IARFC Journal of Personal Finance Volume 18 Issue 2 2019

Volume 18 Issue 2 2019 www.journalofpersonalfinance.com

Journal of Personal Finance

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

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INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

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Journal of Personal Finance

Volume 18, Issue 2 2019 The Official Journal of the International Association of Registered Financial Consultants Š2019, IARFC. All rights of reproduction in any form reserved.

Journal of Personal Finance Volume 18, Issue 2 2019 Editor Benjamin Cummings, Ph.D., CFP®, RFC® The American College of Financial Services

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

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

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

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intended to present timely, accurate, and authoritative information. The editorial staff of the Journal is not engaged in providing investment, legal, accounting, financial, retirement, or other financial planning advice or service. Before implementing any recommendation presented in this Journal readers are encouraged to consult with a competent professional. While the information, data analysis methodology, and author recommendations have been reviewed through a peer evaluation process, some material presented in the Journal may be affected by changes in tax laws, court findings, or future interpretations of rules and regulations. As such, the accuracy and completeness of information, data, and opinions provided in the Journal are in no way guaranteed. The Editor, Editorial Board, the Institute of Personal Financial Planning, and the Board of the International Association of Registered Financial Consultants specifically disclaim any personal, joint, or corporate (profit or nonprofit) liability for loss or risk incurred as a consequence of the content of the Journal.

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

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

Individual financial decision making

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

Volume 18, Issue 2

Contents The Impact of Family Economic Strain On Work-Family Conflict, Marital Support, Marital Quality, and Marital Stability During the Middle Years ��������������������������������������������������������������������������������������������������������������������������������� 9 D. Bruce Ross, PhD, University of Kentucky Jerry Gale, PhD, University of Georgia Kandauda K.A.S. Wickrama, PhD, University of Georgia Joseph Goetz, PhD, University of Georgia Matthew Vowels, MS, University of Surrey Over the past few decades, US families have been faced with several economic recessions. The regularity and severity of these economic crises lends to the importance of having an understanding of how these events affect families. The present study investigates the effects of family economic strain on marital quality and marital stability through individual and dyadic models, incorporating work-family conflict and marital support as mediators.

The study analyzed secondary data from the Iowa Youth and Families Project (IYFP) and Midlife Transition Project (MTP) collected from 370 middle-aged married couples across a 12-year time period that encompassed the US early 1990s recession and the recession of 2001, which occurred between the years 1989 and 2001. The results support the hypotheses that the impact of economic strain upon marital quality and marital stability is mitigated through a positive perception of work-family conflict and marital support. Implications for future research and for financial practice are discussed.

Financial Education and Demand for Debt Counseling Advice ������������������������������������������������������������������������������� 25 Nilton Porto, MBA, PhD, University of Rhode Island Jing Jian Xiao, PhD, University of Rhode Island The purpose of this study is to examine potential effects of consumer financial education on demand for debt counseling advice using a large and representative national dataset. Previous research has examined demographic, economic, and financial capability factors on the demand for debt counseling advice. After controlling for these factors and eliminating the effect of bankruptcy, financial education, a variable not examined in previous research, is positively associated with the demand for debt counseling advice. Education attainment is similarly associated with debt counseling. These results suggest evidence of the complementary nature of financial education. Our results indicate that consumers who have been exposed to financial education are more likely to seek debt counseling advice.

An Exploration of Overconfidence in the Utilization of Financial Advisors ���������������������������������������������������������� 39 Marc B. Lewis, DBA, Central Connecticut State University Households are not seeking the assistance of financial advisors in planning for retirement. This study explores why individuals are not utilizing a financial advisor. A survey was conducted on individuals who completed the 2012 National Financial Capability Study, which was organized by Financial Industry Regulatory Authority. The results revealed that overconfidence is statistically significant in explaining the likelihood of an individual utilizing a financial advisor. The results also revealed that men utilize financial advisors more frequently than women.

Furthermore, the results indicated that an individual might utilize a financial advisor once their overconfidence decreases. Moreover, as education increases, so too does the likelihood that an individual will utilize a financial advisor. However, there is a need for additional research on overconfidence and the utilization of financial advisors in the current economy, as well as researching the relationship between overconfidence and other variables such as employment and health status of an individual.

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From Financial Education to Financial Literacy to Financial Stability: Evidence of National and Regional Connections ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 51 Stephanie R. Yates, PhD, CFP®, AFC®, University of Alabama at Birmingham The present study uses data from the National Financial Capability Study to analyze the degree of impact of financial education on financial literacy based on the related socialization factors. We then analyze the degree to which financial literacy explains financial stability. We find higher levels of financial literacy in those who have been exposed to financial education and that those with higher levels of financial literacy are more financially stable. Further, we find that a lack of exposure to financial education has a negative impact on financial literacy that is more severe than the positive impact of financial education. Lastly, we find that financial education socialization varies by race, gender, and socio-economic status. These findings provide a roadmap for financial educators and practitioners to account for limited exposure to financial education and financial overconfidence in order to have the greatest impact.

The Efficacy of Publicly Available Retirement Planning Tools ����������������������������������������������������������������������������������� 61 Taft Dorman, PhD, AFC®, Western Governors University Barry Mulholland, PhD, MBA, CFP®, ChFC®, The University of Akron Qianwen Bi, PhD, Utah Valley University Harold Evensky, CFP®, AIF®, Evensky & Katz/Foldes Financial Wealth Management Publicly available retirement planning tools are advertised as aiding households in their retirement planning efforts, but households are likely overestimating their effectiveness. The authors conclude that the advice provided by a majority of these tools is extremely misleading to households and propose a more systematic approach to tool development by improved choice of input variables. Analyzing theory and professional advisor opinion, critical input variables are identified and recommended. Using a case study that incorporates these critical variables, the efficacy of 41 publicly available retirement planning tools was tested.”

2019 IARFC National Financial Plan Competition ��������������������������������������������������������������������������������������������������������� 79 Rebecca Boyle, GreenCourse Financial Advisors Winning Financial Plan by Rebecca Boyle, Undergraduate Student, University of North Texas; Paraplanner, GreenCourse Financial Advisors Edited by Benjamin F. Cummings, PhD, CFP®, RFC® For the 2019 National Financial Plan Competition, students were given a fictional case study of a family that included an overview of their financial picture. Students were asked to analyze the case and prepare a financial plan for the family, including recommendations to help the family reach their goals. Written case submissions were judged, and winners advanced to the semi-finals, after which the top three teams made in-person presentations at the IARFC annual conference in Nashville, Tennessee.

The case that was provided to students is summarized below, followed by a near-complete summary of the winning financial plan, prepared and presented by Rebecca Boyle from University of North Texas. As editor of the Journal of Personal Finance, Benjamin Cummings edited the case and Rebecca’s plan for inclusion in the journal. .

79 Note: The views expressed in this financial plan are those of the author and do not represent the views of GreenCourse Financial Advisors, nor does this financial plan constitute investment advice.

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

Volume 18, Issue 2

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Editor's Notes

I am pleased to introduce the Fall 2019 issue of the Journal of Personal Finance. The research included in this issue explores some important aspects of personal finance. This issue also includes the case and winning plan from this year’s IARFC National Financial Plan Competition. The intent is that all our readers – practitioners and academics alike – will enhance their understanding of personal finance and financial planning as they read this issue.

In the first article, D. Bruce Ross, PhD and his colleagues, Jerry Gale, PhD, Kandauda K.A.S. Wickrama, PhD, Joseph Goetz, PhD, and Matthew Vowels, MS, perform a complex analysis on how financial stress impacts marital relationships. They find that financial strain is a common source of stress for middle-aged adults; however, they also find financial strain less impactful on a marriage when spouses are supportive of one another, and there are fewer conflicts between one’s work and family life.

The next three articles all use data from the National Financial Capability Study. We still have much to learn about financial capability in America, and this dataset—administered by the FINRA Investor Education Foundation in consultation with the Department of the Treasury and other agencies—is an excellent source of information for such analyses.

Nilton Porto, MBA, PhD and Jing Jian Xiao, PhD examine financial advice seeking in the second article. Specifically, they analyze the effect of financial education on the demand for debt counseling advice. They find that individuals with higher levels of financial education—as well as higher levels of educational attainment—are more likely to seek help managing their debt.

In the third article, Marc B. Lewis, DBA, investigates the under-utilization of financial advisors. He finds that overconfidence is a significant reason why more individuals do not employ a financial advisor. He also finds that individuals with higher levels of formal education are more likely to use an advisor and that men are more likely to use a financial advisor than women.

Stephanie R. Yates, PhD, CFP®, AFC®, uses the National Financial Capability Study in the fourth article to investigate the impact of financial education on financial literacy, and in turn, the impact of financial literacy on financial stability. She finds that those with more financial education are more financially literate and that increased literacy leads to greater stability.

In the fifth article, Taft Dorman, PhD, AFC®, and his colleagues, Barry Mulholland, PhD, MBA, CFP®, ChFC®, Qianwen Bi, PhD, and Harold Evensky, CFP®, AIF®, analyze publicly available retirement planning tools. Their findings suggest that many publicly available tools provide misleading results. Often, a high degree of personalization is needed for an adequate analysis of one’s preparedness for retirement, and the tools frequently fail to incorporate all the necessary variables. Further, many users of the tools are naïve consumers who may not realize the inadequacy of the analyses. Fortunately, Dr. Dorman and his colleagues propose a more systematic approach to tool development by enhancing the quality of the input variables.

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Journal of Personal Finance

The final article brings you the winning financial plan of the 2019 IARFC National Financial Plan Competition, authored by Rebecca Boyle, an undergraduate student from the University of North Texas and now a paraplanner at GreenCourse Financial Advisors in St. Louis, MO. Rebecca prepared a comprehensive financial plan for George and Emily Hathaway, clients who were seeking help as they work towards their education and retirement 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 Rebecca’s impressive analysis and recommendations.

With a 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

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

Volume 18, Issue 2

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The Impact of Family Economic Strain On Work-Family Conflict, Marital Support, Marital Quality, and Marital Stability During the Middle Years D. Bruce Ross, PhD, University of Kentucky Jerry Gale, PhD, University of Georgia Kandauda K.A.S. Wickrama, PhD, University of Georgia Joseph Goetz, PhD, University of Georgia Matthew Vowels, MS, University of Surrey

Abstract Over the past few decades, US families have been faced with several economic recessions. The regularity and severity of these economic crises lends to the importance of having an understanding of how these events affect families. The present study investigates the effects of family economic strain on marital quality and marital stability through individual and dyadic models, incorporating work-family conflict and marital support as mediators. The study analyzed secondary data from the Iowa Youth and Families Project (IYFP) and Midlife Transition Project (MTP) collected from 370 middle-aged married couples across a 12-year time period that encompassed the US early 1990s recession and the recession of 2001, which occurred between the years 1989 and 2001. The results support the hypotheses that the impact of economic strain upon marital quality and marital stability is mitigated through a positive perception of work-family conflict and marital support. Implications for future research and for financial practice are discussed.

Key Words family economic strain; family stress and coping theory; finances; financial counseling; financial management; marital stability; marital support; marital quality; work-family conflict

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Many researchers have identified how financial stress may cause marital conflict, as well as significantly and negatively impact couples (Bryant, Taylor, Lincoln, Chatters, & Jackson, 2008; Dew & Dakin, 2011). Approximately one-third of couples in relationship counseling have reported financial stress as one of their most salient issues (Aniol & Synder, 1997). Furthermore, approximately fifteen percent of marital satisfaction is predicted by financial factors, such as financial management quality and financial problems (Kerkmann, Lee, Lown, & Allgood, 2000), and a partner’s spending behaviors and level of financial stress has a direct impact on their relationship satisfaction (Britt, Grable, Goff, & White, 2008). Conversely, individuals reporting higher financial satisfaction have a significantly lower probability of considering divorce (Grable, Britt, & Cantrell, 2007).

Martin (2010) provide a review of the potential mechanisms and theoretical frameworks that account for the associations between socioeconomic status, marital satisfaction and marital stability within romantic relationships. Altogether, the longitudinal data from the IYFP and MTP provides researchers with substantial evidence for the association between economic hardship, resilience, and risk factors for lower- and middle-income populations in rural locations.

Previous literature investigating the relationships between economic strain, marital quality, and marital stability is often cross-sectional and concerns individuals. As a result, the existing research generally does not consider the possibility of the protracted and chronic nature of the effects of economic strain that has been experienced by families in the US. One exception is the Iowa Youth and Families Project (IYFP) and the follow-up Midlife Transition Project (MTP), which is an ongoing longitudinal study investigating the long-term effects of socioeconomic influence on family processes (Conger, et al., 1990, Conger, Conger, & Martin, 2010). It comprises data from 451 rural, Midwestern, two-parent families with a 7th grade adolescent and a near-age sibling in the home. The families in this sample lived through the agricultural crisis of the late 1980s, and thus likely experienced a reduction in wages and working hours, as well as farm foreclosures and increased unemployment (Rosenblatt, 1989). The longitudinal nature of the data collected by the IYFP and MTP allows for the exploration of causality in relation to the effects of economic strain on the relationships and mediating cumulative processes of these families. The IYFP and MTP data have been utilized to explore the Family Stress Model (FSM) (Conger, Wallace, Sun, Simons, McLoyd, & Brody, 2002; Conger, Rueter, & Conger, 2000; Conger, Conger, Elder, Lorenz, Simons, & Whitbeck, 1992), the negative impact of economic hardship and strain on family processes (Conger & Elder, 1994; Conger, et al., 1990; Wickrama, Surjadi, Lorenz, Conger, & O’Neal, 2012), couple and family resilience factors to economic pressure (Conger & Conger, 2002; Conger, Rueter, & Elder, 1999), marital quality and stability (Yeh, Lorenz, Wickrama, Conger, & Elder, 2006), and divorce and marital instability (Lorenz, Wickrama, Conger, & Elder, 2006; Wickrama, Lorenz, Conger, Elder, Abraham, & Fang, 2006). Additionally, Conger, Conger, and

The current study investigated data collected in Midwestern U.S. between 1989 and 2001, a period which encompassed two major recessions and was marked with economic instability.

Indeed, it is valuable to study the middle-aged cohort in this period, since that they experienced a number of economic down turns, including the rural farm crisis (late 1980s), the early 1990s recession and the recession of 2001, as well as their subsequent recoveries (Wickrama, Surjadi, Lorenz, Conger, & O’Neal, 2012). The early 1990s recession lasted from July 1990 until March 1991 and was the worst economic recession since World War II (Ekerdt, 2010). Although each family may experience different economic pressures, the economic decline from the recession placed significant economic stress on many families due to reduced employment opportunities, increased pricing of consumer goods, and a lack of trust in the strength of the economy following such a quick increase of inflation in the late 1980s. After the period of strong economic growth during mid-to-late 1990ss, the recession of 2001 occurred, which lasted from March to November of 2001 and was attributed to the collapse of the “dot-com bubble” and the sudden impact of the September 11th terrorist attacks (Hall, Feldstein, Bernanke, Frankel, Gordon, & Zarnowitz, 2001). This again, created a period of economic distress, decreased industrial production, and increased unemployment in the US (Bureau of Labor Statistics, 2003; Hall et al., 2001).

LITERATURE REVIEW The Recessions of the 1990s and 2000s

Several studies concerning the association between family processes and economic hardship were published in the early 1990s (Conger et al., 1990; Conger et al., 1992; Conger, Ge, Elder, Lorenz, & Simons, 1994). One 1990 study found that economic strain or hardship increased hostility and decreased warmth and support between married partners, thereby indirectly

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

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negatively affecting the perception of the marriage (Conger, et al., 1990). The hostility was more associated with economic strain for husbands than for wives, revealing possible gender differences.

Subsequent assessment of the same Midwestern cohort suggested that specific financial conditions, such as per capita family income and work instability, were linked to the parents’ behaviors (e.g. conflict) and emotional well-being (e.g. demoralization and depression) via the perception of increased financial strain (Conger et al., 1992). In this later study, and in contrast with the previous study, Conger et al. (1992) found that financial difficulties affected emotions (e.g., depressed mood) and behaviors (hostility) equally for husbands and wives. Conger theorized that this gender shift, from husbands primarily associated with irritability and hostility due to financial difficulties to both genders, can be explained in part by more contemporary wives assuming a greater role in family economic life. As to the number of dual career families increase, often brought about by pressure of economic strain due to the recession, both spouses feel the increased burden and responsibility of financial difficulties. Additionally, the authors suggest that historically wives are largely responsible for the emotional work in the family relationships, and as they withdraw from this role to take on other tasks (e.g., earning money for the family), this may result in more marital friction, producing more irritability and hostility in the family and for both spouses in the relationship. They also noted how the disruption in family interaction, resulting from the economic hardship, impacted adolescent development. Later investigation also found that economic strain was positively linked with marital conflict and parental dysphoria, which, in turn, influenced parental practice (Conger et al., 1994).

The results from one of the most generalizable studies of the 1990s, by Porter and Garman (1993), highlighted significant variability in each individual’s perception of their financial well-being. As such, it may be that individuals do not have accurate or reasonable impressions of their financial situations.

The economic uncertainty and instability of the early 1990s may have contributed to this confusion.

Research that emerged after the recession of 2001 began to investigate aspects of resilience and financial management behaviors in relation to finances. One study noted how, in general, individuals who sought advice from financial professionals showed higher financial satisfaction, increased likelihood of homeownership, greater financial risk tolerance, and exhibit-

ed positive financial behaviors (Grable & Joo, 2001). Similarly, Conger and Conger (2002) found that parents who successfully managed financial difficulties were more likely to have increased problem solving skills, a greater sense of emotional control, higher marital support, less hostile parenting, and better parental nurturing practices. The children of these parents also showed resilience to economic strain, through support from siblings, parents, and other adults outside of the family.

Further research highlights the importance of financial knowledge in exhibiting financial management abilities. For example, one study demonstrated how individuals with financial knowledge are more competent at managing savings, investments, and credit (Hilgert, Hogarth, & Beverly, 2003). Participants in a similar study exhibited positive financial behaviors and improved health after participating in a debt management program (O’Neill, Sorhaindo, Xiao, & Garman, 2005).

Economic Strain, Marital Quality, and Marital Stability Marital quality may be defined as a global measure of satisfaction and happiness in a marriage, whereas marital stability is the probability that the marriage will stay intact, without dissolution or divorce, which is typically measured as a reverse-coded questionnaire on the frequency a married couple discusses or considers separation (Matthews, Conger, & Wickrama, 1996). Although low marital quality does not necessarily imply low marital stability (Booth, Johnson, White, & Edwards, 1984), longitudinal research indicates that the two constructs tend to both respond negatively or positively together (Karney & Bradbury, 1995).

Research has suggested that financial concerns explained approximately fifteen percent of variance of marital satisfaction (Kerkmann, Lee, Lown, & Allgood, 2000), and a partner’s spending behaviors and level of financial stress has a direct impact on their relationship satisfaction (Britt, Grable, Goff, & White, 2008).

Conversely, individuals reporting higher financial satisfaction have a significantly lower probability of considering divorce (Grable, Britt, & Cantrell, 2007) and as such, would have higher levels of marital stability.

Economic Strain and Work-Family Conflict

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Due to the increase in the number of dual-earner couples in the workforce (Bond, Galinsky, & Swanberg, 1998), it becomes increasingly important to consider the balance of work-family conflict. Wickrama, Florensia, Surjadi, Lorenz, and Elder (2008) presented results that suggest that the physical and mental health problems of men may be associated with changes in levels of perceived work control. Having to fulfill both the financial responsibilities of the family (e.g. paying for food, paying the bills) and work responsibilities can contribute to the pressures of general economic hardship and may foster insecurity about the future of the family (Mirowsky & Ross, 2003). Economic strain has been found to be associated with increased work-family conflict for both husbands and wives, but slightly more so for husbands (Schieman & Young, 2011).

Economic Strain and Marital Support Stress and coping theory is a framework for describing how individuals manage difficulties in order to maintain a state of homeostasis in the face of adversity and stress (Lazarus, 1993), and was adjusted by Bodenmann (1997) in order to account for dyadic and family interactions. The dyadic version of the theory may be applied, with the use of an actor-partner interdependence model (APIM), in the context of family economics in order to understand the strategies adopted by husbands and wives to mitigate the impact of economic strain on themselves and each other. Marital support is one psychosocial resource that research has identified for couples as a direct resilient factor with economic strain (Conger, Rueter, & Elder, 1999). Conversely, economic strain may erode or decrease the amount of spousal support as well (Lorenz, Conger, Montague, & Wickrama, 1993). Marital support, however, may also reduce the perceived level of work-family conflict, particularly for wives (Erwins, Buffardi, Casper, & O’Brien, 2001).

Although research has suggested that marital support alone is not sufficient to decrease the negative link between economic strain and marital distress (Conger & Conger, 2002), a positive perception of work-family conflict, in addition to marital support, may together be enough to increase the resilience to the point where the negative impact of economic strain is mitigated. A family’s overall marital support, marital stability, and work family conflict may also be the result of a transactional association between each individual partner’s measures of these criteria. Dyadic research is becoming more frequent and suggests that a partner’s own behaviors and perceptions

influence, and are influenced by, their partner’s behaviors and perceptions (Kenny, Kashy, & Cook, 2006; O’Brien, 2005).

The Current Study The current study draws on family stress and coping theory (Conger & Conger, 2002) in assessing the impact of economic strain on husbands’ and wives’ marital quality and marital stability over a twelve-year, five-wave longitudinal study. Family stress and coping theory account for the ways in which internal or external life stressors are mediated by family psychosocial resources to mitigate their impact on functional status. In relation to the present study, family economic strain due to the early 1990s recession is the life stressor, the functional status is represented by marital quality and marital stability, and the psychosocial resources are represented by measures of marital support and work-family conflict.

The current study explores the influence of family economic strain on marital quality and marital stability through the perceptions of marital support and work-family conflict. Marital support and work-family conflict have been shown to be important psychosocial resources for positive couple outcomes. In order to fully examine the relationships these two psychosocial resources have as mediators between family economic strain and marital outcomes, two individual models and two dyadic models were utilized. These four models are structured to test similarities and differences at the individual level (husband vs. wife) for work-family conflict, and at the dyadic level (husbands and wives), where work-family conflict marital support are analyzed separately. Through the use of these four models, a more comprehensive examination of the mediation of work-family conflict and marital support is conducted. In considering relationships, it is important to consider that couples have both individual needs and challenges, as well as shared dyadic needs and challenges. These four models consider the confluence of work-family conflict and marital support of the couple at multiple levels to examine how they contribute to couples staying together or separating.

Two hypotheses are proposed: 1) family economic strain in 1989–1991 (averaged across both time points) will directly predict marital stability in 2001; and 2) the relationship described in the first hypothesis will be partially mediated by various indirect paths via marital support, work-family conflict, and by marital quality.

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

Volume 18, Issue 2

DESIGN AND METHODOLOGY The current study made use of data for husbands and wives from the Iowa Youth and Families Project (IYFP) and the follow up Iowa Midlife Transition Project (MTP). The final sample used in study comprised 370 primarily lower-middle and middle class, Caucasian, married couples, living in north-central Iowa.

Families were chosen that included two biological, married parents, a 7th grade child, and a sibling within four years of age of the other child. They were interviewed in 1989, 1991, 1992, and 1994 as part of the IYFP, and then followed up in 2001 by the MTP, which interviewed the same families with the same questions. The attrition rate for the IYFP and MTP combined sample was 11% over the 12-year period, which was the result of divorce or separation, moving, or dropping out of the study.

Detailed information about the MTP and IYFP can be found in Conger and Conger (2002) and Conger and Elder (1994).

At the start of the study, in 1989, all husbands and wives were in early middle-age, with average ages of 40 and 38 respectively, and had been married for an average of 17.63 years. They had three children, on average, and the median age of the youngest child was ten. The husbands and wives had been an average of 13.68 and 13.54 years of education, respectively, a median annual income of $33,240, and reported incomes ranging between $0 to $259,000. Ninety-six percent of husbands and seventy-eight percent of wives were employed in a variety of occupations. Husbands were employed as farmers, foremen, and craftsmen (38.4%); managers, professionals, officials, and owners (23.8%); kindred workers and operatives (16.6%); service workers, sales workers, clerical workers, military service workers, and private household workers (14.4%); laborers (3.3%); or other (3.5%). Wives, on the other hand, were employed as homemakers (19%); clerical workers, private household workers, sales workers, and military service works (46.1%); owners, managers, officials, and professionals (23.7%); kindred workers and operatives (4.3%); laborers (0.7%); or other (3.4%).

Study Measures Family economic strain was measured by standardizing total scores from males and females for three separate subscales: the “material needs” scale; the “financial cutbacks” scale; and the “making ends meet” scale (Matthews, Conger, and Wickrama, 1996; Conger, Rueter, & Elder, 1999; Schieman & Young, 2011).

The combination of the scores provided a measure of economic

13

strain for the couple. Example questions from the subscales include asking participants whether they “Strongly Agree” (1) to “Strongly Disagree” (5) about whether the family has enough money for clothing, home, a car, food, household items, medical care, and recreational and leisure activities; whether or not they had taken certain measures (such as “Cut back on social activities and entertainment expenses”) in the last year as a response to financial difficulties; and asking respondents whether their family had experienced difficulties paying their bills within the last year where (1) corresponded with “A great deal of difficulty” and the maximum (5) corresponded with “No difficulty at all”. The final measure for economic strain had a high internal consistency, where α = .94 in 1989–1991, and α = .86 for 2001.

Work-family conflict was measured using a two-item scale (Matthews et al., 1996) in which each participant was asked how often the demands of work interfere with their family life and vice versa. The answers were rated between “Often” (1), and “Never” (4). Work-family conflict was reported for husbands and wives for 1992 and 1994.

Marital support describes the perceived amount of warm and positive interactions a partner receives from their partner (Simons, Lorenz, & Conger, 1993). It was measured using the behavioral affect rating scale (BARS) by subtracting results from the coercive behaviors scales from the supportive behaviors scale. The respondents were asked 13 questions in 1992 and 1994, such as how often did your partner “Get angry at you” or “Act loving and affectionate toward you”. The answers coded so that higher scores corresponded with higher levels of support.

The scale had a Cronbach’s alpha values of .95 and .94 in 1992 and .94 and .93 in 1994 for husbands and wives, respectively.

Marital quality was measured in 1994 and 2001 using a two item scale (Matthews et al., 1996) which asked husbands and wives to rate: “How happy are you, all things considered, with your relationship?” where (1) corresponded with “Extremely unhappy” and (6) corresponded with “Extremely happy”; and “All in all, how satisfied are you with your relationship?’ where (1) corresponded with “Completely satisfied” and (5) corresponded with “Not at all satisfied.” The answers from the two questions were combined for each participant and coded and standardized so that high scores indicated high marital quality.

Time-invariance analyses using the confirmatory factor analysis approach indicated that the factor structure was the same for the measure of Marital Quality in 1994 and 2001. This scale had a Cronbach’s alpha of .84.

Marital stability was measured in 1991 and 2001 using a 5-item

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Table 1 Correlations among the Study Variables 1 1. Fam Economic Strain 19891991

2

3

4

5

6

7

8

9

10

11

--

2. H. Work Family Conflict 1992

.013

3. W. Work Family Conflict 1992

.154**

.139*

--

4. H. Work Family Conflict 1994

.122*

.498***

.037

5. W. Work Family Conflict 1994

.212***

.157**

.536***

.104

6. H. Marital Support 1992

-.181**

-.094

-.060

-.124*

-.106

7. W. Marital Support 1992

-.139*

-.176**

-.233***

-.112

-.192**

.740***

8. H. Marital Support 1994

-.145**

-.115*

-.043

-.246***

-.073

.699***

.594***

9. W. Marital Support 1994

-.131*

-.176**

-.178**

-.114*

-.194***

.395***

.699***

.534***

10. Marital Quality 1994-2001

-.174**

-.120*

-.105

-.137*

-.133*

.444***

.516***

.624***

.678***

11. Marital Stability 2001

-.152**

-.059

-.026

-.147**

-.069

.161***

.079

.204***

.079

.325***

Mean

.01

2.60

2.44

2.61

2.41

3.00

3.10

2.81

3.04

-.024

3.89

SD

.76

.60

.71

.59

.67

1.82

1.70

1.74

1.74

.68

.28

Skewness

.36

-.33

-.05

-.30

.031

-.83

-.67

-.52

-.55

-.29

-4.93

-1.65-

1.00-

1.00-

1.00-

-3.36-

-3.02-

-2.74-

-3.00-

-2.63-

1.00-

2.10

4.00

4.00

4.00

6.00

5.86

6.00

6.00

1.25

4.00

Range

--

1.00-4.00

---------

Note: H = Husband; W = Wife; * p < .05; ** p < .01; *** p < .001

version of the Marital Instability Index (Booth, Johnson, & Edwards, 1983) that asked participants questions such as how often “Have you or your spouse/partner serious suggested the idea of ending your relationship” where (1) corresponded with “Not in the last year” and (4) corresponded with “Within the last three months”. All the items were reversed coded and summed to create the marital stability measure. The scale had Cronbach’s alpha values of .74 in 2001 and .89 in 1991.

Analysis Structural equation modeling (SEM) was used to test the hypotheses and analyze the impact of economic strain on marital quality and marital stability, as mediated by the psychosocial resources of marital support and work-family conflict, for both husbands and wives. SEM analyses were run at both the individual and dyadic levels over time. Participant sex, age, educational attainment, and number of children in the family were accounted for in all analyses as control variables. The analyses were conducted using SPSS 21 and AMOS 24.0 (Arbuckle, 2011). Missing data was accounted for using Full Information

Maximum Likelihood.

Research has indicated that standardized coefficients for dyadic analyses incorporating actor and partner effects may have biased variances (Kenny et al., 2006). The biased variances associated with standardized coefficients may affect the standard errors of test statistics, which in turn may bias the degrees of freedom and tests of statistical significance. Therefore, only unstandardized coefficients are reported. A chi-square statistic/ degrees of freedom ratio, Tucker-Lewis index (TLI), Comparative Fit index (CFI), and root mean square error approximation (RMSEA) were used in order to assess goodness-of-fit. Finally, a Sobel test was employed in order to assess the statistical significance of indirect effects (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). x2/df ratios less than 3.0 (Carmines & McIver, 1981) TLI values greater than .90 (Cheung & Rensvold, 2002), CFI values greater than .95 fit (Hu & Bentler, 1999), and RMSEA values less than .08 fit (Hu & Bentler, 1999) indicate acceptable model fit.

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Volume 18, Issue 2

Figure 1 Husbands’ Individual Model: Impact of Family Economic Strain on Husbands’ Work-Family Conflict, Husbands’ Marital Support, Marital Quality, and Marital Stability.

Note: Results are unstandardized coefficients with corresponding t ratios.

RESULTS Preliminary Analyses All variables were tested to make sure assumptions of uni- and multivariate normality were achieved (Behrens, 1997). The skew of all variables, except for marital stability, lay between -1 and 1. Marital stability had a negative skew of -4.93. It was rated between 1.0 and 4.0, had a standard deviation of .28, and a mean of 3.89, where high scores correspond with higher perceived marital stability. This indicates that a large number of participants reported a high marital stability in their enduring marriage. A zero-order, or bivariate, correlational matrix for the variables utilized in the four models is presented in Table 1.

--Insert Table 1 here--

The Individual Model - Husbands The individual model for husbands is shown in Figure 1a. The model fit indices indicate that the model fits the data well (x2(41, 370) = 93.80, p <.01; RMSEA = .06; 95% CI [.043, .075]; CFI = .96; TLI = .90) where x2/df = 2.29. As average family economic strain across 1989-1991 increased, husbands’ marital support in 1992 decreased (b = -0.419, p < .001), and work-family conflict in 1994 increased (b = 0.079, p < .05). Furthermore, for increased average perceived economic strain across 1989– 1991, average perceived marital quality across 1994 and 2001 decreased (b = -0.080, p < .05). As husbands’ marital support in 1994 increased, so did the couple’s average marital quality across 1994 and 2001 (b = 0.235, p < .001). As the couples’ perceptions of average marital quality across 1994 and 2001 increased, their perceptions of marital stability in 2001 also increased (b = 0.117, p < .001). Among control variables, marital stability was significantly and positively associated with marital

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Journal of Personal Finance

Figure 2 Wives’ Individual Model: Impact of Family Economic Strain on Wives’ Work-Family Conflict, Wives’ Marital Support, Marital Quality, and Marital Stability.

Note: Results are unstandardized coefficients with corresponding t ratios.

stability in 2001 (b = 0.185, p < .001).

The following actor effects were identified: as husbands’ perception of work-family conflict in 1992 increased, their own perception of work-family conflict in 1994 increased in turn (b = 0.488, p < .001); as husbands’ perceptions of marital support in 1992 increased, their perceptions of marital support in 1994 also increased (b = 0.673, p < .001).

A Sobel test was undertaken in order to test the significance of indirect effects (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). Average family economic strain across 1989– 1991 was found to indirectly affect marital stability in 2001 via average marital quality across 1994 and 2001 (z = -1.98, p < .05); average family economic strain across 1989–1991 was indirectly linked to husbands’ marital support in 1994 via husbands’ marital support in 1992 (z = -3.24, p < .001); husband’s marital support in 1992 was indirectly linked to average marital quality across 1994 and 2001 via husbands’ marital support in 1994 (z = 10.79, p < .001); and finally, husbands’ marital support in 1994

was indirectly linked to marital stability in 2001 via average marital quality across 1994 and 2001 (z = 5.39, p < .001).

The Individual Model—Wives The individual model for wives is shown in Figure 1b. The model fit indices indicate that the model fits the data well (x2(41, 370) = 83.90, p <.001; RMSEA = .05; 95% CI [.037, .069]; CFI = .97; TLI = .92), where x2/df = 2.05. As average family economic strain across 1989-1991 increased, wives’ perceptions of work-family conflict in 1992 increased (b = 0.143, p < .01), wives’ perceptions of marital support in 1992 decreased (b = -0.262, p < .05), and their perceptions of work-family conflict in 1994 increased (b = 0.107, p < .01). As average family economic strain across 1989– 1991 increased, average marital quality across 1994 and 2001 decreased (b = -0.082, p < .05). Increased expression of marital support from wives in 1994 increased the couples’ perceptions of average marital quality across 1994 and 2001 (b = 0.261, p <

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Volume 18, Issue 2

Figure 3 Dyadic Model for Work-Family Conflict: Impact of Family Economic Strain on both Husbands’ and Wives’ Work-Family Conflict, Marital Quality, and Marital Stability.

Note: Results are unstandardized coefficients with corresponding t ratios.

.001). Finally, as couples’ perceptions of average marital quality across 1994 and 2001 increased, their perceptions of marital stability in 2001 also increased (b = 0.117, p < .001). Among control variables, marital stability in 1992 was significantly and positively associated with marital stability in 2001 (b = 0.185, p < .001).

average family economic strain across 1989–1991 was found to be indirectly linked to wives’ marital support in 1994 via wives’ marital support in 1992 (z = -2.20, p < .05); wives’ marital support in 1992 was found to be indirectly linked to average marital quality across 1994 and 2001 via wives’ marital support in 1994 (z = 12.35, p < .001); and finally, wives’ marital support in 1994 was found to be indirectly linked to marital stability in 2001 via average marital quality across 1994 and 2001 (z = 5.54, p < .001).

The following actor effects were identified: as wives’ perceptions of work-family conflict in 1992 increased, their perceptions of work-family conflict in 1994 also increased (b = 0.474, p < .001); and, as wives’ perceptions of marital support in 1992 increased, their perceptions of marital support in 1994 also increased (b = 0.719, p < .001).

The Work-Family Conflict, Dyadic Model

A Sobel test used to check the significance of indirect effects showed average family economic strain across 1989–1991 was found to be indirectly linked to marital stability in 2001 via average marital quality across 1994 and 2001 (z = -2.17, p < .05);

The dyadic work-family conflict model is shown in Figure 1c.

The model fit indices indicate that the model fits the data well (x2(41, 370) = 87.25, p <.001; RMSEA = .06; 95% CI [.039, .071]; CFI = .96; TLI = .89). The x2/df ratio was 2.13. As average family

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Figure 4 Dyadic Model on Marital Support: Impact of Family Economic Strain on both Husbands’ and Wives’ Marital Support, Marital Quality, and Marital Stability.

Note: Results are unstandardized coefficients with corresponding t ratios.

economic strain across 1989-1991 increased, wives’ work-family conflict in 1992 increased (b = 0.149, p < .01), and husbands and wives both expressed increased work-family conflict in 1994 (b = 0.091, p < .05 and b = 0.114, p < .01, respectively). As average family economic strain across 1989–1991 increased, average marital quality across 1994 and 2001 decreased (b = -0.123, p < .01). As both husbands’ and wives’ work-family conflict in 1994 increased, their average marital quality across 1994 and 2001 decreased (b = -0.127, p < .05 and b = -0.117, p < .05, for husbands and wives respectively). As the average marital quality for the couple across 1994 and 2001 increased, their marital stability in 2001 also increased (b = 0.117, p < .001).

Among control variables, marital stability in 1992 was positively associated with marital stability in 2001 (b = 0.185, p < .001).

The following actor effect was identified: for both husbands and wives, as each of husbands’ or wives’ perception of work-family conflict in 1992 increased, so did their respective

perceptions of work-family conflict in 1994 (b = 0.497, p < .001 and b = 0.478, p < .001, for husbands and wives respectively).A Sobel test was undertaken in order to test the significance of indirect effects: average family economic strain across 1989– 1991 was found to be indirectly associated with marital stability in 2001 via average marital quality across 1994 and 2001 (z = -2.39, p < .01); average family economic strain across 1989– 1991 was found to be indirectly linked to wives’ work-family conflict in 1994 via wives’ work-family conflict in 1992 (z = -2.86, p < .01); wives’ work-family conflict in 1992 was found to be indirectly associated with average marital quality across 1994 and 2001 via wives’ work-family conflict in 1994 (z = -2.08, p < .05); average family economic strain across 1989-1991 was found to be indirectly linked to average marital quality across 1994 and 2001 via wives’ work-family conflict in 1994 (z = -1.69, p < .05); wives’ work-family conflict in 1994 was found to indirectly associated with marital stability in 2001 via average marital quality across 1994 and 2001 (z = -2.00, p < .05); average family

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Volume 18, Issue 2

economic strain across 1989-1991 was found to be indirectly associated with average marital quality across 1994 and 2001 via husbands’ work-family conflict in 1994 (z = -1.56, p < .05); and finally, husbands’ work-family conflict in 1994 was found to be indirectly associated with marital stability in 2001 via average marital quality across 1994 and 2001 (z = -1.93, p < .05).

The Marital Support, Dyadic Model The dyadic marital support model is shown in Figure 1d. The model fit indices indicate that the model fit the data well (x2(41, 370) = 80.13, p < .001; RMSEA = .05; 95% CI [.034, .067]; CFI = .98; TLI = .95), where the x2/df ratio was 1.95. As average family economic strain across 1989–1991 decreased, husbands’ and wives’ reported marital support in 1992 (b = -0.417, p < .001 and b = -0.308, p < .01, for husbands and wives respectively).

As both husbands’ and wives’ perceptions of their own marital support in 1992 increased, so did their perceptions of marital support in 1994 (b = 0.590, p < .001 and b = 0.874, p < .001, for husbands and wives respectively). As both husbands’ and wives’ marital support in 1994 increased, average marital quality across 1994 and 2001 increased (b = 0.136, p < .001 and b = 0.190, p < .001, for husbands and wives respectively). As average marital quality across 1994 and 2001 increased, so did marital stability in 2001 (b = 0.117, p < .001). As average family economic strain across 1989-1991 increased, average marital quality across 1994 and 2001 decreased (b = -0.061, p =.054).

Among control variables, marital stability in 1991 was positively associated with marital stability in 2001 (b = 0.185, p < .001).

The following partner effects were identified: as wives’ marital support in 1992 increased, so did husbands’ marital support in 1994 (b = 0.114, p =.062); husbands’ marital support in 1992 was negatively associated with wives’ marital support in 1994 (b = -0.226, p < .001), however, in contrast, there was no significant association between wives’ marital support in 1992 and husbands’ marital support in 1994.

A Sobel test was undertaken in order to test the significance of indirect effects: Average family economic strain across 1989– 1991 was found to be indirectly associated with husbands’ and wives’ marital support in 1994 via husbands’ and wives’ marital support in 1992 (z = -3.13, p < .001 and z = -2.55, p < .01 for husbands and wives respectively); husbands’ and wives’ marital support in 1992 was found to be indirectly associated with average marital quality across 1994 and 2001 via husbands’ and wives’ marital support in 1994 (z = 6.33, p < .001 and z = 9.20,

p < .001 for husbands and wives, respectively); average family economic strain across 1989-1991 was found to be indirectly associated with wives’ marital support in 1994 via husbands’ marital support in 1992 (z = 2.53, p < .01); and finally, husbands’ and wives’ marital support in 1994 was found to be indirectly associated with marital stability in 2001 via average marital quality across 1994 and 2001 (z = 4.72, p < .001 and z = 5.25, p < .001 for husbands and wives, respectively).

DISCUSSION In relation to the first hypothesis, the models failed to indicate a direct link between average economic strain across 1989–1991 and marital stability in 2001. This result may be due to the nature of the sample—only married couples were included, so those that were impacted the most by family economic strain may have divorced or separated. However, it would be pertinent to further explore the underlying reasons behind the absence of a direct link between family economic strain and marital stability.

As predicted in the second hypothesis, various indirect links were identified. The results indicate that higher marital quality was a result of successful management of work-family conflict through marital support, in spite of the negative impact of family economic strain. Indeed, all four models identified marital quality as a mediating factor between family economic strain and marital stability. The fact that, for the individual models, a link was identified between economic strain and marital quality and marital stability via marital support but not via work family conflict suggests that marital support may be a more influential factor than work-family conflict.

In contrast, several indirect paths were identified in the first dyadic model indicating that work-family conflict and marital support are both important in mediating the link between family economic strain and marital quality and marital stability.

There were generally more actor than partner effects, in that a partner’s perception of their marital support or work-family conflict in 1992 was more likely to predict their own subsequent perception of their marital support or work-family conflict in 1994 than to predict their partner’s perceptions. Four indirect paths were identified showing that family economic strain negatively impacted marital quality and marital stability through the mediators of work-family conflict for husbands and wives.

The second dyadic model identified three indirect paths linking

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Journal of Personal Finance

family economic strain with marital stability via intermediate marital support, thereby further supporting the second hypothesis as well as family stress and coping theory. The first two indirect paths link average family economic strain with marital quality and marital stability via husbands’ and wives’ perceptions of marital support. The third path shows that an increase in average family economic strain resulted in a decrease in husbands’ marital support in 1992, which, interestingly, resulted in an increase in wives’ marital support in 1994, and a resulting subsequent increase in marital quality and marital support.

The negative association between husbands’ marital support in 1992 and wives’ marital support in 1994 may be due to the high multi-collinearity of the measures for marital support, and general model instability. Further, it may be argued that, due to a significant influence of wives’ marital support in in 1992 on their marital support in 1994, wives’ marital support in 1994 may not be able to increase any further due to influence from husbands’ marital support in 1992, resulting in a ceiling effect.

nomic strain with marital quality and marital stability.

The results of the models used in the current study are consistent with existing research in suggesting that family stress and coping theory provide a compelling framework for investigating how families manage stressful economic hardships. The findings indicate that high perceptions of marital support, and low perceptions of work-family conflict may act as psychosocial for those experiencing economic strain, and, as such, provide evidence for the resilience of middle-aged married couples in the face of the organizational restructuring, competition for jobs, and corporate downsizing concomitant with economic recessions (Elder & O’Rand, 1994; Lorenz, Elder, Bao, Wickrama, & Conger, 2000).

Cohort Perspectives and Future Directions

The results from the second dyadic model also indicate that work-family conflict may have an earlier and more significant impact on wives than on husbands. Previous research has suggested that lower levels of marital quality were partly due to wives’ extended hours of work and wives’ work responsibilities (Amato, Johnson, Booth, & Rogers, 2003). The researchers also discovered that an increase in husbands’ apportionment of housework decreased husbands’ marital quality, but increased wives’ marital quality. Furthermore, wives traditionally take on more family responsibilities (Arendell, 2000). As such, a gendered effect may exist insofar as wives take on more work and family responsibilities, and they may do so earlier, than husbands.

In reviewing the results of the current study, it is worth noting the temporal context of the sample. Research has suggested that as many as 64% of the baby boomer cohort is not financially healthy, and have few assets and little savings (Baek & DeVaney, 2004). As such, the economic recessions in the early 1990’s and 2001 may have been particularly problematic for this cohort. In contrast, the results actually indicate that the couples in the present study maintained fairly consistent levels of work-family conflict and marital support in spite of the economic strain. This may be due to the fact that, between the recessions of 1989 and 2001, the mid- to late-1990s represent a decade of significant economic growth (Mankiw, 2001). The effects of the economic strain may, therefore, have been mitigated by the improving economic conditions.

Family economic strain was found to be positively associated with husbands’ work-family conflict and wives’ work-family conflict. This link does not come as a surprise in light of previous research which has repeatedly identified a negative association between work-to-family/family-to-work conflict and marital quality (Bellavia & Frone, 2005). Furthermore, work-family conflict is positively related between 1992 and 1994 for both husbands and wives, which means that, for both husbands and wives, as perceptions of work-family conflict increased in 1992, it also increased in 1994. The differences between the significance of indirect links via marital support versus those via work-family conflict may be due to marital support being of greater importance in relation to marital quality, as compared with work-family conflict for marital quality. Marital support functioned as the primary mechanism associated family eco-

Research has shown that, in contrast with other cohorts, people from the same birth cohort as that used in the current study have significant divorce rates (Stevenson & Wolfers, 2007). In spite of this, a large majority of the sample stayed together.

This may in part be due to the fact that in the current sample, each family consisted of at least two children. Previous research has indicated that having children increases marital stability (Twenge, Campbell, & Foster, 2003). This positive effect may, however, be somewhat reduced, given that marital quality often declines throughout the middle-age years as a result of increased parental and family responsibilities (Adelmann, Chadwick, & Baerger, 1996). It is worth noting that over the course of the 12-year study, the children may have transitioned to adulthood themselves, thus relieving the parents of the burden of care and thus increasing marital quality and stability. Ad-

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ditionally, age may play a role in that more mature individuals, with more life experience, may be more able to strike a more effective balance between family and work responsibilities, as well as being able to provide spousal support. However, this aspect is not explored in the current study and presents itself as an opportunity for further research.

Limitations Some limitations of the current study should be highlighted.

The sample may be limited in terms of generalizability in that it comprised only rural, Caucasian, low-to-middle income, married, middle-aged couples from the Midwest U.S. who had at least two children. However, previous research has indicated that the negative impact of economic strain has similar effects across demographics, including low-to-middle income Latino, African American, and Caucasian families (Conger, Wallace, Sun, Simons, McLoyd, & Brody, 2002; Jackson, Krull, Bradbury, & Karney, 2017). Nonetheless, future research should aim to incorporate more diverse demographics, and investigate differences between ethnicity, race, cohort, age, and location. Additionally, the current study utilized a secondary dataset and, as such, limited the selection of measures to those previously decided for each construct. Further, some of the individual items in each measure were not used across all time points. It may be that other measures could more effectively represent the constructs than those used in the present study.

Practice Implications For many adults, the midlife developmental period is a sensitive time, particularly for families, requiring a balancing of parenting practice, employment and job responsibilities, and preparation for retirement. Individuals in their middle years represent an important demographic group, being the largest U.S. cohort, and also one which takes the brunt of economic recessions. The results of the present study indicate that family economic strain may be a chronic source of stress for families, and, therefore, financial practitioners should account for current and lasting effects of economic strain when establishing financial plans.

The findings indicate that lower perceived levels of work-family conflict can act as a mediator between family economic strain and marital stability. As such, the balancing of work and family responsibilities need not be negative in itself. For example,

an individual may derive satisfaction from their work, and as a result, this positive experience may facilitate more positive interactions with their family. Financial practitioners should therefore be mindful before assuming that work demands necessarily impact families negatively, and instead, focus on establishing a healthy balance between work and family.

In addition to work-family conflict, the results of the current study indicate that the perception of marital support may also be an effective psychosocial resource. Previous research has supported this finding, and highlighted how economic strain and marital quality is mediated via a combination of positive and hostile marital interactions, where the positive interactions explain more of the variation in marital quality than those that were hostile (Ross, Oâ&#x20AC;&#x2122;Neal, Arnold, & Mancini, 2017). As such, financial practitioners should also focus on both reducing the number of hostile interactions and increasing the positive interactions with couple relationships when seeking to address family economic strain.

CONCLUSION The current study investigated direct and indirect associations between family economic strain and marital stability via the mechanisms of work-family conflict, marital support, and marital quality for a sample of 370 married couples over the course of 12 years. Individual and dyadic structural equation models were used to explore these associations for husbands and wives. The results are consistent with family stress and coping theory, as well as previous research highlighting how resilience/support mechanisms, such as marital support, predict relationship outcomes (Conger & Conger, 2002; Newton & Kiecolt-Glaser, 1995). Specifically, work-family conflict and marital support may act as positive, psychosocial resources, helping couples maintain marital quality and marital stability in the face of family economic strain.

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Volume 18, Issue 2

25

Financial Education and Demand for Debt Counseling Advice

Nilton Porto, MBA, PhD, University of Rhode Island Jing Jian Xiao, PhD, University of Rhode Island

Abstract The purpose of this study is to examine potential effects of consumer financial education on demand for debt counseling advice using a large and representative national dataset. Previous research has examined demographic, economic, and financial capability factors on the demand for debt counseling advice. After controlling for these factors and eliminating the effect of bankruptcy, financial education, a variable not examined in previous research, is positively associated with the demand for debt counseling advice. Education attainment is similarly associated with debt counseling. These results suggest evidence of the complementary nature of financial education. Our results indicate that consumers who have been exposed to financial education are more likely to seek debt counseling advice.

Key Words financial education; debt counseling; financial advice

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Journal of Personal Finance

INTRODUCTION In personal finance and consumer economics research, there is a growing interest in the interplay between financial education and seeking financial advice. In addition to acquiring financial education, receiving professional and unbiased financial advice might provide with consumers the necessary tools for making better financial decisions. Financial education is the formal teaching of a curriculum of money management knowledge and skills to nonfinancial professionals. This teaching can be delivered in a classroom or an online setting. Financial education can be provided in high schools, colleges, and workplaces.

Financial education contributes to financial capability and financial wellbeing (Xiao & O’Neill, 2016; Xiao & Porto, 2017).

Financial literacy can be broadly defined as “peoples’ ability to process economic information and make informed decisions about financial planning, wealth accumulation, debt, and pensions” (Lusardi & Mitchell, 2014, p. 2) and also narrowly defined as financial knowledge indicated by financial knowledge quiz scores (Lusardi & Mitchell, 2014). To avoid confusion, we use financial knowledge to refer to the narrow definition of financial literacy in this paper. Consumers may choose between seeking financial advice or attending a financial education training based on their perception of their own level of financial knowledge and based on the type of financial issues they are facing.

Moreover, consumers that attend financial education training might learn the benefits of seeking help from a financial professional such as a financial advisor or financial counselors.

In the literature, the terms “financial literacy” and “financial capability” are often used interchangeably (Lusardi & Mitchell, 2010) since financial literacy refers to not only financial knowledge but also to its applications (Huston, 2010). In this study, we define financial capability as the ability to apply basic financial knowledge and engage in desirable financial behavior to achieve financial wellbeing (Xiao, Chen, & Chen, 2014).

This definition of financial capability is equivalent to the broad definition of financial literacy and used in this paper. Financial education provides the foundation to improve financial capability, or both financial knowledge and financial behavior.

Financial knowledge has been shown to act as a complement to all-purpose financial advice (Collins, 2012; Gentile, 2016).

While financial education is a possible source of financial capability, financial advice might also substitute or complement financial knowledge. The present study examines the relationship between financial education and the demand for debt counseling advice, a type of advice most likely sought by

consumers at times of financial distress. A key component of our analysis is to determine if financial education serves as a complement or substitute in the demand for debt counseling advice. If the complementary assumption holds, consumers who participated in a formal financial education training would display higher levels of financial capability, appreciate benefits of financial advice, and more likely to seek financial advice if needed. If the substitute assumption holds, consumers lacking financial capability might be more likely to seek financial advice and rely on financial advisers for their money management decisions.

While the broad category of financial advice deserves research attention, advice on specific topics may affect different sets of consumers. For instance, a consumer seeking investment advice might be very different from one seeking debt counseling; the former has funds available to invest while the latter is most likely experiencing financial stress.

Unlike previous research concerning the five topics of financial advice, this study focuses only on debt counseling advice.

Researching a single topic of financial advice may provide specific implications for financial professionals and policymakers.

Among various forms of financial advice, the use of debt counseling advice has been shown to be the least prevalent among American households (Collins, 2012; Robb, Babiarz, & Woodyard, 2012). Unlike other advising topics, debt counseling advice is usually remedial following an event of financial distress.

This study examines potential effects of a relevant variable, financial education, on debt counseling. To the best of our knowledge, previous research has not yet directly examined past participation as a potential determinant for seeking debt counseling advice. We believe that this study’s findings generate helpful information for financial service professionals who provide advice for consumers with debt problems.

PREVIOUS RESEARCH AND HYPOTHESIS Previous research has studied financial advice from a number of perspectives: financial literacy (Calcagno & Monticone, 2015), issues relating to older adults (Cummings & James, 2014), payment schemes (Finke, Huston, & Winchester, 2011), wealth volatility (Grable & Chatterjee, 2014), risk tolerance (Hanna, 2011), and portfolio diversification (Von Gaudecker, 2015). More generally, the literature on help-seeking behavior can be found in Grable and Joo (1999, 2001) and Lim, Heckman, Letkiewicz,

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Volume 18, Issue 2

and Montalto (2014).

In recent years, researchers have used data from the National Financial Capability Study (NFCS) to examine factors associated with financial advice and their potential effects on consumer financial wellbeing. Several studies used data from the 2009 NFCS to study determinants of financial advice usage such as trust (Lachance & Tang, 2012), financial literacy and capability (Collins, 2012; Robb et al., 2012), and income (Tang & Lachance, 2012). Two other recent articles used the 2012 wave of the NFCS to study the demand for financial advice. Porto and Xiao (2016) explored whether financial literacy overconfidence (a miss calibration between objective and subjective financial knowledge) is associated with five types of financial advice while Xiao and Porto (2016) examined the relationship between financial advice and financial satisfaction.

The studies mentioned above show the breadth and complexity involved in the utilization of financial advice and a few themes emerge. First, demographic, financial, and financial capability factors are correlated with the demand for financial advice. Second, financial knowledge may play an important role in people’s decision to seek financial advice.

The literature regarding debt counseling (sometimes referred to as debt advice) is more limited, but a few relevant studies have been published in recent years. Collins (2012) used the 2009 NFCS national data set, which includes 1,468 observations, and found that difficulty paying bills and a large drop in income are both positively associated with debt counseling.

Robb et al. (2012) used the 2009 NFCS state level data with 28,146 observations to find that higher levels of financial knowledge, financial satisfaction and financial confidence all have negative associations with the demand for debt counseling. Using the same dataset, Tang and Lachance (2012) conducted discriminant analyses to reveal that the five top contributors to the demand for debt counseling are bankruptcy, checking account overdraws, having high cost loans, difficulty paying bills, and financial satisfaction (which was negatively correlated). These studies seem indicative of the remedial nature of debt counseling where its demand increases due to financial stress or a detrimental financial event.

Financial education in this study refers to any education program delivered at work or at an educational institution teaching a curriculum of money management knowledge and skills to nonfinancial professionals. Research shows that consumer education contributes to consumer financial capability and wellbeing (Xiao & O’Neill, 2016; Xiao & Porto, 2017).

The exposure to formal financial education may contribute to the demand for debt counseling advice, even after controlling for financial capability factors. Financial education may benefit consumers in multiple ways, including knowledge acquisition, confidence enhancement, and action encouragement (Xiao & O’Neill, 2016). Mediation research implies that financial education has stronger effects on confidence in clients’ self-assessment of financial knowledge (subjective financial knowledge) and self-reported financial behavior (Xiao & Porto, 2017). This study also implies that financial education has unique contributions to financial satisfaction after including financial capability variables as mediating factors. This implies that financial education may serve purposes not only measured by knowledge transmission, confidence enhancing, or behavior encouragement, but also by peer effects and resource supplies. Consumers with a higher financial knowledge level are also more likely to engage in desirable financial behaviors, such as financial advice seeking (Porto & Xiao, 2016).

Research suggests that debt counseling may improve clients’ behaviors and their financial wellbeing, which corresponds with findings about the impact of other types of financial advice (Agarwal, Amromin, Ben-David, Chomsisengphet, & Evanoff, 2010; Disney Gathergood, & Weber, 2015; Elliehausen, Lundquist, & Staten, 2007; Moulton, Collins, Loibl, & Samek, 2015; Roll & Moulton, 2016; Xiao & Wu, 2008). When considering previous research on the effects of financial education on financial behavior, it seems possible that in the context of debt counseling, financial education may help consumers realize the importance of debt counseling, enhance their confidence in taking control of debt, and further promote their engagement in more beneficial financial behaviors. In addition, financial education may provide additional benefits for students, such as peer support and needed resources. Based on these findings, we test the following hypothesis: H: Consumers who have received financial education are more likely to seek debt counseling advice.

METHODS Data This study used data from the 2012 National Financial Capability Study (NFCS) funded by the Financial Industry Regulatory Authority Investor Education Foundation (FINRA IEF). The NFCS

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was an online survey of over 25,000 American adults from all states and the District of Columbia. FINRA IEF conducted the study to better understand the concept of financial capability and its key components. The sample is representative of the American population (for more information about this data set, see FINRA IEF, 2013).

included being a homeowner, having difficulty paying bills, and having experienced a drop in income in our analyses to capture household wealth and potential financial stressors. Willingness to take financial risks was measured using a survey question with a 1-10 scale where 1 represents “Not at all willing” while 10 represents “Very willing.”

Variables

Data Analyses

Debt counseling advice. One question on the survey asked: “In the last five years, have you asked for any advice from a financial professional about any of the following?” where debt counseling was one of the options. The possible answer was yes or no. Respondents that answered “I don’t know” or “Prefer not to say” to the debt counseling question were dropped from the analyses, reducing the sample size to 24,932.

We used probit regression models reporting average marginal effects (AME) with corrections for heteroskedastic errors in our analyses. In our models, debt counseling advice is a function of demographic and financial, financial capability, and financial education variables. We controlled for regional economic variation by including the four census regions listed in the survey as fixed effects. Hierarchical models were constructed by adding different sets of independent variables to show their unique contributions. A separate set of analyses were performed, splitting our sample into those that have or have not filed for bankruptcy in the last 12 months, since credit counseling is required during the bankruptcy process. We also included a model limiting our sample to those that attended college and another model containing a series of interaction terms.

Financial education. Financial education was measured in the study in a two-step process. Respondents were first asked if they had ever been offered financial education in the past.

Conditional on whether they were offered and participated in financial education, respondents were next asked if the education was offered at high school, college, or from an employer.

Roughly 23% of respondents had received financial education from one of these sources.

Financial capability. We used four variables to measure financial capability variables. Objective financial knowledge is the score of five financial knowledge questions included in the survey, questions that have come to be known as the “Big Five” (Lusardi & Mitchell, 2010). Subjective financial knowledge is a self-assessment of financial knowledge with the wording “On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall financial knowledge?”

Perceived financial capability is measured using a survey question asking respondents to rate the following statement using a Likert-type 1-7 scale: “I am good at dealing with day-to-day financial matters, such as checking accounts, credit and debit cards, and tracking expenses.” The financial behavior variable is the sum of three dummy variables: spending less than income, having emergency funds for 3 months of expenses, and having obtained a copy of credit report in the last 12 months.

Other control variables. Following previous research (see the previous section), this study also investigated a number of demographic and financial variables to better understand the demand for debt counseling. These included gender, race, age, education, income, and number of dependent children. We also

RESULTS Descriptive Statistics Table 1 presents descriptive statistics of the sample. A total of 9% of households reported using debt counseling advice in the last five years. Among the respondents, 10% reported having received financial education in high school, 11% in college, and 8% in the workplace. On average, respondents were able to correctly answer less than three financial knowledge questions while scoring themselves relatively high in both subjective financial knowledge and perceived financial capability (5.18/7 and 5.68/7, respectively). Respondents self-disclosed to having performed slightly more than one positive financial behavior while other descriptive statistics are similar to those reported in previous research (FINRA IEF, 2013).

Table 2 compared those that received debt counseling with those who did not. Respondents who used debt counseling were more likely to participate in financial education, an indication of the potential complementary nature of financial ed-

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Volume 18, Issue 2

Table 1 Summary Statistics Mean

Standard Deviation

0.09

0.28

High School Fin. Education

0.10

0.31

College Fin. Education

0.11

0.32

Workplace Fin. Education

0.08

0.28

Objective Financial Knowledge (0–5)

2.94

1.45

Subjective Financial Knowledge (1–7)

5.18

1.28

Perceived Financial Capability (1–7)

5.68

1.57

Financial Behaviors (0–3)

1.21

0.97

Female # of Children

0.51 0.72

0.50 1.08

Homeowner

0.59

0.49

Married

0.55

0.50

Under $25,000

0.26

0.45

$25,000–50,000

0.26

0.44

$50,000–100,000

0.30

0.46

Over $100,000

0.18

0.38

Difficulty Paying Bills

0.57

0.49

Income Drop Last Year

0.30

0.46

Bankruptcy last 12 months

0.04

0.19

Under 35

0.27

0.47

35–50

0.37

0.48

Over 50

0.36

0.47

White

0.74

0.43

African American

0.11

0.32

Hispanic

0.09

0.34

Asian

0.04

0.21

Other Race

0.02

0.14

Lower than high school

0.07

0.31

High School Diploma or GED

0.25

0.45

Incomplete College

0.33

0.48

College Graduate

0.34

DV: Debt Advice

Financial Education

Financial Capability Variables

Controls/Demographics

Income

Financial Stressors

Age

Race/Ethnicity

Education Attainment

Observations

Source: 2012 National Financial Capability Study

0.44 24,932

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Table 2 Comparison by Whether Respondent Received Debt Counseling No Counseling

Counseling

Two-tailed T-test using unequal variances p-value

Mean

Standard Deviation

Mean

Standard Deviation

High School Fin. Education

0.11

0.31

0.15

0.36

0.000

College Fin. Education

0.12

0.33

0.17

0.37

0.000

Workplace Fin. Education

0.09

0.28

0.13

0.34

0.000

Difficulty Paying Bills

0.55

0.50

0.76

0.43

0.000

Income Drop Last Year

0.27

0.44

0.48

0.50

0.000

Bankruptcy

0.02

0.13

0.18

0.38

0.000

Willing to take risks

4.69

2.57

5.27

2.89

0.000

Objective Financial Knowledge

3.05

1.44

2.82

1.36

0.000

Subjective Financial Knowledge

5.19

1.26

5.21

1.36

0.424

Perceived Financial Capability

5.75

1.55

5.42

1.67

0.000

Financial Behaviors

1.23

0.97

1.24

0.91

0.900

Savings/Investment Advice

0.29

0.45

0.52

0.50

0.000

Mortgage/Loan Advice

0.19

0.40

0.46

0.50

0.000

Insurance Advice

0.30

0.46

0.62

0.49

0.000

Tax Planning Advice

0.17

0.38

0.39

0.49

0.000

Education Attainment

Financial Stressors

Financial Capability Variables

Financial Advice

Observations

22,761

Source: 2012 National Financial Capability Study

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

2,171

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Volume 18, Issue 2

ucation on the demand for debt counseling advice. Those who sought debt counseling were also more likely to have received financial advice in any of the other four categories, perhaps an indication that some people are advice seekers while others are not.

The presence of financial stressors such as having difficulty paying bills, a drop in income, or a recent bankruptcy filing were also related to debt counseling. However, those who carried out positive financial behaviors were not significantly different between debt counseling advice users and non-users. Debt counseling advice users scored lower in objective financial knowledge and subjective financial capability. No difference between the two groups was found on subjective financial knowledge.

Regression Analyses Table 3 presented results of the probit regression reporting marginal effects at the mean. We used three hierarchical models. Model 1 included only demographic and financial stressor indicators. In model 2, financial capability variables were entered. In model 3, financial education variables (the variable of interest), were included. Comparing the results of these models, we observed several interesting findings. First of all, while financial education variables were entered into the model last, the high school and workplace financial education variables still showed significant associations with debt counseling usage. Second, all four financial capability variables were associated with the demand for debt counseling. Higher scores in the objective financial knowledge or perceived ability to manage oneâ&#x20AC;&#x2122;s own finances (subjective financial capability) were negatively associated with debt counseling usage, which is consistent with previous research (Robb et al., 2012). Subjective financial knowledge was positively associated with debt counseling, a possible indication of overconfidence as shown in previous research (Porto & Xiao, 2016). Debt counseling is most likely sought after a financial difficulty such as loan delinquency or severe credit card debt. Overconfident consumers might fail to notice the warning signs and fail to seek debt counseling when it is needed (Kramer, 2016). Performing more desirable financial behaviors was also positively associated with debt counseling, which makes sense. Debt counseling includes reviewing the clientsâ&#x20AC;&#x2122; credit reports, one of the beneficial behaviors included in this variable. This finding might indicate reverse causality, where those who sought debt advice were asked to

pull their credit report during one of the counseling meetings.

Third, education attainment had a positive relationship with debt counseling, while financial stressors were negatively associated with debt counseling. Taken as whole, these results point to the complementary nature of debt counseling and financial education: individuals who have been exposed to financial education are more likely to seek advisors, as observed in past research by Collins (2012).

We also found that the demand for debt counseling diminished with age and that African-Americans were more likely to seek debt counseling than Whites. Demand for debt counseling advice rose with the number of dependent children, a variable that has often indicated financial stress in previous research.

Bankruptcy was also a strong indicator of debt counseling.

The federal mandate requiring credit counseling during the bankruptcy process gives this regressor a distinctive role in this analysis since most of other explanatory variables are, arguably, endogenously determined. People who file for bankruptcy may be different from those that have never filed regarding their demand for debt counseling. In order to test this assumption, we conducted additional analyses by splitting our sample between those who have and those who have not filed for bankruptcy in the last 12 months (Table 4).

In addition to identifying differences between the bankruptcy and non-bankruptcy groups, Table 4 also helped distinguish endogenous factors that have explanatory impacts on the demand for debt counseling as opposed to those that were forced into seeking this type of advice due to a bankruptcy.

Comparing the two groups, a number of variables were significant for those without a bankruptcy in their record but were not significant for those who had filed bankruptcy recently. For instance, receiving financial education in high school or from an employer was associated with debt counseling in the first column (non-bankruptcy), but the association disappeared when bankruptcy is present. Higher educational attainment was positively associated with debt counseling only for those that have not filed for bankruptcy in the last 12 months.

Robustness Check In the initial sample, 32.79% of respondents either had a high school diploma or an equivalent certification or had not completed high school. This group of respondents could only obtain financial education from either the workplace or during

32

Journal of Personal Finance

Table 3 Probit Results on Debt Counseling Advice

Age (ref: under 35) 35 to 50 Over 50 Female Married # of Children Homeowner Race/Ethnicity (ref: White) African American Hispanic Asian Other Race Educational Attainment (ref: no H.S.) High School or GED Incomplete College Graduate College Income (ref: under $25,000) $25,000–$49,999 $50,000–$99,999 Over $100,000 Financial Stressors Difficulty Paying Bills Income Drop Last Year Bankruptcy Standardized Financial Capability Variables Objective Financial Knowledge Subjective Financial Knowledge Perceived Financial Capability Financial Behaviors Financial Education High School Fin. Education

(1) Debt Counseling Advice estimate (standard error)

(2) Debt Counseling Advice estimate (standard error)

(3) Debt Counseling Advice estimate (standard error)

-0.022*** (0.005) -0.035*** (0.005) -0.007* (0.003) -0.004 (0.004) 0.012*** (0.002) -0.002 (0.004)

-0.017*** (0.005) -0.030*** (0.005) -0.007 (0.003) -0.003 (0.004) 0.011*** (0.002) -0.005 (0.004)

-0.016*** (0.005) -0.028*** (0.005) -0.006 (0.003) -0.002 (0.004) 0.011*** (0.002) -0.005 (0.004)

0.039*** (0.005) 0.010 (0.006) -0.009 (0.008) 0.010 (0.009)

0.035*** (0.005) 0.007 (0.006) -0.011 (0.008) 0.011 (0.009)

0.035*** (0.005) 0.007 (0.006) -0.011 (0.008) 0.010 (0.009)

0.018** (0.006) 0.028*** (0.006) 0.049*** (0.006)

0.019** (0.006) 0.030*** (0.006) 0.050*** (0.007)

0.018** (0.006) 0.028*** (0.006) 0.047*** (0.007)

0.025*** (0.005) 0.025*** (0.005) 0.022*** (0.007)

0.025*** (0.005) 0.025*** (0.005) 0.019** (0.007)

0.025*** (0.005) 0.024*** (0.005) 0.017** (0.007)

0.050*** (0.004) 0.041*** (0.004) 0.181*** (0.007)

0.056*** (0.004) 0.039*** (0.004) 0.177*** (0.007)

0.056*** (0.004) 0.038*** (0.004) 0.176*** (0.007)

-0.005* (0.002) 0.005*** (0.002) -0.009*** (0.002) 0.011*** (0.002)

-0.006** (0.002) 0.005** (0.002) -0.009*** (0.002) 0.010*** (0.002)

College Fin. Education Workplace Fin. Education Census Region fixed effects Yes Yes N 24,932 24,932 Source: 2012 National Financial Capability Study * p < 0.05, ** p < 0.01, *** p < 0.001 ©2019, IARFC. All rights of reproduction in any form reserved.

0.013* (0.006) 0.007 (0.006) 0.023*** (0.006) Yes 24,932

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Table 4 Probit Results on Debt Counseling Advice: Non-Bankruptcy versus Bankruptcy

Age (ref: under 35) 35 to 50 years old Over 50 Female Married # of Children Homeowner Race/Ethnicity (ref: White) African American Hispanic Asian Other Race Educational Attainment (ref: no H.S.) High School or GED Incomplete College College Graduate Income (ref: under $25,000) $25,000â&#x20AC;&#x201C;$49,999 $50,000â&#x20AC;&#x201C;$99,999 Over $100,000 Financial Stressors Difficulty Paying Bills Income Drop Last Year Risk Taking Standardized Financial Capability Variables Objective Financial Knowledge Subjective Financial Knowledge Perceived Financial Capability Financial Behaviors Financial Education High School Fin. Education College Fin. Education Workplace Fin. Education Census Region fixed effects Observations Source: 2012 National Financial Capability Study * p < 0.05, ** p < 0.01, *** p < 0.001

(1) Debt Counseling Advice No Bankruptcy estimate

(2) Debt Counseling Advice Bankruptcy estimate

(standard error)

(standard error)

-0.013** (0.004) -0.023*** (0.005) -0.003 (0.004) -0.001 (0.004) 0.010*** (0.001) -0.007 (0.004)

-0.046 (0.044) -0.063 (0.055) 0.042 (0.036) 0.032 (0.040) 0.012 (0.016) 0.132*** (0.036)

0.032*** (0.005) 0.005 (0.005) -0.015 (0.008) 0.010 (0.009)

-0.007 (0.048) 0.011 (0.054) 0.084 (0.082) -0.031 (0.096)

0.025** (0.008) 0.036*** (0.008) 0.052*** (0.009)

0.059 (0.070) 0.051 (0.070) 0.129 (0.072)

0.026*** (0.005) 0.025*** (0.006) 0.015* (0.007)

-0.018 (0.049) -0.037 (0.055) 0.081 (0.069)

0.057*** (0.005) 0.035*** (0.004) 0.004*** (0.001)

-0.004 (0.043) 0.110** (0.036) -0.002 (0.007)

-0.005*** (0.001) 0.003 (0.002) -0.006*** (0.001) 0.010*** (0.002)

0.050*** (0.013) -0.013 (0.014) -0.000 (0.011) 0.040* (0.020)

0.011* (0.006) 0.005 (0.006) 0.020*** (0.006) Yes 24,142

0.120 (0.062) 0.026 (0.068) 0.083 (0.063) Yes 790

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Journal of Personal Finance

their high school years. Moreover, another 36% of respondents were either college dropouts or had graduates from a twoyear college and would have limited or no access to financial education during their college years. The outputs in Table 5 were from the subsample of 16,437 respondents that did not graduate from college. We found that the pattern of associations between our variables of interest remained unchanged from the main sample, supporting the initial claim of a positive relationship between financial education and the demand for debt counseling. For this limited sample, both high school education and workplace education were positively associated with a higher demand for debt counseling.

The models in Table 6 used a different specification to examine the impact of each financial stressor variable independently.

Demographic and financial capability factors were also included in those regressions. All financial stressors were positively correlated with debt counseling when entered into the model separately. The last column of this table includes three interaction terms between attending financial education and each financial stressor. Since the interaction terms were not significant, we speculate that the demand for debt counseling can be driven by financial stress or financial education independently, but not together. Respondents who received financial education and faced recent financial stress were not common in the sample (see percentages in Table 6).

DISCUSSION The purpose of this study is to examine potential effects of financial education on the demand for debt counseling. Based on a large national dataset, the study’s results show that consumers who participated in financial education at work or in high school are more likely to seek debt counseling advice.

This pattern holds even after other demographic and financial variables are ruled out and after disentangling the effect of a recent bankruptcy, where debt counseling is mandated. Our results provide evidence that financial education may encourage consumers in need to seek debt counseling. Financial education aims to, among other objectives, increase people’s financial knowledge. As such, consumers who have been exposed to financial education might be more able to properly identify when outside advice is needed to solve their financial issues.

Financial advisors have an important part to play due to their in depth understanding of their clients’ financial situation.

Financial advisors may advise clients that display low finan-

cial literacy to attend a free financial education training. After reviewing their client’s financials, financial advisors may also uncover the need for debt counseling. Debt counselors can potentially benefit from this finding by supporting more prevalent financial education and by partnering with those delivering financial education.

The measurement of objective financial knowledge, the “Big Five”, has become a mainstay in personal finances to compare levels of financial literacy. In this study, stronger performance in this measurement and people’s perception of their financial capability are associated with lower demand for debt counseling. More financially literate consumers and those with more confidence about their financial capabilities may have less need to seek debt counseling, which is a likely remedial form of financial advice for them.

Financial stressors play an important role in the process of seeking and receiving debt counseling. The interplay between debt counseling and financial education is of great importance to policymakers, financial educators, and financial counselors.

In this sample, it was rare to find respondents who participated in financial education and were faced with a stressful financial event. In fact, less than one percent of those who filed for bankruptcy had received previous financial education. This suggests another way in which financial education can help consumers improve their financial wellbeing.

Financial advisors who have debt counseling businesses may consider offering or sponsoring financial education courses at high schools and workplaces to introduce the benefits of using financial advice and teach ways of selecting appropriate advisors in addition to conveying basic financial knowledge. This practice can demonstrate their corporate social responsibilities, help improve local consumers’ financial capability, and expand their own clientele base.

REFERENCES Agarwal, S., Amromin, G., Ben-David, I., Chomsisengphet, S., & Evanoff, D. D. (2010). Learning to cope: Voluntary financial education and loan performance during a housing crisis. American Economic Review, 100(2), 495–500. Retrieved from http://www. aeaweb.org/articles.php?doi=10.1257/aer.100.2.495 Calcagno, R., & Monticone, C. (2015). Financial literacy and the demand for financial advice. Journal of Banking & Finance, 50, 363–380. https://doi.org/10.1016/j.jbankfin.2014.03.013 Collins, J. M. (2012). Financial advice: A substitute for financial

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

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Table 5 Probit Results on Debt Counseling Advice, Excluding College Graduates

Age (ref: under 35) 35 to 50 years old Over 50 Female Married # of Children Homeowner Race/Ethnicity (ref: White) African American Hispanic Asian Other Race Income (ref: under $25,000) $25,000â&#x20AC;&#x201C;$49,999 $50,000â&#x20AC;&#x201C;$99,999 Over $100,000 Financial Stressors Difficulty Paying Bills Income Drop Last Year Bankruptcy Standardized Financial Capability Variables Objective Financial Literacy

(1) Debt Counseling Advice estimate (standard error)

(2) Debt Counseling Advice estimate (standard error)

(3) Debt Counseling Advice estimate (standard error)

-0.016** (0.005) -0.025*** (0.006) -0.009* (0.004) -0.006 (0.005) 0.009*** (0.002) 0.004 (0.005)

-0.014* (0.006) -0.024*** (0.006) -0.007 (0.004) -0.005 (0.005) 0.009*** (0.002) 0.000 (0.005)

-0.013* (0.005) -0.023*** (0.006) -0.007 (0.004) -0.004 (0.005) 0.009*** (0.002) -0.000 (0.005)

0.038*** (0.006) 0.011 (0.007) 0.008 (0.012) 0.009 (0.011)

0.036*** (0.006) 0.009 (0.007) 0.005 (0.012) 0.010 (0.011)

0.035*** (0.006) 0.010 (0.007) 0.005 (0.012) 0.010 (0.011)

0.028*** (0.005) 0.029*** (0.006) 0.038*** (0.009)

0.026*** (0.005) 0.025*** (0.006) 0.030** (0.009)

0.026*** (0.005) 0.024*** (0.006) 0.028** (0.009)

0.043*** (0.005) 0.036*** (0.004) 0.167*** (0.008)

0.051*** (0.005) 0.034*** (0.004) 0.165*** (0.008)

0.051*** (0.005) 0.033*** (0.004) 0.164*** (0.008)

0.002 (0.002) 0.004 (0.002) -0.007** (0.002) 0.014*** 0.002

0.001 (0.002) 0.003 (0.002) -0.007** (0.002) 0.013*** 0.001

Yes (0.002)

0.019** (0.007) 0.026** (0.008) Yes (0.002)

Yes 16,437

0.013* (0.006) 0.007 (0.006) 0.023*** (0.006) Yes 16,437

Subjective Financial Literacy Perceived Financial Capability Financial Behaviors Financial Education High School Fin. Education Workplace Fin. Education Census Region fixed effects

Yes

Financial Education High School Fin. Education College Fin. Education Workplace Fin. Education Census Region fixed effects N Source: 2012 National Financial Capability Study * p < 0.05, ** p < 0.01, *** p < 0.001

Yes 16,437

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Journal of Personal Finance

Table 6 Probit Results on Debt Counseling Advice with Interaction Terms (1)

(2)

(3)

(4)

Debt Counseling Advice

Debt Counseling Advice

Debt Counseling Advice

Debt Counseling Advice

estimate (standard error)

estimate (standard error)

estimate (standard error)

estimate (standard error)

Demographic Controls

Yes

Yes

Yes

Yes

Standardized Financial Capability Variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Financial Education Indicator

Yes

Financial Stressors Difficulty Paying Bills

0.078*** (0.004)

Income Drop Last Year

0.064*** (0.004)

Bankruptcy

0.194*** (0.007)

0.054*** (0.005) 0.037*** (0.004) 0.173*** (0.008)

Interactions (% full sample) Financial Education and

0.007

Difficulty Paying Bills (10.4%)

(0.008)

Financial Education and

0.007

Income Drop Last Year (5.8%)

(0.008)

Financial Education and Bankruptcy (0.06 %)

0.016 (0.017)

Census Region fixed effects N

Yes 24,932

Yes 24,932

Source: 2012 National Financial Capability Study * p < 0.05, ** p < 0.01, *** p < 0.001

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

Yes 24,932

Yes 24,932

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literacy? Financial Services Review, 21(4), 307. http://dx.doi. org/10.2139/ssrn.2046227

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Volume 18, Issue 2

39

An Exploration of Overconfidence in the Utilization of Financial Advisors Marc B. Lewis, DBA, Central Connecticut State University

Abstract Households are not seeking the assistance of financial advisors in planning for retirement. This study explores why individuals are not utilizing a financial advisor. A survey was conducted on individuals who completed the 2012 National Financial Capability Study, which was organized by Financial Industry Regulatory Authority. The results revealed that overconfidence is statistically significant in explaining the likelihood of an individual utilizing a financial advisor. The results also revealed that men utilize financial advisors more frequently than women. Furthermore, the results indicated that an individual might utilize a financial advisor once their overconfidence decreases. Moreover, as education increases, so too does the likelihood that an individual will utilize a financial advisor. However, there is a need for additional research on overconfidence and the utilization of financial advisors in the current economy, as well as researching the relationship between overconfidence and other variables such as employment and health status of an individual.

Key Words financial advisor; overconfidence

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INTRODUCTION Households in the United States are not confident that they will have enough money for retirement and are not seeking the advice from a financial advisor to assist them with their planning (Hopkins, 2014). They tend to perceive a financial advisor as a resource that is only available to those individuals with large investible assets and high net worth (MacDonald, 2006). Despite these misconceptions, households appear to be unaware that advisors tailor how they charge their clients based on income, assets, and retirement goals (MacDonald, 2006). One example of an advisory service that tailors its services to clients based on income is Garrett Planning Network. This service is provided by advisors who commit to a fee-only service e.g., they do not charge commissions or sell products and promise to devote a portion of their practice to clients of lower income status (Updagrave, 2011). However, despite these options, a study conducted by the Certified Financial Planner Board revealed that 19% of households engage in comprehensive planning and 33% are limited planners who do not utilize a financial professional related to savings, investing, and debt management (CFP Board, 2013).

One misconception that has been identified is that cost is a reason for why households do not utilize financial advisors (MacDonald, 2006). However, this article will identify the overconfidence that exists for individuals within households that may contribute to their decision-making related to the utilization of financial advisors.

Role of Decision Making and Overconfidence Decision Making Decision Making. Psychiatrists have revealed that humans by nature do not make logical decisions (Macdonald, 2014). In most cases, when a decision is made using logic, an individual tends use their logic to move towards their emotions. These two distinct systems of thinking are called the deliberate and intuitive minds (Macdonald, 2014). The intuitive mind is our ability to react quickly to situations without analyzing the facts to determine whether or not a decision is logical or sound.

Conversely, the mind’s logical side of the mind requires time and effort to sift through all the facts and then decide based on this information. In either case, decision-making influences all types of situations including those related to finances and over-

confidence. Poor financial decisions can impact a household’s current and future situation and should be addressed to help clients understand how they are remaking financial decisions (Macdonald, 2014). Furthermore, decision-making could also impact a consumer’s decision to utilize a financial advisor to assist them in planning for retirement. This decision of whether or not to use a financial advisor could lead to a successful or unsuccessful retirement. A successful retirement can be defined under different parameters, since each retiree’s circumstances and long-term desires and goals are different. However, the retirees’ finances are a main factor on determining a successful retirement. From this perspective, most of the personal needs, wants, and goals should be achieved if retirees have the financial capacity to support their retirement lifestyle, e.g. achieving financial retirement success (Abich, 2018).

Overconfidence and Behavioral Finance. Behavioral finance is a mixture of various disciplines. Its founding researchers blended psychology, sociology, and finance to create a theory that was applicable to the thinking behind how individuals or groups arrive at their financial decisions (Ricciardi & Helen, 2000). For example, consider an individual who purchased securities as a long-term investment and subsequently sells the stock prematurely. The individual does this not because the security is decreasing in value, but because it is increasing in value. This propensity to sell while the security is high is related to behavioral finance in that the psychological and sociological aspects of the investor are affected by both external sources such as the media and internal sources such as the investor. In addition, because overconfidence can play a role in an individual’s ability to make decisions, it can affect their ability to execute and plan for the future (Ricciardi & Helen, 2000). Therefore, the concept of overconfidence as it exists within behavioral finance can be extended in explaining the likelihood of a household making decisions on utilizing a financial advisor.

Utilizing Financial Advisors. It can be somewhat challenging when utilizing financial advisors to determine their value (Warschauer & Sciglimpaglia, 2012). For the consumers who are using the services of a financial advisor, they appear to be either somewhat or very confident in their financial future despite the cost of their services (McClune, 2010). Conversely, the consumers who do not employ financial advisors to assist them with their financial concerns are said to be less confident in their financial future (McClune, 2010). Despite these differences, McLune (2010) is unable to capture the true value that an advisor adds for their clients. However, she does reveal that for those consumers that do utilize a financial professional, they

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

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Volume 18, Issue 2

appear to be more confident than those that do not.

One common view among consumers about utilizing a financial advisor is that one common goal is planning for retirement (“Consumers and financial planning,” 2008). In addition, when defining the value of a financial advisor, respondents stated that receiving good returns from their investment was a common indicator of receiving value for their services (“Consumers and financial planning,” 2008). Moreover, previous research has revealed consumers recognize the value of a financial advisor based on the services they are receiving (“Consumers and financial planning,” 2008; McClune, 2010; Schulaka, 2009; Warschauer & Sciglimpaglia, 2012). Yet, if the consumer realizes that a financial advisor can produce positive returns for them and give them more confidence in their current and future financial position, why are only 61% of all Americans working with a financial advisor (“Consumers and financial planning,” 2008)? Regardless of the costs of their services, advisers appear to be adding value to their clients’ lives.

Overconfidence. Behavioral finance is a blend of two distinctly different disciplines: psychology and finance. Although its origins date back to the early 1970s, it has grown into a compelling field that has generated numerous studies on various topics (Jaiswal & Kamil, 2012). Regardless of the topic being researched, one common theme that behavioral finance addresses is that individuals often make financial decisions that can be viewed as illogical or irrational. In addition, these behaviors may not align with the individual’s perception of themselves and be completely contradictory (Agrawal, 2012). Daniel Kahneman and Amos Tversky began researching this problem in 1974 and argue that the theory of behavioral finance was valid and provable when applied to investment decision-making (Jaiswal & Kamil, 2012). It should be noted that a majority of behavioral finance research does in fact relate to investment decision-making.

When viewed in its entirety, behavioral finance is comprised of various concepts that attempt to explain the behavioral aspects of an individual in relation to making financial decisions (Jaiswal & Kamil, 2012).

This study aims to assist individuals within households and advisors in identifying how overconfidence relates to their decisions to not utilize a financial advisor. Because households may make decisions that are not in-line with logical thinking (Sela & Berger, 2012), this may lead them to making financial decisions that may not benefit them monetarily (McClune, 2010). In addition, regardless of the state of their emotions

(negative or positive), decision-making can be impaired if an individual or household is not aware of inputs that might affect their decision-making (Carlson et al., 2013).

Because this study will explore how overconfidence can affect the utilization of a financial advisor, research conducted by Robb et al., (2012) will be the basis of this study. Moreover, it will be the intent of this study to extend their analysis beyond that of demand for financial advisors but more towards the understanding of how overconfidence relates to an individual’s choice to utilize a financial advisor.

METHOD The purpose of this study is to understand the association of overconfidence in an individual utilizing a financial advisor. The following research question relates to this study.

Research Question. Does overconfidence affect the likelihood of an individual utilizing a financial advisor?

The following alternate hypothesis was created in order to address the primary research question: H1: There is an association between the likelihood of utilizing a financial advisor and overconfidence.

The research design for this study is a secondary data analytical study in order to test the research question and hypothesis created to explore the association between overconfidence and an individual utilizing a financial advisor. Although this data was not collected primarily for the use of this study, the breadth of the data can extend beyond the capabilities of collecting primary data, and the time element of allowing the researcher to utilize their time effectively by being able to analyze the data to its fullest potential.

Research Design In conducting this study, frequency distributions, descriptive statistics, and binary logistic regression were used to analyze the data. By utilizing frequency distributions and descriptive statistics, the researcher was able to identify any skewed data that exists within the dataset. A cross tabulation was conducted in order to determine if any variables were missing data.

Respondents who were missing data for any of the primary variables identified for this study were removed.

Binary logistic regression was used to address identifying the relationship between the independent and dependent vari-

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Journal of Personal Finance

ables. Because the outcome variable is dichotomous and the hypothesis is associational, the logistic regression model was the preferred method because it is designed to determine relationships between dichotomous dependent variables and independent variables of nominal, ordinal, or scale type (Field, 2009).

The model parameters below relate to the binary logistic regression model. Based on the previous research on overconfidence, the expectations will be that all of the independent variables will be positive contributors to the model with the exception of overconfidence. In viewing overconfidence, it is predicted that it will have a negative impact on the likelihood of an individual utilizing a financial advisor. This prediction contradicts previous research which has recognized a positive effect on confidence and the utilization of a financial advisor. log(p/1-p) = -b0 - b1gender + b2age - b3married – b4single – b5separatedordivorced + b6highschool + b7somecollege + b8collegegrad + b9postgraduate – b10overconfidence + e

Population and Sample The dataset used in this analysis is the 2012 National Financial Capability Study (NFCS), which is comprised of a state-by-state sample size of 25,509 adult respondents who are 18 years of age or older (FINRA, 2012). Each online panel utilized standard survey techniques in order to ensure that the participants identity could be verified in addition to their demographic information provided (FINRA, 2012). The online survey was conducted over a four-month period ranging from July 2012 to October 2012 (FINRA, 2012). Applied Research and Consulting estimates that the sample has a margin of error of .05% (FINRA, 2012).

The NFCS dataset has been used for various research projects.

In 2014, the United States Military Academy conducted a study that reviewed the financial welfare of military families within the United States (Skimmyhorn, 2014). The Teachers Insurance and Annuity Association - College Retirement Equities Fund (TIAA-CREF) performed two studies in which they reviewed working women’s financial capability in addition to college-educated millennials by reviewing their personal financial habits (Scheresberg, Lusardi, & Yakoboski, 2014a, 2014b). Millennials are defined as those individuals who become adults by the

year 2000 (Scheresberg, Lusardi, & Yakoboski, 2014a, 2014b).

Moreover, the Securities Exchange Commission reviewed the financial literacy of financial retail investors by utilizing the data compiled within the NFCS. Because of its robust sample size and usage across many organizations and industries, this dataset enabled the researcher to perform analysis on the data in identifying the relationship of overconfidence and the utilization of a financial advisor.

Data Collection For the purposes of this study, the NFCS will be used in order to determine how overconfidence relates to an individual’s utilization of a financial advisor. The NFCS was funded by the FINRA Investor Education Foundation in an effort to identify the financial capabilities of Americans across the United States (FINRA, 2012). It was originally commissioned in 2009 by the U.S. Department of Treasury and President Bush’s Advisory Council on Financial Literacy (FINRA, 2012). In 2012 another survey was conducted under the Advisory Council on Financial Capability under President Barack Obama (FINRA, 2012). The survey was conducted by Applied Research and Consulting which utilized three online panels: Survey Sampling International (SSI), EMI Online, and Research Now (FINRA, 2012).

The data collection process began with a total of 1,165,405 emails sent out to respondents. An estimated 68,229 participants started the survey by clicking on the provided link (FINRA, 2012). Of those participants that started the survey, 33,866 had their survey terminated for reason such as quotas or not willing to provide specific information required for the survey (FINRA, 2012). Of the remaining 34,363 participants, 8,854 voluntarily ended their survey before completion. This resulted in a total of 25,509 successfully completed surveys or 2.19% of the total population originally emailed (FINRA, 2012). The datasets provided by the NFCS are obtainable through their website and provided as a tool for future research related to financial management and literacy (FINRA, 2012).

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Volume 18, Issue 2

Analysis

single respondents make up 19.5% of the sample.

In employing a quantitative approach, this associational design will be used in order to determine the relationship of overconfidence and the utilization of a financial advisor. Overconfidence was derived in SPSS by first selecting responses from survey questions from the NCFS that pertained to an individual’s financial knowledge and behavior. In addition, age, marital status, education, and gender were used in the analysis in order to provide additional insight into sample population. Each of these independent variables are nominal, clearly defined, and non-overlapping pursuant to their coding in the NFCS 2012 survey (Applied Research & Consulting, 2012).

Evaluable Population Selection criteria were developed to remove those respondents who failed to answer pertinent questions in the survey relating to financial behavior and the utilization of a financial advisor.

Respondents who selected “I don’t know” or “Prefer not to say”, were removed from the dataset. These criteria applied to questions, K_2, J3, J6, J8, J11, J12, and J20 (Applied Research & Consulting, 2012). Respondents who selected “Neither Agree nor Disagree” for question M1_1 were removed from the dataset.

The results of removing respondents from the dataset created an evaluable population of 7,122 respondents who answered all questions relating to financial behavior and the utilization of a financial advisor. As a result, 28% of the respondents from the NFCS were selected in order to accurately derive overconfidence based on their financial behavior.

In order to ensure that removing respondents did not jeopardize the integrity of the data, frequencies were performed on gender, age, education, and marital status on the evaluable population. The analysis illustrates the distribution of gender for the 7,122 respondents. There are 2,878 (40.4%) males and 4,244 (59.6%) females within this population (see Table 1).

Frequency Distribution by Marital Status Marital Status

Frequency Distribution by Gender

Frequency

Percent

Married

5,060

71.1

Living with partner

670

9.4

Single

1,388

19.5

Total

7,122

100.0

The largest percentage of participants within the evaluable population by age fell in the 35-44 brackets. This group represented 2,245 (31.5%) of the population. The smallest group of 107 (1.5%) were 65 plus.

Table 3 Frequency Distribution by Age Age

Frequency

Percent

18-24

493

6.9

25-34

1,745

24.5

35-44

2,245

31.5

45-54

1,854

26.0

55-64

678

9.5

65+

107

1.5

Total

7,122

100.0

Concerning education, the largest population 1,818 (32%) had some college education versus the smallest group of 449 (6.3%) who did not complete high school.

Table 4 Frequency Distribution by Education Frequency Education

Table 1

Gender

Table 2

Percent

No high school

457

6.4

Regular high school

1,166

16.4

GED

449

6.3

Frequency

Percent

Some college

2,281

32.0

Male

2,878

40.4

College graduate

1,818

25.5

Female

4,244

59.6

Post graduate

951

13.4

Total

7,122

100.0

Total

7,122

100.0

Married respondents make up 71.1% of the sample, while those who are living with a partner make up 9.4% of the sample, and

The analysis of the evaluable population revealed that there

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Journal of Personal Finance

are similarities between gender, marital status, and education.

However, there is a small variation regarding age. The larger population in the full dataset is 45 to 54 versus 35 to 44 in the evaluable population. Likewise, the smallest population in the full dataset are those respondents 18 to 24 versus those respondents in the evaluable population who are 65 plus.

Measures In order to determine a respondent’s overconfidence, two variables will need to be determined. First, how does the respondent view their ability to manage their finances on a day-to-day basis. Second, what behavior is the respondent actually displaying in managing their finances e.g., positive or negative. Scoring was accomplished from respondents based on 14 questions identified in the 2012 NCFS. The sections below explain how each variable was calculated and used within the analysis.

Advisor Utilized Variable. In order to determine if a respondent used a financial advisor, the dependent variable K2AdvisorUtilized was derived by recoding question K2 (Applied Research & Consulting, 2012) that related to a respondent utilizing a financial professional for savings and investments. Survey question K2 was asked in order to learn if survey participants consulted with a financial advisor to manage their savings and investments. This variable helped the researcher to determine if participants were taking steps to plan their future retirement by seeking professional help. For those respondents who answered ‘Yes’ to question K2, they were given a score of 1 for variable K2AdvisorUtilized. All other respondents were given a score of 0. Because this study is based on the utilization of a financial advisor, it was decided not to include questions K1, and K3-5. These questions related to a respondent seeking advice related to debt counseling, mortgage, taxes, or insurance of any type. In some cases, these professional services can be obtained from a financial advisor but in other cases a specialized individual would handle these types of advice.

Confidence Dummy Variable. In order to assess a respondent’s feeling on their ability to manage their finances on a day-to-day basis, question M1_1 (Applied Research & Consulting, 2012) was used in order to identify those respondents who self-assessed their financial knowledge. This question relates to how a respondent viewed their ability to manage their financials on a day-to-day basis and whether or not their view was positive or negative. Dummy variables were created within SPSS using the transform and recode functionality. Question M1_1 was scored using a Likert scale ranging from 1 through 7. Respondents

who chose 1 through 3 did not feel that they were good at managing their finances. Respondents who chose 5 through 7 did feel that they were good at managing their finances.

Selecting values 1 through 3 from question M1_1 created dummy variable M1_1NotConfident. These selections where given a value of 1 and all other selections where given a value of 0. Selecting values 5 through 7 from question M1_1 created dummy variable M1_1Confident. These selections where given a value of 1 and all other selections where given a value of 0. Respondents who chose a score of 4 (neutral) or 98 (don’t know) where removed from the sample because of the inability to identify their confidence level.

Positive and Negative Behavior Dummy Variables. In order to determine if a respondent is displaying negative or positive financial management behavior, questions J3 through J6, J8, J11, J12, and J20 relate to positive and negative financial management behavior. Questions J3, and J4, were developed to analyze how participants manage their income in order to afford their current and future expenses. Question J8 was asked to determine what the participants’ position and readiness are about planning for retirement. Questions J11, and J12, aimed to learn whether participants know their current credit position, thus, whether they are actively managing their credit as part of their personal finances. Questions J6 relates to those households with children and whether or not they are saving for their children’s higher education costs. It should be noted that only respondents who answered that they have dependent children to question A11 (How many children do you have who are financially dependent on you?) were asked question J6.

Lastly, question J20 analyzed in order to determine if participants are currently saving, therefore, their attitude about it can determine whether or not their saving habits will contribute to planning for retirement.

Dummy variables where created for questions J3 through J6, J8, J11, J12, and J20 (See Table 5). Using question J3 as an example, respondents who chose that they are spending less than their household income were given a score of 1 by the survey.

Selecting values 1 from question J3 created dummy variable J3SpendLess with a value of 1. All other selections where given a value of 0. Respondents who chose that they are spending more than, or about equal their household income were given a score of 2 or 3 by the survey. Selecting values 2 or 3 from question J3 created dummy variable J3SpendMoreEqual with a value of 1. All other selections where given a value of 0. Dummy variables for questions J6, J8, J11, J12, and J20 were created

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Volume 18, Issue 2

Table 5 Dummy and Computed Variable Derivation Logic Survey Variable J3 J4 J5 J6 J8 J11 J12 J20

Selected Response

Derived Variable

Behavior Category

Computed Variable

1

J3SpendLess

Positive

PositiveComputed

2,3

J3SpendMoreEqual

Negative

NegativeComputed

3

J4PayBillsNotDifficult

Positive

PositiveComputed

1,2

J4PayBillsDifficult

Negative

NegativeComputed

1

J5EmergencyFundsYes

Positive

PositiveComputed

2

J5EmergencyFundsNo

Negative

NegativeComputed

1

J6ChildCollegeSaveYes

Positive

PositiveComputed

2

J6ChildCollegeSaveNo

Negative

NegativeComputed

1

J8RetirementPlanningYes

Positive

PositiveComputed

2

J8RetirementPlanningNo

Negative

NegativeComputed

1

J11CreditReportYes

Positive

PositiveComputed

2

J11CreditReportNo

Negative

NegativeComputed

1

J12CreditScoreYes

Positive

PositiveComputed

2

J12CreditScoreNo

Negative

NegativeComputed

1,2

J20Have2KYes

Positive

PositiveComputed

3,4

J20Have2KNo

Negative

NegativeComputed

using the same logic as question J3.

was used in order to calculate overconfidence:

Positive and Negative Behavior Computed Variables. Dummy variables where created for questions J3 through J6, J8, J11, J12, and J20 with each question having either a positive or negative response. In order to determine a respondent’s financial behavior, computed variables were created within SPSS by summing all positive responses into variable PositiveComputed and all negative responses into variable NegativeComputed.

For example, if a respondent had a PositiveComputed score of 2 and a NegativeComputed score of 1, the assumption from these scores would state that the respondent overall has positive financial behavior. This is in-line with the definition of overconfidence within this study, which states overconfidence is an individual believing that his skillset is higher than expected regardless of his actual behavior. Conversely, if a respondent had a PositiveComputed score of 3 and a NegativeComputed score of 4, the assumption would be that the respondent overall has negative financial behavior. Respondents with a PositiveComputed score equal to their NegativeComputed score will receive a value of 0 for both computed variables.

Overconfidence = 1 If M1_1Confident = 1 and NegativeComputed > PositiveComputed

Overconfident Variable. In order to derive a respondent’s overconfidence, variables M1_1Confident, PositiveComputed, and NegativeComputed were examined. The following algorithm

This algorithm illustrates that if a respondent is confident in their ability to manage their finances (M1_1Confident = 1) and their negative financial habits outnumber their positive financial habits (NegativeComputed > PositiveComputed), then they are viewed as being overconfident about their ability to manage their finances. Respondents with M1_1Confident scores of 0 with NegativeComputed <= PositiveComputed were considered to be not overconfident and their overconfidence indicator was set to 0. In addition, respondents with an M1_1NotConfident=0 were considered not overconfident and their overconfidence indicator was set to 0. Respondents who answered any of the above-mentioned questions with a response of either “don't know” or neutral were removed from the sample.

In order to assess whether the data from questions M1_1, J3 through J6, J8, J11, J12, and J20 demonstrated a reliable score, the Cronbach’s alpha for these variables was calculated to test their reliability, which is defined as the data presenting consistent results regardless of the point in time when the analysis is being conducted (Field, 2009). The alpha score for these

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Journal of Personal Finance

variables was .74. This provided an indication that questions M1_1, J3 through J6, J8, J11, J12, and J20 together provide a reasonable internal consistency reliability score as they relate to identifying a respondentâ&#x20AC;&#x2122;s level of confidence in their ability to manage their finances.

In order to understand the relationship between overconfidence and the utilization of a financial advisor, a secondary data analysis was performed using the 2012 NCFS dataset. This dataset is comprised of 25,509 respondents who were surveyed on their ability to manage their finances on a day-to-day basis.

Dummy variables were created to derive overconfidence in each respondent. In analyzing the data, a binary logistic regression model was created which used a dependent variable, which was computed within SPSS using question K2 (Applied Research & Consulting, 2012) along with the dummy independent variables.

sion model was used in order to determine the associations between all of the independent variables and the dependent variable in their likelihood to explain if an individual would utilize a financial advisor. For those respondents that utilized a financial advisor (See Table 6), 512 were overconfident (22.4% of the total) and 1,773 were not overconfident (77.6% of the total). Furthermore, for those respondents that did not utilize a financial advisor, 2,548 were overconfident (52.7% of the total) and 2,289 were not overconfident (47.3% of the total). Similarly, 16.7% of those identified as overconfident utilized a financial advisor and 83.3% did not. For the total amount of respondents identified as not overconfident, 43.6% utilized a financial advisor and 56.4% did not. In summary, the cross tabulation illustrates that those who are not overconfident utilize a financial advisor more than those who are overconfident.

Table 6

RESULTS

Frequency Between Overconfidence and Advisor Utilization

A secondary data analysis was performed using data from the National Financial Capability Study (NFCS). Exploratory data analysis was first performed on the initial dataset in order to verify that all survey questions were answered in their entirety that were applicable to this study. Any pertinent survey questions that were missing were removed from this study using case statements within SPSS. The binary logistic regres-

Advisor Utilization No

Yes

Total

Yes

2,548

512

3,060

No

2,289

1,773

4,062

4,837

2,285

7,122

Total

A model was created using overconfidence and other independent variables and it was examined how these variables

Table 7 Logistic Regression Predicting Likelihood of Using a Financial Advisor based on Gender, Age, Marital Status, Education, and Overconfidence.

B

S.E.

Wald

df

Sig.

Exp(B)

Gender

-0.244

0.056

19.237

1

0

0.783

Age

0.019

0.026

0.545

1

0.46

1.019

Married

-0.362

0.238

2.315

1

0.128

0.696

Over Confident

-1.136

0.06

359.026

1

0

0.321

Single Separated Or Divorced High School

-0.337

0.25

1.819

1

0.177

0.714

-0.584

0.249

5.507

1

0.019

0.558

0.55

0.164

11.246

1

0.001

1.733

Some College

0.912

0.159

32.996

1

0

2.49

College Grad

1.515

0.16

89.96

1

0

4.551

Post Graduate

1.589

0.168

89.953

1

0

4.897

Constant

-0.697

0.316

4.858

1

0.028

0.498

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47

Volume 18, Issue 2

contributed to the model in explaining the likelihood of an individual utilizing a financial advisor. Table 7 shows that gender (p < .001) divorced or separated (p < .001), high school (p < .001), some collage (p < .001) college graduate (p < .001), and post-graduate (p < .001), and overconfidence (p < .001) are significant predictors in determining if an individual will utilize a financial advisor. Females are .78 times more likely to utilize a financial advisor versus males. Furthermore, divorced or separated respondents are .55 times likely than their counterparts to employ the services of a financial advisor. Concerning education, the data reveals that an increase in education increases the propensity of an individual to utilize a financial advisor, See Table 7. High school graduates are 1.7 times more likely to utilize an advisor versus non-high school graduates. However, respondents who at least attended college are approximately 2.5 times more likely to utilize an advisor. Additionally, if a responded were to graduate from college, the data reveals these individuals show a propensity to utilize a financial advisor as 4.5 times greater than others in the dataset. Post-graduates also show an increase in the likelihood of using a financial advisor at 4.8 times, which is a small increase when moving from graduating from college to obtaining a post-graduate degree. Moreover, overconfidence displayed a large odds ratio, which can be viewed as any decrease in overconfidence yielded an increase of 3.12 times in the likelihood that an individual would utilize a financial advisor.

Neither age (p = .460), single (p = .177), or married (p = .128) was significant in predicting the likelihood of an individual utilizing a financial advisor.

The study examined the independent variable and how it related to an individual financial behavior. Additionally, behavioral finance was reviewed as it relates to overconfidence.

The participants within this study included 7,122 respondents within the United States with varying ages, marital statuses, and educational levels.

Overall, the results of the logit showed statistically significant associations amongst overconfidence, as it is independently associated with the dependent variable utilization of a financial advisor. The logistic regression results supported the theory that overconfidence is statistically significant in predicting an individualâ&#x20AC;&#x2122;s propensity to utilize a financial advisor. For this reason, the findings using the independent variable overconfidence support the hypotheses.

DISCUSSION

Overconfidence is as one of the most prevalent concepts within behavioral finance (Prasad & Mohta, 2012). Overconfidence can be defined as an overstatement of oneâ&#x20AC;&#x2122;s own limitations (Cheng, 2007). Furthermore, how overconfidence is measured is not a consistent process. However, for the purpose of this study, overconfidence was defined by utilizing data from the National Financial Capability Study. Robb et al., (2012) utilize this same dataset in order to understand confidence and the demand for financial professionals.

This study was based on the research question; how is overconfidence associated with the likelihood of an individual utilizing a financial advisor?

In addition, this study replicated the use of overconfidence used by previous research by utilizing data collected by the Financial Industry Regulatory Association (FINRA). Based on the findings utilizing the FINRA dataset, overconfidence has a significant association with utilizing a financial advisor. Although there were no studies found that related overconfidence with the utilization of a financial advisor, the study revealed that overconfidence increases the tendency of an investor to make decisions that may not be directly correlated with their ultimate financial goals ( Glaser, Langer, & Weber, 2013). This was evident from the fact that the logistic regression revealed that overconfidence was not only statistically significant, but also that as each unit of overconfidence decreased, the likelihood of an individual utilizing a financial advisor increased 3.21 times.

Hence, a decrease in confidence can be associated with an increase in the utilization of a financial advisor.

The logistic regression tests further supported the literature that when analyzed, overconfidence is a significant predictor of an individual utilizing a financial planner.

The results of the present study reveal significant predictors that can assist consumers and practitioners in determining the likelihood of an individual utilizing a financial advisor. In addition, by having an understanding and being able to identify overconfident behavior, consumers and practitioners will be able to better equip themselves with additional knowledge that would assist them in making financial decisions. If consumers were provided with a questionnaire that is comprised of similar questions derived from the 2012 NFCS, they would possibly make a more informed financial decision based on the results of the survey. Moreover, practitioners could administer these same questions to each of the current and potential clients to identify behaviors related to overconfidence. This perhaps could lead to practitioners being more of an educator on

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Journal of Personal Finance

financial decision-making versus rationalizing by their clients or prospects should utilize their services.

Because the data revealed that the less overconfident an individual is, the more likely they are to use a financial advisor, both clients and practitioners would be aware of the effects of overconfidence on making financial decisions and lead them to make more informed decisions related to the financial goals.

This study’s findings reveal that overconfidence is a significant predictor in determining if an individual will utilize a financial advisor. A study utilizing the same independent variable in addition to other variables that take into consideration certain externalities such as consumer price index, inflation, and the current performance of the Dow Jones Industrial Average (DJIA) will assist in not only determining how gender, education, and overconfidence predict behavior, but also how externalities can affect decision making beyond the findings in this study. This research can further be expanded to study specific areas of the population such as LGBTQ couples and explore how that community’s decision-making dynamics compare to those of heterosexual couples. In addition, collecting additional quantitative data via a survey that expands upon the questions derived from the 2012 NFCS will further improve the definition of overconfidence by creating more questions related to financial management behavior. The concept of overconfidence as it relates to financial behavior has not been extensively studied in relation to utilizing a financial advisor. Additional research on this topic will assist both consumers and practitioners in understanding financial decision-making. By understanding the associations and predictors of utilizing a financial advisor, this data will provide guidance that consumers can use to make certain that the financial decision they are making are in-line with their long-term financial goals. Furthermore, practitioners can utilize this same data to assist their prospects or clients in making sound financial decisions.

When analyzed overconfidence displayed a large odds ratio, which can be viewed as any decrease in overconfidence yielded an increase of 3.12 times in the likelihood that an individual would utilize a financial advisor. In other words, as an individual’s overconfidence decreased, this would have a large effect on an individual’s utilization of a financial advisor e.g., the less overconfident you are, the more likely you will use a financial advisor.

REFERENCES

Abich, A. (2018). Seven Steps Toward A Long-Lasting Retirement. Forbes. Retrieved from https://www.forbes.com/sites/ impactpartners/2018/03/12/seven-steps-toward-a-successful-retirement/#168e3dec5c97 Agrawal, K. (2012). A conceptual framework of behavioral biases in finance. Journal of Behavioral Finance, 7–18.

Applied Research & Consulting. (2012). 2012 National Financial Capability Study. Retrieved from https://www.usfinancialcapability.org/downloads/NFCS_2012_State_by_State_Qre.pdf Carlson, K., Meloy, M., & Miller, E. (2013). Goal reversion in consumer choice. Journal of Consumer Research, 39(5), 918–930.

CFP Board. (2013). New research shows most American households do financial planning, but the extent of this planning varies greatly. CFP Board. Retrieved from http://www.cfp.net/ news-events/latest-news/2013/09/18/new-research-sh…-do-financial-planning-but-the-extent-of-this-planning-varies-greatly Cheng, P. (2007). The trade interaciton effect on the impact of overconfidence on trading performance: an empirical study.

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Consumers and financial planning. (2008). Journal of Financial Planning, 21(9), 1–9.

Field, A. (2009). Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: SAGE Publications, Inc. FINRA. (2012). National financial capability study. FINRA Investor Education Foundation. Retrieved from http://www.usfinancialcapability.org Glaser, M., Langer, T., & Weber, M. (2013). Overconfidence in interval estimates: Evidence based on a new measure of miscalibration. Journal of Behavioral Decision Making, 26(5), 405–417.

Hopkins, J. (2014). Not enough people have financial advisers and new research shows they should. Forbes, 1–3.

Jaiswal, B., & Kamil, N. (2012). Gender, behavioral finance and the investment decision. IBA Business Review, 7(2), 8–22.

Lusardi, A., & Mitchell, O. (2007). Financial literacy and retirement preparedness: Evidence and implications for financial education. Business Economics, 42(1), 35-44.

MacDonald, J. (2006). Financial planners: Not just for millionaires anymore. Bankrate.com. Retrieved from http://www.bankrate.com/finance/savings/financial-planners-not-just-for-millionairesanymore- 1.aspx Macdonald, T. (2014). How do we really make decisions? BBC

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News. Retrieved from http-//www.bbc.com/news/science-environment-26258662 McClune, S. (2010). Consumer demographics: What do financial planning services consumers look like now? Journal of Financial Planning, 23(3), 4–7.

Prasad, H., & Mohta, B. (2012). Loss aversion and overconfidence: Does gender matter? International Journal of Business Studies and Research, 4(1), 48–54.

Ricciardi, V., & Helen, S. (2000). What is behavioral finance? Business, Education, and Technology Journal, 2(2), 1–9.

Robb, C., Babiarz, P., & Woodyard, A. (2012). The demand for financial professionals advice: The role of financial knowledge, satisfaction, and confidence. Financial Services Review, 21(4), 291–305.

Scheresberg, Lusardi, & Yakoboski. (2014a). College-educated millennials: an overview of their personal finances. TIAA-CREF Institute.

Scheresberg, Lusardi, & Yakoboski. (2014b). Working women financial capability: an analysis across family status and career stages. TIAA-CREF Institute.

Schulaka, C. (2009). What is the value of financial planning?

Journal of Financial Planning, 22(3), 14.

Sela, A., & Berger, J. (2012). Decision quicksand: How trivial choices suck us in. Journal of Consumer Research, 39(2), 360– 370.

Skimmyhorn. (2014). The financial welfare of military households: descriptive evidence from recent surveys. United States Military Academy.

Updagrave, W. (2011). Should I hire a financial adviser or go it alone? CNNMoney. Retrieved from http://money.cnn. com/2011/11/22/pf/expert/financial_adviser.moneymag/ Warschauer, T., & Sciglimpaglia, D. (2012). The economic benefits of personal financial planning: An empirical analysis.

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51

From Financial Education to Financial Literacy to Financial Stability: Evidence of National and Regional Connections Stephanie R. Yates, PhD, CFPÂŽ, AFCÂŽ, University of Alabama at Birmingham

Abstract The present study uses data from the National Financial Capability Study to analyze the degree of impact of financial education on financial literacy based on the related socialization factors. We then analyze the degree to which financial literacy explains financial stability. We find higher levels of financial literacy in those who have been exposed to financial education and that those with higher levels of financial literacy are more financially stable. Further, we find that a lack of exposure to financial education has a negative impact on financial literacy that is more severe than the positive impact of financial education. Lastly, we find that financial education socialization varies by race, gender, and socio-economic status. These findings provide a roadmap for financial educators and practitioners to account for limited exposure to financial education and financial overconfidence in order to have the greatest impact.

Key Words financial education; financial literacy; financial stability

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Journal of Personal Finance

This paper addresses some key questions in personal finance.

First, it seeks evidence for whether or not financial education works. Then, it investigates how consumers learn about personal finance by paying close attention to whether these socialization methods vary based on gender, life stage, race, socio-economic status, education and ethnicity. Finally, this paper looks for a correlation between financial literacy and financial stability.

Previous literature on financial education has attempted to address the question “Does financial education work?” The results are mixed. Some articles argue that financial education does lead to better financial outcomes for participants, while other research indicates that financial education is effective for only a segment of the population. Lastly, other research shows that due to deficiencies in evaluation, financial education cannot be proven to be effective.

behavior focusing on cash flow management, credit management, saving, and investing. Using data from the Surveys of Consumers and the Survey of Consumer Finances, they find that greater knowledge about credit, saving, and investment practices correlates with participation in financial management practices. Courchane and Zorn (2005) used survey information on finances, attitudes, experiences, and behaviors matched with credit information from 12,140 respondents to address how consumer literacy affects financial behavior. They find a significant positive causal link from financial knowledge to behavior to outcomes. Hogarth, Anguelov, and Lee (2005) explore factors affecting bank account ownership using data from the Survey of Consumer Finances. They find that poorly educated consumers are more frequently among the unbanked. Kimball and Shumway (2006) develop an index of investor sophistication from a set of 15 quiz-like questions and find a large positive correlation between financial sophistication and portfolio choice. Perry and Morris (2006) examine the relationship between consumer financial knowledge, income, and locus of control on financial behavior. They find that consumers with higher levels of financial knowledge were more likely to budget, save and plan. Lusardi and Mitchell (2007) review the state of financial literacy and financial education programs. They ponder whether consumers possess the financial literacy necessary to process financial information and formulate adequate savings plans. They find that retirement education increases liquid wealth by 18% and mostly affects those at the bottom of the income distribution. Calvert, Campbell, and Sodini (2009) analyze the individual portfolios of Swedish households. They find that more financially sophisticated households were less risk averse and invest more efficiently. Hastings and Mitchell (2010) use experimental evidence from Chile to explore how financial illiteracy and impatience relate to poor financial decisions. They find that financial literacy is associated with greater retirement saving.

Financial Education Works

Financial Education Might Work

Chang and Hanna (1992) apply Stigler’s economic model of information to the external search for consumer credit. They find increased levels of financial information resulting in decisions that are more efficient. Bernheim, Garret, and Maki (2001) find that adults in states with mandated financial education programs had increased rates of savings and wealth accumulation compared to those in other states. Hilgert, Hogarth, and Beverly (2003) explore the connection between knowledge and

Muller (2002) examines the relationship between retirement education and the saving of lump sum distributions. She finds that retirement education in the workplace increased the probability that persons under 40 will save for retirement but finds no evidence in financially vulnerable groups defined as individuals over 40 or persons who did not graduate from college. Hathaway and Khatiwada (2008) analyze research on the impact of financial education programs on consumer financial

These are important questions in the area of financial education and financial inclusion. Schools, governments, and businesses earmark significant resources for financial education.

However, there is little guidance regarding how to direct this funding in order to be most impactful. Many questions are still unanswered such as when to provide financial education, how to deliver it, and how to measure its impact.

The present study uses the National Financial Capability Study data to examine these questions. After reviewing the literature in this area, we analyze the impact of financial education by relating the level of financial literacy of study respondents to their exposure to financial education to determine if there is a statistically significant relationship. Then, we determine if those respondents with higher levels of financial literacy tend to be more financially stable.

REVIEW OF THE LITERATURE

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behavior. Based on this analysis, they recommend that financial literacy interventions be highly targeted to a specific audience and activity and that they should occur just before the corresponding financial event.

Financial Education Does Not Work While Gartner and Todd (2005) found that completion of online credit education correlates with more responsible credit card usage, their evidence does not support a causal relationship.

Fox, Bartholomae, and Lee (2005) found that arguments against financial education are more likely to be the result of inadequate evaluation methods than ineffective programming. For example, in high school settings, they found little evidence of rigorous evaluation and reporting. Thus, many programs measure effectiveness only by completion rates. Lyons, Palmer, Koralalage, and Scherpf (2006) found that the major shortcomings in financial education are related to evaluation. They identify three primary areas of deficiency: lack of evaluation capacity, evaluation treated as an afterthought, and the lack of standards for program evaluation. Willis (2008) provides one of the most negative views of financial education, arguing against the notion that financial education will make consumers more responsible and better financial decision makers. Further, Willis argues that promoting such logic results in consumer overconfidence. Alternatively, Willis suggests that better consumer protections would be more effective in bringing about positive financial outcomes.

Other Findings Garrison and Gutter (2010) studied the relationship between the financial social learning opportunities and the financial behaviors of 15,797 college students age 18 and over throughout the United States during spring and fall semesters of 2008. They found that females have higher exposure to financial social learning opportunities across four dimensions (parents, peers, observation of parents and observation of peers).

Kim and Chatterjee (2013) study the association between childhood financial socialization and the financial practices and asset choices of young adults. They find that childhood financial socialization experiences are positively associated with beneficial practices and financial asset ownership in young adulthood. Respondents who owned bank accounts and had

their spending monitored by parents in childhood were more likely to own financial assets.

DATA AND METHODOLOGY Our focus is on how respondents receive financial education, if at all, whether or not that socialization leads to increased financial literacy, and whether or not increased financial literacy leads to a higher level of financial stability as evidenced by better financial choices. We use data from the 2015 National Financial Capability Study (NFCS), which is a large-scale, multi-year project that measures Americans’ financial capability based on four components: making ends meet, planning, managing financial products, and financial knowledge. In 2009, the FINRA Investor Education Foundation commissioned the study in consultation with the U.S. Department of the Treasury and other federal agencies. The polling firm Applied Research and Consulting conducts the study every three years and draws from a nationwide sample of over 25,000 respondents. It strives to benchmark key indicators of financial capability and evaluate how these indicators vary with underlying demographic, behavioral, attitudinal, and financial literacy characteristics.

We gather data on various socialization factors related to financial education. The NFCS asks respondents two questions regarding these socialization factors. The first question was “Was financial education offered by a school or college you attended, or a workplace where you were employed?” The second question was “Did your parents or guardians teach you how to manage your finances?” Table 1 presents the results of this survey. The table shows the number (and percentage) of respondents who received financial education at school or work, from family, both or not at all in the first row. Subsequent rows break down those figures using various demographics, such as gender, age, life stage, race, income, education, and geographic region. Note that in order to protect respondent anonymity; the NFCS aggregates selected variables, such as race, into larger groupings in the publicly available data.

As the table shows, we find that over 40% of respondents do not receive financial education at school, work, or at home. In addition, the study reveals that respondents who receive financial education are more likely to receive it from family members than at school or work. We also find differences in socialization factors across specific demographic groups. We find evidence that male respondents are more likely to receive financial education at school or work AND at home while women are more likely to neither receive financial education at school or work

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Table 1 Socialization Factors Related to Financial Education This table presents the socialization factors that influence the way that individuals receive financial education as self-reported in the National Financial Capability Study.

Neither Family School or Family AND Family (only) NOR School or Full Sample Work (only) School or Work Work 3,735 7,674 4,978 11,177 27,564 N (13.55%) (27.84%) (18.06%) (40.55%) (100.00%) Panel A: Gender Male 50.01% 40.63% 51.73% 42.34% 44.60% Female 49.99% 59.37% 48.27% 57.66% 55.40% Panel B: Age 18 – 24 14.27% 9.90% 17.48% 7.94% 11.07% 25 – 34 19.36% 16.80% 22.52% 16.82% 18.19% 35 – 44 16.63% 16.74% 16.41% 16.68% 16.64% 45 – 54 17.08% 18.44% 16.63% 19.19% 18.23% 55 – 64 15.66% 18.86% 13.32% 18.85% 17.42% 65+ 17.00% 19.26% 13.64% 20.52% 18.45% 18-44, Single w/o Children 18-44, Single with Children 18-44, Married w/o Children 18-44, Married with Children 18-44, DSW w/o Children 18-44, DSW with Children 45+, Single w/o Children 45+, Single with Children 45+, Married w/o Children 45+, Married with Children 45+, DSW w/o Children 45+, DSW with Children

18.82% 5.78% 4.63% 18.26% 0.75% 2.01% 5.38% 1.10% 20.24% 10.71% 9.29% 3.03%

White Alone Non-Hispanic Non-White

64.63% 35.37%

$0 - $15,000 $15,000 - $25,000 $25,000 - $35,000 $35,000 - $50,000 $50,000 - $75,000 $75,000 - $100,000 $100,000 - $150,000 $150,000+

10.66% 8.89% 10.33% 13.60% 21.23% 14.51% 14.08% 6.69%

Did not complete HS HS graduate GED Some college 2-year degree 4-year degree Graduate study

1.12% 10.82% 3.16% 29.16% 12.26% 28.01% 15.48%

Northeast Region Midwest Region South Region West Region

18.39% 23.00% 33.15% 25.46%

Panel C: Life Stage 17.10% 3.24% 5.41% 15.16% 0.87% 1.67% 7.14% 0.94% 24.09% 10.18% 11.73% 2.48% Panel D: Race/Ethnicity 77.59% 22.41% Panel E: Income Level 10.49% 11.44% 10.99% 15.62% 20.78% 13.24% 11.83% 5.60% Panel F: Education 2.12% 17.45% 6.12% 26.94% 10.70% 22.78% 13.89% Panel G: Region 17.98% 24.86% 33.19% 23.96%

22.02% 5.40% 5.87% 21.41% 0.64% 1.06% 5.28% 0.98% 17.92% 9.98% 7.15% 2.27%

14.38% 4.53% 4.60% 15.09% 1.01% 1.83% 7.81% 0.92% 24.73% 9.63% 12.50% 2.98%

17.12% 4.50% 5.06% 16.68% 0.87% 1.67% 6.84% 0.96% 22.71% 9.99% 10.88% 2.72%

69.39% 30.61%

71.70% 28.30%

71.96% 28.04%

9.58% 9.06% 8.74% 11.81% 20.23% 16.41% 15.91% 8.26%

15.62% 13.03% 13.34% 16.44% 19.13% 11.17% 7.96% 3.31%

11.47% 10.84% 10.84% 14.69% 20.50% 13.59% 12.22% 5.85%

0.60% 9.96% 3.33% 26.62% 11.67% 30.07% 17.74%

3.19% 19.75% 8.45% 29.15% 10.41% 18.14% 10.92%

2.14% 16.13% 6.16% 28.08% 10.97% 22.92% 13.59%

16.59% 24.75% 32.06% 26.60%

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

18.93% 22.54% 32.62% 25.91%

18.17% 23.65% 32.75% 25.43%

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Table 2 Financial Literacy Measures This table presents descriptive statistics for two measures of financial literacy: self-assessment and the results of a six-question test.

Means Medians Region

N

Self-Assessed

Test-Based

Difference

Self-Assessed

Test-Based

Northeast

5,009

72.98%

54.90%

18.07%***

71.43%

50.00%

Midwest

6,518

73.30%

56.31%

16.99%***

71.43%

66.67%

South

9,027

73.57%

52.40%

21.17%***

71.43%

50.00%

West

7,010

73.48%

56.80%

16.67%***

71.43%

66.67%

Full Sample

27,564

73.38%

54.90%

18.48%***

71.43%

50.00%

*** indicates that the t-test is significant at the 0.001 level.

NOR at home. We also find a difference in socialization across life stages. We group respondents into 12 life stage categories adapting and applying the methodology from Baek and Hong (2004) to the NFCS data. These groupings take into consideration age, marital status, and the presence of children in the home. We classify age as under 45 or 45 and older. We classify marital status as single, married, or other where other could be either divorced, separated, or widowed. We also include the presence of children in the home as a demographic. We then create the 12 possible life stage combinations of these seven classifications. The majority of the respondents (22.71%) are 45 or older and married with no children present in the home. The number of respondents in this life stage who received financial education both at school or work and at home is disproportionately small (17.92%) compared to their representation in the total sample. We also see a difference across educational attainment. Respondents who do not have a college degree have a disproportionately high rate of non-exposure to financial education while those who do have a college degree have a disproportionately low rate of non-exposure to financial education. We find a similar result with regard to income with the breakpoint being an income of $50,000. That is, those respondents with income less than $50,000â&#x20AC;&#x201D;like those without a college degreeâ&#x20AC;&#x201D;most likely have not received any financial education while those with income greater than $50,000 are more likely to receive financial education. With respect to race, white respondents have disproportionately less exposure to financial education at school or work relative while non-whites have disproportionately more exposure to financial education

at school or work. We do not find any remarkable differences across geographic regions.

The NFCS asks respondents several questions regarding their level of financial literacy. One question asks â&#x20AC;&#x153;On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall financial knowledge?â&#x20AC;? Six additional questions ask specific, test-type questions regarding personal finance. We include these questions in the Appendix.

We calculate a test-based financial literacy measure as the percentage of correct answers. For comparison, we calculate a self-assessed financial literacy measure by converting the 1â&#x20AC;&#x201D;7 scale to a percentage, in which seven is set as equal to 100 percent. We use both the self-assessed measure and the testbased measure. Table 2 presents descriptive statistics for these two variables. While the study finds little difference in financial literacy across regions, there is a significant difference between self-assessed financial literacy and test-based financial literacy.

We compare the mean values of the self-assessed and testbased financial literacy measures to determine if there is evidence of overconfidence that may lead to poor financial decision-making. Table 2 also presents the results of a paired t-test of two sample means. These results indicate that respondentsâ&#x20AC;&#x2122; self-reported financial literacy is statistically significantly higher than their tested financial literacy.

We test two hypotheses. First, we propose that financial literacy is a function of financial education. To test this hypothesis, we estimate the equation: FINANCIAL LITERACY= đ?&#x203A;ź0+đ?&#x203A;˝1 FINANCIAL EDUCATION +đ?&#x153;&#x20AC;

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Table 3 Multivariate Analysis of Self-Assessed Financial Literacy This table presents estimates of the degree to which financial education socialization factors can explain variations in financial literacy as measured by a self-assessment and a standardized test.

Intercept SCHL/WRK FAMILY BOTH NEITHER F Adjusted R2

Intercept SCHL/WRK FAMILY BOTH NEITHER F Adjusted R2

Model 1 0.7165*** 0.0581*** 0.0381***

Panel A: Self-Assessed Financial Literacy Model 2 Model 3 0.6905*** 0.7546*** 0.0712*** 0.0418*** 0.0606***

-0.0286*** 468.2062*** 0.0484

689.8931*** 0.0476

Model 1 0.5309*** 0.0622*** 0.0122*

Panel B: Test-Based Financial Literacy Model 2 0.5119*** 0.0718*** 0.0286***

-0.0209*** 159.9285*** 0.0170

236.2054*** 0.0168

where FINANCIAL LITERACY is measured as either self-assessed or test-based financial literacy and FINANCIAL EDUCATION indicates how a respondent received financial education if at all. In other words, this equation estimates the degree to which having financial education explains the variability in financial literacy.

Second, we propose that financial stability is a function of financial literacy. To test this hypothesis, we estimate the equation: FINANCIAL STABILITY= đ?&#x203A;ź0+đ?&#x203A;˝1 FINANCIAL LITERACY + đ?&#x203A;˝2CONTROL +đ?&#x153;&#x20AC;

where we use income as a proxy for FINANCIAL STABILITY and FINANCIAL LITERACY is measured as either self-assessed or testbased financial literacy. In other words, this equation estimates the degree to which financial literacy explains the variability in income after controlling for other factors that impact income such as age, education, gender, and race. We recognize that researchers frequently use other measures as proxies for financial stability, but income is the only such variable available in the NFCS. Gadanecz and Jayaram (2009) mention five commonly used measures of financial stability in the household sector.

They are household assets and household debtâ&#x20AC;&#x201D;which allow

-0.0668*** 674.1431*** 0.0466

Model 3 0.5431*** 0.0570*** -0.0331*** 238.2851*** 0.0169

Model 4 0.7589*** 0.0537*** -0.0711*** 691.84*** 0.0477

Model 4 0.5540*** 0.0514*** -0.0440*** 203.178*** 0.0145

researchers to compute net household assets â&#x20AC;&#x201D; and household income, household consumption, household debt service, and principal payments â&#x20AC;&#x201D;where researchers can combine the latter three to compute net disposable income.

RESULTS We first test the hypothesis that financial literacy is a function of financial education.

We estimate a multivariate equation to explain the variability in financial literacy using self-assessed financial literacy as our dependent variable. We estimate all possible models except for those that include BOTH and SCHL/WRK or HOME. Our results are presented in Panel A of Table 3. Exposure to any type of financial education is positively and significantly related to financial literacy and the absence of financial education is negatively and significantly related to financial literacy.

We repeat our multivariate analysis using test-based financial literacy as our dependent variable and find similar results but, as in our univariate analysis, our adjusted R2 values are much lower. We present these results in Panel B of Table 3.

We then test our second hypothesis that financial stability is a

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Volume 18, Issue 2

Table 4 Multivariate Analysis of Financial Stability This table presents estimates of the degree to which financial literacy can explain variations in financial stability as measured by income after controlling for age, education, race, and gender.

Intercept SELF-ASSESSED

TEST-BASED AGE EDUCATION FEMALE NON-WHITE F Adjusted R2

Model 1

Model 2

1.9319*** 1.6163***

2.3977***

0.1173*** 0.4152*** -0.3184*** -0.4114*** 1426.991*** 0.2055

function of financial literacy. We estimate a multivariate equation controlling for other factors cited in the literature to explain differences in financial stability. These are age, education, race, and gender. We find evidence of a positive and significant relationship between financial literacy and financial stability regardless of the measure of financial literacy used. We present these results in Table 4.

CONCLUSION We test two hypotheses regarding the relationship between financial education, financial literacy, and financial stability.

First, we hypothesize that exposure to financial education leads to an increased level of financial literacy whether that literacy is self-assessed or test-based. We not only find support for this hypothesis, but we find that the absence of financial education has a negative impact that is more severe than the positive impact of receiving financial education. Specifically, we find that receiving financial education both at home and outside the home leads to a 5% increase in financial literacy, whereas not receiving financial education at all is related to a 7% decrease in financial literacy.

Second, we hypothesize that a higher level of financial literacy leads to a higher-level financial stability, which is measured using income. We proposed that this finding would hold even after controlling for other factors that often explain variability in income. We find support for this hypothesis as well. In fact, the study shows that a 1% increase in test-based financial literacy leads to a $30,000 increase in income.

1.3993*** 0.0872*** 0.3900*** -0.2677*** -0.3254*** 1445.083*** 0.2076 The implications of these findings are multi-faceted. First, this study provides evidence that financial education is impactful because it helps individuals make better financial choices, which lead to a greater level of financial stability. Because of the significant number of individuals who do not receive financial education, these results indicate that these individuals may be at risk. Furthermore, there are a disproportionate number of women, older adults (over 55), and African-Americans who are not exposed to financial education. This is also true of low-income and less-educated populations. The findings of this study should serve to inform financial educators as to where to target their efforts.

Finally, future research should focus on alternative measures of financial literacy and financial stability and measuring the impact of specific interventions on their target populations.

After identifying the gap, future research should serve to determine the best way to address the gap in the form of targeted programs and services.

These findings are especially relevant for financial practitioners.

While every client is different, a better understanding of the linkage between financial education, financial literacy and financial stability can help practitioners communicate more effectively with clients. Financial practitioners often provide services such as seminars and webinars as a means to promote their organizations and add value. The studyâ&#x20AC;&#x2122;s results suggest that targeting these types of offerings to those who traditionally are less often exposed to financial education at school, at work, or through family members may be more effective than broadly offering these services to the public. Specifically, these

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Journal of Personal Finance

results suggest that programming specifically targeting women and older adults could fill a significant need.

The present study’s findings also suggest a statistically significant difference between the levels of financial literacy that individuals self-report and their responses to specific financial questions. This implies some degree of overconfidence.

Knowing that this phenomenon exists, financial practitioners may want to account for overconfidence in their practice. A practitioner could do this at various stages of the client relationship. For example, during the intake process, a practitioner may want to avoid vague statements regarding risk tolerance in favor of more specific questions that ask a client how he or she might react in a specific situation. In addition, the financial practitioner should manage the practitioner-client relationship in such a way that the practitioner regularly checks for client understanding by asking probing questions, restating client concerns, and using other means that invite discussion as opposed to asking more simple, objective questions.

Lastly, regarding how a financial practitioner might incorporate these findings into daily practice, it is important to recognize that if the ultimate goal is to improve a client’s level of financial stability, a better understanding of those factors that lead to stability is critical. This study finds that individuals with greater exposure to financial education tend to be more financially literate, which ultimately leads to a higher level of financial stability. Given varying client backgrounds, incorporating financial education into a financial practice and adjusting for client overconfidence may help clients make better decisions, adhere to the advice of the financial practitioner, and ultimately achieve the goals developed with the practitioner’s assistance.

APPENDIX

was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?

M8: If interest rates rise, what will typically happen to bond prices?

M9: A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be less.

M10: Buying a single company’s stock usually provides a safer return than a stock mutual fund.

M31: Suppose you owe $1,000 on a loan and the interest rate you are charged is 20% per year compounded annually. If you didn’t pay anything off, at this interest rate, how many years would it take for the amount you owe to double?

REFERENCES Baek, E., & Hong, G.-S. (2004). Effects of Family Life-Cycle Stages on Consumer Debts. Journal of Family and Economic Issues, 25(3), 359–385.

Bernheim, B. D., Garrett, D. M., & Maki, D. M. (2001, June).

Education and Saving: The Long-Term Effects of High School Financial Curriculum Mandates. Journal of Public Economics, 80(3), 435–465.

Campbell, J., Calvert, L. E., & Sodini, P. (2009). Measuring the Financial Sophistication of Households. The American Economic Review, 99(2), 393–398.

Chang, Y.-C. R., & Hanna, S. (1992, September). Consumer Credit Search Behaviour. International Journal of Consumer Studies, 16(3), 207–227.

Self-assessed financial literacy is measured in the NFCS using question M4 which reads as follows:

Courchane, M. J., & Zorn, P. M. (2005). Consumer Literacy and Credit Worthiness. Wisconsin Department of Financial Institutions Task Force on Financial Literacy.

M4: On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall financial knowledge?

Fox, J., Bartholomae, S., & Lee, J. (2005). Building the Case for Financial Education. The Journal of Consumer Affairs, 39(1), 195–214.

The NFCS measures test-based financial literacy using questions M6, M7, M8, M9, M10 and M31 as follows:

Gadanecz, B., & Jayaram, K. (2009). Measures of Financial Stability —A Review. Basel, Switzerland: Bank for Intrenational Settlements.

M6: Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?

M7: Imagine that the interest rate on your savings account

Gartner, K., & Todd, R. M. (2005). Effectiveness of Online "Early Intervention" Financial Education for Credit Cardholders. Unpublished Working Paper.

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

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Gutter, M. S., Garrison, S., & Copur, Z. (2010, June). Social Learning Opportunities and the Financial Behaviors of College Students. Family and Consumer Sciences Research Journal, 38(4), 387–404.

Hastings, J., & Mitchell, O. S. (2010). How Financial Literacy and Impatience Shape Retirement Wealth and Investment Behaviors. NBER Working Paper.

Hathaway, I., & Khatiwada, S. (2008). Do Financial Education Programs Work? FRB of Cleveland Working Paper.

Hilgert, M. A., Hogarth, J. M., & Beverly, S. G. (2003). Household Financial Management: The Connection Between Knowledge and Behavior. Federal Reserve Bulletin, 89(7), 309.

Hogarth, J. M., Anguelov, C. E., & Lee, J. (2005). Who Has a Bank Account? Exploring Changes Over Time, 1989 - 2001. Journal of Family and Economic Issues, 26(1), 7–30.

Kim, J., & Chatterjee, S. (2013). Childhood Financial Socialization and Young Adults' Financial Management. Journal of Financial Counseling and Planning, 24(1), 61–79.

Kimball, M. S., & Shumway, T. (2006). Investor Sophistication and the Participation, Home Bias, Diversification, and Employer Stock Puzzles. Unpublished University of Michigan Working Paper.

Lusardi, A., & Mitchell, O. S. (2007, January). Financial Literacy and Retirement Preparedness: Evidence and Implications for Financial Education. Business Economics, 42(1), 35.

Lyons, A. C., Palmer, L., Koralalage, S. J., & Scherpf, E. (2006). Are We Making the Grade? A National Overview of Financial Education and Program Evaluation. The Journal of Consumer Affairs, 40(2), 208–235.

Muller, L. A. (2002). Does Retirement Education Teach People to Save Pension Distributions? Social Security Bulletin, 64(4).

Perry, V. G., & Morris, M. D. (2006). Who Is In Control? The Role of Self-Perception, Knowledge, and Income in Explaining Consumer Financial Behavior. The Journal of Consumer Affairs, 39(2), 299–313.

Willis, L. E. (2008). Against Financial Literacy Education. Iowa Law Review, 101(3), 429–434.

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61

The Efficacy of Publicly Available Retirement Planning Tools Taft Dorman, PhD, AFC®, Western Governors University Barry Mulholland, PhD, MBA, CFP®, ChFC®, The University of Akron Qianwen Bi, PhD, Utah Valley University Harold Evensky, CFP®, AIF®, Evensky & Katz/Foldes Financial Wealth Management

Abstract Publicly available retirement planning tools are advertised as aiding households in their retirement planning efforts, but households are likely overestimating their effectiveness. The authors conclude that the advice provided by a majority of these tools is extremely misleading to households and propose a more systematic approach to tool development by improved choice of input variables. Analyzing theory and professional advisor opinion, critical input variables are identified and recommended. Using a case study that incorporates these critical variables, the efficacy of 41 publicly available retirement planning tools was tested.”

Keywords retirement planning; retirement planning tools

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Journal of Personal Finance

“Annual income twenty pounds, annual expenditure nineteen six, result happiness. Annual income twenty pounds, annual expenditure twenty pound ought and six, result misery”

Charles Dickens, David Copperfield

Retirement planning is complex and continues to challenge American households, financial professionals, and researchers due to uncertainty about the future (Bernheim, Skinner, & Weinberg, 2001; Skinner, 2007). Consumer questions abound: Will I be able to retire? Do I have enough to retire? Will I run out of money in retirement? How do I find the best answer to these fundamental questions? Even after decades of research, producing a lifetime income remains challenging (Siegel 2015).

Therefore, it is no surprise that the CFA Institute’s Future of Finance initiative considers “Retirement Security” to be one of its six areas of focus (CFA Institute, 2015).

One approach to answering the difficult retirement questions is for households to seek professional guidance (Campbell, 2006; Fischer & Gerhardt, 2007); however, not all households can afford professional advice, and even those who can afford advice struggle to discern good advice from bad advice (Mullainathan, Noeth, & Schoar, 2012; Hung, Clancy, Dominitz, Talley, & Berrebi, 2008; Inderst & Ottaviani, 2012; Bodie, 2003; Del Guercio & Reuter, 2012). Finding a trustworthy advisor may be difficult because not all financial professionals provide unbiased, high quality advice. Consequently, cost for searching high for many households who looking for financial advice, thus causing them to seek alternatives for their retirement planning needs, even do-it-yourself options. One alternative is to conduct a webbased search for a publicly available retirement planning tool that may attempt to answer a plethora of retirement questions in one place. Some households may trust these tools because they come from well-known third-party sources, or they may significantly reduce search cost.

We define publicly available retirement planning tools as tools that provide an answer as to whether a household can meet its unique retirement goal(s). These are tools that households can find and use online, either for free or for a modest fee, without the assistance of a financial professional. There are dozens of publicly available retirement planning tools, many of which are found on well-known company websites. The variation of tool inputs and default settings invites the question: How does the use of theoretical variables impact tool outcomes?

This article investigates the necessary inputs and default settings of publicly available retirement planning tools while focusing on three important outcomes. We identify the rele-

vance of the advice provided to households using these tools.

We highlight the risk households may face by using these tools.

Finally, we provide tool developers with critical information to improve their tools, so they are more valuable guides.

The use of publicly available retirement planning tools likely serves households in different ways. Households that plan their retirement savings on a do-it-yourself basis are likely looking for tools that will help them make informed decisions. Households that use financial professionals may be looking for ways to improve their level of trust in the advice they receive. Thus, households may use these tools to double check the advice given by financial advisors (Powell, 2015).

Providers of publicly available retirement planning tools can exploit naïve users through information asymmetry (Akerlof, 1970). Many tools market themselves as sufficient for answering the most common retirement questions: How much do I need to save for retirement? Will I run out of money in retirement? However, if the tools are designed as a marketing tool for product or service providers, it follows that the tool inputs and default settings are likely established to guide households into purchasing financial products or to speak with a financial professional who may be paid for selling proprietary products.

Households may believe the tool results reflect independent, non-biased retirement advice, without the understanding that the tools provide directed “advice” that benefits the vendor, not the investor.

Our primary purpose is to understand how the use of theoretical variables among publicly available retirement planning tools impacts tool outcomes. First, we look at a brief background of publicly available tools. Then, we discuss prior research related to both retirement savings and the effectiveness of publicly available tools. Next, we discuss our methodology for identifying appropriate variables and testing the effectiveness of current tools. We finish by presenting our results and discussing our conclusion. To the best of our knowledge, this is the largest study regarding the efficacy of publicly available retirement planning tools.

BACKGROUND In January of 2000, a patent was granted to a life insurance company for a computer-based tool that performed financial planning calculations, primarily for retirement. This tool aimed to create graphical results for financial advisors in an understandable format to present to households. However, this tool

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Volume 18, Issue 2

was never meant for direct personal use. By 2003, there were at least twenty-four electronic tools for retirement analysis. Six of those tools were for public use, eleven were for professionals, at least two were proprietary, and five tools were unidentified (Sondergeld, 2003). The number of publicly available tools has grown substantially.

Publicly available retirement planning tools have long been known to be inefficient for retirement planning (Warshawsky & Ameriks, 2000). Many have imprecisely calculated critical retirement planning variables such as social security, investment rates of return, longevity risk, and income replacement rates (Turner & Witte, 2009). These issues may cause households to make detrimental retirement planning decisions (Turner, 2014).

We argue that, at a minimum, a respectable tool will be goal oriented, provide both accumulation and decumulation strategies, and use inputs and default settings that agree with theory.

We focus on the variability in tool inputs and default settings as it pertains to theory, questioning how theoretical variable use impacts tool outcomes.

PRIOR RESEARCH Industry and academic literature has examined both professional and publicly available retirement planning tools. While professional software attracts most of the attention, a considerable amount of the literature draws common conclusions on, and recommendations for, improving publicly available retirement planning tools.

The consumer press focuses almost exclusively on the publicly available retirement planning tools. A recent Google search for the term “recommended retirement calculator” generated over 1 million results. Many of those results are investment companies or news sites that are recommending their own tools. It is possible to find search results for financial planning sites that are not pushing their own tools, but those articles appear to have a similar focus—the authors have tried several available tools and recommend tools, with minimal due diligence about their effectiveness.

Consumer bloggers and members of the financial planning press who have more closely examined and compared retirement planning tools are generally critical of the available tools.

Tresidder (2011) argues that the available tools appear easy to use but suggests that they employ inaccurate assumptions.

These include important variables such as inflation, longevity, investment returns, and retirement spending. Kirkpatrick

63

(2014a) adds that assumption problems exist for social security timing, marital status, tax rates, and other retirement income sources. Tresidder (2011) also argues that user input flexibility impacts tool results, with many tools relying on rules of thumb or assuming an average value for user inputs. Real retirees are unique and few actually retire at exactly age 65 and live exactly to the average life expectancy. Consequently, rules of thumb are at best meaningless and at worst, dangerous.

Clear, unbiased guidance is difficult to obtain with publicly available retirement planning tools when they provide opaque advice. Consumer Reports (2011) suggests that tools offered by investment companies are biased because they focus on proprietary products or make assumptions that inefficiently demonstrate what retirement planning really is.

Non-academics have suggested different approaches to improving the user experience with retirement planning tools.

Vernon (2010), an actuary, identifies important features of publicly available tools. These include detailed expenses—not just rules of thumb on expenses; savings withdrawal flexibility—to see the impact of changes; saving the inputs and results—to compare current and prior projections; adjustable future economic scenarios—to see the impact of future downturns; outputs that show the components of retirement income sources—to understand the impact and quality of each source on retirement income; and “what if” scenario testing—to assess different situations that households are interested in testing.

Kirkpatrick (2014b) attempts to categorize a large number of available tools based upon several criteria to allow his blog readers the opportunity to choose the better tool that meets their needs. His sorting criteria includes the level of realism of the output, how stock returns are modeled, the software platform the tool operates on, and cost.

Academic research shows that publicly available retirement planning tools fail to meet retirement planning needs for a variety of reasons. Warshawsky and Ameriks (2000) suggest that many programs fail to recognize household uncertainties, such as life expectancy and returns on financial and human capital.

Sondergeld, Chamerda, Drinkwater, and Landsberg (2003) explore how programs work with retirement decumulation when attempting to address different types of risk such as longevity, health, investment, and inflation. They find that not only do inputs vary tremendously from tool to tool, but results are just as inconsistent. Bodie (2003) examines four online retirement planning tools and finds that they lack sufficient recognition of post-retirement risk, thus making them dangerous for public

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use. Dowd, Atherly and Town (2008) suggest that publicly available retirement planning tools be improved to include either annual or monthly health care spending.

Researchers continue to explore the challenges of planning for retirement with a mix of both pessimism and optimism. Mayer, Zick, and Marsden (2011) show that determining retirement needs increases the probability of a household saving for retirement. Skinner (2007) examines the conflicting views that Americans are either not saving enough or are saving too much. He concludes that even the most sophisticated retirement planning tools produce inaccurate results. Sexauer and Siegel (2013) believe that retirement preparation by individuals is possible as long as the individual sets up a savings plan and sticks to it. However, determining the optimal savings and distribution plan is a complicated and dynamic procedure. The key is that different people may need unique yet accurate retirement solutions or combinations of solutions (Siegel, 2015).

For example, a single replacement rate may not be practical because retirees’ spending ratio may change based on their investment returns (Waring & Siegel, 2015; Scott & Watson, 2013).

Kotlikoff (2006) concludes that retirement planning programs should use a dynamic programming style instead of a Monte Carlo simulation because households make spending adjustments over time.

Each retirement tool provides its own information to consumers for use, but there is evidence that information given to consumers is biased. Lee, Haley, Yun, and Chung (2011) investigate the relationship between financial information and consumer investment choice. They discuss the dangers of complex information, alluding to the idea that consumers may overvalue the credibility of investment information due to the tool’s extensive fine print. An increase in information complexity may lead to information overload, triggering an increase in informational trust. They suggest that information provided to consumers needs to be free of default bias. Financial professionals play an important role in helping households make rational financial decisions (Fischer & Gerhardt, 2007). Additionally, Kucuk (2016), who discusses “digital consumerism,” notes that consumers are increasingly vulnerable to digital information.

Several researchers have conducted academic studies of publicly available retirement planning tools, with John Turner being the primary contributor. Turner and Witte (2009) suggest that households may lack sufficient financial literacy to understand how to use tools properly. Turner (2010) also suggests that tool rating systems are subjective to input criteria and likely provide

inaccurate advice to users. Later, he finds that publicly available retirement planning tools may harm risk-averse investors because tools may be biased toward equity investing (Turner, 2012). Turner’s work, to date, concludes that users of publicly available retirement planning tools may be making irrational retirement planning decisions because the tools provide sub-optimal advice (Turner, 2014).

Prior academic researchers, industry press, and consumer advocates conclude that publicly available retirement planning tools are lacking attention to important factors. The tools give highly variable and confusing results, and households are naive to this fact. As suggested by Kirkpatrick (2013), while households may be using these tools to determine how they are doing in their savings plans or how much to save, these tools are not meant to be used only once, without adjusting the planning as the household situation changes. Yet, the consensus of most literature on these tools is that they are useful if their limitations are understood by the users. Furthermore, if the number of available tools is any indication, clearly there is a demand by households for help with retirement planning. Turner suggests in an interview about retirement calculators that calculators, even with their flaws, provide some help (Powell, 2010). We challenge Turner's suggestion.

THEORETICAL FRAMEWORK Our theoretical framework draws directly from the work of Bi, Finke, and Huston (2015). For their examination of five retirement planning tools used by large 401(k) providers, they first create a theoretical framework to guide their selection of variables. Starting with the traditional Modigliani and Brumberg (1954) life-cycle model, they made an initial assumption of no retirement income source other than savings, and they reinterpret the model to identify the important input variables required for consumption smoothing throughout the life-cycle.

We group the variables into four major categories: Ages: Current age, target retirement age, and life expectancy Income: Current annual income, and expected future income Assets: Current accumulated assets, and bequest amounts Rates: Expected constant rates of return on assets, inflation rates, and tax rates Of these variables, life expectancy, the average age that a person will live to, given their current age, has proven to be the most problematic in retirement needs calculations. Since

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life expectancy determines the planning horizon, Bi, Finke, and Huston (2015) suggest the life expectancy assumption is the most important assumption in this group of variables, though many folks get it wrong while planning for their future. A MetLife (2008) survey finds that about 60% of respondents underestimate the likelihood of a person living to age 85. If a tool relies on the typical resource for determining life expectancy in the U.S., the Social Security Administrationâ&#x20AC;&#x2122;s Period Life Tables (Bell & Miller, 2005), then there is a 50% chance the individual will surpass their indicated life expectancy.

Studies indicate that smoking significantly affects life expectancy. Several studies estimate that individuals who smoke throughout adulthood will shorten their life expectancy by 6.5 to 15 years (Shaw, Mitchell, & Dorling, 2000; Gohlke & Yusef, 2007), suggesting smoking is an important factor in determining longevity. With such strong evidence of the negative impact of smoking on an individualâ&#x20AC;&#x2122;s life expectancy, the following additional variable should be included:

non-married partner in the planning model seems appropriate.

Thus, a fifth major category, household structure, is included: Household Structure: Marital status, with a non-married partner option In conclusion, the life cycle hypothesis, with adjustment for current social norms, suggests the variables listed in Table 1 are foundational for any retirement savings tools to be effective.

Table 1 Theoretical Variables by Major Category Variable Category

Variable

Ages

Current Age Target Retirement Age Life Expectancy

Income

Current Annual Income Expected Future Income (Wage Replacement) Government or Private Pensions

Lifestyle: Smoker/Non-smoker.

Bi, Finke, and Huston (2015) note that households with retirement income from pensions or social security during the decumulation (retirement) stage will have lower income replacement rates, reducing required savings during the accumulation (earnings) stage. We suggest that any form of household income during either the accumulation or decumulation stages will have the same effect on the required savings needs for retirement. This includes forms of income supplements such as the receipt of public or private pensions, annuity income, rental income, financial windfalls such as inheritance or life insurance death benefit, or replacement of income through disability insurance payments. Therefore, the following additional variables should be included: Income: Government or private pensions, social security income, trust income, disability insurance income Assets: Windfall receipts The life cycle hypothesis relates to household consumption (Modigliani & Brumberg, 1954). All household income and savings should be included in any calculation of savings needs for the retirement stage of the life cycle. Because most government social insurance includes benefits for spouses, the marital status of the household members becomes an important variable. While marital status naturally indicates that retirement need planning should carry through the lifespan expectations of both spouses, current social norms suggest the inclusion of a

Social Security Income Trust Income Disability Insurance Income Assets

Current Accumulated Assets Windfall Receipts Bequest Amounts

Rates

Expected Rate of Return on Assets Inflation Rate Tax Rate

Household Structure

Marital Status Non-Married Partner Option

Lifestyle

Smoker

DATA AND METHODS Our data gathering process consists of three parts. Analysis begins with the creation of a basic retirement planning scenario to be tested with each tool. This base case scenario is then processed through the most widely used professional financial planning software. Next, we compile a list of 41 publicly available retirement planning tools to test and categorize each tool by Google search popularity from seven different Google searches. Lastly, using the base case scenario results as the benchmark, we test the 41 tools and collect data on each toolâ&#x20AC;&#x2122;s

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variable use and output.

Assumptions

Sondergeld et al. (2003) and Dowd, Atherly, and Town (2008) are two of the few prior studies that focus on specific variables.

They note that variable use among tools is inconsistent and suggest that households should not rely on any given tool for retirement planning because it is difficult to compare tools.

Because of the challenge of comparing tools with variable inconsistency, the development of a testable case scenario across all tools requires simplicity. Our base case scenario consists of a debt-free married couple nearing retirement who simply have a goal of having their basic retirement living expenses covered.

Details of the base case scenario are outlined in Table 2.1

Additional Information

Table 2 Best Case Scenario Assumptions Assumptions Current Age Health (Tobacco Use) Annual Salary State of Residency

Husband

Wife

59

57

Good (No)

Good (No)

$50,000

$50,000

Texas

Texas

Expected Retirement Age

65

63

Wage Replacement Ratio (WRR)

70%

70%

66

66

Program Estimate

Program Estimate

91

93

$250,000

$200,000

Annual IRA Contributions

$5,000

$5,000

Taxable account assets

$50,000

$50,000

No

No

Total investment assets

$300,000

$250,000

Expected Return Prior to Retirement

5.72%

5.72%

Expected Return During Retirement

5.72%

5.72%

Portfolio Allocation

60/40

60/40

Age to begin SS Annual Social Security Income Life Expectancy Traditional IRA assets

Pension

1 2

Husband

Wife

Household Risk (on a scale of 100)

60

Annual Inflation

3%

Market value of primary residence Mortgage balance of primary residence

$300,000 $0

According to a BlackRock report, Baby Boomers (age 55 to 65), who are closest to retirement age, want to have $45,500 annual income in retirement while the average savings is only $136,200 (Greaney, 2015). To represent the demand for retirement planning, our base case scenario is a baby boomer couple that is approaching retirement, who are currently 59 and 57. The median annual income for workersâ&#x20AC;&#x2122; age 55 to 64 is approximately $46,000 based on the 2017 survey data from the Bureau of Labor Statistics (BLS, 2017). We use an annual income of $50,000 for each spouse for simplification. Research suggests that reasonable target income replacement rates, or wage replacement ratios (WRR), are around 70% (Jeszeck et al., 2016; Yoonkyung, 2011; Munnell, Anthony, Webb, & Wenliang, 2014).

We use a 70% target replacement rate for our sample household. Under current Social Security law, the full retirement age for retirees is increasing from 65 to 67 depending on birth year, thus we choose a middle ground and have the base case couple begin Social Security benefits at age 66. Males reaching age 65 today have an average life expectancy of age 84.3 while the same figure for females is age 86.6. Moreover, about 25 percent of those 65 years old will live beyond age 90, and about 10 percent will live beyond age 95 (Social Security, 2017).

To be conservative in our basic case scenario, we use 91 and 93 respectively for our couple as their life expectancy.

To evaluate tool precision, we employ a professional software package as our benchmark.2 Figure 1 shows that MoneyGuidePro is, and has been for several years, the primary professional software package. As a result, MoneyGuidePro acts as our reference point. With this software, we process our benchmark scenario representing a potential user of a publicly available

Replicating the base case may not yield the exact same results as this study due to market movements or other changes in economic factors since the timing of the study, but the resulting Monte Carlo probability should be very close to base case scenario used in this study.

A copy of the software was available to us without purchase through the financial planning programs where the authors are employed and where it is available to all students in the academic programs at no charge. PIEtech, creator of MoneyGuidePro, did not request that we use their software for this study, nor did they provide any funding in support of this study. We further chose to purchase a retail license to MoneyGuidePro for this study to eliminate any incidence of support from PIEtech. For purposes of full disclosure, one author has a 1.5% ownership stake in PIEtech.

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Figure 1 Financial Planning Software by Market Share

retirement planning tool (Bruckenstein, 2014, 2017).

Table 3 Google Search Phrases

Our base case deliberately indicates that the client (tool user) has a high longevity risk and a less than acceptable probability of a successful retirement. At the time of testing, the base case produces a 52% probability of success in MoneyGuidePro, an indication that we classify as “cannot retire.”

1. Free Retirement Calculator

Dozens of retirement planning tools are available to the public on the Internet, many of which are found on well-known company websites. To build our sample of testable tools, we perform seven different Google searches. Table 3 shows the seven different phrases used to search for testable tools3. Selected tools for this study are required to meet the following criteria:

6. Can I Retire Now Calculator

•

The tool portrays itself as one that provides the user “an answer”; i.e., helps the user understand how much he or she needs to save for retirement and/or indicates the user can realistically achieve his or her retirement goal(s).

•

The tool is publicly available, either for free or for a modest user fee.

•

The tool targets households.

3

2. How Much Do I Need for Retirement 3. Can I Retire 4. When Can I Retire Calculator 5. Calculate My Retirement 7. Retirement Calculator The tools that were tested in our study are listed in Table 4.

However, the Appendix shows that tools may change or get removed over time. Some free or modest fee tools require tool users to have accounts established with the respective financial institution. For the purposes of our study, each tool is categorized as either a “popular” (TPS ≤ 100), “semi-popular” (101 ≤ TPS ≤ 150), or “unpopular” (TPS > 150) tool based on its Google search popularity score. The popularity category is important because it implies the likelihood that an individual or household may use the tool. We determine each tools Google search popularity score:

Authors do not guarantee similar search results to any attempt to reproduce similar Google search results.

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Journal of Personal Finance

Table 4 Tool Name by Internet Search Result Popularity Popular TPS ≤ 100

Semi-Popular 101 ≥ TPS ≤ 150

Unpopular TPS > 150

AARP

Finra

Ameriprise

Bankrate

Merrill Edge

Chase

CalcXML

Motley Fool

Choose to Save Ballpark

CNN Money

MSN Money

ESPlanner

Fidelity

Personal Capital

E-Trade

Financial Mentor

U.S. News

FireCalc

Kiplinger

T. Rowe Price

Flexible Retirement Planner

MarketWatch

Voya (myOrangeMoney)

Nerdwallet

Voya (Retirement Outlook)

Schwab

Wells Fargo (MRP)

In Charge Newretirement.com Lincoln Wells Fargo (Quick View)

Smart Assets

MetLife

TIAA

Nationwide

Vanguard

Prudential

Betterment

TD Ameritrade USAA WealthTrace

Tool Popularity Score (TPS) = R1 + … + R7, where Rn = Google Search Result Rank for the nth Search Phrase, with a lower R indicating a higher rank Tools with a low TPS may experience greater visits because its search result web link appears as one of the earlier web links on the list.

We test our base case scenario and collect individual tool data regarding variable use and outcomes. These data identify the tools that use variables consistent with theory. Most importantly, tool output data details tool deviation from the benchmark. In addition, tool data contains information on default settings and user flexibility. For data gathering purposes, we homogenize variables that fluctuate in their input description, but serve a similar purpose, because variables may be defined differently from tool to tool.

After testing the benchmark scenario in each tool, we classify the results of each tool as “inefficient” or “efficient”. If a tool indicates that the user can retire with a significant degree of success such as a 70%+ probability, or if it provides opaque results such as saying “Congratulations, you can retire” without

any quantitative justification, the tool is classified as an “inefficient” tool because its results are in direct conflict with the benchmark results. Tools that indicate the user cannot retire are classified as “efficient” tools because they align with the benchmark results. We further investigate the efficient tools by identifying which variables are used and identify any commonalities.

RESULTS Table 5 shows the number and percentage of the tested sample tools that are classified as inefficient and efficient by popularity cohort. Results illustrate a significant level of tool inefficiency.

Nearly 7 of every 10 tools are inefficient at producing trustworthy results. Of the entire sample, 13 tools indicate that the tool user would not be able to retire. Only five of the efficient tools indicate a probability of retirement success, the average being 47.60% (high of 61% and low of 34%).

Among the popularity cohorts, the most popular tools make up one-third of the entire sample and only 12.2% of the entire sample is popular and efficient. Semi-popular tools represent

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the smallest cohort of the entire sample, less than a quarter of the tested tools, with 20% of these tools being efficient. Unpopular tools serve as the largest cohort of the tested sample at nearly 42%, and like the other cohorts shows a lower portion of the cohort being efficient at 35% of the cohort.

Table 5 Number & Percentage of Sample Tested by Popularity (N=41) Inefficient

Efficient

Total

Popular

9 (21.95%)

5 (12.20%)

14 (34.15%)

Semi-Popluar

8 (19.51%)

2 (4.88%)

10 (24.39%)

Unpopular

11 (26.83%)

6 (14.63%)

17 (41.46%)

Total

28 (68.29%)

13 (31.71%)

41 (100%)

Sample cohorts and tool outcomes in Table 6 imply higher levels of efficiency with unpopular tools. Tools that appear further down on a Google search result page (or even onto the 2nd, 3rd, or 4th page) represent more than 60% of the efficient tools. Likewise, unpopular tools serve as the largest cohort of inefficient tools (39.29%), ahead of popular tools (32.14%), but is the only cohort where more than 4 in 10 of its tested tools were efficient. The percentages of efficient tools in the popular (38.46%) and semi-popular (15.38%) cohorts are much lower than unpopular tools (46.15%).

Table 6 Study Results by Popularity and Tool Outcomes Inefficient

Efficient

Total

Popular

64.29%

35.71%

100.00%

Semi-Popluar

80.00%

20.00%

100.00%

Unpopular

64.71%

35.29%

100.00%

Total

68.29%

31.71%

100.00%

Inefficient

Efficient

Total

Popular

32.14%

38.46%

34.15%

Semi-Popluar

28.57%

15.38%

24.39%

Unpopular

39.29%

46.15%

41.46%

4

Data only available from a portion of the sample.

Total

100.00%

100.00%

100.00%

Table 7 notes the average percentage of theoretical variables used by tool outcome and cohort popularity. On average, the results show, using more theoretical variables may lead to a more efficient tool. Of the entire sample, tools use nearly 60% of the theoretical variables, on average, with inefficient tools (56%) using nearly 10% fewer theoretical variables than efficient (65%) tools. Efficient, but unpopular, tools are ahead of popular and semi-popular tools in implementing more theoretical variables. The unpopular cohort uses significantly more (72%) theoretical variables than the popular (60%) and semi-popular (53%) cohorts. The smallest percentage of theoretical variables employed by an efficient tool is 33% (Current Age, Retirement Age, Current Income, Pension Income, Trust Income, and Accumulated Assets) with the largest being 94% (all variables less Smoker). In contrast, the smallest percentage of theoretical variables employed by inefficient tools is 39% (Current Age, Marital Status, Retirement Age, Current Income, Social Security, Accumulated Assets, and Investment Return) with the largest being 83% (all variables less Smoker, Expected Future Income, and Tax Rate).

Table 7 Average Percentage of Theoretical Variables Used by Tool Outcome & Popularity Results (N = 41) Inefficient Popular Semi-Popular Unpopular Efficient Popular Semi-Popular Unpopular Total

% of Theoretical Variables Employed 56% 52% 53% 62% 65% 60% 53% 72% 59%

Table 8 presents average default variable data by tool result and popularity cohort. On average, efficient tools default to slightly fewer years in retirement, lower wage replacement rates, and lower investment returns during retirement4 compared to inefficient tools. For the entire sample, tools default to approximately a 25-year retirement, which is close to the base case scenario we used to test each tool. Semi-popular tools that indicate the user cannot retire employ an average of 19

Table 8 Average and Default Variable Data Inefficient

Efficient Total

Popular

Semi-Popular

Unpopular

Total

Grand Total

62%

56%

60%

53%

72%

65%

59%

$678,500

$545,401

$521,340

N/A

N/A

N/A

N/A

$521,340

25

24

27

24.88

26

19

22

24.37

24.34

88%

81%

82%

83%

77%

100%

68%

76%

82%

Default Ave Retirement Age

66

66

66

66

67

67

66

66

66

Default Ave Life Expectancy

90

90

91

90

93

86

88

90

91

2.7%

2.8%

3.0%

2.9%

2.3%

3.4%

2.6%

2.5%

2.8%

6.5%

6.7%

6.3%

6.5%

5.3%

5.0%

4.6%

4.9%

6.0%

6.5%

6.7%

5.4%

6.0%

5.0%

5.0%

4.6%

4.8%

5.4%

Ave % of Theoretical Variables Used Ave Ending Balance (end of plan) Default Ave # of Yrs. in Retirement Default Ave Wage Replacement Rate

Default Ave Inflation Rate Default Ave Return Before Retirement Default Ave Return During Retirement

Popular

Semi-Popular

Unpopular

52%

53%

$392,505

years compared to inefficient semi-popular tools (24 years). Popular tools have a minimal spread (1-2 years) between inefficient and efficient tools. However, there is a half-decade difference when comparing the efficiency of unpopular tools.

Many tools start users with a predetermined wage replacement rate. The average default wage replacement rate for the entire sample is 82%. The average default wage replacement rate for inefficient tools (83%) is much higher than our base case (70%).

Tool users replace 7% less pre-retirement income when planning with efficient tools (76%) compared to inefficient tools.

A clear difference in default wage replacement rates is found within the popular cohort. Inefficient popular tools use a default wage replacement rate of 88% compared to their efficient counterparts which begin with a default wage replacement rate of 77%.

Investment returns play an integral role in planning for and drawing down retirement assets. On average, tools have a default post-retirement investment return of 5.4% and a default pre-retirement investment return of 6.0%. By default, inefficient tools use higher pre and post-retirement investment returns, by 0.5% and 0.6% respectively, than efficient tools.

Figure 2 displays the average ending balance of the base case scenario tested among inefficient tools. If a household can successfully retire, it implies that they can meet all of their stated goals. Efficient tools have no data on plan ending balances

because the household runs out of assets prior to the end of the plan. Eight inefficient tools (28.5%) offered the tool user an asset value at the end of the plan. The average ending plan balance is $521,340. Popular tools have lower average ending balances ($392,505). Semi-popular tools produce, on average, an ending balance of $678,500. Unpopular tools have an average ending balance of $545,401. The majority (62.5%) of these tools produce an ending balance of less than $500,000. The highest ending balance comes in at $1.3 million and the lowest at about $57,000.

Table 9 shows the comparison of individual variable use and variable flexibility by tool outcome. Not every tool allows for every variable to be manually entered or changed from its default setting. Retirement planning is an event for household customization. Efficient tools provide users with greater flexibility to change variable input information. Efficient tools have greater input flexibility than inefficient tools with 66.67% of the variables being flexible. However, inefficient tools allow for more manual changes for Life Expectancy, Current Income, Pension Income, Social Security Income, Expected Rate of Return, and Marital Status. Like Tresidder (2011), we find that variable input flexibility may have a positive impact on tool results. The tested samples show the Ages variable category is commonly found and flexible with nearly every tool, except for the life expectancy variable. Additionally, all tools allow for users to manually enter

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Figure 2 End of Plan Balances of Inefficient Tools

their assets to date, but few allow for recognizing windfalls and bequests. Only two tools allow users to manually indicate their use of tobacco, and both tools were found to be efficient.

The Income variable category offers tool users the ability to self-identify sources of income prior to and during retirement.

The current annual income variable is present in 88% of all tools, and nearly all of these tools allow for users to change the input. However, 93% of inefficient tools permit tool users to change their income amount, compared to 77% of efficient tools. Inefficient tools focus primarily on the userâ&#x20AC;&#x2122;s current income and social security income, and less on other income sources. Efficient tools focus primarily on expected future income (i.e. WRR) and social security income. Trust income, and the flexibility to manually enter in its details, is found in twice as many efficient tools (62%) than inefficient tools (29%).

The only variables that are consistent across all tools are current accumulated assets (Assets variable category) and target retirement age (Age variable category). Every tool allows all users to manually enter their accumulated assets and expected retirement age. Although asset accumulation is critical to a retirement plan, tools offer variations as to how the assets are entered. Some tools only allow for a single dollar amount of all assets, while other tools allow tool users to input specific account types (i.e. sheltered and taxable).

Similar to the Income variable category, tool users may find the

ability to identify, and customize, other sources of lump sum assets (windfall receipts and bequests/inheritance) in a larger percentage of efficient tools. Only 25% of the inefficient tools allow for users to manually enter windfall receipts, such as tax refunds. The base case scenario we test in this study does not include any bequest information, but a small percentage (7%) of inefficient tools offer a bequest variable. It is important to note that 85% of publicly available retirement planning tools allow users to identify how much they are setting aside for retirement each year.

Both efficient and inefficient tools place great emphasis on investment returns. Nearly 80% of all tools employ an investment rate of return variable. A larger percentage of inefficient tools (71%) consent to users manually changing the expected return they receive each year on their investment assets, compared to efficient tools (62%). Tool users are forced to guess their risk tolerance when using publicly available retirement planning tools, as no tool provided a risk tolerance questionnaire. The ability to pick an investors risk tolerance is only available in 28% of all tools.

Tax rates are a top concern that professional advisors have when advising their client on their portfolios, but producers of publicly available retirement planning tools appear to disagree.

Nearly 90% of professional advisorâ&#x20AC;&#x2122;s rate taxes as the most influential topic on client portfolios (TD Ameritrade, 2018). Our

72

Journal of Personal Finance

Table 9 Individual Variable Use and Variable Flexibility by Tool Outcome Variable Category

Ages

Income

Assets

% of Tools with Variable (n=41)

% of Tools Allow for Manual Entry (n=41)

% of Efficient Tools with Variable (n=13)

% of Efficient Tools Allow for Manual Entry (n=13)

% of Inefficient Tools with Variable (n=28)

% of Inefficient Tools Allow for Manual Entry (n=28)

Current Age

95%

95%

100%

100%

93%

93%

Target Retirement Age

100%

100%

100%

100%

100%

100%

Life Expectancy

78%

66%

77%

62%

79%

68%

Current Annual Income

88%

88%

77%

77%

93%

93%

Expected Future Income (WRR)

83%

76%

92%

77%

79%

75%

Government or Private Pensions

78%

73%

77%

69%

79%

75%

Social Security Income

90%

68%

85%

62%

93%

71%

Trust Income

39%

39%

62%

62%

29%

29%

Disability Insurance Income

22%

22%

38%

38%

14%

14%

Current Accumulated Assets

100%

100%

100%

100%

100%

100%

Windfall Receipts

34%

32%

46%

46%

29%

25%

Bequest Amounts

15%

12%

23%

23%

11%

7%

Expected Rate of Return on Assets

78%

68%

77%

62%

79%

71%

Inflation Rate

66%

54%

77%

62%

61%

50%

Tax Rate

15%

12%

38%

31%

4%

4%

Variable

Rates

Household Structure

Marital Status

54%

54%

46%

46%

57%

57%

Non-Married Partner Option

22%

22%

31%

31%

18%

18%

Lifestyle

Smoker

5%

5%

15%

15%

0%

0%

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

73

Volume 18, Issue 2

results show that only 15% of all tools bring a tax rate variable to user attention, and only 12% allow users to personalize their tax information. Inefficient tools are even worse than the entire sample, with only 4% of the tools employing a tax rate variable.

Efficient tools pay closer attention to user tax information with 38% of the tools using the tax rate variable and 31% allowing for manual adjustment. A larger percentage of efficient tools allow for users to self-identify their tax rates, but this is still less effective than financial professional concern.

Household Structure and Lifestyle variable categories are of lesser importance to tool producers. Only slightly more than half of all tools recognize a user’s marital status (54%) with only 22% allowing for a user to identify a non-married partner. Inefficient tools prompt for marital status more often than efficient tools, but users will find a greater probability of entering a non-married partner in efficient tools. Only two tools allow for users to self-identify their use of tobacco; both of these tools are efficient. When changing the Smoker variable in the tested scenario, user life expectancy declines by 8 years.

CONCLUSION Planning for one’s retirement requires orchestrating many different variables, including variables for both pre- and post-retirement time periods, with life expectancy being one of the most important variables. The opportunity for a secure retirement is becoming increasingly elusive for many investors.

The financial services industry has responded to this issue by developing, and offering to the public, a variety of retirement planning tools. However, the use of a publicly available retirement planning tool may be less appropriate for accomplishing retirement goals that require a high level of personalization.

The tools that fail to include all the appropriate variables are likely adding to the potential failure of successful retirement planning with their inaccuracy. Tool users may be too naive to know the tool they are using may be giving misleading advice.

The traditional three-legged stool—pensions, social security, and savings—are morphing into a system where retirement income is being transferred from government and private-sector employers to the individual investor. Compounding the risk of this transition, the investment universe has become increasingly complex, longevity risk is growing, and individual investors lack the knowledge and experience to plan adequately for their retirement income needs.

This study tested the largest known national sample of tools to investigate the efficacy of retirement planning tools that

are available to the public for free, or for a modest fee. We examined how the use of different variables impact tool results.

Unlike prior studies, which reviewed a limited number of tools and primarily focused on the assumptions made by the tools or the lack of user sophistication in tool use, we considered the issue from an academic perspective. We found that the majority of tools are inaccurate, making it dangerous for the public to trust tool outcomes. A high tool failure rate combined with the extreme variation in tool outputs increases the difficulty of individual users properly conducting their retirement planning. Households may be made less well off if they rely on the advice from these tools. We are concerned that households that receive conflicting advice while using several of these tools may make the wrong savings decisions or they may be driven to paralysis in making any retirement savings decision.

We found that more accurate tools employ a greater quantity of theoretical variables. All tools that employ at least 89% of the theoretical variables were efficient. If tool users seek accurate tools, they will likely find better tools by skipping the first page of their Internet search results. Unpopular tools, which are usually found on the later search pages, are more efficient at producing trustworthy results than well-known brand name tools. This may be because unpopular tools incorporate a larger percentage of theoretical variables; however, this requires tool users to ignore popular brand names, which is problematic because Google search users trust higher ranked search results more than lower ranked search results (Pan, Hembrooke, Joachims, Lorigo, Gay, & Granka, 2007).

We also found that more accurate tools allow greater user input flexibility. The study’s results are consistent with prior research in that the results from the available tools are highly variable.

With results from our test scenario ranging from having a plan ending balance of $1.3 million to a 34% probability of being able to retire successfully, we concluded that, in most cases, the available offerings are extremely misleading and disagree with Turner’s suggestion that tools, even with their flaws, provide some help to consumers (Powell, 2010).

Our study provides evidence that suggests that inefficient tools go wrong in several ways, beginning with an opaque wage replacement rate variable, lack of user customization capabilities, and the use of fewer theoretical variables. We found that efficient tools tend to use more theoretical variables, have lower wage replacement ratios, allow for more user personalization, and more conservative pre- and post-retirement real investment returns.

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Journal of Personal Finance

Since many of these tools are offered by well-known and respected organizations, we conclude that considering the issues raised by our study, a reevaluation of the underlying inputs in the current offerings is in order to allow these tools to better serve the needs of consumers. Furthermore, updates to the existing tools should start with the inclusion of the variables noted in our study. In order to properly aid households in their retirement planning efforts, we propose the following 10 improvements for all publicly available retirement planning tools, including efficient tools: Utilize, at a minimum, the variables outlined in this study. We find a larger percentage of efficient tools use more variables.

Most importantly, tools should improve their use of the life expectancy variable to better account for longevity risk. We also suggest that tools allow for users to self-identify their use of tobacco, as there is a significant reduction in life expectancy for smokers when compared to non-smokers. A tool that ignores the user’s smoking status assumes the user is a non-smoker by default. Like the Social Security Administrations website, which helps tool users obtain accurate social security income data, respected longevity tools, such as the Actuaries Longevity Illustrator from the American Academy of Actuaries and the Society of Actuaries, can help tool users determine their life expectancy.

Allow for user customization. Like Tresidder, we find that input flexibility may have a positive impact on tool results.

Incorporate withdrawal options into tool results. Tools fail to account for retirement distribution strategies. A decisive element of retirement planning is knowing how to accomplish the goal. Without the “how”, a goal is idle. Current tool results imply tool users will or will not be able to make their retirement savings last through retirement and suggest, directly or indirectly, a suboptimal static retirement income withdrawal strategy focused on constant inflation-adjusted withdrawal amounts, ignoring the reality of variable goals (i.e. fixed interest rate mortgages, increasing medical cost, etc.) and other options such as dynamic strategies (variable annual spending based on longevity, risk, and annual asset valuation), or annuitization strategies (Finke & Blanchett, 2016). Additionally, employer plan participation is positively related to the number of decumulation options offered to participants (Brown & Weisbenner, 2014). Yet, publicly available retirement planning tools may reduce the probability of employer plan participation because they fail to provide, or inform about, efficient decumulation options. For example, if a non-participant in an employer plan

produces a misleading tool result that indicates they are on track for a successful retirement, the employee is less likely to make the rational choice of participating in the employer plan.

Tools also ignore that users may prefer to retire gradually, rather than identifying a specific age that acts as a light switch that instantly changes them from "not retired" to "retired."

Increase tool workflow. Tools should, at a minimum, allow users to input all variable information prior to running the calculated result. For example, some tools do not allow tool users to address the inflation rate or life expectancy variables until after the tool user has hit the “submit” button.

Improve wage replacement rate consistency. The use of a fixed replacement rate may provide misleading results, since such a gross measure fails to account for the complexity of the financial life of an investor. Many financial goals are limited in time (i.e. paying off a mortgage, travel, providing support for family members, funding education for children or grandchildren, etc.).

Retirement planning is more complex with higher wage replacement ratios. Although 92% of efficient tools allow for users to customize their wage replacement, inefficient tools have higher wage replacement rates, which may be a result of tool inflexibility. A wide distribution of wage replacement ratios across tools exists. For example, one inefficient tool indicates a 60% wage replacement rate is healthy, while two other inefficient tools believe 75% and 80% are appropriate. Although wage replacement ratios vary by household circumstance, we suggest tools guide users to using wage replacement rates that are supported by research.

Eliminate the “Fine” from the fine print. Tool accuracy may be contingent upon a user’s level of financial sophistication.

Often, fine print is treated a substitute for lack of financial literacy, but tool users are not likely to read the fine print (Obar & Oeldorf-Hinsch, 2016). Tools may improve tool effectiveness by spreading out and simplifying the “fine print” throughout the mainstream appearance of the tool.

Do not use past performance to predict future performance.

Retirement planning is a long-term endeavor, not a short-term speculation. Some tools base their future returns on historical returns. For example, an inefficient tool says in their fine print, "Growth and volatility assumptions are based on historical asset class returns and correlations using well diversified portfolios." Studies debate the topic of past performance predicting future performance, but the evidence suggests that although there are some instances where past performance may support

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

Volume 18, Issue 2

short term future performance, there is insufficient evidence that past performance predicts long term future performance (Droms, 2006).

Incorporate and improve consistency with risk tolerance variables. Retirement planning should not be conducted without understanding one’s risk tolerance because risk tolerance is directly connected with portfolio asset allocation and investment rates of return. Risk tolerance is defined differently among the diverse tools. For example, one inefficient tool considers an annual return of 6.1% to be "Moderately Aggressive", but another inefficient tool indicates that an annual return of 6.2% is the most conservative option available. An efficient tool takes another approach by allowing users to pick from seven different investment risk tolerance styles, ranging from "Conservative" to "Aggressive", based on a broad asset allocation. Currently, tool users are forced to guess their risk tolerance, which investors are likely to dangerously overestimate, without guidance (Pan & Statman, 2012). We suggest that tools offer risk tolerance questionnaires.

Greater transparency with tool results. Tool users anticipate results and clarity. Currently, tools produce a mix of outcomes.

Inefficient tools tend to give opaque answers such as whether the user is “On Target” or not but without any indication of variation. Some tools use probability success rates, occurring in both efficient and inefficient tools, but the issue lies in how they define success. For example, one inefficient tool indicates that users need a 90% success rate to be prepared for retirement, but another indicates that a 50% success rate constitutes being prepared for retirement. Efficient tools tend to use a defined range of acceptable success, such as 80% to 95%.

Improve clarity of tool use and purpose. Several of the wellknown websites offer consumers multiple retirement planning tools, which may make it difficult to discern between tools for their specific situation. For example, one popular, but inefficient website employs two different tools, pre- and post-retirement tools. The post-retirement tool requires tool users to know and input up front how much they have or will have at the start of retirement, however, this is not provided in the pre-retirement tool, making the post-retirement tool is unusable.

The findings of our study have implications for the financial services industry. The industry seeks greater trust from households. Our study’s results suggest that households that use inefficient publicly available tools may be confused if the results from the tools contradict the advice they receive from profes-

75

sional advisors, and these tools may subsequently promote distrust towards financial professionals. Therefore, it is important that households be informed that these tools cannot be trusted until significant improvements are made. Financial professionals should take note that their clients may be using these tools to understand the trustworthiness of the advisor’s advice. This trust may be dependent upon dangerously misleading results.

Until the publicly available tools are improved, financial professionals should be ready to help their clients understand the major issues with these tools.

In this Study?

No Yes No Yes

No

No No Yes Yes

No

Yes Yes No No No Yes No Yes No No Yes No No

No

Yes No Yes Yes Yes No No No Yes No No No No Yes No Yes Yes Yes Yes

Still Exist?

Yes Yes Yes Yes

No

No No Yes Yes

No

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes

Yes

Yes No Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes

AARP Retirement Nest Egg Calculator AARP Retirement Planning Calculator American Funds Ameriprise Retirement Planner Annplan (ANNROC Retirement Financial Planning Corp.)

AOL Retirement Planning Calculator Bloomberg.com Retirement Planner Choose to Save Ballpark CNNMoney.com EBSA, Taking the Mystery Out of Retirement Planning Economic Security Planner (ESPlanner) Fidelity Retirement Income Planner Fidelity Retirement Quick Check Fidelity Investments (Fidelity Customers only) Financial Engines FINRA Retirement Calculator First American Bank InCharge Education Foundation Calculator ISG Personal Version 1.0 (ISG) John Hancock Retirement Planning Calculator Kiplinger Retirement Savings Calculator LifeFocus V 7.5-0405 English (The Rouse Companies) Mass Mutual Retirement Income Calculator McRetire (Monte Carlo Retire) by Efficient Solutions Inc. MetLife Retirement Income Snapshot Mind Your Finances Motley Fool Retirement Calculator MSN Money Retirement Planner Nationwide Pacific Life Plan for life after Work (Fidelity) Principal Financial Group Prudential Retirement Calculator Quicken Retirement Calculator Simple Joe Simple Planning.com Smart Money (Now MarketWatch.com) SunTrust Retirement Planner T. Rowe Price Retirement Income Calculator TIAA-CREF Retirement Goal Evaluator Vanguard Retirement Income Calculator Wells Fargo Retirement Planning Calculator

Tool

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

x

x

x

x

x

Turner (2010)

x x x x x

x

x x x

x

x

x x

x

x

x

x

x

x x x x

x

x x

Turner (2014)

APPENDIX

x

x

x

x x

x

x

Turner & Witte (2009)

x

x x x

x

x

x

x

x

x x

x

et al., (2008)

Dowd Madden (2008)

x

x

x

x

x

x

KotliKoff (2006)

x

x

x

x

x

x

Sondergeld et al., (2003)

x

x

x

x

Bodie (2003)

x

Warshawsky & Ameriks (2000)

76 Journal of Personal Finance

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Volume 18, Issue 2

79

2019 IARFC National Financial Plan Competition Rebecca Boyle, GreenCourse Financial Advisors Winning Financial Plan by Rebecca Boyle, Undergraduate Student, University of North Texas; Paraplanner, GreenCourse Financial Advisors Edited by Benjamin F. Cummings, PhD, CFP®, RFC®

Abstract For the 2019 National Financial Plan Competition, students were given a fictional case study of a family that included an overview of their financial picture. Students were asked to analyze the case and prepare a financial plan for the family, including recommendations to help the family reach their goals. Written case submissions were judged, and winners advanced to the semi-finals, after which the top three teams made in-person presentations at the IARFC annual conference in Nashville, Tennessee.

The case that was provided to students is summarized below, followed by a near-complete summary of the winning financial plan, prepared and presented by Rebecca Boyle from University of North Texas. As editor of the Journal of Personal Finance, Benjamin Cummings edited the case and Rebecca’s plan for inclusion in the journal.

Note: The views expressed in this financial plan are those of the author and do not represent the views of GreenCourse Financial Advisors, nor does this financial plan constitute investment advice.

Key Words case study; financial plan

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Journal of Personal Finance

CASE NARRATIVE

remodel 5 years ago. They hope the home will appreciate at 3% annually based on its location, even though the value has declined. Emily feels that the remodel was worth it because she loves her new kitchen.

The Hathaway Family First Name

Relationship

DOB

Age

George Emily William (Billy) Alicia

Husband Wife Son/Step-son Daughter

07-12-1970 09-24-1978 09-02-2004 07-28-2010

48 40 14 8

George is Vice President of Operations for Sloan Dealy Manufacturing, where he has been working for 20 years. His current salary is $152,000, which makes him the second highest paid employee. He believes his salary will increase at 4% annually.

Emily, George’s second wife, is a teacher at Carmel Catholic. Her annual salary is $40,000 with no bonus or overtime pay. She expects her pay increases to be modest at 3% annually. She recently returned to the classroom.

Emily went back to school for her Master’s degree in teaching when she and George married in 2007. Emily briefly stopped working after Alicia was born.

George has a son, Billy, from his first marriage who lives with his mother in Fishers, Indiana but spends every other weekend with George and Emily. In the divorce, instead of child support, George agreed to pay for Billy’s private schooling. The annual tuition is $9,600.

With their daughter Alicia, they live in Carmel, Indiana, a suburb of Indianapolis with excellent public schools. They both like their home. Emily says she would prefer they never move.

Financial Information George owns 3 acres on a lake in Kentucky. Emily says it has been a great investment since the value has gone from $22,000 to $90,000 over the 20 years George has owned it. The taxes are only $850 a year, but would increase if they built on it. George would like to build a cottage and eventually retire there. It would be at least $175,000 in today’s dollars to build the home he envisions. George would like to start building while the kids are younger so that the family can enjoy it year-round. Right now, the family can only camp on the property in the summer.

Their home (owned JWROS) cost $450,000 8 years ago. It is now worth $390,000, despite spending about $50,000 on a kitchen

When they bought the home, they took out a 30-year mortgage for $380,000 at 5% with a monthly payment of $2,930, including taxes and insurance. The insurance is an HO-2 broad form policy from Indiana Farmers. The policy has neither exclusions nor riders and a $100 deductible with an annual premium of $2,275.

To complete the kitchen, they took out a home equity loan for $25,000 with monthly payments of $262. It was a 10-year loan, but neither of them remembers the interest rate. The rest of the kitchen was paid for from George’s cottage fund, which no longer has a balance. They are no longer contributing to it.

George and Emily are both well-educated and would like each of their children to attend a 4-year university. Billy wants to attend Miami University in Oxford, OH, which currently costs $34,000 for an out-of-state student plus on-campus room and board of $16,900. They have $50,000 in Series E Bonds earmarked for Billy. The original cost for the bonds was $27,750.

They are not currently contributing to Billy’s college fund.

George and Emily would like to fund $40,000 per year for Alicia’s college tuition. George and Emily have a joint account for Alicia’s education invested at the local bank with a current balance of $4,500, earning 1.5% per year. George and Emily are making $200 monthly contributions to Alicia’s college fund until Alicia’s and Billy’s college accounts are equal.

Both George and Emily are offered benefits through their employers such as group health and life insurance, defined contribution plans, and defined benefit plans.

For group health insurance through his employer, George pays $675/month which covers the entire family. He believes it’s a robust plan, since it was the most expensive and covers vision and dental. In addition to health insurance, his employer supplies officers of the company with a short-term disability benefit of 6-months at full salary. The company’s practice is to not report the premium as W-2 income unless the employee is fully disabled (long-term).

To minimize this risk, George purchased a standalone personal long-term disability policy. It has a 6-month elimination period, coverage to age 65, and a monthly benefit of $5,000 with a 4% COLA, not to exceed 60% of his pre-disabled wages. He pays an annual premium of $748.

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

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Volume 18, Issue 2

George’s group term life is provided to him at no cost. It had an original face amount of $35,000 when he first started working at Sloan Dealy Manufacturing. Over the 20 years he’s worked there, the policy has increased to $304,000, even though he’s never made any changes to it. His company also offers supplemental group term life insurance. He can buy up to five times his salary. Based on his age and health, the supplemental group life insurance is slightly less expensive per thousand dollars of coverage than a fully underwritten policy from a highly rated life insurance company.

George currently has a Universal Life policy from Ohio National with $1,000,000 death benefit. The annual premium is $2,600 until his retirement, at which time no further premiums are necessary as projected. Current cash value is $15,750, with a minimum crediting rate of 4% per year. Currently, the average cost of insurance is $80 a month.

George’s 401(k) retirement plan has a current balance of $251,813 with Emily as the beneficiary. George contributes 5% of his salary and there is a 100% match up to 10%. It is invested in a balanced fund with an estimated annual total return of 7%.

The plan has a full selection of funds, including actively managed, target date, and index funds.

Although George’s employer plan provides health insurance for the entire family, Emily also has health insurance through her employer. It covers only Emily, and she pays $125 per month.

Emily has $65,000 of group term life insurance at no cost to her.

She also bought a 20-year level term policy with a $250,000 death benefit in 2007. The annual premium is $312. George is the beneficiary of both policies.

Emily participates in the school’s 403(b) plan. Her current balance is $5,070, all invested in US Government Securities earning 2.5%. In addition, her plan offers 6 index funds (Asia Pacific Stocks, U.S. Large Cap. U.S. Small Cap, U.S. Balanced, U.S. Long-Term Bonds and European Stocks). George is the beneficiary. She says she’s contributing 15% of her salary; there is no match because there is a State Teacher’s Pension available.

George’s mother, Mary, is a widow. She is 75 years old and lives in Columbus, Ohio. She is well-established financially and she withdraws the maximum annual gift exclusion each year to give to George. George believes there's about $750,000 currently in the trust which he hopes will supplement his and Emily's retirement. George will be the sole beneficiary at his mother’s death.

The Hathaway family has no wills, trusts, prenuptial or any other legal agreements. Indiana is a separate property state.

Other Family Living Expenses •

Food: $850/month

•

Vacation: $6,000/year

•

Charitable: $12,000/year

•

Clothing: $300/month

•

Home maintenance: $250/month

•

Utilities: $575/month

•

Auto maintenance and fuel: $225/week

•

Personal spending allowances: George: $250/month

––

Emily: $125/month

––

Billy: $50/month

––

Alicia: $25/month

•

Car insurance: $175/month for George; $125/month for Emily

•

Cell phone: $300/month for the family (four lines)

•

Internet, phone, cable (bundled): $250/month

•

Subscriptions services (Netflix, Hulu, etc.): $50/month

Other Liabilities •

Emily has an estimate of her future pension that is based on her continued employment and nominal salary increases. If she retires at age 58 (when George plans to retire at 66) she would be entitled to a monthly pension of $4,000 with a 1% COLA and a 50% survivor benefit.

Emily also had a 403(b) plan at her old employer. There is a $54,745 balance invested in US Government Securities and she expects it to grow at 2.5% annually.

––

•

George ––

Cadillac: loan balance $16,000; 1.9% APR; $425/month; FMV $30,000

––

Visa: loan balance $12,000; 25.99% APR; $300/month; new charges each month keep the balance about the same

Emily ––

Jeep: loan balance $500; 5.9% APR; $200/month; FMV $19,000

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Journal of Personal Finance

Other Investments and Properties George Bank CD: 5% APY

$20,000

Credit Union: 2.5% APY; contributing $250/month

$23,500

Aggressive Stock Fund: cost basis $18,000; was told he would earn 12%/year

$12,000

Stock: inherited from grandmother; estate valuation was $25,000

$75,000

Arizona land: 2 acres inherited from grandfather in 1991; estate valuation was $4,000; taxes are $500/ year

$14,000

Gun collection

$14,000

per month for the surviving spouse. Since they are both in good health, they would like to plan for each spouse to live to age 90. Emily plans to work until age 58, but is willing to work until 62.

Survivorship income. George and Emily would like to know how much income they need while Alicia and Billy are both in school, when one is in school, and finally when they are both out of the house.

Investment Attitudes Willingness to hold investments for 5 years Importance of converting investments into cash

Emily Growth Stock Fund: cost basis $6,000; contributing $100/month; earning 9%/year Savings Account: 2.5% APY; not contributing to it Jewelry: inherited from grandmother in 1994 Jewelry: engagement ring from George

$7,250 $9,000 $8,000

Family Assets Checking Account: 2.5% APY

$7,500

Camping equipment

Agree

$100,000

Art (signed prints): purchased as investment 7 years ago for $2,500; expected growth of 6%/year

Neutral

Discomfort with market fluctuations Importance of achieving highest return

Agree Neutral

Do not need current income from investments

Agree

Important to receive maximum current income

Disagree

Comfort with many investments

Strongly Agree

$10,000

Household furnishings

Disagree

Concern with inflation Concern with market fluctuations

$22,000

Agree

Tax advantaged Investments are important

Agree

$4,000

Tax Information (W-2) George

Stated Goals

Employer’s name

Sloan Dealy Manufacturing

Education. Funding for Billy and Alicia based on the schools that they and their parents are considering.

Wages, salary, tips

$136,300

Social Security wages

$128,400

Wedding for Alicia. George and Emily would like to provide $20,000 in today’s dollars for a wedding for Alicia when she is 25.

Medicare wages and tips

$152,000

Federal income tax withheld

$15,277

Social Security tax withheld

$7,960

Lifestyle. George and Emily would like to build a cabin on the lake lot. Current cost for the type of structure they want to have would be $175,000 and building costs are increasing 5–7% a year in this area.

Medicare tax withheld

$2,204

12c

$311

12d

$7,600

State income tax withheld (Indiana)

$5,452

Local income tax withheld (Carmel)

$2,726

Retirement. Probably at George’s age 66, but possibly sooner.

They want to maintain their preretirement standard of living, but they have no idea what that will require or how reachable it may be. After a recent discussion with their advisor, they said they might need a retirement income of $10,000 per month in today’s dollars (net after taxes) while both are alive, and $6,000

Emily Employer’s name

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

Carmel Catholic School

83

Volume 18, Issue 2

Wages, salary, tips

$37,300

Social Security wages

$40,000

Medicare wages and tips

$40,000

Federal income tax withheld

$6,000

Social Security tax withheld

$2,480

Medicare tax withheld

$580

12c

$15

12d

$1,200

State income tax withheld (Indiana)

$1,492

Local income tax withheld (Carmel)

$746

WINNING FINANCIAL PLAN Note: Items in italics indicate notes from the editor and areas where the submission was summarized.

Engagement Letter Dear Mr. and Mrs. Hathaway, This letter will confirm the terms of our agreement regarding the financial planning services we will provide for you.

Engagement Objectives The primary objective of our engagement is to review and analyze your personal financial situation and make recommendations for your financial plan. This review will identify your personal financial goals and objectives and include possible strategies to achieve them. Our analysis and recommendations are based on information provided by you that will be relied upon for representations.

Activities The initial phase involves accumulating and organizing facts about your current financial status, identifying specific goals and objectives, and agreeing upon planning assumptions. This information will be obtained during an initial meeting or conversation with you and/or from the use of a financial planning data questionnaire. We will also review copies of pertinent documents, such as wills, company-provided fringe benefit booklets, prior tax returns, investment account statements, and insurance documents.

After the information has been received, the data will be analyzed and projections will be made. A subsequent meeting will be held to verify the accuracy of the data and will allow you to validate the assumptions used. Alternative courses of action to meet goals and address any issues will be comprehensively discussed.

The projections will then be updated for any required changes and a comprehensive financial planning report containing recommendations in all relevant areas of your financial situation will be presented. We will work with you to finalize the choice of strategies, to set time goals, and to establish responsibilities for your implementation of the plan.

The methods that you choose to follow for the implementation of the financial planning recommendations are at your discretion.

You will be responsible for all decisions regarding implementation of the recommendations, except where otherwise noted in the implementation plan.

Your plan should be reviewed with us informally on a semiannual basis and more formally on an annual basis. These update sessions are essential so that adjustments can be made for changes in circumstances, economic conditions, and income, gift, or estate tax law revisions.

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Journal of Personal Finance

Fees The fee for your Comprehensive Financial Plan has been determined by our mutual agreement and is $2000, which is due and payable upon return of this Engagement Letter. Please note that this fee is for the written financial plan alone and the plan shall contain all of our recommendations to you through the date of its delivery.

This agreement and fee does not provide for any product sales that may be offered at no obligation to you. This is a separate service that may be considered a conflict of interest because commissions and/or additional fees may be paid in connection with products purchased. We will inform you if there is any conflict of interest.

After the initial plan is created, additional implementation support, plan monitoring, and plan updates are provided through a retainer fee of $100/month, to be billed annually at the beginning of the engagement year. If at any point you choose to discontinue services, you will be refunded for any month of the engagement that has not yet begun.

We reserve the right to discontinue services if billings are not paid when due.

If at any time you are dissatisfied with our services, you may terminate this agreement. If you do so within three business days of your acceptance, you will receive a full refund. Subsequently, any fees that you have paid to us in advance will be charged for the time and effort that has been devoted, up to that termination time, to prepare your written report and any remaining balance will be refunded.

We anticipate beginning the engagement immediately. If this letter meets with your approval, please sign the enclosed copy in the space provided and return it to us in the enclosed envelope.

We thank you for the opportunity to be of service, and we welcome you as a valued client.

Sincerely, Rebecca Boyle RJB Wealth Management I/We agree to the above terms & conditions: Client Signature: _____________________________ Date: _______________________ Client Signature: _____________________________ Date: _______________________ Note: Template for engagement letter taken from Dalton, Dalton, Gillice, & Langdon (2019). Fundamentals of Financial Planning (6th ed.) USA: Money Education.

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

Volume 18, Issue 2

85

Goals and Objectives Throughout the plan, we will incorporate recommendations that will help you achieve the following goals: 1. Build a cabin on the lake lot in Kentucky a. Approximate Cost of $175,000 inflating at 5–7% yearly b. Built before retirement so the family can enjoy it while the kids are young, but can also serve as a retirement home 2. Pay for Billy to attend school at Miami University a. Tuition is currently $34,000/year, not including additional $16,900 in room and board 3. Fund $40,000/year in college costs for Alicia a. Retire at George’s age 66 with $10,000/month in after-tax income 4. Understand income needs a. While Billy and Alicia are both in school b. While Alicia is in school c. When both children are out of the house

Assumptions Since financial planning requires making informed predictions about an uncertain future, several assumptions are made in the financial planning process. We also made assumptions where full details were not provided. These assumptions are stated below: •

Billy cannot be claimed as a dependent on Emily and George’s tax return, but a percentage of the living expenses do go to providing for him while he is with them

•

The long-term return of stocks is 10.4% and the long-term return of bonds is 5.6%

•

Base inflation is 3.97%

•

College Cost inflation is 6%

•

Building cost inflation is 5%

•

George’s salary will increase 4% per year and Emily’s will increase 3% per year

•

All information provided as “X years ago” is assumed to have happened in September of that year

•

The Cadillac loan is assumed to have been a 5-year loan taken out at the end of 2016

•

The annual gift tax exclusion remains at current levels through 2022, then inflates to $16,000

•

401(k) contribution limits are adjusted for inflation before George’s contributions exceed them

Income Tax Analysis The table shows your projected federal and state tax income tax liability through 2022. Since 2018 is the first year under the new tax law, your projected taxes may look different than what you have paid in previous years. The main change that affects you is the doubling of the Child Tax Credit. The standard deduction has increased significantly as well, but with your level of itemized deductions, it is better to itemize yearly. 2018 taxable wages were taken from the provided W-2s. Taxable wages are adjusted yearly for changes in pre-tax insurance premiums and savings, but all values are shown in today’s dollars.

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Journal of Personal Finance

Federal Tax Projections Line 1 2b

Item

2018

2019

2020

2021

2022

Taxable Wages (Form W-2, box 1)

$173,600

$173,900

$173,900

$166,300

$166,300

Taxable Interest (Estimate)

$2,050

$2,050

$2,050

$2,050

$2,050

Net Capital Gain (Land Sale)

$0

$4,000

$0

$0

$0

6

Total Income

$175,650

$179,950

$175,950

$168,350

$168,350

7

AGI

$175,650

$179,950

$175,950

$168,350

$168,350

8

Standard or Itemized Deduction

$38,395

$35,643

$35,191

$34,719

$34,225

10

Taxable Income

$137,255

$144,307

$140,759

$133,631

$134,125

11

Tax

$22,075

$23,017

$22,846

$21,278

$21,387

12

Child Tax Credit

$2,000

$2,000

$2,000

$2,000

$2,000

12

American Opportunity Tax Credit

$0

$0

$0

$0

$1,900*

13

Line 11 - Line 12

$20,075

$21,017

$20,846

$19,278

$17,487

15

Total Tax

$20,075

$21,017

$20,846

$19,278

$17,487

16

Tax WH

$21,227

$21,227

$20,900

$19,500

$17,500

18

Total Tax Payments

$21,227

$21,227

$20,900

$19,500

$17,500

19

Overpaid

No

No

No

No

No

22

Amount Owed

($1,152)

($210)

($54)

($222)

($14)

Yearly Itemized Deductions

2018

2019

2020

2021

2022

R/E & State Income Taxes ($10,000 cap)

$10,000

$10,000

$10,000

$10,000

$10,000

Charity

$12,000

$12,000

$12,000

$12,000

$12,000

Mortgage Interest

$16,395

$13,643

$13,191

$12,719

$12,225

Total

$38,395

$35,643

$35,191

$34,719

$34,225

*Your current income is within the phase out range for the American Opportunity Credit, so if income rises faster than projected you may no longer be eligible.

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

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Volume 18, Issue 2

State and Local Tax Projections Line

Item

2018

2019

2020

2021

2022

1

Federal AGI

$175,650

$179,950

$175,950

$168,350

$168,350

3

$175,650

$179,950

$175,950

$168,350

$168,350

$2,500

$2,500

$2,500

$2,500

$2,500

5

Lines 1 and 2 Deductions (Property Tax) Line 3-Line 4

$173,150

$177,450

$173,450

$165,850

$165,850

6

Exemptions

$4,500

$4,500

$4,500

$4,500

$4,500

7

Line 5 - Line 6

$168,650

$172,950

$168,950

$161,350

$161,350

8

State AGI Tax

$5,447

$5,586

$5,457

$5,212

$5,212

9

County Tax

$1,687

$1,730

$1,690

$1,614

$1,614

11

Line 8 + Line 9

$7,134

$7,316

$7,147

$6,825

$6,825

12

Credits (529 Cont.)

$0

$1,000

$1,000

$1,000

$1,000

$7,134

$6,316

$6,147

$5,825

$5,825

12

Total Tax Credits (Withholding) Amount Owed

$10,416

$6,450

$6,200

$6,100

$6,100

($3,282)

($134)

($53)

($275)

($275)

4

26

Note that federal and state withholdings are adjusted yearly in the plan based on the projected tax liability. Accurate withholding will maximize your take home pay and have the money in your hands more quickly than overpaying and waiting for a refund.

Cash Flow Analysis Depicted below is your provided cash flow for 2018. All of your expense categories are reasonable in relation to one another; however, your savings rate is low. The minimum range for retirement savings levels is 10â&#x20AC;&#x201C;18%. Since you have additional goals besides retirement, your current savings rate is not adequate for you to reach your goals. Adjusted savings targets are discussed later in the plan. Your emergency fund ratio (excluding Aliciaâ&#x20AC;&#x2122;s college savings) is on the upper end of the recommended range, which is appropriate for a high-ranking employee who might not be able to find another job at the same level easily. Both your housing ratio 1 and 2 are within acceptable ranges, showing that you have done an excellent job of maintaining housing costs and debt that are appropriate for your level of income.

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Journal of Personal Finance

Savings Rate = $23,000/ $192,000 = 11.9%

Benchmark: 10–18%

Emergency Fund Ratio = $60,000 / $10,081 = 5.95

Benchmark: 3–6

Housing Ratio 1 = $35,120 / $192,000 = 18.3%

Benchmark: < 28%

Housing Ratio 2 = ($35,120 + 14,044) / $192,000 = 25.6%

Benchmark: < 36%

The following tables show your projected income, savings, and expenses for the next 11 years, allowing you to see the cash flow through when Alicia starts college. From these projections, you can estimate how much income you will require in different stages of your family life. The living expense numbers are not adjusted when the children go to college based on the assumption that you may still want to provide for some of their basic needs during the parts of the year when they are still home. After both children are out of the house, I anticipate that your basic living expenses will go down about $10,000/year. Because the money you are spending on their tuition is redirected to retirement savings and mortgage payoff when Alicia graduates, I would not anticipate that your income needs go down by more than the reduction in basic living expenses.

Inflows George's Salary Emily's Salary Gift Arizona Land Sale Aggressive Stock Fund Sale Total Inflows

Savings George's 401(k) Savings Emily's 403(b) Savings Emily's Stock Fund Savings Credit Union Savings Billy's College Savings Alicia's College Savings Alicia's Wedding Savings Kentucky Home Savings Taxable Investment Account Total Savings

2018

152,000 40,000 15,000 0 0 207,000

7,600 1,200 1,200 3,000 0 2,400 0 0 0 15,400

2019

158,080 41,200 15,000 14,000 12,000 240,280

7,904 2,436 0 0 12,500 2,400 0 25,400 37 50,677

2020

164,403 42,436 15,000 0 0 221,839

8,220 2,473 0 0 12,500 2,400 0 500 2,608 28,701

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

2021

170,979 43,709 15,000 0 0 229,688

17,098 2,511 0 0 12,500 2,400 0 500 1,804 36,813

2022

177,819 45,020 15,000 0 0 237,839

17,782 2,551 0 0 0 12,656 0 500 3,198 36,686

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Volume 18, Issue 2

Expenses Non-Desc Food Clothing Billy's Tuition Alicia's Tuition Home maintenance Utilities Cell Phone Internet, Phone & Cable Auto/maintenance Group health premiums Disability premiums Life Insurance Premiums Long Term Care Premiums Umbrella Insurance Premiums Mortgage P&I Kentucky Mortgage P&I Mortgage Refinance Fee Home Insurance Home Property Taxes Kentucky Property Tax Kentucky Home Insurance Arizona Property Tax Auto insurance premiums Financial Planning Fees Visa Payment Cadillac Loan Jeep Loan Home Equity Loan Total Non-Desc.

Desc.

Entertainment/Vacation Charitable Personal Allowances - fun $ Subscription Total Desc.

Taxes Federal Income Tax SS/Medicare Tax State and Local Income Tax Total Taxes

Total Expenses

Surplus/(Deficit)

2018

10,200 3,600 9,600 0 3,000 6,900 3,600 3,000 11,700 9,600 748 2,912 0 0 24,440 0 0 2,275 8,405 850 0 500 3,600 2,000 3,600 5,100 2,200 3,144 120,974

6,000 12,000 5,400 600 24,000

20,075 13,225 7,134 40,434

200,808

6,192

2019

10,605 3,743 9,981 0 3,119 7,174 3,743 3,119 12,164 8,422 5,040 1,791 0 200 24,300 0 1,595 2,365 9,617 884 0 0 3,000 1,200 6,600 5,100 0 3,144 126,907

3,500 12,476 4,340 624 20,940

21,851 13,405 6,501 41,757

240,280

0

2020

11,026 3,892 10,377 0 3,243 7,459 3,892 3,243 12,647 8,756 5,240 1,791 0 208 24,300 0 0 2,459 9,999 919 0 0 3,119 1,248 6,600 5,100 0 3,144 128,661

4,000 12,972 4,512 649 22,133

22,177 13,591 6,576 42,344

221,839

0

2021

11,464 4,046 10,789 0 3,372 7,755 4,046 3,372 13,150 9,104 5,448 1,791 0 216 24,300 0 0 2,557 10,396 955 0 0 3,243 1,297 2,863 5,100 0 3,144 128,407

4,159 13,487 4,691 674 23,011

21,295 13,784 6,378 41,457

229,689

0

2022

11,919 4,207 23,718 0 3,506 8,063 4,207 3,506 13,672 9,465 5,664 1,791 0 225 24,300 0 0 2,658 10,808 993 0 0 3,372 1,349 0 0 0 3,144 136,565

4,324 14,022 4,878 701 23,925

20,048 13,983 6,631 40,663

237,839

0

90

Income George's Salary Emily's Salary Gift Arizona Land Sale Aggressive Stock Fund Sale Total Income

Savings George's 401(k) Savings Emily's 403(b) Savings Emily's Stock Fund Savings Credit Union Savings Billy's College Savings Alicia's College Savings Alicia's Wedding Savings Kentucky Home Savings Taxable Investment Account Total Savings

Journal of Personal Finance

2023

184,931 46,371 16,000 0 0 247,302

18,493 2,591 0 0 0 12,656 0 500 3,199 37,439

2024

192,328 47,762 16,000 0 0 256,091

19,233 2,633 0 0 0 12,656 0 500 5,704 40,726

2025

200,022 49,195 16,000 0 0 265,217

20,002 2,676 0 0 0 12,656 0 500 962 36,796

2026

208,022 50,671 16,000 0 0 274,693

20,802 2,720 0 0 0 12,656 11,000 0 10,764 57,942

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

2027

216,343 52,191 16,000 0 0 284,534

21,634 2,766 0 0 0 12,656 11,000 0 13,062 61,118

2028

224,997 53,757 16,000 0 0 294,754

22,500 2,813 0 0 0 0 0 0 1,414 26,726

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Volume 18, Issue 2

Expenses Non-Desc Food Clothing Billy's Tuition Alicia's Tuition Home maintenance Utilities Cell Phone Internet, Phone & Cable Auto/maintenance Group health premiums Disability premiums Life Insurance Premiums Long Term Care Premiums Umbrella Insurance Premiums Mortgage P&I Kentucky Mortgage P&I Mortgage Refinance Fee Home Insurance Home Property Taxes Kentucky Property Tax Kentucky Home Insurance Arizona Property Tax Auto insurance premiums Financial Planning Fees Visa Payment Cadillac Loan Jeep Loan Home Equity Loan Total Non-Desc.

Desc.

Entertainment/Vacation Charitable Personal Allowances–fun $ Subscription Total Desc.

Taxes Federal Income Tax SS/Medicare Tax State and Local Income Tax Total Taxes

Total Expenses

Surplus/(Deficit)

2023

12,392 4,374 25,141 0 3,645 8,383 4,374 3,645 14,214 9,841 5,889 1,791 0 234 24,300 0 0 2,764 11,238 1,033 0 0 3,506 1,402 0 0 2,800 2,096 143,060

4,496 14,579 5,071 729 24,875

20,844 14,190 6,895 41,928

247,302

0

2024

12,884 4,547 26,650 0 3,789 8,716 4,547 3,789 14,779 10,232 3,914 1,791 1,937 243 24,300 0 0 2,874 11,684 1,074 0 0 3,000 1,458 0 0 2,800 0 145,007

6,000 15,158 5,200 758 27,115

21,672 14,403 7,168 43,243

256,091

0

2025

13,395 4,728 28,249 0 3,940 9,062 4,728 3,940 15,365 10,638 4,110 1,791 2,014 253 24,300 2,034 0 2,988 12,148 2,958 1,546 0 3,119 1,516 0 0 2,800 0 155,620

6,238 15,759 5,406 788 28,192

22,532 14,625 7,453 44,609

265,217

0

2026

13,927 4,915 0 0 4,096 9,421 4,915 4,096 15,975 11,060 4,315 1,791 2,094 263 24,300 12,203 0 3,106 12,630 3,075 1,607 0 3,243 1,576 0 0 2,800 0 141,411

6,486 16,385 5,621 819 29,311

23,426 14,853 7,749 46,029

274,693

0

2027

14,480 5,111 0 0 4,259 9,795 5,111 4,259 16,610 11,500 4,531 1,791 2,177 273 24,300 12,203 0 3,230 13,131 3,198 1,671 0 3,372 1,638 0 0 2,800 0 145,438

6,743 17,035 5,844 852 30,475

24,356 15,090 8,056 47,503

284,534

0

2028

15,055 5,314 0 37,679 4,428 10,184 5,314 4,428 17,269 11,956 4,757 1,791 2,263 284 24,300 12,203 0 3,358 13,652 3,324 1,738 1 3,506 1,704 0 0 2,800 0 187,307

7,011 17,712 6,076 886 31,685

25,323 15,336 8,376 49,035

294,754

0

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Debt Repayment Mortgage: Your current mortgage has 22 years remaining at a 5% interest rate. If you refinance this year, you can lower your monthly payments and pay off the mortgage 2 years earlier. Refinancing to a 20-year mortgage would give you an interest rate of 4.375%, saving you $42,817 in interest over the life of the loan after refinance fees.

Home Equity Loan: Your home equity loan has an interest rate of approximately 4.75% and should be paid off in 2023.

Visa Card: Given the high interest rate on the Visa Card, I recommend that you stop using it and focus on paying down the debt.

Continue to make the $300 monthly payments, but do not accrue any additional charges. Doing this alone would pay off the debt in 2026 with total interest of $15,985. I recommend that by 2019, you put the money you were contributing to the credit union savings toward this debt instead. If you do this, you will pay off the debt in 2021 with only $5,291 in interest, saving you over $10,000.

Car Loans: The Jeep loan is assumed to be paid off in 2018 and the Cadillac loan is assumed to be paid off in 2021. In 2033, the Jeep loan reenters the plan with the assumption that you may want to replace the vehicle at that time.

Discretionary Expenses Though your current spending levels are not unreasonable, some cuts to entertainment/vacation and personal allowances were required for you to reach your other goals.

Savings Savings for retirement and college are discussed in later sections of this plan. Your yearly projected surplus was put into a taxable investment account. This money is not allocated to anything specific nor used in your retirement projections; however, I suggest you leave it in the account so that it can cover closing costs and capital gains taxes you will face when building the home.

Insurance Recommendations for life, long term care, and disability insurance are discussed later in the plan. Additionally, I recommend that you add a $1,000,000 personal liability insurance policy. This protects your assets if someone is injured accidentally by you on your property. This coverage will cost approximately $200/year.

Kentucky Home Building Building your home in Kentucky would require you to have approximately $35,000 in todayâ&#x20AC;&#x2122;s dollars for a down payment, as well as the excess cash flow to pay a second mortgage payment. The next few years to not have enough excess cash flow to save for a down payment, but, if you are willing to sell the Arizona land and invest the proceeds, you could have enough for a down payment by 2025.

This would be achieved by selling the Arizona land and the aggressive stock fund in 2019. The gains from the land would be partially offset by the loss from the aggressive stock fund, leaving you with an estimated tax on the proceeds of only $600. The proceeds from both would be invested as discussed in the asset allocation section. The money you were putting toward Arizona property tax yearly would also be invested.

This would give you the funds necessary to put a 20% down payment on a mortgage to build your dream home in 2025, when, due to debt repayment, you have the excess cash flow to pay a mortgage. In 2025, the cost of the home you would like is projected to cost $246,243 (based on 5% building cost inflation), and your savings are projected to be $49,492. This would leave you with a $197,000 mortgage. At a 5% interest rate, a 30-year mortgage would give you payments just above $1,000/month.

Closing costs and taxes on the savings used for the down payment are not included in cash flow. These can be paid for using the surplus savings or money from your cash reserves if you are comfortable with a lower emergency fund. The funds used can be replaced with the high surplus in the next two years when neither child is in college.

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Volume 18, Issue 2

Net Worth Statement Assets

Liabilities

Cash & Cash Equivalents

Current Liabilities (Due within One Year)

Credit Union

$23,500

Mortgage

$8,357

Bank CD

$20,000

Cadillac Loan

$4,835

Savings Account

$9,000

Home Equity Loan

$2,537

Checking Account

$7,500

Visa Card

$555

Alicia Education Fund

$4,500

Jeep Loan

$500

Investment Assets

Long Term Liabilities (Due Later than One Year)

George 401(k)

$251,813

Mortgage

$317,884

Inherited Stock

$75,000

Visa Card

$11,445

Emily’s Old 403(b)

$54,745

Home Equity Loan

$11,437

Series EE Bonds

$50,000

Cadillac Loan

$11,220

LI Cash Value

$15,750

Jeep Loan

Aggressive Stock Fund

$12,000

Growth Stock Fund

$7,250

Emily Current 403(b)

$5,070

Total Liabilities

Personal Assets

House

$390,000

Household Funishings

$100,000

Kentucky Property

$90,000

Cadillac

$30,000

Inherited Jewelry

$22,000

Jeep

$19,000

Arizona Land

$14,000

Gun Collection

$14,000

Camping Equipment

$10,000

Engagement Ring

$8,000

Art

$4,000

Total Assets:

Net Worth

$0 $368,770

$868,358

$1,237,128

Alicia’s Wedding In 17 years, the cost of a $20,000 wedding is estimated to cost almost $39,000. By saving $11,000 in 2026 and 2027, the two years you are not paying college tuition for either child and investing that money with the same moderately conservative allocation discussed in the college funding section, you are projected to have enough funds to cover the future value cost of the wedding in 2035, when Alicia is 25.

Health Insurance George’s health insurance covers the whole family and Emily has a policy of her own, resulting in double coverage for Emily. I rec-

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ommend that either Emily stops paying for her own coverage or is taken off of George’s to avoid paying double premiums for her.

The cash flow projections assume her policy is cut.

Taxes Federal, state and local income taxes are shown as projected in the income tax analysis. The numbers shown in cash flow are inflated, while the numbers in the income tax analysis are not, which is why you will see different numbers between sections. After 2022, specific tax projections were not made and the numbers shown are the same as the previous year adjusted for inflation.

Current Ratio = $64,500 / $16,784 = 2.84

Benchmark: >1

Debt to Total Assets = $368,770/ $1,237,128 = 29.8%

Benchmark: Declines with Age

Net Worth to Total Assets = $868,358/ $1,237,128 = 70.2%

Benchmark: 32-74%

Investment Assets to Gross Pay = $536,128/$192,000 = 2.79

Benchmark: 3-4

Your current ratio, debt to total assets ratio, and net worth to total assets ratio are all strong. You investment assets to gross pay ratio is slightly below the benchmark, but with the increased savings discussed in the retirement section should soon be back on track.

Education Funding Billy Billy’s current desire is to attend Miami University, which cost $34,000/year in tuition and $16,900 in room and board, for a total cost of $50,900. With college costs inflating at 6% yearly, the cost of a year at Miami University in 2022 is estimated to be $64,620.

After considering the current value of your savings bonds, covering the full cost amount of these costs would require you to save $19,000/year until Billy starts college, and fund an additional $19,000/year out of your cash flow in the years he is in college on top of the yearly amount you are currently paying for his high school tuition.

Given your current cash flow, the cuts required to save this amount would compromise your ability to reach your other goals, including Alicia’s college, building a home in Kentucky, and retirement. I believe a more reasonable target would be to aim to cover $40,000 of the cost, the same amount you wish to provide Alicia. To achieve this, you would need to save $12,500/year until Billy starts college and pay for the remaining tuition out of your normal cash flow and savings bonds. The amount you would be required to pay out of your normal cash flow is equal to what you are currently paying for Billy’s high school tuition and what you will be putting into savings for Billy, adjusted for tuition inflation yearly.

Currently, you have $50,000 in Series EE bonds earmarked for Billy’s college. Given the low risk of these bonds and short time horizon until Billy begins college, I recommend that you keep them until they are cashed in for qualified education expenses. Because of your high income, the plan assumes that you are not eligible to for the tax exclusion of Series EE bond interest used for education.

For the $12,500/year in savings pre-college, I recommend that you open an Indiana 529 plan. Within this plan, you can invest 60% in the Bond Index Portfolio and 40% in the stock index portfolio. This allocation can be expected to return around 7.45% annually.

Using the Indiana 529 plan will allow you to claim a tax credit on your state tax return.

There are a variety of options available for covering the gap between the cost of attendance and what you are planning to pay.

For example, Miami University offers guaranteed merit scholarships if you apply by the priority deadline. Since Billy is still in his first year of high school, encouraging him to maintain a high GPA and prepare well for college entrance exams can help guarantee maximum scholarship eligibility. Depending on his mother’s income at the time she files Billy’s FAFSA, he may also qualify for subsidized government loans. If there is still a gap, Billy can also choose to work part time to cover the excess.

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Alicia By the year Alicia starts college, the $40,000 you wish to provide her today will have grown to over $71,000. In order to fund this amount, you can continue to contribute the same amount you are contributing now to her college savings while you focus on paying off debt. Starting in 2022, that amount should be increased to $12,656/year until she begins college. While she is in college the remaining costs can be funded out of your cash flow. The amount required will be the amount you were paying for Billy’s tuition plus a portion of what you were saving. The exact dollar value you are projected to cash flow is $37,679. The two years in between when Billy ends and Alicia begins are discussed in the above cash flow section.

I recommend that the current and future savings for Alicia are redirected to an Indiana 529 plan invested in the same fund mentioned above for Billy.

Retirement Analysis The following percentages are based on running 1000 trials with differing market conditions. The percentage shown is the percent of those trials that allowed you to successfully fund all of your goals (college, wedding, building a home in Kentucky, and retirement). Because the software operates in a way that considers the trial a failure if your funds go below 0 at any point, the success rate appears much more dire than your current situation actually is. In reality, you would make adjustments along the way to ensure that you don’t spend every last dollar you own on college or a second home, giving you a higher success rate even without implementing the recommendations.

Current Scenario: 1% Probability of Success Recommended Scenario: 84% Probability of Success

Current Scenario •

Provided savings rates continue indefinitley into the future

•

Nominal growth rate is assumed to be 6.23%, based on the calculation of a weighted average of the growth rates you provided to us excluding Emily’s savings account and the family checking account

•

Assumes you are paying for the full cost of Billy’s education

•

Assumes you receive a $250,000 inheritance in 2038

Recommended Scenario •

Investment assets are placed into a balanced portfolio (60% stock/40% bond) earning 8.46%

•

Emily’s monthly contributions to the growth stock fund are redirected into her 403(b) for additional tax savings

•

Recommendations for education savings, Kentucky home savings, and wedding savings are implemented

•

George’s 401(k) savings are increased to 10% in 2021

•

After Alicia graduates, you contribute $20,000/year to a taxable brokerage account until retirement

•

The mortgage on your Indiana home is paid off before retirement, giving you approximately $120,000 of living expenses/year, as you estimated. After the refinance, your mortgage is scheduled to be paid off two years into retirement. After Alicia graduates, the remaining surplus that you have (after accounting for the savings above) can be put toward the mortgage, or saved to cover the remaining mortgage payments not accounted for in retirement living expenses

•

Living expenses for Alicia alone are $100,000. This is higher than the estimate you provided, but it accounts for the fact that you may still have two homes worth of expenses to pay for

•

George retires at 66 and Emily retires at 58

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Journal of Personal Finance

•

George begins receiving $36,000 in social security at 67, and Emily received $4,000/month in pension benefits at her retirement

•

Assumes you receive a $250,000 inheritance in 2038. We used a lower estimate than what you believe is currently in the trust in case she is spending more than the trust is earning and much of the value is depleted before you receive it. Additional monitoring through time is important because you will need to save more if the inheritance does not come

•

Assumes that you move to the Kentucky home after retirement and receive $17,280/year in rental income from renting out the Indiana home. Selling the Indiana home after retirement would also give you a success rate in the 80-90% range, but you will require additional savigns if you want to keep both homes and not rent them out

•

After retirement, your assets are invested in a capital preservation portfolio with a 7.42% growth rate. This return can be achieved by transitioning your portfolio to be 60% bonds and 40% stock

•

George’s plan ends at 92 and Emiy’s plan ends at 94. This is slightly older then you originally desired to plan for, but helps lower the risk of you living longer than expected and outliving your money

Life Insurance Analysis Current Coverage George: $304,000 Group Term Life Policy Premium: None

$1,000,000 Universal Life Policy

Premium: $2,600/year until Retirement

Emily: $65,000 Group Term Life Policy

Premium: None

Premium: $312/year

$250,000 Term Policy

Needed Coverage The amount of life insurance necessary is based on the amount of earnings that is used to provide for your family. This is calculated by subtracting the expenses that would no longer occur if they were to die from their gross income. Below are estimated of your family’s share of each of your earnings.

George’s Gross Income

$152,000

Taxes (assumed to be approximately 75% of the total tax liability since George makes approximately 75% of the family income)

$30,000

1/4 Share of food, clothing, cell phone, entertainment and allowances

$7,200

½ Share of Auto Insurance and Expenses

$7,650

Family’s Share of Earnings

$107,150

Using the assumption that any life insurance proceeds will grow at 8.5%, inflation will be 3.97%, and there are 18 years left of George’s working career, we can calculate his need for life insurance.

The present value of 18 years of $107,150 payments with a real rate of return of 4.4% is equal to $1,317,915. This is George’s life insurance need.

His current coverage covers almost this amount, so it is not necessary to add additional life insurance. However, by switching the universal life policy to a term policy, we can achieve greater growth of assets without compromising the family’s security. Since you will be financially independent at retirement and are not projected to have an estate tax burden at George’s death, it is not

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Volume 18, Issue 2

97

necessary to have life insurance into retirement. The information below show the comparison between the current insurance policy’s estimated cash value at retirement compared to the value of investing the cash value and yearly premium savings from switching to term.

•

•

Current Cash Value: $15,750 ––

Minimum Crediting Rate: 4%

––

Yearly Additions (Premium-Cost of Insurance): $1,640

––

Projected Cash Value at Retirement: $73,965

Cash Surrender Value: $15,750 ––

Estimated Investment Portfolio Returns: 8.5%

––

Yearly Savings from Switching Policies: $976

––

Projected Value at Retirement: $106,773

These calculations assume that there would not be surrender charges. If there are, we recommend that you keep your current policy in-force and provide us with the surrender charge amounts for further analysis.

Based on these calculations, I recommend that you surrender your Universal Life Policy and replace it with $1,000,000 of 20-year term coverage. This coverage will cost approximately $1,624/year. I recommend that you do not obtain this coverage through your employer so you can keep this coverage even if you change jobs.

In order to realize the full benefit of switching policies, I recommend that you invest the cash surrender value and the annual savings in premium in the portfolio discussed in the financial planning section.

Although the above recommendations are the mathematically better solution, you are giving up a guaranteed minimum return and the security of knowing that your family will receive funds no matter when you die. If these factors are more important to you than the extra $30,000, then disregarding the above suggestions and keeping the universal life policy may be appropriate. The cash flow projections assume that you switch policies.

Emily’s Gross Income is $40,000. Taxes associated with her income are assumed to be approximately 25% of the total tax liability since Emily makes approximately 25% of the family income, which results in estimated taxes of $10,000. 1/4 share of food, clothing, cell phone, entertainment and allowances is estimated at $7,200, and 1/2 share of auto Insurance and expenses is $7,650. As a result, Emily’s share of earnings (after reductions for associated costs) is estimated at $15,150.

The present value of 18 years of $15,150 payments with a 4.4% real return is equal to $186,341, therefore Emily’s Life Insurance need is approximately $186,000. Her current work and outside policies currently provide $315,000 of coverage at a relatively low cost. I do not recommend that any changes be made to Emily’s life insurance.

Estate Analysis Proper estate planning is essential to ensuring that your assets go where you want them to after you pass. Some of your assets are structured in a way where you have already provided for what happens if you die. These assets include: 1. Your home: Since you own the property in Joint Tenancy with Right of Survivorship, if either of you were to pass away the full ownership of the home would pass to the surviving spouse 2. Your retirement accounts: You have declared each other to be the beneficiaries on your retirement accounts, therefore they will not pass through probate and will go straight to the surviving spouse Since Indiana is a separate property state, all assets and liabilities that you purchased or received with your own funds are owned separately. Since you don’t currently have a will, the transfer of this property will be determined by Indiana state intestacy laws.

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Journal of Personal Finance

All of George’s separate property will be divided between Emily, Billy, and Alicia. All of Emily’s separate property will be divided between George and Alicia.

Since Billy is not Emily’s biological or adopted child, Emily will get a lower share of George’s assets under intestacy laws. This could prevent her from receiving some of the property she intends to live off of or use later in life if George passes. It also means that none of her property will go to Billy if she passes, even if they are close and she would like to leave him something.

I recommend that you meet with an estate planning attorney to draft documents that dictate exactly what you would like to happen to your assets when each of you die. While you are there, it is also a good idea to create Powers of Attorney for your finances and person to determine who can make decisions for you and establish a guardian for Alicia to determine who will take care of her if you both pass.

For George, the best solution may be to place his assets in a trust. By using a trust, he can give Emily the power to use his assets or the income from them during her life, but he would retain the power to control what happens to them when Emily passes.

Without a trust, all of George’s 401(k) will go to Emily when he passes. While this may be what you want for Emily, when she dies the remaining funds will go solely to her heirs, unintentionally disinheriting Billy. With a trust, you can dictate that the remainder be split between the children when Emily dies and still give her the power to use the assets as she needs them during her life.

For Emily, a will may be sufficient. However, if that will dictates that all of her property be left to George, any remaining assets at his death will be split between Billy and Alicia. If she wants Alicia only to receive the remainder of her assets, a trust may be necessary for her as well.

I recommend that you consider both privately and together what you would like to happen to each of your respective property when you die. I and an estate planning attorney can help facilitate this conversation if you wish. The appropriate documents need to be drafted as soon as possible.

Disability Analysis Achieving your financial goals is dependent on your ability to earn income. If either of you faces a disability, part of that income disappears, seriously dampening your ability to reach your goals. For this reason, it is important to have adequate disability insurance.

Current Coverage George: •

6 months of short-term coverage provided by employer

•

$5,000/month standalone policy

•

Current gross monthly income is $12,666

•

$5,000/$12666 = 39.5% of wages

Emily: •

No current coverage

Benchmark 60-70% of gross income

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Recommendations George: •

•

Obtain a long-term disability policy that provided at minimum $7600/month in coverage, with an ideal policy covering $8867 ––

Policy should include coverage for illness and injury

––

Given George’s high income, the policy should cover own occupation. This ensures that the policy will provide coverage if George is unable to keep his current career, even if he is able to work in a different occupation

––

A policy that meets these requirements is estimated to cost $3,840/year

Assuming current coverage does cover both illness and injury and is not own occupation, cancel current disability policy. If the current policy does meet these requirements, you can also choose to simply supplement it with an additional policy

Emily: •

Since your cash reserves are adequate to cover the elimination period on a long-term disability policy, short term disability is not necessary

•

Obtain a long-term disability policy that covers at minimum $2000/month, with an idea policy covering $2,333 ––

Policy should include coverage for illness and injury

––

Policy should be own occupation if you would not be happy working in a different career

––

A policy that meets these requirements is estimated to cost $1,200/year

Long-Term Care Analysis Preparing for a potential long-term care need is an essential part of any financial plan. Approximately 58% of men and 79% of women will require some form of long-term care during their lives, and without adequate preparation, these costs can easily drain savings. Currently in Indiana, the average cost of two common forms of long-term care are: Full-Time Home Health Aide: $50,336/year inflating at 3% Private Room in a Nursing Home: $98,915, inflating at 3% Given that Emily is younger than George and you would likely prefer to stay together, I believe it would be appropriate to plan for the possibility of needing a home health aide to assist Emily in taking care of George if he experiences a significant care need.

By the time that George reaches the age of 90, the projected cost of a home health aide is $174,197/year. Since your retirement plan would experience significant stress with this additional expense, a long-term care insurance policy is necessary. I recommend a policy that has a $150 (today’s dollars) daily benefit with a 3% inflation rider that covers 3 years of care.

This policy would cost approximately $1,475/year in today’s dollars and should be purchased at age 55.

Since Emily is younger and women tend to live longer, it is reasonable to prepare for the possibility that George may not be around to care for her and a full-time nursing room stay may be needed.

By the time that Emily reaches the age of 90, the projected cost of nursing home care reaches $433,634, creating the need for a long-term care policy for her as well. I recommend a policy with a $275 (today’s dollars) daily benefit with a 3% inflation rider that covers 4 years of care.

This policy would cost approximately $3,857 in today’s dollars and should be purchased at age 55.

Notes: Coverage length recommendations are based on the average length of a long-term care need for men (2.2 years) and women (3.7 years); long-term care quotes and cost estimates are provided by Genworth Financial; data on percentages of people requiring care are from Forbes.

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Asset Allocation This section shows the current and proposed asset allocations for your non-cash reserves, non-college fund investment assets. The current cash accounts can remain in cash or CDs in case of an emergency. The recommended allocation for the college savings is discussed above in the college finding section.

Non-College Portfolio Assets Asset

Asset Class

Value

Growth Rate

Cont to. Total Ret.

George's 401(k) Stock

60% Stock/40% Bond

251813

7%

4.34%

Old 403(b)

Government Securities

54745

2.50%

0.34%

Current 403(b)

Government Securities

5070

2.50%

0.03%

Aggressive Stock Fund

Stock

12000

12%

0.35%

Growth Stock Fund

Stock

7250

9%

0.16%

Inherited Stock

Stock

75000

10%

1.85%

Total

405878

7.07%

Your current allocation is comprised of 60% stock, which I believe is appropriate given your age, goals, and attitudes toward risk.

However, I would allocate some of that stock exposure to the international markets for additional diversification. Since you have adequate cash reserves and a long time horizon until retirement, I do not believe you need as high of an allocation to government-specific investments. Focusing on the total bond market can give you a greater return. Though this also comes with more risk, I believe you have the time horizon to recover from short term fluctuations and a need to take risk to meet your goals.

For George’s 401(k), I recommend using the index funds to mirror the proposed allocation. Place 45% in a total stock market index fund, 40% in a total market corporate bond fund, and 15% in an international index fund.

For Emily’s 403(b) the options are more limited, so the allocation may not exactly mirror the recommended allocation. I recommend putting 85% into the US Balanced index fund, 7.5% into the Asia Pacific index fund and 7.5% in 7.5% in European stocks.

This will slightly underweight bonds compared to the base allocation recommendation. If you are uncomfortable with this additional risk you can slightly overweight bonds in your non-retirement assets to compensate.

For non-retirement assets, the same 45/15/40 allocation to index funds can be used. I recommend using Fidelity’s FZROX for Total

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Stock Market and FZILX for the International Index because both of these funds have a 0% expense ratio. For bonds, FXNAX would be appropriate.

Based on historical data, we estimate that the long term returns of stock will be 10.4% and the long term returns of bonds will be close to 5.6%, giving a portfolio with 60% stocks and 40% bonds an expected long-term return of approximately 8.5%.

Please keep in mind that this does not mean that you will see 8.5% returns each year. In order to achieve an 8.5% return you are taking risk—and that means in any individual year you could experience a wide range of returns. Over the long term, however, we expect it to average out to 8.5% (before inflation).

Implementation Recommendation

Responsible Party

Deadline

Decrease state tax withholding by $3,900 in 2019

George and Emily

12/31/2018

Refinance your mortgage through Consumer Direct or your local bank to a 20-year loan with 4.375% interest.

George

12/31/2018

Stop using your Visa card and make $550/month payments.

George

12/31/2018

Reduce entertainment/vacation expenses to $4,000/year

Emily

12/31/2018

Reduce personal allowances to $4,400/year.

Emily

12/31/2018

Obtain $1,000,000 in personal liability coverage.

George

9/30/2018

Sell the Arizona land and invest the proceeds

George

6/1/2019

Sell the aggressive stock fund and reinvest in a balanced portfolio

George

6/1/2019

Save $11,000/year in 2026 and 2027 for Alicia’s Wedding

Emily

12/31/2027

Cancel Emily’s duplicate health insurance

Emily

12/31/2018

Open an Indiana 529 plan and contribute $12,500/year

George

13/31/2019

Open a 529 plan for Alicia and move her college savings into it

Emily

12/31/2018

Date Completed

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Journal of Personal Finance

Starting in 2022, increase contributions to Alicia’s 529 plan to $12,656/year

Emily

12/31/2022

Increase 403(b) contributions to 6% of income

Emily

12/31/2018

Reallocate retirement accounts

George and Emily

12/31/2018

In 2021, increase George’s 401(k) savings to 10%

George

12/31/2020

When Alicia graduates, begin investing $20,000/year for additional retirement savings

George

12/31/2032

Emily

5/1/2032

George

9/30/2018

George and Emily

9/30/2018

Obtain a disability insurance policy for George that covers at least $7,600/month and cancel current coverage

George

9/30/2018

Obtain a disability insurance policy for Emily that covers at least $2,000/month

Emily

9/30/2018

At George’s age 55, obtain a long-term care insurance policy with a $150 daily benefit

George

12/31/2024

At Emily’s age 55, obtain a long-term care insurance policy with a $275 daily benefit

Emily

12/31/2033

When Alicia graduates, increase your monthly mortgage payments by $700 If you are comfortable switching to term insurance, cancel your current policy and purchase a $1,000,000, 20-year term policy Meet with an estate planning attorney to create wills and trust documents

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

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

1. The theory which accounts for internal or external life stressors and how they are mediated by family psychosocial resources in order to mitigate the impact on functional status is a. Family Stress and Coping Theory b. Family Systems Theory c. Social Choice Theory d. Exchange Theory 2. Psychosocial resources seen to mediate the relationship of family economic strain with marital quality and stability are a. Employment status and gender roles b. Marital support and employment status c. Work-family conflict and marital support d. Gender roles and work-family conflict 3. Work and family responsibilities a. Should assume a healthy balance to provide more positive spousal support. b. Has no effect on spousal relationships. c. Always impacts the family negatively. d. Are not increased during middle-age years of parents.

4. Research indicates that consumers who have been exposed to financial education are _____ to seek debt counseling service. a. more likely b. less likely c. indifferent d. not motivated 5. The research literature suggests that financial education ________ consumer financial capability. a. decreases b. increases c. weakens d. is irrelevant to 6. In the research literature, financial literacy is often referred as a. financial market b. financial risk c. financial capability d. financial asset 7. Financial capability refers to a. financial knowledge only b. financial behavior only c. neither financial knowledge nor financial behavior d. both financial knowledge and financial behavior 8. Consumers who do not utilize a financial advisor; a. Never reach their retirement goals because of bad investments.

9.

10.

11.

12.

13.

b. Possess the skillset needed to manage their finances independently. c. Normally cannot afford the services of a financial advisor. d. May understand the importance of an advisor but choose not to use them.

How does education level relate to a person’s use of an advisor to plan for retirement? a. Education does not affect a person’s ability to plan for retirement. b. Education only effects utilizing an advisor if you are a college graduate. c. Higher levels of education increase the utilization of an adviser. d. Higher levels of education increase knowledge of retirement planning.

Based on the research in this issue, if an individual or couple is overconfident, this will usually result in the following? a. They are more likely to use an advisor with a full understanding investing. b. They are more likely to do their own investing. c. They are less likely not to do their own investing. d. They are less likely to use an advisor.

In what ways does a person’s age and marital status play a role in choosing an advisor? a. Age and marital status do not affect a person’s ability to plan for retirement. b. Age and marital status only effects utilizing an advisor if you are older and married. c. Age and marital status increase the utilization of an adviser. d. Age and marital status increase knowledge of retirement planning.

What percentage of female respondents in the Yates (2019) study received financial education through family members and at school or work? a. 59.37% b. 48.27% c. 57.66% d. 55.40% At what life stage did the greatest percentage of respondents in the Yates (2019) study receive financial education through family members only? a. 18-44, Single w/o Children b. 18-44, Married w/o Children c. 45+, Married w/o Children

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d. 45+ DSW w/o Children 14. Which explanatory variables are negatively correlated with financial stability in the Yates (2019) study? a. Financial literacy b. Age c. Education d. Female 15. According to the study on free publicly available retirement planning tools, efficient tools tend to _______________. a. Use fewer theoretical variables b. Have higher wage replacement ratios c. Allow for more personalization d. Use more aggressive pre-retirement real investment returns 16. According to the study on free publicly available retirement planning tools, tools that show up at the top of Google search results for how to calculate retirement __________ . a. Should be trusted less than those tools that show up towards the end of the list of the search results b. Should be trusted more than those tools that show up towards the end of the list of the search results c. Are always used by retirement DIY households d. Have results that are shared frequently with financial planners during the financial planning process 17. What percentage of publicly available retirement planning tools guide tool users in evaluating their personal level of risk tolerance? a. 0% b. 25% c. 50% d. 100% 18. The study on free publicly available retirement planning tools finds that _______________. a. Most publicly available retirement planning tools consider smoker vs non-smoker as a critical variable when evaluating a mortality target b. Most publicly available retirement planning tools consider smoker vs non-smoker as a semi-important variable when evaluating a mortality target c. Most publicly available retirement planning tools consider smoker vs non-smoker as a minor variable when evaluating a mortality target d. Most publicly available retirement planning tools donâ&#x20AC;&#x2122;t consider smoker vs non-smoker as a variable at all when evaluating a mortality target

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IARFC National Financial Plan Competition

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

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

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

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

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

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

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

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

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

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

The IARFC invites professional financial service individuals and corporate entities to take part in the National Financial Plan Competition by participation as sponsors for the National Financial Plan Competition. This is mutually beneficial and allows for various levels of interaction with the students and members.

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

IARFC

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INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

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

IARFC NATIoNAl FINANCIAl PlAN ComPeTITIoN The IARFC invites participation in the National Financial Plan Competition Spring 2020

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

To learn more call 800.532.9060 or visit www.IARFC.org

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