efforts (h) can alter this environment (R). The financial social environment encompasses the characteristics of a variety of individuals (e.g. friends, family, coworkers, financial planner, financial media, other financial experts, etc.) who might have a positive, negative, or neutral effect on the client’s financial social environment and, consequently, their capacity to produce financial stability. For example, let’s assume the client has a financially dependent adult child; this child is part of the client’s natural financial social environment (DC). The client can exert effort (h) to change the negative effects of the child’s financial dependency by reducing or completely removing financial support, or by investing in the child’s human capital (e.g., education and experience). The client may also need to exert effort by improving their communication skills and employing conflict resolution strategies to work through the situation with their child.

In summary, the client’s production function—the goods and services, time, human capital, other personal characteristics, and their financial social environment—defines the client’s available production technology set that is utilized to produce financial stability. This set of production tools directly determines the client’s capacity (i.e., scope of functioning) to produce the commodities necessary to maximize utility. The client could theoretically procure these commodities (and maximize utility) through their current production function. If the client cannot, FPCIT suggests the client will seek ways to expand their production function to do so.

The Client’s Production Function. The client’s production function is depicted in Figure 2. The y-axis represents financial stability (the commodity); the x-axis represents the inputs required to produce financial stability. Given the complexity of representing two or more inputs on a multi-dimensional graph (Rasmusen, 2012), Figure 2 assumes that all inputs are held constant except for time. The function for Figure 2 is represented as C (t ;x ,EC,Rl , . . . ,Rp ). (1.7) ZFS=fFS FS FS FS FS

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Figure 2 The client’s production function for financial stability (ZFS)

R

FSR FSQ FSP

00

ZFS = f CFS (T;X,E,R)

Q P

TQ TP

TR

Time

}

}

Gain Opportunity

Scope of Functioning

The semi-colon dividing time (tFS) from the remaining inputs indicates that the amount of time spent in pursuit of financial stability can vary, whereas the level of goods and services(xFS), personal environmental characteristics(EC), and social environmental characteristics (RlFS, . . . ,RpFS) are fixed (Bryant & Zick, 2006). While this reduced production function is insufficient in describing how all inputs interact to produce financial stability, it provides a simple illustration demonstrating the conceptual relationship between an input (i.e., time) and an output (i.e., financial stability) within FPCIT, while holding other inputs constant.

FPCIT assumes the client’s production function incorporates the law of diminishing marginal returns, a necessary precondition to a production function (Besanko & Braeutigam, 2013; Rasmusen, 2012). The production function curve from the origin (0,0) to Q represents positive and increasing marginal returns, where returns increase at an increasing rate for every one-unit of additional input. The curve from point Q to point R denotes a shape of diminishing marginal returns, where returns increase at a decreasing rate for every one-unit of additional input. The region to the right of R represents a decline in total returns for every one-unit of additional input. The depiction of the client’s production function in Figure 2 is consistent with Bryant and Zick’s (2006) conceptualization of the household production function.

The production function curve represents the combination of inputs and outputs that are technically efficient and feasible (Besanko & Braeutigam, 2013). Input and output combinations above the production function curve are not attainable with the client’s current production set (i.e., inputs). Input and output combinations located beneath the production function curve are possible but are technically inefficient. Thus, points Q and R are examples of technically efficient combinations of time and financial stability; in other words, the client is max-

imizing their financial stability as much as possible given the amount of time they are contributing. Point P represents an inefficient client who is falling short of maximizing financial stability for the amount of time they are spending on their financial situation. The client has reached their maximum production point for financial stability at point R. After the client attains point R, any additional time input results in a decline in financial stability, as any additional inputs beyond point R result in negative returns.

The client’s current production set defines their scope of functioning. As discussed, point R represents the maximum efficient combination of inputs and outputs and defines the upper scope of functioning boundary. The area above point R represents the opportunity for additional financial stability gains should the client expand their current production set, resulting in an upward shift of their production function. A client with an advanced set of production inputs will have a greater scope of functioning than a client with a set of basic production inputs.

Figure 3 represents the financial stability possibility for a client with a basic production set and a client with an advanced production set. As illustrated in Figure 3, a client with an advanced production set can more efficiently produce a greater level of financial stability (FSRA) than the client with a basic production set (FSRB).

Figure 3 The client’s production function for financial stability (ZFS) with basic and advanced inputs Financial stability (FS)

Advanced Inputs

RA

FSRA FSR

R RB

FSRB

ZFS = f CFS (T;X,E,R)

Basic Inputs

FSQ FSP

00

}}

Scope of Functioning (advanced)

TQ TP

TR

Time

Scope of Functioning (basic)

Shifts in the Client’s Production Function. Production functions can shift over time (Besanko & Braeutigam, 2013). Upward shifts of the client’s production function imply a production expansion for financial stability with an efficiency gain. For example, the client could invest their time or monetary resources into technology, education, or expert advice. These investments strengthen the client’s inputs and expand their scope of functioning and possibility for financial stability while reducing the net inputs required to maintain the pre-shift financial stability level (i.e., investment benefits exceed the additional input costs).

Shocks trigger downward shifts that create a production con-

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Journal of Personal Finance

traction for financial stability with an efficiency loss; consequently, the client is unable to maintain and improve financial stability without additional inputs that expand their production function. Shocks might consist of life events and circumstances that are both challenging and productive. For example, an inheritance expands the client’s financial resources and their situation complexity from both a financial and psychological standpoint. This shock necessitates additional time and/or monetary inputs (e.g., additional liability insurance or trust planning, financial counseling or therapy as it relates to inherited wealth, and financial education) to return the client to their pre-shock financial stability level or higher.

The Financial Planner’s Utility Function We can assume that profit, social capital, and relationship satisfaction are commodities (e.g. wants and needs) the financial planner seeks to procure within a client relationship. Profit refers to the net financial gain resulting from a client relationship (income minus expenses). While the financial planner brings social capital as an input to the client relationship, the financial planner also seeks social capital from clients through referrals.

Clients that contribute social capital to the relationship aide in maximizing the financial planner’s utility. Relationship satisfaction results from working with clients that fit the financial planner’s business model, values, goals, and personality. The importance of “client fit” is well known within PFP and is a staple topic within business development discussions (e.g., see Bowen Jr., 2016). While there are certainly other commodities that contribute to financial planner utility, profit, social capital, and relationship satisfaction are examples of fundamental commodities that are commonly sought within the PFP profession.

Thus, the financial planner’s (FP) utility function is described as: UFP=UFP (Zprofit(P),Zsocial capital (SC), Zrelationship satisfaction (RS)). (1.8) Function 1.8 implies that various combinations of profit, social capital, and relationship satisfaction will maximize the financial planner’s utility. Using profit as an example, the financial planner’s production function for profit is defined according to function 1.9: ZP=fPFP (xP,tP,EFP,RPl , . . . ,RpP ).

(1.9)

Function 1.9 suggests that the financial planner can secure a profit through goods and services that reduce costs, create efficiency, and increase income (xP), such as client relationship management software, analytical software programs, and an allied professional referral network (e.g., accountants, attorneys,

financial product experts, etc.). The amount of time (tP) spent in the client relationship has a direct impact on profit, with greater time inputs causing costs to increase and profits to decrease.

The financial planner’s tangible human capital (e.g. education and experience) and intangible human capital (e.g. psychological factors, communication skills, mental health status, etc.) affect their ability to conduct financial planning competently and efficiently, which affects client retention and, consequently, profit (EFP). Like the client’s production function, the financial planner’s production function estimates the maximum commodity output that can be obtained from a given combination of inputs.

The financial planner can increase their tangible and intangible human capital through time and/or monetary investments that achieve profitability. It is important to note that the financial planner’s psychological composition is a component of EFP. Their personality traits (e.g., openness, conscientiousness, extroversion, agreeableness, and neuroticism) and other psychological characteristics (e.g., relationship self-efficacy, mental health status, well-being, etc.) can affect their ability to work with clients and achieve a profit. Additionally, the financial planner’s communication ability (e.g., verbal, nonverbal, conflict resolution, etc.) and overall demeanor (e.g., bedside manner) can affect whether the client perceives financial planning services positively or negatively (Sharpe, Anderson, White, Galvan, & Siesta, 2007; Christiansen & DeVaney, 1998), thereby affecting profitability. The EFP component of the financial planner’s production function illuminates the role financial planner-specific attributes play in converting the financial planner’s expertise into profit within the client relationship.

Last, the financial planner’s social environment (RlP, . . . ,RpP) affects their ability to produce a profit. The financial planner’s business social environment is defined by equation 2.0: R = DFP + h.

(2.0)

Equation 2.0 states that while the financial planner’s natural business social environment (DFP) has an effect on their ability to produce commodities (ZP ; profit), the financial planner can alter this environment (R) with their own efforts (h). The financial planner’s business social environment encompasses access to markets that enhance services delivered to clients (e.g., brokerage firm or broker dealer access, discounted investment transaction fees, access to mutual funds, access to debt products, etc.). Their network of allied professionals (e.g., accountants, attorneys, product experts, etc.) plays two major roles in the financial planner’s production function within (R):

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(1) this valued network is a good or service offered to clients through referrals, and (2) it is a source of support and guidance for the financial planner when encountering challenging client situations.

The financial planner’s production function directly impacts their ability to produce the commodities necessary to maximize utility. Where the client has the possibility of maximizing utility independent of a financial planner, the financial planner is squarely dependent upon the client to maximize utility. For example, a financial planner cannot realize a profit without revenue from client fees. A client will only become dependent upon a financial planner for utility if the financial planner is able to enhance the client’s capacity to produce commodities (i.e., expand the client’s scope of functioning).

The Financial Planner’s Production Function. The financial planner’s production function is depicted in the same manner as the client’s production function in Figures 2 and 3, with the y-axis representing the commodity output (i.e., profit in this example); similarly, the x-axis is represented by time with all other inputs held constant. As with the client, a financial planner with a more advanced production set can efficiently produce a profit level that is greater than that of a financial planner with a basic production set.

Shifts in the Financial Planner’s Production Function. The financial planner’s production function curve can shift for a variety of reasons. Upward shifts imply a production expansion for profit with an efficiency gain. For example, the financial planner could invest their time and money in improving their ability to communicate, understand behavioral biases, resolve money arguments, or advance their technical capability. These investments expand the financial planner’s ability to work with complex client situations (and therefore charge higher fees), while simultaneously realizing efficiency gains (i.e., can more effectively navigate challenging client situations)—representing an upward shift of the financial planner’s production function curve.

Shocks from the client’s situation or the regulatory environment trigger downward shifts that create a production contraction and efficiency loss; the financial planner must expand their scope of functioning (by increasing production inputs) to maintain current client service and efficiency levels. If the financial planner lacks existing inputs required to address these client and/or regulatory issues, they will need to expand their scope of functioning (e.g., through additional education, training, practice, and/or hiring/outsourcing new expertise);

whereas the financial planner with a more extensive scope of functioning will simply need to leverage existing resources.

Within a financial planning firm, downward shifts can also result from employee turnover and the corresponding loss of human capital talent.

The Financial Planner/Client Interaction Effects The financial planner acts as a catalyst within the client’s production function that enables the client’s curve to shift upward or remain constant (i.e., obtain a more optimal level of financial stability or maintain the status quo at a reduced net cost). For this financial planner/client interaction effect to occur, the financial planner’s scope of functioning (as defined by the financial planner’s production function curve) must be at least equivalent to the client’s scope of functioning (as defined by the client’s production function curve) or greater, as demonstrated in Figure 4. Client relationship utility is highly dependent upon this interaction because it creates opportunity for expected net gains and reduces the variance in actual net gains due to the financial planner’s ability to deliver value through a production function that is at least equivalent to or more extensive than the client’s.

In Figure 4, the client is operating at a suboptimal financial stability level due to their production function constraints. The client is also operating in a technically inefficient area within the financial planner’s production function. The financial planner’s production function will be more extensive than the client’s production function if the financial planner possesses resources (e.g., goods and services, personal environmental characteristics, and social environment) that add value to the client’s ability to achieve financial stability. The upward arrow in Figure 4 indicates the possible upward shift in the client’s production function curve and the expected net gain in the client relationship as a result of the financial planner/client interaction. This expected net gain translates into increased financial stability for the client and increased profit for the financial planner. The financial planner in Figure 4 has the incentive (and ability) to shift the client’s production function upward so that the client relationship operates in a more technically efficient area of the financial planner’s production function curve.

FPCIT posits that it is also possible for a client relationship to exist when the financial planner and client production function curves are equivalent. This means that a client may possess the necessary resources to produce the same financial stability level as what the financial planner can produce for them,

20

Journal of Personal Finance

yet they still choose to enter into a client relationship due to comparative advantage and opportunity cost. In this situation, the client would experience an initial production function contraction for financial stability upon entering the client relationship due their reduced time input, thereby creating a discrepancy in the financial planner/client production function curves. This discrepancy generates expected gains for the client relationship: expected client relationship gains (utility) exist in the extent to which the financial planner can shift the client’s production function curve back to the pre-client relationship financial stability level at a net cost reduction to the client. In other words, the client chooses to outsource financial planning services to maintain their current financial situation. FPCIT suggests this outcome is possible due to comparative advantage and opportunity costs.

Figure 4 Financial Planner/Client Interaction: Expected Client Relationship Gain Financial stability (FS) Profit (P) RFP

PRFP

ZP = fPFP (T;X,E,R)

RC Expected Gain

FSRC

ZFS = f CFS (T;X,E,R)

00

Time

TRFP

TRC

}}

Scope of Functioning (financial planner)

Scope of Functioning (client)

As illustrated in Figure 4, FPCIT suggests that it is optimal for the financial planner’s scope of functioning to be equal to or exceed that of the client’s. Therefore, it is suboptimal for the client’s scope of functioning to exceed that of the financial planner. If the client’s scope of functioning exceeds the financial planner’s, then there is no expected gain in the client relationship and consequently, a limited (or no) value proposition.

When the client’s scope of functioning exceeds the financial planner’s, expected net gains decline and eventually turn into losses; this situation results in more variance in actual net gains realized by the client. Figure 5 illustrates the client operating at a scope of functioning greater than that of the financial planner. Under this scenario, the financial planner would need to expand their production set to shift their curve upwards, or the financial planner would need to target clients that fit within their existing scope of functioning.

Figure 5 Financial Planner/Client Interaction: No Expected Client Relationship Gain Financial stability (FS) Profit (P) RC

FSRC

RFP

PRFP

0, 0

ZFS = f CFS (T;X,E,R)

ZP = fPFP (T;X,E,R)

Time

TRC

}}

Scope of Functioning (client)

No Expected Gain

TRFP

Scope of Functioning (financial planner)

A client relationship is unlikely to exist for an extended period of time in the event that the client’s scope of functioning exceeds the financial planner’s. This is primarily due to the financial planner operating in a technically inefficient region of the client’s production set. If a client relationship does exist in this situation, it is likely one focused on basic technical advice, products, or services that are exchangeable and attainable from a variety of sources in the marketplace. This financial planner provides a valuable service, but is simply incapable of generating expected client relationship gains that facilitate utility maximization. It is important to note that Figures 4 and 5 place the financial planner and client production functions on the same coordinate plane for conceptual purposes; however, more research is needed to determine an appropriate mathematical comparative analysis.

Discussion The purpose of this paper is to bring theory to the forefront of academic and practitioner discussions by addressing the call for a theory that provides a foundation for the PFP profession (Altfest, 2004; Grable, 2017). This paper introduces Financial Planning Client Interaction Theory (FPCIT). FPCIT is set apart from other PFP theories (e.g., see Altfest, 2004; Overton, 2008) because it is grounded in the financial planner/client relationship, a unique phenomenon that distinguishes PFP from other finance-oriented professions. FPCIT provides a theoretical foundation for PFP and addresses Altfest’s (2004) stated objectives for PFP theory development.

First, Altfest (2004) asserts that “a theory of PFP would validate the individuality of its characteristics, lead to further academic research, and enhance the stature of the profession” (p. 58).

FPCIT describes the core characteristic of PFP that distinguishes it from other areas of research and practice—the financial planner/client interaction. Within FPCIT, the financial planner acts as a catalyst within the client’s production function that propels

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the client to new possibilities that are not otherwise attainable.

This is a unique and powerful outcome that historically has been hard to quantify and describe (Tharp, 2017), even though it is the essence of the value the financial planner brings to the table. The relationship between the financial planner’s production function and the client’s production function determines the extent to which the financial planner is able to effect positive change on the client and actually cause the client to shift to these new utility heights.

FPCIT also underscores that the financial planner/client interaction is complex with outcomes that vary substantially in magnitude from negative, to neutral, to positive. If PFP were grounded in a pure technical and transactional exchange, then it would be less complex with reduced variability in client outcomes. The recognition of this complexity and outcome magnitude within the context of a client relationship illuminates the need for rigorous academic research and enhances the stature of PFP as a profession. Future research addressing the value of financial advice stands to benefit significantly from FPCIT, since it will guide the researcher towards a set of interconnected ideas that explains the financial planner/client interaction phenomenon. This set of interconnected ideas provides a theoretical foundation to generate hypotheses and evidence that supports or falsifies FPCIT. PFP stakeholders (financial planners, consumers, academics, regulators, and policy makers) will gain a much deeper understanding of the special PFP client relationship by incorporating a theoretical foundation through FPCIT from which to apply a rigorous scientific method.

Second, Altfest (2004) states that “it [PFP theory] would result in such things as broadening academic finance, now almost exclusively focused on financial assets, providing further linkage of the parts of the financial plan for financial practitioners” (p.

58). FPCIT provides a link between the technical and non-technical aspects of financial planning. It also provides theoretical support that the non-technical components of PFP, such as communication, conflict resolution, counseling, psychology, therapy, cognitive biases, and business development, should have a greater emphasis in education, research, and practice than are currently. FPCIT suggests the financial planner’s scope of functioning and ability to help clients will increase through human capital investments in these areas.

This is not to say that technical knowledge has no value; technical competency is essential to PFP. Advanced technical skills create an expanded scope of functioning for the financial planner and add value to the client relationship. However,

technical recommendations and outcomes are insufficient in quantifying the value of PFP advice when viewed in isolation.

For example, Hanna and Lindamood (2010) provided an analysis for quantifying PFP advice based upon three technical PFP outcomes: increasing wealth, preventing loss, and smoothing consumption. Hanna and Lindamood concluded that the value of technical advice varies with risk aversion and the percentage of wealth subject to gain or loss. In other words, the value of technical PFP advice is highly dependent upon contextual client factors. FPCIT suggests that the combination of technical and non-technical competencies will provide the financial planner with a greater capacity to help clients maximize utility than technical competencies alone. Researchers and experts have noted that expanding PFP to non-technical areas may become a necessity as technical financial planning advice (e.g. investment management) becomes more commoditized and exchangeable in the market place (Cheng, Browning, & Gibson, 2017; Kitces, 2017).

FPCIT suggests that financial planning education programs focused predominantly on technical knowledge will produce financial planners with a more limited scope of functioning than education programs centered upon the human element of PFP. PFP education programs that encompass topics such as advanced communication and conflict resolution skills, psychology, therapy, and behavioral finance will have a competitive advantage over programs focused predominantly on technical knowledge.

Fourth, PFP theory would help “…the public better understand why personal financial planning is performed” (Altfest, 2004, p.

58). FPCIT explains that a consumer could invest in their own production function inputs to improve their financial situation and maximize utility; alternatively, a consumer could hire a financial planner due to production function constraints and the increasing complexity of the financial environment. FPCIT suggests that a financial planner and client will enter into a client relationship when each party expects a net gain from the relationship. This means the financial planner can help a client maximize utility net of any financial or psychological costs. The financial planner adds value when the client realizes a net gain within their production function; similarly, the client is worth retaining if the financial planner realizes a net gain to their production function as a result of the relationship. Thus, FPCIT provides a framework for both financial planners and the public to value PFP advice and services.

A significant limitation to FPCIT is a lack of available data to

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Journal of Personal Finance

empirically support or falsify the theory. Several national data sets exist that focus on client attributes and behavior; however, these data sets lack reliable and valid financial planner related measurements (Hanna & Lindamood, 2010; Heckman, Seay, Kim, & Letkiewicz, 2016; Tharp, 2017). To deepen our understanding of the financial planner/client relationship and value of PFP, data that encompasses both client and financial planner definitions, characteristics, and behaviors that are valid and reliable and derived from longitudinal and experimental research methods is essential. This need for client and helper focused data is well-known within the traditional helping professions (e.g., psychotherapy, counseling, etc.), as it has been shown that helper characteristics do indeed impact client outcomes within the therapeutic relationship (Ackerman & Hilsenroth, 2001, 2003; Brammer & MacDonald, 2003). FPCIT provides a foundation to construct hypotheses about how financial planner attributes may affect the client relationship and overall client PFP outcomes. While FPCIT can continue to develop without a comprehensive source of data, it will be a necessary next step to develop data sets that facilitate empirical testing of FPCIT and to quantify the value of the financial planner/client interaction effects.

Implications and Conclusion FPCIT has several implications for PFP stakeholders. FPCIT provides a mechanism to quantify and communicate the value of PFP advice and services. This helps financial planners with a broader scope of functioning to differentiate themselves in the marketplace. For clients, it allows them to directly compare financial planner value and the cost of services, which increases the level of transparency in PFP—a necessity for PFP to develop as a profession. Regulators and policy makers can use FPCIT as a foundation for regulation and policy governing PFP services and client standards of care. Educational institutions can use FPCIT as a foundation to expand curriculum to develop future financial planners with a broad and well-rounded scope of functioning, or an extensive scope of functioning within a particular specialty. Last, there is currently only one topic (principles of communication and counseling) out of 72 principal knowledge topics for CFP® Professionals dedicated to non-technical skills (CFP Board, 2015). The CFP Board can consider expansion of the non-technical requirements for CFP® Certificants. Existing helping professions (e.g., therapist, psychologists, counselors, etc.) already possess and apply a rigorous framework to test, certify, and license helping skills (“non-technical skills”) in terms of education, testing, expe-

rience, supervision, mentorship, etc. This process is licensed and regulated by States and can typically be found on State government websites. The CFP® certification process follows a similar structure as existing helping professions by requiring a certain level of education, experience, and supervision; however, the CFP® certification requirements are broader and generally less rigorous. To properly educate, train, and assess financial planner’s non-technical abilities, the CFP Board might consider revising the education, examination, experience, and ethics requirements in conjunction with expanding the topic list.

References Ackerman, S. J., & Hilsenroth, M. J. (2001). A review of therapist characteristics and techniques negatively impacting the therapeutic alliance. Psychotherapy: Theory, Research, Practice, Training, 38(2), 171–185. http://dx.doi.org/10.1037/00333204.38.2.171 Ackerman, S. J., & Hilsenroth, M. J. (2003). A review of therapist characteristics and techniques positively impacting the therapeutic alliance. Clinical Psychology Review, 23(1), 1–33. http:// dx.doi.org/10.1016/S0272-7358(02)00146-0 Altfest, L. (2004). Personal financial planning: Origins, developments and a plan for future direction. The American Economist, 48(2), 53–60. http://dx.doi.org/10.1177/056943450404800204 Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70(5), 9–49. http://dx.doi. org/10.1086/258724 Becker, G. S. (1976). The economic approach to human behavior.

Chicago, IL: The University of Chicago Press.

Besanko, D., & Braeutigam, R. (2013). Microeconomics (5th Ed.).

Hoboken, NJ: John Wiley & Sons, Inc. Black, K., Jr., Ciccotello, C. S., & Skipper, H. D., Jr. (2002). Issues in comprehensive personal financial planning. Financial Services Review, 11(1), 1–9.

Bowen Jr., J. J. (2016). Finding the perfect client fit. Financial Planning. Retrieved November 5, 2017 from https://www.financial-planning.com/news/finding-the-perfect-client-fit Brammer, L. M., & MacDonald, G. (2003). The helping relationship: Process and skills (8th ed.). Boston, MA: Allyn and Bacon.

Britt, S. L., & Huston, S. J. (2012). The role of money arguments in marriage. Journal of Family and Economic Issues, 33(4), 464–476. http://dx.doi.org/10.1007/s10834-012-9304-5

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Bryant, W. K., & Zick, C. D. (2006). The economic organization of the household (2nd ed.). New York, NY: Cambridge University Press.

Horwitz, E. J., & Klontz, B. (2013). Understanding and dealing with client resistance to change. Journal of Financial Planning, 26(11), 27–31.

CFP Board. (2015). 2015 Principal knowledge topics (72 Topics).

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Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs, (44)2, 296–316. http://dx.doi.org/10.1111/j.17456606.2010.01170.x

CFP Board. (2017a). CFP® professional demographics. CFP Board News and Events. Retrieved October 27, 2017 from https:// www.cfp.net/news-events/research-facts-figures/cfp-professional-demographics

Joo, S. H., & Grable, J. E. (2004). An exploratory framework of the determinants of financial satisfaction. Journal of family and economic Issues, 25(1), 25–50. http://dx.doi.org/10.1023/B:JEEI.0000016722.37994.9f

CFP Board. (2017b). Terminology. CFP Board Standards of Professional Conduct. Retrieved October 27, 2017 from: https:// www.cfp.net/for-cfp-professionals/professional-standards-enforcement/standards-of-professional-conduct/terminology

Kitces, M. (2017). Finology and finding the higher purpose of the financial planning profession. Nerds Eye View at Kitces.com.

Retrieved October 29, 2017 from https://www.kitces.com/blog/ dick-wagner-finology-integral-finance-higher-purpose-planning-profession/

Cheng, Y., Browning, C., & Gibson, P. (2017). The value of communication in the client-planner relationship. Journal of Financial Planning, 30(8), 36–44.

Klontz, B., Kahler, R., & Kontz, T. (2016). Facilitating financial health: Tools for financial planners, coaches, and therapists (2nd ed.). Erlanger, KY: The National Underwriter Company

Christiansen, T., & DeVaney, S. A. (1998). Antecedents of trust and commitment in the financial planner-client relationship.

Journal of Financial Counseling and Planning, 9(2), 1–10.

Overton, R. H. (2008). Theories of the financial planning profession. Journal of Personal Finance, 7(1), 13–41.

Doherty, W. J., Boss, P. G., LaRossa, R., Schumm, W. R., & Steinmetz, S. K. (2009). Family theories and methods: A contextual approach. In Sourcebook of family theories and methods. New York, NY: Springer Science+Business Media, LLC. Foa, U. G. (1971). Interpersonal and Economic Resources.

Science, 171(3969), 345–351. http://dx.doi.org/10.1126/science.171.3969.345 Grable, J. (2017). Frontiers of financial planning research and their expansion beyond the core areas of financial planning.

Presentation delivered at the CFP Board Academic Research Colloquium for Financial Planning and Related Disciplines.

Retrieved from https://centerforfinancialplanning.org/ initiatives/academic-homebody-of-knowledge/academic-research-colloquium-for-financial-planning-and-related-disciplines-cfp-board-registered-program-conference/ speakers/#Grable Hanna, S. D., & Lindamood, S. (2010). Quantifying the economic benefits of personal financial planning. Financial Services Review, 19(2), 111–127.

Heckman, S. J. Seay, M. C., Kim, Kyoung Tae, & Letkiewicz, J. C. (2016). Household use of financial planners: Measurement considerations for researchers. Financial Services Review, 25(4), 427–446.

Prawitz, A. D., Garman, E. T., Sorhaindo, B., O’Neill, B., Kim, J. & Drentea, P. (2006). InCharge financial distress/financial well-being scale: Development, administration, score interpretation.

Journal of Financial Counseling and Planning, 17(1), 34–50.

Rasmussen, S. (2012). Production economics: The basic theory of production optimisation. Springer Science & Business Media.

Rogers, C. R. (1958). The characteristics of a helping relationship. Journal of Counseling & Development, 37(1), 6-16. http:// dx.doi.org/10.1002/j.2164-4918.1958.tb01147.x Sharpe, D. L., Anderson, C., White, A., Galvan, S., & Siesta, M. (2007). Specific elements of communication that affect trust and commitment in the financial planning process. Journal of Financial Counseling and Planning, 18(1), 1–17.

Tharp, D. (2017). Can we trust research on the use and benefits of financial advisors? Nerds Eye View at Kitces.com. Retrieved from: https://www.kitces.com/blog/trust-research-advisor-planner-use-benefits-value-vanguard-alpha-morningstar-gamma/ Warschaeuer, T. (2002). The role of universities in the development of the personal financial planning profession. Financial Services Review, (11)3, 201–216.

Yeske, D. (2010). Finding the planning in financial planning.

Journal of Financial Planning, 23(9), 40–51.

Volume 18, Issue 1

25

A Mechanistic Model of Personal Finance

Joseph L. Galatowitsch, Bachelor of Science, Biomedical Engineering, MBA

Abstract Widespread personal finance education and advice have not been proven to materially impact the financial health and wellbeing of most households in the U.S. Ironically, a clear and comprehensive understanding of the operating model of how income, expenses, spending and asset accumulation are interconnected has not been seriously explored or defined. Without an adequate understanding of this operational subsystem, the impact of spending and asset accumulation decisions cannot be objectively assessed. This model provides a robust and actionable tool to help improve understanding of the operating implications and consequences of these decisions. Broad adoption and use of a standard model of income, expenses, spending, and asset accumulation in financial literacy education may have a positive and sustainable impact on individuals and personal finance professionals alike.

Key Words personal finance model, mechanistic model, emergency fund, cash reserves, working capital, financial well-being, financial literacy, free cash flow, replacement rate, asset allocation, financial decision-making

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Journal of Personal Finance

Background Personal finance includes a broad set of components, factors, decisions, and dynamics that ultimately deliver (or fail to deliver) financial well-being. Financial well-being is generally defined as the ability to meet immediate, intermediate, and long-term financial commitments and goals and the financial freedom to enjoy life. Broadly, these personal finance components include: savings, investments, investment strategies, spending and purchase behaviors, financial management, financial literacy, motivations, planning behaviors, and others.

The literature has deep roots in investment engagement and strategies, behavioral economics, modeling of underlying characteristics that drive buying behaviors and wealth creation, the value and use of various personal financial tactics like budgeting, saving and assessing the robustness of financial tools and strategies. However, the body of peer-reviewed published literature provides little commentary on or study of the mechanistic interrelationships between income, expenses, spending, and asset accumulation decisions. Furthermore, there has been no consideration of the potential linkage between this personal finance sub-system and financial well-being.

Personal finance education and advice alone have not been proven to materially impact the financial health and well-being of most households in the U.S. (Bricker et al., 2017). A pervasive, chronic, and frustrating problem exists for individuals and families as they attempt to apply personal finance advice and tools to their personal finance operations. Almost everyone can appreciate the value of axioms such as “live below your means” or “have an emergency fund,” but how does one operationalize this advice? The fundamental issue and question is: Mechanistically, what defines our personal finances? Is it checking and savings accounts? Investment accounts? Debts? Assets?

Expenses? In fact, these are all elements of this system, but they are not the system itself. The literature and government-sponsored training programs and resources on financial literacy (CFPB, 2017 – Financial Literacy) provide a wealth of information on financial decisions and general implications or tradeoffs of those decisions; however, they, and all other identifiable resources, are silent on the operating mechanisms of personal finance. What is this elusive beast and what does it look like?

This article defines the operating mechanism of personal finance as a mechanism that includes income, expenses, spending, and the accumulation and allocation of all owned assets.

In contrast to other personal finance related literature, this operating mechanism does not include investment strategies,

planning, behavioral dynamics, asset management, insurance, or other important considerations related to the totality of personal finance nor the means of effectively using this operational sub-system. A proposed model for this operating mechanism that effectively describes its components, interdependencies, and dynamics is illustrated by the Personal Finance Operating Model (PFOM), which is defined and described below.

The hypothesis to be explored is that a lack of understanding and operationalization of two critical factors (see below) creates an environment where it is much more difficult to achieve and sustain a healthy level financial well-being.

•

Factor one: Historically, there has been a mischaracterization of the role and value of an “emergency” fund as a “nice to have” backstop against unforeseen expenses or disruptions in income. In fact, these funds are not for “emergencies”—rather, they are an essential and integral component of a well-functioning personal finance operating system.

•

Factor two: A lack of understanding of the operational dynamics associated with their financial decisions limits individual’s ability to maintain a healthy level of “emergency funds” and therefore a well-functioning personal finance operating system that can be controlled and harnessed to achieve and maintain satisfactory levels of financial well-being.

Given these mischaracterizations and gapes in understanding, a key assertion emerges: a mechanistic Personal Finance Operating Model can help individuals more effectively achieve financial well-being by helping them build and maintain a meaningful level of excess cash and providing a mechanism to more effectively understand the impact of their financial decisions on their actual financial well-being.

Financial Well-Being: The Consumer Financial Protection Bureau (2015) summarized financial well-being in the following way: Financial well-being is a state of being wherein a person can fully meet current and ongoing financial obligations, can feel secure in their financial future and is able to make choices that allow them to enjoy life. More specifically, an individual’s financial well-being corresponds to the extent to which the individual feels that he or she: (1) has control over day-to-day and month-to-month finances; (2) has the capacity to absorb a financial shock; (3) is on track to meet his or her financial goals; and (4) has the financial freedom to

©2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

make the choices that allow one to enjoy life.

Recently, a standard instrument has been developed and validated to measure personal financial well-being and directly correlate to actual financial circumstances (See CFPB 2015, 2017).

This validated instrument was then used to evaluate the financial well-being of U.S. households. The results were published in 2017 (See CFPB, 2017 Well-Being). From the CFPB survey: the average Financial Well-Being score was 54 (0-100 scale). Those households who scored below 50 had a very high probability (> 50%) of being unable to make ends meet or to be able to absorb even a small unexpected expense. Those who scored above 61 had a very low probability (< 10%) of having difficulty making ends meet or covering a modest to large unexpected expense.

Of the many variables evaluated, two variables had very significant contributions to overall financial well-being scores.

Specifically, the size of an individual’s cash cushion was the single largest contributor to differences in financial well-being scores. In fact, those with liquid cash savings of more than $75k had average scores of 68, independent of income. Another significant factor that contributed to higher scores was the individual’s financial know-how, confidence, money management skills, and ability to exert control over their financial behaviors.

Importantly, these variables are relatively modifiable vs. other positively correlated demographic variables like age, level of education and income.

These findings suggest that a large portion of the U.S. households would benefit from any tool that can help them build and maintain their cash savings or “emergency fund” and provide more financial know-how and insight into the operational characteristics of their personal finances. As demonstrated in the Financial Well-being study, these factors can have a material impact on user’s sense of financial well-being as well as their underlying financial health.

Cash Reserves The literature contains many references and analysis of the size and use of an emergency fund to support the needs of individuals and households (Chang, 1997). Separately, the CFPB has demonstrated a strong statistical correlation between higher levels of financial well-being and higher levels of cash savings. To merge these important and similar constructs into a single term to be used in this article from this point forward:

emergency funds, cash savings, money market funds, checking, short-term savings, etc. will be replaced with the term “cash reserves” to create a stronger alignment between their collective function as working capital within the PFOM and its description.

The goals of this Personal Finance Operating Model (PFOM) are several-fold: 1.

2.

3.

4.

5.

6.

7.

Provide a framework for understanding how the operational mechanism of personal finance “works.”

Provide new insight into mechanistically how the system can, and frequently does, break-down.

Provide a set of core measures or KPIs that prospectively monitor and reflect the health of the system.

Provide a standard model for understanding the operating mechanism of personal finance and the impact of decisions on the overall health of the user’s personal finances—specifically on the ability to build and maintain a healthy level of Cash Reserves.

Provide a standard operating model for further study, aggregation, comparison and analysis of the current and future financial performance of households individually or in the aggregate.

Provide a tool to translate and operationalize financial education, tools, or other strategies designed to inform decision-making and improve financial health and well-being.

Lastly, provide a practical tool for individuals and families to directly influence their financial health, wellbeing, and net worth.

Personal Finance Operating Model (PFOM) definition and components The PFOM is comprised of two interconnected macro components: - Cash Flow Component (CFC) (Figure 1) - Asset Holding Component (AHC) (Figure 2) The Cash Flow Component has two fundamental stages (1 and 2). Stage 1 is comprised of the following sub-components: - One Input: Gross Income (GI) (income from all sources) - Taxes (Tx) (Fed, State, Local) - Asset Burden (AB) (All costs and expenses associated with owned assets)

28

-

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Journal of Personal Finance

Routine Living Expenses (RLE) (Basic expenses including utilities, food, clothing, personal care, insurance and other necessary household operating expenses) o Routine Operating Expenses (ROE) = AB + RLE One Output: Free Cash Flow (FCF) = GI – Tx - ROE

Stage 1 of the Cash Flow Component encompasses the basic costs and expenses associated with personal financial responsibilities and household management. The organization is designed to capture expenses that are necessary, routine, relatively consistent in size from month to month or that are directly associated with owned assets. The output of Stage 1 of the Cash Flow Component is Free Cash Flow. Free Cash Flow is a direct measure of excess cash after accounting for Routine Operating Expenses and taxes.

1,.<%$'A

Cash Reserves is expected to be a sufficiently large pool (about six times one’s Routine Operating Expenses) to be able to absorb a significant episodic expense (e.g. vacation), string of large Unpredictable Expenses or to weather a significant disruption in Gross Income. Cash Reserves has often been mischaracterized as an emergency fund and as a “nice to have” backstop for basic savings and checking resources. In fact, a user’s personal finances cannot function without this essential sub-component.

The Asset Holding Component also has several sub-components:

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management: discretionary spending and relatively large unpredictable expenses. The input of Stage 2 is Free Cash Flow and the output is Asset Holding Component Contributions.

These contributions feeds into the Asset Holding Component of the PFOM. Asset Holding Component Contributions represent the residual operating funds available at the end of each monthly income-expense cycle.

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- Stage 2 of the Cash Flow Component has the following sub-components: - One Input: Free Cash Flow - Cash Reserves (CR)—Large pool of working capital to cover non-routine expenses - Lifestyle Expenses (LSE)—Entertainment, vacations, convenience, hobby/interests, and comfort-related expenses - Unpredictable Expenses (UPE)—e.g. broken water heater, car accident, storm damage o Non-Routine Expense (NRE) = LSE + UPE - One Output: Asset Holding Component Contributions (AHCC) = FCF - NRE Stage 2 helps to effectively manage the most difficult and variable aspects of day-to-day and month-to-month financial

-

Living Assets (LA) — Traditional investments (stocks, bonds, mutual funds) and cash.

Marginal Assets (MA) — Owned assets that require maintenance and upkeep to protect their value. These are also much more difficult to value and liquidate (e.g. house, collectables, art, etc.).

Dead Assets (DA) — Owned assets that depreciate over time and with use (e.g. cars, recreational vehicles, boats, furnishings, etc.).

Soft Debt (SD) — All debt tied to Marginal Assets and where interest expense is tax deductible.

Hard Debt (HD) — All consumer debt. Secured (tied to cars, boats, RVs, etc.) and unsecured (credit cards).

The Asset Holding Component of the PFOM produces two key outputs: - Output One: Asset Burden (AB) — the very same Asset Burden that must be covered in Stage 1 of the Cash Flow Component - Output Two: Net Worth Gain (NWG) — this is the composite of annual depreciation and appreciation of all owned assets.

Asset Burden provides an important (negative) feedback loop from the Asset Holding Component back into the Cash Flow Component. Net Worth Gain simply measures the combined

©2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

impact of appreciation and depreciation on the Net Worth of the user’s Asset Holding Component. Both Asset Burden and Net Worth Gain document and quantify the impact of asset allocation decisions within the user’s PFOM. Over time, Net Worth Gain has a cumulative impact on Net Worth. Within Net Worth resides Retirement Value, which is simply the value of investment assets dedicated to funding retirement income (Retirement Income from Investments —RIFI).

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Operating Dynamics

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Personal Finance Operating Model–PFOM (Figure 3) Functionally, the PFOM provides a closed-loop, dynamic model for understanding the mechanics of personal finance, the

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Maintaining Cash Reserves: Maintaining a functionally sufficient level of Cash Reserves is the result of managing the inflow and outflows of Stage 2 of the Cash Flow Component and is central to a healthy PFOM; it also directly contributes to developing a high level of financial well-being. The inflow is straightforward and relatively consistent: Free Cash Flow. The outflows are more complex as they include three elements: Lifestyle Expenses, Unpredictable Expenses and Asset Holding Component Contributions.

Collectively, these outflows can be described as Cash Burn (CB) and they have two important characteristics that need to be considered: magnitude and frequency. Any monthly outflow that is based on routine behaviors (e.g. eating out, morning coffee shop), or by design (e.g. saving for a replacement car, future college expense, or down-payment on a house), or through contractual obligation (e.g. Cable TV subscription) is identified and categorized as Baseline Cash Burn (BCB). All other outflows

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specific measurable impact of decisions, and how to effectively operationalize, control, and manage personal finances over time. In practice, this model can clearly demonstrate and provide insight into the dynamics that cause individuals and families to become trapped by a dysfunctional or compromised PFOM. It can also provide insight into decisions before they are made to help users avoid actions that can compromise an otherwise well-functioning PFOM, or to optimize or shape a well-functioning PFOM to better align with the user’s lifestyles and financial goals.

30

Journal of Personal Finance

are designated as Episodic Cash Burn (ECB). Episodic Cash Burn includes all Unpredictable Expenses and episodic Lifestyle Expenses (e.g. vacations, week-end getaways, seasonal spending, significant events, etc.) CB = BCB + ECB = LSE + UPE + AHCC For all Episodic Cash Burn related expenses, the user can estimate (e.g. Unpredictable Expenses) or budget (e.g. vacations) the annual cost of these expenses and convert this into a monthly average that for many months may be zero and in others as large as two or more months of Routine Operating Expenses. Thus, the user can estimate and target total Cash Burn to be approximately equal to Free Cash Flow, creating a stable (though highly fluctuating) Cash Reserves balance (see Figure 4)

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Additionally, by separating outflows from Cash Reserves into baseline and episodic components, we can now measure the short- and long-term impact of various decisions on the user’s ability to maintain Cash Reserves. The two critical dynamics we can now measure are the impact of structural and operating changes on a PFOM and the ability to maintain or sustain Cash Reserves. An example of structural changes would include decisions that increase Asset Burden or otherwise decrease Free Cash Flow. An example of an operating change would be a significant episodic expense (e.g. vacation) that has an immediately impact on Cash Reserves balance but does not alter the fundamental inflow (Free Cash Flow) or outflow (Cash Burn) characteristics of Stage 2 of the Cash Flow Component.

An index that can help quantify these important dynamics can be described as Replacement Rate (RR). The Replacement Rate is the measure of the number of months required to replace one month’s worth of Routine Operating Expenses in Cash Re-

serves. Mathematically: RR = ROE / (FCF – BCB), where all values are in monthly amounts. If we also measure Cash Reserves as a multiple of Routine Operating Expense (CR / monthly ROE), then Replacement Rate and Cash Reserves can be understood in both relative and absolute terms that are specific to an individual PFOM. To see how these measures are relevant in practice, consider a PFOM where Cash Reserves = 6 months of Routine Operating Expense and Replacement Rate = 4 months of Routine Operating Expense. This would suggest that if an episodic expense equal to one month’s worth of Routine Operating Expense were to occur, Cash Reserves would be reduced to 5 months of Routine Operating Expense and it would take about 4 months for Cash Reserves to recover to its original level. This presumes that there are no additional large episodic expenses during the 4-month recovery period.

Conversely, if the starting Cash Reserves = 3 months and the Replacement Rate = 22 months and the same episodic expense occurred, the exact same mechanism would operate, but in practical terms this PFOM is extremely vulnerable. The first vulnerability is in having Cash Reserves drop to 2 months of Routine Operating Expense, which would be an operating issue. This is a dangerous level of “working capital”; if it were to be depleted, it would be necessary for the user to utilize consumer credit to bridge any gaps in resources to cover expenses.

In addition, with a Replacement Rate of 22 months (structural issue), the time it will take to replenish Cash Reserves is impractically long. It is very unlikely that there will not be several significant and unavoidable episodic expenses over 22 months.

Without intervention, this operating and structural condition creates a dynamic where a self-funded Cash Reserves becomes unsustainable and will eventually become fully depleted and replaced by Virtual Cash Reserves (VCR), which is the chronic use of easily accessible revolving credit card debt.

In figure 5, we can see this dynamic play out over time. In this example, Free Cash Flow = $2800, Baseline Cash Burn = $1900, Episodic Cash Burn average = $770. Total Cash Burn = $2670 and target Cash Reserves = $32,000. Overall average Free Cash Flow is slightly larger than average Cash Burn (as intended), resulting in a stable, though volitile Cash Reserves balance. In months when Episodic Cash Burn events are small or non-existent, Cash Reserves will grow (months 1–8) until a significant Episodic Cash Burn event. As a large Episodic Cash Burn event occurs in a random month (month 9), the Cash Reserves drops dramatically, but then recovers over the following months

©2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

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Figure 5

$36,000 $34,000

Primary Asset Allocation is defined as the allocation of assets within an individual’s household assets and together with all assets that comprise an individual’s net worth. Primary Asset Allocation is not the same as (Secondary) Asset Allocation in the investment context. In the investment context, Secondary Asset Allocation is the distribution of investments across asset classes with the intent to diversify and achieve certain investment and risk balance objectives.

4*% 5)4 6+7 $/%

based on the Replacement Rate. The slope of the response to this “acute injury” to Cash Reserves is 1/RR. When Replacement Rate is unreasonably long relative to the frequency and magnitude of larger Episodic Cash Burn events, then Cash Reserves cannot effectively recover even when Free Cash Flow and Cash Burn are in balance, resulting in a higher risk of depleting Cash Reserves. One way to address a long Replacement Rate is to have excessively large Cash Reserves. However, this is often an impractical solution. Likewise if Replacement Rate is healthy, but Cash Reserves is small (e.g, less than double the Routine Operating Expense), the user will be vulnerable to a single large, or untimely string of moderate Episodic Cash Burn events that could drain their Cash Reserves and force the individual to shift to Virtual Cash Reserves, thereby crippling their PFOM.

4*%5)9$%$/7$% :4;4)<&*=$)>?

892

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E

,-.-/(# $-/.)0&1)/&+# 200)&0 B

!"#$%&'($)*+,) -+./$,"0&*1($)23.$+%$%

Monthly Impact on Cash Reserves $6,000

Episodic Cash Burn

$5,000 $4,000 $3,000 $2,000

Baseline Cash Burn

Average Episodic Cash Burn = $770/mo.

Free Cash Flow

BCB ECB FCF

$1,000 $0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Months

All PFOMs have a Cash Reserve pool. The only question is its funding source. Once a PFOM becomes dependent on virtual Cash Reserves (credit card debt), the ability to return to a healthy status is significantly hampered. However, individuals with a compromised PFOM can operate for years or decades in this stable steady state without falling into bankruptcy. These “locked” PFOMs effectively pump all the individual’s potential investment contributions and Retirement Value out of their PFOMs for as long as they remain locked. The key to controlling these negative and highly significant consequences is to maintain a healthy Cash Reserves balance and a low Replacement Rate.

The most significant factors that drive these variables can be traced back to decisions relative to the acquisition of assets, asset mix and the use of debt, which are collectively called Primary Asset Allocation (PAA).

Primary Asset Allocation is the allocation of assets across Dead, Marginal and Living assets within an individual’s PFOM. Primary Asset Allocation is the most significant set of decisions individuals make relative to establishing and maintaining a healthy PFOM. Figure 6 shows the various Primary Asset Allocation decisions and their impact on a PFOM. Cash Burn can be separated into Non-Routine Expenses (A) and Asset Holding Component Contribution (B). Asset Holding Component Contributions can be further delineated as B1 (Living Asset Investments), B2 (Dead Asset Purchases), B3 (Marginal Asset Purchases), and B4 (Consumer debt). If Asset Holding Component Contributions are primarily allocated to B2 and B3 and amplified by B4, these have a powerful impact on Asset Burden. As Asset Burden grows, Free Cash Flow falls, which in turn drives the Replacement Rate higher and puts significant pressure on Cash Reserves until eventually, Cash Reserves collapses and becomes virtual using credit card debt. As consumers are forced to modify their behaviors in response to their growing Asset Burden and shrinking Free Cash Flow, and as credit limits are reached, user’s settle into a “paycheck to paycheck” cycle they cannot break. Figure 7 represents a locked PFOM. When a PFOM becomes locked credit card payments (C) dominate Cash Burn

32

Journal of Personal Finance

and consequently Asset Holding Component Contributions (B) drops to zero or near zero, thereby forcing the Asset Holding Component to effectively go dormant. Non-Routine Expenses (A) also gets constrained, which reduces lifestyle spending and forces all future Unpredictable Expenses to go onto credit cards. This increasing credit card debt further drives up Asset Burden and reduces Free Cash Flow in a downward spiral to the point where the only way to maintain solvency is to budget everything and live very frugally.

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Table 1

While this process can progress slowly or quickly, it is fundamentally triggered by Primary Asset Allocation decisions related to Dead and Marginal Assets. What is even more challenging is that the impact of these Primary Asset Allocation decisions typically plays out over many months or even years. Making it difficult for individuals to see the eventual consequences of their decisions and actions. This mechanistic model clearly shows the dynamics that can lead to chronically low levels of financial well-being scores that are extremely difficult to overcome due to the trapping nature of credit card debt (high interest, low minimum payments).

For example, discerning between taking an expensive vacation (2.5x Routine Operating Expense) versus buying a new car can now be fully understood in terms of the decision’s operating and structural impact on the user’s PFOM. Using cash from Cash Reserves to go on vacation has no structural impact whatsoever. Conversely, taking (for example) 2.5 months of Routine Operating Expense from Cash Reserves for the down payment on a car and taking on a car payment that represents 5–10% of monthly Gross Income can have material and unappreciated consequences on the purchaser’s PFOM. These consequences can be effectively measured using these Key Performance Indicators (KPIs):

Asset Burden (%); (AB/GI)—Structurally difficult to modify and often contractually obligated.

Free Cash Flow (%): (FCF/GI)—Measures the flexibility of a PFOM. Cash Reserves (mo. of ROE): (CR/ROE)—Measures a PFOM’s safety margin (ability to consistently cover Non-Routine Expenses).

Replacement Rate (months of ROE): (ROE/(FCF-BCB)—measures the operating resiliency of a PFOM. Cash Reserves Stability ($): (FCF-CB)—measures the structural stability of Stage 2 of the Cash Flow Component.

Living Asset Contributions (%): (LAC/GI)—Measures the level of funding for medium and long-term goals.

By evaluating the potential impact of significant financial decisions on these KPIs, a user can objectively assess and quantify the actual impact of decisions before they are made. This is particularly true for the decision’s impact on non-obvious, but extremely important, operating dynamics of a PFOM like Replacement Rate and Free Cash Flow. To extend this example, if an individual is contemplating an automobile purchase or vacation decision, here is a PFOM-based summary of the consequences: Table 1 Impact of Purchase Summary Gross Income (GI) After tax Income Asset Burden Routine Living Expenses Routine Operating Expense Free Cash Flow % of Gross Income Baseline Cash Burn BCB as % of Free Cash Flow Replacement Rate Cash Reserves Months of Cash Reserves

Before Purchase $8,000 $6,560 $2,398 $1,550 $3,948 $2,612 33% $1,780 68% 4.7 $32,000 8.1

After Buying $40K Car $8,000 $6,560 $3,098 $1,550 $4,648 $1,912 24% $1,780 93% 35.2 $22,000 4.7

After Buying $10K Car $8,000 $6,560 $2,398 $1,550 $3,948 $2,612 33% $1,780 68% 4.7 $22,000 5.6

Assume that the user has a healthy Cash Flow Component with Free Cash Flow of 33%, Routine Operating Expense of approximately 50% of Gross Income, Baseline Cash Burn of 68%, Replacement Rate of 4.7 months, and a starting Cash Reserves of 8.1 times their Routine Operating Expense (see leftmost column in Table 1). If the user goes on a $10k vacation that they planned for and included in their estimated annual Episodic Cash Burn, the impact on their Cash Flow Component can be seen in the rightmost column in Table 1. Their Cash Reserves are

©2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

reduced to 5.6 months of Routine Operating Expense, and their Replacement Rate remains at 4.7 months. Consequently, the rate of replacing that reduction in Cash Reserves is about 8.5% per month ($10k ECB expense divided by ~$4k ROE equals 2.5x ROE times RR of 4.7 mo. equals 11.75 mo. to replace the $10k or about 8.5% per month). Because this is a dynamic process, Cash Reserves will not return to exactly $32k in 11.75 months; however, over the course of the next 12 months, CR will equilibrate to an average target level of $32k, if the average Free Cash Flow, Baseline, and Episodic Cash Burn remain as budgeted or estimated. Replacement Rate is a very useful measure but should be viewed less in absolute terms and more as a relative rate of replacement or a measure of the Cash Flow Component’s ability to respond to a financial “injury.” Alternatively, it can be considered as a measure of financial resiliency.

In looking at the new car purchase decision, one can see in the middle column of Table 1, a dramatic impact on both Free Cash Flow and Replacement Rate as well as the impact on Cash Reserves and Cash Reserves Stability. In this case, the decision had a material impact on the structure of the user’s PFOM, however, the only observable impact is in higher Asset Burden and lower Cash Reserves. While these observable impacts are important, they do not tell the whole story.

From the perspective of the KPIs, Asset Burden increased by nearly 30% (+$700) and went from 30% of Gross Income to 39%, Free Cash Flow (-$700) went from 33% to 24% of Gross Income, a 28% drop. Cash Reserves dropped from 8.1 months to 4.7 months of Routine Operating Expense, a 42% drop. Most importantly, Replacement Rate increased by sevenfold to 35.2 months and Cash Reserves Stability went from zero to -$700. A Replacement Rate of 35 months and a Cash Reserves Stability of -$700/month is not practical or acceptable if the user wants to replenish or avoid further erosion of their Cash Reserves. Given their nature, Free Cash Flow and Routine Operating Expense are not readily modifiable, therefore, Baseline Cash Burn is the only variable that can be influenced. Consequently, this user’s ability to maintain their existing lifestyle and long-term savings rates are not possible. Asset Holding Component Contributions will need be reduced to absorb some or even all the structural changes imposed by this decision. These changes, if fully absorbed by Baseline Cash Burn will improve Replacement Rate to 5.6 months ($4648 / ($1912 – ($1780-$700)) = 5.6 months).

The cost of returning the user’s Cash Reserve Stability to a healthy state is a material reduction in Asset Holding Component Contributions. Reductions in Asset Holding Component Contributions weaken the user’s ability to have sufficient funds

to support medium- and longer-term financial goals (e.g. saving for a replacement car or adequately funding retirement).

Additional financed Dead Asset purchases will further erode the health of this users PFOM. By seeing these consequences before or after making this decision, the user can make the necessary adjustments to protect Cash Reserves and long-term investments rather than merely watching helplessly as Cash Reserves erodes over time and finally collapses.

It is important to note that if this user had set aside funds in their Asset Holding Component to pay for the vacation or for the down payment on the new car, then the impact on their Cash Flow Component would be different. In the case of the vacation, there would be no impact whatsoever. For the new car purchase, there would be no impact to their Cash Reserves balance, but all other structural impacts would be the same as described above. Also note, if the user had not planned for the $10k vacation to come from their CR (by structuring BCB + ECB = FCF inclusive of the $10k ECB event), but took it from Cash Reserves anyway, then Cash Reserves would have a new equilibrium point, assuming all else in their Cash Flow Component stays the same (in this example, $32k - $10k = $22k).

As these examples demonstrate, the PFOM captures the obvious and non-obvious impact of typical personal financial decision and highlights the operational and structural impact of decisions like these. Now, these “generally understood” short- and long-term consequences can be easily modeled, quantified and documented to better inform decision-making.

Some Primary Asset Allocation decisions (e.g. financed new car) will have a material structural impact on a PFOM, while others do not. Table 2 shows the impact of various Primary Asset Allocation decisions (converting cash into assets) on the two outputs of the Asset Holding Component.

Table 2 - revised Table 2

Primary Asset Allocation

AB Impact

NWG Impact

Cash

0

0

Living Assets

0

+++

Marginal Assets

-

0/+

Dead Assets

-

--

Soft Debt

-

-/0/+

Hard Debt

-

0

34

Journal of Personal Finance

By having a clear understanding of the structural impact of various Primary Asset Allocation decisions on the Asset Holding Component’s outputs and the user’s PFOM, users can make much more informed decisions and have the tools to finetune their PFOM to more effectively support the lifestyle they most want to live. Furthermore, understanding the operational dynamics of their PFOM and being able to measure their KPIs before and after decisions are made, will help users be in a much stronger position to build and maintain a healthy Cash Reserves. Achieving this important milestone on a consistent basis is highly associated with higher levels of financial well-being.

The PFOM highlights several obvious and non-obvious functional and operational characteristics and dynamics of personal finance operating decisions and management.

- Cash Reserves is not a nice to have financial resource, nor is it an emergency fund that is used infrequently or only for “emergencies.” It is an essential component of every individual’s and household’s financial operations.

The only question is its funding source (self-funded or using consumer credit).

- Making Primary Asset Allocation decisions that dominantly favor Marginal and Dead Assets will inevitably fully compromise the functionality of the user’s PFOM by destroying their ability to maintain a sufficiently robust self-funded Cash Reserves.

- Excessive use of debt to fund large Dead and Marginal Asset purchases will accelerate the rate of compromise.

- A compromised, or locked, PFOM is not the same as bankruptcy; rather, this is a state of operation that can (and frequently does) go on for long periods of the user’s working life, effectively eliminating any chance of building a sufficiently large Living Asset Base to facilitate or support traditional retirement.

- It is necessary to direct a (significant) portion of Asset Holding Component Contributions into Living Assets or increased lifestyle spending to avoid compromising the healthy functionality of a user’s PFOM. Otherwise, these funds will be used to drive up Asset Burden through the purchase of Dead and/or Marginal Assets and set in motion the inevitable collapse of Cash Reserves.

- Effectively maintaining Cash Reserves requires a nuanced understanding of a user’s PFOM, particularly

-

the relationships and dynamics between Primary Asset Allocation, Free Cash Flow and Replacement Rate.

Effectively maintaining a large Free Cash Flow and Cash Reserves facilitates the user’s ability to achieve and maintain a higher level of financial well-being as well as desired lifestyle spending and Living Asset Contributions, which the clear majority of U.S. households have consistently failed to achieve (Bricker et al., 2017).

Discussion Ironically, accounting and finance practitioners have spent over 500 years developing, refining, and standardizing the financial operating models for traditional businesses (Sangster, 2010), but have largely overlooked doing the same for personal finance. The early codification of the mechanisms of traditional business accounting began in the late 15th century in Italy, helping business leaders effectively quantify their operations and determine the potential impact of their business decisions for the first time. In the 1930s, U.S. standards were established for business accounting (Generally Accepted Accounting Principles—GAAP). Today, the Financial Accounting and Standards Board is well on its way to standardize business accounting globally (FASB, 2018). This highly evolved financial accounting system is the foundation for business leader’s ability to assess and understand the financial health of their businesses.

Furthermore, these tools allow business leaders to model the impact of strategies and investments and facilitate development and refinement of business strategies that advance the overall value of the enterprise over time.

Apart from the IRS’s development and use of personal income tax forms for the purposes of standardizing the assessment and collection of taxes, no such effort or model has ever evolved to do the same for personal finance. The model presented in this article was developed over years of studying personal finance and through extensive research into the financial status of households in the U.S. While guided by statistical assessments of government-based consumer financial databases and various other resources, this model was developed primarily based on an experiential assessment of how the operating system of personal finance works (Galatowitsch, 2018). By their nature, models are imperfect reflections of complex realities. However, the recently published Financial Well-being survey (CFPB, 2017 Well-Being) statistically validated the value and correlation of a healthy Cash Reserves on individual’s perceptions of their own financial well-being. Additionally, there is also a high correla-

©2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

tion between an individual’s financial knowledge and their perceived ability to exert control over their finances. This suggests that anything that can help households improve and maintain Cash Reserves, improve their understanding of the operating mechanisms of their personal finances, and better inform their financial decisions may have significant utility. The intent of this model is to be robust and comprehensive enough to sufficiently capture and reflect functional reality and to balance that with a tool that is practical, actionable and relatively easy to work with.

It can be easily envisioned that the codification of this PFOM into a dynamic web-based tool with a robust user interface could have a meaningful and immediate impact on user’s ability to improve their financial well-being.

While the industry pays inordinate attention to budgets and budgeting as a means of achieving financial control, this approach has proven to be largely ineffective in helping households achieve operational control or their long-term goals.

Furthermore, nearly 45% of all households rely on virtual Cash Reserves to support their financial operations (Bricker et al., 2017). Dieting and budgeting are examples of strategies that address the symptoms of a deeper, more structural problem.

Motivations and behaviors certainly play a central role in failed budgets and poorly managed personal finances, but, these are not the only factors. Understanding the underlying operational structure of how personal finances work provides critical insights that can help motivated users achieve what has eluded them by focusing on budgets and budgeting alone. Structuring a PFOM to avoid the need for detailed budgets, provides the user with an immediate sense of operational financial control and freedom and lays the foundation for achieving the structural financial freedom associated with being financially prepared for retirement.

This model makes several critical components visible, which are essential to maintaining operational control. For example, Free Cash Flow is rarely observed or measured by households, largely because expenses are not organized in a way to facilitate “seeing” it. However, measuring and tracking Free Cash Flow is essential to being able to manage Cash Reserves to reach a target level and make appropriate decisions relative to Lifestyle Expenses, Asset Holding Component Contributions, and Primary Asset Allocation Decisions. This model and associated KPIs also provide a useful framework and set of tools to facilitate the a priori modeling of financial decisions. This can help users reduce the time and effort required to maintain the health

of their personal finances, avoid having to budget individual expense categories, better align their personal finances to their lifestyles and financial goals, and more effectively build their net worth.

Imagine for a moment if traditional business leaders ran their businesses exclusively based on a long list of platitudes and “good sound advice”? Chaos would ensue. While personal finance is neither as complex nor as expansive as traditional businesses, it can benefit just as much from having a standard operating model for making operational and strategic decisions that ensure smooth operations, financial health, and enhanced enterprise value.

This personal finance operating model can also help to open and foster important conversations, guide future study, and potentially set the standard for the thing that is our personal finance operations. From this foundational framework, we can more effectively hang our financial literacy work, apply the many fine financial planning and decision-making tools, and provide a standard context for many other analyses and studies of personal finance. Potentially, we may now have a new field of study and a tool to operationalize that “good sound advice” offered to consumers and clients.

Additional publications will explore and provide a data-driven foundation and understanding of healthy operating ranges for the KPIs defined here and elucidate other more advanced measures and indexes that can further inform our understanding of the operating mechanism of personal finance and the impact of decisions on consumers’ financial well-being and health.

Conclusions Over the last 50+ years, the study of personal finance has amassed a significant body of peer-reviewed published work related to nearly all aspects of personal finance with the notable exception of the operational mechanism, dynamics, and quantified consequences of personal financial decisions.

This proposed standard operating model provides a simple, comprehensive, actionable, and forward-looking framework that provides individuals and families with the understanding and tools to more effectively control and harness their personal finance operations to improve or sustain their financial health and well-being and achieve their long-term financial and lifestyle goals.

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Journal of Personal Finance

References Bricker, J., Dettling, L. J., Henriques, A., Hsu, J. W., Jacobs, L., Moore, K. B., ... & Windle, R. A. (2017). Changes in U.S. family finances from 2013 to 2016: Evidence from the survey of consumer finances. Federal Reserve Bulletin, 103 (3).

CFPB. (2017). Financial Literacy Annual Report. Consumer Financial Protection Bureau. Retrieved from https://s3.amazonaws.com/files.consumerfinance.gov/f/documents/cfpb_financial-literacy-annual-report-2017.pdf CFPB. (2015). Financial well-being: The goal of financial education. Consumer Financial Protection Bureau. Retrieved from https://files.consumerfinance.gov/f/201501_cfpb_report_financial-well-being.pdf CFPB. (2017). CFPB Financial Well-Being Scale: Scale Development Technical Report. Consumer Financial Protection Bureau.

Retrieved from https://s3.amazonaws.com/files.consumerfinance.gov/f/documents/201705_cfpb_financial-well-being-scale-technical-report.pdf CFPB. (2017). Financial well-being in America. Consumer Financial Protection Bureau. Retrieved from https://s3.amazonaws. com/files.consumerfinance.gov/f/documents/201709_cfpb_financial-well-being-in-America.pdf Chang, Y., Hanna, S., & Fan, J. (1997). Emergency fund levels: Is household behavior rational? Financial Counseling and Planning, 8(1), 1–10.

FASB. (2018). Compatibility in international accounting standards: A brief history. Financial Accounting Standards Board.

Retrieved from https://fasb.org/jsp/FASB/Page/SectionPage&cid=1176156304264 Galatowitsch, J., & Engstrom, C. (2018). Manufacturing wealth.

Maitland, Florida: North Loop Press.

Sangster, A. (2010). Using accounting history and Luca Pacioli to put relevance back into the teaching of double entry accounting. Business & Financial History 20(1), 23–39.

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Volume 18, Issue 1

37

“As Soon As…” Finances: A Study of Financial Decision-Making

Barbara O'Neill, Ph.D., CFP, CRPC, AFC, CHC, CFEd, CFCS, Extension Specialist in Financial Resource Management and Distinguished Professor Yilan Xu, Ph.D., Assistant Professor, University of Illinois at Urbana-Champaign Carrie Johnson, Ph.D., AFC, Assistant Professor and Extension Specialist in Personal & Family Finance D. Elizabeth Kiss, Ph.D., Associate Professor and Extension Specialist, Kansas State University Steven Buyske, Ph.D., Associate Professor, Rutgers University

Abstract This article reports findings from a study of financial decision-making featuring analyses of responses to open-ended questions.

The target audience was young adults with 69% of the sample under age 45. Four key financial decisions were explored: financial goals, homeownership, retirement planning, and student loans. Results indicated that many respondents were sequencing financial priorities instead of funding them simultaneously, and they were delaying homeownership and retirement savings. Threeword phrases like “once I have…,” “after I [action],” and “as soon as…” were noted frequently, indicating a hesitancy to fund certain financial goals until achieving others (i.e., sequential goal pursuit). This article also provides implications for financial practice.

Key Words financial decision-making, open-ended questions, sequential goal pursuit, homeownership, retirement savings, student loans

38

Journal of Personal Finance

Introduction

financial practitioners.

To effectively attract and serve clients, financial practitioners (i.e., advisors and educators) need to be “generation-savvy.” In other words, they need to understand their own generational biases and have an awareness of, and empathy for, the biases of others (Schulaka, 2017). Perhaps no other generation needs to be understood by financial practitioners as much as Generation Y (or, the Millennials), who represent a quarter of the nation’s population and an increasing proportion of the nation’s workforce. Millennials are a large demographic cohort, 83.1 million people in 2015 (U.S. Census Bureau), with unique financial characteristics. Compared to Generation X and the Baby Boomers who preceded them, they have higher levels of student loan debt, poverty, and unemployment, and lower levels of wealth and income at the same stage of life (Cutler, 2015).

Review of Literature

A report by the National Endowment for Financial Education (2016) characterized Millennials’ financial position as “more fragile than expected.” For example, 34% of Millennials have used an alternative financial services (AFS) provider within the previous five years. Additionally, they have a preference for experiences over things and a strong desire for work/life balance (Finke, 2016). There is also evidence that Millennials, who were born between 1982 and 2000, are “unmoored” from major American social institutions such as politics, religion, and marriage and family (Pew Research Center, 2014).

Research that investigates how different generations make financial decisions can inform successful practice management efforts. For example, if young adults prefer experiences over things, financial practitioners may have to reframe investing for retirement as an investment in future experiences and flexibility in later life, rather than savings for future financial security (Finke, 2016). This article reports findings from qualitative research of financial decision-making related to four key personal finance topics: financial goals, homeownership, retirement planning, and student loans. The sample consisted of 1,538 individuals who completed an online survey during June 2016.

The target audience for the study was young adults; 69% of the sample was under age 45. Basic quantitative analyses were performed for three age cohorts (aged 34 and below, aged 35–54, and aged 55 and above) to determine sample characteristics and the frequency of performing certain financial practices (e.g., saving for retirement) and qualitative data were analyzed in a systematic way to determine how respondents made financial decisions. This article describes findings for each key personal finance topic and concludes with implications for

This study investigated financial decision-making with respect to four key areas of personal finance: financial goal-setting, homeownership, retirement planning, and student loans.

Studies relating to each of these topics are described below.

This study adds to existing literature by targeting young adults as research subjects and by analyzing key word sequences in respondents’ open-ended comments.

Financial Goal-Setting Many young adults put off saving for future financial goals, thinking “I’ll start saving when I pay off my student loans,” or “I’ll save in a few more years when I make more money” (Olson, 2014, p. 51). This can lead to a $1 million mistake. Someone who invests for retirement starting at age 32 and accumulates $1 million at age 67 after 35 years of diligent saving could have accumulated $2 million if they had only begun their retirement savings nine years earlier at age 23 and had almost another decade of compound interest to grow their money.

For many consumers, clarifying values and setting concrete financial goals are the first steps to long-term financial security.

When asked in a recent survey, the majority of those under 40 years old (72%) reported having set financial goals (iQuantifi, 2015). General savings, saving for retirement, buying a home, and reducing debt are frequently reported financial goals, regardless of age (American College of Financial Services, n.d.; iQuantifi, 2015; Wells Fargo Bank, 2016).

Among younger consumers (40 years old and younger), 45% have a routine for reviewing their finances and 54% have a budget (Wells Fargo Bank, 2016). A report of actions taken by women to achieve their financial goals suggests priorities vary by age (American College of Financial Services, n.d.). For example, among women 40 years old and younger, those working to increase their savings reported their priorities to be reducing spending (69%), saving bonuses and tax refunds (61%), and saving more regularly (32%). Those working to reduce debt reported reducing their spending (77%) and creating a schedule or plan (71%). When it comes to protecting their financial security, very few reported having disability insurance or investing on their own to grow their wealth.

Women 41–52 years old who are working to decrease debt reported attempting to cut back on spending (80%), creating a

©2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

plan or schedule (40%), and calculating how much they need (39%) (American College of Financial Services, n.d.). Not surprisingly, another goal for women in this age group is saving and paying for college. Those focusing on this goal report attempting to cut spending (44%), saving regularly (37%), researching tips and information about how to save for education (33%), calculating how much is needed (25%), and using a 529 plan (14%).

Women in the next older cohort, ages 53 to 71, reported insuring what is important to them as one of their top goals and their actions included purchasing financial products and insurance (68%), speaking to financial professionals (24%), and calculating the amount they need (20%) (American College of Financial Services, n.d.). In addition to saving for retirement, another top goal for this group was to improve their cash cushion and reduce debt.

Homeownership In economics literature, homeownership decisions can be explained by a life cycle model that is subject to borrowing constraints and a budget for consumption and saving (Artle & Varaiya, 1978). Under this framework, in order to enjoy the potentially lower future housing costs resulting from owning a home, individuals need to forgo part of their current consumption in order to save for the down payment. The homeownership decision then depends on the relative size of the present value of homeownership benefits and that of the foregone consumption. In practice, homeownership is a personal financial decision—provided that the down payment has been accumulated and credit access is available, a decision-maker compares the annual costs of renting to the annual costs of owning.

A decision-maker may also factor in the non-pecuniary benefits of homeownership. In particular, the economics literature has shown that homeownership leads to better behaviors and positive educational outcomes of children (Boehm & Schlottmann, 1999; Haurin, Parcel, & Haurin, 2002), higher political participation, and more investment in social capital (McCabe, 2013). These benefits are mediated by the residential stability of homeownership (Aaronson, 2000; Haurin et al., 2002), perceived control over life events (Lindblad & Quercia, 2015), and home equity (Cooper & Luengo-Prado, 2015). The equity built by homeownership not only permits higher investment in children’s education (Cooper & Luengo-Prado, 2015), but also fosters entrepreneurship (Corradin & Popov, 2015). Nevertheless, renting can be a wise choice for those who want the flex-

ibility to move around and freedom from home maintenance responsibilities.

The housing market collapse and the mortgage credit crunch during the Great Recession impacted millions of U.S. households. As a result of a challenging labor market and high student loan debt, young adults were more likely to live with parents during this period, just like all other historical recessions (Bitler & Hoynes, 2015; Bleemer, Brown, Lee, & Van der Klaauw, 2014; Dettling & Hsu, 2018). Young adults who witnessed a parental foreclosure during the 2007–2009 Great Recession and housing crisis were less likely to become homeowners than those who did not experience parental foreclosures (Xu, 2017).

Their homeownership during this period was complicated by a credit crunch, limited financial resources, high student loan debt, and postponed marriage and parenthood (Xu, Johnson, Bartholomae, O'Neill, & Gutter, 2015).

According to the Pew Research Center, in 2014, for the first time in more than 130 years (i.e., since the 1880s), living with parents edged out all other living arrangements (e.g., married or cohabitating, living alone or as a single parent, and other living arrangements) for 18-to-34 year olds (Fry, 2016). Almost a third (32.1%) were living in the home of their parents versus 31.6% living with a spouse or partner in their own household. With so many so-called “boomerang kids,” there is less stigma today in living with one’s parents. There may be long-term ripple effects, however, including multigenerational family tensions, unanticipated inter-generational financial transfers, and delayed marriage and parenthood (Mitchell, 2009). According to the Pew Research Center (2015), 61% of Americans with adult children helped out their children financially in the past 12 months.

Retirement Planning Unlike shorter-term financial goals like buying a house or saving for a child’s education, retirement planning can take place over seven or eight decades, from the start to the end of someone’s adult life (i.e., 20s through 80s or 90s). Even workplace retirement planning programs target a wide demographic swath, ranging from recent college graduates in their 20s to soon-to-retire employees in their 50s, 60s, and beyond (Walstad et.al, 2017). In recent decades, responsibility for financial security in retirement has been transferred, for the most part, from government and employers to individuals (McGuinness, 2013).

Many Americans are financially unprepared for retirement, including millions of “middle Americans” with annual household incomes of $30,000 to $100,000 who have low savings,

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Journal of Personal Finance

high debt, and a tendency not to consult professional advisers (Neiser, 2009).

Perhaps the best insight into how people are thinking about and planning for retirement is the annual Retirement Confidence Survey (RCS), which has been conducted by the Employee Benefit Research Institute (EBRI) since 1996. Results from the 2017 study (Greenwald, Copeland, and VanDerhei, 2017) indicate that many American workers feel stressed about retirement and are not taking steps to prepare for it. Specifically, the RCS found that 31% of 1,082 surveyed workers reported feeling mentally or emotionally stressed about preparing for retirement, 61% had saved money for it, and 56% were currently saving. RCS respondents who felt stressed out about retirement preparation were more likely than others (30% vs. 12% of workers not feeling stressed) to say that their debt level was a major problem.

Among RCS respondents reporting savings data, 47% said the total value of their household savings, excluding a primary home and defined benefit (DB) pension, was less than $25,000.

This included 24% who said they had less than $1,000 in savings. A recent study found that Millennials who graduate college and begin their careers at age 22 with $30,000 in student loan debt could wind up with $325,000 less in retirement savings than their debt-free peers (Bier, 2015). The analysis assumed that they are able to pay back their student loans within 10 years, which is often not the case since debt often persists well into middle age (Bier, 2015). Thus, the retirement savings gap experienced by some student loan borrowers could actually be larger than $325,000.

A study by the Center for Retirement Research at Boston College (Munnell, Hou, and Sanzenbacher, 2017), using the National Retirement Risk Index (NRRI), found that slightly more than half (52%) of working-age U.S. households are at risk of being unable to maintain their living standard in retirement. Almost 1 in 5 (19%) did not recognize that they are at risk for a shortfall and are unlikely to take remedial action. Increases in life expectancy and the shift from DB pensions to 401(k) plans have contributed to the increasing proportion of at-risk households.

The 2015 National Financial Capability Study (NFCS) conducted by the FINRA Investor Education Foundation (FINRAIEF, 2016) found that over half (58%) of 27,564 respondents had some kind of retirement savings account, either employer-based (e.g., 401(k) plan) or independent (e.g., IRA), but only 39% had tried to calculate how much they need to save. Over half (57%) of NFCS respondents age 18–34 were worried about running

out of money in retirement, second to those age 35–54 (65%).

More than 4 in 10 (42%) Millennials in the NFCS sample did not have any type of retirement savings account (NEFE, 2016). Two obstacles to saving are student loan debt and fear of losing savings resulting from the 2008 financial crisis (Obstacles to Millennial Retirement, 2016).

Student Loans The student loan debt burden of American households, especially young adults, is sobering with 44.2 million Americans owing student loan debt (A Look, 2017). The highest frequency of student loan use is among borrowers age 18–29 (Federal Reserve Board, 2016; Fry, 2012). Student loan debt has increased to the point that it is crowding out other purchases such as homes and cars, according to research by the Federal Reserve Bank of New York (Brown & Caldwell, 2013). The co-authors of the study noted, “While highly skilled young workers have traditionally provided a vital influx of new, affluent consumers to U.S. housing and auto markets, unprecedented student debt may dampen their influence in today’s marketplace” (Brown & Caldwell, 2013, Student Debt and Total Borrowing, para 6).

Young adults have also been found to shun stocks after witnessing the Great Recession bear market. According to a 2014 Gallup poll, just 27% of 18- to 29-year-olds reported owning shares outright or in mutual funds (Smialek, 2014).

Approximately 37% of households headed by an adult under age 40 have some student debt (Letkiewicz & Heckman, 2017).

According to the Federal Reserve Bank of New York (FRBNY) (2016), in 2015 those under age 30 held 30.6% of the total student loan debt, borrowers 30–39 held 33.2%, borrowers 40–49 held 18.7%, borrowers 50-59 held 12.2%, and borrowers over the age of 60 held 5.4% of the total student loan debt. Most notable is the increase of student loan debt of individuals 60 years and older. According to data from 2005, only 2.1% of the total student loan debt was held by this age group and in 10 years it increased by 3.3% (FRBNY, 2016).

Dependence on student loans has increased, at least in part, because of the shift in financial aid policy toward greater individual responsibility for the costs of education beyond high school (Center for Social Development, 2013). Student loan debt can negatively affect other areas of personal finance including housing and retirement. Evidence suggests that students do not act rationally when deciding whether or not to take on student debt (Center for Social Development, 2013).

A focus group study conducted by Johnson, O’Neill, Worthy,

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Volume 18, Issue 1

Lown and Bowen (2016) found that student loan borrowers did not think they had any choice but to take student loans, but they did think that the debt would negatively affect their financial future.

An in-depth review of student loan literature conducted by Cho, Xu, and Kiss (2015) found that there are many considerations and impacts related to student loan borrowing. They concluded that an increase in human capital is a big contributing factor to taking on student loan debt. They also determined that student loan debt can negatively affect borrowers’ health as well as life transitions and wealth accumulation. Johnson, Gutter, Xu, Cho, and DeVaney (2016) conducted a study to determine reasons for attending college and student loan debt satisfaction. Their results indicated that students were attending to build social capital as well as human capital. They also determined that younger borrowers had higher student loan debt and were less satisfied with their debt.

Financial Goal Setting and Financial Actions: Theory and Practical Challenges The focus of this study was financial decision-making featuring quantitative analyses of responses to open-ended questions.

Specifically, respondents were asked about actions taken, goals set, and plans formulated to buy a home and save for retirement as well as the impact of student loans on these two key financial planning decisions. The conceptual base of this study is the Theory of Planned Behavior (TPB), which links intentions to perform behaviors with attitudes toward the behavior (i.e., a person’s belief about whether a certain action makes a positive contribution to his or her life), subjective norms (i.e., everything around an individual such as social networks, cultural norms, and group beliefs), and perceived behavioral control (i.e., a person’s belief about how easy or hard it is to act in a certain way) (Ajzen, 2006, 1991).

According to the TPB, a positive attitude toward a behavior, favorable social norms, and a high level of perceived behavioral control are predictors of forming a behavioral intention which, in turn, leads to an actual displayed behavior. If people believe that taking a certain action is a good idea, believe that others think it is a good idea, and believe that they can accomplish a task, they will take action. If beliefs about one or more of these three constructs are unfavorable, the likelihood of people taking action decreases. The TPB posits that intentions are the best predictor of behavior. If people plan to do something, they are more likely to do it. The theory has been well supported by

empirical evidence as a highly accurate and reliable way to predict human behavior and is widely used in consumer product marketing research (Taylor & Todd, 1995; Koufaris, 2002). Gaining knowledge about people’s thought processes, behavioral intentions, and emotions related to key financial decisions can help inform effective financial education interventions as well as client-practitioner relationships.

Regardless of what the TPB predicts, financially constrained individuals will find it impossible, or at least difficult, to accommodate multiple financial goals at the same time. To illustrate this point, imagine a representative college graduate who graduates at age 22 with a student loan balance of $34,144 (U.S. national average), makes a household income of $62,500 (median of sample in this study), and plans to retire at age 67 (full retirement age) with a life expectancy of 81 (average for U.S. men). With a 4.45% interest rate and a 10-year term, the monthly student loan payment is $353.04 (i.e., 8% of after-tax income, assuming an average tax rate of 15%). Under the assumptions of replacing 100% of pre-retirement income, a 25% Social Security income replacement ratio, 3% inflation, and a 7.5% investment return, this individual has to save $478.70 monthly (i.e., 10.8% of after-tax income) for retirement if savings start immediately after graduation at age 22. This amount will increase if retirement savings does not start immediately.

Consider these additional basic math calculations. To buy a house costing $200,000 (U.S. median housing value) with a 20% down payment, 4.5% interest, 30-year mortgage, the monthly mortgage payment is $810.70 (i.e., 18.3% of after-tax income) and a $40,000 down payment (20% of $200,000) needs to be saved. Assume the annual homeowner’s insurance is $1,000 and the property tax is $2,400. The monthly housing expense, including the principal, interest, taxes, and insurance (PITI), is a total of $1,094.03 (24.7% of after-tax income). Accommodating all three goals, namely, a student loan payment, retirement savings, and homeownership, will require a monthly allotment of $1,925.77, which is equivalent to 43.5% of monthly after-tax income. This level of savings is unattainable for a majority of U.S. households. For the following scenario analyses, an ambitious 36% of the individual’s after-tax income, i.e., $1,593.75, was assumed for savings to support the three goals every year until retirement. It is also assumed that this person lives with his or her parents to save aggressively, as many young adults today do (Fry, 2016; Mitchell, 2009). The individual needs to prioritize three expensive competing financial planning priorities.

Do different prioritization strategies of financial goals affect

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Journal of Personal Finance

individualsâ&#x20AC;&#x2122; long-term financial well-being? The answer is yes.

Since defaulting on student loans can have a tremendous negative impact, student loan repayment should be a top priority.

Thus, two scenarios are compared: Scenario 1, accommodating a student loan payment and housing at the same time while postponing retirement savings, and Scenario 2: accommodating a student loan payment and retirement savings, while postponing homeownership.

Under Scenario 1, all remaining savings after the student loan payment are earmarked for a house down payment, and it takes 2.7 years to save the down payment while paying off the student loan. However, at the age of 24.7, when the down payment has been accumulated, the required monthly retirement savings has increased to $586.41, which is 13.2% of after-tax income. This savings goal cannot be accommodated when 8% of after-tax income is first allocated to student loan repayment and then 24.7% is allocated to PITI. If retirement savings starts after the student loan is paid off at age 32, the required monthly savings will become $1,030.51, which comprises 23.3% of after-tax income. This goal is still unattainable, even without the burden of student loan payments, because 24.7% of after-tax income is allocated for PITI. Thus, under Scenario 1, the person can achieve homeownership at age 24.7 and pay off the mortgage at age 54.7, but postponing retirement savings eventually will have to result in delayed retirement.

In Scenario 2, the individual prioritizes student loan payments and retirement savings and uses the remaining savings to accumulate a house down payment. It will take 4.4 years to save the down payment. After having saved enough for the down payment, the housing expense (i.e., PITI) cannot be accommodated by the total budget of 36% of after-tax income until the student loan is paid off. Hence, homeownership will be achieved at the age of 32, and the mortgage will be paid off at age 62, which is 7.3 years later than in Scenario 1. However, the goal of retiring at age 67 can be achieved by prioritizing retirement savings over homeownership. These calculations were done using an Excel spreadsheet that is available upon request.

To summarize, the scenario analyses show that student loans have a major impact on homeownership and retirement savings for average student loan borrowers. In order to pay off student loans on time, homeownership will be delayed. Moreover, retirement financial security will be severely affected if retirement savings is not prioritized. The results of the scenario analysis are in line with previous studies indicating that repayment of outstanding debt is a key goal of young adults and may

be associated with deferred homeownership and retirement savings, resulting in reduced lifetime wealth accumulation (Oâ&#x20AC;&#x2122;Neill, Xu, Johnson, & Kiss, 2018). They also invite an empirical question of how people prioritize and fund multiple competing financial goals; i.e., sequentially or concurrently.

Research Questions Based on the three constructs in the TPB theoretical model (i.e., attitudes toward a behavior, subjective norms, and perceived behavioral control) and findings from the scenario analysis, the following research questions were investigated: Q1: What evidence was found about the priority of financial goals and actions that people are taking toward fulfilling them?

Q2: What evidence was found about factors affecting housing decisions?

Q3: What evidence was found about factors affecting retirement planning decisions?

Q4: What evidence was found that student loans affect housing and retirement planning decisions?

Methodology Data Data were collected using an online Qualtrics survey instrument during June 2016. Respondents were directed to the survey via social media posts made by the principal investigator, invitations at public presentations, and from WiseBread (http:// www.wisebread.com), a personal finance website targeted toward young adults. A total of $1,500 in Amazon gift cards ($500 grand prize and five $200 prizes) was awarded as incentives to encourage survey participation.

Most of the survey questions were open-ended and required a text response. Thus, qualitative data were analyzed using regular expressions to determine themes for each response; that is to say, keyword analysis. To determine these themes, n-grams were used; n-grams are a contiguous sequence of a specific number of words, where n is the designated number of words (i.e., combinations of adjacent words of different lengths) (What are N-Grams?, 2014).

Tables of the most frequent n-grams for each item were used to suggest topics. These topics were augmented by other topics determined by the authors on the basis of a sample of responses. Regular expressions, also known as grep searches, are a

Š2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

Table 1: Search Terms for Common Themes Themes

Search Terms

Top three financial goals (Q3) Save for:

house|college|car|retire|emergency |401|travel|\save|savi|build

Reduce debt/Pay off:

loan|debit|credit

Buy a:

house|car|buy|own|have

Debt free:

debt free|debt-free|debtfree

Have enough:

have enough

What, if any, actions are you taking right now? (Q4) Save more:

401|ira|retirement|%|save|saving

Pay off:

debt|credit card|load|student loan|house|mortgage|pay|credit karma

Spend less:

spend|track|mint

As much as possible:

trying

Describe your homeownership decision (Q7) Comparison to rent:

rent|cheaper|less|more|economical|higher|cost

Family reasons:

home|family|kids|married|marry|neighborhood|crime|location

As an investment:

investment|equity|return|appreciate

Problem with renting:

tired of living in apartment|renting|bad landlord

Low mortgage rate or housing price

rate|market|crisis|price

Like the ownership:

own home|ownership

Lifestyle choices:

pet|dog|cat|change|improvement|flexibility afford|have money|had money

Tax benefits:

tax|itemize|deductible

Affordability:

afford|have money|had money

Describe your housing decision if you are not currently a homeowner (Q9) Plan to buy:

buying|buy|purchase|in the next|in the future|wait|save up|save for|saving for|in the process|not yet|purchasing|saving

Living with parents:

parent|dad|mom|father|mother|as dependent|student|college

Uncertainty about locations:

move|where to live|same city|which city|where

Lifestyle:

move|travel|job|travel a lot|job transfer|military|grad school

Down payment constraint:

no savings|couldn't afford|no money|not enough|down payment

Credit or debt constraints:

poor credit|bad credit|debt|student loan

How you are saving for retirement? (Q12) 401(k)/403(b)/457 plan:

401|403|457

IRA/Roth IRA:

ira|roth

Unnamed employer-sponsored plans:

work|employer|company|paycheck

Through spouseâ&#x20AC;&#x2122;s plan:

spouse|husband|wife

SEP/SEP-IRA:

Sep

When do you think you will begin regular retirement savings deposits? (Q13) Conditional:

When

Time horizon:

next few months|((within|in |next few) & (month|year|decade))

No plan:

donâ&#x20AC;&#x2122;t know|not sure|unsure

Never:

never|not ever

Sequential:

as soon as|after

(topic selection made by rater not algorithm) How have student loans affected retirement savings? (Q19) (topic selection made by rater not algorithm)

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Journal of Personal Finance

concise language for pattern matching in text and were used to determine topics in the responses (Linux Grep Command, 2016). Table 1 summarizes the research questions, search terms, and common themes.

the middle-aged cohort (p = 0.0034), and their desire to “reduce debt” was also higher than the oldest cohort (p=0.0003). Other top goals included being debt free and having enough resources to feel financially stable, financially secure, or able to achieve long-term financial goals.

Sample

When asked, “What, if any, actions are you taking now to achieve your financial goals?” again, nearly all survey participants responded to the question (n = 1,520). Regardless of age, the most frequently reported actions included saving more and paying off something. The youngest cohort (aged 34 and below) was more likely to list “saving more,” “paying off,” and “spending less” as their actions taken than those aged 55 and above (p = 0.0004, 0.0011, and 0.0148, respectively).

The full sample consisted of 1,538 respondents. Among these, 1,476 (96%) provided information about their demographic characteristics. These respondents were 78% White and 75% female with over two-thirds (69%) under age 45. Among the 1,538 respondents, 758 reported being homeowners while the remaining 760 were non-homeowners. Younger adults under age 35 in the sample expected to retire almost 3 years earlier than those age 55 and older. Detailed data about sample characteristics are shown in Table 2.

Homeownership

Findings Financial Goal-Setting Nearly all survey participants (n = 1,520) responded to the question, “What are your top three financial goals? Briefly list and explain.” As identified through the keyword analysis and summarized in Table 3, the top three goals for the full sample involved saving for something (91%), buying something (62%), or reducing debt/paying off a previous purchase (24%). Regardless of age, the top goal, on average, was to save for something.

A distinct pattern for those aged 34 and below is that they listed “buying” among their financial goals more frequently than

Homeowner respondents were asked to describe how they made the homeownership decision. In total, 752 of the 758 homeowners (99%) answered this question. Table 4 reports the frequency of their mentioning common themes. For the entire subsample of homeowners, the top reasons to become a homeowner were (ranked from the most mentioned to the least): cheaper than renting, family reasons, investment, problems with renting, low mortgage rates or low housing prices, like ownership, and lifestyle choices. In particular, 45.9% of respondents mentioned that owning is cheaper than renting for them. Almost 30% mentioned family reasons such as providing a good environment for family and children, marriage, and considerations for the neighborhood, crime rates, and location.

Table 3. Financial Goals and Actions Taken to Achieve Them ≤34

3554

≥55

Total p-value, p-value, ≤34 v ≤34 v 35-54 ≥55

Save for

89.9

92.5

89.2

90.9

0.1866

0.9485

Buy a

66.9

58.4

59.5

62.4

0.0034

0.1050

Reduce Debt / Pay off

28.0

21.8

14.4

23.6

0.0205

0.0003

Debt Free

8.9

10.1

4.6

8.8

0.7077

0.1088

Have Enough

5.3

5.8

8.7

6.0

0.8678

0.1430

Save More

51.7

46.2

36.4

47.4

0.0976

0.0004

Pay Off

37.1

32.2

23.6

33.3

0.1331

0.0011

Spend Less

10.6

7.5

4.1

8.5

0.1012

0.0148

As Much as Possible

7.1

8.5

4.6

7.4

0.6050

0.3737

Top Financial Goals

Actions Taken

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Volume 18, Issue 1

Table 2: Summary Statistics ≤34

35-54

≥55

Total

Gender Female

71.6

77.9

77.9

75.1

Male

28.4

22.1

22.1

24.9

Income Less than $25,000

16.8

15.7

21.5

17.0

$25,000 to $49,999

27.8

20.9

28.2

25.0

$50,000 to $74,999

23.6

27.1

22.6

24.9

$75,000 to $99,999

12.0

15.6

14.4

13.8

$100,000 or greater

19.7

20.6

13.3

19.2

Asian

6.6

4.5

1.0

5.0

Black/African-American

8.4

8.8

5.6

8.2

Hispanic/Latino

6.8

5.2

1.5

5.4

Native Hawaiian or Pacific Islander

0.5

0.2

0.0

0.3

Other

4.5

2.8

2.1

3.5

White

73.2

78.6

89.7

77.6

Race/ethnicity

Education Some high school or less

1.1

1.8

0.0

1.2

High school graduate

12.0

12.2

16.4

12.7

Some college, trade, or vocational training

20.3

21.8

23.6

21.3

Associate’s degree

8.1

12.2

11.3

10.2

Bachelor’s degree

38.5

32.3

27.7

34.5

Graduate or professional degree

20.0

19.8

21.0

20.1

Household size 1

12.2

13.1

22.6

14.0

2

33.2

27.3

50.8

33.1

3

19.5

21.3

15.4

19.7

4

19.4

22.6

7.2

19.1

5 or more

15.6

15.7

4.1

14.2

Homeowner

31.0

63.1

74.4

50.1

Non-homeowner

69.0

36.9

25.6

49.9

Homeownership

Outstanding Student Loan Balance Has outstanding balance

44.7

25.3

7.2

31.6

No outstanding balance

55.3

74.7

92.8

68.4

Expected Age at Retirement

62.4

65.4

65.0

64.0

p-value, ≤34 v 35-54

p-value, ≤34 v ≥55

0.0182

0.1477

0.8279

0.2400

0.2064

0.0164

0.4684

0.9999

0.9982

0.0002

<0.0001

<0.0001

<0.0001

<0.0001

0.9328

0.0243

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Journal of Personal Finance

Table 4. Homeownership Decision-making ≤34

35-54

≥55

Total

p-value, ≤34 v 35-54

p-value, ≤34 v ≥55

65.7

39.3

35.9

45.9

< 0.0001

< 0.0001

Homeowners Cheaper than renting Family reasons

33.8

28.7

27.6

29.9

0.3297

0.3582

As an investment

21.6

20.2

16.6

19.8

0.8893

0.4018

Problems with renting

25.0

13.2

10.3

15.9

0.0007

0.0016

Low mortgage rates/ housing prices

14.7

5.2

2.8

7.3

0.0003

0.0018

Like Ownership

4.4

4.1

7.6

4.9

0.9803

0.3433

Lifestyle choices

7.4

3.9

2.1

4.5

0.1346

0.0753

28.4

32.3

42.0

30.6

0.5174

0.0950

Non-homeowners Renting without elaboration of reasons Planning to buy

20.0

17.7

8.0

18.5

0.7320

0.0934

Living with parents

21.3

6.4

4.0

15.6

< 0.0001

0.0206

Uncertainty about locations

6.0

1.8

2.0

4.4

0.0444

0.4636

Lifestyle

4.9

3.2

4.0

4.3

0.5236

0.9514

About 20% of respondents considered homeownership to be an investment, and 15.9% owned a home because of problems with renting. Less than 10% of respondents mentioned that they owned a home because of lower mortgage rates/housing price, they liked owning, or homeownership fit their lifestyle.

The three age cohorts displayed similar rankings of reasons to own a home, except that the youngest group (aged 34 or under) listed problems with renting more frequently than investment potential as a reason for homeownership. The former was mentioned by 25% of the homeowners of this age cohort and the latter by 21.6%. Overall, the younger cohorts mentioned specific reasons more frequently than older cohorts.

For instance, 65.7% of those age 34 and below mentioned that owning is cheaper for them, which was more frequent than the 39.3% of those aged 35 to 54 and 35.9% of those age 55 and above who mentioned owning was cheaper (p < 0.0001 for both comparisons). The youngest age cohort was more likely to take advantage of lower mortgage rates and lower housing prices, with 14.7% of them mentioning it as a reason for homeownership, compared to 5.2% for the middle-aged cohort and 2.8% for the older cohort (p = 0.0003 and p = 0.0018, respectively).

The remaining 760 respondents who reported being non-homeowners were asked to explain how they made their housing arrangement decisions. About 31% mentioned they were renting, but they did not explicitly explain their reasons.

Other reasons for not becoming a homeowner included saving to buy (18.5%), living with parents (15.6%), uncertainty about location (4.4%), and lifestyle (4.3%). The rankings of those reasons are the same for all age cohorts. The youngest age cohort was the most likely to mention living with parents (21.3%) compared to the middle-aged cohort (6.4%) and the older cohort (4%) (p< 0.0001 and p = 0.0206, respectively). The frequency of mentioning specific reasons also decreased with age.

Retirement Planning A total of 1,507 respondents (98% of the total sample) answered the question “Are you currently saving for retirement?”

Of this number, slightly less than two-thirds (62%) said that they were currently saving and 38% were not. In response to the question “At what age do you think that you will retire (or leave a full-time career?),” the median age response was 65 and the mean was 65.4. However, as shown in Table 2, the mean expected retirement age of young adults under age 35 was almost 3 years younger than older respondents (p=0.0243). In

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Volume 18, Issue 1

response to the question, “At what age do you anticipate collecting Social Security benefits?,” the median age response was age 66 and the mean was age 66.7.

Respondents who indicated that they were saving for retirement were directed to the question “Describe how you are saving for retirement. What retirement savings accounts are you using?” Responses are shown in Table 5. The top choice of retirement savings vehicle was employer-sponsored retirement savings accounts, like 401(k), 403(b), and 457 plans, which were mentioned by 56.4% of the sample. Almost 40% of the sample mentioned two or more retirement savings vehicles, more than a third of the sample mentioned using some type of IRA, and about a third mentioned unnamed employer-sponsored retirement plans. A small portion (6.2%) of the sample counted on their spouse’s retirement plans. The youngest age group mentioned specific and unnamed employer-sponsored retirement plans more frequently than the oldest cohort (p = 0.0079 and p=0.0002, respectively), and they were also more likely to mention two or more retirement savings vehicles than the oldest cohort (p=0.0006).

Respondents who were not saving for retirement were asked: “When do you think that you will begin regular retirement savings deposits?” The words and word combinations “job,” “money,” “debt,” “bills,” “when I,” “not sure,” “as soon as,” “when I get,” “I don’t know,” “when I have,” “as soon as I,” “when I get a,” “in a few years,” and “get a job with” were used frequently. Those answers were grouped into “conditional” (18.9%), “time horizon”

(15.9%), and “sequential/as soon as…” (11.6%) categories. The youngest age cohort, aged 34 and below, was more likely than other age cohorts to condition retirement saving on having accomplished other goals (p=0.0365 and p = 0.0297, respectively). A small portion of the sample had no plan (9.5%) or indicated they would “never” take action to save for retirement (8%). A distinct pattern is that the youngest cohort was less likely than the other two cohorts to use “never” in their answer to the question (p=0.0062 and p = 0.0005, respectively).

Student Loans A total of 1,495 persons (97% of the total sample) responded to the question “Do you have outstanding student loan balances?” Thirty-two percent (473) said they had student loan debt. Age affected the amount of student loan debt; younger cohorts were more likely to have an outstanding student loan balance. Of those individuals aged 34 and under, 44.7% had an outstanding balance, 25.3% of individuals ages 35–54 had a balance, and 7.2% of respondents 55 and older had an outstanding balance.

Respondents were asked if their student loans affected other financial decisions such as housing and retirement decisions. Almost three-quarters (73.9%) indicated that their student loans affected decisions about housing choices. For housing decisions, the age group most affected by student loans was the age group 34 and under (76.7%). Among the older age groups, 69.7% of those 35–54 and 42.9% of those 55 and older also

Table 5: Retirement Savings and Decision Making ≤34

35-54

≥55

Total

p-value, ≤34 v 35-54

p-value, ≤34 v ≥55

401(k)/403(b)/457 plan

58.3

58.4

43.2

56.4

0.9991

0.0079

Two or more retirement savings vehicles

45.2

35.7

26.3

38.7

0.0141

0.0006

IRA/Roth IRA/SEP/SEP-IRA

41.9

31.9

31.4

36.4

0.0138

0.1275

Unnamed employer-sponsored plans

40.4

27.6

20.3

32.4

0.0004

0.0002

Through spouse’s plan

6.7

6.4

3.4

6.2

0.9855

0.3375

Conditional

23.9

16.9

8.2

18.9

0.1244

0.0110

Time horizon

18.5

16.4

5.5

15.9

0.8007

0.0226

Sequential/As soon as . . .

16.5

8.7

4.1

11.6

0.0365

0.0297

No plan

6.2

12.8

11.0

9.5

0.0316

0.2962

Never

3.3

10.5

16.4

8.0

0.0062

0.0005

Retirement Accounts

When to begin regular retirement savings

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Journal of Personal Finance

indicated that student loans affected their housing choices.

Four prominent ways that individuals were affected by student loans (see Table 6) stood out in the data. Of those affected by student loans, over a third (38.2%) had less money to spend on housing, 8.1% said their credit had been affected, 6.7% felt they had to pay off loans first, 4.8% lived with family, and 3.4% rented instead of purchasing a home. Respondents were also asked if their student loans affected decisions about saving for retirement. Seventy-five percent of individuals with outstanding student loan balances indicated that their retirement savings decisions were affected by their student loan debt. Two ways that retirement preparation was affected by student loan debt were that respondents either contribute less money (27.5%) or nothing (30.1%) to retirement savings accounts. Due to the small sample size of student loan holders, the p-values for the cohort comparison are higher than the conventional threshold value to draw conclusions about any cohort differences in how student loans affect housing and retirement.

Data Analysis Agreement To validate the topics determined by regular expressions, a native speaker of English selected a topic or topics for each response from the set of established topics. Agreement between the human and algorithm selected topics on a sample of responses was measured using Cohen’s Kappa (Landis and Koch, 1977). Cohen’s Kappa has a range of -1 to 1, with 0 indicating agreement equivalent to random selection and 1 indicating perfect agreement. The interpretation of the Cohen’s

Kappa depends on the context, but in general Landis and Koch (1977) suggested that 0.21 to 0.40 be considered fair, 0.41 to 0.60 moderate, 0.61 to 0.80 substantial, and 0.81 to 1.00 almost perfect agreement. Comparisons across the three age groups were tested with logistic regression followed by Dunnett’s correction for comparisons against a reference group, those 34 and under. Comparisons of that age group to older participants were tested using the chi-squared test. All statistical analyses were performed using the R statistical platform, including the tidyverse, multcomp, and psych packages.

Table 7 shows the estimates of Cohen’s Kappa, along with 95% confidence intervals, for the assignment of topics to the open-ended questions. Most of the items showed substantial or better agreement (i.e., kappa greater than 0.60) between the human rater and the regular expression searches. However, two items, Q18 and Q19, regarding the effect of student loans on decisions about housing choices and saving for retirement, respectively, showed only fair agreement. For those items, a second human rater assigned topics for all responses and used those instead of the keyword assigned topics. The agreement between the two human raters is shown in Table 7 for those two items.

Discussion This study of financial decision-making featured analyses of responses to open-ended questions. Four key financial decisions were explored: financial goals, homeownership, retirement planning, and student loans. The study had several limitations.

Table 6: Effects of Student Loans on Financial Decisions ≤34

35-54

≥55

Total

p-value, ≤34 v 3554

p-value, ≤34 v ≥55

Yes

76.7

69.7

42.9

73.9

0.3082

0.1120

Less money

40.4

35.8

0.0

38.2

0.6527

0.9995

Credit

8.3

6.4

28.6

8.1

0.7838

0.1674

Pay off loans first

7.9

4.6

0.0

6.7

0.4531

0.9998

Live with family

5.8

2.8

0.0

4.8

0.4009

0.9999

Rent

4.2

1.8

0.0 a

3.4

0.4833

0.9999

Yes

72.7

79.8

88.9

75.4

0.2945

0.5163

Contribute less to retirement

28.6

26.6

11.1

27.5

0.9135

0.4770

Contribute nothing to retirement

29.4

31.2

33.3

30.1

0.9329

0.9605

Affecting Housing

Affecting Retirement

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Volume 18, Issue 1

Table 7: Cohen's Kappa Estimate of Kappa

Lower limit of 95% CI

Upper limit of 95% CI

Q3

0.62

0.56

0.70

Q4

0.85

0.75

0.96

Q7

0.75

0.65

0.85

Q9

0.78

0.65

0.91

Q12

0.96

0.91

1.00

Q13

0.92

0.76

1.00

Q18

0.58

0.45

0.71

Q19

0.79

0.62

0.95

Note: 95% confidence intervals, showing agreement on a random sample between a rater and the topic classification algorithm for all items except Q18 and Q19. For those two items, the values show the agreement between two raters.

First, respondents came from a convenience sample of online survey respondents who did not match characteristics of the U.S. population as a whole. Second, respondents were recruited from a website, tweets, and public presentations that focused on financial topics and may have been more conscientious about their personal finances than others, resulting in sample selection bias. The website and Twitter chat that recruited respondents also attracts primarily females. Third, a wide variety of words and phrases were used to derive common topic themes. Nevertheless, the findings are instructive and reliability testing was used to validate the connection between themes and search terms derived from the n-grams.

Financial Goal-Setting The top financial goals reported by participants in this study involved saving for something, reducing debt or paying off something, and buying something. The keyword analysis also identified two additional goals: being debt free and having enough. Respondents reported a range of things they wanted to have enough money for. Some respondents mentioned being financially stable. For example, the ability to pay bills on time and in full, start a family or take care of family members, or afford health costs. Others mentioned being financially secure.

For example, being able to handle emergencies, live comfortably, travel, retire, and leave bequests. Having enough money for a down payment on a house and for their own education or their childrenâ&#x20AC;&#x2122;s education were also mentioned.

In terms of actions taken, study participants described their actions as saving more, spending less, and paying off financial obligations. Several mentioned they save and invest as much as possible each month. Others were focused on paying off as

much credit card or other debt as possible. For those who were spending less, key actions involved staying within a budget, living frugally, and deliberately stretching the life of products as much as possible.

Homeownership The analysis of survey participantsâ&#x20AC;&#x2122; responses suggests that people are making housing decisions based on their personal situations as well as financial considerations. For instance, cheaper cost than renting was the top reason reported by homeowners for owning a home, followed by family reasons and investment considerations. It appears that the youngest cohort aged 34 and below was more likely than other age cohorts to report owning because of problems with renting, and they were also more likely to take advantage of low housing prices and low mortgage interest rates to become homeowners.

Among those who were not homeowners, the top reason for their housing arrangement was that they were saving for a home. A second common reason was living with parents, which was disproportionately mentioned more often by the youngest age cohort. Interestingly, living with parents is not a unique phenomenon among young people as 6.4% of non-homeowners aged 35â&#x20AC;&#x201C;54 and 4% of non-homeowners aged 55 and older reported this housing arrangement as the reason for not owning a home. This could be partially explained by cohabitating with, and caring for, aging parents.

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Journal of Personal Finance

Retirement Planning Analyses about retirement planning also revealed useful insights. Clearly, 401(k)s and Roth IRAs were favored retirement savings vehicles among the respondents and many were saving in tax-deferred accounts through their place of employment.

Interestingly, respondents aged 34 and below identified retirement accounts at higher frequencies than their counterparts in older age cohorts. This may reflect a higher awareness of retirement accounts among Millennials.

When respondents who were not saving for retirement were asked when they thought they would begin regular retirement savings deposits, their answers indicated that retirement savings was viewed as being contingent on some other life event. The youngest age cohort, aged 34 and below, was more likely than other age cohorts to condition retirement saving on having accomplished other goals; meanwhile, they were less likely than the other two groups to totally give up savings for retirement.

Student Loans According to participant responses, both housing and retirement decisions are affected by outstanding student loan debt.

The most prominent ways through which student loans affect housing are “(having) less money,” “credit,” “paying off loan first,” “living with family,” and the effects on “rent.” At the mean level, the youngest cohort was more likely than the other two age cohorts to indicate that student loans affect their housing, but they were less likely to indicate that their retirement decision was affected. Although statistical differences cannot be inferred due to the small subsample size, these differences are supported by other studies. Between 2003 and 2012, homeownership rates among 30-year-olds with no history of student loan debt declined by five percentage points while homeownership rates for the same age group with student debt fell by more than ten percentage points (Brown & Caldwell, 2013). The Center for Retirement Research at Boston College (Rutledge, Sanzenbacher, & Vitaglina, 2016) found that retirement asset levels of 30-yearolds were unrelated to the size of student loan balances.

Implications As noted above, financial practitioners need to be generation-savvy and understand the biases of clients at different ages and stages of the life cycle. By doing so, they can target their services appropriately and effectively frame the conver-

sations that they hold with clients or students. For example, generation-savvy advisors can help Millennials overcome their pessimism about investing in stocks by reassuring them that they have time on their side (Obstacles to Millennial Retirement, 2016). Each generation has a distinct set of experiences and expectations and older financial practitioners may need to change their business models and practices to gain and hold the attention of younger clientele (Rabe, 2015). This study focused on key financial decisions and provides useful insights into the “money mindsets” of Americans, especially young adults who will become future financial planning clients. Below are implications for professional practice: Promote Concurrent Financial Planning. There are two ways to pursue multiple goals: sequential and concurrent goal pursuit (Orehek and Vazeou-Nieuwenhuis, 2013). Results of this study provided evidence of sequential “as soon as…” financial decision-making where respondents said that they wanted to complete one goal before moving on to the next through a mechanism known as goal shielding (Orehek and Vazeou-Nieuwenhuis, 2013). In other words, some respondents appeared to be living a postponed financial life; for example, delaying retirement savings until a life event occurred or another financial goal, such as repaying student loan debt, was achieved. By postponing savings, however, the wealth-building effects of compound interest are not being maximized. Financial education and planning interventions need to stress the importance of saving for financial goals early in life and show people, with case study examples and attractive graphics, that multiple goals can be funded concurrently, instead of consecutively. When multiple goals are pursued concurrently, financial practitioners need to help clients understand the potential consequences of the order that goals are combined. For example, it is important to demonstrate the benefits of concurrently repaying student loans while also investing for retirement so that retirement savings can take place throughout four to five decades of a young adult’s working life rather than focusing on retirement only after paying off student loans and buying a house.

Encourage Retirement Savings. A worrisome 38% of survey respondents was not currently saving for retirement. Clearly, this makes a case for auto-enrollment in employer savings plans and “financial wellness” programs at worksites that walk people through the process of deciding to make a retirement plan contribution, how much to save, and what plan investment options are available to select from. Encouraging savings of even small amounts can make a big difference over a young

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Volume 18, Issue 1

adult’s lifetime. Many respondents reported that automating retirement savings was a key to their savings success.

Address Risk Management Concerns. Having adequate insurance coverage to protect against the risk of financial losses is a basic financial planning recommendation. Though it didn’t emerge as a top financial goal in the n-grams, some survey participants did report insurance related goals. Adequate auto, health, home, and life insurance coverage were all mentioned.

Over two-thirds of this sample was younger than 45 years old.

As reported by the American College of Financial Services (n.d.), a significant proportion of women aged 41–52 years old feel under-insured or don’t know if they have enough protection.

Financial planning interventions need to continue making the connection between adequate insurance coverage, protection from risk, and preservation of both health and wealth. Practitioners can provide tools and personalized analyses for determining adequate coverage levels.

Promote Income-Driven Student Loan Repayment Plans.

Many of the respondents indicated that student loan debt affects other aspects of their finances (e.g., housing and retirement). Income-driven repayment plans on federal student loans could reduce monthly payments, freeing up more money to save additional funds for a down payment and/or increased retirement savings. There are a variety of income-driven repayment plans. Examples include Income Based, Income Based for New Borrowers, Income Contingent, Pay as You Earn, and Revised Pay as You Earn. Each of these repayment plans can make it easier for student loan borrowers to set and fund multiple financial goals.

Help People Plan Future Financial Goals. As seen in the previous implications, planning is a key factor in achieving financial goals. This connects well to the Theory of Planned Behavior. If consumers feel they have control over their life and develop a plan to achieve their goals, they will be more likely to follow through. This study suggests avenues for financial planning interventions (e.g., addressing clients’ multiple priorities) to assist consumers in gaining a sense of control and encouragement to turn behavioral intentions into specific goals and action steps to achieve them.

Conclusion This study of financial decision-making by primarily young adults included both quantitative and qualitative data. Four key financial decisions were explored: financial goals, homeownership, retirement planning, and student loans. Results indicated

that many respondents were sequencing financial priorities, instead of funding them simultaneously, and delaying homeownership and retirement savings. Almost half (45%) of the sample had student loan debt and many respondents indicated that it was a key factor affecting their financial decisions.

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Credit Card Use of College Students: A Broad Review

Alex Yue Feng Zhu, Ph.D., Visiting Research Assistant Professor, Lingnan University (Hong Kong)

Abstract Using credit cards is an effective way for college students to learn about credit and shape their credit behavior in preparation for financial independence. However, negative outcomes are associated with unhealthy credit card use. By reviewing articles in the past 15 years, this article summarizes the factors influencing credit card use among college students, and identifies the research gaps in previous studies. The purpose of this article is to direct future studies in promoting healthy and responsible credit card use, which promotes financial wellbeing of college students.

Key Words credit card behavior, credit card use, college students

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On university campuses in the U.S., between 56.0% to 68.0% of the overall student population has at least one credit card (Hancock, Jorgensen, & Swanson, 2013; Hayhoe, Leach, Allen, & Edwards, 2005; Norvilitis & Mao, 2013). In Brazil, 84.0% (Norvilitis & Mendes-Da-Silva, 2013) of students have a credit card; in China 29.0% (Norvilitis & Mao, 2013) do. Undergraduate students acquire credit cards through a variety of commercial solicitations, including store offers, direct mail solicitations, on-campus solicitations, and bank offers for account holders (Mattson, Sahlhoff, Blackstone, Peden, & Nahm, 2004). Students usually get credit cards from the commercial sector rather than from an educator or parent. Indeed, because of the amazing purchasing power of modern college students, credit card companies have developed solicitation strategies that specifically target them (Hayhoe, Leach, Turner, Bruin, & Lawrence, 2000).

Students occupy the watershed between financial dependence and financial independence; in the past, university students were empowered to make independent financial decisions (Feltham, 1998). Companies use social media to cultivate brand recognition and loyalty in these newly independent customers, resulting in large increases in consumption by college students (Hoffman, Pinkleton, Weintraub Austin, & Reyes-Velázquez, 2014). More importantly, university students are likely to keep their first credit card brand for more than fifteen years (Commercial Law Bulletin, 1997). This motivates credit card companies, who earn significant profits from commissions on transactions, to promote their cards to students.

However, college students are poorly prepared to be card holders. They lack an understanding of the proper function and use of credit cards and they do not know how to track their own use. In particular, students did not understand the core nature of credit cards as an extension of personal credit and responsibility. Warwick and Mansfield (2000) found that the majority of students only saw the credit cards as a method of payment.

Most did not know their card’s interest rate, and they did not recognize the heavy personal financial burden of credit card interest, which they had to shoulder alone. Joo, Grable, and Bagwel (2003) found that a majority of students (93%) thought it was acceptable to use credit cards to buy items related to school. Parents, the most important agents of socialization for young people before college, seem to inadequately educate their children on credit card use (Braunsberger, Lucas, & Roach, 2005). Instead, they sometimes help students pay their credit card debt (Braunsberger et al., 2005).

Most of the credit card debt accumulated by college students was accumulated inadvertently. Sotiropoulos and d'Astous

(2012) reported that, on average, university students in the U.S. who owned at least one credit card carried a balance of $944.

Williams, Waterwall, and Giardelli (2008) found that 84% of U.S. college students who possessed credit card(s) reported having less than $2000 in credit debt. Norvilitis, Szablicki, and Wilson (2003) found that only 22.2% of all college students (from a sample including students with and without a credit card) reported that they did not have any credit card debt.

There are a variety of negative effects associated with carrying a credit card balance. Excessive credit card debt has been linked to psychological problems such as anxiety and stress (Grable & Joo, 2006; Hancock et al., 2013; Hayhoe et al., 2000; Peltier, Pomirleanu, Endres, & Markos, 2013), decreased financial wellbeing (Nelson, Lust, Story, & Ehlinger, 2008; Norvilitis et al., 2006), decreased physical health (Berg et al., 2010; Nelson et al., 2008), and behavioral problems such as substance abuse (Adams & Moore, 2007; Berg et al., 2010). It has also been linked to a series of negative consequences, which are listed here according to severity. These possible consequences consist of low academic performance (Hogan, Bryant, & Overymyer-Day, 2013; Zhang & Kemp, 2009), extended their studies or delayed graduation (Horn, 2006), dropping out of school (Cleaver, 2002, Gallo, 2006), experienced difficulties obtaining employment (Manning, 2001), blemished credit histories (Hoover, 2001), increased bankruptcy claims (Palmer, Pinto, & Parente, 2001), and even suicide (Marcus, 2001).

The present article reviews 52 empirical English language studies that analyzed quantitative data on factors associated with credit card use in university students. The peer reviewed papers were identified through keyword searches of “university students” (or “college students”) and “credit cards” in the Business Source Premier of Academic Search; papers from the past fifteen years were included in this literature review. The present article seeks to 1) systematically summarize the individual, family-related, and social factors associated with the credit card use and identify how these factors influence credit card use; 2) identify perceived gaps in the existing research for future studies.

Research Overview Most of the studies (90.4%) were conducted in the U.S. A few studies used data from Brazil (5.8%), China (1.9%), or Norway (1.9%). Most of the studies were done by marketing researchers (44.2%), followed by psychologists (42.3%) and consumer science researchers (13.5%). The majority of studies (94.2%)

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used random samples. In all of the samples, most student participants were undergraduates between 18 and 22 years old; in most of the samples (71.2%), the number of female participants exceeded the number of male participants.

Because there is no one commonly accepted way to measure credit card use, the studies measured poor credit card behaviors in different ways. Scholars used three measures of irresponsible credit card use: the number of credit cards under a student’s name (15.4%), the amount of credit card debt not cleared within a specific period of time (42.3%), and scales designed for systematically measuring risky credit card behavior (Lyons, 2004, 2008; Palmer et al., 2001; Pinto, Palmer, & Parented, 2000, 2001a, & 2001b; Roberts & Jones, 2001) (51.9%). Most studies (84.6%) evaluate personal factors, 26.9% consider family-related factors, and only 23.1% of them study social factors.

Factors Associated with Credit Card Use of College Students Personal Factors Student credit card use is associated with individual, family-related, and social factors. Personal factors include demographic attributes such as age (Hancock et al., 2013; Ludlum et al., 2012; Mattson et al, 2004; Norvilitis et al., 2006; Williams et al., 2008) and gender (Limbu, Huhmann, & Xu, 2012; Wang, 2011). Senior college students are more likely to have one or more credit cards than freshmen (Hancock et al., 2013; Mattson et al, 2004; Williams et al., 2008); seniors also often have more credit card debt than freshmen (Hancock et al., 2013; Norvilitis et al., 2006).

Female students are more likely than male students to have one or more credit cards (Moore, 2004), to engage in more risky credit card behaviors (Limbu et al., 2012), and to have higher credit card debt (Wang, 2011).

Economic factors, including employment status (Fogel & Schneider, 2011; Ludlum et al., 2012; Wang, 2011) and income (Fogel & Schneider, 2011; Lyons, 2008), could also influence credit card use. Fogel and Schneider (2011) found that students with part-time jobs are more likely than unemployed students to use credit cards irresponsibly. However, Wang (2011) found that students with part-time jobs carry a lower credit card balance than those without jobs. Furthermore, higher earnings are consistently associated with risky credit card use (Fogel & Schneider, 2011; Lyons, 2008).

Financial factors, including financial knowledge and education

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(Anderson & Card, 2015; Grable & Joo, 2006; Hayhoe et al., 2005; Moore, 2004; Mendes-Da-Silva, Nakamura, & Moraes, 2012; Norvilitis et al., 2006; Robb, 2007, 2011; Robb & Sharpe, 2009; Xiao, Serido, & Shim, 2011; Xiao, Tang, Serido, & Shim, 2011) and practical financial experience (Ludlum et al., 2012), shape university students’ credit card behavior in various ways. Financially educated students are less likely than other students to have four or more credit cards (Hayhoe et al., 2005). Some studies found that increased objective and subjective financial knowledge decreased risky credit card behavior (Mendes-Da-Silva et al., 2012; Robb, 2007, 2011; Xiao, Serido et al., 2011; Xiao, Tang, et al., 2011). However, according to Grable and Joo (2006), students with high subjective levels of financial knowledge tended to report irresponsible credit card behavior. Similarly, objective financial knowledge has been positively associated with credit card debt (Moore, 2004; Norvilitis et al., 2006; Robb & Sharpe, 2009).

Some psychological factors have also been associated with credit card use. These factors include the student’s credit and general attitude towards money (Hayhoe et al., 2005; Hayhoe et al., 2000; Moore, 2004; Norvilitis & Mao, 2013; Palan, Morrow, Trapp, & Blackburn, 2011), their financial confidence (Braun Santos, Mendes-Da-Silva, Flores, & Norvilitis, 2016; Xiao, Serido et al., 2011), their ability to delay gratification (Norvilitis et al., 2006), locus of control (Joo et al, 2003; Sotiropoulos & d’Astous, 2013; Watson, 2009; Xiao, Tang et al., 2011), their attitude towards risk (Sidoti & Devasagayam, 2010), and their compulsivity (Peltier et al., 2013; Wang & Xiao, 2009). Specifically, a student’s affective credit attitude (a tendency to derive happiness from using credit), retention money attitude (an aversion to spending money), and a belief that credit cards are problematic increase the likelihood that a student will have four or more credit cards (Hayhoe et al., 2005; Hayhoe et al., 2000; Norvilitis & Mao, 2013). A power-prestige attitude towards money—that is to say, the belief that money symbolizes success—increases the chance of risky credit card behavior (Palan et al., 2011), and an unhealthy attitude towards credit cards (a failure to understand the nature, operation, and risks of credit cards) increases the likelihood of credit card debt (Moore, 2004). Financial confidence and the ability to delay gratification could reduce students’ debt-to-income ratios (Xiao, Serido et al., 2011).

Sotiropoulos and d’Astous (2013) found that self-control is negatively associated with credit card overspending, and Watson (2009) found that an internal (rather than an external) locus of control is more likely to result in responsible credit card use.

As expected, compulsivity causes university students to apply

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for more credit cards and accumulate more debt (Peltier et al., 2013; Wang & Xiao, 2009). Willingness to take risks also increases credit card misuse (Sidoti & Devasagayam, 2010).

In addition to demographic, economic, financial, and psychological factors, other personal factors can also influence students’ credit card use. These include materialism (Pinto et al., 2000; Pirog & Roberts, 2007; Sidoti & Devasagayam, 2010), emotional extroversion (Pirog & Roberts, 2007), academic performance (Lyons, 2008; Williams et al., 2008), and type of credit card use (Mansfield, Pinto, & Parente, 2003). Materialism and emotional introversion are associated with increased credit card misuse (Pirog & Roberts, 2007; Sidoti & Devasagayam, 2010). Low academic performance is also associated with risky credit card behavior (Lyons, 2008); students who reported high GPAs were less likely to have credit card debt (Williams et al, 2008). Convenience users (those who use credit cards because they are an easy way to pay) are likely to have fewer credit cards than those who use credit cards for specific financial reasons (such as carrying a balance) (Mansfield et al., 2003).

rectly from their parents (Mattson et al., 2004). Frequent credit card use by parents has also been shown to promote positive attitudes towards credit cards in students (Joo et al., 2003).

However, children of parents who often argued about finances are more likely to have high credit card debt and to have two or more credit cards (Hancock et al., 2013). Norvilitis and MacLean (2010) found that parental reticence increases students’ debt levels and risky credit card behaviors, in contrast to facilitation (parental hands-on assistance in handling money).

In addition to parental socialization, other parental factors can also influence students’ credit card use, including parents’ social and economic status (Xiao, Tang, et al., 2011) and parental worries (Norvilitis & Mao, 2013). Specifically, negative parenting positively predicts a higher student debt-to-income ratio (Norvilitis & Mendes-Da-Silva, 2013). Students whose parents had a higher social and economic status were less likely than others to engage in risky credit card behavior (Xiao, Tang, et al., 2011).

Parental worries could motivate students to apply for multiple credit cards (Norvilitis & Mao, 2013).

Family-related Factors

Social Factors

College students’ credit card use is shaped by parental socialization factors (Peltier et al., 2013). These can include parental arguing (Hancock, et al., 2013), students’ interaction and communication with parents (Hayhoe et al., 2005; Kim & Chatterje, 2013; Mattson et al., 2004; Thorson & Kranstuber Horstman, 2014), parents’ credit card use (Joo et al., 2003), parental attitudes towards credit (Norvilitis & MacLean, 2010), parental norms (Xiao, Tang, et al., 2011), and parental involvement in students’ acquisition of credit cards (Mattson et al., 2004). Peltier et al. (2013) examined several aspects of parental involvement, including parental access to students’ credit card statements, parents providing financial assistance for students’ credit card balances, parents’ awareness of students’ credit card consumption, parental influence over student credit card use, and their involvement with the issuance of students’ first credit cards. This study found that parental involvement is a negative predictor of credit card debt. Frequent, pleasant interactions with parents consistently decrease the likelihood that students will have four or more credit cards. Conversations between parents and students increase the students’ likelihood of being open about their credit card behavior (Thorson & Kranstuber Horstman, 2014). Students who communicate with their parents about credit cards are also less likely to carry a credit card balance, as are those who receive their first credit card di-

Several social factors also influence college students’ credit card use. These include whether the credit card was acquired from a stand or table on campus (Lyons, 2008; Mattson et al., 2004; Norvilitis et al., 2003), the norms of students’ friends (Xiao, Tang, et al., 2011), social comparisons (Braun Santos et al., 2016; Norvilitis & Mao, 2013), social norms (Sotiropoulos & d’Astous, 2012, 2013), social support (Wang & Xiao, 2009), and community characteristics (Friedline, West, Rosell, Serido, & Shim, 2017).

Students who acquire credit cards through campus solicitation are more likely to engage in risky behavior (Lyons, 2008), to carry a credit card balance (Mattson et al., 2004), and to have high debt-to-income ratios (Norvilitis et al., 2003). Xiao, Tang, et al. (2011) found that friendships with people who have positive norms regarding spending, borrowing, and saving are associated with healthy credit card behavior. Similarly, Sotiropoulos and d’Astous (2013) found that social norms favoring credit card spending (social acceptance of carrying a credit card balance and a high number of friends with maxed-out credit cards) are associated with credit card overspending. Wang and Xiao (2009) suggested that encouragement from friends to manage credit cards is crucial to decreasing debt, especially for college students who are in a new environment and who lack parental support. Friedline et al. (2017) found that a community with a higher unemployment rate and a lower number of bank

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branches increases the debt level of college students.

Pathways to University Student Credit Card Use Hayhoe et al. (2000) examined financial stressors and pathways to credit card debt among university students. This study used a cross-sectional sample of 480 university students in the U.S. and compared the pathways of male and female students. For male and female students, an affective credit attitude and positive financial practices may affect the number of credit cards on which the student carries a balance (Hayhoe et al., 2000). In a similar study, Palan et al. (2011) examined a cross-sectional sample of 260 U.S. undergraduate students to confirm a model that connects an attitude correlating money with prestige to compulsive buying through the mediation of credit card misuse. However, in another cross-sectional sample of 406 U.S. undergraduate students, Roberts and Jones (2001) found that misuse of credit cards has a moderating rather than a mediating effect between a power/prestige attitude towards money and compulsive buying.

Moore (2004) tested two more complex models using cross-sectional data collected from 2,113 undergraduate students in the U.S. The latent variables of Moore’s two models are nearly the same, but their developmental stages are different.

The first model describes a three-stage process that leads to debt. Background variables in this study include age, gender, GPA, and year in school. These variables influence students’ financial knowledge and the health of their financial attitudes, which further impact their total debt directly or indirectly through the mediation of credit card use (Moore, 2004). The second model considers financial knowledge to be a separate stage and describes a four-stage developmental process, affected by background variables, knowledge, and attitude towards total debt (Moore, 2004).

The models discussed above focus on personal factors. By contrast, a study by Norvilitis and MacLean (2010) used cross-sectional data from 173 university students in the U.S. to test a model composed mainly of parental factors. According to this study, four parental factors—parental reticence, parental bailout, parental facilitation, and parental instruction—affect students’ ability to delay gratification and credit card disinhibition, which then affect credit card debt through the mediation of problematic credit card use.

Sotiropoulos and d’Astous (2013) tested a model that includes

individual and social factors affecting students’ credit card use. They examined a cross-sectional sample of 335 university students in the U.S. and found that students’ attitudes towards credit card overspending, self-efficacy regarding credit card overspending, and credit card related credit card descriptive social norms all affect students’ propensity to overspend on credit cards.

A five-stage model constructed and tested by Xiao, Tang, et al.

(2011) is the most sophisticated one examined in the present study. This model incorporates individual, parental, and social factors that affect students’ credit card use. Xiao, Tang, et al.

(2011) examined a sample of 1,242 college freshmen and found that the participants’ subjective and objective financial knowledge and their parents’ economic and social status predict psychological factors that can influence credit card use. These psychological factors include students’ attitudes, parental norms, friends’ norms, financial efficacy, and financial controllability. In the next stage of this model, all these factors together influence behavioral intention and affect risky purchasing and borrowing. In the final stage of the model, both of these types of behaviors may impact debt levels (Xiao, Tang, et al., 2011).

Discussion Although previous studies have extensively explored the factors associated with credit card use by college students, their findings should be viewed with caution. Not all young adults attend college and this study has only reviewed college students’ credit card use. While college students comprise a significant percentage of emerging adults, this does not mean that credit card use by other young adults is unimportant. The factors associated with credit card use by other young adults are likely to differ from those affecting college students’ credit card use.

Additionally, in some of the earlier studies, the age ranges of the samples were quite large; this means that the samples included some students who were returning to university after working for an extended time. Undergraduate students were the target group of most of the studies, because starting college is the most important transformational moment in one’s life. First-time college students psychologically or physically leave their parents’ homes and enter a new social environment with increased autonomy to make independent decisions. The correlation patterns, multivariable patterns, and developmental processes observed in these studies are likely based on the fact that most participants are young undergraduates. This means

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that including older returning students (rather than limiting the samples to emerging adults) probably limits the interpretive power of the estimated parameters.

Previous studies on this topic have used data collected in the U.S., China, Brazil, and Norway. However, samples of participants drawn from a limited social background may limit a study’s power to explore the effect of social factors on credit card use. Very few studies simultaneously incorporate all aspects of credit card use, which include debt level, number of credit cards, and risky purchasing and borrowing. Most studies focus on the personal factors affecting credit card use and insufficient research has been done on family-related and social factors. There are a number of correlation and multi-variant studies on this topic, but too few studies have explored the development of credit card behaviors. The non-random cross-sectional samples and correlational analysis of most studies make it hard to establish valid causality.

Therefore, future studies may consider including young adults who have not attended college and college students from more culturally diverse backgrounds. Future studies might also limit the samples by age to exclude older and returning students. A standard measurement of students’ credit behaviors, debt levels, and number of credit cards should be developed and validated for use in future studies. Future studies may consider examining the relationships between credit card use and family-related factors or friend/coworker socialization factors.

Such studies might also track credit card use over time to see how these factors shape responsible and irresponsible credit card use.

The thorough investigation of factors associated with credit card use offers effective entry points for comparing whether financial education or credit regulation more effectively promotes responsible credit card use among college students.

Lusardi and Tufano (2015) emphasized the positive impact of financial education and credit literacy on responsible credit use.

However, Willis (2008) questioned the effectiveness of financial education and highlighted the importance of credit regulation.

The current study has summarized several factors associated with credit card use. These factors offer implications for the possible design of various financial education programs (e.g., financial education programs that include parents) and different credit regulations (e.g., limiting the credit access of students without part-time jobs). Policymakers can determine whether financial education or credit regulations are more effective after analyzing the outcomes of studies of a large pool of different

financial education programs and credit regulations.

It is interesting to note that more of the reviewed studies were conducted by marketing researchers than by consumer science researchers. This review is intended for researchers in the field of consumer science. The authors hope that this review will increase interest in research on college students’ credit card use that serves the interests of consumers rather than business corporations.

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Using donor images in marketing complex charitable financial planning instruments: An experimental test with charitable gift annuities

Russell N. James III, Ph.D, JD, CFP®, Professor, Texas Tech University

Abstract Previous experimental research has demonstrated simple charitable giving decisions can be influenced by examples of another’s donation. This article uses a series of experiments to explore how the example of another’s complex donation influences interest in a complex charitable financial planning arrangement (a charitable gift annuity). The influence of an example donor varied depending upon the presence and age of the example donor image. A key factor in determining the effectiveness of an example donor image was matching the age of the participant with the age of the example donor image. When the age of the donor image was similar to (differed substantially from) the participant’s age, including the donor image generated higher (lower) interest in the complex gift as compared with the overall interest generated when including a non-donor image or no image. This effect from age matching arose through the impact on perceived similarity and identification with the example donor. The value of a donor image for increasing interest in a complex charitable financial planning instrument depends upon its ability to advance the idea that “people like me do things like this.”

Keywords planned giving, charitable financial planning, charitable gift annuity

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Introduction Complex charitable financial planning can provide a range of attractive benefits for financial planning clients with charitable interests (Clontz, 2017; Yeoman, 2014). However, some planners may be less familiar with client motivations for funding such charitable instruments, given that charitable motivations (James, 2008; James, 2017) will differ from the simple economic accumulation and consumption motivations that are the typical focus of personal financial planning (Ando & Modigliani, 1963). This article reviews the previous research on the power of examples to influence simple charitable gifts, and then presents experimental results extending the application of these concepts to the field of complex charitable financial planning using the specific instrument of charitable gift annuities.

Past research suggests that providing examples of what others have done can influence simple charitable giving decisions.

These examples can become more influential when the decision-maker perceives the people in the example to be more similar to themselves. The current study expands this line of inquiry by exploring two additional questions. First, do these past results from experiments involving simple cash donations also apply to the marketing of complex charitable financial planning instruments? Second, how should these concepts be applied to the practical issue of using donor images when sharing a complex charitable financial planning example?

The following article reviews past research, explores related hypotheses in two sets of experiments involving 4,103 participants, and discusses the findings and implications.

Literature Review A range of experimental research has demonstrated that providing an example of another’s previous charitable donation can influence a client’s decision to donate. For example, in an experiment involving donors to a public radio station, donation amounts tended to increase when donors were provided information about another’s donation that was larger than the subject’s last donation. Conversely, when an example of a donation smaller than the subject’s last donation was presented, the donation amounts tended to decrease (Croson & Shang, 2008).

In an experiment with a transparent donation box at a museum, seeding the box with large bills generated fewer, but larger gifts, than seeding the box with smaller coins (Martin & Randal, 2008). Similarly, including a reference to a relatively large gift by another donor in a charitable appeal letter increased the average donation size, but decreased the likelihood of donat-

ing (Jackson, 2016). When the allowed donation amount was fixed, informing students that many other students at their university donated to specific charitable funds increased the students’ subsequent willingness to contribute to the funds (Frey and Meier, 2004). The influence of others’ examples has been demonstrated in experiments involving other pro-social behavior such as volunteering (Chen, Harper, Konstan, & Xin, 2010), financial sharing in a dictator game (Cason & Mui, 1998), or stopping to help a motorist with a disabled car (Bryan & Test, 1967).

Further, examples of others’ giving tend to be more influential when the others are more similar to the subject. Shang, Reed, and Croson (2008) experimentally demonstrated an “identity congruency effect”, in which referencing the gift of a previous donor was more influential when the previous donor was described as sharing the subject’s gender. Agerström, Carlsson, Nicklasson and Guntell (2016), found that donations from university students increased when they were informed that 73% of university students in their country made a donation when asked; they also found that donation rates increased even more when the students were informed that 73% of students at their particular university made a donation when asked. Similarly, Hysenbelli, Rubaltelli and Rumiati (2013) found that the positive effect from referencing larger prior donations dropped if the past donations were described as coming from those of another nationality. In a study, Bennett, Kim and Loken (2013) found that when students were asked for donations, they were more likely to give if they saw that other students and faculty at their school had previously shown support for the cause, in comparison to when students were given no information about other donors; students were also found to be less likely to give when they learned about prior support from corporations. In the context of volunteering for a charitable cause, Fishbach, Henderson, and Koo (2011) found that students engaged in more volunteering when they felt a greater identification with and similarity to other unseen group members who had already completed the volunteering project. Thus, examples of others’ charitable activity are most influential when they promote the idea that “people like me do things like this.”

While these previous experimental results demonstrate the effect of others’ donations on decisions to make simple gifts of cash, the current experiments explore whether this same concept can be effectively applied to the marketing of complex charitable financial planning instruments, specifically charitable gift annuities. This article also investigates how these concepts should be applied to the practical issue of using example cli-

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ent/donor pictures when sharing another’s donation example.

Understanding complex charitable planning is a significant issue, given the economic importance of such complex vehicles. For example, in the United States, charitable remainder trusts distribute over $4 billion to charities annually (Internal Revenue Service, 2017a). Donor advised funds account for more than 7 percent of all charitable giving in the U.S., holding almost $54 billion in assets (Colinvaux, 2015). Charitable private foundations in the U.S. hold over $791 billion (Internal Revenue Service, 2017b); these kinds of foundations are also growing in other countries, including the United Kingdom and India (Murray, 2016, March). Charitable gift annuities, in which donors exchange a gift for lifetime income, have existed in the U.S. since 1831 (Brown, 2017), and now exceed $15 billion in assets (Clontz, 2010). Such complex charitable giving arrangements may provide a range of financial benefits for donors, such as income (as with charitable remainder trusts or charitable gift annuities) or employment (as when U.S. private foundations pay donors or donor family members to perform personal services). Although previous experimental work has not explored donor examples in complex charitable giving, some experiments suggest that donor examples may influence charitable bequest gifts. In the U.K., Sanders and Smith (2016, p. 183) found that when solicitors added a statement beginning with, “Many of our customers like to leave a gift to charity in their will,” estate-planning clients became significantly more likely to include a charitable bequest gift. In the U.S., James (2016) found that the use of a similar phrase significantly increased interest in leaving a bequest gift to charity.

Experimental research regarding complex charitable planning is difficult because it is difficult or impossible to recreate actual transfers in a laboratory or experimental setting, but it is possible to measure interest in pursuing such gifts. Previous research has found that experiments measuring expressions of charitable intentions have produced results similar to those using real donation requests (Dickert, Sagara, & Slovic, 2011).

Further, experiments measuring hypothetical intentions to donate revealed similar motivations (Nilsson, Erlandsson, & Västfjäll, 2016) and similar choices (Carlsson & Martinsson, 2001) as compared to those measuring actual donations. Specific to the current investigation, Alzipar, Carlsson, and Johansson-Stenman (2008) found no significant differences in the effect of others’ donations between hypothetical contribution experiments and actual contribution experiments.

When providing the example of another’s donation, marketers

may be faced with the practical question of whether or not to include a picture of the donor. It appears only one previous charitable giving experiment has explored this issue. In a working paper, Sanders, Reinstein, and Tupper (2014) reported that including a picture of a co-worker donor in a workplace giving request more than doubled participation than when the same message was used without a picture. The positive effect of a donor picture corresponds with research demonstrating that charitable giving increases in the presence of watching eyes or eyespots (Krupka & Croson, 2016).

Hypotheses Hypotheses: The use of an example donor image of similar (dissimilar) age to the respondent will increase (decrease) the influence of the donor example in generating interest in pursuing a complex charitable gift.

Methods and Results The charitable gift annuity is a complex charitable giving device that is common only in the U.S. Accordingly, all experimental participants were U.S. residents. Participants were recruited through an online panel (MTurk) that has been proven to generate a more diverse group of experimental participants when compared with typical university experiment pools (Buhrmester, Kwang, & Gosling, 2011; Stewart, et al., 2015).

Researchers in a variety of disciplines have found recruiting experimental participants from this panel produces reasonable and consistent results similar to other methods of participant recruitment (Buhrmester, et al., 2011; Goodman, Cryder, & Cheema, 2013).

Two sets of experiments were conducted sequentially and included 4,103 participants in total. The 1,537 participants in experiment 1 had an average age of 36.8 years, 56.1% were female, and the majority (51%) had at least a bachelor’s degree.

The 2,566 participants in experiment 2 had an average age of 36.0 years, 51.4% were female, and the majority (52%) had at least a bachelor’s degree. All responses were collected online using the Qualtrics online response platform.

Experiment 1 In experiment 1, participants were randomly assigned to receive one of three different descriptions, each labeled with the heading “Gift Annuity”. To test for the reasonableness of using donor examples, as compared with simply describing

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what the reader could do, one group received the text, “You make a gift and receive a tax deduction and yearly income for life. Any unused gift amount will go to the charity at the end of your life.” Another group instead received the text, “Sara made a gift and received a tax deduction and yearly income for life.

Any unused gift amount will go to the charity at the end of her life.” As a first exploration of the use of a donor image, the third group received the same text, but also with a picture of Sara as an approximately 65 to 75-year-old white female. In each case, under the description was the question, “Please rate your interest in pursuing the above described charitable giving arrangement,” with the options of 1= “I will never be interested,” 2= “Not now, but maybe in the future,” 3= “Not now but probably in the future,” 4= “Not now but definitely in the future,” 5= “Slightly interested now,” 6= “Somewhat interested now,” 7= “Definitely interested now.”

on interest for donors, especially among those under age 55.

This initial result, which showed that adding a donor picture has a negative effect, and the possible connection between the age of the donor picture and the age of the respondent, led to the second experiment further exploring this relationship.

The numerical average interest in a charitable gift annuity was 3.46 (n=526) for the text referencing “you,” 3.64 (n=497) for the text referencing Sara, and 3.45 (n=514) for the text referencing Sara and including a picture of Sara. In two-tailed t-tests the significance of the difference between the “you” text and Sara text was p=.115, and the difference between the Sara text with and without a picture was p=.094. However, when the analysis was restricted only to donors (defined as those who had given at least $100 in any year to charity), the reduction in interest from adding the donor picture changed from -.19 to -.32 (significant at p=.023). Furthermore, looking only at donors under age 55, the reduction in interest from adding the donor picture was -.36 (significant at p=.018). In contrast, there was no significant impact from the donor picture (p=.823) among respondents age 55 and older.

Similarly, in an ordered logistic regression on the level of interest in the charitable gift annuity comparing against the omitted category of the text referencing Sara, the text referencing “you” was negative, -.183 (p=.099), as was the addition of the picture of Sara, -.181 (p=.104). However, as before, restricting the analysis to previous donors ($100+) resulted in coefficients of -.206 (p=.125) for the “you” text and -.307 (p=.024) for the picture of Sara. Restricting the analysis to previous donors ($100+) under age 55 resulted in coefficients of -.122 (p=.398) for the “you” text and -.349 (p=.017) for the picture of Sara.

Taken together, these results suggest that sharing an example of what someone else has done might be weakly more influential than sharing an example of what the reader can do, but that adding the older donor picture had a significant negative effect

Experiment 2 In the new experiment, participants all received the same text describing Sara using a charitable gift annuity as in the first experiment. However, participants were randomly assigned to have the description accompanied by 1) no image, 2) a picture of Sara as a younger female, 3) a picture of Sara as a middle-aged female, 4) a picture of Sara as an older female, and 5) a picture of a university campus green space. The Sara pictures were all of white females in a similar portrait pose. On the page following the rating of interest in pursuing the gift arrangement, participants were asked the additional questions, “How old do you think Sara is?” and “How much do you identify

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with Sara?â&#x20AC;?

Table 1 reports descriptive statistics from this experiment. The highest average interest in the complex charitable gift resulted from the use of the younger donor image. However, this difference was not statistically significant in any comparison except that the younger donor picture generated significantly greater interest in the complex charitable gift than the older donor picture. This difference in the effectiveness of the younger and older donor image corresponded with a difference in the estimated age gap between the participant and the example donor. Although the older donor image generated lower interest in the complex charitable gift, these results, and those from

Table 1: Experiment 2 group averages Younger donor picture

Middle Older donor aged donor picture picture 3.64

3.45**

Campus picture

Text only

3.64

3.70

Interest in charitable gift annuity

3.73

Respondent age

35.8

36.0

36.1

35.1

36.8

Estimated example donor age

31.5

48.6***

73.7***

48.6***

49.3***

Estimated absolute age difference

8.5

16.2***

37.6***

17.2***

16.8***

n

498

499

524

517

528

Notes: Reporting significance levels from two-tailed t-test comparing against younger donor picture, *p<.05, **p<.01, ***p<.001

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experiment 1, hint that the effectiveness of the image may depend upon the difference in the participant age and the age of pictured donor, rather than the absolute age of the pictured donor.

Further analysis provides additional support for this suggestion. Participants reported both their own age and their estimated age of Sara using the options under 18, 18–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and 85+. Thus, the differences in age levels between the respondent and the donor image could vary from 0 to 8. The numerical average interest level in pursuing the charitable gift annuity arrangement depending on the differences in age levels between the participant and the example photo were 0=4.02, 1=3.79, 2=3.65, 3=3.31, 4=3.32, 5=3.34, 6=3.08, 7=3.00.

Table 2: Ordered logistic regression on interest in a charitable gift annuity (1)

(2)

(3)

Younger donor picture

0.081 (0.037)*

-0.106 (0.047)*

-0.145 (0.048)**

Middle-aged donor picture

0.211 (0.096)*

0.014 (0.101)

-0.026 (0.101)

-

-

-

Text only

0.223 (0.109)*

-0.174 (0.126)

-0.251 (0.127)*

Campus picture

0.032 (0.022)

-0.047 (0.025)

-0.067 (0.026)**

-0.019 (0.003)***

-0.022 (0.003)***

(Omitted comparison) Older donor picture

Estimated age difference (absolute value) Respondent age

-0.018 (0.003)***

Female

-0.050 (0.078)

Education level

-0.016 (0.031)

Largest donation level

0.116 (0.028)***

Table 2 presents results from ordered logistic regressions on the level of interest in pursuing the complex charitable giving arrangement. Column 1 shows that the older donor picture (the omitted comparison category) performed significantly worse than the younger donor picture, middle-aged donor pic-

ture, and text alone. Column 2 indicates that controlling for the difference between the participant’s age and the participant’s estimate of Sara’s age caused the negative association with the older donor picture to disappear. With this factor controlled, no presentation was significantly better than the older donor picture and the younger donor picture was significantly worse.

Finally, Column 3 includes controls for other demographic and donation factors. When including these additional controls, the older donor picture generated the highest level of interest in pursuing a charitable gift annuity, with significantly lower interest generated by the younger donor picture, campus picture, and text alone.

Thus, although the use of the older donor picture was associated with lower interest in the complex charitable giving arrangement in both experiments 1 and 2, this appears to have been driven largely by the perceived difference in age between the respondents and the donor picture. Once this perceived age difference was controlled for, the older donor picture was quite effective. The positive, highly significant coefficient for the estimated age difference variable in columns 2 and 3 of Table 2 demonstrates that matching the perceived age of the example donor with the age of the experimental participant was important in generating interest in the complex charitable giving arrangement.

These results confirm the importance of age matching an example donor with the participant. As a practical matter, when is it more effective to use a donor image as compared with no image or a non-donor image? The two non-donor image treatments combined generated an average interest of 3.67 and the three donor image treatments combined for an average interest of 3.60, a statistically insignificant difference.

Thus, it is not the case that using or not using donor images is a universally superior strategy to increase interest in making a complex charitable gift.

Not surprisingly, using a donor image increased the precision of the estimated age of the example donor. Using range midpoints (and 17 and 90 for endpoints), the standard deviation for the estimated age of Sara was 15.0 years for the text alone treatment and 15.2 years for the campus image treatment, but was 6.5 years for the younger donor image, 7.7 years for the middle aged donor image, and 9.3 years for the older donor image. Given the importance of age matching, the effect of this increased precision in estimating the example donor’s age will depend upon the similarity with the participant’s age.

Thus, when the example donor’s age and the participant’s age

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were in the same range (here using ranges of under 18, 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85+), the average interest level resulting from any donor picture was 3.92. This was significantly (p<.05) greater than the 3.67 overall interest level from non-donor-image treatments. When the ages differed by one reporting level, the average interest resulting from any donor picture was 3.85. At a two-level age difference, this fell to 3.45. When the ages differed by three or more levels, the interest resulting from a donor picture treatment was 3.39, significantly lower (p<.01) than the overall interest level from non-donor-image treatments of 3.67. Thus, when the donor picture was in the same age level as the respondent, the donor picture was significantly more effective at generating interest in the complex charitable gift than the overall result from using no picture or a non-donor picture. When there was an age difference of three age levels or more for the donor picture, using the donor picture was significantly less effective.

Why is this age matching important? Past research suggests that an important factor influencing the effectiveness of a donation example is the similarity between the decision-maker and the example donor. Increased similarity can cause a decision maker to identify more closely with the example donor, resulting in a greater level of influence for the social example.

To explore this potential mechanism, participants were subsequently asked, “How much do you identify with Sara?” with the options of 1= “She is not at all like me,” 2= “She is not really like me,” 3= “She is a little bit like me,” 4= “She is somewhat like me,” 5= “She is a lot like me.” Using a simple ordered logistic regression with two variables, there was a significant relationship between the outcome (interest in a charitable gift annuity) and the predictor variable (absolute difference between respondent age and respondent’s estimate of the example donor age), β=-.146 (p<.0001). Additionally, there was a significant relationship between the mediator (the perceived similarity with the example donor) and the predictor variable, β=-0.324 (p<.0001). The previously significant relationship between the outcome and predictor became non-significant (β =-.-.031, p=.20) when the mediator variable was added to the regression. Therefore, following the method from Baron and Kenny (1986), the respondent’s perceived similarity with the example actor mediates the relationship between age difference and respondent interest in a charitable gift annuity.

Discussion In this first experiment, the use of a donor example (“Sara”)—as compared with an otherwise identical description of what the participant could do (“You”)—generated greater interest in the complex charitable gift, but this difference was statistically insignificant (p=.115). Thus, at a minimum, communicating information about this complex charitable gift using the example of another’s gift appears not to produce lower interest than simply describing what the participant could do.

The remaining results provided support for the hypotheses that the use of an example donor image of similar (dissimilar) age to the respondent will increase (decrease) the influence of the donor example in generating interest in pursuing this complex charitable gift. In the first experiment, accompanying the complex gift example with an image of the example donor as a 65–75 year old white female significantly reduced interest in the complex gift among those who had given to charity ($100+) in the past. However, no such reduction in interest occurred among those previous donors who were aged 55 and above.

In the second experiment, participants read identical text about the example donor’s use of a charitable gift annuity. In addition to the text, respondents saw an image picturing the donor as younger, middle-aged, or older, a campus image, or no image. The use of donor pictures was overall not more or less effective than the campus image or no image. But, the effectiveness of the examples with a donor picture depended upon the difference between the participant age and the perceived age of the example donor picture. When the participant age and donor age were similar (substantially different), the use of a donor image generated significantly higher (lower) interest in the complex charitable giving instrument as compared with the overall average interest using identical text with no image or a campus image.

This suggests that if the example donor image can be age matched with the target audience, the image will increase the effectiveness of the message. However, if the example donor image is substantially different in age than the target audience, it is preferable to use a non-donor image or no image. Avoiding the use of the donor image creates greater uncertainty in the age of the example donor. If resolving this uncertainty – via a donor image – confirms (disconfirms) the similarity of the example donor with the target audience, that resolution is helpful (harmful) in generating interest in the complex charitable gift.

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The reason why age matching with an example donor was important is that the degree of age matching significantly influenced (p<.0001) the degree of perceived identification/ similarity between the participant and the example donor (i.e., identity congruency as in Shang, Reed, and Croson, (2008)).

Thus, the degree of age matching strongly (p<.0001) predicted subsequent interest in the complex gift, but after controlling for the participant’s perceived level of identification/similarity with the example donor, this relationship was no longer significant (p=.20).

As a practical matter, it appears that donor examples are most powerful when they successfully convey the idea that “people like me do things like this.” Although this idea was tested here specifically with regard to age, this is likely true for other characteristics (like gender in Shang, Reed, and Croson, (2008)). Thus, where a donor example differs from the intended audience, it may be better not to specify or draw attention to those differences, but instead emphasize areas of similarity.

Correspondingly, if an advisor wishes to provide an influential example of what another client has done in a conversational setting, it may be important also to reference some point of similarity with the example client.

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Martin, R., & Randal, J. (2008). How is donation behaviour affected by the donations of others?. Journal of Economic Behavior & Organization, 67(1), 228–238.

Murray, S. (2016, March 11). Private foundations—the charitable impulse. Financial Times. Retreived from https://www.ft.com/ content/23df51e0-d1a8-11e5-831d-09f7778e7377 Nilsson, A., Erlandsson, A., & Västfjäll, D. (2016). The congruency between moral foundations and intentions to donate, self-reported donations, and actual donations to charity. Journal of Research in Personality, 65, 22–29.

Sanders, M., Reinstein, D., & Tupper, A. (2014). Worth 1000 words: The effect of social cues on a fundraising campaign in a government agency: A field experiment. Centre for Market and Public

Yeoman, J. C. (2014). The economics of using a charitable remainder trust to fund a retirement portfolio. The Journal of Wealth Management, 17(1), 40–50.

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Issues with the Transition Mechanism in the Actuarial Approach to Retirement Spending Ken Johnston PhD CFA, Associate Professor of Finance, Berry College John Hatem PhD, Professor of Finance, Georgia Southern University Thomas Carnes PhD, Professor of Accounting, Berry College Arman Kosedag PhD, Associate Professor of Finance, Berry College

Abstract This article highlights some issues with the actuarial retirement withdrawal strategy. There are potential problems with the transition mechanism (present value of an annuity calculation, PVAN) as the retiree ages. With poor initial returns, the actuarial approach does not cut spending quickly enough, due to the mathematics of the annuity formula. Additionally, spending rates can initially be too high or too low depending on the assumed discount rate, which can result in the inefficient spending down of wealth. If a future value is specified as a bequest/safety net, the minimum annual spending over the 30-year period decreases as the future value increases. The effect of a desired future value on annual spending volatility depends on whether the actual subsequent compounding rate is high or low. An increase in the desired future value results in smaller initial withdrawals, with the portfolioâ&#x20AC;&#x2122;s recovery dependent on future returns. As remaining longevity declines with a constant future value, large (small) positive returns as the retiree ages force the transition mechanism (PVAN) to significantly increase (not significantly increase) the annual withdrawal, thus increasing (decreasing) the standard deviation. This effect can possibly turn annual spending negative.

Therefore, while the actuarial approach provides solutions to some issues, it also creates new ones.

Key Words actuarial approach, retirement withdrawal strategies

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Introduction The actuarial approach compares the market value of the retiree’s assets with the market value of the retiree’s spending liabilities. This process is implemented with a transition mechanism that determines the first actuarial withdrawal and how that withdrawal will change periodically as the mechanism responds to input changes.

The actuarial approach transition mechanism recalculates retiree’s spending each year using some form of the present value of an annuity (PVAN) or the present value of a growing annuity (PVGAN) formula (see: Blanchett, Kowara, and Chen (2012); Frank, Mitchell and Blanchett (2011, 2012a, 2012b); Blanchett (2013); Steiner (2014); and Waring and Siegel (2015)). As discussed in Pfau (2015), these approaches can be represented with the Excel PMT function: PMT(rate,nper,pv,fv,type)

(1)

where rate = the expected future return on the retirement portfolio nper = the remaining longevity pv = the portfolio balance at the time of the PMT calculation fv = amount of ending wealth desired type = if payments made at beginning (1) or end (0) of period This approach ensures that the investor ends up with no significant surplus unless a surplus is specified as a fv in the Excel calculation or smoothing techniques are employed (spending adjustments relative to changes implied by the formula). One benefit of this approach is that it adjusts to shorter periods as the retiree ages (each year the retiree lives, he or she has fewer years remaining) and the value of nper is reduced based on life expectancy in the following year’s calculation. This contrasts with the traditional approach of using a single fixed retirement period (e.g., 35, 30, 25 years) and an annual decision rule to calculate withdrawals over this fixed period. The choice of a fixed period is a common practice, but retirement is anything but a “set it and forget it” single simulation or calculation. Retirement is a series of adjustments that must be made periodically as the retiree ages, lifespan is reduced, and realized returns differ from what is expected. The actuarial approach has the potential to 1.

2.

help in that regard; it provides flexibility in spending while the retiree is alive, while also providing reserves for potential future years. This approach can ensure the investor ends up with no significant surplus. Hence, in theory, the actuarial approach offers a higher standard of living when compared to other dynamic withdrawal approaches, which typically end up with significant ending wealth.

Issues with the Actuarial Approach This article highlights potential problems with the transition mechanism (PVAN). Some dynamic approaches help to set up the retiree for the long term by drastically cutting spending if initial returns are bad, which gives the portfolio the ability to bounce back. Actuarial dynamic approaches, which use PVAN/ PVGAN as their transition mechanism, do not cut spending quickly enough (due to the mathematics of the annuity formula). Additionally, initial spending rates can be too high. For example: if a retiree starts with a $1,000,000 portfolio, the actuarial table indicates that 29.60 years of retirement is a reasonable estimate for the first year1; there is no cushion/bequest (fv=0) and the entire withdrawal for the year is made at the beginning of the year. Assume the retiree’s portfolio is allocated as follows: 50% large company stock (historical returns 10%); 50% corporate bonds (historical returns 6%). The expected return (based on historical averages) is 8% (.5*10% + .5*6%). This is reduced by 1% to be conservative, so the initial discount rate equals 7%.

Using the actuarial approach, the first year’s cash flow would be $75,596.28 (using Excel, pmt(.07, 29.64,-1000000,0,1)). This is much higher than the first-year withdrawal using other typical dynamic approaches. The fixed percentage (endowment) approach, with a 5% withdrawal rate, has an initial withdrawal of $50,000 ($1,000,000 * .05).2 To match the first-year actuarial withdrawal with the fixed percentage approach, the retiree’s withdrawal rate would have to be 7.56%. Few advisors would suggest that high of an initial withdrawal rate.

Now assume that the portfolio return in year 1 is -30%, the portfolio’s ending values would be:

Based on figures from the 2015 Social Security actuarial life table, for a 50-year old male.

Based on the value of the portfolio at the end of the year, a fixed percentage (5%) is taken out and put in the bank. That is what the retiree has available to spend over the next year. If the portfolio at the end of the next year is higher than last year, the retiree gets an increase in benefits for the next year.

If the portfolio declines in value, the retiree takes a pay cut. Since the retiree is taking out a fixed percentage each year, he or she can never run out of money, but depending on the return sequence and how long the retiree lives, the annual amount available to spend will be volatile.

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Actuarial approach: $1,000,000 - $75,596.28 = $924,403.72(1-.3) = $647,082.60

the financial cushion that accumulates as protection from the inevitable crash.

Fixed rate approach: $1,000,000 -$50,000 = $950,000(1-.3) = $665,000

In the example above, to get the initial withdrawal to match the fixed percentage initial withdrawal ($50,000), the discount rate would have to equal 3.016%. Constructing a portfolio so that the expected return is about 3.016% would suggest a portfolio composed of 100% money market securities. For example, the average annual return for T-bills (from 1926–2014), taken from the Ibbotson Stocks, Bonds, Bills and Inflation Yearbook (2015) is 3.5%. By choosing a more conservative portfolio, variability in spending would be reduced. However, choosing a more conservative portfolio results in compounding at a much lower return. It may help in a period with large early losses, but it will hurt the retiree in cases where the returns are strong initially. As discussed in Frank and Braymen (2016), advisors using actuarial approaches often select high mortality numbers to provide safety buffers for their clients. Choosing a high mortality number does not solve the problem in the initial example. Assuming retirement lasts 76.33 years (nper = 76.33, life expectancy for a 0-year-old male according to 2014 Social Security Actuarial Life Table), the actuarial approach first-year cash flow equals $65,796.69 (pmt(.07, 76.33,-1000000,0,1)). The year 1 ending portfolio values, given a -30% return, are:

Year 2 cash flows would be:

Actuarial approach: pmt(.07, 28.75,-647082.60,0,1) = $49,371.63

Fixed rate approach: $665,000 *.05 = $33,250

In this case, the ending portfolio value is now $17,917.40 smaller for the actuarial approach, but the second-year cash flow is $16,121.63 larger. That could be problematic.

Table 1 extends this example for three years. The returns in section A are such that the average geometric return over the 3-year period is 7% per annum. Column 2, part B shows the cash flows for the actuarial approach. The fourth-year withdrawal is approximately equal to the first. The cash flows in column 5 are for the 5% fixed percentage approach. The issue with the actuarial approach is that the retiree is now taking roughly the same amount out of the portfolio in the 4th year as in the 1st year. This is from a portfolio that has declined by 3.46% ($965,427.09 vs $1,000,000), leaving $890,261.59 at the beginning of the fourth year. With potentially 27+ years left in retirement, the withdrawal rate has increased from 7.56% to 7.79% as the portfolio has declined in value. With the fixed percentage approach, the retiree is taking out $2,516.09 (5.03%) more than the first-year withdrawal, but the portfolio has grown by 5.03% ($1,050,321.77 vs $1,000,000) leaving $997,805.68 at the beginning of year 4.

This result is driven by the mathematics of the PVAN formula as nper declines over time. As shown in section C of Table 1, for the actuarial approach, when the ending portfolio declines over the year, next year’s cash flow declines at a lower rate (-.0301 vs. -.0205) and when the ending portfolio increase over the year the cash flow increases at a higher rate (.5383 vs. .5543). With the fixed percentage approach, the cash flow increases and decreases at the same rate as the portfolio.

There is a sequence of return risk with all dynamic strategies.

Just because the actuarial approach will adjust and ensure that money does not run out, this does not mean there are not spending issues in retirement. If the consumption horizon is initially underestimated or if a severe market downturn occurs, spending may have to be cut to unacceptable levels in the future due to the lack of an available cushion. In a bubble, annual spending may increase greatly and consume part of

Actuarial approach: $1,000,000 -$65,796.69 = $934,203.31(1-.3) = $653,942.32

Fixed rate approach: $1,000,000 -$50,000 = $950,000 *(1-.3) = $665,000 Year 2 cash flows would be:

Actuarial approach: pmt(.07, 75.81,-653942.32,0,1) = $43,036.10

Fixed rate approach: $665,000 *.05 = $33,250

Even with the nper starting at 76.33 years, the ending portfolio value will still be $11,057.68 smaller for the annuity approach, but the second-year cash flow is $9,786.10 larger. The potential problem remains.

Examination of a Historical 30-year Period To further examine some of these issues, the retiree’s accumulated savings is the exclusive focus (assuming $1,000,000), thereby simplifying the analysis and highlighting the potential

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issues with the transition mechanism (the mathematics of the present value of annuity calculation).3 A more aggressive portfolio composed of 100% large-company stocks is examined. This allocation is chosen since it highlights the issues with the actuarial approach, given the portfolio’s increased volatility. Again, 1% is subtracted from the historical return (when determining the discount rate, 10% -1% = 9%) to create a more conservative downward bias in the results.

Annual returns for large-company stocks (LCS) are taken from the Ibbotson Stocks, Bonds, Bills and Inflation Yearbook (2015).

An era is chosen in which the returns are extremely poor early in the 30-year period (1929–1958). Poor early returns tend to put a retiree’s spending plan under significant stress, which is a primary concern.

Longevity is based/adjusted using the 2014 Social Security Actuarial Life Table. The United States Census Bureau shows that the average retirement age in the United States is about 63. Based on figures from the Social Security 2014 actuarial life table, average life expectancy at retirement is 19.3 years for a male.4 This value is used as the initial nper when calculating the first withdrawal for the actuarial approach.

Results Table 2 compares the annual spending and ending wealth of an actuarial approach with a 5% and 10.19% fixed percentage approach over the period 1929–58. The 10.19% is examined so the fixed percentage approach will have the same first-year withdrawal as the actuarial approach. The actuarial approach assumes that there is no cushion/bequest (fv=0), and all the money is taken out at the beginning of the year. The historical average return on LCS 10% - 1% = 9% (adjusted downward) is used as the expectation of future returns, and this expectation does not change. Total spending, average spending, and median spending are larger for the fixed percentage approach.

The actuarial approach leaves little ending wealth ($42,608), as no fv was chosen in this example. The 5% and 10.19% fixed 3.

4.

5.

6.

percentage approaches have ending wealth of $2,463,528 and $457,008, respectively. The fixed percentage approach leaves a significant surplus, but not at the expense of consumption when compared to the actuarial approach. Proponents of the actuarial approach argue that its small amount of ending wealth is indicative of its ability to maximize a retiree’s standard of living. The benefit of the 5% fixed percentage approach, compared to the actuarial approach, is that initial withdrawals are much smaller. This mitigates the impact of the initial poor returns, giving the portfolio the ability to bounce back when the economy turns around. However, over the 30-year period, the actuarial approach has a lower standard deviation of spending ($19,055 vs. $25,506) but lower minimum annual spending ($13,703 vs. $14,570). With the 10.19% fixed percentage approach, when initial returns are bad, spending is cut slightly more than the actuarial approach. Columns 9 and 10 in Table 2 show the annual percentage change in spending for the fixed percentage (10.19%) and the actuarial approaches.

The fixed percentage approach spending cuts (increases) are larger (smaller) in percentage terms for the first 12 years. The last column shows the difference in annual spending (Fixed percentage (10.19%) - Actuarial); the dollar spending differences are smaller for the fixed percentage approach for the first 19 years. These spending reductions allow the portfolio to recover to a greater extent. In this case, the standard deviation is slightly lower for the fixed percentage ($18,278 vs. $19,055), while the minimum is higher ($18,314 vs. 13,703).5 Table 3 uses the same data and assumptions as Table 26, except here the retiree, using the actuarial approach, seeks to leave a bequest or preserves a portion of the portfolio for other purposes (i.e., a safety net). As shown in Table 3, as the future value is increased, total, average and median spending increases.

This is expected since the initial cash flows are smaller, allowing the portfolio to rebound after some poor returns. However, the standard deviation increases and the minimum spending declines as the fv increases. From these traditional measures, downside risk increases as the retiree creates, in a sense, a safety net. Pfau (2015) discusses that the PVAN formula makes

Examples of other sources of income include traditional (defined benefit) pensions, reverse mortgages, and social security. These examples would place a floor under a retiree’s income and increase the market value of the retiree’s assets.

Average life expectancy is 22.07 for a female. Results do not change significantly using the female table.

Although the year 2 decrease in spending under the actuarial approach is greater than the decrease under the 5% fixed percentage approach, this is not a good comparison. The 5% fixed percentage approach has a first-year withdrawal that is $51,877 ($101,877–$50,000) smaller than the actuarial, so it would be expected that the spending cut does not need to be as drastic. The 10.19% fixed percentage approach is examined so this dynamic approach will have the same first-year withdrawal as the actuarial.

Columns 7 and 8 of Table 2 are the same as the FV = 0 columns of Table 3.

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it clear that return volatility is the primary source for spending fluctuations. Here, it is shown that fv can also play an important role. This result is driven by the mathematics of the PVAN calculation when there is a positive fv. As n declines as the retiree ages, a constant fv increases the dispersion of the pmt for a return series where positive significant returns occur later in the return series. In addition, in the columns where the fv is $1,000,000 or more, there are years when the payments turn negative.

Waring and Siegel (2015) suggest that retirees who want less volatile spending should use a more conservative portfolio. In Table 4, the actuarial approach is examined over the same time period with two different asset allocations: the 100% stock used in Tables 2 and 3 and an asset allocation that has a return expectation similar to the “risk-free interest rate” to determine the withdrawal amount (100% T-bills). The discount rate chosen for the 100% T-bills allocation is 3.5%, the Ibbotson average annual return of T-bills over the period 1926–2014. Total spending is slightly lower for the stock portfolio, $976,094 (100% stocks) vs. $1,090,888 (100% T-bills), while the standard deviation is larger for T-bills ($21,177 vs. $19,055) and the minimum is smaller ($5,240 vs. $13,703). The T-bill portfolio would be expected to provide more downside protection as the initial withdrawals are much smaller, early T-bill returns are positive when stock returns are poor, and the T-bill returns have lower volatility.

When examining the annual withdrawal differences in Table 4, the 100% stock portfolio has much larger withdrawal reductions early on due to lower asset returns and a higher discount rate. If the retiree is able to withstand larger annual reductions during a market crash, subsequent returns are much larger for stocks, putting the retiree in a better position to withstand the long-term effects of a crash. The cumulative effect of early larger withdrawal reductions and compounding at a higher rate actually reduce risk.

Using 100% T-bills in the portfolio and a discount rate that reflects this asset allocation (3.5%) along with an increasing future value (Table 5) results in total, average, and median spending declining significantly. This result is opposite of Table 3 (using 100% stock and a 9% expected return), where increasing future value result in spending increases. Standard deviation and minimum spending decline as the fv increases, whereas in Table 3 standard deviation increases and minimum spending decreases as fv increase. These differing results are driven by the lower compounding rate of T-bills vs. stock. In this case, as n declines, with a constant fv, there are not large positive returns overall as the retiree ages, and therefore there is no pressure on

the transition mechanism (PVAN) to significantly increase the annual withdrawal. Therefore, the standard deviation does not increase.

Some financial planners who employ the actuarial approach use a lower rate than the expected rate of return on the retiree’s portfolio. Using a lower discount rate than what is expected from a given asset allocation lowers the initial withdrawals, but not at the expense of a lower future compounding rate. If the retiree gets higher than expected returns in the future, this enables the portfolio to increase to a greater degree and gives the retiree larger withdrawals later on. Steiner (2014) suggests an actuarial approach with a withdrawal amount based on the “risk-free interest rate” regardless of the portfolio asset allocation. Steiner justifies this approach with the following statement: “…as the higher expected returns associated with investment in riskier assets, such as equities, also carry a higher volatility, meaning the returns might vary significantly over time. Therefore, assuming a risk-free rate for all asset classes is more conservative and automatically adjusts for the extra risk inherent in investing in riskier asset classes…” (p. 52) However, disengaging the link between asset allocation and expected return on the portfolio weakens one of the main advantages espoused for using the actuarial approach (i.e., the assurance that the retiree ends up with little surplus), thus in theory allowing for higher standards of living compared to other dynamic withdrawal approaches which typically end up with significant ending wealth.

By disengaging the link between the discount rate and the investment risk or by not linking remaining longevity to the mortality table, the actuarial approach may now result in significant surpluses. This weakens one of the main arguments for its use.

An additional proposed benefit is that the actuarial approach allows for increased spending in early years (Frank and Braymen, 2016). If the link between asset allocation and the expected return is disengaged, spending may or may not be higher in early periods, depending on how conservative is the discount rate. Lastly, proponents talk about how the actuarial approach spends down wealth more efficiently. Table 6 shows the results using the actuarial approach, for 100% stocks with two different discount rates: 9% (LCS -1%) and 3.5% (T-bills). Total, average and median spending are much higher with the lower discount rate as withdrawals will initially be smaller; given the same asset allocation, this will allow the portfolio to grow faster

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and provide larger withdrawals in the future. Disengaging the discount rate from the asset allocation results in a surplus that is 2.52 ($42,608 vs. $107,521) times larger at the end of the 30-year period. Overall, using a lower discount rate results in a lower standard deviation ($10,964 vs. $19,055) and a larger minimum ($20,742 vs. $13,703). The lower standard deviation is driven by the smaller withdrawals in the early years with the 3.5% discount rate.

Table 7 shows average annual spending and standard deviation from Table 6 broken up into three 10-year periods. This table clearly shows that the spending pattern may be increasing or decreasing depending on the discount rate. With the 9% discount rate, spending is higher in the early periods and standard deviation declines over time. With the 3.5% discount rate, spending is highest in the last 10-period, and the standard deviation decreases from the first 10-year period to the second, then increases from the second 10-year period to the third.

Given the low discount rate of 3.5% and the same realized returns of the 9% portfolio, results in much higher spending in the last 10 years, as the mathematics of the PVAN forces (with no fv) the exhaustion of the portfolio by the end of the actuarial life. Does this demonstrate the efficient spending down of wealth? Retirees may prefer spending that increases or decreases over time depending on their individual utility function, and the choice of discount rate plays a significant role in spending patterns.

volatility depends on whether the compounding rate is high (stocks) or low (T-bills).

Future value increases results in smaller initial withdrawals.

The degree the portfolio bounces back, if there are early poor returns, depends on the size of the future returns. As the remaining longevity declines, with a constant fv, large (small) positive returns as the retiree ages would force the transition mechanism (PVAN) to significantly increase (not significantly increase) the annual withdrawal, thus increasing (decreasing) the standard deviation. Since large positive returns result in an increased standard deviation and a lower minimum annual spending, traditional risk measures increase as the retiree creates a safety net. This effect, a decline in remaining longevity with a constant future value, forces spending volatility to change for a given return series, and can possibly turn spending negative.

We are not suggesting that the actuarial approach has no value. This paper highlights some potential issues associated with it. All dynamic strategies have strengths and weaknesses, and the profession seems consumed with “finding the one true best approach”. There is not one. Examining different potential retirement glide paths, given different dynamic strategies and assumptions, provides valuable information both to the financial advisor and the future retiree.

References Conclusion Many financial planners advocate the actuarial approach withdrawal strategy, believing it to be a superior to other dynamic approaches. They argue that the actuarial approach results in better overall spending strategies. Our analysis shows that the actuarial approach is not the panacea proponents suggest.

Although it provides solutions to some issues, it creates new ones. There are some potential problems with the transition mechanism (PVAN calculation) as the retiree ages. If initial returns are poor, actuarial approaches do not cut spending quickly enough due to the mathematics of the annuity formula.

In addition spending rates can be too high or too low initially depending on the assumed discount rate. This could result in the inefficient spending down of wealth.

If a future value (fv) is specified as a bequest /safety net, minimum annual spending over the 30-year period decreases as fv increases. The effect of a future value on annual spending

Blanchett, D. M. (2013). Simple formulas to implement complex withdrawal strategies. Journal of Financial Planning, 26(9), 40–48.

Blanchett, D. M., Kowara, M., & Chen, P. (2012). Optimal withdrawal strategy for retirement-income portfolios. Retirement Management Journal, 2(3), 7–20.

Frank, L. R., Mitchell, J.B., & Blanchett, D. M. (2011). Probability-of-failure-based decision rules to manage sequence risk in retirement. Journal of Financial Planning, 24(11), 44–53.

Frank, L. R., Mitchell, J.B., & Blanchett, D.M. (2012a). An agebased, three-dimensional distribution model incorporating sequence and longevity risks. Journal of Financial Planning, 25(3), 52–60.

Frank, L. R., Mitchell, J. B., & Blanchett, D.M. (2012b). Transition through old age in a dynamic retirement distribution model.

Journal of Financial Planning, 25(12), 42–50.

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Frank, L. R., & Braymen, S. (2016). Combining stochastic simulations and actuarial withdrawals into one model. Journal of Financial Planning, 29(11), 44–53.

Ibbotson R., Grabowski, J., Harrington, J., & Nunes, C. (2015).

Ibbotson Stocks, Bonds, Bills, and Inflation Classic Yearbook. Chicago, IL: Morningstar, Inc. Steiner, K. (2014.) A better systematic withdrawal strategy—The actuarial approach. Journal of Personal Finance, 13(2), 51–56.

Waring, B. and Siegel, L. (2015). The only spending rule article you will ever need. Financial Analysts Journal, 71(1), 91–107.

Pfau, W. D. 2015. Making sense out of variable spending strategies for retirees. Journal of Financial Planning, 28(10), 42–51.

Table 1 A. Year

Return

1 2 3

-0.3 0.05 0.666726 product gm

B.

Life (1+ Return) Expectancy 0.7 1.05 1.666726 1.22504 0.07

29.64 Year 28.79 27.94 27.11

Actuarial

Beginning Year Cash Portfolio Flow 1 75,596.28 924,403.72 2 49,371.63 597,710.97 3 48,360.89 579,235.63 4 75,165.50 890,261.59

C.

% change Actuarial Cash Flow 2 -0.3469 3 -0.0205 4 0.5543

% change Fixed Percentage 5% Ending Cash Portfolio Flow -0.0301 -0.3350 0.5383 -0.0025 0.5834

Ending Portfolio -0.0025 0.5834

Cash End Flow Portfolio Difference 665,000.00 25,596.28 663,337.50 16,121.63 1,050,321.77 15,194.01 22,649.41

Ending Port Value Difference -17,917.40 -35,740.98 -84,894.68

Fixed Percentage 5% End Portfolio 647,082.60 627,596.52 965,427.09

Cash Flow 50,000.00 33,250.00 33,166.88 52,516.09

Beginning Portfolio 950,000.00 631,750.00 630,170.63 997,805.68

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Table 2 100% percent stocks portfolio, expected return for actuarial based on historical returns (10% -1% (to be conservative) = 9%)

Year 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958

LCS Returns -8.42 -24.90 -43.34 -8.19 53.99 -1.44 47.67 33.92 -35.03 31.12 -0.41 -9.78 -11.59 20.34 25.90 19.75 36.44 -8.07 5.71 5.50 18.79 31.71 24.02 18.37 -0.99 52.62 31.56 6.56 -10.78 43.36 total average median stdev min Max

Fixed percentage 5% annual withdrawal 50,000 43,501 31,035 16,705 14,570 21,315 19,958 27,998 35,620 21,985 27,386 25,910 22,207 18,652 21,323 25,503 29,013 37,607 32,843 32,983 33,057 37,305 46,677 54,995 61,843 58,169 84,338 105,408 106,706 90,443 1,235,056 41,169 32,913 25,506 14,570 106,706

Ending Wealth 870,010 620,709 334,109 291,408 426,302 399,155 559,961 712,405 439,707 547,717 518,197 444,142 373,033 426,462 510,070 580,268 752,132 656,863 659,652 661,136 746,095 933,548 1,099,897 1,236,850 1,163,375 1,686,766 2,108,154 2,134,126 1,808,864 2,463,528

Fixed percentage 10.19% annual withdrawal 101,877 83,794 56,518 28,761 23,715 32,799 29,033 38,505 46,313 27,024 31,824 28,465 23,065 18,314 19,794 22,381 24,071 29,497 24,354 23,122 21,908 23,374 27,649 30,797 32,741 29,114 39,907 47,153 45,127 36,161 1,047,156 34,905 29,073 18,278 18,314 101,877

Actuarial ending annual wealth withdrawal 822,501 101,877 554,769 85,086 282,309 58,246 232,783 30,058 321,943 25,119 284,981 35,162 377,958 31,469 454,595 42,160 265,261 51,112 312,376 30,027 279,402 35,522 226,396 31,854 179,765 25,819 194,290 20,443 219,691 21,955 236,278 24,588 289,535 26,073 239,053 31,341 226,959 25,260 215,048 23,234 229,430 21,169 271,397 21,558 302,296 24,075 321,374 25,036 285,776 24,536 391,717 19,823 462,841 24,233 442,958 25,059 354,944 20,496 457,008 13,703 976,094 32,536 25,189 19,055 13,703 101,877

ending wealth 822,501 553,799 280,780 230,188 315,787 276,583 361,960 428,277 245,044 281,930 245,397 192,659 147,503 152,904 164,864 167,981 193,620 149,183 130,999 113,692 109,909 116,366 114,459 105,849 80,508 92,618 89,967 69,166 43,424 42,608

Fixed percentage 10.1877% Actuarial annual annual spending spending change change -17.75% -32.55% -49.11% -17.54% 38.30% -11.48% 32.63% 20.28% -41.65% 17.76% -10.56% -18.97% -20.60% 8.08% 13.07% 7.55% 22.54% -17.44% -5.06% -5.25% 6.69% 18.29% 11.39% 6.31% -11.08% 37.07% 18.16% -4.30% -19.87%

-16.48% -31.54% -48.39% -16.43% 39.99% -10.50% 33.97% 21.24% -41.25% 18.30% -10.33% -18.95% -20.82% 7.40% 11.99% 6.04% 20.21% -19.40% -8.02% -8.89% 1.84% 11.67% 3.99% -2.00% -19.21% 22.25% 3.41% -18.21% -33.14%

17 Š2019, IARFC. All rights of reproduction in any form reserved.

$ Spending difference Fixed % 10.19% Actuarial 0 -1,292 -1,727 -1,297 -1,403 -2,364 -2,436 -3,654 -4,799 -3,003 -3,698 -3,389 -2,754 -2,129 -2,162 -2,206 -2,001 -1,844 -906 -112 740 1,815 3,574 5,761 8,204 9,291 15,674 22,094 24,631 22,458 71,062

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LCS Year Returns 1929 -8.42 1930 -24.9 1931 -43.34 1932 -8.19 1933 53.99 1934 -1.44 1935 47.67 1936 33.92 1937 -35.03 1938 31.12 1939 -0.41 1940 -9.78 1941 -11.59 1942 20.34 1943 25.90 1944 19.75 1945 36.44 1946 -8.07 1947 5.71 1948 5.50 1949 18.79 1950 31.71 1951 24.02 1952 18.37 1953 -0.99 1954 52.62 1955 31.56 1956 6.56 1957 -10.78 1958 43.36 Total Average Median Stdev Min Max

Table 3 100% percent stock portfolios, expected return for actuarial based on historical returns (10% -1% (to be conservative) = 9%), fv changing FV = 0 FV = 500,000 FV = 1,000,000 FV = 1,500,000 Actuarial Actuarial Actuarial Actuarial Actuarial Actuarial Actuarial Actuarial annual ending annual ending annual ending annual ending withdrawal wealth withdrawal Wealth withdrawal wealth withdrawal wealth 101,877 822,501 92,223 831,342 82,569 840,183 72,915 849,025 85,086 553,799 75,561 567,592 66,036 581,385 56,511 595,178 58,246 280,780 48,393 294,178 38,541 307,575 28,689 320,973 30,058 230,188 19,251 252,410 8,444 274,632 -2,363 296,854 25,119 315,787 14,267 366,717 3,415 417,647 -7,437 468,577 35,162 276,583 26,444 335,374 17,725 394,164 9,006 452,954 31,469 361,960 22,553 461,941 13,638 561,923 4,722 661,904 42,160 428,277 36,852 569,280 31,543 710,284 26,235 851,287 51,112 245,044 49,553 337,667 47,993 430,290 46,433 522,914 30,027 281,930 21,392 414,699 12,758 547,469 4,123 680,239 35,522 245,397 30,537 382,588 25,551 519,778 20,566 656,968 31,854 192,659 26,044 321,674 20,233 450,689 14,423 579,704 25,819 147,503 17,386 269,021 8,954 390,538 521 512,056 20,443 152,904 9,272 312,582 -1,900 472,260 -13,072 631,938 21,955 164,864 14,373 375,445 6,791 586,026 -791 796,607 24,588 167,981 22,708 422,402 20,829 676,823 18,950 931,244 26,073 193,620 29,240 536,430 32,408 879,240 35,575 1,222,051 31,341 149,183 47,181 449,766 63,022 750,350 78,862 1,050,933 25,260 130,999 32,779 440,798 40,298 750,596 47,817 1,060,395 23,234 113,692 30,784 432,564 38,335 751,436 45,885 1,070,307 21,169 109,909 28,728 479,716 36,288 849,524 43,847 1,219,332 21,558 116,366 37,306 582,699 53,053 1,049,032 68,801 1,515,365 24,075 114,459 58,394 650,243 92,713 1,186,027 127,032 1,721,811 25,036 105,849 74,148 681,923 123,260 1,257,997 172,372 1,834,071 24,536 80,508 83,455 592,543 142,374 1,104,579 201,292 1,616,614 19,823 92,618 64,071 806,555 108,319 1,520,493 152,566 2,234,430 24,233 89,967 121,492 901,269 218,752 1,712,570 316,011 2,523,872 25,059 69,166 153,051 797,301 281,043 1,525,435 409,035 2,253,570 20,496 43,424 129,384 595,916 238,271 1,148,408 347,159 1,700,900 13,703 42,608 71,552 751,728 129,401 1,460,849 187,250 2,169,969 976,094 1,488,374 2,000,655 2,512,935 32,536 49,612 66,688 83,765 25,189 34,815 38,438 44,866 19,055 36,593 72,708 110,478 13,703 9,272 -1,900 -13,072 101,877 153,051 281,043 409,035

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Journal of Personal Finance

Table 4 100% percent stock portfolio, expected return for actuarial based on stock historical returns vs. 100% T-bills portfolio, expected return based on T-bill historical returns 100% stocks 100% t-bills Actuarial Actuarial Actuarial Actuarial Annual LCS T-Bills annual ending annual ending withdrawal Year Returns Returns withdrawal wealth withdrawal wealth difference 1929 -8.42 4.75 101,877 822,501 69,698 974,491 -32,179 1930 -24.9 2.41 85,086 553,799 69,804 926,491 -15,282 1931 -43.34 1.07 58,246 280,780 68,308 867,365 10,062 1932 -8.19 0.96 30,058 230,188 65,899 809,160 35,841 1933 53.99 0.30 25,119 315,787 63,461 747,936 38,343 1934 -1.44 0.16 35,162 276,583 60,608 688,428 25,446 1935 47.67 0.17 31,469 361,960 57,722 631,778 26,253 1936 33.92 0.18 42,160 428,277 54,924 577,892 12,765 1937 -35.03 0.31 51,112 245,044 52,120 527,402 1,007 1938 31.12 -0.02 30,027 281,930 49,459 477,847 19,432 1939 -0.41 0.02 35,522 245,397 46,654 431,280 11,132 1940 -9.78 0.00 31,854 192,659 43,923 387,356 12,069 1941 -11.59 0.06 25,819 147,503 41,238 346,326 15,419 1942 20.34 0.27 20,443 152,904 38,600 308,557 18,156 1943 25.90 0.35 21,955 164,864 36,058 273,452 14,103 1944 19.75 0.33 24,588 167,981 33,589 240,654 9,002 1945 36.44 0.33 26,073 193,620 31,126 210,220 5,053 1946 -8.07 0.35 31,341 149,183 28,680 182,175 -2,661 1947 5.71 0.50 25,260 130,999 26,294 156,661 1,034 1948 5.50 0.81 23,234 113,692 23,942 133,794 708 1949 18.79 1.10 21,169 109,909 21,691 113,336 523 1950 31.71 1.20 21,558 116,366 19,558 94,903 -2,000 1951 24.02 1.49 24,075 114,459 17,444 78,613 -6,631 1952 18.37 1.66 25,036 105,849 15,421 64,241 -9,615 1953 -0.99 1.82 24,536 80,508 13,476 51,689 -11,061 1954 52.62 0.86 19,823 92,618 11,617 40,416 -8,206 1955 31.56 1.57 24,233 89,967 9,730 31,168 -14,503 1956 6.56 2.46 25,059 69,166 8,049 23,688 -17,010 1957 -10.78 3.14 20,496 43,424 6,553 17,673 -13,943 1958 43.36 1.54 13,703 42,608 5,240 12,624 -8,463 Total 976,094 1,090,888 Average 32,536 36,363 Median 25,189 34,824 Stdev 19,055 21,177 Min 13,703 5,240 Max 101,877 69,804

Š2019, IARFC. All rights of reproduction in any form reserved.

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Volume 18, Issue 1

Year 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958

T-Bills Returns 4.75 2.41 1.07 0.96 0.30 0.16 0.17 0.18 0.31 -0.02 0.02 0.00 0.06 0.27 0.35 0.33 0.33 0.35 0.50 0.81 1.10 1.20 1.49 1.66 1.82 0.86 1.57 2.46 3.14 1.54 Total Average Median Stdev Min Max

FV = 0 Actuarial annual withdrawal 69,698 69,804 68,308 65,899 63,461 60,608 57,722 54,924 52,120 49,459 46,654 43,923 41,238 38,600 36,058 33,589 31,126 28,680 26,294 23,942 21,691 19,558 17,444 15,421 13,476 11,617 9,730 8,049 6,553 5,240 1,090,888 36,363 34,824 21,177 5,240 69,804

Table 5 100% T-bills portfolios, expected return based on T-bill historical returns, fv changing FV = 500,000 FV = 750,000 Actuarial Actuarial Actuarial Actuarial Actuarial Ending annual ending annual ending Wealth withdrawal wealth withdrawal wealth 974,491 51,757 993,284 42,787 1,002,681 926,491 52,243 963,721 43,462 982,336 867,365 51,097 922,389 42,492 949,900 809,160 49,000 881,773 40,550 918,080 747,936 46,850 837,428 38,545 882,174 688,428 44,251 794,446 36,073 847,455 631,778 41,596 754,129 33,534 815,305 577,892 39,001 716,415 31,040 785,677 527,402 36,427 682,097 28,580 759,444 477,847 33,985 647,982 26,248 733,049 431,280 31,356 616,749 23,707 709,484 387,356 28,798 587,951 21,236 688,248 346,326 26,272 562,016 18,788 669,862 308,557 23,820 539,649 16,431 655,195 273,452 21,542 519,921 14,283 643,155 240,654 19,355 502,218 12,238 632,999 210,220 17,195 486,623 10,230 624,825 182,175 15,083 473,190 8,285 618,698 156,661 13,039 462,452 6,411 615,348 133,794 11,170 454,938 4,784 615,510 113,336 9,603 450,234 3,558 618,683 94,903 8,320 447,217 2,701 623,374 78,613 7,206 446,567 2,087 630,543 64,241 6,426 447,447 1,929 639,050 51,689 5,884 449,599 2,088 648,554 40,416 5,581 447,837 2,562 651,547 31,168 4,350 450,449 1,661 660,090 23,688 4,113 457,317 2,145 674,131 17,673 5,100 466,416 4,374 690,787 12,624 6,950 466,542 7,805 693,500 717,371 530,613 23,912 17,687 20,448 13,261 16,927 14,993 4,113 1,661 52,243 43,462

FV = 1,000,000 Actuarial Actuarial annual ending withdrawal wealth 33,816 1,012,077 34,682 1,000,951 33,887 977,412 32,100 954,387 30,239 926,920 27,894 900,464 25,471 876,481 23,078 854,939 20,733 836,791 18,511 818,117 16,059 802,219 13,674 788,545 11,305 777,707 9,041 770,741 7,025 766,389 5,121 763,781 3,264 763,026 1,486 764,205 -217 768,244 -1,602 776,081 -2,486 787,132 -2,918 799,531 -3,031 814,520 -2,569 830,653 -1,708 847,509 -456 855,258 -1,029 869,731 176 890,945 3,647 915,159 8,660 920,459 343,854 11,462 7,843 13,260 -3,031 34,682

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Table 6 100% stock portfolio, with different discount rates (expected returns) i=.09 i=.035 Actuarial Actuarial Actuarial Actuarial Annual LCS annual ending annual ending withdrawal Year Returns withdrawal wealth withdrawal wealth difference 1929 -8.42 101,877 822,501 69,698 851,971 -32,179 1930 -24.90 85,086 553,799 61,027 593,998 -24,059 1931 -43.34 58,246 280,780 43,794 311,746 -14,452 1932 -8.19 30,058 230,188 23,685 264,468 -6,372 1933 53.99 25,119 315,787 20,742 375,314 -4,377 1934 -1.44 35,162 276,583 30,413 339,935 -4,749 1935 47.67 31,469 361,960 28,502 459,892 -2,967 1936 33.92 42,160 428,277 39,981 562,345 -2,178 1937 -35.03 51,112 245,044 50,717 332,404 -395 1938 31.12 30,027 281,930 31,173 394,975 1,145 1939 -0.41 35,522 245,397 38,563 354,951 3,040 1940 -9.78 31,854 192,659 36,150 287,623 4,296 1941 -11.59 25,819 147,503 30,621 227,215 4,802 1942 20.34 20,443 152,904 25,324 242,956 4,881 1943 25.90 21,955 164,864 28,392 270,136 6,437 1944 19.75 24,588 167,981 33,182 283,752 8,594 1945 36.44 26,073 193,620 36,700 337,078 10,627 1946 -8.07 31,341 149,183 45,987 267,600 14,646 1947 5.71 25,260 130,999 38,624 242,050 13,364 1948 5.50 23,234 113,692 36,991 216,337 13,757 1949 18.79 21,169 109,909 35,074 215,323 13,905 1950 31.71 21,558 116,366 37,158 234,661 15,600 1951 24.02 24,075 114,459 43,133 237,534 19,057 1952 18.37 25,036 105,849 46,596 226,013 21,560 1953 -0.99 24,536 80,508 47,411 176,834 22,874 1954 52.62 19,823 92,618 39,743 209,227 19,921 1955 31.56 24,233 89,967 50,370 208,993 26,137 1956 6.56 25,059 69,166 53,969 165,193 28,910 1957 -10.78 20,496 43,424 45,699 106,613 25,203 1958 43.36 13,703 42,608 31,612 107,521 17,909 Total 976,094 1,181,031 Average 32,536 39,368 Median 25,189 37,860 Stdev 19,055 10,964 Min 13,703 20,742 Max 101,877 69,698

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Volume 18, Issue 1

Table 7 10-year intervals from Table5 Average Standard deviation Spending of Spending

Discount rate 9.00% 3.50% 1st 10 years 49,031.59 39,973.32 2nd 10 years 26,608.88 35,053.37 3rd 10 years 21,968.89 43,076.46

9.00% 3.50% 25,868.94 16,304.22 4,798.45 5,911.01 3,512.22 7,083.01

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Volume 18, Issue 1

CE Exam for Members of the IARFC Members of the IARFC can earn CE credit by reading the Journal of Personal Finance (JPF). Two hours of IARFC CE credit will be awarded to members who achieve a 70% or higher on this multiple choice quiz. Only one submission per IARFC member is allowed. Please read the articles in the JPF, and then take the quiz online. The questions are provided here for your reference.

A link to register for the quiz (or for quizzes on prior JPF issues), is available on the JPF website (www.journalofpersonalfinance. com). Once you have registered, you will receive an email with a link to access the quiz. As of this printing, JPF Online CE quizzes cost $20 for each Volume, Issues 1 and 2.

1. The FPCIT posits that the value of PFP advice is primarily derived from: a. The interaction between a financial planner and client. b. Technical advice only. c. Behavioral coaching. d. The financial planner’s credentials.

6. Poor Primary Asset Allocation has the biggest impact on what in the mechanistic model? a. Long-term investment returns b. Short-term investment risk c. Asset Burden d. Secondary Asset Allocation

2. In the FPCIT, which of the following production function inputs recognize the influence family and friends have on the client’s ability to produce commodities within personal financial planning? a. Goods and services b. Social environment c. Time d. Personal environment

7. What theory was used by O’Neill et al. to study the financial decision-making of young adults? a. Transtheoretical Model of Change b. Theory of Planned Behavior c. Big Five Personality Theory d. Diffusion of Innovations Theory

3. According to the FPCIT, an upward shift in the client’s production function curve implies: a. A production contraction for commodities and an efficiency loss. b. The client no longer needs the financial planner. c. A production expansion for commodities and an efficiency gain. d. It is no longer efficient for the financial planner to serve the client.

4. According to the mechanistic model, investing excess cash from operations has two primary benefits, one of which is to build long-term wealth. What is the other? a. Saving for college or other long-term financial goals. b. Saving for replacement of Dead Assets. c. Building an additional operating cushion. d. Keeping Asset Burden in check and protecting Free Cash Flow.

5. What is the impact of a low Replacement Rate in the mechanistic model? a. Signals the ability to quickly recover from a large episodic expense. b. Reflects a low savings rate required to replace depreciating assets. c. Insufficient savings to replace current income in retirement. d. Quickly running out of accumulated Cash Reserves after a loss of income.

8. What was the top financial goal of the full sample of the O’Neill et al. study of financial decision-making? a. Reducing debt b. Buying something c. Saving for something d. Traveling abroad 9. The most frequently used retirement savings vehicle cited by respondents in the O’Neill et al. study of financial decision-making was: a. A Roth and/or traditional IRA. b. A SEP-IRA. c. A tax-deferred employer savings plan. d. Two or more retirement savings vehicles.

10. With respect to pursuing multiple financial goals, the O’Neill et al. study of financial decision-making found evidence of: a. No goal-setting. b. Concurrent goal-setting. c. Shared goal-setting. d. Sequential goal-setting.

11. Which of the following is the most serious negative consequence associated with carrying a credit card balance? a. Difficulties of obtaining employment b. Low academic performance c. Delayed graduation d. Dropping out of school

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Journal of Personal Finance

12. Which of the following factors associated with credit card use of college students are understudied? a. Personal factors b. Social factors c. Family related factors d. Family related and social factors 13. Adding a picture of a donor to an explanation of the donor’s use of a charitable gift annuity had what effect on participants’ interest in pursuing a charitable gift annuity? a. The donor picture increased interest if it was similar in age to the participant, but decreased interest if it was very different in age from the participant. b. The donor picture decreased interest if it was similar in age to the participant, but increased interest if it was very different in age from the participant. c. The donor picture increased interest in all tests. d. The donor picture decreased interest in all tests.

17. A large difference between the discount rate and the expected return for a given asset allocation causes the actuarial approach to have: a. Larger ending wealth. b. Average spending in early years that is higher than later years. c. Lower average spending over the entire period. d. A higher standard deviation of spending over the entire period.

18. For a 100% stock portfolio, a larger bequest amount causes the actuarial approach to have a: a. Higher total withdrawal amount. b. Lower average withdrawal. c. Lower standard deviation of withdrawals. d. Higher minimum withdrawal..

14. What was the mechanism that, statistically, explained how matching the age of the example donor picture with the age of the respondent increased interest in pursuing a charitable gift annuity? a. Perceived financial benefit, i.e., “This will provide a good return.” b. Perceived mortality, i.e., “This made me think about death.” c. Perceived complexity, i.e., “This transaction feels complicated.” d. Perceived identification/similarity, i.e., “She is a lot like me.”

15. The article by James on charitable gift annuities suggests that, as a practical matter, donor examples are more powerful when they successfully convey the idea that: a. Older people do things like this. b. People like me do things like this. c. Younger people do things like this. d. Women do things like this.

16. The actuarial approach for retirement planning differs from the fixed percentage approach in that it: a. Leaves no significant surplus. b. Ensures that the retiree never runs out of money. c. Accounts for decreasing life expectancy through time. d. Uses a variable discount rate.

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IARFC National Financial Plan Competition

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

The International Association of Registered Financial Consultants (IARFC) invites undergraduate students in financial planning programs at U.S. based universities, to submit a sample plan based on a case narrative provided by practicing consultants. Plans are judged through a comprehensive process and the winners are determined and recognized by national media exposure.

This is an excellent opportunity for students in an undergraduate financial services curriculum to gain real world experience – judged by industry professionals.

The National Financial Plan Competition promotes handson development to young people of critical skills — especially those of producing quality plans.

Most important is the educational process. Some university professors actually require this competition as part of their curriculum. In every case, students learn the basics of creating a sample plan and get experienced feedback for consideration.

Designed exclusively for undergraduate degree programs, these competition teams advance through three phases.

• Case Study and Plan Development • Plan Presentation of Recommendations • Live Financial Plan Delivery

Advanced Students and Professors attend IARFC Annual Conference and compete in a live Financial Plan Delivery.

The 2020 IARFC Annual Conference is set to be held in the “Queen City” Cincinnati, Ohio.

Participate Register For the IARFC National Financial Plan Competition Contact the IARFC today for your students to participate PlanComp@iarfc.org or 800.532.9060 Sponsor Financial Professional have an inherent responsibility to continue the legacies and skills of their profession.

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Join the IARFC in developing the next generation of financial professionals. Visit www.iarfc.org to learn more.

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sponsor the collegiate IARFC National Financial Plan Competition!

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