IARFC Journal of Personal Finance Volume 19 Issue 2 2020

Volume 19 Issue 2 2020 www.journalofpersonalfinance.com

Journal of Personal Finance

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Journal of Personal Finance

Volume 19, Issue 2 2020 The Official Journal of the International Association of Registered Financial Consultants Š2020, IARFC. All rights of reproduction in any form reserved.

Journal of Personal Finance Volume 19, Issue 2 2020 Editor Benjamin Cummings, Ph.D., CFP®, RFC® Utah Valley 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, 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.

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

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Volume 19 • Issue 2

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

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Household portfolio choice

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Retirement planning and income distribution

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Household risk management

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Life cycle consumption and asset allocation

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Investment research relevant to individual portfolios

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Household credit use

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Professional financial advice and its regulation

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Behavioral factors related to financial decisions

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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, Ph.D., CFP®, RFC® Editor jpfeditor@gmail.com www.journalofpersonalfinance.com

Volume 19 • Issue 2

Contents An Investigation of the Relationship Between Advisor Engagement and Investor Anxiety and Confidence 9 Matthew Sommer, CFA, CFP® HanNa Lim, PhD, CFP® Maurice MacDonald, PhD The purpose of this paper is to investigate whether working with an advisor is related to investor financial anxiety and investment confidence. Using data collected from 1,005 U.S. households, we found no evidence that working with an advisor was related to anxiety; however, a positive relationship was found with confidence.

Further, couples that make decisions jointly were found to have significantly more investment confidence than couples where one partner makes decisions alone. These findings highlight an additional benefit advisors provide to their clients, and a compelling reason for couples to consider making financial decisions jointly.

Financial Anxiety in the Orthodox Jewish Community ����������������������������������������������������������������������������������������������� 25 Daria Auciello Newfeld, PhD This paper investigates financial stress of 243 Orthodox Jews in the United States of America using two financial anxiety measures. Consistent with previous research, the participants’ financial anxiety is positively associated with credit card debt, automobile and student loans, and rent and mortgage payments and inversely related to income, education, and emergency funds. With respect to religion, the results show that regular minyan (daily prayer service) attendance and self-identifying as Yehivish and or Chassidish, two sects of Orthodox Judaism often identified by the media as “Ultra-Orthodox” are inversely correlated with financial anxiety even after controlling for the other factors listed above.

Antecedents of Financial Practices among Latina/o University Students ������������������������������������������������������������� 37 Scott W. Plunkett, PhD Yoko Mimura, PhD Joan Koonce, PhD, AFC®, CPFFE Wen Chin Hsu, PhD Earlier studies have often identified the Latina/o population as having financial disadvantages over non-Latina/o populations. To identify controllable factors associated with better financial practices among Latina/o individuals and families, this study explored demographic and family qualities related to financial practices (budgeting, tracking spending, setting immediate or intermediate financial goals, and saving a portion of earnings). The data came from a self-report survey completed by 607 Latina/o university students. After controlling for demographics, hierarchical multiple regression models showed parental financial socialization, financial socialization from other family members, living with parents, and students’ years of education were positively related to some financial practices. Parents’ education and family wealth were not significantly related to any of the financial practices. Immigration generation status was not related to any financial practices once family qualities were included. The findings support the potential to empower Latino families to engage in better financial practices through factors they can control.

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Finding the Next Major Donor: The Relationship between Financial Planning Horizon and Charitable Giving ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 47 Zhikun Liu, Ph.D., CFP® Russell James III, Ph.D., J.D., CFP® Abbas Aboohamidi, Ph.D. Previous research has studied the characteristics associated with the presence and amount of charitable giving.

However, few of these studies have explored the relationship between financial planning horizon and philanthropic donations, especially large donations. Using cross-sectional and longitudinal analysis of the Health and Retirement Study data, this paper explores the factors associated with charitable giving over time with a particular focus on financial planning horizon. This paper concludes that American adults who have longer financial planning horizons are more likely to make charitable donations compared to those whose planning horizon is short, i.e., less than six months. Among donors, major gifts are associated with long-term financial planning horizon, wealth, religious activity frequency, volunteer experience, and education.

Charitable Planning, Financial Planning Horizon, Financial Decision Making Determining A Portfolio’s Range of Probable Wealth - Without Monte Carlo Simulations ������������������������������� 65 John M Hogan PhD Saving for retirement, saving for a child’s education, or outliving your nest egg during retirement all depend on the wonders of compounding. For typical investment applications the annual compounding rate is not fixed and not certain. For the past several decades Monte Carlo simulations have been used to address this uncertainty. A Monte Carlo simulation combines multiple trials of a portfolio’s potential performance. For a given time period and time horizon a Monte Carlo trial will calculate the final worth of a portfolio based on compounding random returns consistent with each time period’s probability distribution. Upwards of 1,000 trails are conducted forming the probability distribution of the portfolio’s worth at the end of the time horizon. These simulations typically assume a normal distribution for the portfolio’s annual probability of return and can account for annual contributions or withdrawals. In this paper a closed form solution is derived for a portfolio’s probability of future wealth in the presence of annual contributions or withdrawals and normally distributed annual returns. While closed form solutions are mathematically preferable to approximations, in this case other practical considerations arise. Few practitioners (or their clients) have easy access to Monte Carlo simulations. Those available on the internet lack the input flexibility, ease of use, or speed of the closed form solution developed in this paper. This closed form solution has been implemented in excel making use of excel’s probability functionality. This implementation accepts user inputs for the mean and standard deviation of equity and fixed income returns and their correlation, the time horizon over which the probability of wealth will evolve, annual contributions or withdrawals, and the funding allocation between equities and fixed income. The output of the model is the cumulative distribution function of net wealth along with values highlighted at the 25%, 50%, and 75% confidence levels. Results are compared to the author’s Monte Carlo simulations, the Monte Carlo simulations reported by Pfau (2016), the Monte Carlo simulations executed in Portfolio Visualizer (2019), and historical S&P 500 returns. Excel programing for a particular scenario is displayed in the appendix.

FINANCIAL ANALYSIS - Prepared for The Casey Family (Nov 13, 2019 - April 2, 2020) ��������������������������������������� 71 Zachary J. Wakamatsu Abigail M. Adams, Associates in Accounting

©2020, IARFC. All rights of reproduction in any form reserved.

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Editor's Notes It is my pleasure to introduce the Fall 2020 issue of the Journal of Personal Finance. The topics in this issue help to celebrate diversity. As you read the articles in this issue, my hope is that we can expand not only our understanding of the financial decisions people make, but that we can also come to greater understanding of some of the cultures we enjoy in America.

In the first article, Matthew Sommer, CFA, CFP®, HanNa Lim, PhD, CFP®, and Maurice MacDonald, PhD, investigate the potential impact of working with an advisor. Using primary data, they find that using an advisor is associated with investor confidence, but they do not find any evidence of an association between using an advisor and financial anxiety.

Daria Newfeld, PhD, on the other hand, examined the financial stress of Orthodox Jews in the US and found that those who identified with the most Orthodox sects of Judaism reported lower levels of financial anxiety. Those who engaged in regular minyan, a daily prayer service, also reported lower levels of financial anxiety.

In the third article, Scott Plunkett, PhD, Yoko Mimura, PhD, Joan Koonce, PhD, AFC®, CPFFE, and Wen Chin Hsu, PhD, investigate Latina/o university students and found that those with higher levels of financial socialization from parents and other family members were more likely to engage in positive financial practices. The authors suggest that practitioners could play a role in encouraging parents to teach their children about effective financial practices.

Zhikun Liu, PhD, CFP®, Russell James III, PhD, JD, CFP®, and Abbas Aboohamidi, PhD, use the Health and Retirement Study (HRS) in the fourth article to explore charitable giving over time. They found that adults with longer financial planning horizons were more likely to make charitable donations. In a longitudinal analysis of the HRS, they also found that as a donor’s planning horizon increase, so does the likelihood that they make larger donations.

In the fifth article, John Hogan, PhD, challenges the common use of Monte Carlo analysis and presents an alternative approach of using a closed form solution. John thoroughly analyzes a variety of cases showing both a Monte Carlo solution as well as a closed form solution, highlighting that they are quite similar. One of the big benefits of the closed form solution that John highlights is that it can be performed in Excel, which is a common software program used by many practitioners.

The final article brings you the winning financial plan of the 2020 IARFC National Financial Plan Competition, authored by Zachary Wakamatsu and Abigail Adams, undergraduate students at Utah Valley University. Zachary and Abigail prepared a comprehensive financial plan for Robert and Jane Casey, fictitious clients who wanted help working towards their financial goals. The facts of the case are provided at the beginning of the article, in case you want to perform an analysis of your own before reading Zachary and Abigail’s analysis and recommendations.

With this robust line-up of articles, I hope you enjoy the latest research and ideas found in this issue of the Journal of Personal Finance.

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

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Errata There was an error in an article published in the Spring 2019 issue, titled, “A Mechanistic Model of Personal Finance,” by Joseph L. Galatowitsch. In Table 1, the last column should be labeled, “After $10K Vacation” instead of, “After Buying $10K Car.” There was also an error in the credentials of one of the authors of an article published in the Spring 2020 issue titled, “The Impact of Financial Software Use on Financial Literacy Education – Evidence from China.” Qianwen Bi, MBA, PhD was incorrectly listed as being a CFP® professional; however, she has not yet completed the certification requirements.

These corrections have now been made in the digital versions of the journal issues.

©2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 • Issue 2

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An Investigation of the Relationship Between Advisor Engagement and Investor Anxiety and Confidence

Matthew Sommer, CFA, CFP®1 HanNa Lim, PhD, CFP®2 Maurice MacDonald, PhD3

Abstract The purpose of this paper is to investigate whether working with an advisor is related to investor financial anxiety and investment confidence. Using data collected from 1,005 U.S. households, we found no evidence that working with an advisor was related to anxiety; however, a positive relationship was found with confidence. Further, couples that make decisions jointly were found to have significantly more investment confidence than couples where one partner makes decisions alone. These findings highlight an additional benefit advisors provide to their clients, and a compelling reason for couples to consider making financial decisions jointly.

Key Words financial anxiety, investment confidence, joint decision making

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2.

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Senior Managing Director, Janus Henderson Investors; PhD candidate at Kansas State University; Matthew.sommer@janus.com Assistant Professor, Kansas State University; hlim@ksu.edu Professor, Kansas State University; morey@ksu.edu

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INTRODUCTION

LITERATURE REVIEW

Use of financial advisors is growing. According to the CFP Board of Standards (2015), consumer use of financial advisors has increased from 28% in 2010 to 40% in 2015. Additionally, most respondents feel financial advisors have become more important in the last five years rather than less important (41% vs. 12%) and have hired them for better financial guidance, especially for long-term goals such as retirement. Advisors are perceived to have the tools, training and expertise the general population lacks and, therefore, can help individuals make the most informed decisions possible when facing complex circumstances (Letkiewicz, Robinson, & Domian, 2016).

Given the growing use and importance of financial advisors, our literature review will begin by identifying the benefits and costs of using an advisor. A discussion will follow that explores how the value of a financial advisor has been measured. Finally, prior research that examined the relationship between advisor use and the constructs of financial anxiety and investment confidence will be highlighted.

If the benefits received from advisors outweigh the costs incurred, a net gain accrues to the client. Most of the research regarding the net gain investors receive, however, has focused primarily on portfolio performance and return generation (Kitces, 2016). While arguably more difficult to quantify, one of the expected outcomes of receiving professional advice is a reduction in financial anxiety and increase in investment confidence. We posit these relationships may be explained by social support theory. Social support theory is the process by which individuals manage the psychological and material resources available through their social network to enhance their coping with stressful events, meet their social needs, and achieve their goals (Rodriguez & Cohen, 1998). Financial advisors provide not only technical expertise but also act a coach, mentor, and confidant (Dubofsky & Sussman, 2009). Therefore, it stands to reason that working with an advisor will help ease worries regarding financial matters while providing the confidence boost people need to achieve their financial goals.

These hypotheses were tested using a sample that consists of high-net-worth individuals who currently engage a financial advisor and high-net-worth individuals who plan to engage a financial advisor within the next 24 months. Surprisingly, no relationship was found between using an advisor and financial anxiety, however, a positive relationship was found with investment confidence. One of the more interesting findings was the relationship between the decision-making dynamics among couples and investment confidence; those respondents in relationships in which financial decisions were made equally were more confident than respondents in relationships where one person was the primary decision maker.

The Benefits and Costs of Using an Advisor Research regarding the benefits and costs of an advisor, both perceived and actual, is extensive. Plewa, Sweeney and Michayluk (2014) suggested that advisors offer the perceived benefits of expertise, education, support, relationship, convenience, and motivation. The perceived costs include monetary, time and effort, emotional, and lifestyle. According to their model, the desired outcomes of receiving financial advice are peace of mind, satisfaction with the planners and financial decisions made, financial quality of life, trust, and willingness to provide a referral. Finke, Huston, and Winchester (2011) suggested the benefit of an advisor may lead to a more optimized portfolio, improved borrowing and savings behaviors, decreased tax payments, enhanced estate planning, and the maximization of other financial resource decisions. In addition to explicit costs, they suggested skilled experts also present potential agency costs as agents may seek to provide recommendations that are not perfectly aligned with those of a client. Agency costs reduce benefits and lead to higher monitoring and bonding costs. Cummings and James (2014) indicated that professional advice includes both the financial and psychological costs and benefits. Major life events were found to alter these costs and benefits over time. In a study of Canadian households, Montmarquette and Viennot-Briot (2015) found that having a financial advisor for at least four years had a positive and significant impact on financial assets, after controlling for demographic factors, and concluded that the greater savings discipline acquired through advice played a major role.

Measuring the Net Gain of Using a Financial Advisor Presumably, if the benefits provided by an advisor exceed the costs incurred, the client will experience a net gain. To help illustrate this concept, Asebedo (2019) introduced the Financial

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Planning Client Interaction Theory. This theory suggests that both clients and financial planners bring inputs to the client/ planner relationship. The financial planners’ inputs include human capital, resources, and their business social environment.

The clients’ inputs include human capital, resources, and their financial social environment. The ability for financial planners and clients to use these inputs effectively is reflected by a production function curve. Only if the financial planners’ production function curve is greater than the client’s production function curve, will the client expect a net gain and experience reduced volatility in actual net gains.

basis points per year, on average. Kitces (2016) pointed out that these comparisons are problematic, as it assumes advisors would have provided, and investors would have followed, such advice. Rather than pinpointing an exact measure, Kitces offered a continuum of the potential advisor benefits. Certain benefits such as free-up time, ensure things actually get done, de-biasing and financial coach for implementation were described as, among other things, ‘priceless.’

Recently, industry whitepapers have emerged that attempt to quantify the incremental return that advice receiving investors enjoy. Blanchett and Kaplan (2013) found that the benefit of financial advice for retirees improved their outcomes by the equivalent of a 1.59% increase in returns. The improved performance was primarily due to asset location and tax-savvy investing, effective use of annuities, and other factors. Researchers at the Envestnet Quantitative Research Group (2016) concluded that the value added by advisors is approximately 3 percent per year in incremental returns, largely due to the ability to select low cost investments and utilize tax loss harvesting.

Anxiety

Research has also emerged that identifies the behavioral benefits of working with a financial advisor. For example, Kinniry, Jaconetti, DiJoseph, and Zilberg (2014) estimated the economic benefits of a financial advisor’s advice to be an incremental 3 percent per year, half of which is attributable to what is called ‘behavioral coaching.’ Gennaioli, Shleifer, and Vishny (2015) compared financial advice to medicine. These researchers provided the example of unknowledgeable patients being guided toward treatment by a trusted doctor, even if the medical advice is routine and would be suggested by almost any other doctor. Similarly, the role of an advisor is to build trust that allows people to make risky investments even if the recommendation is costly or generic. Prati and Prati (2010) discussed the role of a financial advisor as an emotion manager, charged with helping clients manage the nervous feelings that arise with the ups and downs of the financial markets.

Quantifying the behavioral benefits advisors provide can be complex. For example, in calculating the benefit of ‘behavioral coaching,’ Kinniry, et al. (2014) compared the performance of over 58,000 Vanguard self-directed accounts that made one exchange between 2008 and 2012 to the performance of the appropriate Vanguard Retirement Target Fund benchmark.

The benchmark outperformed self-directed investors by 150

Advisor Impact on Reducing Financial

The American Psychological Association (2019) defined anxiety as an emotion characterized by feelings of tension, worried thoughts and physical changes. People with anxiety disorders usually have recurring intrusive thoughts or concerns, and may avoid certain situations out of worry. Anxiety can also lead to physical symptoms such as sweating, trembling, dizziness, or a rapid heartbeat. Eastman (1951) suggested that anxiety is more than experiencing fear but also, the inability of an individual to escape or flee its root cause. Where the source of a person’s anxiety is internal, stress is typically triggered by an external event. The closely related concept of financial stress involves two distinct features: the chronic stress of lower socioeconomic status and the acute stress of financial events (Skinner, Zautra, & Reich, 2004). Wheaton (1994) described chronic stressors as continuous strains without resolve that occur within our lives.

Acute daily stressors, on the other hand, are defined as minor events that arise in everyday life, such as having an expected expense. According to the American Psychological Association (2017), money-related and financial factors are the second most cited source of stress among U.S. adults (62%), following the state of the nation (63%).

Prior research has investigated the role a financial advisor plays in mitigating both chronic and acute client financial stress to help lower overall anxiety levels, however, the results are not conclusive. Collins (2010) suggested that advisors may play several roles such as reducing anxiety although the effect has not yet been empirically tested. Taylor, Jenkins, and Sasker (2011) did not explicitly consider financial planners; however, they did show that making good financial decisions improves people’s psychological health. In a study of financial counseling among college students (Britt, Canale, Ferant, Stutz, & Tibbetts, 2015), a positive relationship emerged with subjective financial knowledge and attitudes, but no relationship was found regarding

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financial stress. Conversely, credit counseling was found to reduce financial stress and indirectly improve financial health and well-being (Kim, Garman, & Sorhaindo, 2003). Archuleta et al. (2015) reported that financial distress decreased in a pilot study involving the use of solutions-focused therapy.

Advisor Impact on Increasing Confidence According to Glidewell and Livert (1992), confidence differs from self-efficacy, as defined by Bandura, in that confidence is a relatively stable state of certainty and not a situation-specific individual ability. Glidewell and Livert suggested, however, that self-efficacy is probably a source of confidence. Further, confidence about what one can do is also confidence about knowing what one cannot do and perhaps identifies the need to refer to others. Glidewell and Livert found evidence that clarity surrounding goal attainment was positively related to confidence. Similarly, O’Brien, Hanton, and Mellalieu (2005) found that participants who reported positive goal attainment expectations and contributed to goal formation experienced higher levels of self-confidence.

Regarding investment confidence, in a poll of U.S. adults who currently work with a financial advisor, approximately 75% were more confident in their financial future as a result of the relationship, 62% reported that the reason they work with a financial advisor was to set realistic goals, and 56% sought a comprehensive financial plan (Fischer, 2018). A similar survey (Wells Fargo/Gallup, 2017) found that investors who use a personal financial advisor are more likely to have confidence in their investment plans and in their own knowledge of stock investing than those who handle their own finances. Respondents who work with an advisor were almost three times as likely to have a financial plan. These survey results suggest that goal setting and having a written financial plan are important advisor services that help increase client confidence levels.

THEORETICAL FRAMEWORK Social support is one of the most important functions of social relationships and is always intended by the sender to be helpful, thus distinguishing it from intentional negative interactions such as criticism (University of Pennsylvania, 2019). A number of definitions have been offered to define social support.

Cobb (1976) explained social support as knowing one belongs to a socially coherent community and that one is loved and esteemed. House (1981) described social support as an inter-

personal transaction involving concern, aid and information about oneself and the environment. More recently, Rodriguez and Cohen (1998) suggested that social support is the process by which individuals manage the psychological and material resources available through their social network to enhance their coping with stressful events, meet their social needs, and achieve their goals.

Veilel (1985) suggested that social support has two dimensions.

The first dimension is a temporal element which accounts for support that enhances the well-being irrespective of the client’s stress and confidence levels (i.e. everyday support) and support that is provided to directly reduce the impact of specific adverse scenarios (i.e. crises management). A key implication is that the client perceives that the everyday support is available, even in the absence of an adverse situation. The type of social support provided is the second dimension. This dimension also has two elements: psychological and instrumental. The psychological element is comprised of transactions aimed at changing the client’s intra-psychic parameters such as mood, attitudes, or cognitive processing. The instrumental element provides the necessary tools, resources, and information necessary for helping the client’s performance directly.

Asebado’s (2019) Financial Planning Client Interaction Theory can be considered an example of how planners provide social support to their clients. For instance, planners contribute a basic and advanced category of resources to their client relationships. The basic category is defined as concrete and universal resources. Specifically, these resources include goods, services, information, time, and money. Examples of planner deliverables are a comprehensive financial plan, portfolio rebalancing, account paperwork, meeting time, and investments in human capital. The advanced category of resources, however, are theorized to have a more significant impact on the financial planner’s scope of functioning. According to the theory, only the planners with a scope of functioning greater than the client’s scope of functioning are able to provide a net gain to the client. One example of an advanced resource is status. Status is important because it enhances a financial planners’ social capital, thereby expanding their scope within the professional community. Another example of an advanced resource is love.

Love is typically portrayed in a symbolic manner and encompasses how the planner communicates, both verbally and nonverbally, displays emotions, and expends time and energy to service the client. While routine information is considered a basic resource, the ability for a planner to tailor the message based on the uniqueness of the client is categorized as an

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advanced resource. Both dimensions of when social support is provided and what type of social support is provided is included in the Financial Planning Client Interaction Theory. Clients are aware that their planner is available to provide assistance both in times of need and on an ongoing basis. Further, the types of support offered ranges from the tangible and basic to the intangible and personal.

Evidence is plentiful that social support enhances people’s well-being along a range of dimensions including health, happiness, and positive self-concept (Demo, 1992; Gray & Keith, 2003; Schwartz et al., 2010). Further, social support can reduce stress when certain forms of support such as empathy, attachment, and trust are available (Cohen & Wills, 1985; House, Umberson, & Landis, 1988). Adults who indicated having emotional social support report lower stress and stress-based depression symptoms than those who indicated having no emotional social support (APA, 2015). Results from a meta-analysis on social support and mortality indicated the risk of death for individuals with high social support is approximately 11% lower compared to those with low levels of social support (Shor, Roelfs, & Yugev, 2013).

The literature suggests a negative relationship between social support and levels of financial stress. In a study of 700 U.S. adults, Park, Heo, Ruiz-Menjivar, and Grable (2017) found that social support was negatively related to the level of perceived stress. In this study, a structural equation model was used and found that one’s negative perceptions of their situation was positively associated with financial hardship; the negative perceptions of one’s situation was positively related to perceived stress; and social support mediated the impact of holding a negative perception of one’s situation with perceived stress.

Social support was operationalized as support received from friends, family and spouse. In a study about the financial strain among the spousal caregivers, Lee and Zurlo (2014) found the social support mechanisms of affectionate, emotional/information, and positive social interaction, singularly and combined, lessened financial strain from caregiving. Garer, Businelle, and Wetter (2017) found perceived appraisal and belonging support moderated the effects of financial strain on health-related quality of life. Finally, Viseu et al. (2018) compared the results of structural equation models with and without social support as a moderator. They found social support reduces the magnitude of the relationship between negative economic factors and stress, anxiety and depression.

The literature also suggests a positive relationship between

social support and confidence. For example, Freeney and Collins (2014) discussed how social support can lead to ‘thriving.’

Specifically, the dimension of ‘eudaimonic well-being’ consists of having or progressing toward meaningful life goals, mastery, efficacy, and movement towards full potential. In a study of social support through informal networks, Rodriguez and Cohen (1998) cited an advantage of a group over one-one interactions is the enhanced self-efficacy gained through helping others.

Leahy-Warren, McCarthy, and Corcoran (2011) conceptualized a model in which social support provides maternal parental self-efficacy, defined as a mother’s beliefs in her abilities to organize and execute a set of tasks related to parenting a child.

HYPOTHESIS Social support theory suggests that individuals rely on their network to help reduce stress and achieve goals. In the context of a planner-client relationship, we posit that an advisor is a critical component of an individual’s network, particularly when it comes to financial matters. Based on theory, we expect having an advisor reduces anxiety that may be associated with the chronic, day-to-day stressors of money management tasks and economic shocks, such as market corrections or job loss.

As the theory implies, this dimension of support is considered always available even in the absence of an immediate crises. In forming our first hypothesis, we reason that advisors are in the position to provide clients the information they need to better understand their options and make informed decisions, while at the same time, helping clients manage their attitudes and moods given the uncertainty of the capital markets and dealing with unexpected and unpleasant life events.

A second dimension of social support is the emotional and tangible tools provided by an individual’s network. We reason that advisors provide clients various resources, most notably an opportunity to engage in a formal goal-setting exercise. Advisors who follow the CFP Board of Standard’s (2019) financial planning process incorporate goal setting as a fundamental element of the planner-client relationship. In forming our second hypothesis, we posit that the encouragement and education obtained through goal-setting and other collaborative activities with an advisor will positively impact client confidence.

The formally stated two hypotheses, therefore, are: H1: Individuals who work with a financial advisor have lower levels of financial anxiety compared to individuals who do not work with a financial advisor.

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H2: Individuals who work with a financial advisor have higher levels of investment confidence compared to individuals who do not work with a financial advisor.

METHODOLOGY Data For this investigation, an online survey instrument was administered through the Research Now SSI National Online Panel in January 2018. Research Now SSI is a leading panel and data collection company. The panel has over 17 million panelists in over 90 countries. The sample was sent to U.S. residents between ages 25 and 85, with a specific gender/asset quota (400 males and 400 females with investable assets between $250,000 and $1,000,000 and 100 males and 100 females with investable assets over $1,000,000). Only those respondents with a net worth (excluding primary residence) of $250,000 and who currently engage, or would be willing to engage an advisor within the next two years, were allowed to complete the survey.

A net worth of $250,000 was chosen because it is the required minimum amount needed to open accounts at many large financial services companies, including Merrill Lynch (Levaux, 2012) and Morgan Stanley (Brokerage-Review, 2018). Comparing users of financial advisors to individuals who do not currently but are likely to engage a financial advisor rather than “do-it-yourselfers” is helpful because these two groups are more likely to share similar attitudes regarding the financial services industry. The survey responses were collected until the number of responses reached approximately 1,000 with each gender evenly distributed. The survey instrument did not allow respondents to skip any questions.

Dependent Variable The first dependent variable was self-reported financial anxiety.

The survey question asked, “Please indicate where you put yourself on the following spectrum.” The four choices were: ‘Describes me completely – I rarely worry about my finances,’ ‘Describes me somewhat – I rarely worry about my finances,’ Describes me somewhat – Thinking about my finances fills me with anxiety,’ and ‘Describes me completely – Thinking about my finances fills me with anxiety.’ Only 72 respondents answered ‘Describes me completely – Thinking about my finances fills me with anxiety’ and 182 respondents answered ‘Describes me completely – I rarely worry about my finances.’ We decided

to combine the rarely worry about my finances and thinking about my finances fills me with anxiety responses and created a binary variable: has financial anxiety or does not have financial anxiety.

The second dependent variable was self-reported investment confidence. Respondents were asked, “Overall, how confident are you in your ability to meet your investment goals?” The choices they were very confident, somewhat confident, not too confident, or not confident. Only 5 respondents answered ‘Not confident’. These answers were combined with the 79 respondents who were ‘not too confident.’ Similarly, the 374 ‘very confident’ and 549 ‘somewhat confident’ responses were also combined to create a binary variable: has investment confidence or does not have investment confidence.

Variable of Interest The variable of interest was whether an individual works with a financial advisor or is planning to hire an advisor within the next two years. Specifically, the survey question was, “Does your household currently work with a professional financial advisor?” By professional financial advisor, we mean a paid individual who provides ongoing customized advice and service to you about your investments – not just a one-time transaction?”

Respondents who indicated ‘no’ were directed to the question, “How likely are you to begin working with a professional financial advisor in the next two years?” Those respondents who indicated that they were ‘somewhat unlikely’ or ‘very unlikely’ to begin working with an advisor were thanked and their surveys were terminated.

Control Variables Control variables included in our model were gender, marital status, education, employment, age, income, investable assets, and ethnicity. The four categories of marital status/primary decision maker were single/sole decision maker, couple/respondent is the primary decision maker, couple/partner is the primary decision maker, and couple/the respondent and partner make decisions equally. A couple consisted of individuals who were married or live together. The four education attainment categories were completed high school, attended some college or technical school, earned a four-year college degree, and completed graduate school. The five employment status categories were work full-time, work part-time, self-employed or consultant, retired, and not presently working (students, unemployed, and stay-at-home parent). Age, income and

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Volume 19 • Issue 2

investable assets were continuous variables. The four ethnicity categories were white, African American, Hispanic, and other.

Two variables were included to capture the respondents’ investment style and objectives. The investment style measure captured whether the respondents conduct research when making investment decisions or if they are impulsive and go with their gut. The objective variable indicated whether the respondents were more concerned about building wealth or securing wealth. Finally, to account for more general aspects of anxiety and confidence, respondents were asked if they experienced a major life event within the last twelve months. Specifically, a professional life event was defined as any of the following: starting a new job, losing a job, starting a new business, selling a business, and entering retirement. A binary variable was created as either having experienced or not having experienced any professional life event. Respondents were also asked if they had been diagnosed with a major illness within the last 12 months. A yes/no health life event binary variable was created.

Analysis To conduct our analysis, two logistic regression models were specified. The first model regressed financial anxiety and the second model regressed investment confidence. A logistic regression was appropriate since the dependent variables were binary.

RESULTS Descriptive Statistics The sample contained 1,005 responses of which about eight in ten (79%) of respondents currently work with a financial advisor, and the balance indicated they are very likely or somewhat likely to work with a financial advisor within the next two years.

Among the four marital status/primary decision maker groups, 23% were single/sole decision maker, 39% were couple/respondent primary decision maker, 7% couple/partner primary decision maker, and 31% couple/make decisions equally. Given the low number of respondents who were divorced, widows, and widowers, these individuals were combined with those who never married and categorized as single. Slightly more than four in ten (41%) of respondents were retired and approximately 89% were white. The average age of respondents was 58.

The average investable assets was $968,000 and the average annual income was $87,000. The study’s descriptive statistics are found in Table 1. The table provides descriptive statistics on

the total sample and is broken out by those who have financial anxiety compared to those who do not have financial anxiety and those who are confident compared to those who are not confident.

Multivariate Analysis Results The results of the financial anxiety model can be found in Table 2. The model reported a Wald chi-squared of 73.406, an adjusted R-squared of 10.72%, and a c-statistic of 0.668. Testing Hypothesis 1, no relationship was found between the use of a financial advisor and financial anxiety (p=0.264). There were, however, control variables that did have a relationship with financial anxiety. Males were found to have marginally less financial anxiety than females (p=0.088). Specifically, males had 32% lower odds of reporting financial anxiety than females.

Respondents who were full-time employees were found to have 68% higher odds of reporting financial anxiety than the reference group of retirees. Two additional controlled variables that had a relationship with financial anxiety were investment style and having experienced a professional life event within the last 12 months. Specifically, those who conduct research on their investment decisions were found to have 50% lower odds of reporting financial anxiety than those who are impulsive and make decisions using their gut. Also, respondents who experienced a professional life event had 53% higher odds of reporting financial anxiety than those who did not experience a professional life event. The final control variable that had a relationship with financial anxiety was log investable assets.

Using a log function was necessary because investable assets displayed a positive skewness, however, the results can be difficult to interpret. Therefore, for a given percentage change in investable assets, we can use the equation [(1 + % change)Beta – 1] to transform the variable. For example, the log investable asset beta coefficient was -0.365. Using the above formula and assuming a 10% increase in investable assets, we can determine that there will be a 3.42% decrease in the odds of reporting financial anxiety.

The results of the investment confidence model can be found in Table 3. The Wald chi-squared was 47.670, the adjusted R-squared was 12.43%, and the c-statistic was 0.731. Support was found for Hypothesis 2 as a positive, significant relationship existed between the use of a financial advisor and investment confidence (p=0.004). Specifically, respondents who had an advisor had two times the odds of being confident, compared to respondents who did not have an advisor. A limited number

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Table 1: Descriptive Statistics Variables

Total Sample (%)

Financial Anxiety (%)

Investment Confidence (%)

Yes

No

Yes

No

N=1,055

N=378

N=627

N=921

N=84

Yes

79.20

75.13

81.66

80.35

66.67

No

20.80

24.87

18.34

19.65

33.33

Use of financial advisor:

Marital status/primary decision maker: Single-sole

22.89

21.69

23.60

22.04

32.14

Couple-respondent

39.40

38.89

39.71

39.63

36.90

Couple-partner

6.57

8.99

5.10

5.97

13.10

Couple-equal

31.14

30.42

31.58

32.36

17.86

High school

5.67

6.61

5.10

5.54

7.14

Some college

20.80

21.69

20.26

20.20

27.38

College graduate

45.27

44.44

45.77

45.39

44.05

Graduate school

28.26

27.25

28.87

28.88

21.43

Full-time employee

37.51

44.97

33.01

37.35

39.29

Part-time employee

6.87

6.35

7.18

6.73

8.33

Self-employed

8.06

8.20

7.97

8.58

2.38

Retired

41.00

32.28

46.25

41.15

39.29

Not presently working

6.57

8.20

5.58

6.19

10.71

Age (mean):

57.89

55.42

59.39

58.08

55.90

Income (mean):

$87,002

$79,067

$91,786

$87,704

$79,315

Investable assets (mean):

$968,035

$742,724 $1,103,867 $995,656

$665,168

Education attainment:

Employment status:

Ethnicity: White

88.76

88.89

88.68

88.93

86.90

African American

2.59

2.12

2.87

2.61

2.38

Hispanic

2.59

2.65

2.07

2.17

3.57

Other

6.37

6.35

6.38

6.30

7.14

Research

86.27

81.22

89.31

86.75

80.95

Impulsive

13.73

18.78

10.69

13.25

19.05

Secure wealth

77.21

74.60

78.79

76.66

83.33

Build wealth

22.79

25.40

21.21

23.34

16.67

Yes

16.32

20.11

14..04

16.29

16.67

No

83.68

79.89

85.96

87.71

83.33

Yes

10.95

12.17

10.21

10.75

13.10

No

89.05

87.83

89.79

89.25

86.90

Investment Style:

Investment Objective:

Professional life event:

Health life event:

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Volume 19 • Issue 2

Table 2: Financial Anxiety Logistic Regression Model Variables (N=1,005) Intercept Use of a financial advisor: Yes No (ref. group) Gender: Male Female (ref. group) Marital status/primary decision maker: Single-sole Couple-respondent Couple-partner Couple-equally (ref. group) Education attainment: Some college College graduate Graduate school High school (ref group) Employment status: Full-time employee Part-time employee Self-employed Not presently working Retired (ref. group) Age Age squared Log income Log investable assets Ethnicity: African American Hispanic Other White (ref. group) Investment style: Research Impulsive (ref. group) Investment objective: Secure wealth Build wealth (ref. group) Professional life event: Yes No (ref. group) Health life event: Yes No (ref. group) Model fit: Wald chi-squared Adjusted R squared c statistic

Odds Ratio

B

SE

P

6.750

1.865

<0.001

-0.190

0.170

0.264

0.827

-0.250

0.146

0.088

0.779

-0.154 -0.074 0.354

0.191 0.174 0.298

0.423 0.668 0.219

0.858 0.928 1.425

-0.179 -0.364 -0.237

0.318 0.301 0.316

0.589 0.227 0.451

0.842 0.695 0.788

0.518 -0.005 0.245 0.461

0.214 0.293 0.283 0.312

0.015 0.986 0.387 0.139

1.679 0.995 1.277 0.598

<0.001 <-0.001 -0.099 -0.365

0.044 <0.001 0.065 0.112

0.984 0.775 0.125 0.001

1.001 1.000 1.679 0.694

-0.514 -0.072 -0.106

0.451 0.451 0.282

0.254 0.874 0.707

0.598 0.931 0.900

-0.686

0.195

<0.001

0.504

-0.112

0.167

0.502

0.894

0.425

0.183

0.020

1.530

0.296

73.406 10.72% 0.668

0.218

0.174

1.344

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Journal of Personal Finance

of the control variables were also found to have a relationship with investment confidence. All three marital status/primary decision maker groups had less confidence than the reference group of respondents in relationships where partners made decisions equally. Specifically, single individuals who made their own decision had 66% lower odds of being confident, respondents in relationships and who was the primary decision maker had 53% lower odds of being confident, and respondents whose partner was the primary decision maker had 70% lower odds of being confident. Self-employed respondents had six times the odds of having investment confidence compared to the reference group of retirees. Age was also found to have a significant relationship with investment confidence. Because the relationship between age and investment confidence was not linear, a quadratic equation was used by adding a second variable – age squared. Age was found to have a negative relationship with confidence but age squared was found to have a positive relationship with confidence. Using the formula [Bage + (2(Bage-squared) x Age)], we were able to determine the inflection point of when the relationship between age and the dependent variable turned positive. In this case, the inflection point was age 48. Finally, a positive relationship was found between log investable assets and investment confidence. Using the formula described above, a 10% increase in investable assets results in a 5% increase in the odds of being confident regarding one’s investment decisions.

DISCUSSION & IMPLICATIONS The purpose of this paper was to identify whether financial anxiety and investor confidence were related to using a financial advisor. As stated in the literature review, both these items have significant implications regarding overall investor emotional and psychological well-being. As such, when evaluating the decision to hire, or continue to use a financial advisor, the impact regarding anxiety and confidence should be included within the cost-benefit analysis. Surprisingly, no relationship was found between using a financial advisor and financial anxiety. As anticipated, however, a positive relationship was found between using a financial advisor and investment confidence. In untangling these results, important lessons can be learned by practitioners, and suggestions for future analysis by researchers.

Upon closer examination, the sample consisted of current users of financial advisors and respondents who do not currently use a financial advisor but have indicated an intention to formalize

a relationship within the next 24 months. Referring to Grable and Joo’s (1999) framework for help-seeking behavior, the subsample of those without a financial advisor have completed Step 4, that is, they have made the decision to seek help. Unlike the subsample who currently have a financial advisor, these respondents are currently on Step 5 where they are evaluating their help-seeking alternatives. Prior research has found a positive relationship between the decision to seek help and high levels of anxiety (Gino, Brooks, & Schweitzer, 2012). A more recent study, however, found that engagement of a financial advisor is highest when investors have less financial anxiety and more physiological arousal (Grable, Heo, & Rabbani, 2014).

Based upon these findings, one possible explanation for these results is that since the decision to seek help has been made, it is possible the non-users of financial advisors had existing low levels of financial anxiety at the time of the survey. This prior research is a helpful reminder to financial advisors that high-pressure sale techniques that are designed to raise stress levels may in fact be counter-productive, and lower levels of anxiety are necessary to obtain client commitment.

A number of interesting conclusions can be drawn from the financial anxiety model. For example, the results found that males have marginally lower levels of financial anxiety than females. The financial challenges females face have been well documented by the financial services industry (Merrill Lynch, 2019). Advisors who are adept at understanding these challenges and incorporate ‘gender-smarts’ into their approach are likely in the best position to attract and retain female clients (Blayney, 2010). Surprisingly, only full-time employees were found to have higher levels of financial anxiety than the reference group of retirees. In a study that analyzed the relationship between daily financial and interpersonal life events, employment was not found to be a financial stress buffer (Sturgeon, Zautra, & Okun, 2014). The authors hypothesized that daily financial stressors may be more prevalent for those employed because they serve as reminders that holding a job may not be sufficient to protect oneself against the threat of financial loss.

Another possible explanation was that employed individuals need to balance the competing demands of work and family, resulting in additional financial stress. The exact nature of this relationship may warrant additional research. Finally, experiencing a professional life event did have a positive relationship with financial anxiety. Professional life events have very broad financial implications and are likely to impact all aspects of an individual’s life. It is, therefore, not unreasonable to infer that losing a job or entering retirement is a predictor of financial

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Volume 19 â&#x20AC;˘ Issue 2

Table 3: Investment Confidence Logistic Regression Model Variables (N=1,005) Intercept Use of a financial advisor: Yes No (ref. group) Gender: Male Female (ref. group) Marital status/primary decision maker: Single-sole Couple-respondent Couple-partner Couple-equally (ref. group) Education attainment: Some college College graduate Graduate school High school (ref group) Employment status: Full-time employee Part-time employee Self-employed Not presently working Retired (ref. group) Age Age squared Log income Log investable assets Ethnicity: African American Hispanic Other White (ref. group) Investment style: Research Impulsive (ref. group) Investment objective: Secure wealth Build wealth (ref. group) Professional life event: Yes No (ref. group) Health life event: Yes No (ref. group) Model fit: Wald chi-squared Adjusted R squared c statistic

Odds Ratio

B

SE

P

-2.027

3.781

0.592

0.774

0.270

0.004

2.169

0.312

0.260

0.232

1.364

-1.086 -0.751 -1.222

0.348 0.348 0.443

0.002 0.031 0.006

0.337 0.472 0.295

-0.077 0.309 0.358

0.509 0.492 0.528

0.880 0.530 0.498

0.926 1.362 1.431

0.596 0.218 1.793 0.014

0.372 0.469 0.778 0.487

0.110 0.642 0.021 0.976

1.814 1.244 6.005 1.015

-0.171 0.002 0.062 0.510

0.086 <0.001 0.118 0.227

0.048 0.029 0.602 0.024

0.843 1.002 1.064 1.666

-0.341 -0.400 -0.071

0.782 0.677 0.472

0.663 0.555 0.881

0.711 0.671 0.932

0.444

0.312

0.155

1.559

-0.444

0.325

0.166

0.638

0.168

0.324

0.604

1.183

-0.312

47.670 12.43% 0.731

0.361

0.388

0.732

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Journal of Personal Finance

anxiety, while the use of a financial advisor is not.

Support was found for the second hypothesis, with a positive relationship emerging between use of a financial advisor and investment confidence. This research did not answer the question why working with an advisor instills a higher level of confidence, but as referenced in the literature review, it appears that help setting realistic and attainable goals is one likely determinant. Advisors who are adept at helping clients articulate their goals and who regularly benchmark progress, are in the best position to raise their clients’ levels of confidence. Further increasing clients’ confidence has been found to increase the amount of trustworthiness in sales people (Crosby, Evans, & Cowles, 1990). Joiner, Leveson, and Langfield-Smith (2002) believe this relationship applies in a financial planner context, and they found that clients were more likely to follow advice when levels of trustworthiness were higher.

The investment confidence model found strong evidence that respondents in relationships in which both partners are equal decision makers are more confident than respondents in relationships where only one partner is the primary decision maker. This finding may have significant implications for both academic and practitioner understanding of household decision-making. It appears from the literature that financial decisions among couples are typically delegated to one partner (Dobbelsteen & Kooreman, 1997; Nyman, 1999; Skogrand, Johnson, Horrocks, & DeFrain, 2011). The literature, however, does provide examples of how joint decision making regarding financial matters can prove advantageous for couples. Barnett and Stum (2013) found that obtaining spousal consensus plays an important role in the decision to purchase long-term care insurance. Britt, Grable, Nelson-Goff, and White (2008) found that as a partner spent more money without consultation, the respondent’s marital satisfaction decreased. Archuleta (2013) suggests that if couples do specialize in financial roles, it is important for practitioners to assess each person’s involvement and levels of satisfaction. If one or both persons are not happy with their assigned tasks, this may have a negative impact on their financial and relationship satisfaction. Of course, practitioners should also be on guard for overconfidence. In general, men have been found to exhibit much more overconfidence than women (Lundeberg, Fox, & Puncochar, 1994). Barber and Odean (2001) concluded that overconfidence contributed to men trading 45% more than women in an analysis of 35,000 brokerage accounts from 1991 to 1997. Excessive trading increases transaction costs resulting in suboptimal performance.

At a minimum, couples should be informed that a potential

advantage to joint investment decision-making may be an increase in both partner’s levels of investment confidence.

Perhaps not surprising, self-employed respondents had higher levels of investment confidence than the reference group of retirees. Prior research that examined the personality traits of entrepreneurs has found that these individuals generally have higher levels of self-confidence than non-entrepreneurs (Chaudhary, 2017; Rhee & White, 2007). Similarly, the literature has also found that overconfidence can lead to poor decision making and entrepreneurs would benefit from tempering optimism with reality (Trevelyan, 2008; Von Bergen & Bressler, 2011). Financial advisors could be instrumental helping entrepreneurs manage their expectations regarding investment returns.

This study is not without limitations, namely the measurement of financial anxiety and investment confidence. These constructs were measured using responses to single-item, self-assessed questions. A more robust methodology using scales that have been tested for validity and reliability would likely enhance the generalizability of our results. Further, we did not learn from our models why an advisor contributed to higher levels of investment confidence. A number of possible explanations may be responsible, including the demographic similarities of the advisor and respondent, the approach of the advisor, or the past investment history of the respondent. These shortcomings offer the potential for future study.

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House, J. (1981). Work stress and social support. Reading, MA: Addison-Wesley.

House, J., Umberson, D., & Landis, K. (1988). Structures and processes of social support. Annual review of sociology, 14(1), 293-318. doi:10.1146/annurev.so.14.080188.001453 Joiner, T., Leveson, L., & Langfield-Smith, K. (2002). Technical language, advice understandability, and perceptions of expertise and trustworthiness: The case of the financial planner. Australian Journal of Management, 27(1), 25-43. doi:10.1177/031289620202700102 Kim, J., Garman, T, & Sorhaindo, B. (2003). Relationships among credit counseling clients’ financial well-being, financial behaviors, financial stressors, and health. Journal of Financial Counseling and Planning, 14(2), 75-87. Retrieved from https://papers. ssrn.com/sol3/papers.cfm?abstract_id=2265623 Kinniry, F., Jaconetti, C., DiJoseph, M., & Zilbering, Y. (2016).

Putting a value on your value: Quantifying Vanguard advisor’s

alpha. Retrieved from https://www.vanguard.com/pdf/ISGQVAA.pdf Kitces, M. (2016). Evaluating financial planning strategies and quantifying their economic impact. Journal of Personal Finance, 15(2), 7-28. doi:10.2139/ssrn.2201323 Leahy-Warren, P., McCarthy, G., & Corcoran, P. (2011). Postnatal depression in first-time mothers: Prevalence and relationships between functional and structural social support at 6 and 12 weeks postpartum. Archives of Psychiatric Nursing, 25(3), 174184. doi:10.1016/j/apnu.2010.08.005 Lee, Y., & Zurlo, K. (2014). Spousal caregiving and financial strain among middle-aged and older adults. The International Journal of Aging, 79(4), 302-321. doi:10.1177/0091415015574181 Letkiewicz, J., Robinson, C., & Domian, D. (2016). Behavioral and wealth considerations for seeking professional financial planning help. Financial Services Review, 25(2), 105-126. doi:10.2139/ ssrn.2666727 Levaux, J. (2012). Merrill Lynch boosts client minimum, earns expert’s kudos. ThinkAdvisor. Retrieved from https://www. thinkadvisor.com/2012/01/05/merrill-lynch-boosts-client-minimums-earns-experts/ Lundeberg, M. A., Fox, P. W., & Puncochar, J. (1994). Highly confident but wrong: Gender differences and similarities in confidence judgements. Journal of Education Psychology, 86(1), 114-121. http://dx.doi.org/10.1037/0022-0663.86.1.114 Montmarquette, C., & Viennot-Briot, N. (2015). The value of financial advice. Annals of Economic and Finance, 16(1), 69-94. doi:10.1080/10293523.2016.1201292 Nyman, C. (1999). Gender equality in ‘the most equal country in the world?’ Money and marriage in Sweden. The Sociological Review, 47(4), 766-793. doi:10.1111/1476-954X.00195 O’Brien, M., Hanton, S., & Mellalieu, S. (2005). Intensity and direction of competitive anxiety as a function of goal attainment expectation and competitive goal generation. Scandinavian Journal of Medicine and Science in Sports, 8(4), 423-432. doi:10.1111/j/1600-0838.2004.00389.x Park, N., Heo, W., Ruiz-Nenjivar, J., & Grable, J. (2017). Financial hardship, social support, and perceived stress. Journal of Financial Counseling and Planning, 28(2), 322-332. doi:10.1891/10523073.28.2.322 Plewa, C., Sweeney, J., & Michayluk, D. (2014). Determining value in a complex service setting. Journal of Service Theory and Practice, 25(5), 568-591. doi:10.1108/JSTP-03=2104-0059

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Prati, M., & Prati, R. (2009). Managing ups and downs with clients: Managing emotions by financial advisors. Journal of Personal Finance, 8, 128-146. doi:10.2139/ssrn.2568754 Rhee, K. & White, R. (2007). The emotional intelligence of entrepreneurs. Journal of Small Business and Entrepreneurship, 20(4), 409-426. doi:10.1080/08276331.2007.10593408 Rodriguez, M., & Cohen, S. (1998). Social support. Encyclopedia of Mental Health, 3, 535-544.

Schwartz, B., Albert, I., Trommsdorff, G., Zheng, G., Shi, S., & Nelwan, P. (2010). Intergenerational support and life satisfaction: A comparison of Chinese, Indonesian, and German elderly mothers. Journal of Cross-Cultural Psychology, 41(5-6), 706-722. doi:10.1177/0022022110372197 Shor, E., Roelfs, D., & Yugev, T. (2013). The strengths of family ties: A meta-analysis of self-reported social support and mortality. Social Networks, 35(4), 626-638. doi:10.1016/j.socnet.2013.08.004 Skinner, M., Zautra, A., & Reich, J. (2004). Financial stress predictors and the emotional and physical health of chronic pain patients. Cognitive Therapy and Research, 28(5), 695-713. doi:10.1023/B:COTR.0000045572.33750.60 Skogrand, L., Johnson, A., Horrocks, A., & DeFrain, J. (2011). Financial management practices of couples with great marriages.

Journal of Family and Economic Issues, 32(1), 27-38. doi:10.1007/ s10834-010-9195-2 Sturgeon, J., Zautra, A., & Okun, M. (2014). Associations between financial stress and interpersonal events: A daily diary study of middle-aged adults and their life circumstance. Psychology and Aging, (29)4, 803-813. doi:10.1037/a0037961 Taylor, M., Jenkins, S., & Sacker, A. (2011). Financial capability and psychological health. Journal of Economic Psychology, 32(5), 710-723. doi:10.1016/j/joep.2011.05.006 Trevelyan, R. (2008). Optimism, overconfidence and entrepreneurial activity. Management Decisions, 46(7), 986-1001. doi:10.1108/00251740810890177 University of Pennsylvania (2019). Health behavior and health education. Retrieved from https://www.med.upenn.edu/ hbhe4/part3-ch9-key-constructs-social-support.shtml Veiel, H. (1985). Dimensions of social support: A conceptual framework for research. Social Psychiatry, 20(4), 156-162. doi:10.1007/BF00583293 Viseu, J., Leal, R., Neves de Jesus, S., Pinto, P., Pechorro, P., &

Greenglass, E. (2018). Relationship between economic stress factors and stress, anxiety, and depression: Moderating role of social support. Psychiatry Research, 268(10), 102-107. doi:10.1016/j.psychres.2018.07.008 Von Bergen, C.W. & Bressler, M. (2011). Too much positive thinking hinders entrepreneur success. Journal of Business and Entrepreneurship, 23(1), 30-52. Retrieved from http://homepages.se.edu/cvonbergen/files/2012/11/Too-Much-Positive-Thinking-Hinders-Entrepreneur-Success.pdf Wells Fargo/Gallup (2017). Investor and Retirement Optimism Index. Retrieved from https://news.gallup.com/poll/220547/ personal-financial-adviser-tied-investor-confidence.aspx Wheaton, B. (1994). Sampling the stress universe. In W.R. Williams & J.H. Gotlib (Eds.). Stress and mental health: Contemporary issues and prospects for the future (pp.77-114). NY: Routledge.

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25

Financial Anxiety in the Orthodox Jewish Community Daria Auciello Newfeld, PhD1

Abstract This paper investigates financial stress of 243 Orthodox Jews in the United States of America using two financial anxiety measures.

Consistent with previous research, the participants’ financial anxiety is positively associated with credit card debt, automobile and student loans, and rent and mortgage payments and inversely related to income, education, and emergency funds. With respect to religion, the results show that regular minyan (daily prayer service) attendance and self-identifying as Yehivish and or Chassidish, two sects of Orthodox Judaism often identified by the media as “Ultra-Orthodox” are inversely correlated with financial anxiety even after controlling for the other factors listed above.

Key Words

1.

Assistant Professor of Finance, Albright College; 1621 N 13th St, Reading, PA 19604; dnewfeld@albright.edu

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Journal of Personal Finance

INTRODUCTION Aside from infancy and early childhood, when are we not stressed out about finances? College loans, car loans, rent, mortgage, childcare, retirement, and, oh yeah, we need to eat!

It is no wonder that the American Psychological Association reports that the most prominently cited sources of stress among Americans are financial stressors such as, “having enough money,” housing costs, and job stability (American Psychological Association, 2015). Almost half of all Americans report trouble keeping up with monthly expenses and bills (FINRA Investor Education Foundation, 2010) and these pressures are leading to stress and anxiety. Financial stress, as defined by Northern et al (2010), is the inability to meet one’s economic responsibilities and is influenced by attitudes, beliefs, and other psychological factors. Similarly, Burchell (2003) defines financial anxiety as “a psychosocial syndrome whereby individuals have an uneasy and unhealthy attitude toward engaging with, and administering their personal finances.” Many causes of financial stress, ranging from age and gender to income, debt, and financial knowledge/mastery/ literacy, have been investigated in academic literature. Not surprisingly, those with higher debt levels tend to experience more financial anxiety and those with higher incomes and more financial knowledge tend to experience a better sense of financial wellbeing. Specifically, high debt levels are associated with a decreased sense of financial wellbeing and ability to manage personal finances, lowered self-esteem and productivity, as well as increased levels of overall stress (Garman, Leech, & Grable, 1996; Joo & Grable, 2000; Lange & Byrd, 1998; Britt et. al, 2015; Bell et. al 2014; Norvilitis et al., 2006; Norvilitis, Szablicki, & Wilson, 2003). Conversely, higher income levels, total assets, and perceived net worth are associated with financial satisfaction and a sense of overall financial wellbeing (Bonke & Browning, 2009; Porter & Garman, 1992). While the studies sited above cover diverse groups of subjects ranging from college students to military families, to the best of my knowledge, no study has investigated financial anxiety among the orthodox Jewish community in America. Orthodox Jews observe halacha (Jewish law) which governs ritual behavior, such as the observance of the Sabbath and Jewish holidays, as well as every day actions such as diet, dress, and “appropriate behavior in a wide range of business and other personal relationships” (Silverman, 2014), each of which involve a financial cost. On February 9, 2015, The Times of Israel ran an article entitled, “For US Orthodox, ‘upper-class’ incomes often not enough.” Similarly, on February 16, 2015, The New York Jewish News ran an article entitled, “Modern Orthodoxy Has Its

Costs – Not Just Financial, When cost of living pushes $300,000, what else is sacrificed?” Financial pressures are often topics of conversation of orthodox Jewish message boards such as Yeshiva World News’s “Coffee Room” and imamother.com (a website for orthodox women). In fact, imamother.com (a website for Orthodox women) has an entire subsection devoted to financial complaints and the “tuition crisis.” Based on methodologies of Bell et. al (2014) and Britt et. al (2015), this study examines the predictors of financial stress among a sample of 243 orthodox Jewish Americans. Consistent with previous research, the participants’ financial anxiety is positively associated with credit card debt, automobile and student loans, and rent and mortgage payments and inversely related to income, education, and emergency funds. With respect to religion, the results show that regular minyan (daily prayer service) attendance and self-identifying as Yeshivish or Chassidish (two sects of Orthodox Jews often identified as “Ultra-Orthodox”) are inversely correlated with financial anxiety even after controlling for the other factors listed above.

LITERATURE REVIEW Several studies have been done over the years examining the financial costs of living a halachically observant life. The most commonly cited costs are synagogue memberships, day school tuition, and kosher food (Wertheimer, 2010; Monson & Feldman, 1995; Chiswick and Chiswick, 2000; Bubis, 2005).

“Orthodox Jewish parents find certain financial choices, such as putting their children in private religious schools or living in an orthodox neighborhood, as essential, even if it puts additional financial strain on the families” (Silverman, 2014). The estimated cumulative cost of a halachicilly observant life ranges widely from region to region. Wertheimer (2010) conservatively estimates that, “an actively engaged Jewish family that keeps kosher and sends its three school-age children to the most intensive Jewish educational institutions can expect to spend somewhere between $50,000 and $110,000 a year at minimum just to live a Jewish life.” However, as Nathan Diament of the Orthodox Union explained in a 2014 interview with The Times of Israel, the costs and associated financial strain may be much higher since “if you are in the Modern Orthodox community and you’re making $200,000 or even $300,000 a year, you’re struggling. That’s very difficult to say and we’re aware that it’s much higher than the average income in the United States, but if you’re paying tuitions of $20,000 to $30,000 a year per child and you have four or five children, it’s very, very challenging”

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Volume 19 • Issue 2

(Shapiro, 2015). Let’s begin by examining some of the costs specific to an orthodox Jewish lifestyle, specifically: synagogue membership, tuition, keeping kosher, and real estate prices in the Jewish community.

Costs of an Orthodox Jewish Lifestyle The cost of synagogue membership can vary widely by location and the age of the congregation since newer congregations tend to tack on building fees. As Wertheimer (2010) notes, dues can range from a few hundred to well over $3,000 with some synagogues setting “suggested dues” for families earning $250,000 per year or more at $6,000 (Wertheimer, 2010).

“Day School tuition is the cost many parents believe they must bear if their children are to retain their heritage” (Wertheimer, 2010), but tuition is expensive! Estimates of costs vary; according to Wertheimer (2010), most schools charge between $15,000 and $20,000 per child per year, however, Bubis (2005) estimated an annual cost of $5,000 to $18,000 per student.

These differences are likely driven by year and location. In 2016, a Google Spreadsheet entitled “Jewish Day School Tuition” was widely discussed in newspapers and online (Nathan-Kazis, 2016). This spreadsheet listed the advertised tuition for Jewish Day Schools in the US as well as internationally along with their websites, email addresses, and phone numbers. The US tuitions listed for kindergarten through high school range from $4,000 to $43,900 per student per year.

Kashrut, the Hebrew term meaning "fit or proper," defines the collection of “Jewish laws and customs pertaining to the types of food permitted for consumption and their preparation" (Roth, & Wigoder,1971). These laws restrict the types of meat, poultry, wine, cheese, and (in some cases) milk that the kosher consumer may purchase (Masoudi, 1993). There are various different estimates of the cost differential between kosher and non-kosher foods. According to Wertheimer (2010), poultry slaughtered according to Jewish law costs 50-100 percent more than the non-kosher equivalent. A more conservative estimate

by New York NonProfit Media in conjunction with the Metropolitan Council on Jewish Poverty estimates that kosher food is approximately “20 percent more expensive to nearly twice as expensive” as non-kosher food (Popovici, 2016).

Financial Stress and Its Impacts Financial stress, as defined by Northern et al (2010), is the inability to meet one’s economic responsibilities and is influenced by attitudes, beliefs, and other psychological factors.

On the personal side, drinking problems (Peirce et al., 1996), depression and reduced psychological wellbeing (Jackson, Iezzi & Lafreniere, 1997), family issues (Mills et al., 1992), and personal health problems (Drentea & Lavrakas, 2000) are linked to tension surrounding one’s finances. On the professional side, this stress can lead to absenteeism (Jacobson, et. al., 1996) and a loss of productivity (Garman et al, 1996; Joo, 1998).

The primary predictors of financial stress are often debt levels and income. A negative sense of financial “wellbeing” is associated with higher debt levels (Garman et al, 1996; Norvilitis et al., 2006; Britt et al., 2015; Dunn and Mirzaine, 2016). Conversely, higher income levels are associated with a greater sense of financial “wellbeing” (Norvilitis et al., 2006; Bonke & Browning, 2009).

The relationships between Religion, Stress and Financial Decisions Frequency of religious attendance at religious services and the belief in an afterlife are inversely associated with feelings of anxiety and positively associated with feelings of tranquility, as shown by Ellison, Burdette and Hill (2009). Specifically, they note that frequency of prayer helps to create a buffer against the adverse effects of poor health and financial decline on anxiety. Hess (2012) investigated the relationship between of “religiosity” on personal financial decisions and found that individuals residing in areas with strong religious social norms tend to have significantly higher credit scores as well as significantly lower levels of credit card balances, foreclosures, and bankruptcies compared to those individuals residing in areas with lower levels of religiosity.

CONCEPTUAL FRAMEWORK AND HYPOTHESIS DEVELOPMENT Financial stress is measured using the 7-question Financial Anxiety Scale developed by Archuleta et al (2013) as well as the single question version used by Britt et al. (2015), which is the first question in the 7-question models. Using a sample of US military personnel, Bell et al. (2014) analyzed the correlations of financial resources and constraints on financial anxiety. They define financial resources as salary, emergency funds, and a self-assessed net worth measure, and constraints as credit card and automobile debt (Bell et al. 2014). Not surprisingly, their findings indicate that financial anxiety rises with financial

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Journal of Personal Finance

constraints and falls with financial resources (Bell et al. 2014).

Using a similar measure, Britt et al. (2015) investigated the predictors of financial stress using a sample to college students.

They found strong correlations between financial stress, debt (a constraint), and perceived net worth (a resource). I hypothesize that financial anxiety in the Orthodox Jewish community is inversely related to resource constraints and orthodox specific factors, and positively related to financial resources. I also suspect that financial stress may be tempered to some degree by regular attendance at minyan, as suggested by Ellison, Burdette and Hill (2009).

Hypothesis 1: There is an inverse relationship between financial anxiety and financial constraints measured by credit card, automobile and student loan debt, as well as tuition and mortgage or rent payments.

Hypothesis 2: There is a positive relationship between financial anxiety and financial resources measured by education, salary, and emergency funds Hypothesis 3: Those who regularly attend minyan or Sabbath services, or who self-identify as members of more “religious” sects (such as Chassidish & Yeshivish), have lower levels of financial anxiety because of the “buffer” offered by religion.

METHODS Data Collection I recruited survey participants primarily from ten Orthodox Synagogues in and around the Chicago metropolitan area who agreed to distribute the survey to their congregants via email lists and associated Facebook pages, as well as. Through posting on imamother.com, a web forum for Orthodox Jewish

women. Participating adults completed the anonymous online Qualtrics survey. Because they survey was anomalous and the requests to participate in the survey were sent on the same day, the response rate from each of these source is sources is unknown. Aggregate geographic data provided by Qualtrics shows that the respondents were primarily located in the Chicago, New York and Los Angeles metropolitan areas. Ultimately, two hundred fifty-three subjects participated in the study, with two hundred forty-three of those responses being useable. Five subjects were eliminated because they did not attend an orthodox synagogue, two were eliminated because they did not live in the United States, and three were eliminated because they failed to complete the financial anxiety scale.

Dependent variable The dependent variables were the survey respondents’ score on the seven question Financial Anxiety Scale (FAS) developed

by Archuleta et al.(2013) and on the single question version used by Britt et al. (2015)), which is the first question seven question model. All of these measurements are based on the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association [APA], 2000), and, consistent with Bell et al (2014), the questions were answered using a scale ranging from 1 to 5, where 1 = they never experience the symptoms and 5 = they always experience the symptoms. On the survey, the FAS appeared as “Financial Anxiety Questions.” The two different scores were used because there is not yet a standard measure in the literature. Prior to being reduced to a factor score, the 7-question FAS scores ranged from 7 to 37 with higher levels indicating more financial anxiety. The average score was 16.89 with a standard deviation of 7.78 points, and the Cronbach’s Alpha is .939, indicating a strong degree of reliability.

Table 1: Summary Statistics for Demographics among Orthodox Jewish Adults (n=243) Standard Deviation

Mean Age

Minimum Maximum

33.69

8.76

20

76

Household size

5.07

2.02

1

14

Number of Children

3.16

1.99

0

10

Gender

Frequency

Percent

Female

185

75.82

Male

57

23.36

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Volume 19 • Issue 2

Independent variables The average age of the contributors was 33.69 years, the majority of the survey participants were female (75.81%), and the average household size was 5.07 with 3.16 children under age 18. The descriptive statistics for this data are shown in Table 1.

Survey participants’ financial resources are shown in Table 2.

According the 2014 U.S. census, 60% of households have a mean annual income of $ 54,041 or less (census, 2014). Although the surveyed orthodox Jewish adults’ annual household incomes were widely dispersed, they were comparatively better off financially, with 62.71% of participants earning more than $75,000 per year. The surveyed population was highly educated, with 59.83% of participants holding at least a bachelor’s degree.

The subjects’ financial constraints are shown in Table 3. These were measured based on their credit card, automobile, and student loan debt, and mortgage or rent payments. Roughly half of orthodox Jewish adults had little to no credit card, automobile, or student loan debt. Forty-three percent of the surveyed population spent more than $2,000 per month on rent or mortgage payments, with several participants having contacted me via email or message boards to suggest that I increase the upper end of the rent/mortgage range. One respondent stated, “Our monthly rent/mortgage payment is twice the max number and it's pretty typical for anyone moving into my area who is just getting into the rental or home ownership market.

That accounts for a lot of our financial anxiety! ” The orthodox-specific factors were addressed using a series of seven yes/no questions; the summary statistics are shown in Table 4. The surveyed orthodox Jewish adults were asked whether they attend an orthodox synagogue, keep kosher, pay membership dues to an orthodox synagogue, attend daily minyan, regularly attend services on the Sabbath and Jewish holidays, and currently pay or have previously paid tuition for day school/Yeshiva/Seminary. In order to address lifestyle differences among the various orthodox Jewish ethnicities and sects, subjects were asked if they identify as Modern Orthodox, Chassidish, Yeshivish, Lubavitch, Charadi, or other.

Table 2: Resource Distributions among Orthodox Jewish Households (n=243) Percentage Household Income Less than $30,000 $30,001-$45,000 $45,001-$75,000 $75,001-$100,000 $100,001-$150,000 $150,001-$200,000 $200,001-$250,000 $250,001-$300,000 $300,001 or more Emergency Fund $0 $1-$500 $501-$1000 $1,001-$2,000 $2,000 or more Education Level Less than a high school degree High School Degree/ GED Some College or Associates Degree Bachelor's Degree Graduate Degree Residence Type Own Rent

6.56 14.75 15.57 18.85 15.57 10.66 5.74 6.15 5.74 9.84 13.12 12.71 11.89 52.05 3.28 14.75 21.72 23.36 36.48 43.85 55.74

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Table 3: Financial Constraint Distributions among Orthodox Jewish Households (n=243)

Table 4: Distribution of Religiosity and Religious Identity

Percentage Credit Card Debt None/ payoff each month

55.74

less than $1,000

10.66

$1,001-$2,500

7.79

$2,501-$5,000

10.25

$5,001 or more

12.70

Automobile Debt None

53.69

Less than $5,000

15.57

$5,001-$10,000

15.57

$10,001-$20,000

12.30

$20,000 or more

2.05

Student Debt None

52.46

less than $10,000

15.16

$10,001-$20,000

4.10

$20,001-$50,000

6.97

$50,001-$100,000

10.25

$100,001 or more

10.66

Monthly Mortgage or Rent less than $1,000

10.66

$1,001-$1,500

20.90

$1,501-$2,000

24.18

$2,001-$2,500

19.67

$2,501 or more

23.36

Pay Membership Dues

59.43

Day School/Yeshiva Tuition

74.59

Regularly Attend Daily Minyan Attend Services on Shabbat and Yom Tov Religious Identity Modern Orthodox Chassidish Yeshivish Lubavich Charadi Other

Percent 70.08 84.84 33.20 6.56 31.97 11.89 2.46 13.52

Results The data was analyzed using ordinary least squares regression models with SPSS version 23 for the seven and single question FAS scores. The model explained 39.59% (p=.000) of the variation in the 7- question FAS and 55.75% (p=.000) of the variation in the single question version. Table 5 showing the results of the full OLS Model is broken into three panels: constraints, resources and religious factors for ease of analysis; however, the results shown are for the full regression of all independent variable against the dependent variable.

Hypothesis 1, an inverse relationship between financial anxiety and financial constraints is evaluated in Panel A of Table 5: Credit card debt had the expected positive coefficient in most cases, indicating that larger amounts of credit card debt contribute to increased financial anxiety as compared to those without credit card debt. This result supports hypothesis 1 and is statistically significant for those who carry $1,000 to $2,000 worth of debt. Oddly, credit card debts less than $1,000 had a negative coefficient in in the single question FAS model, however, this result is not statistically significant.

When compared with those with less than $5,000 of auto debt, auto debt in excess of $10,000 had the expected positive coefficient, indicating that higher amounts of auto debt are associated with higher degrees for financial anxiety, as predicted by hypothesis 1. Interestingly, low levels of automobile debt (less than $5,000) had a negative coefficient, indicating that they are associated with lower levels of financial anxiety, however, this result is not statistically significant.

Student Debt in excess of $20,000 had a positive coefficient in

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Volume 19 • Issue 2

both models and was statistically significant for those carrying more than $100,000 of student debt, as predicted by hypothesis 1. However, the coefficients on debts less than $20,000 were negative, although not statistically significantly so. These results may stem from the overall increase in the acceptance of student debt among the community.

Mortgage and rent are evaluated relative to those paying less than $1,000 per month. Predictably, the coefficients for all rent segments were positive, indicating that larger rent or mortgage payments are associated with increased financial anxiety. This finding supports hypothesis 1 and is statistically significant for mortgages and rents in excess of $2,500 per month in both models.

Tuition was often cited as a reason for financial anxiety in the orthodox community If we view tuition payments as a financial constraint, then the positive coefficient in both models also supports hypothesis 1.

Hypothesis 2, a positive relationship between financial resources and financial anxiety, is evaluated in Table 5, Panel B. Household income is compared relative to those earning less than $30,000 per year. For incomes ranging from $30,001 to $200,000, the coefficients are negative in both models, indicating that higher household incomes are associated with lower financial anxiety, which supports hypothesis 2. Conversely, for incomes in excess of $200,000, the coefficients become statis-

tically significantly positive when financial anxiety is measured with the single question scale! This result is counterintuitive but consistent with the idea that these families are less likely to receive financial assistance with tuition or synagogue membership dues and more likely to live in expensive areas, which contributes to their anxiety. The data set does give longitude and latitude which can identify the city, however, these sub-sub samples were too small to yield any significant results.

As expected, having an emergency fund does appear to be is associated with lower financial anxiety, especially if this fund in in excess of $2,000. This result, which supports hypothesis 2, is statistically significant in both models.

Education is compared with those without a high school diploma. The coefficients were negative in both models, indicating that higher education is associated with lower financial anxiety, which supports hypothesis 2, although the results are not statistically significant for either anxiety measure.

Hypothesis 3 is that those who regularly attend daily minyan or Sabbath services, or who self-identify as members of more “religious” sects (such as Chassidish & Yeshivish), have lower levels of financial anxiety because of the “buffer” offered by religion.

This hypothesis is analyzed in Table 5, Panel C. The results show a mildly statistically significant inverse relationship between self-identifying as Yeshivish and Chassidic and financial anxiety. Additionally, regular minyan and Sabbath

Figure 1a: Seven Question Financial Anxiety Score (FAS-7) vs. Sect

Figure 1b: Single Question Financial Anxiety Score (FAS-1) vs. Sect

This figure shows the distribution of financial anxiety scores using the 7- question model vs. the self-selected sect of Orthodox Judaism.

This figure shows the distribution of financial anxiety scores using the single question model vs. the self-selected sect of Orthodox Judaism.

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Journal of Personal Finance

attendance is inversely related to financial anxiety, although these results are not statistically significant after controlling for other factors. These result provides support for hypothesis 3.

Chassidish participants reported less financial anxiety despite their markedly lower incomes!

The following figures show the financial anxiety by the religious sects for both financial anxiety measures. Both figures show that those who identify as Modern Orthodox reported higher levels of financial anxiety, as compared to the other sects, even factors controlling for other financial resources and constraints.

This finding is further supported when one takes into account the income distributions as shown in figure 2. The Yeshivish and

Table 5a: Financial Constraints Regression Results The table reports the beta values for the financial resources.

Dependent Variable FAS-7 R-Squared 0.3959 F-Statistic 3.3980 *** β Credit Card Debt less than $1,000 0.0344 $1,001-$2,500 0.5253 * $2,501-$5,000 0.0525 $5,001 or more 0.1455 Automobile Debt $5,001-$10,000 -0.2006 $10,001-$20,000 0.0022 $20,000 or more 0.5913 Student Debt less than $10,000 -0.1115 $10,001-$20,000 -0.1404 $20,001-$50,000 0.2277 $50,001-$100,000 0.2610 $100,001 or more 0.6206 ** Mortgage or Rent $1,001-$1,500 0.2946 $1,501-$2,000 0.2562 $2,001-$2,500 0.2735 $2,501 or more 0.7532 ** Pay Membership Dues -0.1730 Day School/Yeshiva Tuition 0.1184 * The stars indicate statistical significance at the 1% (***), 5%(**) and 10%(*) levels.

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FAS-1 0.5575 5.7725 *** β -0.0004 0.6002 * 0.5537 0.0009 -0.4771 ** 0.1413 1.0511 * -0.3083 0.0422 0.2587 0.2908 0.6540 ** 0.3124 0.1151 0.0678 0.4507 * -0.2635 0.1117 *

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Table 5b: Financial Resources Regression Results The table reports the beta values for the financial resources.

Dependent Variable FAS-7 R-Squared 0.3959 F-Statistic 3.3980 *** β Household Income $30,001-$45,000 -0.3534 $45,001-$75,000 -0.1662 $75,001-$100,000 -0.4237 $100,001-$150,000 -0.3952 $150,001-$200,000 -0.3116 $200,001-$250,000 0.2421 $250,001-$300,000 0.2000 $300,001 or more 0.2399 Emergency Fund $1-$500 -0.2932 $501-$1000 -0.0068 $1,001-$2,000 0.0660 $2,000 or more -0.3614 Education Level High School Degree/ GED -0.4678 Some College or Associates Degree -0.3273 Bachelor's Degree -0.2888 Graduate Degree -0.4904 Homeowner -0.1809 The stars indicate statistical significance at the 1% (***), 5%(**) and 10%(*) levels.

Table 5c: Regression Results for Religious Factors The table reports the beta values for the financial resources.

Dependent Variable FAS-7 R-Squared 0.3959 F-Statistic 3.3980 *** β Religious Identity Modern Orthodox 0.1199 Chassidish -0.2029 * Yeshivish -0.2752 * Lubavich -0.1287 Charadi -0.2030 Religious Factors Minyan -0.2067 Shul on Shabbat and Yom Tov -0.1730 The stars indicate statistical significance at the 1% (***), 5%(**) and 10%(*) levels.

FAS-1 0.5575 5.7725 *** β -0.8560 ** -0.2955 -0.6898 * -0.5956 -0.4671 0.4340 0.5574 1.1455 ** -1.0194 ** -0.8198 -0.6041 ** -0.8067 ** -0.1857 -0.0433 0.5466 0.1571 -0.1500

FAS-1 0.5575 5.7725 *** β 0.0942 -0.4723 * -0.7349 ** -0.4884 -0.2033 -0.2391 0.2310

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Journal of Personal Finance

Figure 2: Income distribution by self-selected sect

school/yeshiva tuition, and religious identity.

This figure shows the percentage of survey participants’ reported income ranges by sect.

The results show a statistically significant inverse relationship between financial anxiety and self-identifying as Yeshivish and Chassidish, despite the fact their incomes are on the lower end of the sample’s range, tend to live in large cities with more expensive rent, observe the strictest version of kashrut, and almost exclusively send their children to Jewish day schools!

One explanation for this counterintuitive finding may be the community’s heavy reliance on “gemachs.” As Bindman (1993) explains, “a gemach is both a free-loan society and a framework for the organized borrowing and gifting of items. Money and goods are donated or lent out by members of the community.”

Gemachs can be relied upon to set up a young couple for marriage and to supply furniture and loans to those in need, thus reducing financial anxiety.

Pearson correlation tests between financial anxiety and regular attendance at Sabbath services and daily minyan provide further support for hypothesis 3 as shown in Table 6.

DISCUSSION & CONCLUSIONS Consistent with previous studies, the constraints (credit card debt, automobile and student loans, tuition, and rent and mortgage payments) are positively related to both measures of financial anxiety and the resources (education, income, and emergency funds) are inversely related to both measures of financial anxiety, although their degrees of effect varied based on the anxiety measure being examined (Britt et al, 2015; Bell et al, 2014; Garman et al, 1996; Norvilitis et al, 2006; Dunn and Mirzaine, 2016; Bonke and Browning, 2009). The contribution of this study is that it targets its analysis on the orthodox Jewish community in the United States and extends it to include factors specific to the community such as minyan attendance, day

This information is important to financial advisors because it allows the advisor to have a deeper understanding of the client’s culture and its impact on their financial needs and goals.

Additionally, we know from Grable et al., (2015) that are those who exhibit high financial anxiety and low physiological arousal are least likely to seek the help of a financial adviser, which is an important consideration, particularly when dealing with the Modern Orthodox sect.

LIMITATIONS This study is limited by the sample size and broad distribution across geographic areas and sects. A follow up study should focus on a single community and incorporate subject interviews.

The relationship between of financial literacy and financial anxiety was not examined in this study and should be examined in the follow-up research taking place in a single community.

Additionally, costs associated with residing within the eruv, a

Table 6: FAS vs. Religious measures This table shows the Pearson Correlations between the financial anxiety measures and measures of religiosity. The stars indicate statistical significance at the 1% (***), 5%(**) and 10%(*) levels.

Do you or your spouse regularly attend serDo you or your spouse vices on Shabbat and attend daily minyan?

Yom Tov? FAS-1 Pearson Correlation -0.157 ** -.001 FAS-7 Pearson Correlation -.084 -0.211 ***

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Volume 19 â&#x20AC;˘ Issue 2

boundary constructed around the community to allow for carrying on the Sabbath, were initially considered in the study but were removed due to ambiguity in wording. A follow-up study should include an analysis of this relationship as well.

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Archuleta, K. L., Dale, A., & Spann, S. M. (2013). College students and financial distress: exploring debt, financial satisfaction, and financial anxiety. Journal of Financial Counseling and Planning, 24(2), 50-62. Retrieved from: http://afcpe.org Bayme, S., & Bubis, G. B. (n.d.). Occupy Judaism: Lowering the Cost of Jewish Education. Retrieved from: http://huc.edu/ckimages/files/Digest/April%205,%202012/Occupy%20Judaism.pdf Bell, M. M., Nelson, J. S., Spann, S. M., Molloy, C. J., Britt, S. L., & Goff, B. S. N. (2014). The impact of financial resources on soldiers' well-being. Journal of Financial Counseling and Planning, 25(1), 41- 52. Retrieved from: https://ssrn.com/abstract=2466556 Bindman, R. Y. (1993). If you lend to my people: Gemachs (free loan funds) and their operation. Spring Valley, NY: Feldheim.

Bonke, J., & Browning, M. (2009). The distribution of financial well-being and income within the household. Review of Economics of the Household, 7(1), 31-42. doi: 10.1007/s11150- 0089044-3 Britt, S. L., Canale, A., Fernatt, F., Stutz, K., & Tibbetts, R. (2015).

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changes in household debt, and the Great Recession. Economic Inquiry, 54(1), 201-214. doi: 10.1111/ecin.12218 Ellison, C. G., Burdette, A. M., & Hill, T. D. (2009). Blessed assurance: Religion, anxiety, and tranquility among US adults.

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Retrieved from: http://hdl.handle.net/10504/64313 Jackson, T., Iezzi, A., & Lafreniere, K. (1997). The impact of psychosocial features of employment status on emotional distress in chronic pain and healthy comparison samples. Journal of Behavioral Medicine, 20(3), 241-256. doi: 10.1023/A:1025552710949 Joo, S. H., & Grable, J. E. (2000). Improving employee productivity: The role of financial counseling and education. Journal of Employment Counseling, 37(1), 2-15. doi: 10.1002/j.21611920.2000.tb01022.x Roth, C., & Wigoder, G. (Eds). (1971). Encyclopedia Judaica. Jerusalem: Keter Publishing.

Kim, J., & Garman, E. T. (2003). Financial stress and absenteeism: An empirically derived model. Journal of Financial Counseling and Planning, 14(1), 31- 42. Retrieved from: https://pdfs.semanticscholar.org/9b08/216e7aebda9173ec2f97f2f8d1674c1bf9ef. pdf Lange, C., & Byrd, M. (1998). The relationship between perceptions of financial distress and feelings of psychological well-being in New Zealand university students. International Journal of Adolescence and Youth, 7(3), 193-209. doi: 10.1080/02673843.1998.9747824 Masoudi, G. F. (1993). Kosher food regulation and the religion clauses of the first amendment. The University of Chicago Law

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Review, 60(2), 667-696. doi: 10.2307/1600082 Mills, R. J., Grasmick, H. G., Morgan, C. S., & Wenk, D. (1992). The effects of gender, family satisfaction, and economic strain on psychological well-being. Family Relations, 4, 440- 445. Retrieved from: http://www.jstor.org/stable/585588

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Monson, R. G., & Feldman, R. P. (1995). The cost of living Jewishly in Philadelphia. Journal of Jewish Communal Service, 68(2), 1991-1992. Retrieved from: http://wordpress.jpro.org/aboutthe-journal/ Nathan-Kazis (2016). Jewish day school parents reveal tuitions in crowdsourced spreadsheet. Forward. Retrieved from: https:// forward.com/news/349337/jewish-day-school- parents-reveal-tuitions-in-crowdsourced-spreadsheet/ Northern, J. J., O'Brien, W. H., & Goetz, P. W. (2010). The development, evaluation, and validation of a financial stress scale for undergraduate students. Journal of College Student Development, 51(1), 79-92. doi: 10.1353/csd.0.0108 Norvilitis, J. M., Merwin, M. M., Osberg, T. M., Roehling, P. V., Young, P., & Kamas, M. M. (2006). Personality factors, money attitudes, financial knowledge, and credit‐card debt in college students 1. Journal of Applied Social Psychology, 36(6), 13951413. doi: 10.1111/j.0021-9029.2006.00065.x Peirce, R. S., Frone, M. R., Russell, M., & Cooper, M. L. (1996).

Financial stress, social support, and alcohol involvement: A longitudinal test of the buffering hypothesis in a general population survey. Health Psychology, 15(1), 38- 47. doi: 10.1037/02786133.15.1.38 Popovici, A. (2016). new york non-profit media: The cost of kosher. Retrieved from: http://www.marklevine.nyc/new_york_ nonprofit_media_the_cost_of_kosher Porter, N. M., & Garman, E. T. (1992). Money as part of a measure of financial well- being. American Behavioral Scientist, 35(6), 820-826. doi: 10.1177/0002764292035006016 Shapiro, D. (2015). The times of Israel: For US orthodox, ‘upper-class’ incomes often not enough.. Retrieved from: https:// www.timesofisrael.com/for-us-orthodox-upper-class- incomesoften-not-enough/ Silverman, S., & Force, S. T. (2014). Working with couples and families in the orthodox Jewish community. The National Resource Center for Healthy Marriage and Families,1, 1-11. Retrieved from: https://pdfs.semanticscholar.org/69c0/0f1a069bf3d9356c493583d6c8635ebd7745.pdf Wertheimer, J. (2010). The high cost of Jewish living. Commen-

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Volume 19 • Issue 2

37

Antecedents of Financial Practices among Latina/o University Students Scott W. Plunkett, PhD1 Yoko Mimura, PhD2 Joan Koonce, PhD, AFC®, CPFFE3 Wen Chin Hsu, PhD4

Abstract Earlier studies have often identified the Latina/o population as having financial disadvantages over non-Latina/o populations. To identify controllable factors associated with better financial practices among Latina/o individuals and families, this study explored demographic and family qualities related to financial practices (budgeting, tracking spending, setting immediate or intermediate financial goals, and saving a portion of earnings). The data came from a self-report survey completed by 607 Latina/o university students. After controlling for demographics, hierarchical multiple regression models showed parental financial socialization, financial socialization from other family members, living with parents, and students’ years of education were positively related to some financial practices. Parents’ education and family wealth were not significantly related to any of the financial practices. Immigration generation status was not related to any financial practices once family qualities were included. The findings support the potential to empower Latino families to engage in better financial practices through factors they can control.

Key Words Generation status, financial socialization, Hispanic, immigrants, Latina/o, saving

1.

2.

3.

4.

Professor, California State University; 18111 Nordhoff Street, Northridge, CA 91330-8255; scott.plunkett@csun.edu Associate Professor, California State University; 18111 Nordhoff Street, Northridge, CA 91330-8308; yoko.mimura@csun.edu Professor, University of Georgia; 230 Hoke Smith Annex, Athens, GA 30602; jkoonce@uga.edu Assistant Professor, California State University; 18111 Nordhoff Street, Northridge, CA 91330 hsu.wen-chin@csun.edu

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Journal of Personal Finance

A developmental challenge during emerging adulthood (18 to mid- to late-20s) is to become financially independent from oneâ&#x20AC;&#x2122;s parents (Arnett, 2000), yet two-thirds of emerging adults felt underprepared to handle personal finances (Clark University, 2015). Existing literature suggests young adults who are immigrants, born to immigrant parents, and/or from ethnic minority backgrounds were less likely to exhibit optimal financial practices (Amuedo-Dorantes & Pozo, 2002). Also, young adults of Hispanic origin (Robb & Woodyard, 2011) reported worse financial practices than young non-Hispanic White adults, and young Hispanic adults were more likely to have difficulty paying off credit cards than their non-Hispanic White counterparts (Lyons, 2004). Latina/o householdsâ&#x20AC;&#x2122; lower median incomes than Caucasian households in 2015 ($45,148 vs. $62,950; Proctor, Semega, & Kollar, 2016) might partially explain these findings.

Understanding the association between financial practices and family factors, in addition to demographic characteristics, helps financial professionals serve Latina/o individuals and families better. Identifying factors that relate to the financial practices of young Latina/o adults could also be a first step in alleviating income inequity. Thus, this study examined demographic and family factors that may relate to financial practices of Latina/o university students. Some studies have combined different aspects of financial practices into a single variable (e.g., Mimura, Koonce, Plunkett, & Pleskus, 2015; Robb & Woodyard, 2011); however, following the lead of other studies (e.g., Kim & Chatterjee, 2013; Robb, 2011; Robb & Sharpe, 2009; Solheim & Yang, 2010; Xiao, Chatterjee, & Kim, 2014), this study examined five common financial practices (i.e., budgeting, tracking spending, setting immediate financial goals, setting intermediate financial goals, and saving a portion of earnings) related to better economic outcomes, such as perceived financial wellbeing in adults (Xiao, Tang, & Shim, 2009) and university students (Gutter & Copur, 2011), separately.

This paper identified family factors, such as living with parents and amount learned about money from parents and other family members, that were positively associated with better financial practices. While some demographic characteristics, such as being male and more years in college (classification), were associated with better financial practices as anticipated, other family characteristics that some studies found as negative predictors of financial practices --- family wealth, parental education, and immigration generation --- had no association with any of the financial practices. The results of this study could be of interest to financial planners who currently work with or would like to work with Latina/o families in the future, as well

as those who educate families in the area of finance, and assist them in perceiving Latinas/os as individuals, rather than as a financially disadvantaged group.

DEMOGRAPHIC CHARACTERISTICS AND FINANCIAL PRACTICES While there is a wealth of studies on immigrant versus native-born differences in financial practices among adults (Chartterjee, 2009; Fang, Hanna, & Chatterjee, 2013) and older adults (Jang, Kim, & Chiriboga, 2006), few studies have focused on generation status and financial practices among young adults. Amuedo-Dorantes and Pozo (2002) found less financial preparation toward future economic uncertainty among immigrant young adults than their native-born counterparts.

For example, Hmong refugees took 15 to 20 years to adapt to the U.S. financial system (Paulson & Rhine, 2008). Yet, compared to their US-born children, Hmong parents in immigrant families emphasized saving over spending and borrowing (Solheim & Yang, 2010). The parents viewed saving regularly and avoiding debt as traditional values and placed higher priorities on these.

Young adults valued spending more than saving and satisfied their wants by using credit cards (Solheim & Yang, 2010).

Latinas/os, especially in immigrant families, generally have strong family networks and place a high value on families (i.e., familism; Halgunseth, 2004). Familism is reflected in how Latinas/os save, spend, and invest money (e.g., for family rituals) and also how they pool money within extended families (Falicov, 2001). Given the value of familism is stronger among immigrant Latinos than those from later generations (Lugo Steidel & Contreras, 2003), it is likely the generation status of Latina/o university students is related to their financial practices.

Besides generation status, other demographic characteristics relate to financial practices. For example, classification or class rank was positively associated with financial literacy (Chen & Volpe, 2002). Specifically, upperclassmen reported better financial literacy than lower classmen, possibly due to taking more classes in accounting, business, economics, consumer affairs, and/or personal finance. Similarly, age was positively associated with better financial practices among Asians and non-Hispanic Whites (Grable, Joo, & Park, 2015). Better financial knowledge or literacy may lead to having better financial practices.

Also, a few studies have examined the association between gender and financial practices of young adults. For example, Kim and Chatterjee (2013) found no gender differences in the

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Volume 19 • Issue 2

odds of financial asset ownership or the amount of debt. However, they found men were less likely to have debt than women.

Yet, another study identified young male adults were more prone to risky financial practices than young female adults (Xiao, Ahn, Serido, & Shim, 2014).

FAMILY QUALITIES RELATED TO FINANCIAL PRACTICES Gudmunson and Danes (2011) argued that while demographic characteristics are important, family socialization plays a significant and more direct role in affecting financial practices among individuals. Various family qualities correlate with young adults’ financial practices. For example, in a study of households in Italy, household wealth was positively associated with young adults’ financial knowledge (Monticone, 2010). Also, a study of first year college students identified a positive, indirect association between parental socioeconomic status and financial practices through parental financial socialization (Shim, Barber, Card, Xiao, & Serido, 2010).

Similarly, parents’ educational attainment is related to financial practices among young adults. For example, young adults whose parents had less formal education were more likely to borrow (Kim & Chatterjee, 2013; Robb & Sharpe, 2009). Also, college students whose parents had bachelor’s degrees were less likely to have a balance on a credit card and have a lower balance than first generation college students (Robb & Sharpe, 2009). However, after controlling for childhood financial socialization and other demographic variables, fathers’ educational attainment was not associated with the odds of having financial assets or debt and the amount of debt among a nationally representative sample of young adults (Kim & Chatterjee, 2013).

The living arrangements of college students may explain variations in financial practices. Young adults between the ages of 17 and 21 years who lived with their parents were less likely to have higher debt than those who lived away from their parents (Kim & Chatterjee, 2013). On the other hand, college students who lived with their parents were less likely to be financially independent from their parents than those who lived away from their parents (Xiao, Chatterjee, & Kim, 2014). College students who live with their parents may also have more opportunities to learn financial practices from their parents, and it is likely parents have more opportunities to monitor their children’s financial practices and assist them financially when they live at home.

While individual and family demographic backgrounds may

39

explain variations in financial practices among young adults, the actual source of variations in financial practices may be due to family experiences while growing up. Family financial socialization processes affect financial practices among young adults. The significance of parental roles in socializing children in the area of personal finance, with respect to both attitudes and practices, is well documented (Hira, 1997; Jorgensen & Savla, 2010; Kim & Chatterjee, 2013; Kim, LaTaillade, & Kim, 2011; Koonce, Mimura, Mauldin, Rupured, & Jordan, 2008; Norvilitis & MacLean, 2010). Specifically, parents influenced young adults’ financial attitudes (Jorgensen & Savla, 2010) and practices (Gutter, Garrison, & Copu, 2010; Jorgensen & Savla, 2010; Shim et al., 2010).

Some studies focused on practices related to credit cards.

Parents were the most significant source of information about credit cards for young adults (Pinto, Parente, & Mansfield, 2005), more important than other sources such as friends, school, and the media. The more young adults learned about credit cards from their parents, the lower their credit card debt (Pinto et al., 2005), and parental hands-on mentoring was the key determinant of lower levels of credit card debt (Norvilitis & MacLean, 2010). In another study, having a parent who communicated about savings accounts and monitored a child’s spending while growing up had no association with the odds of carrying debt or the amount of debt (Kim & Chatterjee, 2013).

Similarly, the more the young adults learned about finance from their parents, the better their overall financial practices (Gutter et al., 2010; Mimura et al., 2015). Having a parent who communicated about savings accounts and monitored children’s spending while growing up was positively associated with financial asset ownership as young adults (Kim & Chatterjee, 2013). Perceptions about parental influence on saving explained if college students were thinking further into retirement and retirement planning (Koposko & Hershey, 2014), and there was no difference in the associations between college students in Mexico and the United States (Koposko, Bojórquez, Pérez, & Hershey, 2016).

While not as significant as the parents, financial socialization at home with family members other than parents also influences the financial practices of young adults. University students who reported financial socialization noted the significance of grandparents and older siblings, whom they saw as role models, coaches, and teachers of financial rules (Solheim, Zuiker, & Levchenko, 2011).

Family dynamics and purposive financial socialization are two components of the financial socialization process (Gudmunson

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Journal of Personal Finance

& Danes, 2011). Overall, individual and family characteristics explain the variations in such socialization processes, and these socialization processes affect the financial knowledge, attitudes, and capability of individuals.

RESEARCH QUESTIONS The following two research questions guided this study. Does generation status, university classification, or gender relate to Latina/o university studentsâ&#x20AC;&#x2122; financial practices (i.e., setting up a budget, tracking spending, setting immediate and intermediate financial goals, and saving a portion of earnings)? Do family qualities contribute to the frequency of these financial practices after accounting for demographics? The specific family qualities examined included perceived family wealth, parental education level, living with parent, financial socialization through parents, and financial socialization through other family members.

HYPOTHESES I.

Latina/o university students practice favorable financial practices (i.e., setting up a budget, tracking spending, setting immediate and intermediate financial goals, and saving a portion of earnings) more often: A. The longer the family has been in the United States (i.e., parents are native born, one parent is native born, both parents are immigrants, and the student themselves are immigrants); B. The higher their university classification status is (i.e., freshman, sophomore, junior, and senior); C. If they are male vs. female.

II. When controlling for demographic characteristics, Latina/o university students with the following family characteristics are more likely to practice favorable financial practices (i.e., setting up a budget, tracking spending, setting immediate and intermediate financial goals, and saving a portion of earnings).

A. Higher family financial wealth; B. Higher parental educational attainment; C. Live with their parents; D. Learned about money from their parents; E. Learned about money from other family members.

METHOD Procedures The Institutional Review Board at the university where data collection took place approved the study prior to the collection of the data. A convenience sample of students was recruited in two departments in two colleges at one public, Hispanic-serving, regional comprehensive university in Southern California.

In the Family and Consumer Sciences Department, self-report, paper-pencil surveys were administered in multiple sections of an upper-division, general education course. Individual instructors agreed for the survey to be administered in their classes.

Students could opt out with no penalty, and those who chose to take the survey received no compensation for completing the survey. Around 95% of the students participated in the study. Trained student research assistants coded, entered, and verified the data.

In the Psychology Department, data were collected from students in a subject pool. Specifically, students in a general education introduction to psychology course were required to participate in studies or complete an alternative written assignment. Because the students could choose from various research projects or the alternative assignment, a response rate does not apply. For the current study, students completed the survey online.

For both departments, the survey took approximately 15 minutes to complete. Participation was voluntary. The inclusionary criteria for this paper were 18- to 25-year-old undergraduate students (i.e., freshmen to seniors), who identified as men or women and identified as Latina/o. Other respondents were excluded from the analyses.

Sample Characteristics The 607 Latina/o student participants were 78.6% women and 21.4% men, ranging in age from 18 to 25 years (mean = 19.5).

Regarding class rank, 45.3% were freshmen, 23.2% were sophomores, 26.0% were juniors, and 5.4% were seniors. The reported birth countries included: 88.5% United States, 5.9% Mexico, 1.7% U.S. Virgin Islands, 1.5% El Salvador, and 0.7% Guatemala, and the remaining 1.7% were born in six other countries. The most frequently reported birth countries of their mothers were Mexico (58.0%), El Salvador (17.9%), Guatemala (5.5%), and the United States (12.4%). The most frequently reported birth countries of their fathers were Mexico (61.7%), El Salvador (14.4%),

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Volume 19 • Issue 2

Guatemala (7.1%), and the United States (8.4%).

parents were averaged.

Most (70.0%) of the Latina/o university students lived at home with their parents. In regard to household structure, 65.8% were from two-parent intact families; 21.6% were from single mother families; 9.1% were from stepfather families (with a biological mother); 0.2% were from stepmother families (with a biological father); 3.0% were from single father families; and the remaining 0.3% came from other family structures.

To measure financial socialization, students were asked, “How much have you learned about savings and investing from each of the following sources?” (Mimura et al., 2015). Responses included (a) “parents or guardians” and (b) “other family members such as grandparents, siblings, and uncles and aunts.” The response choices were: 1 = none, 2 = little, 3 = some, 4 = a good amount, and 5 = a lot. Each item was examined separately.

Measures Financial practices. Five financial practice variables served as the dependent variables (Koonce et al., 2008; Mimura et al., 2015). The five items included setting up a budget or spending plan, tracking spending, setting immediate financial goals, setting intermediate financial goals, and saving from earnings. The items were preceded with the following prompt: “How often do you?” The response options were: 1 = never, 2 = sometimes, 3 = usually, and 4 = always. The items were treated as continuous variables.

Demographic variables. Generation status was ascertained from the birth country of the student, mother, and father. The items were recoded as follows: 1 = student and parents were foreign born; 2 = student was foreign born and parents were U.S. born; 2.5 = student and one parent were foreign born while the other parent was U.S. born; and 3 = student and parents were U.S. born. Preliminary analyses did not show a statistically significant difference between generation status groups on the dependent variables when treating generation status as a categorical variable. Classification was measured through the student’s classification in an undergraduate program (i.e., 13 = freshmen, 14 = sophomore, 15 = junior, and 16 = senior). Gender was coded as 1 = male and 0 = female.

Family background. Family wealth was measured by asking, “How would you describe the wealth of your family?”

The response choices were: 1 = very poor, 2 = poor, 3 = lower middle-class, 4 = middle-class, 5 = upper middle-class, and 6 = upper-class/rich. Parental educational attainment was measured by asking the highest level of education each parent completed. The response choices were: 1 = no schooling completed, 2 = some elementary school (1st-5th grades), 3 = some middle school (6th-8th grades), 4 = some high school (9th-12th grades), 5 = high school graduate or equivalency (GED), 6 = some college but no degree, 7 = associate (technical school) degree, 8 = bachelor's degree, 9 = master's degree, 10 = professional school (medical, law) degree, and 11 = doctorate degree. The education levels of both

Survey type. The survey type, whether from the psychology subject pool (online survey) or family and consumer sciences classes (paper-pencil), served as a possible explanatory variable. The variations in the frequencies of different financial practices among these students may be due to the data collection methods (online vs. paper) or other differences the course selection partially represents (e.g., department, upper-division versus lower-division).

Statistical Analyses Pairwise correlations (Pearson correlations) assessed the correlations between the students’ characteristics, family variables, and each financial practice. Five hierarchical multiple regression models, then, answered the research questions. The five financial practices were the criterion variables. Step one included the demographic characteristics, and step two added the family background characteristics.

One may wonder if the financial practice variables from the survey’s Likert scale items may be better analyzed by dichotomizing or as discrete and ordered variables rather than as continuous variables in the regression analysis. A possible issue with Likert items is they are often highly skewed (Norman, 2010), but this was not the case with the current study with respect to the financial practice variables. Therefore, we treated the financial practices as continuous variables and noted the differences in the results from ordered probit analyses.

Similarly, we treated demographic and family quality variables with more than two values as continuous variables in the regression models rather than dichotomizing them. Generally, dichotomizing predictor variables in regression models is not ideal (Royston et al., 2005). Through dichotomizing, information about the variable will be lost (Cumberland et al., 2004; MacCallum, Zhang, Preacher, & Rucker, 2002). The statistical power is reduced. For example, dichotomizing a variable at the median reduces the statistical power substantially; it is equivalent to discarding one-third of the data (Altman & Royston, 2006; Cohen, 1983; MacCallum et al., 2002). Further, dichotomizing a

42

Journal of Personal Finance

Table 1: Pearson Correlations among Financial Practices and Individual Characteristics, Means, and Standard Deviations 1 1. Set up a budget

2

3

4

5

6

7

8

9

10

11

12

13

14

1.00

2. Track spend- .65*** ing

1.00

3. Set immediate financial goals

.68***

.62***

1.00

4. Set intermediate financial goals

.51***

.40***

.57***

1.00

5. Save earnings

.45***

.42***

.50***

.49***

1.00

6. Generation status

.09*

.06

.04

.03

.01

1.00

7. Classification

.10*

.07

.04

.15***

-.01

.07

1.00

8. Men (1) vs. women (0)

.06

.02

.06

.13**

.09*

-.08

0.05

1.00

9. Family wealth

.04

.06

.02

.05

.00

.11**

.14***

-.03

1.00

10. Parental education

.04

.10*

.09*

.05

.10*

.14***

.01

-.14***

.27***

1.00

11. Living with parents (0 = no, 1 = yes)

.13**

.09*

.10*

-.02

-.05

.04

-.01

.03

-.01

-.02

1.00

12. Financial socialization through parents

.25***

.25***

.30***

.13*** .24***

.03

-.06

-.01

.20***

.11**

.01

13. Financial socialization through other family memb

.17***

.12**

.23***

.21*** .22***

.10*

-.04

-.01

.13**

.10*

.10*

14. Paper (0) vs. online (1) survey

.05

.00

-.01

.11**

-.02

.10*

.54***

.03

.14***

-.09

-.02

.00

.00

1.00

M

2.40

2.75

2.53

1.78

2.34

1.99

13.92

0.21

3.31

4.17

.30

3.64

2.76

0.17

SD

0.96

0.96

1.02

0.97

1.00

0.44

0.96

0.41

0.84

1.64

0.46

1.18

1.3

0.37

*p < .05. **p < .01. ***p < .001.

Š2020, IARFC. All rights of reproduction in any form reserved.

1.00

.44*** 1.00

43

Volume 19 â&#x20AC;˘ Issue 2

Table 2: Multiple Regression Models of Frequencies of Financial Practices among Latina/o College Students Set Immediate Financial Goals Explanatory Variables Intercept Generation status Classification Male (vs. female)

Set up a Budget

Track Spending

Set Intermediate Financial Goals

Financial Goals

Save Earnings

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 .65

-.73

1.54**

.04

1.75**

.04

-.35

-.70

2.46***

1.63*

(.58)

(.25)

(.58)

(.63)

(.62)

(.66)

(.58)

(.64)

(.60)

(.66)

.20*

.17

.12

.08

.11

.05

.06

.00

.03

-.02

(.09)

(.09)

(.09)

(.09)

(.10)

(.05)

(.09)

(.09)

(.09)

(.09)

.09**

.13**

.07

.12*

.04

.10

.14***

.13**

-.02

.01

(.04)

(.05)

(.04)

(.05)

(.04)

(.05)

(.04)

(.04)

(.04)

(.05)

.14

.13

.06

.04

.17

.14

.30**

.29**

.22*

.19

(.09)

(.09)

(.09)

(.09)

(.10)

(.10)

(.09)

(.09)

(.10)

(.10)

Family wealth Parental education Living with parents

.05

-.02

-.08

-.02

-.09

(.05)

(.05)

(.05)

(.05)

(.05)

.00

.04

.03

.01

.04

(.02)

(.02)

(.03)

(.02)

(.03)

.26**

.19*

.18*

-.07

-.12

(.08)

(.08)

(.09)

(.08)

(.09)

Financial socialization through parents

.20***

.20***

.22***

.04

.15***

(.04)

(.04)

(.04)

(.04)

(.04)

Financial socialization through other family members

.04

.00

.09**

.15***

.11**

(.03)

(.04)

(.03)

(.03)

(.03)

Paper (0) vs. online (1) survey

-.30

-.13

-.13

.11

-.01

(.12)

(.12)

(.13)

(.12)

(.12)

R2

.02**

.11***

.01

.08***

.01

.12***

.04***

.09***

.01

.09***

Notes. Model 1 = demographic model (n = 607), Model 2 = full model (n = 601). The table shows the parameter estimates with standard errors in parentheses.

The results using ordered probit were similar to the regression analysis results presented here. Variables significant in the ordered probit but not in regression were generation status in the setup of budget model 2 and family wealth in the save earnings model 2. A31 *p < .05. **p < .01. ***p < .001.

44

Journal of Personal Finance

continuous variable can increase the risk of a type I error (i.e., finding a false positive) (Austin & Brunner, 2004). Finally, when dichotomizing, there can be multiple ways to decide on the cut point, and each will end with different results, thus introducing bias.

RESULTS Most Latina/o students who participated in the study (83%) were from the psychology subject pool, and 17% was from the family and consumer sciences classes. We assumed controlling for survey type accounted for the potential differences in the outcome due to the data collection formats (online vs. paper).

Due to missing data, the models used 607 observations in the demographic models and 601 observations in the full models with both demographic and family background variables.

Pairwise Correlations Table 1 shows pairwise correlations, means, and standard deviations. The five financial practices were significantly and positively correlated with each other, with correlation coefficients ranging from .40 to .68. Generation status was positively correlated with setting up a budget. University classification was significantly and positively related to setting up a budget and setting immediate financial goals. Men were significantly more likely than women to set intermediate financial goals and save a portion of their earnings. Family wealth was not significantly related to any of the financial practices. Parent education level was significantly and positively related to tracking spending, setting immediate financial goals, and saving a portion of earnings. Those who lived with their parents were significantly more likely to set up a budget, track spending, and set immediate financial goals than those who lived away from their parents.

Financial socialization by parents and financial socialization by other family members were significantly and positively related to all five financial practices. Latina/o students in the psychology subject pool who took the online survey were significantly more likely to set intermediate goals than students who took the paper survey in the family and consumer sciences course.

Hierarchical Multiple Regressions Table 2 shows the summary of the multiple regression models for each financial practice item. In the demographic models (Step 1), generation status was significantly and positively related to frequency of setting up a budget, but it was no lon-

ger significant once the family qualities were added in Step 2.

University classification was significantly and positively related to frequency of setting up a budget, tracking spending, and setting intermediate goals, even after the family qualities were added in Step 2. Men reported a significantly higher frequency of setting intermediate goals and regularly saving a portion of their earnings than women. However, once the family qualities were added in Step 2, gender was no longer significantly related to saving a portion of earnings. Family wealth, parental education level, and survey type were not significantly related to the financial practices in the regression models. Similar to the correlations, Latina/o students who lived with their parents were significantly more likely to set up a budget, track spending, and set immediate financial goals than those who did not live with their parents. Financial socialization through parents was significantly and positively related to frequency of setting up a budget, tracking spending, setting immediate financial goals, and saving a portion of earnings. Financial socialization from other family members was significantly and positively related to setting immediate financial goals, setting intermediate financial goals, and saving a portion of earnings. The demographic models only accounted for 1-4% of the variance in the financial practices, while the family qualities added an additional 5-11% of variance in the financial practices above the demographics.

DISCUSSION The purpose of this study was to examine how select demographic characteristics and family qualities related to five financial practices among Latina/o college students. In the final regression models, the following were related to at least one of the financial practices: university classification, gender, living with parents (or not), financial socialization from parents, and financial socialization from other family members. Thus, family qualities appear to be more salient to financial practices in this sample than generation status or other demographic characteristics of the Latina/o university students. Overall, the hypotheses about demographic background and family qualities were partially supported.

Among family background variables, the self-reported financial socialization received from parents was positively associated with the frequencies of all but one financial practice examined.

These findings indicated that when Latina/o university studentsâ&#x20AC;&#x2122; parents communicate financial practices to them, they are more likely to engage in positive financial practices. The sig-

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Volume 19 â&#x20AC;˘ Issue 2

nificance of parental financial socialization for Latina/o young adults confirms the overwhelming literature on other populations (Hira, 1997; Jorgensen & Savla, 2010; Kim & Chatterjee, 2013; Kim et al., 2011; Koonce et al., 2008; Norvilitis & MacLean, 2010). Consequently, as financial service professionals advise and guide parents with their finances, it is likely that parents will pass this financial knowledge to their children. Also, financial service professionals, who understand the impact of financial socialization on the financial practices of young adults, can encourage and guide parents to adopt effective financial strategies as well as communicate the importance of parents teaching these financial principles to their offspring.

Financial socialization from other family members was positively associated with the frequencies of three financial practices (i.e., setting immediate goals, setting intermediate financial goals, and saving a portion of earnings) even when entered into the equation with parental financial socialization. In the Latina/o culture, there is more proximity, connection, and interdependence with extended family members than many other cultures, and this extends to financial practices (Falicov, 2001). Thus, financial service professionals who have Latina/o clients and would like to establish additional relationships and increase their services to Latina/o communities should encourage participation from other family members (e.g., grandparents, Godparents, uncles/aunts) of current clients. Given the strong familial ties within Latina/o families and communities, developing a good relationship with one family member may be the best strategy for financial service professionals to reach and serve this population.

Living with parents was positively associated with frequencies of three financial practices (i.e., budgeting, tracking spending, and setting immediate financial goals). Given financial responsibilities are often shared in Latina/o families (Falicov, 2001), university students who live at home may have less financial burden and more exposure to the financial practices of their parents and other family members. Also, young adults living at home may be more accountable to their families about their financial practices due to the financial interdependence. Living away from parents in Los Angeles may create more financial freedom, as well as financial difficulties, for the Latina/o university students. Most parents want and expect their young adult children to leave home at some point and become financially independent. The ability of young adults to engage in good financial practices can assist them in becoming financially independent. College students living with parents have a longer period of time to learn, develop, and practice better financial

45

practices while still obtaining continued guidance and assistance from parents. At the same time, those living away from parents are less likely or may receive less financial guidance and assistance from their parents. Since living away from home while in college can interfere with studentsâ&#x20AC;&#x2122; positive financial practices, it is important these financial practices are learned and practiced before college. If Latina/o college students who live away from their parents have more financial responsibilities and less financial accountability to their parents than those who live with parents, it is even more important that parents and other family members make a deliberate effort to teach and engage them in better financial practices before they go to college, starting in middle and high school.

Unlike studies that found positive associations between parental formal educational attainment and financial practices (Kim & Chatterjee, 2013; Robb & Sharpe, 2009), the current study did not find such associations. This may be because there was less variability in parental education level than other studies. In this study, the average education level was some high school. Additionally, since the overall education level of parents in our study was lower than other studies examined, it is also likely they had less exposure to education in the area of personal finance.

In the models that only used the demographic variables as explanatory variables, generation status only explained a significant variation in frequencies of setting up a budget.

The longer Latina/o college studentsâ&#x20AC;&#x2122; families had been in the United States, such as having U.S. born parents, the more frequently they had set up budgets. However, this was no longer significant once the family qualities were included. In the full models with family qualities, the only significant demographic variables were classification and gender. Their years in college were positively associated with frequencies of setting up a budget, tracking spending, and setting intermediate goals. This is at least partially likely due to increased exposure to financial education and practices in their courses (e.g., accounting, consumer affairs, and business). This further supports exposing young adults to financial education and practices in the parental home is important. The earlier young adults are exposed to financial education and practices, the more likely they are to engage in good financial practices later in life. Parents with financial knowledge should pass it on to their children.

Men reported setting intermediate goals and saving a portion of earnings more frequently than their female counterparts in the demographics only models. Gender was significant in the full model for saving a portion of earnings. Latino men are

46

Journal of Personal Finance

often in charge of major financial decisions (Falicov, 2001), thus the male university students may have learned the importance of setting intermediate financial goals from their fathers. Female Latinas may receive less financial education in the home (Falicov, 2001). Financial service professionals who want to reach young adult Latina/o families should understand these family dynamics around finances when marketing their services to Latinas/os.

LIMITATIONS AND RESEARCH IMPLICATIONS Certain limitations should be acknowledged. Because this is a cross-sectional study, the ability to infer causality is limited.

Thus, the differences based on demographic and family background characteristics may or may not be the reasons for the variations in frequencies of financial practices. Also, there may be inconsistencies in actual frequencies of financial practices based on self-reporting. While never and always may be comparable among individuals, exact frequency of sometimes and usually could vary among individuals. Finally, the variable distribution may be more appropriately assessed by treating the financial practices differently in the multivariate models.

Literature on financial practices or behaviors is often exploratory in nature and includes individual and family background factors to compare the differences in financial practices based on some sort of group identity or other variations in socio-economic backgrounds. While we agree with Gudmunson and Danes (2011), who argued that the association between variations in family demographic backgrounds and financial outcomes were largely through variations in financial socialization processes, we also considered variations in demographic and family backgrounds as explanatory variables.

With respect to the generalizability of the findings, Latina/o college students in this study were enrolled in one university in Los Angeles. Regardless of variations in demographic backgrounds, their families may share some common financial practices and values, and the participants may also be similar, since they attended the same university (designated a Hispanic-serving institution). This may lead to a more homogeneous group with fewer variations in financial practices based on acculturation levels and other antecedents.

Implications for Financial Service Professionals The findings support the importance of financial education,

counseling, and planning for both immigrant and nonimmigrant Latina/o college students and their families. Before financial planners can engage Latina/o families in financial planning services, financial education and/or counseling is needed. First, they need to be taught positive financial behaviors such as monitoring spending, developing spending plans, setting financial goals, and saving to achieve short-term goals (i.e., purchasing a major appliance). After they have learned and engaged in these financial practices, then financial planners can work with them on more future financial planning issues, including setting and achieving intermediate-term goals (i.e., purchasing a primary residence) and long-term goals (i.e., saving for childrenâ&#x20AC;&#x2122;s education and retirement). Given the results of this study, financial educators, counselors, and/or planners should communicate and encourage Latina/o parents to transfer their personal financial knowledge and effective financial practices to their children. If financial counselors and planners conduct educational seminars or workshops on various financial topics, they can encourage the parents to let their young teen and adult children (middle school, high school, and college) attend.

Empowerment from financial service professionals through a holistic approach to focus on a family as a unit can benefit Latina/o university students as well as adolescents in middle and high school. Focusing on the family as a unit can also benefit current and future financial counselors and planners, as adolescents who are exposed to personal finance and financial practices at a young age are more likely to use the services of these professionals later in life.

For educators in personal finance, focusing financial education efforts on Latinas/os at the secondary and college levels can yield greater rewards such as breaking the cycle of inadequate and/or ineffective financial knowledge and practices of children as they grow into adulthood. In addition to using what they learn to improve their financial situation, emerging adults will be better equipped to pass on financial knowledge and positive financial behaviors to their children in the future.

A primary goal of financial education, counseling, and planning is to provide guidance and assistance to individuals and families to improve their financial practices, so they can be financially self-sufficient and achieve their financial goals.

To have an impact on future generations, it is important for everyone, including financial educators, counselors, and planners, to do what they can to teach children and young adults sound financial principles and practices and the importance of

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47

Volume 19 • Issue 2

passing these principles and practices on to future generations.

Although not the major focus of this study, Latina/o women, lower division students, and those who live away from their parents were less likely to engage in some of the financial practices examined in this study. These groups may be good target audiences for personal finance empowerment on campuses and a target market for financial service professionals.

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Pinto, M. B., Parente, D. H., & Mansfield, P. M. (2005). Information learned from socialization agents: Its relationship to credit card use. Family and Consumer Sciences Research Journal, 33(4), 357367. doi:10.1177/1077727x04274113 Proctor, B. D., Semega, J. L., & Kollar, M. A. (2016). Income and poverty in the United States: 2015. Washington, DC: US Census Bureau. Retrieved from http://www.census.gov/content/dam/

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Volume 19 • Issue 2

49

Finding the Next Major Donor: The Relationship between Financial Planning Horizon and Charitable Giving Zhikun Liu, Ph.D., CFP®1 Russell James III, Ph.D., J.D., CFP®2 Abbas Aboohamidi, Ph.D.3

Abstract Previous research has studied the characteristics associated with the presence and amount of charitable giving. However, few of these studies have explored the relationship between financial planning horizon and philanthropic donations, especially large donations. Using cross-sectional and longitudinal analysis of the Health and Retirement Study data, this paper explores the factors associated with charitable giving over time with a particular focus on financial planning horizon. This paper concludes that American adults who have longer financial planning horizons are more likely to make charitable donations compared to those whose planning horizon is short, i.e., less than six months. Among donors, major gifts are associated with long-term financial planning horizon, wealth, religious activity frequency, volunteer experience, and education.

Key Words Charitable Planning, Financial Planning Horizon, Financial Decision Making

1.

2.

3.

Research Director, Empower Retirement™; 8515 E. Orchard Road, 4T2, Greenwood Village, CO 80111; Zhikun.liu@empower-retirement.com Professor, Personal Financial Planning Department, Texas Tech University; Box 41210, Lubbock, TX 79409-1210; russell.james@ttu.edu Assistant Professor, Oklahoma Panhandle State University; P.O. Box 430 Goodwell, OK 73939; abbas.aboohamidi@opsu.edu

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Journal of Personal Finance

INTRODUCTION In 2016, over 390 billion dollars was donated in the United States (Giving USA, 2017), amounting to over 2% of the total GDP in that year. Living individuals contributed 72% of the total charitable giving, 15% was from foundations, 8% was from bequests, and donations from corporations constituted 5% of the total amount. In other words, more than 312 billion dollars, or 80% of the charitable giving decisions in the United States are made by individuals either directly or through bequests.

The study of individuals’ charitable giving decisions in the United States has been an important topic for many decades due to the notable level of private charitable donations in this country. Previous literature has studied various factors that may affect individuals’ philanthropic decisions, such as religion, education, income, age, marital status, presence of children, employment status, gender, race, volunteering, and cognitive abilities. However, few studies have investigated individuals’ charitable giving decisions from a time discounting or planning horizon point of view. Using the Health and Retirement Study (HRS) data, this paper explores the association of financial planning time horizon with older (age 50 or above) American adults’ charitable donations both cross-sectionally and longitudinally.

This study discovers that controlling for religion, volunteer, health, age, gender, race, marital status, presence of children, retirement status, education, and wealth factors, individuals with longer financial planning horizon are more likely to make charitable donations compared to those who have short-term planning horizon. Among the individuals who donate, longer financial planning horizon is a good predictor for donating larger gifts, ceteris paribus. In addition, within the same individual, shifts between donating larger and smaller gifts over time can be predicted by changes in financial planning horizon, wealth level, religious and volunteer activity frequency, health, retirement, and marital status.

LITERATURE REVIEW A large body of knowledge on philanthropy and individuals’ charitable giving decisions is available in the literature thanks to decades of studies from a variety of disciplines (Bekkers & Wiepking, 2011). A wide variety of factors have been associated with charitable giving. For example, Bekkers (2003) finds that in the Netherlands church attendance frequency is positively and significantly associated with the donation amounts. Brown and Ferris (2007) point out that religion brings people into social

networks and provides a cognitive framework which encourages them to engage in projects aimed at collective good, and pushes their thoughts and identity beyond the personal sphere. This helps to explain why it also demonstrates a positive relationship with charitable giving.

Philanthropic giving is also positively related to the level of education (Brown & Ferris, 2007; Bekkers & Wiepking, 2006), income (Carman, 2003; Bekkers & Schuyt, 2008), age (Alpizar et al., 2008; Bekkers, 2003), and frequency of volunteering (Farmer & Fedor, 2001, Feldman, 2010). Marriage is another factor which positively influences charitable giving (Brown & Ferris, 2007; Feldman, 2010). The findings in gender differences in giving are not consistent in different studies. While most studies find no significant differences between females and males, Andreoni et al. (2003) and Bekkers (2004) report that although women are more likely to give, men give higher amounts. Eschholz & Van Slyke (2002) find that men and women have very similar giving patterns. Regarding race, it is common in U.S. studies that Caucasians are more likely to donate than people of other races (Bielefeld et al., 2005; Feldman 2010). However, Brooks (2004) finds that compared to Caucasians, non-whites give more to religious organizations but less to secular organizations.

Regnerus et al. (1998) discover that people who are of nonwhite race are more likely to donate to the poor and the needy compared to those who are in the white race category. Brown & Ferris (2007) find that black donors give more to religious organizations than white donors, but when organizational membership and trust are controlled, the difference disappears.

Retirement status is another factor which impacts individuals’ charitable giving decisions significantly. While most of the literature finds that the individuals who are working donate more than the unemployed (Banks & Tanner, 1999; Eschholz & Van Slyke, 2002), Schervish and Havens (1997) argue that the retired donate more than those who are still working. Bekkers (2006) claims that people who are in better health tend to donate more. So do those who score higher in a vocabulary test.

Despite the reach of literature regarding different factors that influence individuals’ charitable donations, none have studied the relationship between financial planning time horizon and philanthropic giving. When it comes to large donations, financial planning time horizon can be an important predictor since major donations often require forward planning and are seldom a quick, simple, reflexive choice. Since financial planning horizon is one of the most important areas of economic decision making (Dow & Jin, 2013), and has extensive

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Volume 19 • Issue 2

implications for individuals’ saving, investment, retirement, and estate decision makings, it is useful to investigate whether and what type of influence this factor has on philanthropic giving decisions.

This study advances the research on the characteristics affecting individuals’ charitable donations in several ways. It first explores the cross-sectional relationships between the presence of charitable donations and various behavioral and demographic variables, with a special focus on the impact of individuals’ financial planning horizons. Then, the effects of these predictors on different donor categories based on donation amount are examined through quartile ordered probit regressions. Last but not least, longitudinal analyses are performed to reveal the fact that changes of certain characteristics, such as financial planning horizon, marital and retirement status, wealth level, etc., will predict the donors shift between different donation categories over time.

DATA AND MODEL This paper uses the Health and Retirement Study (HRS) data set, which is a nationally representative longitudinal survey study of adults over the age of 50, conducted by the Institute for Social Research at the University of Michigan. The variables used in this study are selected from the RAND (version P) data file, combined with the cross-wave tracker file as well as the core HRS data from 2002 to 2014. The cross-sectional regressions in this paper are conducted with the 2014 wave data and the longitudinal regressions use all available waves from 2002 to 2014. The sample descriptive statistics for the 2014 wave are described in Table 1.

51

important for the respondent; respondents who choose “5” indicate their planning horizon is “longer than ten years”. Respondents are categorized into five different groups based on their various financial planning horizons. More specifically, out of the 13,347 respondents, 2,197 (16.46%) of them report “next few months” is their most planning horizon. The number of respondents answered “next year” is 1,686 (12.63%), “next few years” is 3,562 (26.69%), “next five to ten years” is 3,910 (29.29%), and “longer than 10 years” is 1,992 (14.92%). As the mean value of the financial planning horizon variable in Table 1 indicates, the average financial planning horizon across respondents is more than a few years but less than five years.

The “religious frequency” variable captures how often the respondents attend religious service during the past year. Their numerical response represents a range of different frequencies: “1” means “not at all”; “2” indicates “one or more times a year”; “3” represents “two or three times a month”; “4” reflects “once a week”; “5” shows that the respondents attend religious services “more than once a week”. Among the 13,347 respondents, 3,403 (25.50%) of them did not attend religious service during the year 2013. The number of respondents who answered “one or more times a year” is 2,875 (21.54%), “two or three times a month” is 1,801 (13.49%). The mean value of the religious frequency reported in Table 1 indicates that the 2014 HRS respondents on average attend religious service “more than once a year”, but “less than two or three times a month”.

Table 1 reports variable means from 13,347 respondents in the 2014 wave HRS weighted to project to a national sample of American adults whose age is 50 or above. Among these variables, the “Donor” variable represents the respondent who has donated $500 or more to charitable organizations in the last year. With the 2014 individual level sample weight applied, 51.6% of the respondents report that they have donated $500 or more in the previous year.

The “volunteer” variable indicates that the respondents have spent time doing volunteer work for different types of charitable organizations in the past 12 months. The “health” variable reflects the respondents’ self-perceived health status: “1” is “poor health” (6.71%); “2” represents “fair health” (22.12%); “3” indicates “good health” (33.72%); “4” reflects “very good health” (29.32%); “5” means “excellent health”(8.12%). The average value of the summary statistics in Table 1 indicates that the participants’ health status is on average between “fair health” and “good health” conditions. The race variable in the HRS data is divided into three base categories: white, black, and other.

Therefore, this study converted the race variable into a categorical variable and set “white race” to be the baseline category.

The financial planning horizon variable includes five categorical responses: “1” means the respondent views the “next few months” as the most important time periods for planning their saving and spending; “2” means the respondent thinks the “next year” is most important; “3” indicates the respondent plans for “the next few years”; “4” reflects “next five to ten years” is most

The economic theory for charitable giving has been developed from a pure altruism model (Becker, 1974), to the “impure altruism” framework which employs both an altruism motive and a “warm-glow” effect (Andreoni, 1989). In the pure altruism theory, donations are modeled as part of a self-interested game to produce public goods, where the sole motive for charitable

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Journal of Personal Finance

Table 1: Descriptive Statistics with/without 2014 Individual Level Weight

Donor 2014 (Percentage) Financial Planning Religious

Horizon1

Frequency2

Volunteer3

2014

2014

2014 (Percentage)

Health4 2014 Male (Percentage) Age 2014 Race5

Black (Percentage)

Race NotBlackNotWhite (Percentage) Married 2014 (Percentage) Have Children 2014 (Percentage) Fully Retired 2014 (Percentage) Years in School (Years) Wealth 2014 (in Dollars) N

Mean

Mean

without Weights

with Weights

47.1%

51.6%

(0.0043)

(0.0105)

3.136

3.213

(0.0111)

(0.0174)

2.815

2.670

(0.0123)

(0.0245)

36.1%

38.4%

(0.0042)

(0.0071)

3.100

3.223

(0.0091)

(0.0183)

41.4%

46.3%

(0.0043)

(0.0049)

66.32

65.99

(0.0931)

(0.2630)

19.7%

10.1%

(0.0034)

(0.0068)

9.48%

6.73%

(0.0025)

(0.0042)

64.2%

65.9%

(0.0041)

(0.0078)

92.3%

90.4%

(0.0023)

(0.0048)

46.0%

43.8%

(0.0043)

(0.0103)

12.86

13.41

(0.0273)

(0.0753)

455,635.9

590,621.9

(9,869.480)

(30,426.739)

13,347

13,347

Notes: Numbers of observations are reported in the last row. Standard deviations are reported in parentheses.

1. The financial planning horizon variable is a categorical variable where detailed descriptions of each category are described below. The average financial planning horizon across respondents is more than a few years but less than five years.

2. The numerical response of the religious frequency variable represents a range of different frequencies. Please see the detailed description in the text. The 2014 HRS respondents on average attend religious service “more than once a year”, but “less than two or three times a month”.

3. The volunteer variable indicates whether the respondents have volunteered in the past 12 months.

4. The health variable also uses numerical categories to reflect the different health status reported by the respondents. The detailed descriptions of the different categories are described in the text following Table 1. The participants’ self-reported health status are on average between “fair health” and “good health” conditions.

5. Omitted race category is “White”, which constitute 70.82% of the population.

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Volume 19 â&#x20AC;˘ Issue 2

giving is the utility derived from the charityâ&#x20AC;&#x2122;s output. Thus, donations by others are perfect substitutes for the donor, and a completed â&#x20AC;&#x153;crowding-outâ&#x20AC;? effect by government grants should be observed. However, substantial empirical evidence (Brooks, 1999; Kropf & Knack, 2003; Marcuello & Salas, 2001) of less than complete crowd-out indicates that the pure altruism model is insufficient for inferring the complete motivation of giving.

Andreoni (1989) proposes that donors get â&#x20AC;&#x153;warm-glowâ&#x20AC;? utility from the fact of self-giving in addition to the public goods utility derived from the charityâ&#x20AC;&#x2122;s output. Warm-glow is the private good or benefit that the donor experiences only by the personal act of giving. Therefore, in the impure altruism model, donations by others and by self are not viewed as perfect substitutes since the charitable behavior is motivated by both altruism and warm-glow.

gift on the overall societal goal. In contrast, big â&#x20AC;&#x153;investmentâ&#x20AC;? gifts are more likely driven by pure altruism, i.e., by the actual perceptible impact on some public good or goal resulting from the gift. Hence, financial planning time horizon should not only predict donations in general but also be particularly effective at predicting major â&#x20AC;&#x153;investmentâ&#x20AC;? gifts. The empirical analysis discussed in the following section supports this suggestion.

METHODS AND RESULTS The following table describes the characteristics of the participants selected in our analysis sample.

Based on Andreoniâ&#x20AC;&#x2122;s (1989) impure altruism model, individuals benefit from charitable donation both from the increase in the charity output and from the warm-glow feeling by the act of giving. Therefore, the donorâ&#x20AC;&#x2122;s utility function can be expressed as:

To study the relationship between older American adultsâ&#x20AC;&#x2122; charitable giving decisions and their financial planning horizon along with other characteristics, we first conducted a series of cross-sectional regressions using the 2014 HRS data. We then use longitudinal analysis to explore the impact of shifting financial planning horizons to the charitable donations decisions over time. The following equation (Equation 2) summarizes the empirical models we use in this study.

đ?&#x2018;&#x2C6;(đ?&#x2018;Ľđ?&#x2018;&#x2013;,đ??ş,đ?&#x2018;&#x201D;đ?&#x2018;&#x2013;), (1)

Donorđ?&#x2018;&#x2013; = Îą0+ Îą1FinPlanHorizonđ?&#x2018;&#x2013;+ Îą2ReligiousFrequencyđ?&#x2018;&#x2013;+ Îą3Vol-

where đ?&#x2018;Ľđ?&#x2018;&#x2013; represents each individualâ&#x20AC;&#x2122;s private consumption, đ??ş is the charityâ&#x20AC;&#x2122;s public good output and đ?&#x2018;&#x201D;đ?&#x2018;&#x2013; represents the gifts that the individual donates, from which private warm-glow benefit or utility is derived. This utility function implies that besides the private consumption, an individual derives utility from charitable giving by two means: The first part is the pure-altruism utility in which the donor benefits from the output of the charity. Since there is usually a time delay from the donation to the enjoyment of the public good produced by the charity, this type of utility may be more favored by the individuals who have a longer time horizon compared to those who discount future heavily. The second part is the warm-glow benefit from the act of giving. Since this type of utility can be derived instantly by the giving action itself, it may not require long-term planning or investment.

Therefore, based on Andreoniâ&#x20AC;&#x2122;s (1989) impure altruism model, where the motivation or benefit from charitable giving can be viewed as two parts â&#x20AC;&#x201C; pure altruism and warm-glow, if financial planning time horizon is a significant factor which impacts peopleâ&#x20AC;&#x2122;s charitable donation decisions, it should affect the donorsâ&#x20AC;&#x2122; utility predominantly from the pure altruism perspective discussed above. In other words, small gifts may be largely driven by the instantaneous warm-glow benefit without any actually perceptible personal benefit resulting from the impact of the

unteerđ?&#x2018;&#x2013;+Îą4HealthStateđ?&#x2018;&#x2013;+Îą5SexMale +Îą6Ageđ?&#x2018;&#x2013;+ Îą7Raceđ?&#x2018;&#x2013;+ đ?&#x2018;&#x2013;

Îą8Marriedđ?&#x2018;&#x2013;+ Îą9HaveChildrenđ?&#x2018;&#x2013;+ Îą10FullyRetiredđ?&#x2018;&#x2013;+

Îą11YearsofEducationđ?&#x2018;&#x2013;+ Îą12ln(Wealth)đ?&#x2018;&#x2013; + Ďľđ?&#x2018;&#x2013; (2)

With the variations of the dependent variables (Donorđ?&#x2018;&#x2013;) and the financial planning horizon variable (FinancialPlannningHorizonđ?&#x2018;&#x2013;), we conduct a total of six cross-sectional regression analyses and three longitudinal regression analyses. For all the cross-sectional regressions, the subscript [ ]đ?&#x2018;&#x2013; represents the year 2014. For the longitudinal analyses, the subscript [ ]đ?&#x2018;&#x2013; ranges from 2002 to 2014 to represent the year range of our longitudinal study. Variables which are not suitable for longitudinal regressions such as Sex, Age, Race, and Years of Education are omitted from the empirical model when conducting the longitudinal analyses. Table 3 below provides a guideline for all the variations of the dependent variable, financial planning horizon variable format and the control variables for each of the proceeding empirical analyses. These empirical analyses not only provide strong evidence supporting our main hypothesis, but also serve as robust checks for each other.

First, let us discuss the results from the cross-sectional regressions. Table 4 and Table 5 shows the results of the probit regressions with a binary independent variable. The average marginal effects of the probit analysis in Table 4 indicate the existence of a positive significant relationship between the respondentsâ&#x20AC;&#x2122;

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Journal of Personal Finance

Table 3: Overview of the Empirical Models Empirical Design Analysis Type

Table Index

Table 4

Regression Descriptions

Dependent Variable Selection

Probit Regression for Factors Affecting Charitable Donation Decisions

Table 5

Probit Regression for Factors Affecting Charitable Donation Decisions (with Financial Planning Dummies)

Table 6

OLS and Tobit Regressions on Dollar Amount Donated (All Respondents)

Financial Planning Horizon Variable Format Scale: 1, 2, 3, 4, 5.

(Larger the longer)

Donor2014 (Yes=1, No =0)

Ln(DonationAMT)2014

Dummy Variables: 1. Next few months (Omitted) 2. Next year 3. Next few years 4. Next 5-10 years 5. Longer than 10 Years Scale: 1, 2, 3, 4, 5.

(Larger the longer) Dummy Variables: 1. Next few months (Omitted) 2. Next year 3. Next few years 4. Next 5-10 years 5. Longer than 10 Years

Table 7

OLS and Tobit Regressions on Dollar Amount Donated with Financial Planning Dummies (All Respondents)

Table 8

Quartile Ordered-Probit Regressions (Average Marginal Effects) - Among Donors

Scale: 1, 2, 3, 4, 5.

(Larger the longer)

Table 9

Quartile Ordered-Pro- DonationQuarbit Regressions (Aver- tile2014 age Marginal Effects) - Among Donors with Financial Planning Dummies

Dummy Variables: 1. Next few months (Omitted) 2. Next year 3. Next few years 4. Next 5-10 years 5. Longer than 10 Years

Table 10

Longitudinal Regressions OLS Fix-Effects

Table 11

Longitudinal Probit and Logit Regressions Donor (Yes=1, No =0) (Average Marginal Effects) Fix & Random - Effects

Scale: 1, 2, 3, 4, 5.

(Larger the longer)

Table 12

Quartile Longitudinal Ordered-Probit Regressions (Average Marginal Effects)

Scale: 1, 2, 3, 4, 5.

(Larger the longer)

Cross-Sectional

Longitudinal

Control Variables

(The natural logarithm of the actual donation amount during the past year.)

ReligiousFrequency2014, Volunteer2014, Health2014, Male, Age2014, RaceBlack, RaceNotBlackNotWhite, Married2014, HaveChildren2014, FullyRetire2014, YearsinSchool, Ln(Wealth)2014.

Donor (Yes=1, No =0); Scale: 1, 2, 3, 4, 5.

Ln(DonationAMT) (Larger the longer)

DonationQuartile

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ReligiousFrequency, Volunteer, Health, Married, HaveChildren, FullRetire, Ln(Wealth)

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Volume 19 â&#x20AC;˘ Issue 2

Table 2: Characteristics of the Participants in the Analysis Sample Participant Characteristics (based on the variables used in the analysis) Donor 2014 (Donated in the last calendar year) Financial Planning Horizon 2014 1. Next few months 2. Next year 3. Next few years 4. Next 5-10 years 5. Longer than 10 Years Religious Frequency1 2014 1. Not at all 2. One or more times a year 3. Two or three times a month 4. Once a week 5. More than once a week Volunteer2 2014 (Spent time doing volunteer work last year.)

Health 2014 Poor Fair Good Very good Excellent Gender Age 2014 Race Black White Not Black nor White Married 2014 Have Children 2014 Fully Retired 2014 Years in School (Years) Wealth 2014 (in Dollars)

Statistics Yes No 6,284 7,063 Number of Participants 2,197 1,686 3,562 3,910 1,992 Number of Participants 1,948 3,320 1,801 2,875 3,403 Yes No 4,813 8,534 Number of Participants 1,084 3,913 4,501 2,953 896 Male Female 5,530 7,817 Mean 66.32 Number of Participants 2,626 9,456 1,265 Yes No 8,572 4,775 Yes No 12,323 1,024 Yes No 6,140 7,207 Mean 12.86 Years Mean $455,635.9

Total Number of Participants 13,347 1. How often does the participant attend religious services during the past year.

2. In the past 12 months, does the participant spent time doing volunteer work.

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Journal of Personal Finance

financial planning horizon and their charitable donation behavior in the past year. Using categorical analysis, the results in Table 5 further tell us that compared to the respondents whose financial planning horizon is just “a few months”, those with longer financial planning horizon are more likely to be a donor.

In particular, Table 5 reflects that the most important predictor is not being in the shortest financial planning horizon category, with relatively little consistent trends as financial planning horizon expands further past one year. Along with these findings, the results in both Table 4 and Table 5 indicate strong relationships between the respondents’ charitable giving decisions and their religious service attendance frequency, volunteer experience, race, health, marriage and retirement status, age, education, and wealth level. These results agree with the findings of previous literature.

Beyond identifying which factors are associated with older American adults’ decision on whether or not to make charitable donations, the next step is to look at the associations of these factors on the dollar amount donated. To answer this question, this study reports both the OLS and Tobit regressions on all respondents in the analysis sample.

The results in Table 6 show that the financial planning horizon factor has a significant positive association with older American adults’ charitable donation amount, controlling for various demographic, religious, wealth, and health factors. Table 7 reports the results of similar regressions but using the categorical analysis approach. These results indicate that compared to the respondents whose financial planning horizon is short (“next few months”), individuals who have longer financial planning horizon donate more to charitable organizations, ceteris paribus. The logarithm transformation is applied to the “Total Donation Amount” dependent variable to better capture the impacts of different factors to the respondents’ total annual donation dollar amount in terms of percentage changes. (Besides the interpretability benefits, the logarithm transformation also pull the outlying data points from a positively skewed distribution closer to the bulk of the data. Since our analysis data sample distribution might be positively skewed by a few larger donors, applying the logarithm transformation is beneficial to our empirical analyses.)

For instance, based on the coefficients of the OLS regression in Table 7, compared to the participants whose financial planning horizon is “next few months”, the annual charitable donation amount is on average 36% higher for those participants whose financial planning horizon is “next year”. If we look at the

Table 4: Probit Regression for Factors Affecting Charitable Donation Decisions Donor2014 (Yes=1; No=0)

Probit Average Marginal Effect on whether Donated Last Year

FinPlanHorizon2014

0.0453*** (0.0109)

ReligiousFrequency2014

0.299*** (0.0105)

Volunteer2014

0.409*** (0.0296)

Health2014

0.0734*** (0.0140)

Male

0.0474 (0.0277)

Age2014

0.00724*** (0.0016)

RaceBlack

0.0883* (0.0384)

RaceNotBlackNotWhite

-0.179*** (0.0503)

Married2014

0.252*** (0.0316)

HaveChildren2014

0.0731 (0.0514)

FullRetire2014

-0.0917** (0.0326)

YearsinSchool

0.102*** (0.0052)

Ln(Wealth)2014

0.216*** (0.0087)

N

11,675

Notes: Number of observation is 11,675. Standard errors are reported in parentheses. ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

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Volume 19 • Issue 2

Table 5: Factors Affecting Charitable Donation Decisions (with Financial Planning Dummies) Donor2014 (Yes=1; No=0)

Probit Average Marginal Effect on whether Donated Last Year

Financial Planning Horizons 2014 Omitted Category: (Next Few Months) FinPlanHorizon2014 (Next Year)

0.158** (0.0512) FinPlanHorizon2014 (Next 0.144** Few Years) (0.0438) FinPlanHorizon2014 (Next 0.216*** 5-10 Years) (0.0433) FinPlanHorizon2014 (Lon- 0.180*** ger than 10 Years) (0.0503) ReligiousFrequency2014 0.299*** (0.0105) Volunteer2014 0.409*** (0.0296) Health2014 0.0732*** (0.0140) Male 0.0464 (0.0277) Age2014 0.00686*** (0.0016) RaceBlack 0.0953* (0.0386) RaceNotBlackNotWhite -0.176*** (0.0504) Married2014 0.252*** (0.0317) HaveChildren2014 0.0718 (0.0515) FullRetire2014 -0.0890** (0.0326) YearsinSchool 0.102*** (0.0052) Ln(Wealth)2014 0.215*** (0.0087) Notes: Number of observation is 11,675. Standard errors are reported in parentheses. ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

participants whose financial planning horizon is “longer than 10 years”, an average of 54% increase in annual donations is observed. (The Tobit regression treats of the potential issue with the clustering of data points around zero-dollar total donation amount, and also serve as a robust check for the OLS regression.) The coefficient of the Tobit regression supports the results of the OLS regression and yield similar conclusions.

For instance, compared to the respondents in the “New Few Months” financial planning horizon group, participants in the “Next 5-10 Years” financial planning horizon group are on average donating 94.4% more to charitable organizations in the past year. Consistent with the previous analysis, Table 6 and Table 7 indicate that the most important predictor is not being in the shortest financial planning horizon category.

Expanding upon these results, the next empirical model uses categorical analysis to explore the factors associated with different donor level categories, defined by their quartile ranking among all donors. (The Health and Retirement Study defines “donors” to be the respondents who donated 500 dollars or more last year. Using the same definition, we now restrict our analysis sample to all the donors and ranking them into four quartiles.) A potential advantage of such a categorical outcome is that it limits the individual effects of very large donations and instead explores factors associated with being in the general “major” donor category, defined as the top quartile of donors. (The range and median for each quartile category are described in the footnotes of Table 8 and Table 9.)

The results of the average marginal effect of the ordered probit regressions on donation level quartiles in Table 8 and Table 9 supports the positive relationship between the donors’ financial planning horizon and their quartile ranking. Financial planning horizon, especially long-term financial planning horizon, plays a significant role in predicting the major donors (defined as donors in the top quartile). In other words, donors with the longest financial planning horizon are more likely to be in the category of giving major donations, ceteris paribus. Table 8 reflects the significance of the financial planning horizon in terms of increasing the likelihood of the donors to be in the high donation quartiles. Table 9 further explores this association by investigating each financial planning horizon separately and reporting a consistent increase in the likelihood of being a major donor (top quarter) as financial planning horizon increases.

For instance, based on the coefficients in Table 9, comparing to the donors who are in the “Next Few Months” financial planning horizon group, donors whose financial planning horizon is “Longer than 10 Years” are on average 5.03% less likely to be

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Journal of Personal Finance

Table 6: OLS and Tobit on Dollar Amount Donated (All Respondents) Ln(DonationAMT)

OLS

Tobit

FinPlanHorizon2014

0.131***

0.220***

(0.0266)

(0.0516)

0.822***

1.567***

(0.0253)

(0.0490)

1.203***

1.853***

(0.0755)

(0.1379)

0.203***

0.382***

(0.0345)

(0.0660)

0.161*

0.212

(0.0679)

(0.1283)

0.0209***

0.0363***

ReligiousFrequency2014 Volunteer2014 Health2014 Male Age2014 RaceBlack RaceNotBlackNotWhite Married2014 HaveChildren2014 FullRetire2014 YearsinSchool Ln(Wealth)2014 N

(0.0039)

(0.0076)

0.235*

0.725***

(0.0946)

(0.1807)

-0.350**

-0.857***

(0.1213)

(0.2439)

0.623***

1.188***

(0.0784)

(0.1518)

0.141

0.305

(0.1271)

(0.2441)

-0.176*

-0.438**

(0.0794)

(0.1500)

0.258***

0.533***

(0.0120)

(0.0249)

0.523***

1.206***

(0.0190)

(0.0419)

11,672

11,672

Notes: Numbers of observations are reported in the last row.

Standard errors are reported in parentheses. ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

Table 7: OLS and Tobit on Dollar Amount Donated with Financial Planning Dummies (All Respondents) Ln(DonationAMT) Financial Planning Horizons 2014 Omitted Category: (Next Few Months) FinPlanHorizon2014 (Next Year) FinPlanHorizon2014 (Next Few Years) FinPlanHorizon2014 (Next 5-10 Years) FinPlanHorizon2014 (Longer than 10 Years)

OLS

Tobit

0.360** (0.1256) 0.268*

0.868*** (0.2462) 0.691**

(0.1069) 0.511***

(0.2113) 1.071***

(0.1058) 0.540***

(0.2080) 0.944***

(0.1222) (0.2395) 0.821*** 1.565*** (0.0253) (0.0490) Volunteer2014 1.204*** 1.852*** (0.0755) (0.1379) Health2014 0.202*** 0.381*** (0.0345) (0.0660) Male 0.160* 0.209 (0.0680) (0.1283) Age2014 0.0205*** 0.0345*** (0.0040) (0.0076) RaceBlack 0.239* 0.753*** (0.0948) (0.1812) RaceNotBlackNotWhite -0.348** -0.842*** (0.1213) (0.2440) Married2014 0.625*** 1.185*** (0.0784) (0.1518) HaveChildren2014 0.142 0.306 (0.1272) (0.2442) FullRetire2014 -0.174* -0.430** (0.0795) (0.1501) YearsinSchool 0.258*** 0.531*** (0.0120) (0.0249) Ln(Wealth)2014 0.522*** 1.203*** (0.0190) (0.0419) N 11,672 11,672 Notes: Numbers of observations are reported in the last row.

Standard errors are reported in parentheses. ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

ReligiousFrequency2014

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Volume 19 â&#x20AC;˘ Issue 2

Table 8: Quartile Ordered-Probit Average Marginal Effects - Among Donors DonationQuartile2014

Bottom Quarter

Second Quarter

Third Quarter

Top Quarter

FinPlanHorizon2014

-0.0113**

-0.00250**

0.00275**

0.0110**

(0.0035)

(0.0008)

(0.0009)

(0.0035)

ReligiousFrequency2014

-0.0788***

-0.0175***

0.0192***

0.0771***

(0.0032)

(0.0010)

(0.0011)

(0.0032)

-0.0611***

-0.0136***

0.0149***

0.0598***

(0.0088)

(0.0020)

(0.0022)

(0.0086)

-0.00846

-0.00188

0.00206

0.00828

(0.0045)

(0.0010)

(0.0011)

(0.0044)

Male

-0.0219**

-0.00486**

0.00533**

0.0215**

(0.0085)

(0.0019)

(0.0021)

(0.0083)

Age2014

-0.000659

-0.000146

0.000160

0.000644

(0.0005)

(0.0001)

(0.0001)

(0.0005)

-0.0397***

-0.00882**

0.00967**

0.0389**

(0.0121)

(0.0027)

(0.0030)

(0.0118)

0.0492**

0.0109**

-0.0120**

-0.0481**

(0.0174)

(0.0039)

(0.0042)

(0.0170)

Married2014

-0.0338**

-0.00750**

0.00822**

0.0331**

(0.0104)

(0.0023)

(0.0026)

(0.0102)

HaveChildren2014

0.00610

0.00135

-0.00148

-0.00597

(0.0165)

(0.0037)

(0.0040)

(0.0161)

0.00166

0.000369

-0.000405

-0.00163

(0.0098)

(0.0022)

(0.0024)

(0.0096)

-0.0197***

-0.00437***

0.00479***

0.0193***

(0.0017)

(0.0004)

(0.0005)

(0.0017)

Ln(Wealth)2014

-0.0599***

-0.0133***

0.0146***

0.0586***

(0.0030)

(0.0009)

(0.0009)

(0.0030)

N

5,962

5,962

5,962

5,962

Volunteer2014 Health2014

RaceBlack RaceNotBlackNotWhite

FullRetire2014 YearsinSchool

Notes: Numbers of observations are reported in the last row. Standard errors are reported in parentheses.

The range, mean, and median of each quartile are described as following: Max

Min

Mean

Median

Bottom Quarter

$900

$500

$610

$600

Second Quarter

$1,800

$950

$1,268

$1,200

Third Quarter

$4,080

$1,900

$2,807

$2,800

Top Quarter

5.00*10^7

$4,100

$8,061,127

$10,000

***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

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Journal of Personal Finance

Table 9: Quartile Ordered-Probit Average Marginal Effects- Among Donors with Financial Planning Dummies DonationQuartile2014 Financial Planning Horizons 2014 Omitted Category: (Next Few Months) FinPlanHorizon2014 (Next Year) FinPlanHorizon2014 (Next Few Years) FinPlanHorizon2014 (Next 5-10 Years) FinPlanHorizon2014 (Longer than 10 Years) ReligiousFrequency2014 Volunteer2014 Health2014 Male Age2014

Bottom Quarter

Second Quarter

Third Quarter

Top Quarter

-0.0220 (0.0171) -0.0228 (0.0148) -0.0357* (0.0145) -0.0503** (0.0166) -0.0788*** (0.0032) -0.0612*** (0.0088) -0.00846 (0.0045) -0.0220** (0.0085) -0.000649

-0.00488 (0.0038) -0.00506 (0.0033) -0.00792* (0.0032) -0.0111** (0.0037) -0.0175*** (0.0010) -0.0136*** (0.0020) -0.00188 (0.0010) -0.00488** (0.0019) -0.000144

0.00536 (0.0042) 0.00555 (0.0036) 0.00869* (0.0036) 0.0122** (0.0041) 0.0192*** (0.0011) 0.0149*** (0.0022) 0.00206 (0.0011) 0.00535** (0.0021) 0.000158

0.0215 (0.0167) 0.0223 (0.0145) 0.0350* (0.0142) 0.0492** (0.0163) 0.0771*** (0.0032) 0.0598*** (0.0086) 0.00828 (0.0044) 0.0215** (0.0083) 0.000635

(0.0005) (0.0001) (0.0001) -0.0396** -0.00877** 0.00962** (0.0121) (0.0027) (0.0030) RaceNotBlackNotWhite 0.0494** 0.0109** -0.0120** (0.0174) (0.0039) (0.0043) Married2014 -0.0338** -0.00749** 0.00821** (0.0105) (0.0023) (0.0026) HaveChildren2014 0.00554 0.00123 -0.00135 (0.0165) (0.0037) (0.0040) FullRetire2014 0.00173 0.000383 -0.000420 (0.0098) (0.0022) (0.0024) YearsinSchool -0.0197*** -0.00438*** 0.00480*** (0.0017) (0.0004) (0.0005) Ln(Wealth)2014 -0.0599*** -0.0133*** 0.0146*** (0.0030) (0.0009) (0.0009) N 5,962 5,962 5,962 Notes: Numbers of observations are reported in the last row. Standard errors are reported in parentheses.

The range, mean, and median of each quartile are described in as following: Max Min Mean Bottom Quarter $900 $500 $610 Second Quarter $1,800 $950 $1,268 Third Quarter $4,080 $1,900 $2,807 Top Quarter 5.00*10^7 $4,100 $8,061,127 ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

RaceBlack

Š2020, IARFC. All rights of reproduction in any form reserved.

(0.0005) 0.0387** (0.0119) -0.0483** (0.0170) 0.0330** (0.0102) -0.00542 (0.0161) -0.00169 (0.0096) 0.0193*** (0.0017) 0.0586*** (0.0030) 5,962

Median $600 $1,200 $2,800 $10,000

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Volume 19 • Issue 2

in the bottom quartile, 1.11% less likely to be in the second quartile, 12.2% more likely to be in the third quartile, and 4.92% more likely to be in the top quartile, keeping everything else equal.

Figure 1: The Variation of Average Donation Amount from 2002 to 2014

Longitudinal Robustness Check After exploring the relationship between older American adults’ charitable donation decisions and their financial planning horizon using a series of cross-sectional analyses, the next analyses examine this relationship over time using longitudinal analyses. The analysis sample for the longitudinal regression is constructed by merging the selected variables from all available waves of the HRS core data files, RAND (version P) file, and the cross-wave tracker file. As Figure 1 indicates, the magnitude of variation for the average donation amount across different waves is not large, indicating a relative consistency of the different sample waves used in this study.

To explore what kind of changes over time may prompt an older American adult to change their status as a donor and/or change the amount they donate, two longitudinal fixed-effects OLS regressions are performed. The results of these regressions are reported in Table 10. Corresponding to the cross-sectional analysis conducted in the previous sections of the paper, the results in Table 10 indicate that over time, changes in financial planning horizon will significantly affect the decision to donate, but not so significantly on the change of dollar amount donated, if the analysis sample is restricted to donors only. The results in Table 11 confirm the first finding of Table 10, using longitudinal probit and logit analyses: Changes in older American adults’ financial planning horizons, along with other social and economic status changes, will significantly influence their charitable donation decisions over time.

The average marginal effects of the quartile longitudinal ordered-probit regression reported in Table 12 explains the second finding of Table 10: Although the change of financial planning horizon over time does not significantly influence the simple dollar amount that the donors give, it does significantly impact the shifts of the donors between the low and high donation quartile categories. In other words, an older American adult is more likely to shift from a lower donation quartile to a

higher donation quartile, if his/her financial planning horizon changes from short-term to long-term over time, ceteris paribus. For instance, based on the coefficients reported in Table 12, an average of one category change from shorter financial planning horizon to longer financial planning horizon will increase the likelihood of shifting to the top donation quartile by 0.58%, keeping everything else equal. Other factor changes, such as an increase in religious activity frequency, annual volunteer times, health status, and wealth level will also increase the likelihood of being in top donation quartiles. Marital and retirement status changes are also significantly related to the shifts in the donors’ quartile category. For example, a donor’s status change from unmarried to married will on average increase the likelihood of her/him being in the top donation quartile by 5.35%. In terms of wealth changes, a 1% level wealth increase will on average cause the donor to increase the likelihood of being in the top quartile by 4.86%.

CONCLUSION This study examines the different factors associated with older American adults’ charitable giving decisions, with a special focus on the financial planning horizon factor. Using cross-sectional and longitudinal analyses with the HRS data, the empirical analysis of this paper supports the hypothesis that there is a relationship between American adults’ charitable donation decisions and their financial planning horizon. Compared to the individuals with short-term financial planning horizon, those with longer financial planning horizons are more likely to make charitable donations. Among those who are donating, longer-term financial planning horizon is associated with being a major donor, i.e., being in the top donation quartile. In other words, among the older American adults who donate, major donors can be predicted in part, by their long-term financial planning horizon, controlling for various health, social, and economic factors.

These findings are confirmed with longitudinal analyses: Changes in financial planning horizon over time were significantly associated with the respondents’ changes in whether or not to donate. Additionally, when an older American adult donor’s financial planning horizon changes from short-term to long-term, she/he is more likely to shift from a lower donation quartile category to a higher quartile category.

The findings of this paper may help charitable organizations and financial planners to identify potential donors, especially donors who have the potential to donate large gifts in the long

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Journal of Personal Finance

Table 9: Quartile Ordered-Probit Average Marginal Effects- Among Donors with Financial Planning Dummies DonationQuartile2014 Financial Planning Horizons 2014 Omitted Category: (Next Few Months) FinPlanHorizon2014 (Next Year)

Bottom Quarter

Second Quarter

Third Quarter

-0.0220 -0.00488 0.00536 (0.0171) (0.0038) (0.0042) FinPlanHorizon2014 (Next Few Years) -0.0228 -0.00506 0.00555 (0.0148) (0.0033) (0.0036) FinPlanHorizon2014 (Next 5-10 Years) -0.0357* -0.00792* 0.00869* (0.0145) (0.0032) (0.0036) FinPlanHorizon2014 (Longer than 10 Years) -0.0503** -0.0111** 0.0122** (0.0166) (0.0037) (0.0041) ReligiousFrequency2014 -0.0788*** -0.0175*** 0.0192*** (0.0032) (0.0010) (0.0011) Volunteer2014 -0.0612*** -0.0136*** 0.0149*** (0.0088) (0.0020) (0.0022) Health2014 -0.00846 -0.00188 0.00206 (0.0045) (0.0010) (0.0011) Male -0.0220** -0.00488** 0.00535** (0.0085) (0.0019) (0.0021) Age2014 -0.000649 -0.000144 0.000158 (0.0005) (0.0001) (0.0001) RaceBlack -0.0396** -0.00877** 0.00962** (0.0121) (0.0027) (0.0030) RaceNotBlackNotWhite 0.0494** 0.0109** -0.0120** (0.0174) (0.0039) (0.0043) Married2014 -0.0338** -0.00749** 0.00821** (0.0105) (0.0023) (0.0026) HaveChildren2014 0.00554 0.00123 -0.00135 (0.0165) (0.0037) (0.0040) FullRetire2014 0.00173 0.000383 -0.000420 (0.0098) (0.0022) (0.0024) YearsinSchool -0.0197*** -0.00438*** 0.00480*** (0.0017) (0.0004) (0.0005) Ln(Wealth)2014 -0.0599*** -0.0133*** 0.0146*** (0.0030) (0.0009) (0.0009) N 5,962 5,962 5,962 Notes: Numbers of observations are reported in the last row. Standard errors are reported in parentheses.

The range, mean, and median of each quartile are described in as following: Max Min Mean Bottom Quarter $900 $500 $610 Second Quarter $1,800 $950 $1,268 Third Quarter $4,080 $1,900 $2,807 Top Quarter 5.00*10^7 $4,100 $8,061,127 ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level. Š2020, IARFC. All rights of reproduction in any form reserved.

Top Quarter

0.0215 (0.0167) 0.0223 (0.0145) 0.0350* (0.0142) 0.0492** (0.0163) 0.0771*** (0.0032) 0.0598*** (0.0086) 0.00828 (0.0044) 0.0215** (0.0083) 0.000635 (0.0005) 0.0387** (0.0119) -0.0483** (0.0170) 0.0330** (0.0102) -0.00542 (0.0161) -0.00169 (0.0096) 0.0193*** (0.0017) 0.0586*** (0.0030) 5,962

Median $600 $1,200 $2,800 $10,000

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Volume 19 â&#x20AC;˘ Issue 2

Table 10: Longitudinal Regressions OLS fix-effects Longitudinal OLS on Longitudinal OLS on Donor Yes/No Ln(DonationAMT) FinPlanHorizon 0.00424* 0.000330 (0.0019) (0.0166) ReligiousFrequency 0.0412*** 0.0988*** (0.0029) (0.0279) Volunteer 0.0345*** -0.0461 (0.0061) (0.0484) Health 0.00245 -0.0124 (0.0031) (0.0277) Married 0.0392*** 0.0952 (0.0096) (0.0912) HaveChildren -0.0432 0.205 (0.0280) (0.2984) FullRetire -0.0192** 0.00815 (0.0061) (0.0516) Ln(Wealth) 0.0204*** 0.0590* (0.0025) (0.0266) N 49,676 25,479 Notes: Numbers of observations are reported in the last row. Standard errors are reported in parentheses. ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

Table 11: Longitudinal Average Marginal Effects Donor

FinPlanHorizon

Longitudinal Probit on Donor Yes/No (Random Effect)

Longitudinal Logit on Donor Yes/No (Random Effect

Longitudinal Logit on Donor Yes/No (Fixed Effect)

0.0642*** 0.111*** 0.0447* (0.0078) (0.0137) (0.0185) ReligiousFrequency 0.422*** 0.741*** 0.358*** (0.0087) (0.0155) (0.0282) Volunteer 0.614*** 1.074*** 0.271*** (0.0221) (0.0390) (0.0557) Health 0.147*** 0.256*** 0.0210 (0.0103) (0.0180) (0.0300) Married 0.368*** 0.647*** 0.269** (0.0248) (0.0436) (0.0858) HaveChildren -0.0377 -0.0638 -0.368 (0.0437) (0.0767) (0.2383) FullRetire -0.0870*** -0.157*** -0.201*** (0.0212) (0.0373) (0.0596) Ln(Wealth) 0.377*** 0.668*** 0.201*** (0.0073) (0.0133) (0.0256) N 49,676 49,676 12,086 Notes: Numbers of observations are reported in the last row. Standard errors are reported in parentheses. ***Statistically significant at 0.1- percent level. **Statistically significant at 1- percent level. *Statistically significant at 5- percent level.

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Journal of Personal Finance

term. It also contributes to the literature of studying the benefit and the promoting of longer-term financial planning horizons.

FUTURE RESEARCH The impure altruism model (Andreoni, 1989) indicates that the motivation or utility behind charitable donations is two-fold: pure altruism and warm-glow. So far, most of the analytical and empirical studies have been focusing on examining this model through the government “crowding-out” effect. Inspired by the implications of the financial planning time horizon study of this paper, future research may benefit from further identifying the difference between warm-glow and pure altruism effects through a time-discounting point of view. Donors’ time-discounting preference might be another crucial element with which the effects of warm-glow and pure-altruism can be disentangled.

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Bekkers, R. (2006). Traditional and health-related philanthropy: The role of resources and personality. Social psychology quarterly, 69(4), 349-366.

Bekkers, R., & Wiepking, P. (2011). A literature review of empirical studies of philanthropy: Eight mechanisms that drive charitable giving. Nonprofit and Voluntary Sector quarterly, 40(5), 924-973.

Bielefeld, W., Rooney, P., & Steinberg, K. (2005). How do need, capacity, geography, and politics influence giving. Gifts of Money in Americas Communities, 127-158.

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Andreoni, J., Brown, E., & Rischall, I. (2003). Charitable giving by married couples: who decides and why does it matter? Journal of Human Resources, 38(1), 111-133.

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Alpizar, F., Carlsson, F., & Johansson-Stenman, O. (2008). Anonymity, reciprocity, and conformity: Evidence from voluntary contributions to a national park in Costa Rica. Journal of Public Economics, 92(5-6), 1047-1060.

Carman, K. G. (2003). Social influences and the private provision of public goods: Evidence from charitable contributions in the workplace. Manuscript, Stanford University, 1-48.

Banks, J., & Tanner, S. (1999). Patterns in household giving: Evidence from UK data. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 10(2), 167-178.

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Bekkers, R. (2003). Trust, accreditation, and philanthropy in the Netherlands. Nonprofit and Voluntary Sector Quarterly, 32(4), 596-615.

Bekkers, R. H. F. P. (2004). Giving and volunteering in the Netherlands: Sociological and psychological perspectives. Utrecht University.

Bekkers, R., & Schuyt, T. (2008). And who is your neighbor?

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Eschholz, S. L., & Van Slyke, D. M. (2002). New evidence about women and philanthropy: Findings from Metro Atlanta. Mimeographed, Georgia State University.

Farmer, S. M., & Fedor, D. B. (2001). Changing the focus on volunteering: An investigation of volunteers’ multiple contributions to a charitable organization. Journal of Management, 27(2), 191-211.

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©2020, IARFC. All rights of reproduction in any form reserved.

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norms and contributions to public broadcasting. Political Research Quarterly, 56(2), 187-197.

Marcuello, C., & Salas, V. (2000). Money and time donations to Spanish Non Governmental Organizations for development aid.

Investigaciones Econรณmicas, 24(1), 51-73.

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67

Determining A Portfolio’s Range of Probable Wealth - Without Monte Carlo Simulations John M Hogan PhD1

Abstract Saving for retirement, saving for a child’s education, or outliving your nest egg during retirement all depend on the wonders of compounding. For typical investment applications the annual compounding rate is not fixed and not certain. For the past several decades Monte Carlo simulations have been used to address this uncertainty. A Monte Carlo simulation combines multiple trials of a portfolio’s potential performance. For a given time period and time horizon a Monte Carlo trial will calculate the final worth of a portfolio based on compounding random returns consistent with each time period’s probability distribution. Upwards of 1,000 trails are conducted forming the probability distribution of the portfolio’s worth at the end of the time horizon. These simulations typically assume a normal distribution for the portfolio’s annual probability of return and can account for annual contributions or withdrawals. In this paper a closed form solution is derived for a portfolio’s probability of future wealth in the presence of annual contributions or withdrawals and normally distributed annual returns. While closed form solutions are mathematically preferable to approximations, in this case other practical considerations arise. Few practitioners (or their clients) have easy access to Monte Carlo simulations. Those available on the internet lack the input flexibility, ease of use, or speed of the closed form solution developed in this paper. This closed form solution has been implemented in excel making use of excel’s probability functionality. This implementation accepts user inputs for the mean and standard deviation of equity and fixed income returns and their correlation, the time horizon over which the probability of wealth will evolve, annual contributions or withdrawals, and the funding allocation between equities and fixed income. The output of the model is the cumulative distribution function of net wealth along with values highlighted at the 25%, 50%, and 75% confidence levels. Results are compared to the author’s Monte Carlo simulations, the Monte Carlo simulations reported by Pfau (2016), the Monte Carlo simulations executed in Portfolio Visualizer (2019), and historical S&P 500 returns. Excel programing for a particular scenario is displayed in the appendix.

Keywords portfolio, closed form, Monte Carlo

1.

Instructor Learning Tree International; 105 Arrowhead Ct Winter Springs FL 32708; jhogan1278@gmail.com

68

Journal of Personal Finance

CLOSED FORM SOLUTION-THE GROWTH OF A NEST EGG 0.045

0.04

0.035

0.03

Frequency Distribution

We first develop a closed form solution for the growth of a nest egg. The ratio of final value to initial value, S, after T years of compounding under uncertainty is assumed to be given by a lognormal distribution. Since the distribution of S is lognormal the log of S is normally distributed. Therefore, to express S we need the mean and standard deviation of the log of S. For continuous compounding the mean of S, denoted as Sm,, is given by

Fig 1 Comparison of Closed Form Solution to Monte Carlo Approach-Growth of a Nest Egg Âľ =.08 đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;=.18 T=20 yrs

0.01

VAR(logS)=Ď&#x192;2đ?&#x2018;&#x2021; (3)

The standard deviation of log S is then Ď&#x192;â&#x2C6;&#x161;đ?&#x2018;&#x2021;.

With the mean and standard deviation of log S defined, the probability density function, pdf, of S can be calculated. For this paper the Excel function for the lognormal distribution LOGNORM.DIST is used. The arguments for this function are S, the mean of log S, and the standard deviation of log S. Also included in the arguments is a binary switch, true/false, indicating if a probability density function (pdf ) or cumulative distribution function (CDF) is desired. The function returns the probability density at the value of the chosen S if the pdf switch is selected.

Figures 1 and 2 show the comparison of the closed form solution to the authorâ&#x20AC;&#x2122;s Monte Carlo simulations for two investment horizons in a stock portfolio. Note that the distribution widens with time along with its expected mean value. This is consistent with Malagoli and Young (2012).

0

0

5

10

15

20

25

Final Value/Starting Value

-0.005

closed form

monte carlo

Fig 2 Comparison of Closed Form Solution to Monte Carlo Approach-Growth of a Nest Egg Âľ =.08 đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;=.18 T=10 yrs

0.1

0.08

Frequency Distribution

r=Âľ â&#x2C6;&#x2019; Ď&#x192;2/2 (2)

0.02

0.015

0.005

Sđ?&#x2018;&#x161;=â&#x201E;ŻrĚ&#x201A; đ?&#x2018;&#x2021; (1)

where rĚ&#x201A; is the geometric rate of return (as opposed to the average rate of return) over the period đ?&#x2018;&#x2021;. The log of the mean of S, log (Sđ?&#x2018;&#x161;), is therefore rĚ&#x201A;đ?&#x2018;&#x2021;. The well- established analysis of Brownian Motion as applied to stock prices, see Brewer (2012), can be brought to bear to define both rĚ&#x201A; and the variance of log S as:

0.025

0.06

0.04

0.02

0

0

5

-0.02

10

15

20

25

Final Value/Starting Value closed form

monte carlo

We next compare the closed form results to the Monte Carlo simulations presented in Pfau (2019) in which a 50/50 allocation between equities and fixed income is analyzed. For this comparison we need the full CDF of the investment strategy.

In general terms, the CDF of a random variable X evaluated at x is the probability that X will take on a value less than or equal to x. For the growth of a nest egg we evaluate the CDF of S, the ratio of the final value to the initial value, at specific goals for the nest eggâ&#x20AC;&#x2122;s growth. Excel functionality allows calculating the CDF for any lognormal distribution given that the mean and standard deviation are specified. Our closed form solutions have derived these values. Therefore, determining the closed form CDF is easy work. Pfau presented growth rates at nine specific probability thresholds determined through 100,000 Monte Carlo trials. These nine values were plotted against the closed form CDF. Figure 3 shows the comparison.

Š2020, IARFC. All rights of reproduction in any form reserved.

69

Volume 19 â&#x20AC;˘ Issue 2

years) the math outlined in Haugen (1990) allows the calculation of the standard deviation of log SF for any value of đ?&#x2018;&#x2021;. The standard deviation of log(SF) ranges narrowly between .688 Ď&#x192;â&#x2C6;&#x161;đ?&#x2018;&#x2021; and .666 Ď&#x192;â&#x2C6;&#x161;đ?&#x2018;&#x2021; for time horizons between 5 and 30 years.

Fig 3 Growth of $1M in 50/50 Portfolio After 30 Years Arithmetic Mean/Std Dev of Equity Growth=9/20.5 Arithmetic Mean/Std Dev of Bond Growth=2.5/5

Probability That Value of Investment Strategy Will Be Achieved at the End of the Time Horizon

1.2

1

With the mean and standard deviation of log SF now defined, the pdf for SF can be constructed with the same Excel functionality as was used for the growth of a nest egg. Figure 4 shows the comparison of the closed form solution to the authorâ&#x20AC;&#x2122;s Monte Carlo simulation of yearly investments in equities over a 20-year period.

0.8

0.6

0.4

0.2

0

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

Fig 4 Comparison of Closed Form Solution to Monte Carlo Approach-Growth of a Yearly Contribution Âľ =.08 đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;=.18 T=20 (stocks)

18000000

Value of Investment Strategy at End of Tome Horizon

Monte Carlo

Closed Form Solution- the Growth of a Constant Yearly Contribution to or Withdrawals from a Savings Plan Over T years For this analysis we define SF as the ratio of the final value to the yearly investment after T years of compounding all yearly investments under uncertainty. With no uncertainty and no annual growth, SF would equal 10 after 10 years, 20 after 20 years, etc. Withdrawals are treated similarly and generate a negative quantity to be subtracted from any initial nest egg. We assume SF will be lognormally distributed and therefore the log of SF will be normally distributed. As before we need to determine the mean and standard deviation of log SF to build a closed form solution.

The mean of SF can be expressed by SF = ereT where re is the effective rate of return for this investment strategy. Since SF is composed of đ?&#x2018;&#x2021; separate investments, we have ereT = ÎŁ1đ?&#x2018;&#x2021;â&#x201E;ŻrĚ&#x201A; đ?&#x2018;&#x2021;

(4)

log SF = reT = log(ÎŁ1đ?&#x2018;&#x2021;â&#x201E;ŻrĚ&#x201A; đ?&#x2018;&#x2021;)

(5)

Therefore, the log of SF is given by, Equation 5 is easily calculated in Excel. Therefore, the mean of the log of SF is now determined for any Âľ or any Ď&#x192;,that is, for any rĚ&#x201A;.

The standard deviation of log SF, Ď&#x192;(logSF), is the standard deviation of the đ?&#x2018;&#x2021; individual investment streams comprising log (SF). The weighting parameters and covariance coefficients of each stream are unique. Although the calculation of the resulting standard deviation is complex (especially for a đ?&#x2018;&#x2021; of 30

0.018

Frequency Distribution

The excel programing used to generate 10 of the values shown on Figure 3 is displayed in the appendix. Readers can build other scenarios accordingly. Access to the calculator is available as detailed below.

0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 -0.002

0

20

40

60

80

100

120

140

160

Final Value/Yearly Contribution

closed

monte carlo

Pfau (2019) also included the effect of annual contributions on portfolio growth. The CDF comparison between his results and the closed form solution is shown in Figure 5.

Fig 5 Growth of $10k per Year After 30 Years Arithmetic Mean/Std Dev og Equity Growth=9/20.5 Arithmetic Mean Std Dev of Bond Growth=2.5/5 1.2

Probability That Value of Investment Strategy Will Be Achieved at the End of the Time Horizon

Closed Form

1

0.8

0.6

0.4

0.2

0

0

200000

400000

600000

800000

1000000

1200000

Value of Investment Strategy at End of Time Horizon Closed Form

Monte Carlo

1400000

1600000

1800000

2000000

70

Journal of Personal Finance

The evolution of a portfolio’s return over a given time horizon is particularly important when annual contributions or withdrawals are made against an initial nest egg. A powerful web application “Portfolio Visualizer” hosts Monte Carlo simulations which implement such scenarios (and many more). The closed form solution was compared to these simulations for two important applications - saving for a child’s education and withdrawing from a nest egg during retirement. These comparisons are shown in Figures 6 and 7.

Fig 6 College Saving Scenario After 10 Years Arithmetic Mean/Std Dev of Portfolio Growth=6.9/9.83 Initial nest egg=$100000 Annual savings =$7000

Probability That Value of Investment Strategy Will Be Achieved at the End of the Time Horizon

1.2

1

0.8

0.6

0.4

the S&P 500 that have occurred since 1920 (1920-1940, 19211941, …,1998-2018). Historical results for the S&P 500 are available on the web pages of Yale University economist Robert Shiller. The closed form solution was based on an r of .115 and a standard deviation of .197 per the analysis of Robert Schwartz and 1000 trails. This comparison is shown in Figure 8. The S&P 500 histogram data is based a bin size of 0.5. For example, Figure 8 shows that 13% of the 20-year holding periods resulted in the final value exceeding the starting value by a factor between 3.5 and 4. Four percent of the periods resulted in a growth factor between 7 and 7.5. Since we are only dealing with 79 periods vice the 1000 trails used in the Monte Carlo analysis, the data displays more randomness but still compares favorably to the trends of the closed form approach. The recent low in the S&P due to the pandemic (March 23, 2020) was only 1.5 times its value 20 years previous. Disappointing and less than the mean, but not an outlier to the closed form solution or historical data.

Fig 8 Comparison of Historical S&P 500 Twenty Year Holding Periods From 1920 to 2018 to Closed Form Solution

0.2

0.14

0

0

100000

200000

300000

400000

500000

600000

0.12

Value of Investment Strategy at End of Time Horizon 0.1

college scenario closed form

college scenario Monte Carlo Portfolio Visualizer

Probability Distribution

0.08

Fig 7 Change in $1M After 25 Years Arithmetic Mean/Std Dev of Portfolio Growth=6.9/9.83 $50000 withdrawn annually 1.2

0.06

Probability That Value of Investment Strategy Will Be Achieved at the End of the Time Horizon

0.04

1

0.02

0.8

0

0.6

-0.02

Final Value/Starting Value

0.4

0.2

-1000000

0

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

9000000

Value of Investment Strategy at End of Time Horizon

retirement scenario closed form

retirement scenario Monte Carlo Portfolio Visualizer

CLOSED FORM SOLUTION COMPARISON TO HISTORICAL STOCK MARKET PERFORMANCE In this section we compare the closed form solution for a 20year holding period to the 79 twenty-year holding periods of

APPLICATIONS The closed form analysis allows investors and financial advisors to quickly conduct sensitivity and “what if” analyses or calculate the future probability of wealth for specific scenarios. For example, the effect of the allocation between equities and fixed income on the probability of wealth after 20 years is shown in Figure 9. The effect is dramatic and supports the mantra of “stocks for the long run”. Figure 10 shows the shrinkage of a nest egg after 20 years for various withdrawal rates. For a 7.5% withdrawal rate there is a 30% chance of insolvency. At a 10% withdrawal rate there is a 70% chance of insolvency. These sensitivity analyses are easily conducted with the excel calculator

©2020, IARFC. All rights of reproduction in any form reserved.

71

Volume 19 â&#x20AC;˘ Issue 2

which implements the closed form solution. Rather than report on a selected subset of such scenarios the author has made the calculator and instructions on its use available as described below.

Fig 9 Probability of Success of Investment Strategy Driven by Equity/Fixed Allocation 20 yr Time Horizon, $100k nest egg, $10k annual contribution

Probability That Value of Investment Strategy Will Be Achieved at End of Time Horizon

1.2

compounding by a fixed rate of return. The closed form results were compared to Monte Carlo results verifying the accuracy of the closed form approach. With these closed form solutions in hand, financial advisors and investors can assess the numerical likelihood of meeting specific financial goals in the presence of uncertainty. The following website contains this link to the calculator which embodies the closed form solution- https:// spreadsheetnut.com/spreadsheets/probability-achieving-financial-goal/

1 100%equities 0.8

80%equities/20%fixed

60%equitire/40%fixed

0.6

40&equities/60%fixed

REFERENCES Haugen, R.A. (1990) Modern Investment Theory (2nd ed., pp.

74-75). Englewood Cliffs, NJ: Prentice Hall

0.4 20%equities/80%fixed 0.2

0

savings goal

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

Value of Investment Strategy at End of Time Horizon (dollars)

Malagoli, Andrea and Young, Chris, Stocks for the Long Run: Historical Facts and Statistical Fallacies (December 31, 2010).

Available at SSRN: https://ssrn.com/abstract=1904992 or http:// dx.doi.org/10.2139/ssrn.1904992

Fig 10: Shrinkage of a Nest Egg, Initially at Si, with an Annual Withdrawal of Sy Âľ =.08 đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;đ?&#x153;&#x17D;=.18 T=20 yrs 1.2

Probability that Savings Goal Will Be Met

1

0.8

Sy = 0 Sy=.025 Si

0.6

Sy=.05 Si Sy=.075 Si Sy=.1 Si 0.4

0

Pfau, Wade D (2016). Capital Market Expectations and Monte Carlo Simulations, Journal of Financial Planning. July, 2016 Shiller, Robert: http://www.buyupside.com/shillerdatainfo/ stockreturncalcinputsp.php Schwartz, Robert: https://seekingalpha.com/instablog/605212-robert-allan-schwartz/4831186-annual-returns-s-and-p-500-1928-2015

0.2

-5

Brewer, Kevin D.; Feng, Yi; and Kwan, Clarence C. Y. (2012) Geometric Brownian Motion, Option Pricing, and Simulation: Some Spreadsheet-Based Exercises in Financial Modeling, Spreadsheets in Education (eJSiE): Vol. 5: Iss. 3, Article 4. Available at: http://epublications.bond.edu.au/ejsie/vol5/iss3/4

0

5

10

15

20

Savings Goal (Final Value over Initial Value)

SUMMARY It this paper we have provided a closed form approach, easily implemented in Excel, for calculating the probability distribution for the growth of a nest egg when compounded under uncertainty, the growth of an annual contribution to an investment vehicle when compounded under uncertainty, and the effect of annual contributions to, or withdrawals from, a nest egg when compounded under uncertainty. Compounding under uncertainty was defined as compounding by an annual rate of return that is normally distributed as opposed to

Portfolio Visualizer https://www.portfoliovisualizer.com/monte-carlo-simulation#analysisResults

72

Journal of Personal Finance

APPENDIX ROW COLUMN 2

B

3

C

D

equities

fixed income

E

F

G

H

nest egg combined

4

Mu

0.09

0.023

fraction in 0.5 equities

Mu

=(C4*F4)+(D4*F5)

5

sigma

0.205

0.05

fraction in 0.5 fixed

sigma

=SQRT((F4^2)*C5^2+(F5^2)* D5^2)

6

time

30

30

time

30

7

mul

=(C4(C5^2)/2)*C6

=(D4(D5^2)/2)*D6

mul

=(H4-(H5^2)/2)*H6

8

sigmal

=C5*SQRT(C6) =D5*SQRT(D6)

sigmal

=H5*SQRT(H6)

9

Initial 1000000 Nest Egg

Initial Nest Egg

1000000

1000000

10 11

Prob

Value at end of 30 years

1.0-Prob

12

0.1

=(LOGNORM.INV(B12,$H$7,$H$8))*$H$9

0.9

13

0.2

=(LOGNORM.INV(B13,$H$7,$H$8))*$H$9

0.8

14

0.3

=(LOGNORM.INV(B14,$H$7,$H$8))*$H$9

0.7

15

0.4

=(LOGNORM.INV(B15,$H$7,$H$8))*$H$9

0.6

16

0.5

=(LOGNORM.INV(B16,$H$7,$H$8))*$H$9

0.5

17

0.6

=(LOGNORM.INV(B17,$H$7,$H$8))*$H$9

0.4

18

0.7

=(LOGNORM.INV(B18,$H$7,$H$8))*$H$9

0.3

19

0.8

=(LOGNORM.INV(B19,$H$7,$H$8))*$H$9

0.2

20

0.9

=(LOGNORM.INV(B20,$H$7,$H$8))*$H$9

0.1

21

0.99

=(LOGNORM.INV(B21,$H$7,$H$8))*$H$9

0.01

©2020, IARFC. All rights of reproduction in any form reserved.

for plotting

Volume 19 • Issue 2

73

2020 IARFC National Financial Plan Competition

2020 IARFC National Financial Plan Competition Winning Financial Plan 1 by Zachary J. Wakamatsu

Abigail M. Adams, Junior in Personal Financial Planning, Utah Valley University

2 Abigail M. Adams Zachary J. Wakamatsu, Senior in Personal Financial Planning, Utah Valley University

Faculty Advisor: Lukas Dean,Associate Associate Professor, Valley University Faculty Advisor: Lukas R.R.Dean, Professor,Utah Utah Valley University Abstract Abstract theIARFC 2020National IARFC National Plan Competition, students givencase a fictional study a familyan For theFor 2020 FinancialFinancial Plan Competition, students were given were a fictional study ofcase a family thatofincluded thatofincluded an overview of their financial picture.

Students were to analyze theplan case a overview their financial picture. Students were asked to analyze the case andasked prepare a financial forand the prepare family, including financial plan for the family, including recommendations to help the family reach their goals.

Written case recommendations to help the family reach their goals. Written case submissions were judged, and winners advanced to the submissions were judged, and winners advanced semi-finals they gave virtual presentation semi-finals where they gave a virtual presentation to a paneltoofthe judges.

The topwhere three teams were ascheduled to present into person a panel of judges.

The top three teams were scheduled to present in person to a panel of judges in Cincinnati, to a panel of judges in Cincinnati, OH; however, they ultimately presented virtually this year due to the coronavirus pandemic.

OH; however, they ultimately presented virtually this year due to the coronavirus pandemic.

In addition to the winning team, the other finalists were Jackie Battles from the University of North Texas, with Dave Ragan, RFC®, as theIn faculty advisor, andwinning Allison Biddix Austin, from Carolinafrom University, with Patrick Payne as the faculty addition to the team,and theMichael other finalists wereWestern Jackie Battles the University of North Texas, with Dave Ragan, RFC®, as the faculty advisor, and Allison Biddix and Michael Austin, from Western Carolina advisor.

University, with Patrick Payne as the faculty advisor.

The case that was provided to students is summarized below, followed by a near-complete summary of the winning financial plan.

The case that was provided to students is summarized below, followed by a near-complete summary of the

winning financial plan.

Keywords

case study; financial plan; competition

Key Words case study; financial plan; competition

1.

2.

Senior, Utah Valley University; 800 W University Pkwy, Orem, UT 84058; Zac.wakamatsu@gmail.com Junior in Personal Financial Planning, Utah Valley University; 800 W University Pkwy, Orem, UT 84058; 10784514@my.uvu.edu

74

Journal of Personal Finance

The Casey Family Relationship

First Name

Age

Husband

Robert

58

Wife

Jane

45

Son

Michael

16

Daughter

Brittney

14

Son

John

10

Plan Preparation Date: Robert owns an architecture firm. He started the firm 25 years ago. He pays himself $150,000 per year, this is expected to raise at 4% per year.

Jane works as an RN at a local hospital. Her salary is $75,000 per year and expects pay increases of 3% annually.

They live in New York and would like to relocate to Delaware in retirement – more specifically Sussex County where there tax rates are lower. Their New York home cost $650,000 which they purchased 13 years ago on a 30 year mortgage at 5%. It’s now worth $800,000. Current balance left is $400,000. They pay $3000/month in P & I. They would love a home where they could walk to the beach from in retirement and expect this will cost over $1.1m. However, they expect property taxes to drop considerably. Currently, they pay $20,000/yr.

The Caseys also own a rental property condo. They’ve already paid this off. It’s worth $200,000 and costs about $600/month in HOA, taxes and maintenance fees. They rent the property for $1200/mo. They’re not sure if this is a good investment or not.

©2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 • Issue 2

Robert would like to create a succession plan for his business, but he does not know where to start. He has two employees that have been around over 15 years that would be dedicated to the future of the firm and willing to take over if needed.

Robert and Jane would like to pay about half of the college education for Michael and Brittney.

They do not anticipate John going to college. They don’t currently have any dedicated vehicles to help plan this. They estimate this will currently cost $12,500 per year for each child – based on $25,000 current tuition. Tuition inflation is expected at 6%.

The Casey’s son John has autism, they would like to set money aside for him for the future in case something happens to both of them. They do not want this money to affect government benefits.

Robert has long-term disability insurance. It replaces 60% of his income and he pays $225/mo.

Jane has group short term disability insurance provided through work.

Robert has a $500,000 term insurance policy which expires next year. Jane has $250,000 group term insurance through work. Robert owns a whole life insurance policy with a death benefit of $50,000. He bought this 25 years ago. He pays $1500 per year and it has a Cash Value of $27,000. The expected real rate of return of premiums is 4%.

Robert has a will but no other estate planning documents. He last updated the will 8 years ago.

Jane has no estate planning documents.

They currently have a home equity line of credit they used to finish their basement. This costs $50,000 and currently have a balance of $35,000 at a fixed 6.25%. The duration is 10 years, this started 6 years ago. They currently pay $600 per month.

The Caseys have an HO2 Homeowners Policy – Coverage A $1,000 ($400k limit), Coverage B $1,000 ($30k limit), Coverage C $500, replacement value ($100k limit), Coverage E $400k limit), Coverage F ($5k/person) Insurance Policy and a 25/50/25 Auto insurance policy. Their auto deductibles – Collision $750, Comprehensive $100.

Robert maxes out his SIMPLE IRA Contribution. Jane contributions up to her hospitals $1 per $1 match up to 6% of her income.

They estimate general inflation will be 3.5%.

75

76

Journal of Personal Finance

Other Family Living Expenses Food: $800/mo Vacation: $6000/yr Charitable: $100/mo Clothing: $300/mo Home Maintenance: $250/mo Utilities/Cable/Internet: $600/mo Auto Maintenance/Fuel: $300/mo HO & Car Insurance: $500/mo

Other Liabilities Automobile 1: 3.5% interest, $400/mo Automobile 2: 4% interest, $425/mo

Š2020, IARFC. All rights of reproduction in any form reserved.

77

Volume 19 â&#x20AC;˘ Issue 2

Jane and Robert Casey Statement of Financial Position January 1, Current Year Assets

Labilities

Financial Assets Cash Equivalents (J) Intermediate-Term Bond Fund (W) Technology Fund (H) Cash Value Life Insurance (H) Total

$21,000 $36,000 $108,000 $27,000 $192,000

Retirement Assets2 IRA Rollover (W) 401(k) Plan (W) SIMPLE IRA Plan (H) Total

$45,000 $153,000 $420,000 $618,000

Use Assets Primary Residence (J) Rental Property (J) Automobiles (J) Total

$800,000 $200,000 $55,000 $1,055,000

Business Assets Business Interest (H)1 Total

$650,000 $650,000

Total Assets

$2,515,000

Net Worth

$2,049,700

Credit Cards (J) Car Loans (J) Primary Mortgage HELOC Total

$6,300 $24,000 $400,000 $35,000 $465,300

Total Liabilities

$465,300

Key: H=Husband W=Wife J=Joint Tenancy with Right of Survivorship 1: Robert estimated the value of his portion of the business 2: Retirement Assets are invested in Growth Allocations (80% stock, 20% bonds)

78

Journal of Personal Finance

General Stated Goals       

Generate 80% of combinate gross preretirement income, beginning at retirement Have John transition out of the business by age 68 and both retire at that point Pay for half of their children’s education expenses Set up a fund for John that won’t affect government benefits in the future Relocate to Delaware in retirement See how much life insurance they need See what estate planning step are needed

 

Pre-Retirement – Growth Retirement – Moderate

Risk Tolerance:

©2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 â&#x20AC;¢ Issue 2

79

80

Journal of Personal Finance

FINANCIAL ANALYSIS Prepared for

The Casey Family Nov 13, 2019 - April 2, 2020

Prepared by:

Abigail Adams Zac Wakamatsu Undergrad Students @ Utah Valley University 800 W University Pkwy Orem, UT 84058

©2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 • Issue 2

1. INTRODUCTION

1. INTRODUCTION a) LETTER TO CLIENT Dear Robert and Jane Casey, Thank you for entrusting us to help you achieve your stated goals. Your trust and cooperation have been essential as we’ve worked hard to create a plan that works best for you.

We want to begin by congratulating you on the many smart decisions you’ve taken thus far in preparing for both your future and the future of each of your children. You have provided a great foundation whereupon we can continue to build your plan. Through analyzing the data you’ve provided us, we’ve been able to model it and make financial projections that show future and potential results. From these projections, we’ve conceived a financial plan specific to you and your circumstances and have provided our professional recommendations in all relevant areas that, when implemented, will help achieve your desired goals and bring you financial peace of mind.

We have organized these recommendations into separate sections corresponding to the goals that they affect. Each section determines the best path forward based on your goals, current financial circumstances, and projected potential. We believe that as we work together to implement this plan by adjusting your savings, investments, documentation, and spending habits as we advise, you will be better prepared for retirement, emergencies, and future education and care as you’ve laid out in your goals.

We would love to discuss any questions or concerns that may arise as you review this plan so that we may best serve your needs and help you feel confident in your financial future. We would also like to continue to monitor your activities and progress. To achieve this, we recommend reevaluating your finances and making the necessary adjustments on an annual basis to keep your plan as accurate and efficient as possible.

We thank you for the opportunity to consult with you and be of service.

We always welcome you as a valued client and hope to hear from you again soon.

Sincerely, Abby Adams Zac Wakamatsu

81

82

Journal of Personal Finance

b) CLIENT INFORMATION Relationship

First Name

Age

Husband

Robert

58

Wife

Jane

45

Son

Michael

16

Daughter

Brittney

14

Son

John

10

Robert: Age: 58 Employer: Self-Employed Position: Owner of Architecture Firm Income: $150,000 annual (expected to raise 4% per year) Jane: Age: 45 Employer: Local Hospital Position: Registered Nurse Income: $75,000 annual (expected to raise 3% per year) Risk Tolerance: Pre-Retirement – Growth Retirement – Moderate

©2020, IARFC. All rights of reproduction in any form reserved.

83

Volume 19 • Issue 2

c) CLIENT GOALS STATED CLIENT GOALS

Education Retirement

Pay for half of your children’s education expenses Have Robert transition out of the business by age 68 and both (Robert and Jane) retire at that point Generate 80% of your combined gross preretirement income, beginning at retirement

Lifestyle

Relocate to Sussex County, Delaware in retirement. You stated you would love a home where you could walk to the beach.

Risk Management Special Needs

Evaluate how much life insurance you both need

Estate Planning

Set up a fund for John that won’t affect his government benefits in the future Determine what estate planning steps are needed

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Journal of Personal Finance

2. ASSUMPTIONS For Income Projection  Robert’s pay increases 4% annually for inflation  Jane’s pay increases 3% annually for inflation  General inflation 3.5% annually For Education Planning:  John doesn’t attend college.  Starting 2019, college tuition is 25,000$ annually per child  College tuition inflation 6% annually  Children start college at 18 and attend college for 4 years  We're assuming 6% returns on conservative-moderate age-based investing For Retirement Accounts before retirement  We assume that Jane contributes the maximum amount annually into her 401(k) (6% of income + 6% employee match = 12% total of Janes’ income)  Retirement Assets are invested in Growth Allocations (80% stocks, 20% bonds)  Retirement account market returns 7%  Assume total allowable SIMPLE contributions are $16,000 annually For Income replacement after Retirement  Retirement Assets are invested in Moderate Allocations (50% stocks, 50% bonds)  Retirement account market returns 6%  Assume you withdraw 4% from your retirement accounts annually  Income tax was approx. 24% of gross total income  Robert and Jane contributed $15,000 total annually on social security  Jane withdraws from her retirement accounts starting at age 60 to avoid penalties (which would be 10% of retirement accounts)  House, Business, and Condo will inflate using the general inflation rate: 3.5%  House, Business, and Condo will achieve some form of growth from now until retirement (10 years)  Assume that Jane and Robert would want to pay off all debts by retirement  Robert’s social security payment starting at age 69: $54,360 (based off ssa.gov)  Janes’ social security payment starting at age 63: $65,544 (based off ssa.gov)  SS inflation rate: 1.52% based off the average COLA from the last 10 years  You will not have moved prior to retirement For Insurance  Assume that funeral costs are $12,000 (funeral expenses, gravestone, grave plot)  Life Insurance Ballpark payments based off https://quote.lifeinsure.com/ For Cash Flows  Minimum monthly payments are being made on all liabilities listed on Statement of Financial Position, such as credit cards.

For Asset Allocation  Current portfolio is organized primarily in high cap stocks with mild diversity in other securities  Risk Tolerance Preretirement: Growth  Risk Tolerance Postretirement: Moderate

1. INTRODUCTION

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Volume 19 • Issue 2

3. INCOME TAX ANALYSIS Casey Family Tax Payment

1. INTRODUCTION

Income $225,000

Federal Income Tax State Income Tax Social Security Tax Medicare Tax Total Tax:

$27,853 $11,025 $12,611 $3,263 $54,752

Tax Percentage 24.3% Current Status: Currently, your tax percentage withheld is 24.3%. Your current federal marginal tax rate is 24% and your current marginal state tax rate is 6.85%.

Recommendations: As you apply the suggestions we recommend throughout this plan, many will come with a tax benefit. Each benefit will decrease the amount you owe on your state and federal income tax and ensure you a greater refund when you file your taxes.

This will allow you to more efficiently manage your income to help achieve your financial goals.

Some benefits will apply now, such as a tax deduction for contributing to your children’s education savings account, while others would apply at a future time, such as payment of a disability insurance policy.

A few Examples of these benefits found in your plan below include:  Up to $10,000 is deductible annually from New York State taxable income for a married couple filing jointly. Which we estimate will save you $2,600 in income tax dollars upfront.  And growth of 529 plan is tax free when used for qualified expenses.  As Robert’s disability insurance is being paid with post tax dollars, should payout ever be necessary, it will be tax free.  Upon retirement, you plan to sell your home. There is a tax exception on the gain on the sale of a primary residence of up to $500,000 when filing jointly, if you have lived in that residence for at least the last five years. Taking advantage of this would save you from excess tax burdens at the start of retirement.

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4. CASH FLOW ANALYSIS

Current: You currently have a surplus at the end of the month. That is a great situation to be in!

1. INTRODUCTION But that also means that there is more potential to achieve your financial goals, pay off liabilities, and get greater returns with investing by allocating those extra funds. So, here’s our analysis on what you can do each month to progress your goals.

Goals:      

Generate 80% combined preretirement income upon retirement Pay off debts, then contribute to savings/investments/funds Minimize expenses that could carry over to retirement (cars, HELOC, etc.)

Pay off most of current expenses and liabilities before retirement Begin saving to buy retirement home and in case of emergency Save 50% tuition cost for Michael and Brittney when they attend college

Recommendations: 







Begin a $500/month contribution to emergency savings fund to cover any vesting period in case of emergency where Robert/Jane are unable to work (such as the elimination period for disability).

Dedicate $1,000/month to pay more towards your liabilities, namely your credit cards, Home Equity Line of Credit (HELOC) and car payments. Once these are all paid off, you would have a surplus of $2,429 per month that would be available for use as you see fit (such as adjusting your insurance premiums, increasing savings/ 529 fund contributions, etc.) We would suggest you start paying off your credit cards first since they usually have the highest interest rate.

Begin a savings fund, starting at $400/month to help pay for retirement home (you can increase this amount as you have extra funds). We would call this the “Delaware Growth Fund”.

Start contributing to Michael and Brittney’s 529 funds as much as possible and increase savings as you get the extra funds.

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Volume 19 â&#x20AC;˘ Issue 2

a) CASH FLOW STATEMENT Statement of Cash Flows for 2019 Inflows 1. INTRODUCTION Active Income

Robert's Salary Jane's Salary Passive Income Rental Income Total Inflows Outflows Family and Living Expenses Groceries Vacation Charitable Donations Clothing-family Auto Maintenance/Fuel Insurance HO & Car Insurance Long-term Disability Insurance (Robert) Term Life Insurance (Robert) Home Utilities/Cable/Internet Home Maintenance Mortgage Equity Line of Credit Rental Property Expenses (HOA, taxes, and fees) Vehicles Automobile 1 Automobile 2 Taxes Property Tax Federal Income Tax (Robert) Federal Income Tax (Jane) New York State Income Tax (Robert) New York State Income Tax (Jane) Social Security (Robert) Social Security (Jane) Medicare (Robert) Medicare (Jane) Total Outflows Net Surplus:

$150,000.00 $ 75,000.00 $ 14,400.00 $239,400.00

$ $ $ $ $

9,600.00 6,000.00 1,200.00 3,600.00 3,600.00

$ $ $

6,000.00 2,700.00 1,500.00

$ 8,400.00 $ 3,000.00 $ 36,000.00 $ 7,200.00 $ 7,200.00

$ $

4,800.00 5,100.00

$ 20,000.00 $ 21,197.00 $ 6,656.00 $ 7,500.00 $ 3,525.00 $ 7,961.00 $ 4,650.00 $ 2,175.00 $ 1,088.00 $180,652.00 $ 58,748.00

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b) MONTHLY BUDGETS Monthly Budget Inflows 1. INTRODUCTION Robert's Salary (Net)

Current $ 9,264.00 $ 4,923.00 $ 1,200.00 $ 15,387.00

Recommended $ 9,264.00 $ 4,923.00 $ 1,200.00 $ 15,387.00

Groceries Vacation Charitable Donations Clothing-family Auto Maintenance/Fuel

$ $ $ $ $

800.00 500.00 100.00 300.00 300.00

$ $ $ $ $

800.00 500.00 100.00 300.00 300.00

HO & Car Insurance Long-term Disability Insurance (Robert) Term Life Insurance (Robert)

$ $ $

500.00 225.00 125.00

$ $ $

500.00 225.00 125.00

Utilities/Cable/Internet Home Maintenance Mortgage Equity Line of Credit Rental Property Expenses (HOA, taxes, and fees) Property Tax

$ $ $ $ $

700.00 250.00 3,000.00 600.00 600.00

$ $ $ $ $

700.00 250.00 3,000.00 600.00 600.00

$

1,666.67

$

1,666.67

Automobile 1 Automobile 2

$ $

400.00 425.00

$ $

400.00 425.00

Simple IRA (Robert) 401(k) (Jane) Delaware Growth Fund Emergency Fund 529 Plan Michael 529 Plan Brittney

$ $

1,083.33 375.00

$ $ $ $ $ $

1,083.33 375.00 600.00 500.00 750.00 587.00

Jane's Salary (Net) Rental Income Total Income Outflows Family/Living Expenses

Insurance

Home

Vehicles

Retirement/ Savings

Other Liabilities Credit Cards Total Outflows Net Surplus:

$ 11,950.00 $ 3,437.00

Š2020, IARFC. All rights of reproduction in any form reserved.

$ 1,000.00 $ 15,487.00

Volume 19 â&#x20AC;˘ Issue 2

5. NET WORTH STATEMENT

Current Status: Congratulations! Your current Net Worth is over $2 million! Your 1. INTRODUCTION

net worth is calculated by subtracting your liabilities (debt) from your total assets and serves as a good indicator of your financial health. You have done a great job of acquiring assets while minimizing liabilities.

Recommendations: As we move forward, your net worth will increase the fastest as you focus on acquiring more in assets, such as saving for retirement as well as paying off your liabilities such as credit cards, HELOC, and car payments. As you apply our recommendations, your net is likely to increase, and the likelihood of you reaching your future goals will also increase.

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a) BALANCE SHEET Statement of Financial Position January 1, 2019 Financial Assets Cash Equivalents (J) Intermediate-Term Bond Fund (W) Technology Fund (H) Cash Value Life Insurance (H) Total

$ 21,000.00 $ 36,000.00 $ 108,000.00 $ 27,000.00 $ 192,000.00

Retirement Assets (2) IRA Rollover (W) 401(k) Plan (W SIMPLE IRA Plan (H) Total

$ 45,000.00 $ 153,000.00 $ 420,000.00 $ 618,000.00

Use Assets Primary Residence (J) Rental Property (J) Automobiles (J) Total

$ 800,000.00 $ 200,000.00 $ 55,000.00 $ 1,055,000.00

Business Assets Business Interest (H)1 Total

$ 650,000.00 $ 650,000.00

Total Assets Net Worth

$ 2,515,000.00 $ 2,049,700.00

Credit Cards (J) Car Loans (J) Primary Mortgage HELOC Total Liabilities

$ 6,300.00 $ 24,000.00 $400,000.00 $ 35,000.00 $465,300.00

Key: H=Husband W=Wife J=Joint Tenancy with Right of Survivorship 1: Robert estimated the value of his portion of the business 2: Retirement Assets are invested in Growth Allocations (80% stock, 20% bonds)

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Volume 19 • Issue 2

6. EDUCATION FUNDING

1. INTRODUCTION Current Goal/Status: We understand that you would like to pay about half of the college education for Michael and Brittney, but don’t know where to start. We’d love to help get you started!

Recommendations: To accomplish this, we propose you invest in a 529 plan for each child. A 529 plan is a college savings plan that offers tax and other benefits. After reviewing multiple 529 plans from states such as Utah, Tennessee, and New York. We’ve decided that the New York 529 Direct plan would be the best because they provide the most benefits to you as residents of the state.

These Benefits include: 

 

You can deduct up $10,000 of your Direct Plan contributions when you file your state income taxes if you're married filing jointly. Therefore, we calculate that if you save for your children’s education through the NY529, you can save $680 in taxes per year (assuming State marginal tax bracket 6.85%) The money in your Direct Plan account grows deferred from federal and state income taxes.

You won't have to pay federal or state income taxes on the money you withdraw to pay for qualified higher education expenses. o Note: a 529 plan cannot be used to purchase a car or transportation. This considered a non-qualified expense.

Given the data you’ve provided, we’ve modeled the approximate costs of what it would take to fulfill your education goals through the 529 plan: NY Direct 529 Plan

Total parent contribution (50%) for 4 yrs. college tuition after inflation

Payment Yearly

Payment Monthly

Observations

Michael

$61,441.48

$18,069.18

$1,505.77

Michael’s payments are larger because his investments have less time to compound before he starts withdrawing

Brittney

$69,035.65

$11,356.07

$946.34

Brittany’s total tuition is higher because tuition inflation went up more before she started attending college

Both Kids TOTAL:

$130,477.13

$29,425.25

$2,452.10

Overall payments are large because there wasn’t as much time to save/invest.

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7. RETIREMENT ANALYSIS OVERVIEW Current Retirement Accounts:

1. INTRODUCTION Retirement Owner Investments Accts

Balance

Growth Rate

Total Annual Contribution

Total at retirement (Year 2029)

7%

0

$87,697.98

(6% Jane Income) + (6% Employer match) 13000

$451,881.01

IRA Rollover (W)

Jane

Growth Portfolio

$45,000

401(k) Plan (W)

Jane

Growth Portfolio

$153,000 7%

Simple IRA Plan (H)

Robert

Growth Portfolio

$420,000 7%

$1,053,717.71

Current Status: You guys have done a great job in getting your retirement funding started, however we are still a way’s off from funding all your retirement goals. We’ve broken down your retirement goals and addressed each one with our analysis and plans.

Retirement Goals: 1. Generate 80% of combined gross preretirement income, beginning at retirement Solution: Replacement Income Retirement Plan 2. Have Robert transition out of the business by age 68 and both retire at that point Solution: Business Succession Plan 3. Relocate to Sussex County, Delaware in retirement where the tax rates are lower. They would love a home where they could walk to the beach.

Solution: House Plan

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Volume 19 • Issue 2

a) INCOME REPLACEMENT PLAN Goal #1: Generate 80% of combined gross preretirement income, beginning at retirement Current Status: n 2029, the last year of work before re rement, we projected that your total gross income would be $322, 30.3 after inflation. We have a few tactics you can use to maximize your retirement income. But first, you should know that after retirement, there are several things that you no longer have to account for. We estimated some of these expenses and subtracted them from your income to get the real income replacement amount you need to live the lifestyle equivalent to 80% replacement income during retirement: $322,830.37 77,479.29 15,000.00 16,000.00 36,000.00 7,200 6,044.38 $165,106.70 * 0.8 =

Income tax (approx 24% of gross total income) Social security contributions SIMPLE IRA contributions Mortgage Payments (P & I) Home Equity Line of Credit (HELOC) Jane’s 401(k) contributions (6% of her 2029 income) $132,085.36 Actual Income Replacement Goal

Recommendations: How do you replace $132,085.36? We recommend that you continue contributing the way you are now! We project that you’re on track to meet that replacement income assuming you can sell your main house, condo, and business at retirement for current or higher value. We suspect that these assets will grow to a higher worth by retirement in 2029, but to give you a basic idea of what you can expect if no growth was made, we’ve calculated the Future value of these combined assets with inflation (3.5%) alone: Current Value (2019) $800,000 House 200,000 Condo Business + 650,000 $1,650,000 Gross

Future Value (2029) $1,128,479.01 282,119.75 + 916,889.19 $2,327,487.96

House Condo Business Gross

Here’s just one way you can use this money: $2,327,487.96 - 1,100,000.00 Completely buy/own Retirement Home $1,227,487.97 Leftover gross amount for retirement, paying off any current liabilities/debt, future goals, funds for John etc.

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Projected Income Post Retirement Snapshots (*not including house, condo or business): Gross Income in 2030 (First Year Robert collects SS and withdraws from retirement ACCTS)

Gross Income in Gross Income in 2034 (First Year Jane 2037 (First Year withdraw from Jane collects SS) retirement ACCTS)

Withdrawal %

IRA Rollover (W)

$0

$4470.27

$5,324.16

4%

401(k) Plan (W)

$0

$22,775.99

$27,126.57

4%

Simple IRA Plan (H)

$42,509.64

$53,667.45

$63,918.79

4%

Robert SS $52,044

$55,281.15

$57,840.49

Based off ssa.gov + ave.

COLA SS inflation rate

Jane SS

0

0

$33,624

Based off ssa.gov + ave.

COLA SS inflation rate

Total Income:

$94,553.64

$136,194.86

$187,834.01

Your decreased spending after retirement plus the combined money from selling your house, condo, and business with your retirement accounts and social security will secure a healthy amount of income every year that should meet your equivalent spending lifestyle at 80% income replacement.

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Volume 19 • Issue 2

b) BUSINESS SUCCESSION PLAN Goal #2: Have Robert transition out of the business by age 68 and both retire at that point Current Status: We understand that you currently have no formally established business succession plans. So, we’ve taken the liberty of providing you with some options to consider: Recommendations: n the case of Robert’s death, we suggest setting up an insured buy–sell agreement (or triggered buyout) on his business. According to Section 7302 of the New York State Education Law, “Architectural services may be provided by: A sole proprietor licensed in New York State. [or] A partnership in which all partners are licensed, at least one [is] an architect.”

Because of this occupation restriction, it’s likely that Jane and the kids can’t own the business. Therefore, we recommend an insured buy-sell agreement, so that Jane can cash out if Robert ever passed away. This agreement that can be made between Robert and any junior, current or future partners that wish to buy/take over the architecture firm. We suggest that Robert find partnerships now so that agreement can be arranged, thus setting up your company’s future ownership.

And, by what you’ve mentioned, we believe you already have two long-term, dedicated, and willing prospective candidates for partnership.

Otherwise, when Robert reaches retirement, we would suggest that you sell your architecture firm, either all at once, if possible, or incrementally. If a complete buyout occurs, you could put the funds into a growth fund and then withdraw from it as needed or in set payments. If you sell your business incrementally, the payments may act similar to an annuity. Either way would allow you to receive a set amount of money per year to help fund your retirement.

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c) HOUSE & RENTAL PLAN Goal #3: Relocate to Sussex County, Delaware in retirement where your tax rates are lower. As mentioned, you had stated you would love a home where they could walk to the beach.

Current House: Current New York home cost $650,000 which was purchased 13 years ago on a 30-year mortgage at 5% interest.

HOUSE DETAILS Purchase Value Current Value Balance Currently Remaining Current Monthly Payment Current Annual Property Tax Expected Value Upon Retirement (Based on Inflation) Expected Balance Upon Retirement: Expected Annual Property Tax (Sussex County)

$650,000 $800,000 $400,000 $3,000 $20,000 $1,128,475 $192,960 $3,718

Future House: Expected cost of the new house is $1.1m estimated. However, we can expect property taxes to drop considerably. (from 5% to .338%) Recommendations: For House:  Sell your current residence at retirement  When you sell your home, we recommend paying off the remainder of the mortgage, which upon retirement would only amount to ~$192,960.  Tax Benefit: There is a tax benefit available when selling your primary residence. The benefit excludes $500,000 of gain when you file jointly if you have used the home as your primary residence for two out of the last five years. Because you plan to live in your current home until retirement, we believe you will qualify for this credit.

For Rental Condo:  We recommend keeping your rental property until retirement and invest the $600.00 net income per month into a growth fund to help pay for your retirement home. We would call this your “Delaware growth fund”. With ~ 7% return, you would have about $120,605.40 gross by retirement to help pay for the new house.  We propose that you sell your condo at retirement. This will have given it 10 additional years to accrue equity and value. Then you can sell it and use the funds towards your new house.  Tax Benefit: All profits from the Rental growth Fund and the sale of the condo will be taxed as long-term capital gain. This would mean that, factoring tax, you would have about $1,274,500 available for the new home upon retirement.

Current Insurance Policies: ©2020, IARFC. All rights of reproduction in any form reserved.

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Volume 19 â&#x20AC;˘ Issue 2

8. RISK MANAGEMENT OVERVIEW

Insurance Whoâ&#x20AC;&#x2122;s Yearly 1. INTRODUCTION Policies Covered Payment

Monthly Current Coverage Payment

Term

N/A

$500,000

Short-term Expires next year

$1500

$125

$50,000 Death benefit $27,000 Cash Value real rate of return of premiums is 4%.

Owned for life, bought 25 yrs ago

Life Jane Insurance

N/A

N/A

$250,000

Group Shortterm Expires when she quits her work

Disability Robert Insurance Disability Jane Insurance

$2,700

$225

Replaces 60% of income

Long-term

N/A

N/A

N/A

Group Shortterm Expires when she quits her work

HO2 Casey Homeowners Family Policy

N/A

N/A

Coverage A-$1,000 (400k limit) Coverage B-$1000 (30k limit) Coverage C-$500 replacement value (100$k limit) Coverage E-(400k limit) Coverage F-($5/person)

N/A

Auto Casey Insurance Family Policy

N/A

N/A

25/50/25 Auto deductibles Collision $750 Comprehensive $100

N/A

Life Robert Insurance

Whole Life Robert Insurance

N/A

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Journal of Personal Finance

a) LIFE INSURANCE ANALYSIS Current Status: Robert owns a whole life insurance policy and currently has a term insurance policy (that’s going to be expiring next year). Jane also has a group term insurance policy, but it will stop when she quits her job at retirement. If either of your deaths results in a financial loss of income, then you should have life insurance to make up for the income loss. In retirement, you will be drawing upon pre-arranged set income that shouldn’t be disrupted upon death.

Therefore, you will only need life insurance until retirement. We propose the following to make sure you’re covered from now until then: Note: Before you choose a policy, we ask that you get actual quotes specifically on each of these policies to see how much it would cost you monthly/annually, and if required, get a physical examination. Then you can make a final decision on the policies that are best suited to how much coverage you want and how much you’re willing to spend. But until then, here are our suggestions:

Disclaimer: Policy prices will differ depending upon your health, age, and other factors.

Recommendations: For Robert: You should keep your Whole Life nsurance policy. As it is, it’s currently valued at $ ,000 total coverage, which shouldn’t expire during your lifetime. This could help with final costs or debts.

We suggest that you replace your current term policy (which is about to expire) with a 10-year term life insurance policy with a coverage of $1,000,000 (low) or $1,500,000 (medium) or $2,000,000 (high) to cover you until retirement. There are pros and cons to each.

Things to remember:  Current Living Expenses will decrease  You don’t have to contribute as much towards retirement (because there’s 1 less person to retire)  Jane and the kids will inherit Robert’s retirement funds All of the following policies can:  Eliminate all current debts ($465,300)  Cover death and funeral costs ($12,000)  Pay for 50% of all your children’s education ($130,4 )  Assuming the previous points are paid off first, the rest of the income can be used to replace Robert’s previous income. ncome replacement % differs between policies.

©2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 • Issue 2

Low: 1,000,000 Insurance policy coverage Would provide about $39,222.3 annual income replacement until retirement if Robert died tomorrow (about 26% income replacement).

This policy yields the least income replacement but is the most affordable of the three. (~280$/mo) If you choose this policy, please be aware that you may have to change your consumer lifestyle to adjust for less income.

Medium: 1,500,000 Insurance policy coverage Would provide about $89,222.287 annual income replacement until retirement if Robert died tomorrow (about 60% income replacement).

This policy yields more income replacement but is less affordable. (~435$/mo) If you choose this policy, you may have to make minor changes to your consumer lifestyle to adjust for less income.

High: 2,000,000 Insurance policy coverage $139,222.28 annual income replacement until retirement if Robert died tomorrow (about 93% income replacement).

This policy yields the most income replacement but is the least affordable of the three. (~590$/mo) If you choose this policy, you should have plenty of income to continue your current consumer lifestyle.

For Jane: Your life insurance policy only needs about ⅓ coverage as Robert, because you currently contribute about ⅓ of the combined gross income. The following policies are about ⅓ policy coverage as the options for Robert (rounded up): Keep in mind:  This is assuming Robert is still alive and still earning ⅔ of the gross income ($150,000)  Current Living Expenses will decrease  You don’t have to contribute as much towards retirement (because there’s 1 less person to retire)  Robert and the kids will inherit Janes’ retirement funds All of the following policies can either:  Cover death and funeral costs ($12,000)  Eliminate some current debts ($465,300)  Pay for some of your children’s education ($130,477) Or  Assuming the previous points are NOT paid, the income can be used to replace Jane’s previous income. Income replacement % differs between policies.

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Low: $350,000 policy Would provide about $35,000 annual income replacement until retirement if Jane died tomorrow (about 46% income replacement).

This policy yields the least income replacement but is the most affordable of the three. (~35$/mo) If you choose this policy, please be aware that you may have to change your consumer lifestyle to adjust for less income.

Medium: $500,000 policy Would provide about $50,000 annual income replacement until retirement if Jane died tomorrow (about 67% income replacement).

This policy yields more income replacement but is less affordable. (~45$/mo) If you choose this policy, you may have to make minor changes to your consumer lifestyle to adjust for less income High: $700,000 policy $70,000 annual income replacement until retirement if Jane died tomorrow (about 93% income replacement).

This policy yields the most income replacement but is the least affordable of the three. (~60$/mo) If you choose this policy, you should have enough income to continue your current consumer lifestyle.

If you would like to be able to get good income replacement and be able to pay off final costs and debts, then you could investigate higher coverage policies.

Š2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 • Issue 2

b) DISABILITY ANALYSIS Current: Robert currently has a long-term disability insurance policy that replaces 60% of his income and Jane has group short term disability insurance provided through work. Disability insurance is important since it’s 33% likely for someone to get disabled. t’s most important to have this insurance while you are still working, so we recommend you have some at least until you retire. We believe your current policies should be enough to cover most of the risk until your retirement. We do however, suggest that Jane get a long-term disability policy just in case. We also have other suggestions that may make paying for this insurance more tax efficient: Recommendations: Since Robert is older, he is at a higher risk for being disabled. Because of this, we suggest that Robert pay post tax dollars to pay his long-term disability premiums.

This would make it so that if he had to collect on his disability, it would be taxfree.

Since Jane is younger, she is less likely to be disabled. Therefore, we propose she pay pretax dollars to pay for her short-term disability premiums. This would make it tax-deferred so that she wouldn’t have to pay taxes unless she had to collect on her disability. But just to be safe we suggest that get Jane long-term disability policy in addition to her short-term to make sure she’s covered since she contributes to ⅓ of the income.

We would like to review both of your disability policies to see what the elimination periods are on those so we can properly budget for emergency savings to replace the income for that time. In the meantime, we suggest budgeting $1,000/mo to your emergency savings.

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c) MISC. INSURANCE Property Insurance

Current: You have an HO2 Homeowners Policy right now, this is a Named Peril’s only policy.

This basic policy has a more limited scope of what it covers, it would be a good policy for a midrange valued home, but your current home is valued at 800k and your planning on purchasing a retirement home valued around 1.1mil. The more value in your home, the more it needs to be protected.

Recommended: We suggest that you work with a property and casualty agent to increase the coverage amounts on your property. We would recommend you increase your HO2 policy to a standard HO3 policy with a minimum $300,000 in personal liability coverage. This policy offers Open Perils for Dwelling coverage and Named Perils for Personal Property coverage. This will cover about 90% of accidents that could occur to your home and your property would be compensated by Actual Cash Value. And in the future, you should see if you need to add on flood coverage insurance to your retirement home since you want to live near the beach in Sussex County, Delaware.

Auto Insurance

Current: We understand you currently have a 25/50/25 Auto insurance policy. As it is now, your car insurance coverage is lower and riskier. t’s especially important that you have good car insurance since you currently have a teenage driver, Michael (16yrs old) and another, Brittney (14yrs old) coming in the next few years.

Recommended: To minimize this risk, we recommend that you meet with a property and casualty agent to consider increasing your auto insurance to 300/300/100. This is a safe amount to have for car insurance and it meets the minimum requirements in case you would like to pick up an umbrella policy on both vehicles.

Umbrella Policy: Optional

If you feel like you want extra coverage on your home or auto insurance then your current or advised insurance policies, then you could get an umbrella policy. I would recommend a $1 million umbrella policy, which is fairly inexpensive for the value. It would only cost an additional $12.50-25 a month, that’s just $150-300 a year. An umbrella policy would provide extra liability insurance coverage and transfer the risk in the case of a major claim.

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Volume 19 • Issue 2

Health Insurance:

Current: Status unknown. We need to make sure that your family has adequate health insurance in case of any major medical emergencies. We would assume that either of you has some form of health insurance through your work as a R.N. or from your business. We would want to confirm what you currently have and see if it’s enough. As for retirement, John will be old enough to qualify for Medicare (he’ll be 6 ), but not Jane (she’ll only be 55). This means that we need to set up some form of health insurance for Jane before retirement to last her until she turns 65 and qualifies for Medicare.

Recommended: For Jane, after retirement, any health Insurance you used to have from your work will no longer be provided. So, for post-retirement, we recommend that you get a Health savings accounts (HSA) under married filing jointly. But before you can qualify for this, you need to currently have a high deductible health insurance plan.

The HSA will cover both of you and it is very tax efficient! You pay pre-tax dollars into the account and any growth is tax deferred, then any money you take out (as long as it’s for medical reasons) is tax free! For this savings account it’s possible that you may never have to pay taxes on it, plus you can withdraw from it at any time.

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d) SPECIAL NEEDS ANALYSIS Current Status: We are aware that your son, John, has autism. We would like to make sure that he will be taken care of, no matter the circumstance.

Your goal was to set up a fund for John that wouldn’t affect government benefits (like Medicaid). To achieve this, we investigated needs-based programs and recommend the following:  A 2nd to die life insurance policy o This is a policy on Robert & Jane where if both passed away, the policy will pay out funds to the disability trust to help take care of the trustee (John). o A 1-million-dollar policy should cover John with about $2,000/mo until age 50  A 3rd Party Special Needs Trust (SNT) o Any assets/money that belong to John (from the Revocable trust) at the death of his parents needs to pour over into third party special needs trust. o We want to utilize the trust, so Michael and Brittney don’t inherit John’s money o We need to set a trustee for the special needs trust (who will take care of John if Jane and Robert die?)

You should consult with a New York Legal Specialist to review and solidify these actions regarding John.

©2020, IARFC. All rights of reproduction in any form reserved.

Volume 19 • Issue 2

e) LONG-TERM CARE ANALYSIS Current Status: You currently do not have any stated LTC. LTC provides money to pay for people to take care of you when you are no longer able to care for yourself.

As it is now, if Robert were unable to take care of himself (which gets more likely as he gets older) then Jane would most likely become the primary caregiver for Robert. This could degrade her own health and leave both of you in need of care.

To prevent this, we would recommend an LTC policy.

Recommendations: We advise that Robert try to purchase an LTC policy as soon as possible if you choose to get an LTC, since they get more expensive and harder to qualify for as you get older. LTC insurance will help Robert be preserved without affecting Jane’s health.

Currently Jane is at less risk, so getting her an LTC policy right now isn’t a priority.

But If you have extra income, you could get her an LTC policy starting at lower premiums. If you don’t want to get an LTC for Jane now, she can revisit this and consider getting one when she’s in her mid-50s.

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9. ESTATE ANALYSIS Current Status: We’ve identified multiple issues with your current estate: The only estate document we received was a will from Robert, which hasn’t been updated in years, and we’re missing the rest of the critical basic estate docs from both of you.

It is important that estate planning documents be complete, accurate, and up to date in the case of serious injury, disability, or death. These forms ensure that in these cases, your intentions are clearly stated, legal representatives are selected, your finances and assets have clear beneficiaries, your children have appointed guardians, and that there is a set succession plan. In addition, you currently live in an expensive probate state (New York) and we would like to avoid all unnecessary probate costs upon death. These expenses can be prevented by providing the proper documentation. You need these documents especially since you have children, and one with special needs. It is even more crucial to have documents as you get older. We want to make sure you and your family have a clear plan, so here’s our recommendations to help.

1. INTRODUCTION

Recommendations: We would like to collect the rest of the required basic estate documents from both of you and update Robert’s will. We suggest you do this by consulting with a legal attorney to complete the following documents: Basic Estate Docs

Purpose

Actions Required (Robert)

Actions Required (Jane)

Advance Directive (AD) or Living Will

Specifies what health actions should be taken when you are no longer able to make or communicate decisions for yourself

Create and complete

Create and complete

Medical Durable Power of Attorney (DPA)

Designates an agent to make medical decisions on behalf of the incapacitated individual

Create and complete

Create and complete

Financial Durable Power of Attorney (DPA)

Designates an agent to make financial decisions on behalf of the incapacitated individual

Create and complete

Create and complete

Revocable Trust (RT)

Is an adjustable trust that manages and protects assets while the owner lives and then transfers assets to beneficiaries upon death. This prevents probate.

Create and complete

Create and complete

Will and Testament

States wishes and how property is to be distributed at death, identifies the beneficiaries that receive the property and possessions, and appoints who manages the estate until its final distribution.

Update

Create and complete

©2020, IARFC. All rights of reproduction in any form reserved.

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Volume 19 â&#x20AC;˘ Issue 2

10. ASSET ALLOCATION Current/Estimated Pre-retirement Growth (80% stocks 20% Bonds)

1. INTRODUCTION

KEY:

Pinks= Stocks

Blues= Bonds

Cash & Cash Equivalents Short Term Bonds Intermediate Term Bonds Long Term Bonds S&P 500 Large Cap Index Fund S&P Mid Cap Fund S&P Small Cap Index Fund Emerging Markets Mutual Funds

Recommended Pre-retirement Growth (80% stocks 20% Bonds) Cash & Cash Equivalents Short Term Bonds Intermediate Term Bonds Long Term Bonds S&P 500 Large Cap Index Fund S&P Mid Cap Fund S&P Small Cap Index Fund Emerging Markets Mutual Funds

Recommended Post-retirement Moderate (50% stocks 50% Bonds) Cash & Cash Equivalents Short Term Bonds Intermediate Term Bonds Long Term Bonds S&P 500 Large Cap Index Fund S&P Mid Cap Fund S&P Small Cap Index Fund Emerging Markets Mutual Funds

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Current: We are glad to see that you understand the value of investing to prepare for the future. We see that you currently have your portfolio growth-focused and allocated as 80% stocks and 20% bonds. This is a great way to improve the rate of return but also exposes you to more risk.

Fortunately, we have some very cost-effective recommendations that will help minimize the risk while maximizing your potential return.

Cash/Cash Equivalent Short Term Bonds Intermediate Term Bonds Long Term Bonds S&P 500 Large Cap Index Fund S&P Mid Cap Index Fund S&P Small Cap Index Fund Emerging Markets Mutual Funds Total (%)

Current/Estimated (Growth) 3% 8% 4%

Recommended (Growth) 3% 7% 5%

Recommended (Moderate) 5% 15% 15%

6% 25%

5% 18%

15% 15%

20%

18%

10%

20%

23%

15%

15%

23%

10%

100%

100%

100%

Recommendations:  The first recommendation is to allocate your stock choices as S&P 500 large, middle, and small cap index funds. The use of index funds will provide you with instant diversification, shielding you from major losses, while allowing your portfolio to grow steadily as it minimizes management costs.  We also recommend using low expense-ratio mutual funds for investing into emerging markets. These are also cheap and diversified and will offer you similar benefits to the index funds. We also recommend set your allocations to focus more on small cap index funds and emerging market mutual funds, as they often provide you with the highest opportunities for growth.  As you reach retirement and your risk tolerance understandably decreases, we recommend shifting your portfolio allocation to be 50% stocks, 50% bonds and cash equivalents, such as treasury bonds. This will lower risk further, while still providing you an opportunity to see returns on your investments.  Upon retirement, we also recommend shifting most of your stock allocation from small cap and emerging markets to large and mid-cap funds as they are traditionally more stable.  Please note that we also recommend that we rebalance your fund annually, this will make sure that we’re maximizing returns throughout, and minimizing risk of loss due to one section becoming overvalued.

©2020, IARFC. All rights of reproduction in any form reserved.

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Volume 19 • Issue 2

11. IMPLEMENTATION

1.

Robert and Jane, we want to thank yo u again for your trust in us and the opportunity you’ve provided us to help you meet your financial goals. We’ve INTRODUCTION Name

Type

Frequency

Amount

Reason

outlined below the main points we recommend from each section. While they are provided above in more detail, these do show the simplicity we strive for as we help you achieve your goals. We know that as you follow these recommendations, you will achieve financial security and accomplish your goals. We look forward to meeting and planning with you again. If you have any questions or concerns about this plan, please reach out to us so we can discuss it.

Implementation Steps:     

 





Contribute $1,000 each month towards paying off your credit cards and other liabilities, such as HELOC and automobiles.

Contribute $1,000 each month to an emergency fund to cover any elimination periods that may arise before disability insurance begins.

Open a 529 plan for both Michael and Brittney. Pay at least $10,000 per year into it as that will maximize your tax benefit and likelihood there will be sufficient funds.

Continue to make maximum contributions to your simple IRA and 401(k) plans.

Organize a buy-sell agreement so that, in case of Robert’s passing, Jane and the children are guaranteed to receive the value of the business. (Pay for this with post tax dollars for maximum tax benefit.

Begin contributing the $400 per month to a growth fund, to save up to buy the retirement home.

Purchase and increase your insurance policies related to: o Term life o Long term disability (Jane) o Home and Auto o Personal Liability Umbrella Policy (PLUP) o Long Term Care (Robert) o Make contributions to an HSA account.

Consult with a lawyer to create: o The five legal basic estate documents o A 2nd to die life insurance policy payable to John. o Organize a 3rd party Special Needs Trust and assign a trustee to look after John.

Allocate your investments into index funds and low-cost mutual funds—focus on small cap index funds and emerging markets mutual funds pre-retirement and bonds and large cap index funds post retirement.

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12. FEE OVERVIEW

1.

Introductory Fee INTRODUCTION Fee

Management Fee

Fee

Referral Fee

Commission

One-time

$500

Fee covers costs of personnel, analyses, and expertise to develop your personalized plan.

Annual

1%

We want your success to be our success. We care about your goals and we’re motivated and prepared to manage your assets to accomplish those goals.

on Assets Under Management (AUM)

One-time

N/A (Depends on Product/Service)

For the times when you do purchase products or services relevant to your plans, we do receive a commission.

Commissions depend on the product or service you choose. (i.e. LTC, Life Insurance, Disability etc.)



Note: This is a general fee overview; any specific fees or commissions can be discussed on a case by case basis.

©2020, IARFC. All rights of reproduction in any form reserved.

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Volume 19 • Issue 2

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. What were the primary findings of the article, “Does Working with a Financial Advisor Reduce Financial Anxiety and Increase Investment Confidence?” a. Working with a financial advisor decreases financial anxiety but does not increase investment confidence b. Working with a financial advisor increases investment confidence but does not decrease financial anxiety c. Working with a financial advisor decreases financial anxiety and increases investment confidence d. Working with a financial advisor does not decrease financial anxiety or increase investment confidence 2. Which of following statements is supported by the financial anxiety regression model? a. Females have marginally lower levels of financial anxiety than males b. Full-time employees have higher levels of financial anxiety than retirees c. Incurring a professional life event within the last 12 months had a negative relationship with financial anxiety d. Attaining a college degree has lower levels of financial anxiety than high school graduates 3. Which of the following statement is supported by the investment confidence regression model? a. Higher levels of income have a positive relationship with investment confidence b. Whites have higher levels of investment confidence than other ethnicities c. Couples in which one partner makes investment decisions has lower levels of investment confidence than couples where partners are equal decision makers d. Males have higher levels of investment confidence than females 4. Additional expenses for Orthodox Jewish families include a. Church dues, Halal food, Charitable contributions b. Tuition, Synagogue dues, Kosher food c. Larger families, modest dress, livestock d. Trips to Israel, Community center membership 5. The Yeshivish and Chassidish sects, often identified as “Ultra-Orthodox” a. Reported more financial anxiety due to family size

6.

7.

8.

9.

b. Reported the same financial anxiety as Modern Orthodox Jews c. Reported less financial anxiety despite having less income on average d. Reported more financial anxiety tied to living costs Regular attendance at minyan (daily prayer service) a. Was found to be positively related with financial anxiety b. Was found to have no impact on financial anxiety c. Was found increase financial anxiety d. Was found to be inversely related to financial anxiety One possible explanation for the disparity in financial anxiety among Orthodox Jewish sects discussed in, “Financial Anxiety in the Orthodox Jewish Community” is a. The reliance on gemachs b. Disparities in tuition costs c. Geographic distribution d. Access to Kosher food In Latino families and other populations, research has shown that when parents engage in more financial socialization (i.e., passing their financial knowledge to their children), their young adult offspring were: a. Less likely to engage in positive financial practices (e.g., budgeting, tracking spending, setting financial goals, saving). b. More likely to engage in positive financial practices (e.g., budgeting, tracking spending, setting financial goals, saving). c. More likely to engage in negative financial practices (e.g., accumulating credit card debt, bankruptcy). d. Not impacted.

In Latino families, financial socialization from other family members (e.g., grandparents, Godparents, uncles/aunts) was: a. Not related to financial practices. b. Not related to financial practices after controlling for parental financial socialization. c. Positively related to financial practices even after controlling for parental financial socialization. d. Negatively related to financial practices.

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Journal of Personal Finance

10. In Latinos and other populations, research has shown that college students who lived AWAY from home: a. Were not impacted in relation to their financial status or practices. b. Had better financial status (e.g., less debt, more savings). c. Were more likely to engage in positive financial practices (e.g., budgeting, tracking spending, setting financial goals, saving). d. Were less likely to engage in positive financial practices (e.g., budgeting, tracking spending, setting financial goals, saving).

11. Studies with Latinos and other ethnic groups on university classification (i.e., class rank) and financial practices have generally found: a. no differences between upper and lower classmen on financial practices b. lower classmen (e.g., freshmen) have better financial knowledge/practices than upper classmen (e.g., seniors) c. upper classmen (e.g., seniors) have better financial knowledge/practices than lower classmen (e.g., freshman) 12. Which of the following factors was positively associated with increased propensity and amount of charitable giving, even controlling for other financial and demographic factors? a. Having children b. Being retired c. Being female d. Increased attendance at religious services 13. Controlling for other financial and demographic factors, those who reported volunteering were a. Less likely to attend religious services than those who did not volunteer b. Less likely to make donations of $500 or greater than those who did not volunteer c. More likely to make donations of $500 or greater than those who did not volunteer d. No different in their likelihood to make donations of $500 or more as compared with those who did not volunteer 14. The economic theory for charitable giving has been developed from a pure altruism model to the “impure altruism” framework which employs both an altruism motive and a “warm-glow” effect. Which one of the following statements

15.

16.

17.

18.

correctly describes the “pure altruism” motivation of charitable giving? a. The good feeling after doing something good for the charitable organization. b. Expecting to enjoy the utility derived from the output (such as public goods) of the charitable organization to which the donor gives or contributes. c. The private good or benefit that the donor experiences only by the personal act of giving. d. The “pure altruism” effect often refers to the utility derived from the giving action itself, hence may not require long-term planning.

The geometric rate of return is a. Greater than the average rate of return b. Less than the average rate of return c. Equal to the average rate of return d. One half the average rate of return The probability distribution of a portfolio’s future wealth a. Widens with time b. Tightens with time c. Is constant with time d. Is not related to time If $1,000 is invested in a portfolio whose probability of growth over one year is characterized by a normal distribution with a mean annual rate of return 10% and a standard deviation of 18% than a. There is a 95% probability that the actual portfolio value after one year will be between $820 and $1,180 b. There is a 95% probability that the actual portfolio value after one year will be between $1,000 and $1,100 c. There is a 68% probability that the actual portfolio value after one year will be between $820 and $1,180 d. There is a 99% probability that the actual portfolio value after one year will be between $820 and $1,180 If a portfolio is characterized by a normally distributed annual rate of return, then we can expect that this portfolio will be characterized in the future by a distribution a. That is normally distributed b. That is symmetric but narrower than the initial distribution c. That is symmetric but wider than the initial distribution d. d. That is lognormally distributed

©2020, IARFC. All rights of reproduction in any form reserved.

MRFC

Volume 19 • Issue 2

®

113

MASTER REGISTERED FINANCIAL CONSULTANT

MRFC® Application Applicant Information

Exam

(please print or type)

______________________________________________________________________________________ Please provide your name only on the line below as you want it to appear on your Certificate.

The Certification staff will review all candidate applications submitted to determine if the candidate is eligible to sit for the MRFC exam and

Business Information ______ _______________________ __________________ __________________________ __________ Prefix First Name Middle Name Last Name Suffix ____________________________________________________________ __________________________ Business Name Preferred Salutation ____________________________________ __________ __________________ _______ ____________ Street Address Ste#/Apt City State Zip

for completeness and payment of fees.

Candidates will be notified of their eligibility to sit for the MRFC Exam. The candidate will have 90 days, after notice of application approval.

____________________________________ _________________________ ________________________ Business Phone Fax Cell Phone __________________________________________________ ____________________________________ Business Email Address Primary Yes No Website

MRFC® Fee Schedule

Home Information

Nonrefundable Application Fee:

$100

____________________________________ __________ ____________________ ______ ___________ Street Address Ste#/Apt City State Zip

Examination/Certification Fee:

$350

______________________________________________ ________________________________________ Home Phone Cell Phone ____________________________________________________ __________________________________ Home Email Address Birth date MM/DD/YY

Please send all mail to my:

Business Address

Choose one

Association Fee (join)

$100

Current Association Member (continue) Total payment:

$0

___________

Home Address

Experience

Annual Recertification Fee

Must have a minimum of four years of experience as a full-time practitioner in the field of financial planning or financial services: Full-time__________ Part-time___________

Commence on anniversary of passing MRFC Exam

Education

MRFC (Certification Only)

$350

MRFC (with Association Membership)

$450

Education criteria requires an applicant to assert and document achievement in any one of these areas: Education, Professional Designations/Credentials or Licensing.

Education Background

(Evidence of license, diploma or documents may be requested. You need not submit evidence with the application.)

School, City, State (Since High School)

Graduated Yes

Major

No

Degree

Payment Options 1. Mail Application with payment to: IARFC P.O. Box 506, Middletown, OH 45042 2. Fax Application to: (513) 345-9479 (credit card only) 3. Email Application to: info@iarfc.org

Professional Designations/Credentials AAMS

CFA

CFP

ChFC

CLU

CPA

EA

LUTCF

RFC

Other _______________________________________________________________________________

Check payable to: IARFC Credit Card: Visa, MC, Amex, or Discover

Licensing Broker/Dealer______________________________________ (Personal) FINRA CRD No. ____________ Securities Licenses:

Series 6 and 63 Series 7 and 66

Series 7 and 63 Series 6 and 66 Series 65 Other_____________________________

Insurance Licenses:

Life Health Variable Contracts Prop. & Casualty Other ____________________________________________________________

Credit Card#

Exp. Date

Security Code

Primary Insurance Company (if any)________________________________________________________ Affiliated with an SEC Registered Investment Advisor (RIA)?

Yes

No

Name of RIA ___________________________________________________________________________

Signature

Code of Ethics (Applicants must subscribe and adhere to the IARFC Code of Ethics) I will at all times put my client’s interest above my own. I will maintain proficiency in my work through continuing education. When fee-based services are involved, I will charge a fair and reasonable fee based on the amount of time and skill required. I will abide by both the spirit and the letter of the laws and regulations applicable to financial planning services. I will give my clients the same service I would give myself in the same circumstances.

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Journal of Personal Finance

Questions relating to business and ethical conduct

Recommend a colleague for the MRFC

(If you check “Yes” to any of the following questions, please attach a written explanation.)

Yes

No

Have you ever been refused a surety bond or other form of employment security?

Full Name

Have you ever been denied or enjoined from selling or dealing in securities or from functioning as an Investment Advisor?

Have you ever been arrested, indicted, or convicted for any felony or misdemeanor, except for minor traffic offenses?

Have you ever been known personally by any other name, or have you ever conducted financial activities, conducted business or carried brokerage or bank accounts in any other name?

Have you ever become insolvent, failed in business or compromised with creditors?

If “Yes” – please provimrfe the date, name, and location of court, disposition, liabilities, and assets.

Address

City

Have you ever had a license, permit, certificate, registration or membership denied, suspended, revoked or restricted, or have you had an application of such type ever withdrawn for cause?

State, Zip

Have you ever been the subject of any order, judgement, decree or other sanction of a foreign court, foreign exchange, or have you ever been the subject of any action by a foreign or domestic governmental or regulatory agency?

Phone

Attestations (Applicants please read carefully) 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

Email

I hereby certify that I have read and understand the foregoing statements and that my responses are true and complete to the best of my knowledge.

I hereby apply for the MRFC credential and in consideration of my application, I submit myself to the jurisdiction of the Association and hereby verify that I agree to abide by all the provisions of the By-Laws and regulations of the Association as they are and may be amended. I agree to comply with all such requirements, subject to right of appeal as provided by law. I agree that any decision as to the result of any exam(s) that I may be required to pass or annual Continuing Education (CE) requirements will be accepted by me as final.

I further agree that neither the Association nor its trustees, directors, officers, or employees shall be liable to me for action taken or omitted in official capacity or in the scope of employment, except as otherwise provided in the statutes, By-Laws, or the Association’s regulations.

I hereby certify that I have a sound record of business integrity with no suspension or revocation of any professional licenses, and I hereby subscribe to the IARFC Code of Ethics, a copy of which I have read and understand.

It is agreed and understood that any material misrepresentation of facts or information given in this or subsequent application or renewal may be cause for immediate revocation of the MRFC credential and all its privileges, without refund of any dues or fees paid.

I understand that failure to disclose any regulatory event, including suspensions or revocations, may disqualify me from initially obtaining the MRFC credential or could result in revocation of the credential.

As an applicant for registration, I understand and agree that my MRFC credential will not become effective until I have met all the eligibility requirements and had have successfully passed the MRFC exam.

I understand that the MRFC credential remains the property of the MRFC Certification Board, (MCB) and must be destroyed or returned to the MCB should my right to display the credential be suspended or terminated.

I understand that the continuation of the MRFC credential requires the successful awarding of forty (40) units of financial services focused CE credits — of which two (2) units every year must be related to Professional Ethics commencing the January of the year following initial acceptance.

I understand this application is valid for sixty (60) days from the date of receipt by MCB’s home office and I have ninety (90) days upon application approval to schedule the MRFC exam.

I authorize the organization to make available to any federal, state or municipal agency, or any securities or commodities industry self-regulatory organization, any information they may have concerning me or to request confirmation of my status, and I release those organizations, employees and agents, from any and all liability of whatever nature by reason of furnishing such information.

I further agree that my contact information contained in this application be divulged to interested parties as part of the member profile on the IARFC website for the benefit of members and the public.

I understand that except for my certification status, written authorization by me is required to release my information.

How did you learn about the MRFC? Advertisement Article Association Broker/Dealer Direct Mail Email Exhibit IARFC Website Insurance Co. Referral FB LI Twitter Other _____________________________

Referred by (if applicable)

Full Name

City, State

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

International Association of Registered Financial Consultants P.O. Box 506 Middletown, OH 45042-0506 P: (800) 532-9060 F: (513) 345-9479 E: mrfc@iarfc.org W: iarfc.org

I attest that I have read and understand the above, that the information I have provided is complete and accurate to the best of my knowledge and belief, and I further understand that my MRFC credential may be revoked if I have provided any false or incomplete information.

Signature of Applicant (required)

Date

©2020, IARFC. All rights of reproduction in any form reserved.

Revised 11/19/19

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

®

NATIONAL FINANCIAL PLAN COMPETITION Cincinnati, Ohio 2021

Corporate Sponsorship Opportunities Take part in the IARFC National Financial Plan Competition as a Corporate Sponsor. This is a give back opportunity to help the NEXT GEN of collegiate undergraduates to develop the skills they need to perform in the Financial Services industry.

SPONSOR LEVELS VISIBILITY AND PROMOTIONS

Diamond

Plan Competition Sponsor Award recognition during banquet

√

Banner Advertisement on Plan Competition web page provided by sponsor (600 pixel x 110 pixel)

√

√

Recognition during Plan Competition

√

Email Advertisement as Sponsor, campaign(s) Logo recognition featured on Plan Competition web page and Competition live promotion Advertisement recognition, in the Register magazine, 1 time Logo recognition, in the Register magazine, during run duration of Plan Competition promotion Advertisement recognition, in the Journal of Personal Finance, 1 time Logo recognition in Journal of Personal Finance, during run duration of Plan Competition promotion Media Release, Individual (personalized), General (pre-event release) Cost

Platinum

Gold

Silver

√

√

√

2

1

1

1

√

√

√

√

full page

1/2 page

1/3 page

√

√

√

full page

1/2 page

1/3 page

√

√

√

√

Individual

Individual

General

General

$25,000

$15,000

$10,000

$5,000

√

A percentage of Plan Competition Sponsorship proceeds are directly donated as monetary prize awards to the winning teams at the Awards Banquet. Sponsorship opportunity promotions run duration: 12 months from the date payments are received. Promotions follow the IARFC advertising guidelines.

International Association of Registered Financial Consuiltants 146 N Breiel Blvd., P.O. Box 506, Middletown, OH 45042 P: 800.532.9060, F: 513.345.9479, E: plancomp@iarfc.org

Journal of Personal Finance International Association of Registered Financial Consultants - IARFC 146 N. Breiel Boulevard P.O. Box 506 Middletown, Ohio 45042

Support the Next Generation of Financial Professionals

IARFC NATIONAL FINANCIAL PLAN COMPETITION The IARFC invites participation in the National Financial Plan Competition Spring 2021

®

To learn more call (800) 532-9060 or visit www.IARFC.org