Journal of Personal Finance Volume 17 Issue 2 sample

Volume 17 Issue 2 2018 www.journalofpersonalfinance.com

Journal of Personal Finance Techniques, Strategies and Research for Consumers, Educators and Professional Financial Consultants

IARFC INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS

IARFC BE THE CHANGE | Guide ◆ Counsel ◆ Consult March 20 20-22, 22 2019

E a r l y R e gOpportunities istration Opened INTERNATIONAL ASSOCIATION OF REGISTERED FINANCIAL CONSULTANTS, (IARFC)

IARFC.ORG | JOGP@iarfc.org | 800.532.9060

Journal of Personal Finance

Volume 17, Issue 2 2018 The Official Journal of the International Association of Registered Financial Consultants Š2018, IARFC. All rights of reproduction in any form reserved.

Journal of Personal Finance

2

Journal of Personal Finance Volume 17, Issue 2 2018 Editor Benjamin Cummings, Ph.D., CFP®, RFC® The American College of Financial Services

Editorial Assistant Amber Lemmon Texas Tech University

Editorial Board Sarah Asebedo, Ph.D., CFP®, Texas Tech University H. Steve Bailey,MRFC, HB Financial Resources, Ltd. 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., University of Missouri

Tao Guo, Ph.D., William Patterson University Sherman Hanna, Ph.D., The Ohio State University Douglas A. Hershey, Ph.D., Oklahoma State University Karen Eilers Lahey, Ph.D., The University of Akron Douglas 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, MD, CFP®, Life Planning Partners Yoko Mimura, Ph.D., California State Uni-

versity, Northridge Robert W. Moreschi, Ph.D., RFC®, Virginia Military Institute David Nanigian, Ph.D., Mihaylo College at Cal State Fullerton Barbara M. O’Neill, Ph.D., CFP®, CRPC, CHC, CFCS, AFCPE, Rutgers Cliff Robb, Ph.D., Kansas State Sandra Timmermann, Ed.D., The American College of Financial Services Jing Jian Xioa, Ph.D., University of Rhode Island Rui Yao, Ph.D., CFP®, University of Missouri Yoonkyung Yuh, Ewha Womans University Seoul, Korea

Mailing Address:

Disclaimer: The Journal of Personal Finance

IARFC Journal of Personal Finance 1046 Summit Drive, P.O. Box 506 Middletown, OH 45042

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

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

Postmaster: Send address changes to IARFC Journal of Personal Finance 1046 Summit Drive, P.O. Box 506 Middletown, OH 45042

Permissions: Requests for permission to make copies or to obtain copyright permissions should be directed to the Editor.

Certification Inquiries: Inquiries about or requests for information pertaining to the Registered Financial Consultant, Master Registered Financial Consultant, or Registered Financial Associate certifications should be made to IARFC, 1046 Summit Drive, P.O. Box 506 Middletown, OH 45042.

Subscription requests should be addressed to: IARFC Journal of Personal Finance 1046 Summit Drive, P.O. Box 506 Middletown, OH 45042 editor@iarfc.org 1-800-532-9060 Subscription Rates, 1yr, 2 issues, add $15 for delivery outside the U.S. Individual Subscription: Member $45, NonMember $65 Institutional: $120, 3 copies, ea. issue Single Copies: Member $25, Non-Members $35 The Journal of Personal Finance ISSN 1540-6717 (Print); 2638-3217 (Online) is published in the U.S. in the months of March and October by the International Association of Registered Financial Consultants (IARFC).

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

3

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

Household portfolio choice

•

•

Retirement planning and income distribution

Investment research relevant to individual portfolios

•

Household credit use

•

Individual financial decision-making

•

•

Household risk management

Professional financial advice and its regulation

•

Life-cycle consumption and asset allocation

•

Behavioral factors related to financial decisions

•

Financial education and literacy

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

Contact Benjamin Cummings, Ph.D., CFP®, RFC®, Editor Email: jpfeditor@gmail.com www.JournalofPersonalFinance.com

Volume 17, Issue 2

Contents Editor's Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Creating Understanding and Interest in Charitable Financial and Estate Planning: An Experimental Test of Introductory Phrases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Russell N. James III, Ph.D, JD, CFP®, Professor, Texas Tech University Charitable financial planning is an important and growing segment of personal financial planning. However, client understanding and interest in learning more about such topics can vary substantially depending on the language used to introduce the topics. This paper reports results from a series of experiments investigating the effectiveness of different phrasing in generating understanding and interest in learning more regarding “planned giving,” “estate giving,” “estate planning,” and “charitable gift annuities.” Results support the proposition that using these standard industry terms to introduce such information reduces both understanding and interest in learning more as compared with simple functional descriptions, such as “other ways to give,” “gifts in wills,” “will planning,” and “gifts that pay you income.” Although formal industry terminology may be technically correct, it can also be detrimental to client understanding and interest in learning more about such topics.

The Consistency Smile: How Consistency of Investment Decisions Relates to Risk Appetite . . . . . . . . . . . . . 23 Sidharth Muralidhar, Freshman, Virginia Polytechnic Institute and State University Using the Kahneman and Tversky (1979) definition of risky gambles, consistency of decision-making is defined as selecting the “same” gamble, regardless of how the gamble is presented. Both expected utility theory (EUT) and prospect theory (PT) make implicit assumptions about the consistency of individual behavior as it pertains to risky gambles that are not borne out in laboratory tests. EUT assumes that individuals are perfectly rational and implicitly consistent in their decisions, whereas, PT implicitly concludes that, in the aggregate, there is zero consistency. This paper develops a new methodology to examine consistency in risky gambles and tests it on a diverse database of 442 people - investment professionals (81), teens (297), and adult non-professionals (64) - to account for factors like literacy, experience, age, and gender. First, consistency of individuals and groups lies well between levels assumed by EUT and PT. Second, using the Risktyle model to calibrate the strength of risk preferences for Kahneman-Tversky gambles, we discover a “Consistency Smile” – individuals with strong risk preferences, across all but one of the sub-groups, tend to have greater consistency in decisions than those with weaker risk preferences. Third, non-investment professionals have the highest level of consistency followed by teens, and investment professionals. This result has interesting investing implications, especially for advisors designing portfolios for a diverse group of clients.

Identifying Overvalued and Undervalued Stock Market and Market Timing in Retirement Funds . . . . . . . 37 Weishen Wang, Ph.D, College of Charleston, SC, USA Seung Hun Han, Ph.D, Korea Advanced Institute of Science and Technology, South Korea This study predicts overvalued or undervalued market ex ante. Based on the arguments of price reversion and momentum in the stock market, the study forms and tests a market timing strategy in managing retirement funds. It provides a simple algorithm for automated trade, which is able to provide performance consistently surpassing the overall stock market in the long run.

5

6

Journal of Personal Finance

Defined Benefit Plans Versus Defined Contribution Plans: An Evaluation Framework Using Random Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Julie Cumbie, Ph.D, Associate Professor of Finance, University of Central Oklahoma Randal Ice, Ed.D, Professor of Finance, University of Central Oklahoma V. Sivarama Krishnan, Ph.D, Emeritus Professor in Finance, University of Central Oklahoma This paper develops a general framework to compare defined benefit (DB) plans and defined contribution (DC) plans. We analyze a proposed defined contribution plan and the current defined benefit plan for a regional university of a mid-western state. We use random investment returns to generate distributions for defined contribution plan accumulations and compare these with the present values of the defined benefit plan under different employee service assumptions. The results indicate no clear dominance for one structure over the other; however, the defined contribution plan appears to be well suited for employees who consider shorter employment tenures with an institution. Employees, in general, should find it beneficial to be offered the choice of both structures, with the ability to choose one or the other, depending on one’s expected longevity with the employer and personal risk tolerance.

Post-Retirement Spending Discomfort and the Role of Preparedness, Preferences, and Expectations . . . 51 Christopher M. Browning, Assistant Professor, Texas Tech University There is much debate about the retirement preparedness of Americans. Despite the debate, many recent studies have found that Americans spend very conservatively in retirement, and in many cases continue to save. Using proprietary data from a survey on retirement risk tolerance, this study explored explanations for such conservative attitudes towards spending. Perceived preparedness, preferences for risk and spending, and expectations for medical costs and longevity were considered. Perceived preparedness and preferences for risk and spending showed consistently strong relationships with spending discomfort, while income, wealth, and estimated longevity were not significant. Better understanding the retirement spending concerns of individuals will help the retirement planning industry improve the framing and structure of retirement products and develop income plans that result in more optimal spending patterns.

2018 IARFC National Financial Plan Competition: Case Solution by Bryant College. . . . . . . . . . . . . . . . . . . . . 63 Solution Written by Victoria Albanese from Bryant College Edited by Walt Woerheide, Ph.D., ChFC©, RICP©, MRFC©, CFP©

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

7

Editor's Notes

As the new editor of the Journal of Personal Finance, I am pleased to introduce the Fall 2018 issue, which is full of the latest research and current thinking in the field of personal finance. I am honored to follow in the footsteps of the previous editors of this key journal serving the academic and practitioner communities in the financial planning profession. Here, I will provide a brief overview of the great articles in this issue.

In the first article, Russell James III, Ph.D, JD, CFP®, challenges us to re-think how we communicate with our clients, particularly about charitable financial planning. He finds that using technical jargon to introduce information can not only turn clients off from wanting to learn more, but it may also reduce understanding. For example, his findings suggest that using simple functional descriptions may better accomplish an advisor’s objective to evoke interest in planned giving. His article provides an opportunity to reflect on the language we use with our clients and how our choice of words might actually be counterproductive to our objectives.

Sidharth Muralidhar, an impressive undergraduate student at Virginia Tech, explains his discovery of a Consistency Smile in the second article. He finds that the consistency in risky investment choices positively correlates with risk appetite and that individuals with a moderate appetite for risk tend to be less consistent in their choices. Inconsistency in risky choices occurs even among investment professionals. Since clients may also exhibit inconsistent preferences, these findings also suggest that assessing a client’s level of investment consistency can be helpful in determining an appropriate portfolio for the client.

The authors of the third article, Weishen Wang, Ph.D, and Seung Hun Han, Ph.D, construct a valuation indicator based on the market phenomena of price reversion and momentum. This indicator can then be used to formulate trading strategies between an equity fund and a bond fund with the aim to enhance portfolio returns. The authors find that performance of these strategies is best for investment periods of 20 years or longer, and they suggest that this strategy would be most easily adopted within retirement accounts.

In the fourth article, Julie Cumbie, Ph.D, Randal Ice, Ed.D, and V. Sivarama Krishnan, Ph.D, develop a framework that can be used to compare define benefit plans with defined contributions plans. They then apply their framework to a case study using the plans offered by a regional university in the mid-west. Their results confirm the benefit of having a choice between the two types of retirement plans and that the most beneficial choice largely depends on one’s expected longevity and risk tolerance.

Journal of Personal Finance

8

Christopher Browning, Ph.D, analyzes post-retirement spending discomfort in the fifth article. He recognizes that many individuals spend conservatively in retirement, and he analyzes what might contribute to the uncomfortable feelings about spending in retirement. Among other insights, he finds that individuals who feel prepared for retirement as well as those who are more risk tolerant are less likely to feel uncomfortable about their spending, while those who are concerned about health care costs are more likely to be uncomfortable spenders.

The final article brings you the winning case of the 2018 IARFC National Financial Plan Competition, authored by Victoria Albanese from Bryant College. Victoria prepared a comprehensive financial plan for Roger and Rebecca Carter, clients who were seeking help with budgeting and debt management 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 Victoria’s solutions.

I hope you enjoy this issue of the Journal of Personal Finance, and I look forward to the bright future of this fine publication.

Benjamin F. Cummings, Ph.D, CFP®, RFC® Editor

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

9

Creating Understanding and Interest in Charitable Financial and Estate Planning: An Experimental Test of Introductory Phrases Russell N. James III, Ph.D, JD, CFP®, Professor, Texas Tech University

Abstract Charitable financial planning is an important and growing segment of personal financial planning. However, client understanding and interest in learning more about such topics can vary substantially depending on the language used to introduce the topics.

This paper reports results from a series of experiments investigating the effectiveness of different phrasing in generating understanding and interest in learning more regarding “planned giving,” “estate giving,” “estate planning,” and “charitable gift annuities.”

Results support the proposition that using these standard industry terms to introduce such information reduces both understanding and interest in learning more as compared with simple functional descriptions, such as “other ways to give,” “gifts in wills,” “will planning,” and “gifts that pay you income.” Although formal industry terminology may be technically correct, it can also be detrimental to client understanding and interest in learning more about such topics.

Key Words: estate planning, charitable financial planning, planned giving, client communications

Journal of Personal Finance

10

Introduction Charitable financial planning and estate planning are growing areas of interest for both financial advisors and nonprofit organizations. Age-related demographics (U.S. Dept. of Health & Human Services, 2016) suggest that estate transfers will continue to grow for many years as part of a substantial intergenerational wealth transfer. Simultaneously, other factors suggest that the share of these transfers designated for charitable causes will increase. For example, charitable estate planning is strongly associated with childlessness and higher education (James, 2009). The forthcoming population of older adults is not only larger than previous generations (U.S. Dept. of Health & Human Services, 2016), but also much more likely to be childless and more highly educated (James, 2015).

It is thus of growing importance for advisors to understand how to communicate effectively regarding charitable financial planning and estate planning. This study compares the effects on client interest of introducing such topics using industry standard terms as contrasted with simple vernacular descriptions. Results suggest that despite the sometimes-greater technical accuracy of industry standard terminology, such language creates a barrier to client understanding and interest in learning more about such topics.

Literature Review Technical terminology or insider jargon is common to most professions and fields of study (Collins & Evans, 2007). When used among insiders such terms can increase communication precision and decrease communication length (Wittgenstein, 1953). However, as Benson (1994) notes, effective personal financial planning service is not simply a matter of having accurate technical knowledge, but rather requires a blend of technical expertise and communication ability. Problems can arise when professionals use technical terminology directly with lay clients or audiences, rather than translating these terms into functional vernacular descriptions. Such problems with client understanding have been identified in a variety of professional fields including medicine, counseling, and financial planning.

Barrier to Understanding Goetz and Bagwell (2006) note that effective communication is essential to successful financial planning practice. A key component of effective communication in personal financial planning is to promote client understanding. However, research

from a variety of professions suggests that the use of technical terms or industry jargon may serve as a barrier to understanding. For lay audience members, increasing the use of jargon and technical language decreases clarity (Dwyer, 1999) and increases the difficulty of understanding the communication (Brown, Braskamp, & Newman, 1978). For example, Jackson (1992) found that using technical language when describing a medical condition reduced both comprehension and recall.

Similarly, in the field of counseling psychology, using technical language rather than conversational language reduced understanding of and comfort with behavioral treatments (Rolider, Axelrod, & Van Houten, 1998).

Barrier to Action The role of a financial advisor is not limited to increasing client knowledge, but ultimately involves affecting client behavior. Communication that creates complexity, uncertainty, or confusion, such as can occur with the use of technical terms, may also serve as a barrier to motivating client action. This fits with the general principle that behavior declines as the effort required increases (Salamone, et al., 2012). Similarly, Jarmolowicz, et al. (2008), found that describing behavioral treatments using conversational – rather than technical – language resulted in caregivers more accurately implementing the suggested actions.

Barrier to Trust In financial planning, effective communication is important not simply as a means of transferring information to clients, but is also critical to building client trust and commitment (Christiansen & DeVaney, 1998; Sharma & Patterson, 2000; Sharpe, Anderson, White, Galvan, & Siesta, 2007). Some evidence suggests that the use of technical terms can serve as a barrier to building such financial planning relationships. Joiner, Leveson, and Langfield-Smith (2002) conducted an experiment in which participants viewed a video of a financial planner giving advice including either high or low usage of technical terms. Fitting with results from other professional fields (Brown, et al., 1978; Dwyer, 1999; Jackson, 1992; Rolider, et al., 1998), the greater use of technical terms resulted in reduced understanding for the financial planning clients (Joiner, et al., 2002). However, the outcomes were not limited to a simple lack of understanding. Additionally, this reduced client understanding negatively affected perceptions of the planner’s expertise and trustworthiness, which in turn reduced client interest in using the planner

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

11

for financial advice (Joiner, et al., 2002).

“Estate” Planning and Repurposing

These results from financial planning fit with experimental results from other fields. Thompson, Brown, and Fergason (1981) found that using technical jargon reduced ratings of an expert’s believability and logicality among both expert and non-expert audience members. Similarly, Elsbach and Elofson (2000) found that explaining an identical decision using easy-to-understand, rather than hard-to-understand, language resulted in increased perceptions of the decision-maker’s competency-based trustworthiness. One explanation for the connection between simple words and advisor trustworthiness is that understandability signals advisor openness and empathy and these, in turn, project trustworthiness (Joiner, et al., 2002; Peters, Covello & McCallum, 1997).

One problem that can arise with the use of technical or industry terms is when such terms repurpose everyday words (Critchfield, et al., 2017). Such repurposing can lead to negative or inaccurate connotations by lay audience members (Critchfield, et al., 2017; Foxx, 1996). For example, to a trained financial advisor, one’s “estate” simply references one’s possessions, particularly at death. However, the common non-expert usage of the term may bring to mind substantial lands and wealth. The Oxford English Dictionary includes a definition of “estate” as “A landed property; usually one of considerable extent. (Now the commonest sense)” (Estate, n.d., Def. 13a). Such an interpretation could cause lay audience members without considerable landed property to disregard “estate planning” as being irrelevant to them. News reports revealing that estate taxes don’t apply to married couples worth less than $11 million could further solidify this association of “estate” with the rich. The use of a term with elite or exclusive connotations may contrast with a simple vernacular term, such as a “will,” that presumably applies to everyone. Using inclusive terminology suggesting, “people like me do things like this,” may be especially important in the areas of estate planning and charitable financial planning as a variety of experimental evidence suggests that social norms are particularly powerful for these type of decisions (Croson, Handy, & Shang, 2009; James, 2016a; 2016b; James & Routley, 2016; Martin & Randal, 2008).

Frequency of Usage Despite these problems, the tendency to use professional jargon when communicating with a lay audience is quite common (Castro, Wilson, Wang, & Schillinger, 2007). This may be due in part to two factors. First, professionals often fail to perceive how frequently they use such jargon with lay clients (Howard, Jacobson, & Kripalani, 2013). Second, professionals may fail to perceive the extent to which using technical terms creates a barrier to understanding, in part because of a tendency to overestimate a lay client’s understanding of technical terms (Cegala, Gade, Lenzmeier-Broz, & McClure, 2004; Makoul, Arntson, & Schofield, 1995).

Hypothesis Translation as a solution As B.F. Skinner (1957) emphasized in his book on verbal behavior, a speaker should select words for their effects on the listener – not their effects on the speaker. Aronoff and Ward (2011) emphasized that part of the role of an advisor is to take complex information and make it understandable to a non-expert client. This translation role is an essential part of a professional’s ability to communicate effectively with clients (DeVito, 1995). Where the use of technical language is unavoidable, the negative effects can be modified by including additional explanatory language (Bradley & Meeds, 2004). Jargon or technical language may be an inherent part of any profession.

However, advisors may need to adopt the vocabulary of the customer by translating such technical terms for clients rather than simply restating them to clients as if they too were experts in the field (Binder, 1994).

Using standard industry terms to introduce planned giving and charitable estate planning information will reduce both understanding and interest in learning more as compared with using simple functional descriptions.

Methods Experimental Design Some previous research has explored the effects of phrasing on people’s willingness to make a charitable bequest gift (James, 2016b). However, financial advisors may be more interested in introducing a charitable financial planning or estate planning topic in a way that generates client interest in learning more about the details, rather than in a simply generating a gift.

Thus, in the current experiment, study participants rated their interest in reading more about a variety of planned giving top-

Journal of Personal Finance

12

ics. Participants were randomly assigned to one of five groups with rotated topic presentation sequences, thus leading to a balanced presentation sequence for each phrasing variation.

Participants rated their interest in each topic by responding to this scenario: Suppose you are viewing the website of a charity representing a cause that is important in your life. In addition to a “Donate Now” button, the following buttons appear on the website.

Please rate your level of interest in clicking on the button to read the corresponding information. (Note: after answering this set of questions, you will be asked to read information about one of these topics. Please rate the ones you are actually interested in more highly than those you are less interested in.)

Participants rated their interest with one of five options labeled numerically: (1) I am definitely NOT interested (2) I don’t think I would be interested (3) I don’t know if I would be interested (4) I might be interested (5) I am definitely interested After rating their interest in all topics, participants were asked to rate their expectations related to some of the previous topics, with the topics differing by randomly assigned group, in response to the following question: Which of the following types of information would you expect when clicking on the button labeled “___________”? where the topic phrase appeared in quotes in bold font, followed by this list of possible types of information: How to make a gift of stocks How to make a gift of bonds How to make a gift of real estate How to make a gift in your will How to make a gift in your living trust How to make a gift by naming a charity as death beneficiary of your life insurance policy How to make a gift by naming a charity as death beneficiary of your IRA or retirement account How to make a gift by naming a charity as death beneficiary of your bank account How to make a gift and, in return, receive lifetime income from the charity

How to avoid capital gains taxes by making charitable gifts How to avoid estate taxes by making charitable gifts How to avoid income taxes by making charitable gifts These are labeled in the following analyses, respectively, as stock gifts, bond gifts, real estate gifts, will gifts, living trust gifts, life insurance TOD gifts (TOD is used here as an abbreviation for “Transfer on Death” referencing non-probate transfers operating by revocable beneficiary designation), IRA TOD gifts, bank TOD gifts, life income gifts, capital gains taxes & gifts, estate taxes & gifts, and income taxes & gifts. For each information type, participants responded to the expectation question with one of five options, labeled numerically as: (1) I definitely did NOT expect this (2) I didn’t really expect this (3) I don’t know if I expected this or not (4) I guess I expected this (5) I definitely expected this Following these ratings, participants provided information regarding gender, age, education, and answered the question, “What is the largest amount (cash or property) you have ever given IN ANY ONE YEAR to charities?”

Sample Experimental participants were collected from an online panel (MTurk) with responses collected using an online platform (Qualtrics). MTurk, a division of Amazon.com, provides an online source of respondents willing to complete tasks such as taking surveys. Demographic comparisons have found that respondents from this panel are roughly representative of U.S. Internet users (Ipeirotis, 2010; Ross, et al., 2010). However, as compared to the general U.S. population, respondents from this panel tend to be younger, more educated, have lower income, and are more likely to be female (Levay, Freese, & Druckman, 2016; Paolacci & Chandler, 2014; Paolacci, Chandler, & Ipeirotis, 2010). This source of participants has produced reasonable and consistent results in a variety of research topics, similar to other methods of participant recruitment (Buhrmester, et al., 2011; Goodman, Cryder, & Cheema, 2013).

The sample group for the planned giving and charitable gift annuity phrases consisted of 2,758 respondents. In the sample, 57.2% of participants were female and 51.6% had a four-year bachelor’s degree. Participant age ranged from 18 to 85+ with a mean age range of 35-44 and 61% of respondents falling in the age range of 25-44.

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

13

Results Planned Giving Phrases Charitable financial planning incorporates a variety of charitable financial planning techniques including tax planning (estate taxes, income taxes, and capital gains taxes), gifts of assets (including stocks, bonds, or real estate), and estate gifts (including gifts from wills, living trusts, and non-probate transfers such as gifts from life insurance, IRAs or retirement accounts, and transfer-on-death designations on financial accounts). Some techniques, such as charitable remainder trusts or charitable gift annuities combine tax planning, a gift of cash or assets, an estate gift, and lifetime income.

The first test compares the effects of a common industry phrase, “planned giving” (along with two variations), against simple functional descriptions, “other ways to give” (along with three variations) and “giving now & later.”

Table 1 reports the numerical average interest rating generated by each phrase overall, and among specific subgroups.

This numerical average usefully reveals shifts in interest at any level. However, as a practical matter, it may be that only those with the highest level of interest will actually proceed to learn more about the topic. Given this practical importance, the first column reports the share of respondents who gave the highest interest rating (i.e., I am definitely interested in clicking on the button to read the corresponding information).

The use of simple descriptive language such as “giving now & later” or variations of “other ways to give” generated substantially greater interest in learning more about the topics as compared with “planned giving.” Every variation of “other ways to give” generated significantly greater interest than did “planned giving,” not only for the sample as a whole, but also when the sample was restricted to women, those age 55+, those with at least a four-year college degree, or those who have made gifts

Table 1: Planned Giving Phrases – Interest in Reading More Definitely interested in reading more

Numerical average interest rating (1-5) Overall

Women

Age 55+

Bachelors graduates

$500+ Donors

Planned giving

4.5%

2.66

2.68

2.56

2.65

2.72

Planned giving options

4.7%

2.68

2.71

2.42

2.66

2.72

Gift planning

3.4%

2.61

2.62

2.35

2.58

2.66

Giving now & later

7.1%***

2.91***

2.97***

2.71

2.94***

3.00***

Other ways to give

15.6%***

3.41***

3.53***

3.40***

3.42***

3.54***

Other ways to 16.4%*** save taxes when you give

3.32***

3.29***

3.21***

3.42***

3.47***

Other ways to give smarter

19.5%***

3.54***

3.63***

3.42***

3.58***

3.65***

Other ways to give cheaper, easier, and smarter

22.6%***

3.60***

3.71***

3.53***

3.66***

3.73***

n

2,757

2,757

1,465

292

1,318

1,204

Notes: *p<.01, **p<.001, ***p<.0001 in two-tailed t-test compared with baseline phrase of “Planned giving”. Interest in reading more rated as 1=I am definitely NOT interested, 2=I don’t think I would be interested, 3=I don’t know if I would be interested, 4=I might be interested, 5=I am definitely interested.

***

***

2.93

***

3.39

***

2.26

2.92

**

3.21

***

3.41

2.79

***

3.34

***

2.29

2.98

3.08

***

3.38

*

3.10

2.88

Real estate gifts

2.90 ***

***

3.37

***

2.31

2.99

3.43

***

2.31

3.20 *

3.13

3.63

3.60

3.47

3.44

Living trust gifts

***

3.61

*

3.70

3.55

3.46

Will gifts

***

2.86

3.22

***

2.37

3.33

*

3.45

3.34

3.28

3.22

Life insurance TOD gifts

***

2.84

3.21

***

2.39

3.36

3.39

3.24

3.24

3.20

IRA TOD gifts

***

2.86

3.22

***

2.36

3.34

3.35

3.27

3.27

3.22

Bank TOD gifts

***

3.06

***

3.58

2.64

***

3.06

***

3.03

***

3.02

2.82

2.69

***

2.96

***

3.35

***

4.14

2.71

2.57

**

2.86

2.65

2.55

***

2.92

***

3.36

***

4.13

2.69

2.60

***

2.87

2.69

2.56

Capital Life in- gains Estate come taxes taxes gifts & gifts & gifts

***

3.08

***

3.46

***

4.24

2.75

2.62

***

2.92

2.72

2.59

Income taxes & gifts n

514

511

497

525

516

525

525

534

©2018, IARFC. All rights of reproduction in any form reserved.

Stock gifts = “How to make a gift of stocks”; Bond gifts = “How to make a gift of bonds”; Real estate gifts = “How to make a gift of real estate”; Will gifts= “How to make a gift in your will”; Living trust gifts= “How to make a gift in your living trust”; Life insurance TOD gifts = “How to make a gift by naming a charity as death beneficiary of your life insurance policy”; IRA TOD gifts = “How to make a gift by naming a charity as death beneficiary of your IRA or retirement account”; Bank TOD gifts = “How to make a gift by naming a charity as death beneficiary of your bank account”; Life income gifts = “How to make a gift and, in return, receive lifetime income from the charity”; Capital gains taxes and gifts = “How to avoid capital gains taxes by making charitable gifts”; Estate taxes & gifts = “How to avoid estate taxes by making charitable gifts”; Income taxes & gifts = “How to avoid income taxes by making charitable gifts”

1=I definitely did NOT expect this; 2=I didn’t really expect this; 3=I don’t know if I expected this or not; 4=I guess I expected this; 5=I definitely expected this;

*p<.01, **p<.001, ***p<.0001 in two-tailed t-test compared with baseline phrase of “planned giving”

Notes:

2.92

***

***

Other ways to give cheap- 2.93 er, easier, and smarter

3.39

3.36

***

*

Other ways to give smarter

2.29

2.81

Other ways to save taxes when you give

2.98

*

* 3.04

3.17

3.14

Other ways to give

Giving now & later

3.43

3.25

*

*

Gift planning

3.13

2.93

Bond gifts

3.15

3.09

Planned giving options

2.95

2.97

Stock gifts

Planned giving

Overall mean

Table 2: Planned Giving Phrases – Types of Information Expected

14 Journal of Personal Finance

Volume 17, Issue 2

of $500+ to charity. In contrast, there was no significant difference in interest generated by the three industry standard terms of “planned giving,” “planned giving options,” or “gift planning.”

The differences which resulted from using the simple functional descriptions were not only statistically significant, but also of such a magnitude as to be practically important. For example, referencing “other ways to give” more than tripled the share of people who reported being “definitely interested” in reading more as compared with the industry standard term of “planned giving.” Referencing “other ways to give smarter” more than quadrupled this share.

However, it is not enough for a term to generate interest if it fails to effectively communicate the type of information referenced. Thus, an effective term must not only promote interest, but must also generate an accurate understanding of the types of information referenced. Table 2 reports the extent to which participants expected to receive different types of charitable financial planning information as a result of clicking on a website button with each term.

The use of the term “planned giving” was relatively ineffective at generating an understanding of the resulting broad range of materials. Indeed, only one intentionally narrow term, “other ways to save taxes when you give,” performed marginally worse overall. Thus, using the term “planned giving” generated little interest in reading more, and little understanding of the broad range of material that would be encountered when reading more.

Instead, participants appeared to interpret the “planned giving” phrase narrowly, focusing on end-of-life gifts only. For example, no term performed significantly better at generating expectations for information regarding living trust gifts, and only “gift planning” was modestly better at generating expectations for information regarding will gifts. The “planned giving” phrase also performed reasonably well at generating expectations for information about transfer-on-death beneficiary designations in life insurance, IRAs, and bank accounts, with only “giving now & later” being modestly better at generating expectations for information regarding life insurance beneficiary designations.

However, the “planned giving” phrase performed poorly in generating expectations for information related to gifts of stocks, bonds, real estate, life income gifts, and tax planning for capital gains, estate, and income taxes. Thus, respondents appeared to associate “planned giving” primarily with death planning, but not with lifetime gifts or any form of tax planning. The other industry term, “gift planning,” generated a significantly greater

15

expectation of receiving a broad range of charitable financial planning information. However, the “gift planning” phrase also generated the lowest level of interest in reading more as compared with any of the other terms tested.

The term generating the greatest expectation of receiving the full range of charitable financial planning information was “Other ways to give smarter.” This term also generated the second highest level of interest in reading more. Using the expanded phrase “Other ways to give cheaper, easier, and smarter” slightly increased interest in reading more, but did so at the cost of reducing the range of charitable financial planning information expected. In particular, the expectation of receiving information about death planning (will gifts, living trust gifts, and transfer-on-death gifts from bank accounts, IRAs, and life insurance) was significantly lower for this phrase, perhaps because such transfers are not viewed as being “cheaper” or “easier” ways to give.

Using the tax-focused variation, “other ways to save taxes when you give,” did increase the expectation of receiving all three forms of tax-related information (income taxes, capital gains taxes, and estate taxes). But, this tax-focused phrase reduced expectations of receiving all other types of charitable financial planning information, making it the only phrase to underperform “planned giving” in generating an expectation of receiving a broad range of charitable financial planning information.

In sum, the most common industry term, “planned giving,” generated the lowest interest in reading more and, except for its association with death planning, the lowest understanding of the range of topics referenced. The other common industry term, “gift planning” generated more understanding of the range of topics referenced, but relatively little interest in reading more. Thus, in this case, using the standard industry terms was relatively ineffective. In contrast, “other ways to give smarter,” generated both high interest in reading more (more than four times that of “planned giving” or “gift planning”) and high expectation of receiving a broad range of charitable financial planning information.

Charitable Gift Annuity Phrases The first test examined terms intended to reference a broad range of charitable financial planning information. The second test explores phrasing to describe a specific planning product, a charitable gift annuity. Charitable gift annuities, in which donors exchange a gift for lifetime income from the charity, have existed in the U.S. since 1831 (Brown, 2017), with total assets

Journal of Personal Finance

16

Table 3: Charitable Gift Annuity Phrases – Interest in Reading More Definitely interested in reading more

All

Women

Age 55+

Bachelor degree

$500+ Donor

Expecting CGA information

Charitable gift 4.5% annuities

2.52

2.50

2.23

2.51

2.58

2.97

Life income gifts

2.71

2.71

2.67

2.69

2.78

3.35

Get a tax de- 25.8% duction and make a gift that pays you income for life

3.56

3.59

3.35

3.63

3.64

3.64

Gifts that pay you income for life

27.5%

3.61

3.62

3.42

3.61

3.64

3.56

Gifts that pay you income

29.3%

3.67

3.74

3.44

3.67

3.68

3.55

n

2,758

2,758

1,464

292

1,318

1,204

527-546

8.8%

Notes: Two-tailed t-tests comparing each phrase against baseline phrase of “charitable gift annuities” was significant at p<.001 for every phrase and every column sub-segment.

CGA information defined as: “How to make a gift and, in return, receive lifetime income from the charity”

exceeding $15 billion (Clontz, 2010). As before, the experiment compares the industry standard term, “charitable gift annuities,” (as well as one less common industry phrase “life income gifts”) against a simple functional description, “gifts that pay you income for life” and two variations.

Using “charitable gift annuities” generated the lowest level of interest of any term tested. This difference was not only statistically significant, but also practically large. The share of respondents indicating they were “definitely interested” in reading more increased more than six-fold when “charitable gift annuities” was replaced with the term “gifts that pay you income for life” or “gifts that pay you income.” This standard industry term,“charitable giving annuities” generated the least interest overall, the least interest among women, among those age 55+, among those with four-year college degrees, and among donors having made gifts of $500 or more. The alternate industry term, “life income gifts,” performed slightly better at generating interest, but not to the levels generated by using any of the simple functional descriptions.

In addition to generating dramatically higher interest in reading more, the simple functional descriptions were much more likely to generate an expectation that the term referenced information about “How to make a gift and, in return, receive lifetime income from the charity.” This is unsurprising given the similarity of the functional descriptive terms and the information description tested. Rather than being simply the result of an artificial experimental construct, this similarity points to an inherent advantage of using functional descriptive terms rather than industry jargon. A simple functional description communicates what the prospect will actually be learning.

One possible justification for introducing a topic by using obfuscatory industry jargon is to “bait and switch” the prospect into learning more when the underlying information might be aversive if labeled with a simple functional description. This dubious strategy is strongly contradicted by the dramatic loss of interest resulting from using industry terms to reference the gift instrument. In sum, introducing this gift instrument using industry standard terms generates little interest and little understanding.

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

17

Table 4: Estate Planning and Estate Giving Phrases – Interest in Reading More Average numerical interest rating (1-5) Definitely interested in reading more

Overall

Women

Age 55+

Bachelors graduates

$500+ Donors

Estate planning 3.3%

2.23

2.17

2.28

2.25

2.31

Legacy planning 3.3%

2.31

2.25

2.09

2.32

2.36

Will planning 7.1%***

2.59***

2.59***

2.61*

2.56***

2.70***

Estate giving 2.2%

2.15

2.10

2.21

2.18

2.20

Legacy giving 2.5%

2.30***

2.23**

2.20

2.31*

2.34*

Bequest gifts 2.4%

2.35***

2.35***

2.43

2.33**

2.42***

2.55***

2.52***

2.54*

2.54***

2.65***

Gifts in wills 5.2%*** Notes:

*p<.01, **p<.001, ***p<.0001 in two-tailed t-test comparing against baseline phrase of “estate planning” or “estate giving”. 1=I am definitely NOT interested; 2=I don't think I would be interested; 3=I don't know if I would be interested; 4=I might be interested; 5=I am definitely interested. Group assignments were random with the exception that group 2 was recruited after group 1. Respondents for “Gifts in wills” & “legacy planning” from groups 2a-2e, for “will planning” from groups 2a-2b & 2d-2e, and all others from groups 1a-1e.

Estate Planning Phrases This next test compares an industry standard term “estate giving” (along with two alternates “bequest giving” and “legacy giving”) against the simple descriptive term “gifts in wills.”

Table 4 reports that the “estate giving” term generated the least interest in reading more while the simple descriptive term “gifts in wills” generated the greatest interest. Although interest in all such topics was low compared to the other charitable financial planning topics described above, the share of people indicating that they were definitely interested in reading about the topic more than doubled when replacing “estate giving” (or alternates “legacy giving” or “bequest gifts”) with “gifts in wills.”

This provides support for the finding in James (2015) that people were much more interested in making a “gift to charity in your will” rather than a “bequest gift” or “leave a legacy gift,” and extends the finding to apply also to interest in simply reading more about such gifts. Additionally, the current test finds that “estate giving,” not tested in James (2015), actually performed worse than “bequest gifts” or “legacy giving” in generating interest in reading more.

Table 4 also reports that people were significantly less interested in reading more about “estate planning” (or “legacy

planning”) than in reading more about “will planning.” Again, the share of those who were “definitely interested” in reading about the topic more than doubled when it was described as “will planning” rather than “estate planning.” The “will planning” term generated greater interest overall, among women, among those age 55+, among those with a four-year college degree, and among donors having made gifts of $500 or more.

Although the “will planning” and “gifts in wills” terms generate significantly more interest in reading more, estate planners may perceive that this comes at a cost. Namely, “will planning” and “gifts in wills” reference a single, narrow planning option, rather than the broad range of instruments possible in estate planning including living trusts, beneficiary designations, and other non-probate transfers. However, the results in Table 5 suggest that this perceived cost in communication is illusory. Technically, it is correct, that “gifts in wills” references a much narrower topic than “estate giving.” But, experimental participants were significantly more likely to expect a broad range of estate planning information resulting from the, technically narrow, term “gifts in wills.” Indeed, as compared with “gifts in wills,” “bequest gifts,” or “legacy gifts,” the “estate giving” term generated a lower expectation of receiving information about will gifts, living trust gifts, life insurance gifts, IRS transfer-on-death gifts, and bank

Journal of Personal Finance

18

Table 5: Estate Planning and Estate Giving Phrases – Types of Information Expected Combined average

Will gifts

Living trust gifts

Life insurance TOD gifts

IRA TOD gifts

Bank TOD gifts

Estate taxes & gifts

n

Estate giving

3.06

3.54

3.24

2.84

2.83

2.87

3.04

524

Legacy giving

3.40***

3.82**

3.65***

3.56***

3.51***

3.49***

2.39***

531

Bequest gifts

3.24*

3.72

3.42

3.29***

3.24***

3.31***

2.47***

518

Gifts in wills

3.41***

4.31***

3.46*

3.41***

3.34***

3.34***

2.60***

516

Estate planning

3.28

3.64

3.33

3.21

3.15

3.17

3.21

516

Legacy planning

3.39

3.79

3.66***

3.53***

3.42*

3.46**

2.49***

519

Will planning

3.42

4.28***

3.52

3.46*

3.36

3.42*

2.49***

533

Notes: *p<.01, **p<.001, ***p<.0001 in two-tailed t-test comparing against baseline phrase of “estate planning” or “estate giving”. 1=I definitely did NOT expect this; 2=I didn’t really expect this; 3=I don’t know if I expected this or not; 4=I guess I expected this; 5=I definitely expected this. Will gifts= “How to make a gift in your will”; Living trust gifts= “How to make a gift in your living trust”; Life insurance TOD gifts = “How to make a gift by naming a charity as death beneficiary of your life insurance policy”; IRA TOD gifts = “How to make a gift by naming a charity as death beneficiary of your IRA or retirement account”; Bank TOD gifts = “How to make a gift by naming a charity as death beneficiary of your bank account”; Estate taxes & gifts = “How to avoid estate taxes by making charitable gifts”.

account transfer-on-death gifts. This suggests the interesting situation that participants interpreted the technically narrow terms, “gifts in wills” and “bequest gifts,” as being associated with a broader range of information than the technically broad term, “estate gifts.” The “estate giving” term generated a greater expectation of receiving only one type of information, “How to avoid estate taxes by making charitable gifts,” possibly because of the shared use of the term “estate.”

Similarly, the term “estate planning,” generated a lower average expectation of receiving a broad range of estate giving information than “will planning” and “legacy planning,” although this overall difference was not statistically significant. Similar to “estate giving,” the “estate planning” term generated a greater expectation of receiving estate tax related information. But, the “estate planning” term was otherwise relatively ineffective at generating an expectation of receiving information about a variety of estate giving techniques. The “will planning” term

was not only more effective at generating an expectation of receiving information about will gifts, but also significantly more effective at generating expectations of receiving information about naming a charity as a transfer-on-death beneficiary of a life insurance policy or a bank account as compared with “estate planning.” Once again, participants appear to have interpreted the technically narrow term, “will planning” as being associated with a broader range of information than the technically broad term, “estate planning.” Thus, in both cases, the increased interest resulting from using the simple, but narrow terms, “gifts in wills” or “will planning,” was not associated with a decreased expectation of receiving a broad range of information.

Sample Variation in Estate Planning Results Although the sample selection process was otherwise identical for each of the estate planning phrases, respondents assigned to the phrases related to “will planning” and “gifts in wills” were

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

selected later in an otherwise identical recruitment process.

Thus, 2,757 participants responded in the initial round, with an additional 2,738 responding to the later round of recruitment.

To check for possible bias resulting from this later selection, both the earlier and later groups responded to an identical comparison phrase, “legacy planning.” The average interest rating for “legacy planning” was 2.31 in the earlier sample group and was 2.38 for the later recruited sample group. Although not significant (p<.01), this difference in sample groups may have contributed to the results shown in Table 4. To explore this, an ordered logistic regression compared “will planning” with “estate planning” controlling for education, gender, age, and the largest prior gift. The coefficient for use of the “will planning” rather than the “estate planning” phrase was positive (0.4992) and significant at p<.0001, suggesting the reported differences did not result simply from different demographic characteristics of the earlier and later sample groups. A similar ordered logistic regression including demographic controls found the coefficient for use of “gifts in wills” rather than “estate giving” was also positive (0.5664) and significant at p<.0001.

Additional Phrase Variations This second round of tests also included a series of variations of terms generating the highest level of interest. However, none of the variations generated a higher level of interest than the original terms reported above. For example, in the second set of tests, the overall numerical average interest rating generated from “Other ways to give cheaper, easier, and smarter” was greater (3.56) than variations such as “How to give cheaper, easier, and smarter” (3.52), “Give other ways” (3.35), “More ways to give” (3.33), or “Other gifts” (2.98). None of the variations produced the low levels of interest generated by the industry standard terms in the first set of tests reported in Table 1. Similarly, in this second set of tests, the overall numerical average interest rating generated from “Gifts that pay you income” (3.73) was greater than “Gifts that pay you guaranteed income” (3.72), “Gifts that pay you” (3.69), “Gifts that pay” (3.56), or “Gifts that pay you income and avoid taxes” (3.55). Again, none of these variations generated the low levels of interest resulting from using the industry standard terms as reported in Table 3.

Conclusion Previous research suggests that in a variety of fields using technical insider terms with non-expert clients can serve as a barrier to client understanding and action. The current experimental

19

results support this proposition and provide several examples where using standard industry terms to introduce charitable financial planning and charitable estate planning information led to reduced understanding and reduced interest in learning more, as compared with simple functional descriptions.

Financial advisors who choose to repeat these insider terms to non-experts, rather than translating such terms into their vernacular functional equivalents, may fail to achieve their goals related to client understanding and action.

These results demonstrate the benefits of translation, but also the challenges. For example, among estate planning experts the terms “will planning” or “gifts in wills” have dramatically narrower meanings than “estate planning” or “estate gifts.” Yet, this appears not to be true of a lay audience’s understanding of these terms. It is understandable that the planning expert might hesitate to use such technically less correct terms, but if part of the role of the advisor is to communicate effectively, then translation may require the use of alternate language relevant to the audience, rather than to the expert.

References Aronoff, C. E. & Ward, J. L. (2011). Why so few business owners get and accept good advice. In C. E. Aronoff & J. L. Ward (Eds.), How to choose and use advisors (pp. 11-18). New York, NY: Palgrave Macmillan.

Benson, L. K. (1994). Profiting as an investment adviser. Practical Accountant, 27(8), 20-31.

Binder, C. (1994). Measurably superior instructional methods: Do we need sales and marketing? In R. Gardner, III, D. M. Sainato, J. O. Cooper, T. E. Heron, W. L. Heward, J. Eshleman, & T. A. Grossi (Eds.), Behavior analysis in education: Focus on measurably superior instruction (pp. 21-31). Belmont, CA: Brooks/Cole.

Bradley, S. D., & Meeds, R. (2004). The effects of sentence-level context, prior word knowledge, and need for cognition on information processing of technical language in print ads. Journal of Consumer Psychology, 14(3), 291-302.

Brown, R. (2017). A history of charitable gift planning: How gift annuities shaped American philanthropy (1830-1959). Charleston, SC: CreateSpace Brown, R. D., Braskamp, L. A. & Newman, D.L. (1978). Evaluator credibility and acceptance as a function of report styles: Do jargon and data make a difference? Evaluation Quarterly, 2(2), 331-341.

20

Journal of Personal Finance

Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science 6(1), 3-5.

Castro, C. M., Wilson, C., Wang, F., & Schillinger, D. (2007). Babel babble: physicians’ use of unclarified medical jargon with patients. American journal of health behavior, 31(1), S85-S95.

Cegala, D. J., Gade, C., Lenzmeier-Broz, S., & McClure, L. (2004).

Physicians’ and patients’ perceptions of patients’ communication competence in a primary care medical interview. Health Communication, 16(3), 289-304.

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

Financial Counseling and Planning, 9(2), 1-10.

Clontz, B. (2010). Managing risks in CGA programs. American Council on Gift Annuities 29th Conference on Gift Annuities, April 28-30, New Orleans, LA. Collins, H. M., & Evans R. (2007). Rethinking expertise. Chicago: University of Chicago Press.

Critchfield, T. S., Doepke, K. J., Epting, L. K., Becirevic, A., Reed, D. D., Fienup, D. M., ... and Ecott, C. L. (2017). Normative emotional responses to behavior analysis jargon or how not to use words to win friends and influence people. Behavior Analysis in Practice, 10(2), 97-106.

Croson, R., Handy, F., & Shang, J. (2009). Keeping up with the Joneses: The relationship of perceived descriptive social norms, social information, and charitable giving. Nonprofit Management and Leadership, 19(4): 467-489.

DeVito, J. A. (1995). Human communication. New York, NY: Addison Wesley Longman Inc. Dwyer, J. (1999). Communication in business: Strategies and skills.

Sydney: Prentice Hall.

Estate. [Def. 13a]. (n.d.). In Oxford English dictionary (2nd edition). Retrieved from http://www.oed.com/view/Entry/64556 Elsbach, K. D., & Elofson, G. (2000). How the packaging of decision explanations affects perceptions of trustworthiness. Academy of Management Journal, 43(1), 80-89.

Foxx, R. M. (1996). Translating the covenant: The behavior analyst as ambassador and translator. The Behavior Analyst, 19(2), 147-161.

Goetz, J. W., & Bagwell, D. C. (2006). Difficult questions from clients: How practitioners respond. Journal of Personal Finance, 5(1), 16-25.

Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26(3), 213-224.

Howard, T., Jacobson, K. L., & Kripalani, S. (2013). Doctor talk: physicians’ use of clear verbal communication. Journal of Health Communication, 18(8), 991-1001.

Ipeirotis, P. (2010). The new demographics of Mechanical Turk. Retrieved from http://www.behind-the-enemy-lines. com/2010/03/new-demographics-of-mechanical-turk.html Jackson, L. D. (1992). Information complexity and medical communication: The effects of technical language and amount of information in a medical message. Health Communication, 4(3), 197-210.

James III, R. N. (2009). Health, wealth, and charitable estate planning: A longitudinal examination of testamentary charitable giving plans. Nonprofit and Voluntary Sector Quarterly, 38(6), 1026-1043.

James III, R. N. (2015). The new statistics of estate planning: lifetime and post-mortem wills, trusts, and charitable planning.

Estate Planning & Community Property Law Journal, 8, 1-39.

James III, R. N. (2016a). An economic model of mortality salience in personal financial decision making: Applications to annuities, life insurance, charitable gifts, estate planning, conspicuous consumption, and healthcare. Journal of Financial Therapy, 7(2), 5.

James III, R. N. (2016b). Phrasing the charitable bequest inquiry.

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(2), 998-1011.

James III, R. N., & Routley, C. (2016). We the living: the effects of living and deceased donor stories on charitable bequest giving intentions. International Journal of Nonprofit and Voluntary Sector Marketing, 21(2), 109-117.

Jarmolowicz, D. P., Kahng, S., Ingvarsson, E. T., Goysovich, R., Heggemeyer, R., & Gregory, M. K. (2008). Effects of conversational versus technical language on treatment preference and integrity. Intellectual and Developmental Disabilities, 46(3), 190199.

Joiner, T. A., Leveson, L., & Langfield-Smith, K. (2002). Technical language, advice understandability, and perceptions of expertise and trustworthiness: The case of the financial planner.

Australian Journal of Management, 27(1), 25-43.

Makoul, G., Arntson, P., & Schofield, T. (1995). Health promotion

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

21

in primary care: physician-patient communication and decision making about prescription medications. Social Science & Medicine, 41(9), 1241-1254.

Paolacci, G., Chandler, J., & Ipeirotis, P. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411-419.

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

U.S. Dept. of Health & Human Services (2016). A profile of older Americans: 2016. Washington, D.C: Administration on Aging.

Retrieved from https://www.giaging.org/documents/A_Profile_of_Older_Americans__2016.pdf

Ross, J., Irani, L., Silberman, M., Zaldivar, A., & Tomlinson, B. (2010). Who are the crowdworkers? Shifting demographics in Mechanical Turk. In K. Edwards & T. Rodden (Eds.), Proceedings of the ACM Conference on Human Factors in Computing Systems (pp. 2863â&#x20AC;&#x201C;2872). New York: ACM. Peters, R. G., Covello, V. T. & McCallum, D. B. (1997). The determinants of trust and credibility in environmental risk communication: An empirical study. Risk Analysis, 17(1), 43-54.

Rolider, A., Axelrod, S., & Van Houten, R. (1998). Donâ&#x20AC;&#x2122;t speak behaviorism to me: How to clearly and effectively communicate behavioral interventions to the general public. Child & Family Behavior Therapy, 20, 39-56.

Salamone, J. D., Correa, M., Nunes, E. J., Randall, P. A., & Pardo, M. (2012). The behavioral pharmacology of effort-related choice behavior: dopamine, adenosine, and beyond. Journal of the Experimental Analysis of Behavior, 97(1), 125-146.

Sharma, N., & Patterson, P. G. (2000). Switching costs, alternative attractiveness and experience as moderators of relationship commitment in professional, consumer services. International Journal of Service Industry Management, 11(5), 470-490.

Sharpe, D. L., Anderson, C., White, A., Galvan, S., & Siesta, M. (2007). Specific elements of communication that affect trust and commitment in the financial planning process. Financial Counseling and Planning, 18(1), 3-18.

Skinner, B. F. (1957). Verbal behavior. New York: Appleton-Century-Crofts.

Thompson, P. A., Brown, R. D., & Furgason, J. (1981). Jargon and data do make a difference. The impact of report styles on lay and professional evaluation audiences. Evaluation Review, 5, 269-279.

Levay, K. E., Freese, J., & Druckman, J. N. (2016). The demographic and political composition of Mechanical Turk samples. SAGE Open 6(1), 1-17.

Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184-188.

Wittgenstein, L. (1953). Philosophical investigations. Oxford: Blackwell Publishing.

Volume 17, Issue 2

23

The Consistency Smile: How Consistency of Investment Decisions Relates to Risk Appetite

Sidharth Muralidhar, Freshman, Virginia Polytechnic Institute and State University

Abstract Using the Kahneman and Tversky (1979) definition of risky gambles, consistency of decision-making is defined as selecting the “same” gamble, regardless of how the gamble is presented. Both expected utility theory (EUT) and prospect theory (PT) make implicit assumptions about the consistency of individual behavior as it pertains to risky gambles that are not borne out in laboratory tests. EUT assumes that individuals are perfectly rational and implicitly consistent in their decisions, whereas, PT implicitly concludes that, in the aggregate, there is zero consistency. This paper develops a new methodology to examine consistency in risky gambles and tests it on a diverse database of 442 people - investment professionals (81), teens (297), and adult non-professionals (64) - to account for factors like literacy, experience, age, and gender. First, consistency of individuals and groups lies well between levels assumed by EUT and PT. Second, using the Risktyle model to calibrate the strength of risk preferences for Kahneman-Tversky gambles, we discover a “Consistency Smile” – individuals with strong risk preferences, across all but one of the sub-groups, tend to have greater consistency in decisions than those with weaker risk preferences. Third, non-investment professionals have the highest level of consistency followed by teens, and investment professionals. This result has interesting investing implications, especially for advisors designing portfolios for a diverse group of clients.

Key Words: consistency smile, consistency, strength of risk appetite, risk tolerance, risk aversion, risk seeking, Kahneman-Tversky, behavioral finance, prospect theory, expected utility theory, risktyle

Journal of Personal Finance

24

Introduction A foolish consistency is the hobgoblin of little minds…

- Ralph Waldo Emerson Ralph Waldo Emerson’s 1841 essay “Self-Reliance” was a call to individuals to avoid conforming to traditional norms and following a false consistency (Emerson 1993). Theories in economics and finance, to greatly simplify models of human behavior, tend to assume conformity of behavior across the entire population. It appears from this empirical research that individuals seem to be heeding Ralph Waldo Emerson’s advice to be unique, and their behaviors seem to be reflecting his comment on consistency.

Kahneman and Tversky (1979) asked respondents to choose between two risky gambles – defined by a probability and a monetary outcome (e.g., 80% chance of winning $4000 vs 100% chance of winning $3000). By presenting many such combinations to the respondents, they were able to detect what they considered behavior inconsistent with expected utility theory (EUT) assumptions. Individuals did not always pick the gambles with the highest expected return, and often flipped their choices depending on how the question was expressed (e.g., 20% chance of winning $4000 vs 25% chance of winning $3000).

These were essentially the same choices, in that the second set of probabilities was just the first divided by a factor of four.

In short, they find individuals were inconsistent in their decisions. While Kahneman-Tversky focused on debunking EUT, the experiments they conducted also lent themselves to examining the consistency of decisions, which they did not specifically address. This paper examines the consistency of decision-making because it has interesting implications for investing and especially for advisors who want to construct portfolios for their clients. Their ability to construct effective portfolios and satisfy clients will depend critically on whether clients are consistent or inconsistent. As a result, this paper presents advisors with a simple methodology to assess the consistencies of investors.

We define consistency of decision-making as selecting the “same” gamble, regardless of how the gamble is presented. In this paper, consistency is evaluated solely on the probabilities, rather than the outcomes in these gambles. In the two examples above, being consistent would require selection of either 1.

2.

the first (lower probability or riskier option) or second option/ gamble (higher probability or safer option) in both questions.

This is important in the management of portfolios, especially when clients delegate decisions to advisors or asset managers.

A more consistent client will respond in a somewhat identical fashion independent of how results are presented, thereby making it easier for an agent/advisor to construct a long-term portfolio. A less consistent client might not react in a similar fashion thereby making it harder for the agent/advisor to construct effective long-term portfolios, and the less consistent client could be buffeted by short-term market corrections or rallies. Hence, knowing the degree of consistency of a client (or even asset manager) is important to the investment practice.

While consistency of managerial decision-making has been examined, as far as we can tell, this is the first attempt to quantify and analyze consistency as an isolated factor in risk-taking behavior.1,2 Both expected utility theory (EUT) and prospect theory (PT) implicitly make assumptions about the consistency of individual behavior as it pertains to risky gambles that are not borne out in laboratory tests. EUT assumes that individuals are perfectly rational and implicitly consistent in their decisions, whereas, PT implicitly concludes that, in the aggregate, there is zero consistency. This paper, using the Kahneman-Tverky (1979) questionnaire which is the basis of PT, develops a new methodology to examine consistency in risky gambles and tests it on a diverse database of 442 people - investment professionals (81), teens (297), and adult non-professionals (64). The first interesting result is that it appears that in large part the consistency of individuals in all sub-groups exists between the implicit assumptions of these two major theories of human behavior.

Both little and large minds, in aggregate, do not seem wedded to the consistency hobgoblin, though we do find a few little minds (i.e., teens) who are perfectly consistent. But, we also find four investment professionals who are perfectly inconsistent, which seems like a controversial result. This begs an important question – are there any factors like literacy, age, gender, etc. that can be used to understand what factors are correlated to the consistency of investment decision-making? Using the Risktyle model to calibrate the strength of risk preferences for Kahneman-Tversky (1979) gambles, we discover a “Consistency Smile” – individuals with strong risk preferences, across all but

Choi et al (2007) use the term consistency in their paper, but as noted later, it is used interchangeably with rationality.

In practice, consistency is being addressed, but the methodology is not in the public domain. Bernard Del Rey and Prof. Shachar Kariv of Capital Preferences have kindly shared with the author the TrueProfile analysis they conduct on individuals. This report provides a consistency score for each individual who answers a series of questions.

©2018, IARFC. All rights of reproduction in any form reserved.

Volume 17, Issue 2

one of the sub-groups, tend to have greater consistency in decisions than those with weaker risk preferences. This may have interesting implications for designing portfolios for individuals because once an advisor analyzes the strength of the client’s risk preference using Risktyle (Muralidhar and Berlik 2017), they can either assume a degree of consistency or use the methodology provided here to quantify their client’s consistency.

Choi, Fisman, Gale, and Kariv (2007), or CFGK as I will refer to their study hereafter, attempt to extract risk preferences of individuals using budget constraints through an innovative graphical interface. This method allows them to examine the rationality/consistency of individuals. This technique is robust and is powerful enough to exclude the possibility that rationality/consistency is a result of random behavior. They report that individuals report high levels of consistency, maximizing unique utility functions. However, CFGK use the terms “rationality” and “consistency” interchangeably as they believe that if an individual is perfectly consistent in their preferences, they are also rational in those same decisions.3 Their definition of rationality and consistency is the standard definition that choices are consistent with maximizing a well-behaved (i.e. piecewise linear, continuous, increasing, and concave) utility function if and only if they satisfy the Generalized Axiom of Revealed Preference (GARP) (Afriat, 1967; Varian, 1982, 1983)4. This paper takes a slightly different approach and allows for the possibility that an individual could be consistently “irrational.”

The paper is structured as follows: Section I reviews EUT and PT and discusses their key (implicit) assumptions of consistency of decision-making. Section II briefly describes the Risktyle model that is used to gauge strength of risk preferences.

Section III describes the consistency scoring method used in this paper. Section IV demonstrates that the general population consistency over risky gambles lies between the implicit assumptions of EUT and PT. It also introduces the Consistency Smile and demonstrates the robustness and dispersion of the result across various sub-groups (gender, age, financial expertise). Section V discusses why consistency is critical to ensuring investment success for investors, advisors and assets managers.5 Section VI examines some possible shortcomings of this approach and extensions. Section VII concludes.

3.

4.

5.

25

Section I: A Review of Expected Utility Theory and Prospect Theory as it Relates to Consistency Von Neumann and Morgenstern (1944) formalized EUT, requiring individual preferences to satisfy five axioms (continuity, transitivity, completeness, monotonicity, and independence).

If satisfied, individuals prefer actions that maximize utility, thus being “rational.” A rational individual in EUT would maximize expected value in risky gambles, thereby maximizing utility.

Implicitly, maximizing utility over risky gambles should result in behavior that is consistent and predictable across those gambles.

The main purpose of Kahneman and Tversky (1979) was to critique EUT, the prevailing model for economic behavior. They sought to show that EUT is based on a representative, rational, consistent, and risk-averse individual (an “econ”), and failed to capture actual human behavior. If EUT’s assumptions were not acceptable, it follows that its recommendations must be overturned as well. As a result, they sought to undermine the basic assumption of EUT, and found various previously unconsidered behaviors such as loss aversion and the certainty effect. However, even in determining that individuals were not rational, as defined in EUT, they did not examine the consistency in decision-making of individuals in any detail. In some part, this is because they focused on the aggregate results of their survey and not on how each individual responded to their questions. For example, if the majority of the individuals chose to gamble to earn 100% of $3000 (vs 80% of $4000) and further, the majority also chose 20% of $4000 (vs 25% of $3000), in their analysis, it confirmed that EUT assumptions about rationality/ consistency were probably incorrect. Since every respondent was not asked the same set of questions, and they did not report individual-level results, it is hard to examine and compare individuals based on their results. Interestingly though, their survey method of asking a range of questions and modifying each marginally (i.e. dividing probabilities by a fixed factor, flipping gains to losses) allows for a detailed analysis of consistency of individuals and sub-groups.

As will be shown in Section III, “econs” from EUT will have a high consistency score, where the aggregate results from

Once again, in practice this may not be true, but the methodology is not in the public domain and hence this paper. TrueProfile appears to provide a separate consistency and coherence score (where coherence is defined as “decisions were in line with principles of economic theory”).

Thank you to Professor Shachar Kariv for this clarification.

Thanks to an anonymous referee for requesting this clarification.