IARFC Journal of Personal Finance Volume 22 Issue 1 2023

Journal of Personal Finance

Volume 22, Issue 1

2023 Editor

Craig W. Lemoine, Ph.D., MRFC®, CFP®

University of Illinois

Editorial Board

Sarah D. Asebedo, Ph.D., CFP®

University of Arizona

Daria J. Auciello Newfeld, Ph.D. Albright College

H. Stephen Bailey, Ph.D., MRFC®

HB Financial Resources, Ltd./IARFC

David Blanchett, Ph.D., CFA®, CFP® Morningstar Investment Management, LLC

Swarn Chatterjee, Ph.D.

University of Georgia

Yuanshan Cheng, Ph.D., CFA, CFP®, FRM

Winthrop University

Preston D. Cherry, Ph.D., CFP®

University of Wisconsin — Green Bay

Chia-Li Chien, Ph.D., CFP®, PMP®

California Lutheran University

Ronnie Clayton, Ph.D.

Jacksonville State University

Ben Cummings, Ph.D., CFP®

Utah Valley University

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

Lu Fan, Ph.D., CFP®

University of Georgia

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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®, CFA William Paterson University

Douglas A. Hershey, Ph.D. Oklahoma State University

Vera Intanie Dewi, Ph.D. Universitas Katolik Parahyangan

Carrie L. Johnson, Ph.D., AFC® North Dakota State University

Kyoung Tae Kim

The Ohio State University

Douglas J. Lamdin, Ph.D. University of Maryland, Baltimore County (UMBC)

David Nanigian, Ph.D., CFP® Dr. Thomas Warschauer Endowed Professor of Finance Fowler College of Business, San Diego State University

Disclaimer: The Journal of Personal Finance 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 Board, and 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.

Blain Pearson, Ph.D., CFP®

Kansas State University

Wade D. Pfau, Ph.D., CFA

The American College of Financial Services

Nilton Porto, MBA/Ph.D.

University of Rhode Island

Abed G. Rabbani, Ph.D., CFP®

University of Missouri

Brandon Renfo, Ph.D., CFP®, RICP®, EA

Eastern Texas Baptist in the Fred Hale School of Business

Laura Ricaldi, Ph.D., CFP®

Utah Valley University

Donald Bruce Ross III, Ph.D., AFC®

University of Kentucky

Zack Taylor, Ph.D. Trellis Company

Jacob Tenney, Ph.D., CFP®

University of Charleston

Sandra Timmermann, Ed.D.

The American College of Financial Services

Walt Woerheide, Ph.D., ChFC®, RICP®

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

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The Journal of Personal Finance is published in the U.S. in the months of March and October by the International Association of Registered Financial Consultants (IARFC®).

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

Call for Papers Journal of Personal Finance

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

• Individual financial decision making

• Household portfolio choice

• Retirement planning and income distribution

• Household risk management

• Life cycle consumption and asset allocation

• Investment research relevant to individual portfolios

• Household credit use

• Professional financial advice and its regulation

• Behavioral factors related to financial decisions

• Financial education and literacy

• Wealth management

• Diversity and inclusion within financial services

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 seeking qualified members for the Editorial Board. Those interested in joining the Editorial Board should send their current CV to the editor at the email address below.

Contact

Craig W. Lemoine, Ph.D., MRFC®, CFP® Editor

jpfeditor@iarfc.org

www.journalofpersonalfinance.com

Beatrix Lavigueur, MS

Jing Jian Xiao, Ph.D.

The purpose of this literature review is to document personal finance scales published in research journals, describe features of each scale, and provide implications for both researchers and practitioners. Through a literature search, 30 scales published in 26 different papers were collected and analyzed based on key factors such as their target population, purpose, number of factors, number of items, reliability and validity. Scales were then divided into five categories: financial wellbeing, financial self-efficacy, financial behavior, financial management and decision styles, and financial attitudes. Implications for researchers and practitioners are provided.

The Role of Financial Advisors in Shaping Investment Beliefs

Blain Pearson, Ph.D., CFP®, AFC®

Thomas Korankye, Ph.D., CFP®

Di Qing, Ph.D.

The objective of this study is to examine the association between financial advisor usage and the association with client investment beliefs. An illustrative model is first introduced, establishing a framework for how financial advisors may influence the investment beliefs of their clients. The authors test the association between financial advisor use and investment beliefs with data collected from the 2016 RAND American Life Panel (N = 1,045). The average age of the sample was 56. The findings suggest an association between the influence of financial advisors and their clients’ investment beliefs. The ensuing discussion highlights the need for financial advisors to be aware of their own investment beliefs, attitudes, and behaviors when working with clients. The conclusions orbit around the need for client communication education to be reinforced as a part of the broader financial planning curricula.

Investment Advisor Use and Stock Market Return Expectations

Miranda Reiter, Ph.D., CFP®

Martin C. Seay, Ph.D., CFP®

This study explored the association between receiving investment advice from a financial professional and investors’ sentiment about expected stock market outlook. Using data from the 2015 National Financial Capability Study, respondents were identified as either being pessimistic, realistic-cautious, realistic-optimistic, or highly optimistic about future stock market performance. Data were provided relative to an investors’ use of financial professionals: being self-directed, using some investment advisor help, or relying upon an investment advisor’s help. Results show that investors using full or some investment advisor help were more likely to expect future stock market returns to align with historical averages. The key implication is that working with an investment advisor is associated with clients having a more realistic view of future stock market returns.

An Exploration of Contributing Factors Related to Retirement Plan Participation

Marc B. Lewis, D.B.A.

Joseph T. Patton, D.B.A.

Approximately 30% of consumers are not participating in their employer-sponsored retirement plans (Topoleski & Myers, 2021). This study examines minorities, non-minorities, and other contributing factors such as age, education, household income, gender, and education as they related to a consumer’s likelihood of participating in their employer’s sponsored retirement plan. A secondary data analysis was conducted using the 2019 National Financial Capability Study. With over 25,000 respondents, the results of the analysis reveal that age, education, and household income are strong contributors in determining the likelihood of an individual participating in an employer-sponsored plan. However, the data reveal that an individual’s age and minority group are less correlated with the likelihood of plan participation. These results provide insight for plan participants, practitioners, and plan sponsors on educating their participants on the various contributors that effect their participation.

Bequest Expectations and Annuity Ownership

Ying Yan

Russell N. James III

This paper uses data from the 9 waves of the Health and Retirement Study (HRS) to examine how bequest expectations impact decisions about annuitization. The estimations of a random-effects model show that people who have a higher expectation of leaving a bequest are more likely to have an annuity, even controlling for housing wealth, non-housing wealth, health, and other demographic characteristics. Previous studies have shown a negative association between bequest motivation and annuitization. The differing relationship of annuitization with bequest motives and bequest expectations reveals a practically and theoretically important distinction between these two types of bequest measurements. The implications of other research findings that use bequest expectations as a proxy for bequest motive may need to be reconsidered.

Editor's Notes

This issue of The Journal of Personal Finance captures the spirit of our mission. Our beloved Journal provides scholarly articles that examine the impact of financial issues on households as well as the practice of financial planning. Our current issue contains articles from respected researchers in our field and I am proud to share their contributions with you. Thank you to our Editorial Board for your tireless work reviewing, editing, and communicating with this edition’s authors.

We kick off our Spring 2023 edition with a review of personal finance scales. In Personal Finance Scales: A Literature Review, Dr. Jing Jian Xio and Beatrix Lavigueur explore scales in financial wellbeing, financial self-efficacy, financial behavior, financial management and decision styles, as well as financial attitudes. This article includes meaningful takeaways for researchers and professionals. This study consolidates and introduces readers to a wide range of scales that add value to practitioner processes and research methods. The paper goes on to provide concise reliability and validity metrics for each scale, providing a launching point for research across a broad number of fields within personal finance.

Our second offering dives deep into investment beliefs. The Role of Financial Advisors in Shaping Investment Beliefs (Pearson, Korankye & Qing) examines associations between financial advisor usage and client investment beliefs. This article utilizes an illustrative model and finds an association between the influence of financial advisors and client beliefs. This article further develops a framework of client outcomes when working with advisors and reinforces the need of advisors to be aware of their own beliefs, attitudes and behaviors when working with clients. This article is valuable to both practitioners and researchers, helping advance additional understanding of the financial planning profession and our impact on those we serve.

In addition to exploring advisor use and investment beliefs, our Spring issue also studies the relationship between investment advisor use and return expectations. In Investment Advisor Use and Stock Market Return Expectations, Drs. Reiter and Seay explore the association of working with financial professionals and investors stock market outlooks. Results showed that investors working with advisors were likely to expect more realistic and historically appropriate returns. The article’s key finding reinforced the role financial advisors play in client outcomes and expectations.

Drs. Lewis and Patton explore factors that determine the likelihood individuals participate in employer retirement plans. An Exploration of Contributing Factors Related to Retirement Plan Participation examines demographic and socioeconomic variables related to retirement plan participation and contributions. Results of this article provide insights for plan participants, practitioners, and plan sponsors on how to target participant outreach and education.

Our Spring edition concludes with a study of Bequest Expectations and Annuity Ownership (Yan & James). This paper uses data from the Health and Retirement Study (HRS) to examine how bequest expectations impact annuitization decisions. The paper found people who have a higher expectation of leaving a bequest are more likely to have an annuity. The differing relationship of annuitization with bequest motives and bequest expectations reveal an important distinction between these two measurements. This study challenges the use of bequest expectations as a proxy for bequest motive.

I hope you enjoy our Spring 2023 edition of the Journal. This edition deepened my understanding of financial scales, the value of working with an advisor, retirement contributions, and annuitization. Our profession is constantly evolving, and we are grateful to be part of that journey! We are looking for additional submissions for our Fall 2023 edition. If you have been working on a manuscript, please send it our way!

Craig Lemoine, Ph.D., MRFC®, CFP® | Editor — Journal of Personal Finance Director of Financial Planning | University of Illinois at Urbana-Champaign

Personal Finance Scales: A Literature Review

Beatrix Lavigueur, MS1

Jing Jian Xiao, Ph.D.2

Abstract

The purpose of this literature review is to document personal finance scales published in research journals, describe features of each scale, and provide implications for both researchers and practitioners. Through a literature search, 30 scales published in 26 different papers were collected and analyzed based on key factors such as their target population, purpose, number of factors, number of items, reliability and validity. Scales were then divided into five categories: financial wellbeing, financial self-efficacy, financial behavior, financial management and decision styles, and financial attitudes. Implications for researchers and practitioners are provided.

Key Words: content analysis, consumer finance, literature review, personal finance, scale

1. Graduate Student, Department of Human Development and Family Science, University of Rhode Island, blavigueur@uri.edu

2. Professor, Department of Human Development and Family Science, University of Rhode Island, jjxiao@uri.edu

INTRODUCTION

Personal finance is defined as “the study of personal and family resources considered important in achieving financial success; it involves how people spend, save, protect, and invest their financial resources” (Garman & Forgue, 2008, p. 4). Positive personal finance strategies are important because they translate into financial career planning, tax management, cash management, credit card, borrowing, expenditures, risk management, investments, retirement, and estate planning (Garman & Forgue, 2008). The use of a financial adviser can be beneficial to consumers because of their expertise and obligation to ensure strategies are best suited to their clients (Kitces, 2016).

To assist an individual’s practice to help clients, a key tool and resource can be a scale. By definition, a scale is “a collection of items, the responses to which scores are combined to yield a scale score” (Lee & Lim, 2008, p. 494) and can be used to measure variables that would otherwise be unobservable (Fayers & Hand, 2002). Common examples include Likert scales, qualitative and quantitative research designs, nominal, ordinal, or interval/ratio levels of measurement and the creation of variables appropriate to the particular research.

The purpose of this article is to conduct an in-depth look at published scales regarding consumer financial knowledge, financial behaviors, and financial wellbeing. In this review, 30 scales from 26 papers were identified from peer reviewed journals. A secondary goal of this study is to provide implications for both researchers and practitioners. The analysis of the scales will create more awareness when investigating how scales have been used, their purpose the results of the use of scales, and implications for future use of scales. The intent is to gain a better understanding of the reliability of the scales, the target population, and how the scales were used in each study.

For this review, three research questions are posed:

1) What consumer finance scales have been published in the last 30 years? 2) What are the contents of the scales?

3) What are the implications of the scales for both researchers and practitioners?

CONCEPTUAL FRAMEWORK

In any scenario that a scale is being used, it is important that it is clear, concise, and comprehensive for it to be effective. Scales are useful when asking a client to self-report their

behavior such as their spending habits or personality traits (Tay & Jebb, 2017). This is important because research has shown that one of the most significant impacts financial practitioners have on a client are not monetary differences, but changes in behavior (Hanna, 2011; Kitces, 2016; Sommer, 2020). When a practitioner is working with a scale, they are working towards understanding a particular phenomenon in a client such as behavior, decisions, feelings, or personality. This can be client’s self-reporting, direct observation, or indirect by collecting records or information from a third party (Appelbaum et al., 2016). According to researchers (Blau & DiMino, 2019; Krosnick & Fabrigar, 1997), a scale that is too long can be overwhelming for respondents, be too lengthy to fully understand and comprehend, and contain too much unnecessary information or questions. On the other hand, a scale that is too short may not have enough questions to fully understand the respondent’s answers and their behavior, attitudes, or perspectives on what is being measured (Krosnick & Fabrigar, 1997). An effective tool is versatile in the setting it can be used and in the population it serves (diverse backgrounds and cultures), sensitive and specific, and having adequate reliability and validity measures (Appelbaum et al., 2016). In any case, it is important to understand that each tool is unique and serves its own purpose.

In the scales collected and analyzed for this literature review, the researchers explicitly explained why their scale is important and practical. According to Xiao (2015) they can be a useful tool in examining financial behaviors of various populations. Furthermore, scales are tools that can bridge the gap as Tomlinson (2015) suggests; professionals can take a deeper look into consumer financial behavior with the use of a scale.

Tay and Jebb (2017) define scale development as a “reliable and valid measure of a construct in order to assess an attribute of interest” (p. 1). One method to create a scale involves using a deductive approach in which the developer focuses on theory and the pre-conceptualized constructs. A scale can also be developed utilizing an inductive approach where there is uncertainty or dimensionalities of the construct (Tay & Jebb, 2017). Regardless of the approach, a critical aspect of the development is a clear, accurate definition of the construct being used. The purpose is to verify the existing theory and see empirical data collected from the target population support predictions specified by the theory. In creating a scale, several factors must be taken into consideration according to Tay and Jebb (2017): the target population, what the items pertain to, the differences in how respondents interpret the items, type

of scale format, and the applicability of reverse scoring. The factors listed by Tay and Jebb (2017) provide an outline in determining which variables will be analyzed when examining each individual scale.

The scales examined in this paper pertains to personal finance and hold implications for both researchers and practitioners. The contents of these specific tools include consumer behaviors relevant to major components of consumer economic wellbeing including spending, borrowing, and saving/investing (Xiao, 2015).

The use of a financial advisor has grown exponentially in recent years (Hanna, 2011) and can impact various aspects of a consumer’s life such as income, estate tax planning, insurance, and retirement planning (Kitces 2016; Tomlinson, 2015).

According to Sommer and colleagues (2020), advisors in the field of personal finance are expected to be the experts and are heavily relied on to help clients make sound decisions. To make these decisions, a tool that could be used to determine best practices for clients could be a scale.

When choosing what scale to utilize for a study, it is important to determine the dimensionality of the construct, or the items that make up the scale (Bhattacherjee, 2012). By definition, a unidimensional scale is one that has only one single dimension whereas a multidimensionality means a scale has two or more dimensions (Bhattacherjee, 2012). A professional not only chooses a target audience to work with, but also determines which items and dimensions are appropriate for the creation of an effective scale. It is paramount that professionals understand the assessment they are using because it can then influence later treatment (Lenz & Wester, 2017).

Analyzing such tools provides professionals the opportunity to assess correlations between measures and values, create item pools, assess reliability and validity of their obtained measures, and can be repeated on different occasions (Tay & Jebb, 2017). Typically, there are two types of scales: 1) criterionreferenced scale measuring ability and 2) norm-referenced scale measuring individuals against a given construct (Lee & Lim, 2008, p. 494). For example, scales can be used to assess a particular behavior or attitude which can aid advisors in better understanding their clients. On the other hand, researchers can study these behaviors and phenomena to assess personality, the psychology behind behaviors, attitudes, or decisions and create hypotheses to contribute to the consumer and personal finance research.

METHODOLOGY

Data collection and analysis of scales for this literature review took place in the fall of 2020. Keywords such as construct, instrument, scale, measure, financial behavior, financial attitudes, money management and scale development were used to identify relevant articles. Databases Scopus, EconLit, and Google Scholar were searched. The literature search identified 26 articles presenting 30 different scales The articles were published between 1990-2020 and most of them used data from the US.

Each article was read and analyzed, and characteristics were documented in an Excel file. Variables used are target population, the number of items, the number of factors, sample size, and the framework used were recorded. From there, scales were put into a table showing the quantitative characteristics of each scale. A second table analyzed the qualitative characteristics of each scale. The tables were combined to display both the qualitative and quantitative characteristics of scales and their respective papers. Following a procedure outlined by Aghdam et al.’s (2019) article, each scale was grouped and categorized based on the topic the scale covers, creating five categories. Table 1 displays the type of scale used, scale reliability, scale validity, the intended purpose of the researcher’s scale, and the framework used in the development of the tool. This allows an understanding of the purpose of each scale as well as the population it intends to serve.

Table 1. Compilation of 30 Scales and their Qualitative and Quantitative Attributes

Lown (2011) Financial SelfEfficacy Scale Bandura’s concept of selfefficacy (1977)

Prochaska’s Transtheoretical Model of Behavior Change

(2019) Women’s Financial Self-Efficacy Scale Bandura’s (2006) suggestions of social cognitive theory

examine the validity and reliability of the Women’s Financial Self Efficacy Scale

Financial Behavior Scales

Dew & Xiao (2011)

The Financial Management Scale

Theory

Means-End

Measure consumption goals encompassing integrative, multidimensional, context sensitive and general uses hoping to analyze gain, hedonic and normative functions

Motivation Scale General 101 5 (13) 5-point Likert .76-.91 construct

Help practitioners and researchers identify and determine the severity of compulsive spending habits in clients

Social

Married

partners

construct,

There were a total of 30 scales in 26 different papers compiled from the literature search. Each scale has its own unique purpose of development and serves various objectives and target populations. The studies published regarding scale development had sample populations ranging from 80 participants to 1,662 participants. Many researchers performed multiple studies to ensure accuracy and reliability in the development of their scales. When reading and analyzing the research, the number of studies was recorded, the number of participants in each study, and Cronbach’s alpha of each study’s internal consistency were also recorded. Unless written otherwise in the articles, only the final study’s participant size was reported in this paper. Reliability values of each factor and all methods of validity measures were recorded. The 30 scales were categorized based on the topics they address: financial wellbeing, financial self-efficacy, financial behavior, financial management and decision styles, and financial attitudes. Table 1 shows the compilation of the articles along with details about the variables that were analyzed, respective framework, and the researcher’s purpose behind the development of the scale(s).

The financial wellbeing category contains eight scales. Financial wellbeing refers to doing well financially, and can be measured with both objective measures such as desirable financial management behavior and subjective measures such as financial satisfaction (Xiao, 2015). These scales all have their unique purposes such as examining different aspects of financial stress (Heo et al., 2020) or working to better understand financial wellbeing such as Financial Wellbeing

Related Scales (Abrantes-Braga & Veludo-De-Oliveria, 2019), Financial Strain Survey (Aldana & Liljenquist, 1998), InCharge Financial Distress/Financial Well-Being Scale (Prawitz et al., 2006) and Financial Anxiety Scale (Archuleta et al., 2012). The final scale, Northern et al.’s (2010) Financial Stress Scale is used to examine undergraduate college student’s levels of financial stress.

The second category is Financial Self-Efficacy and Knowledge, comprised of three scales: Financial Knowledge Scale: Short Form (Houts & Knoll, 2019), Financial Self-Efficacy Scale (Lown, 2011), and Women’s Financial Self-Efficacy Scale (Nguyen, 2019). According to Nguyen (2019), financial self-efficacy is defined as “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments (p. 143) with Bandura’s theory of self-efficacy (1977) used in both Lown and Nguyen’s scales.

The third category of scales focuses on financial behavior, which encompasses the decisions and choices individuals make based on their finances. The scales in this category were created to use in measuring a consumer’s behavior and habits such as saving, spending or money management. There are 10 scales are in this category. Researchers’ goals included measuring consumption goals (Barbopoulos & Johansson, 2017), looking at the psychometric properties of financial management behaviors (Dew & Xiao, 2011), looking at the severity of spending and habits (Edwards, 1993; Weun et al., 1998) and examining financial transparency (Koochel et al., 2020). Other scales such as Ksendzova et al. (2017) and Prochaska-Cue’s (1993) were to measure financial management behaviors while Puustinen et al. (2013) worked to measure investment value associated with investment behavior while Lampenius and Zickar (2005) developed a tool to measure financial risk taking with their scale Measure of Financial Risk Taking.

Category four encompasses financial management and decision styles with three scales. This category looks at consumer money management style or decision style.

Lichtenberg Financial Decision Screening Scale (Lichtenberg et al., 2016) seeks to develop a scale to understand exploitation of consumers and decisional-ability deficits. The second scale, Process of Retirement Planning Scale (PRePS) (Noone et al., 2010) developed a tool to better understand the retirement process in regards to financial preparation. The final scale in this category, Rettig and Schulz’ (1991) Financial Decision Making Styles, assesses decision making styles.

The fifth category is financial attitude scales. Six scales are included, each one measures attitudes towards finances. Researchers such as Loix and colleagues (2005) took a psychological approach to understand attitudes. On the other hand, Hanna et al. (2001) created a scale regarding risk tolerance, while Lown and Cook (1990), and Webb and colleagues (2000) developed scales to understand attitudes towards helping others. The final two scales in this category targeted a specific audience with Beutler and Gudmunson’s (2012) scales studying 16-year-old adolescents.

DISCUSSIONS, LIMITATIONS, AND IMPLICATIONS

Discussions

When examining the 30 scales published in the 26 papers in this literature review, several similarities and differences were noted. While these scales each looked at different topics in the personal finance field to measure variables such as financial attitudes or financial behaviors, each scale was analyzed based on seven different factors: the scale’s target population, the sample size in the original study, the number of factors in the scale, the number of items the scale has, the type of scale the original study used, the reliability measure, and the type of validity the researchers used. Table 1 is comprehensive and includes all important information regarding each scale. One finding from this literature review is the understanding of each scale and its individual purpose and function. Extensively analyzing the characteristics of these types of tools provides transparency for professionals. In other words, having all aspects of a scale displayed in a table and grouped by purpose, allows a professional to find exactly what they are looking for in their practice or research.

Each scale had a unique purpose that was created from specific features as well as the intended audience. Several of the scales reviewed had specific target audiences. Some examples include Beutler and Gudmunson’s New Adolescent Money Attitudes Scale: Entitlement and Consciousness targeting the adolescent population with an average age of participants 16.5 years old. They wanted to better understand adolescents’ development and attitudes towards finances.

A second scale to have a specific audience was Koochel et al.’s (2020) Financial Transparency Scale which was designed for married partners and the initial study’s participants included heterosexual married partners who had been married for less than five years. A third scale in this review to have a targeted audience was Nguyen’s (2019) Women’s Financial Self-Efficacy Scale in which Nguyen used women over the age of 18 in hopes of understanding their levels of self-efficacy when it comes to finances. The final scale with a specific target audience is Northern et al.’s (2010) Financial Stress Scale targeting undergraduate college students working to examine their financial stress.

Another factor analyzed when looking at the scales was the measures of reliability and validity. Reliability and validity are significant pieces of psychometric assessments (Lenz &

Wester, 2017). The purpose of reliability is to ensure scores are consistent and accurate (Bardhoshi & Erford, 2017). Cronbach alpha measures (theoretical average of potential split-half reliability estimates in item scores (Bardhoshi & Ernford, 2017)) ranged from .52 to .95. The validity measures were similar across the 30 scales. Validity is an important measure because it determines whether or not the intended constructs were examined and helps to better understand the correlations between the variables (Lee & Lim, 2008) and is inferred from the scores of the assessment (Lenz & Wester, 2017). Ten scales used construct validity, 10 scales used criterion validity, nine scales used discriminant validity, and eight scales used convergent validity. Other commonly used validity measures included content, confirmatory, concurrent, and face validity. There was a relatively small range in the number of factors each scale used and a large range in the number of items each scale used. The largest number of factors a scale used was seven (n = 1) while most scales (n = 11) used one factor. Five scales used two factors, six scales used three factors, four scales used four factors, two scales used five factors and one scale used six factors. As for the number of items used in each scale, it ranged from one item to 52 items. The average number of items within this analysis was 14.5.

. The majority of the tools analyzed used a Likert Scale ranging from four points to seven points with one scale using two different types of Likert Scales. Two studies did not report their scale type and one scale did not report the number of points on their Likert Scale.

Finally, a large variance was found in each sample size. Some samples came from classroom focus groups while other participants were recruited from listservs sent to people in the United States and university employees while others were selected randomly from different areas within the United States. Dew and Xiao’s (2011) Financial Management Behavior Scale was the only study to use a nationally represented population. The sample sizes ranged from 80 to 1,662. The majority of the studies (n = 24) had a few hundred participants (104 -726) and six studies used over 1,000 participants. When comparing the 30 scales, the average number of participants was 502.5.

Limitations

While there were 26 papers compiled and 30 scales analyzed, a limitation of this paper is the scope. This paper was limited to the articles found and analyzed based on the available databases. Another limitation of the study is the lack of critical analysis. While each scale was analyzed and organized

into a category and compared, there was no statement of the strengths or weaknesses of the scales. These two stated limitations could serve as directions for future research.

Implications

Implications from this literature review are twofold. An extensive table (Table 1) allows aspects of each scale to be clearly displayed. Using this table allows professionals to easily determine which scale could be of use based on the purpose or attributes they are looking for. Additionally, as Tomlinson (2015) states, the use of scales and the fluidity of the tools between researchers and practitioners alike could work to bridge the gaps in the field and in the existing research and practices.

Implications for Researchers. First, these scales provide a strong starting point if a researcher is working towards creating their own scale. They can use these scales as a model and framework in developing their own tools. Secondly, researchers can examine the research gap in the scales. By assessing each tool and understanding its purpose and function, a researcher can see what is missing and what needs to be added to the personal finance scales. A third implication for researchers aiding in testing new theories. Researchers can see how other researchers incorporated scales and theoretical frameworks and use it as their own theoretical framework.

Implications for Practitioners. First, they can use scales to assess a client’s current behavior and attitudes about personal finance. For example, the risk tolerance scales (Hanna et al., 2001; Lampenius & Zickar, 2005) can be directly used to assess clients risk tolerance levels and provide investment strategies for their clients to choose appropriate investment products within their risk tolerance ranges. Second, they can use the scales for educational purposes and motivational tools to help clients see the results of their changes in behavior. For instance, the financial self-efficacy scale (Lown, 2011) can be used to assess clients’ financial capability in terms of goal achievement and help them better understand their strengths and weakness in terms of money management and point out future directions for enhancing their financial capability and wellbeing. A third implication for practitioners is the ability to modify or combine scales. These scales can be changed or used simultaneously for multiple purposes to best serve their professional needs. Several scales are for measuring financial behaviors (Dew & Xiao, 2011; Edwards, 1993; Ksendzova et al., 2017). Financial advisors may select relevant items from these scales to form a new scale to measure the area that they need to know about their clients. If their major work is to provide investment advice, they may use more items relevant to investments. If their major focus is to offer debt counseling services, they may select more items relevant to borrowing and spending behavior.

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The Role of Financial Advisors in Shaping Investment Beliefs

Blain Pearson, Ph.D., CFP®, AFC®3

Thomas Korankye, Ph.D., CFP®4

Di Qing, Ph.D.5

Abstract

The objective of this study is to examine the association between financial advisor usage and the association with client investment beliefs. An illustrative model is first introduced, establishing a framework for how financial advisors may influence the investment beliefs of their clients. The authors test the association between financial advisor use and investment beliefs with data collected from the 2016 RAND American Life Panel (N = 1,045). The average age of the sample was 56. The findings suggest an association between the influence of financial advisors and their clients’ investment beliefs. The ensuing discussion highlights the need for financial advisors to be aware of their own investment beliefs, attitudes, and behaviors when working with clients. The conclusions orbit around the need for client communication education to be reinforced as a part of the broader financial planning curricula.

JEL classification: D14, G29

Keywords: Financial Advice, Financial Counseling, Financial Planning, Investment Advice, Investment Behavior

3. Department of Finance and Economics, E. Craig Wall Sr. College of Business Administration, Coastal Carolina University, bpearson@coastal.edu

4. Personal and Family Financial Planning, Norton School of Family and Consumer Sciences, The University of Arizona

5. Patterson School of Business, Carolina University

INTRODUCTION

Financial advisors, planners, and counselors (financial advisors) play a critical role in facilitating the achievement of their clients’ financial goals. From planning for their clients’ retirement to effectively managing taxes and developing insurance recommendations, financial advisors engage in impactful interactions with their clients when providing financial advisory services.

As a part of the financial planning process, financial advisors may work with their clients to determine appropriate investment recommendations that meet their clients’ longterm financial goals. As a part of this process, financial advisors consider a broad range of information when working to develop appropriate investment recommendations, such as their clients’ time-horizon, goals, and expected return. In addition, many financial advisors consider their clients’ investing beliefs, attitudes, and perceptions as a part of the development of their financial advice.

An area of growing research interest is the formation of clients’ investment beliefs and the implications of how clients’ investment beliefs affect client behavior. This study adds value to this research effort by examining the role of financial advisors in shaping their clients’ investment beliefs. An illustrative model illustrating the formation process of financial advisors’ investment beliefs is first established. This model illustrates how financial advisors’ financial behaviors are formed. Next, an additional illustrative model is presented, which introduces a robust foundation for how financial advisors play a contributing role in their clients’ investment beliefs. Lastly, the authors empirically test the model with newly introduced data from Choi and Robertson (2020).

BACKGROUND

Investment Beliefs

Investment beliefs have been associated with a plethora of factors, such as financial literacy (Van Rooij et al., 2011), recent corporate scandals (Giannetti & Wang, 2016), risk aversion (Vissing-Jørgensen & Attanasio, 2003), and demographics (Gao, 2019; Pearson, 2020). Research has also suggested that investment beliefs are influenced by the interactions, experiences, and contacts with other individuals through the life course. For example, the influence of parental overt and convert financial beliefs have shown to be formative in an

individual’s beliefs regarding money and investing (Klontz et al., 2011). Moreover, Cude et al. (2006) showed that the financial decisions of an individual’s parents are a key factor in their children’s money and investment beliefs.

Investment beliefs have more broadly been studied in parallel with related financial counseling research. Klontz et al. (2008) coined the term “money scripts” to refer to an individual’s beliefs about money. Their research suggests that money scripts are a predictive factor of investment beliefs. Klontz and Britt (2012) go on to suggest that money scripts have both positive and negative impacts on investment beliefs, an outcome that is dependent on the “type” of money script. Types of money scripts include vigilance scripts, money anxiety scripts, and money worship scripts (Klontz & Britt, 2012; Lawson et al., 2015). Harris et al. (2021) suggest that if individuals understand their money scripts, they can improve their interpersonal communication. They ultimately suggest that this can even lead to improved relationship dynamics.

Financial Advisors and Investment Beliefs

The presence of financial advisors may also influence individuals’ investment beliefs. The use of financial advisors has been associated with participation in equity markets (Georgarakos & Inderst, 2014). Linnainmaa et al. (2021) show that a household’s likelihood of owning investment assets increases by 59.2% when households utilize financial advisors. Moreover, Kirchler et al. (2020) shows that financial advisors invest their personal assets in a similar manner as their clients’ investment strategy.

Gerhardt and Hackethal (2009) add to the aforementioned research by analyzing trading data from individuals before and after receiving financial advice. They show that individuals who begin working with a financial advisor increase the likelihood that they will place less risky and speculative trades. Utilizing a survey of over 200 financial professionals, Grable et al. (2020) found that financial advisors with more experience are more likely to recommend portfolios with higher ratios of investment holdings when compared to younger financial advisors.

Research Contributions

A significant limitation in the current literature is the issue of identification. Generally, the current literature has attempted to empirically address the influence of financial advisors on their clients’ investments through indirect methods, such as examining the correlation between utilizing a financial advisor and equity market participation (Georgarakos & Inderst,

2014; Linnainmaa et al., 2021). These methods do not directly evaluate the underlying and veritable client investment belief. An issue of identification is created when an individual seeks the advice of a financial advisor with the goal of investing heavily into equities. Identification issues are seen in other areas of financial planning research, such as the conclusions drawn in studies examining financial advice search and the associations with income and net worth (Hanna, 2011, Joo & Grable, 2001), education planning (Pearson & Lee, 2022; Salter et al., 2010), marital planning (Cummings & James, 2014; Pearson et al., 2021), trust (Sholin et al., 2021), housing (Pearson & Kalenkoski, 2022; Pearson & Lacombe, 2021), and mutual fund selection (Jones et al., 2005; Ramasamy & Yeung, 2003). This study adds value to the current literature in two overarching ways. First, this is the first study, to the authors’ knowledge, that introduces an illustration showcasing how financial advisors can affect the investment beliefs of their clients. Secondly, this study provides empirical evidence of the connection between financial advisors and the investment beliefs of their clients.

BACKGROUND

Financial Advisor Communication

Figure 1 provides a general frameword that illustrates how financial advisors form their communication during an equity event, such as the early 2000s “dot-com” bubble, the 2007-2009 U.S. financial crisis, or the 2019-2020 COVID-19-related equity market decline. This illustration shows how financial advisors’ investment beliefs are formed as a byproduct of financial advisors’ own biases, experiences, firm policies, educational attainment, and other beliefs. It is important to note that the weight of each influence will likely vary among financial advisors.

The formation of financial advisors’ investment beliefs is a paramount consideration when examining the connection between financial advisors’ investment beliefs and the interpersonal communication they engage in with their clients, especially when an equity event occurs. When an equity event occurs, an opportunity presents itself for financial advisors’ interpersonal communication to affect their clients’ investment beliefs. Consequently, a link is created between financial advisors’ investment beliefs and their interpersonal interactions.

Financial Advisor Communication and Investment Beliefs

Figure 2 builds on Figure 1, showcasing how the interpersonal communication between financial advisors’ and their clients forms as an input element in the formation of new client investment beliefs. Clients have varying investing biases, experiences, educational attainment, other beliefs, and past equity market experiences. These influences come together to form clients’ investment beliefs. When an equity event occurs, clients may engage with their financial advisors to seek advice on how to manage their investments during times of market volatility.

When clients engage in interpersonal communication with their financial advisors during equity events, clients are influenced by their financial advisors’ investment beliefs. The influence of these investment beliefs can be present in both financial advisors’ verbal and nonverbal communication. Consequently, financial advisors’ interpersonal communication with clients during equity events results in an additive factor in the formation of new client investment beliefs.

As noted by Kolb (1984), experiences are transformed into beliefs through post-event reflection, particularly when the severity of event increases (Barnett & Pratt, 2000; Weick et al., 2005). Financial advisors who reference equity events in ongoing communications with their clients are engaging in post-event reflection. As a result, an additional opportunity for financial advisors to influence the investment beliefs of their clients presents itself when financial advisors engage in postevent reflection with their clients.

METHODOLOGY Data

Data collected from a survey circulated in the RAND American Life Panel (ALP) are utilized to test the association between the importance of financial advisors’ advice and client investment beliefs. The original purpose of the data collection was to study the determinants of portfolio investment allocation. The survey took place in December of 2016 and survey participants were paid based on anticipated survey completion time. Choi and Robertson (2020) provides a further description of the data and the data collection process. This study uses weighted data from the sample weights provided by the 2016 RAND ALP. The sample size is 1,045.

Investment Beliefs

Survey participants are first asked, “How important are the following factors in determining the percentage of your investable financial assets that is currently invested in stocks?” For each factor, respondents can answer: 1 (Not important at all), 2 (A little important), 3 (Moderately important), 4 (Very important), and 5 (Extremely important). This study examines two equity event investment beliefs.

The first investment belief examined comes from the question, “ The feelings, attitudes, and beliefs about the stock market I’ve gotten from living through stock market ups and downs” (Ups & Downs). The second investment belief examined comes from the question, “The feelings, attitudes, and beliefs about the stock market I’ve gotten from my personal experiences of investing in the stock market” (Personal Experiences).

Influence of Financial Advisor (FA)

To assess whether financial advisors exert influence on their clients’ investment beliefs, this study examines whether survey participants consider financial advisors’ advice important in determining their investment allocation (FA Influence). Survey participants are asked, “How important are the following factors in determining the percentage of your investable financial assets that is currently invested in stocks?” The FA Influence variable is derived from the responses to the statement, “Advice from a professional financial advisor I hired.” Respondents could have answered: 1 (Not important at all), 2 (A little important), 3 (Moderately important), 4 (Very important), and 5 (Extremely important). Missing values are list-wise deleted.

Controls

The control variables utilized in this study include educational attainment, age, gender, racial identification, marital status, income, and investable assets. Educational attainment has been shown to influence financial literacy (Huston, 2010), and financial literacy has been shown to influence investment beliefs (Mandell & Klein, 2009). Age also is an important variable to consider because as individuals age, they enter different periods of their life-course. As individuals enter those periods, their beliefs, attitudes, and perceptions may restructure to match their current stage of life-course. Demographic background, such as gender and race, have been shown to influence investment perceptions (Bhavani & Shetty, 2017; Pearson, 2020). Investment beliefs, such as risk tolerance, have been shown to be influenced by marital status (Pearson & Guillemette, 2020; Yao & Hanna, 2005). Varying levels of income

and investable assets are expected to produce new investment perspectives, and, thus, are also included as controls.

Empirical Model

To examine the role of financial advisors in shaping the investments beliefs of their clients, two ordered probit regression models are estimated:

Ups & Downsi* = β0 + β1 FAInfluencei + βj DVj + εi

Ups & Downs i = { 1 if Ups & Downsi* >0 } 0 if Ups & Downsi* ≤0

Personal Experiencesi* = β0 + β1 FAInfluencei + βj DVj + εi

Personal Experiencesi = { 1 if Personal Experiencesi* >0

0 if Personal Experiencesi * ≤0

where Ups & Downs* and Personal Experiences* are latent variables, while Ups & Downs and Personal Experiences are the observed measures of investment beliefs from the data.

To capture the influence financial advisors’ advice and client investment beliefs, the FAInfluence variable takes the form of a dichotomous variable by assigning a value of “0” for all 1 responses and a “1” for all 2, 3, 4, and 5 responses to the financial advisor importance question.

The matrix DVj contains the controls utilized in the models. The controls include whether the survey participant has a 4-year college degree, a continuous measure for age, whether the survey participant is male, whether the survey participant is white, whether the survey participant is married, and categorical measures for income and wealth. The categorical measure for income utilizes the reference category $0-$9,999, to which values of $10,000 - $24,999, $25,000 - $39,999, $40,000 - $74,999, and $75,000 + are compared. The investable assets variable was developed from the question, “What is the value of all your investable financial assets?” The categorical measure for wealth utilizes the reference category $0 - $999, to which values of $1,000 - $9,999, $10,000 - $49,999, $50,000$99,999, and $100,000 + are compared.

Each of the models are estimated via maximum likelihood. Average marginal effects are calculated to determine the magnitude of the effects. The error term is assumed to follow the standard normal distribution.

RESULTS

Summary Statistics

Table 1 provides the summary statistics of the sample. The averages for the level of importance for the Ups & Downs investment belief are as follows: 23% “Not Important,” 19% “A Little Important,” 31% “Moderately Important,” 19% “Very Important,” and 8% “Very Important.” The averages for the level of importance for the Personal Experiences investment belief are as follows: 19% “Not Important,” 17% “A Little Important,” 33% “Moderately Important,” 21% “Very Important,” and 10% “Very Important.”

Other summary findings reveal that 62% of respondents ranked the FA Influence at “A Little Important” or greater. 50% of the sample holds at least a 4-year college degree, 47% of the sample are male, 82% are white, 58% are married, and the average age is 57. For the categorical income variable, 6% have income between $0-$9,999, 15% have income between $10,000 - $24,999, 22% have income between $25,000$39,999, 20% have income between $40,000 - $74,999, and 37% have income over $75,000. For the categorical investable assets variable, 14% have investable assets between $0-$999, 12% have investable assets between $1,000 - $9,999, 16% have investable assets between $10,000 - $49,999, 11% have investable assets between $50,000 - $99,999, and 46% have investable assets over $100,000.

Main Econometeric Results

Table 2 presents the average marginal effects from the Ups & Downs ordered probit regression. For brevity, only the Not Important and Extremely Important average marginal effects are reviewed. For the Ups & Downs investments belief regression, the FA Influence variable was associated negatively with the Not Important response with an average marginal effect of -0.2010 (p < 0.001). For the Ups & Downs investments belief regression, the FA Influence variable was associated positively with the Extremely Important response with an average marginal effect of -0.1065 (p < 0.001).

Table 3 presents the average marginal effects from the Personal Experiences ordered probit regression. For the Personal Experiences investments belief regression, the FA Influence variable was associated negatively with the Not Important response with an average marginal effect of -0.1434 (p < 0.001). For the Personal Experiences investments belief regression, the FA Influence variable was associated positively with the Extremely Important response with an average marginal effect of 0.1011 (p < 0.001).

Other Econometeric Results

Other results from the regression analysis provide other associations to note. For brevity, only the Not Important and Extremely Important average marginal effects are reviewed. For the Ups & Downs ordered probit regression, being male was associated negatively with the Not Important response with an average effect of -0.0479 (p < 0.05). Being male was associated positively with the Extremely Important response with an average marginal effect of 0.0254 (p < 0.05).

For the Personal Experiences ordered probit regression, being male was associated negatively with the Not Important response with an average effect of -0.0399 (p < 0.05). Being male was associated positively with the Extremely Important response with an average marginal effect of 0.0281 (p < 0.05). A one-year increase in age was associated negatively with Not Important with an average effect of -0.0014 (p < 0.05). A one-year increase in age was associated positively with the Extremely Important response with an average marginal effect of 0.001 (p < 0.05).

DISCUSSION

Discussion of Key Results

This study analyzed the role of financial advisors in the formation of their clients’ investment beliefs utilizing newly introduced data from Choi and Robertson (2020). The empirical findings suggest that considering the advice of a financial advisor as “influential” in making investment decisions is associated with an individual’s investment beliefs. Other results showed that males compared to females, generally, consider the “ups-and-downs” of the market and their own personal investing experience when investing. Increases in age showed similar results for considering personal experiences when investing, however, the results for increases in age and the consideration of the “ups-and-downs” of the market when investing did not result in a statistically significant association.

Implications

Financial advisors play a critical role in facilitating the achievement of their clients’ financial goals. One of the ways that financial advisors act as facilitators is in their input in shaping their clients’ investment beliefs, attitudes, and behaviors. Financial advisors should be aware of their own investment beliefs, attitudes, and behaviors and the impact they have in influencing their clients’ beliefs, attitudes, and behaviors. Moreover, financial advisors should understand how

their beliefs are both overtly and covertly communicated when working with their clients.

As noted by Kolb (1984), experiences are transformed into beliefs through post-event reflection, particularly when the severity of the event increases (Barnett & Pratt, 2000; Weick et al., 2005). When financial advisors reflect on historic equity market events with their clients, such as the early 2000s dotcom bubble, the 2007-2009 financial crisis, or the 2019-2020 COVID-19-related investment downtown, financial advisors should consider how the reflection of those events with their clients play a role in shaping their clients’ beliefs. Training in financial counseling and communication can offer support to financial advisors in developing greater self-awareness. Furthermore, trainings designed to teach self-reflection and self-discovery provides an opportunity for financial advisors to assess their own beliefs and how those beliefs can be communicated in their work with clients. Trained financial counselors and financial therapists also have a unique opportunity to provide financial advisors with these trainings.

An additional avenue for training financial advisors lies in financial planning education programs. Financial planning education programs that incorporate financial counseling and communication as a part of their core curricula provide current and future financial advisors with an opportunity to further explore and develop their understanding of their investment beliefs, attitudes, and perceptions.

Limitations

Despite the findings and the contributions, an empirical limitation to note is that the data only provide a measure assessing the value of survey participants’ investable assets. The data did not provide a measure assessing the net worth of the survey participants. As noted by Klontz and Britt (2012), net worth has been associated with money beliefs, such as money status and money worship beliefs. Although investable assets and net worth are likely correlated, a measure for net worth would have provided a better control. An additional limitation is data availability. The data are cross-sectional. Thus, the authors have an inability to establish evidence of causality.

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Client

Client

Figure 1. Financial Advisor Communication Model

Figure 2. Client Investment Belief Formation during Equity Events

Table 1. Summary Statistics of Sample

Table 2. Average Marginal Effects from Ordered Probit Regression: Ups & Downs

Significance is defined as follows: * significant at p < 0.05; ** significant at p < 0.01; *** significant at p < 0.001 Data collected from the RAND American Life Panel (ALP)

Table 3. Average Marginal Effects from Ordered Probit Regression: Personal Experiences

Significance is defined as follows: * significant at p < 0.05; ** significant at p < 0.01; *** significant at p < 0.001

Data collected from the RAND American Life Panel (ALP) 2016 N = 1,045

APPENDIX

All questions are lead with, “How important are the following factors in determining the percentage of your investable financial assets that is currently invested in stocks?”

Dependent Variables

Ups & Downs

The feelings, attitudes, and beliefs about the stock market I’ve gotten from living through stock market ups and downs (whether or not I was invested in stocks at the time).

1 Not important at all

2 A little important

3 Moderately important

4 Very important

5 Extremely important

Personal Experiences

The feelings, attitudes, and beliefs about the stock market I’ve gotten from my personal experiences of investing in the stock market.

1 Not important at all

2 A little important

3 Moderately important

4 Very important

5 Extremely important

Primary Explanatory Variable

FA Influence

Advice from a professional financial advisor I hired.

1 Not important at all

2 A little important

3 Moderately important

4 Very important

5 Extremely important

Investment Advisor Use and Stock Market Return Expectations

Miranda Reiter, Ph.D., CFP®6

Martin C. Seay, Ph.D., CFP®7

Abstract

This study explored the association between receiving investment advice from a financial professional and investors’ sentiment about expected stock market outlook. Using data from the 2015 National Financial Capability Study, respondents were identified as either being pessimistic, realistic-cautious, realistic-optimistic, or highly optimistic about future stock market performance. Data were provided relative to an investors’ use of financial professionals: being self-directed, using some investment advisor help, or relying upon an investment advisor’s help. Results show that investors using full or some investment advisor help were more likely to expect future stock market returns to align with historical averages. The key implication is that working with an investment advisor is associated with clients having a more realistic view of future stock market returns.

Key Words: Investor sentiment, investor optimism, investment advice seeking, overconfidence

6. Assistant Professor, Texas Tech University, School of Personal Financial Planning, mreiter@ttu.edu

7. Department Head and Professor, Kansas State University, Personal Financial Planning, mseay@ksu.edu

INTRODUCTION

Clients have different expectations and perceptions about their finances, particularly when it comes to their beliefs about the stock market. Depending on individual, behavioral, and psychological characteristics, some clients have pessimistic views about future possible stock market returns, while others are more optimistic. Investor sentiment, the tendency to hold beliefs about stock market performance (Baker & Wurgler, 2007), has been found to have profound influence on the decision-making process for investors, their portfolios’ performance, and the performance of the stock market itself (Chung et al., 2012; Fisher & Statman, 2000; Schmeling, 2009). Investor sentiment can be reflected in stock valuation (Shefrin & Statman, 1994) and unexpected or irrational valuations, which cannot be explained by traditional models and theories, can be influenced by investor sentiment (Barberis et al., 1998; DeBondt & Thaler, 1985).

Psychological factors that influence investor decisionmaking include stock market optimism and confidence. While optimism can have a positive effect on stock returns, overoptimism has been associated with negative impacts on investor stock performance (Puri & Robinson, 2007). Overoptimism is believing that one is more likely, or at less risk, to obtain a favorable outcome regardless of the likelihood of the favorable event occurring (Altman, 2014). There is evidence that highly optimistic investors make inferior financial decisions (Puri & Robinson, 2007).

Allowing a third-party, such as a financial advisor, to play a role in one’s financial and investment decisions can reduce overoptimism (Altman, 2014). Researchers have found that working with a financial professional can curb poor behavioral and financial decision-making (Grable & Joo, 2001; Marsden et al., 2011). Park and Yao (2016) posited that those who worked with financial professionals had more consistent financial risk attitudes and behaviors. In addition, those working with financial professionals were less likely to make impulsive decisions (Park & Yao, 2016). Financial planner use has led to increased financial confidence (Salter et al., 2010) and better investment outcomes (Lei & Yao, 2016). However, it is not evident whether using an investment advisor is associated with a more realistic view of future stock market returns. As such, the purpose of this paper is to investigate the association between working with an investment advisor and investors’ outlook on future market returns.

LITERATURE REVIEW

Factors Related to Seeking Financial Advice

According to the financial help-seeking framework (Grable & Joo,1999), differences exist between consumers who seek financial advice and those who do not. Research suggests that objective financial knowledge and subjective financial knowledge play a role (Grable & Joo, 1999; Grable & Joo, 2001). There is a positive association between objective financial knowledge, purchasing stocks (Van Rooij et al., 2011), and seeking financial assistance (Collins, 2012; Gentile et al., 2016; Grable & Joo, 2001; Seay et al., 2016). At the same time, the literature points to a positive association between low financial literacy and not seeking financial advice (Grable & Joo, 2001; Kramer, 2016). Therefore, those who have a good understanding of financial concepts are more likely to seek advice and, when they do, evidence shows that they stand to obtain greater benefits from that advice (Moreland, 2018).

Consumers who are older (Elmerick et al., 2002; White & Heckman, 2016), have higher incomes (Alyousif & Kalenkoski, 2017) and higher net worths (White & Heckman, 2016) are more likely to seek financial advice. In addition, more educated individuals (White & Heckman, 2016) and those with higher risk tolerance levels (Fan, 2020) are more likely to seek financial advice. Additional factors with a positive relationship with financial advice seeking include financial confidence (Fan, 2020; Kramer, 2016), financial stress (Lim et al., 2014), and trust (Martin et al., 2014; Reiter et al., 2021). Several studies have supported the idea that women are more likely to seek financial advice (Elmerick et al., 2002; Lim et al., 2014) and Black investors are more likely to seek financial advice (Hanna, 2011; Elmerick et al., 2002; White & Heckman, 2016).

Benefits of Working with A Financial Professional

Investors working with financial advisors have better portfolio diversification (Marsden et al., 2011), asset allocation (Winchester et al., 2011), and performance (Lei & Yao, 2016). Financial advice creates a sustained long-term investment outlook (Winchester et al., 2011) and leads to improved saving behavior (Kim et al., 2018), sustainable retirement spending (Harlow et al., 2020), and more appropriate income replacement (Harlow et al., 2020).

While professional advice is associated with tangible financial improvements, it also plays a role in improving the psychological well-being (Kim, 2003), increasing financial confidence (Marsden et al., 2011; Salter et al., 2010), and

boosting financial satisfaction (Schmeiser & Hogarth; 2013). Investors who worked with a financial planner had more consistent risk attitudes and made less impulsive financial decisions (Park & Yao, 2016). Working with an investment advisor specifically has been shown to prevent emotional investing (Maymin & Fisher, 2011).

Investor Sentiment

Longo (2014, p. 507) described investor sentiment as the “feeling and emotion of investors”. More specifically, investor sentiment can be defined as the beliefs that investors have about future cash flows and investment risk which are influenced by market performance over a given period (Winchester et al., 2011). Investor optimism and pessimism, an aspect of investor sentiment, are consumer biases related to one’s psychological disposition that can explain a consumer’s investment decisions (Fabre & François-Heude, 2009). These terms can be defined as degrees of subjective overestimation or underestimation of an event occurring, regardless of the objective probability that the event will happen or of the consumer’s capability to make the event happen (Fabre & François-Heude, 2009). Optimism has been associated with positive actions such as better physical health, higher levels of coping, relationship satisfaction and life satisfaction, among other things (Asebedo & Seay, 2015; Yalçın, 2011). Optimism has also been attributed to positive financial outcomes (Capps et al., 2016; Lim et al., 2014).

While some optimism appears to be good for economic decision-making, too much optimism can result in suboptimal financial decisions (Puri & Robinson, 2007). Dawson (2017) found that optimism leads to an overestimation of success in entrepreneurship, while Puri and Robinson (2007) uncovered that extreme optimists were worse off than mild optimists or those who had more realistic expectations. Investor optimism can lead individuals to invest in specific stocks, rather than in well-diversified portfolios, and can lead to imprudent financial decision making (Puri & Robinson, 2007; Yeske & Buie, 2014). Highly optimistic individuals believe that they are less likely to fail than others, which increases their likelihood to take excessive financial risks compared to those who are more realistic (Altman, 2014; Yeske & Buie, 2014). However, there is evidence that these biases, when detrimental to one’s finances, can be mediated through financial planner assistance and education (Asebedo & Seay, 2015).

THEORETICAL FRAMEWORK

The Theory of Bounded Rationality (Simon, 2000) indicates that individuals are confined or ‘bounded’ in their ability to absorb and process information. While consumers attempt to make rational decisions, they are constrained in their ability to do so due to time or resource constraints. Specifically, an individuals’ decision-making process may be limited by their knowledge, access to information, ability to process information, and having the necessary time or resources available to process information (Simon, 2000). To compensate for these constraints, biases are formed, and decisions are made based on restricted beliefs or information. There is significant evidence that bounded rationality can play a role in financial decision making and attitudes (Altman, 2014; Robb et al., 2015).

The use of a financial professional provides individuals the opportunity to rent expertise. This expertise should mitigate the effects of bounded rationality, leading to more consistent and rational behavior and expectations. Rational expectations would be tied to understanding historical trends, as well as a realization that an individual’s ability to obtain an above average return is limited. Consequently, a rational individual would tend to expect long-term returns to align with historical averages and for their portfolio to perform similarly to the market (Altman, 2014).

Based on the bounded rationality framework and relevant literature, the following hypothesis is proposed.

Hypothesis 1: Financial advice (full investment advisor help or some investment advisor help rather than self-directed) from a broker will be associated with a higher likelihood of investors being realistic about the performance of the stock market.

Data

METHODOLOGY

The 2015 National Financial Capability Study (NFCS), provided by the FINRA Investor Education Foundation, surveyed roughly 27,000 individuals from across the United States for its state-bystate survey. The 2015 version saw the addition of the investor survey, which captures additional data from 2,000 respondents who hold investments outside of a retirement account. For the current study, the data in the state-to-state survey and the investor survey were combined. The key independent variable used in this study is the use of financial professionals

(e.g., investment advisor/broker use). Due to changes in this question after 2015, these data were used to best investigate the question of interest.

Dependent Variables

Investor stock market outlook. Investor stock market outlook was measured using a question which asked, “What do you expect the approximate average annual return of the S&P 500 stock index to be over the next 10 years (without adjusting for inflation)? Respondents were given eight choices: (a) Less than 0%; (b) 0% to 4.9%; (c) 5% to 9.9%; (d) 10% to 14.9%; (e) 15% to 19.9%; (f) 20% or more; (g) don’t know; or (h) prefer not to say. This was consolidated to create one variable with four categories: pessimistic (0 – 4.9%), cautious-realistic 5% to 9.9%, realistic-optimistic (10%-14.9%), and highly optimistic (over 14.9%). These categories were created based on expected rates of return based on long term historical averages (Bogle, 2016). If respondents did not respond, did not know, or preferred not to say, the observation was dropped.

Independent Variables

Variable of Interest

The key independent variable in this study is related to the use of a professional to make investment decisions. The question used from the investor survey was as follows, “Which of the following best describes your current investment style?” Responses included, (a) “I make all my investment decisions on my own without the help of a broker or professional adviser” (self-directed); (b) “I make some decisions on my own and some with the help of a broker or professional adviser” (some professional help) and (c) “I let my broker or professional adviser make all my decisions for me (full professional help).” Responses that included missing, prefer not to say, or don’t know were excluded from the analysis.

Key Control Variables

The Theory of Bounded Rationality indicates an individual’s ability to form rational expectations is informed by their knowledge, ability, and resource constraints. Consequently, a number of key variables may be controlled for. These variables include (a) objective investment knowledge, (b) subjective investment knowledge, (c) formal financial education, (d) comfort using investment products, and (e) risk tolerance.

The investor survey provides ten knowledge questions tied specifically to investments. Three of these questions were found to be highly correlated with seven of the other questions and

therefore were not used in the study. The remaining seven questions were summed to create an objective investment knowledge scale, with the highest possible score being seven and the lowest possible score being zero (Cronbach alpha = 0.67). These questions measured respondents’ knowledge of stocks and bonds, stock market risk, average stock market returns, municipal bonds, margin, and short sells. Respondents with missing data or non-responses were not included in the analysis. It is expected that objective investment knowledge is positively related to rational market and portfolio expectations. Similarly, the investor survey contains a question specifically related to subjective investment knowledge. Respondents were asked “On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall knowledge about investing?”. It is expected that subjective investment knowledge is positively related to rational market and portfolio expectations.

Financial education was measured using the state-by-state survey question, “Was financial education offered by a school or college you attended, or a workplace where you were employed?” Answer choices included “yes and participated”, “yes and did not participate”, or “no”. If respondents chose not to say or did not know, their responses were removed. It is expected that having received financial education is positively related to rational market and portfolio expectations.

Investment comfort, a proxy for investment experience, was measured using the investor survey question, “How comfortable are you when it comes to making investment decisions?”

Responses were offered on a Likert-type scale ranging from one to 10.

Investment risk tolerance was measured using the investor survey question, “Which of the following statements comes closest to describing the amount of financial risk that you are willing to take when you save or make investments?”

Respondents were given six choices: (a) take substantial financial risks expecting to earn substantial returns; (b) take above average financial risks expecting to earn above average returns; (c) take average financial risks expecting to earn average returns; (d) not willing to take any financial risks, (e) don’t know; and (f) prefer not to say. For this study, the first two choices were combined to create the “high risk category”. The third and fourth options were operationalized as the medium and low risk categories, respectively. Answer choices “don’t know” and “prefer not to say” were excluded from the analysis.

Other Control Variables

Control variables included age, income, employment status, education, gender, and race. In the 2015 NFCS, age is composed of five categories including, ages 18 to 34, 35 to 44, 45 to 54, 55 to 64 and over 64. Income is composed of five groups with less than $35,000 being the lowest income group and over $100,000 being the highest income group. Education has four categories including less than high school, some college, college degree, and graduate degree or higher. Gender is described as female and male and race is categorized into two groups in the dataset, White and non-White.

Analytic Methods

The purpose of this paper is to understand the relationship between the use of a financial professional and market expectations. A multinomial logistic regression was performed to identify the factors that influenced being pessimistic, cautious-realistic, realistic-optimistic, and highly optimistic about future returns.

RESULTS

Table 1. Sample Characteristics

Descriptive Statistics

Table 1 provides an overview on the sample, which included 1,545 observations. Half of the respondents were cautiousrealistic regarding their expectations of returns over the next 10 years. A similar number of respondents presented pessimistic and realistic-optimistic beliefs about market returns, with each category representing about 19% of the sample. The remaining 11% of respondents were in the highly optimistic category.

Regarding the use of an investment professional, most of the sample was self-directed (44%) or used some broker help (42%) with the remainder using full broker help (14%). The sample had an average subjective investment knowledge score of 5.10 (out of 7) and objective investment knowledge score of 3.77 (out of 10). These may indicate a general misalignment of objective knowledge and investors perception of knowledge. About 31% of the sample had received financial education in an educational setting or in the workplace. Investors had a relatively high comfort level in making investment decisions (7.37) and were mostly split between having medium risk tolerance (48%) and high-risk tolerance (45%).

Most of the sample was White (80%), over 55 years old (~51%), male (57%), had incomes over $75,000 per year (58%), and had a college degree or higher (72%), In addition, about 45% of respondents were full-time workers and about 30% of them were retired.

Multinomial Results: Investor Stock Market Outlook

To predict a respondent’s stock market optimism, a multinomial logistic regression model was used. Results are shown in Table 2.

Cautious-Realistic (5 - 9.9%) versus Pessimistic (0 - 4.9%)

Compared to being self-directed, investors’ odds of being cautious-realistic versus pessimistic increased when using investment advice. Investors had 1.904 times the odds of being cautious-realistic when using a full broker and 1.611 times the odds when using some broker help.

There were significant additional results related to key variables informed by the theory of bounded rationality. As an investor’s objective investment knowledge increased, they were more likely to be cautious-realistic than pessimistic (OR 1.083).

Compared to those who were 64 and older, respondents between ages 35 and 44 were more likely to be cautiousrealistic than pessimistic. When compared to respondents with low risk tolerance, respondents with high risk tolerance were more likely to be cautious-realistic rather than pessimistic. Compared to those who were unemployed, respondents working full-time were more likely to be cautious-realistic versus pessimistic. Subjective investment knowledge, financial education, comfort, income, educational level, gender and race were not significant.

Realistic-Optimistic (10-14.9%) versus Pessimistic (0 - 4.9%).

If an investor received investment advice rather than being self-directed, their odds of being realistic-optimistic versus pessimistic increased. When using full or some professional investment advice, respectively, investors had 2.125 and 1.561 times the odds of being realistic-optimistic versus pessimistic about future stock market returns.

As an investor’s objective investment knowledge increased, they were less likely to be realistic-optimistic versus pessimistic (OR .838). However, they were more likely to be realisticoptimistic as their subjective investment knowledge (OR 1.191) and comfort with making investment decisions increased (OR 1.193). Compared to those who were 64 and older, respondents between ages 35 and 44 were more likely to be realisticoptimistic than pessimistic. When compared to respondents with low risk tolerance, respondents with high risk tolerance had 2.20 times the odds of being realistic-optimistic rather than pessimistic. Compared to those who were unemployed, respondents working part-time were more likely to be realisticoptimistic. Financial education, educational level, income, gender, and race were not significant.

Highly Optimistic (>15%) versus Pessimistic (0 - 4.9%)

In this comparison, professional investment advice was not significant. However, objective investment knowledge, subjective investment knowledge, comfort, high risk tolerance, and income were significant. As objective investment knowledge and comfort with investments increased, the odds of being highly optimistic versus pessimistic decreased. However, there was an opposite effect with subjective investment knowledge; as it increased, the odds of being highly optimistic versus pessimistic increased. When compared to respondents with low risk tolerance, respondents with high risk tolerance had 2.404 times the odds of being highly optimistic rather than pessimistic. Compared to those earning over $100,000 per year, respondents earning between $75,000 - $100,000 were more likely to be highly optimistic than pessimistic. Financial education, educational level, age, employment status, gender, and race were not significant.

Cautious-Realistic (5 - 9.9%) versus Highly Optimistic (>15%)

Financial advice was not found to be related to whether a respondent was cautious-realistic versus highly optimistic.

When comparing respondents who were cautious-realistic to those who were highly optimistic, objective and subjective investment knowledge, comfort in investing, income, employment status and educational level were significant. Specifically, as objective knowledge increased, the odds of being cautious-realistic versus highly optimistic increased (OR 1.432). On the other hand, as subjective investment knowledge (OR .752) and comfort with investing (0.761) increased, the odds of being cautious-realistic decreased. Compared to those earning over $100,000 per year, respondents earning between $75,000 - $100,000 were less likely to be cautious-realistic versus highly optimistic (OR 0.611). Retirees had 2.402 times higher odds of being cautious-realistic than the unemployed. Those with college degrees were more likely to be cautiousrealistic than highly optimistic (OR 1.669) when compared to respondents with only some college. Financial education, age, risk tolerance, gender, and race were not significant.

Realistic-Optimistic (10-14.9%) versus Highly Optimistic (>15%)

When comparing realistic-optimistic investors to highly optimistic investors, only comfort with investing, income, and employment status were significant. None of the other variables, including the use of a professional, were significant. As comfort with investing increased, the odds of

being realistic-optimistic versus highly optimistic decreased (OR 0.829). Compared to those earning over $100,000 per year, respondents earning between $75,000 - $100,000 were less likely to be realistic-optimistic versus highly optimistic (OR 0.528). When comparing retirees to those who were unemployed, retirees had higher odds of being realisticoptimistic versus highly optimistic (OR 2.213).

Realistic-Optimistic (10-14.9%) versus CautiousRealistic (5 - 9.9%)

Financial advice was not related to differences in individuals that were realistic-optimistic and cautious-realistic. Objective and subjective investment knowledge and educational level were significant factors when comparing the odds of being realistic-optimistic to the odds of being cautious-realistic. As objective investment knowledge increased, the log odds of being realistic-optimistic versus cautious-realistic decreased by 0.256. As subjective investment knowledge increased, the odds of being realistic-optimistic versus cautious-realistic increased by 1.190 times. Respondents with bachelor’s (OR 0.649) and graduate degrees (OR 0.616) had lower odds of being realisticoptimistic versus cautious-realistic. Financial education, comfort in making investment decisions, age, risk tolerance, income level, income, gender, and race were not significant.

Table 2. Multinomial Logit Results - Investor Stock Market Outlook

Note. OR = odds ratio

Investor Stock Market Outlook Ranges: Pessimistic = 0 - 4.9%; Cautious Realistic = 5 - 9.9%; Realistic-Optimistic = 10 - 14.9% ; Highly Optimistic = 15% or greater

*** is significant at the 1 percent level; ** is significant at the 5 percent level; and * is significant at the 10 percent level.

Table 2. Multinomial Logit Results - Investor Stock Market Outlook (Continued)

Note. OR = odds

Realistic-Optimistic = 10 - 14.9% ; Highly Optimistic = 15% or greater

*** is significant at the 1 percent level; ** is significant at the 5 percent level; and * is significant at the 10 percent level.

DISCUSSION & IMPLICATIONS

The purpose of this study was to investigate the association between professional investment advisor and investors’ outlook on the stock market. Results showed that those who had some form of professional assistance when making financial decisions, were more likely to be cautious-realistic and realistic-optimistic about future stock market performance. The key implication is that working with a financial professional rather is associated with having a more realistic view of future stock market returns.

One explanation for this relationship is that investors who work with professionals have access to education and coaching about the financial markets. For example, some clients are not aware of the average historical performance for different asset classes and as a result, do not understand what to expect for long-term returns (Evensky et al., 2011). Financial professionals f-provide education related to market volatility and market performance as a part of their financial planning process. This type of education is beneficial to the client and to the financial professional, as realistic expectation setting makes the relationship more stable in times of volatile markets (Evensky et al., 2011; Gibson, 2013). This is because clients’ expectations

are realistic they can better understand investment strategies and associated performance in their own portfolios. When clients are coached and educated on important topics, there is the possibility to improve client-advisor communication and strengthen the professional bond between client and advisor (Dubofsky & Sussman, 2010).

Investor knowledge was also important in determining investor outlook in the current study. Those who were more knowledgeable about investment concepts were more likely to have realistic expectations of future stock market performance. This finding complements previous research linking financial advice and objective financial knowledge (Gentile et al., 2016), although Kramer (2016) did not find this association. Hirschleifer (2001) asserted that many cognitive biases, such as investor optimism, are more pronounced in consumers with low cognitive abilities when compared to consumers with high cognitive abilities. As such, respondents with lower levels of objective investment knowledge are more bound in their ability to adequately project future market performance. On the other hand, when assessing subjective investment knowledge, results show that there is a correlation between rating oneself highly and being highly optimistic about the market. Having high subjective financial knowledge can be

interpreted as being confident or even overconfident. Research has shown that higher levels of subjective financial knowledge is associated with seeking financial help less often (Gentile et al., 2016; Kramer, 2016) and a higher likelihood to undertake risky financial behaviors (Tokar Assad, 2015).

Comfort, which can be interpreted as another proxy for confidence, was also associated with the investor being more likely to have a highly optimistic outlook of the market. Interestingly, investors who have high confidence are less likely to seek financial advice compared to other investors (Broekema & Kramer, 2021; Lewis, 2019) and are often selfdirected (Hsu, 2021). In addition, overconfidence is often seen as irrational because it has been linked to significant losses (Pitters & Oberlechner, 2014), lack of diversification (Broekema & Kramer, 2021) and excessive trading (Barber & Odean, 2000). In addition, high risk tolerance, which is sometimes attributed to higher financial knowledge (Grable, 2016), rather than low risk tolerance, was also an indicator of being less likely to have a pessimistic view of the stock market. Research shows that those who have higher risk aversion have lower expectations about stock market performance (Lee et al., 2015). High risk tolerance, rather than low risk tolerance, was also attributed to an investor believing that their portfolio would outperform the market. While age was not statistically significant when considering investor outlook on the market overall, results showed that being in a younger age cohort decreased the likelihood of investors believing that their own portfolio would outperform the market.

LIMITATIONS

There are a few limitations with this study. The dependent asked investors, “What do you expect the approximate average annual return of the S&P 500 stock index to be over the next 10 years (without adjusting for inflation)?” They were provided with many choices, but based on historical data, either 5% to 9.9% or 10% to 14.9% would be a realistic answer given that sources typically report a figure between 9-13% for the S&P 500 average. However, both responses include answers which are less accurate. For example, 5%, which could be considered low and pessimistic, or 14.9%, which could be considered high and highly optimistic, do not best reflect past average performance of the S&P 500. Because these answer choices were precategorized in the dataset, there was not much flexibility in determining the categories. Future research may use different data to identify investors’ perceptions of future stock market returns more accurately.

CONCLUSION

Using the 2015 National Financial Capability state-by-state and investor surveys, this study examined the associations between working with an investment advisor and investors’ outlook on the stock market. Results showed that investors who used an investment advisor when making their financial decisions were more likely to be realistic about stock market performance. The key implication is that working with a financial professional rather than being self-directed is associated with clients having a more realistic outlook of future stock market returns.

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An Exploration of Contributing Factors Related to Retirement Plan Participation

Marc B. Lewis, D.B.A.8

Joseph T. Patton, D.B.A.9

Abstract

Approximately 30% of consumers are not participating in their employer-sponsored retirement plans (Topoleski & Myers, 2021). This study examines minorities, non-minorities, and other contributing factors such as age, education, household income, gender, and education as they related to a consumer’s likelihood of participating in their employer’s sponsored retirement plan. A secondary data analysis was conducted using the 2019 National Financial Capability Study. With over 25,000 respondents, the results of the analysis reveal that age, education, and household income are strong contributors in determining the likelihood of an individual participating in an employer-sponsored plan. However, the data reveal that an individual’s age and minority group are less correlated with the likelihood of plan participation. These results provide insight for plan participants, practitioners, and plan sponsors on educating their participants on the various contributors that effect their participation.

8. Associate Professor, Accounting, Central Connecticut State University, mlewis@ccsu.edu

9. Instructor, Department of Management Programs, Florida Atlantic University, jpatton6@fau.edu

INTRODUCTION

Why do consumers not contribute to company sponsored retirement plans? This question has been asked by researchers and journalists for many years. In a recent article published in 2020 by Heritage Capital LLC, they found that in 2018, approximately 30% of employees with access to a 401k plan in the private sector did not participate (Topoleski & Myers, 2021). In addition, they found that 20 percent of the employees who did participate failed to take full advantage of the 401k plans (Schatz, 2020). Schatz (2020) derived some of the reasons why participants did not enroll or take full advantage of their plans. Such reasons were identified as ignorance of the benefits and tax advantages of a 401k plan, priorities in building emergency savings versus investing for retirement, and inertia, which pertains to employees being auto enrolled into a plan with the minimal amount being contributed. These last items were viewed as employees taking a passive approach to their retirement and never considering that a higher contribution might allow them to reach their retirement goals sooner than originally expected (Schatz, 2020)

Further supporting the information culled by Schatz (2020), a survey conducted by the Pew Charitable Trust found that 45% of employees in the private sector participated in their 401k plan versus 55% that did not (Sunagel, 2018). Moreover, it was found that for households with income over $100,000, there was a higher participation rate compared to those who fell under that threshold (Sunagel, 2018). Additionally, Sunagel (2018) found that auto-participation is used less by small- to medium-sized firms in the private sector. The reason for this was that these firms felt that their employees would not like the opt-out approach. However, research has shown that this is not the case (Sunagel, 2018).

As the older generation moves closer to retirement and the younger generation enters their initial years of retirement planning, how does minority status, household income, age, gender, and education associate with one’s participation in an employer-sponsored retirement plan? Do non-minorities participate more in their 401k plans than others? Are there differences in socioeconomic, age, gender, and academic backgrounds among the groups? In this article, we explore the differences in these areas and how they are associated with participation in employer-sponsored retirement plans.

USING FINANCIAL ADVISORS

As the average lifespan of an individual increases, planning for retirement can be viewed as a more specialized approach than just retiring at 65 years of age and living off one’s investments (Hicks, 2021). Outside of participating in employer-sponsored plans, some consumers opt to work with financial advisors. This provides individuals with a holistic approach that covers all areas of financial planning, including but not limited to investments, retirement, insurance, estate and tax planning, and possibly long-term care (Hicks, 2021). However, some consumers view the use of a financial advisor as a service only available to the wealthy (Delfino, 2021). Even though the percentage of individuals who do not use an advisor for planning is declining, those individuals who choose not to use an advisor or participate in their employer plans continue to decrease their chances of obtaining sufficient funding for their retirement needs (McKenna, 2020).

FINANCIAL PLANNING AND MINORITIES

Ethnic background (or minority status) can be seen as a barrier to retirement planning. Tyler (2012) published an article related to a small company in Colorado with 100 employees, 40 of whom were Spanish speakers. However, the company did not realize that language alone was a barrier to entry for these 40 employees. When information was provided to them, they were unable to interpret the information properly to make informed decisions. However, after inviting bilingual presenters to present information to their employees, the company was able to increase participation by 28% (Tyler, 2012). The results illustrate that proper education of the benefits of using an employer-sponsored retirement plan can result in an increase in participation within minority groups. However, access to an employer-sponsored plan can be problematic for minorities. Hence, the AARP Public Policy Institute noted that black, Asian, and Hispanic employees (minorities) have less access to employer-sponsored employees than white (non-minority) employees (Harvey, 2017). More specifically, when analyzing the ethnic group independently, 50% of blacks, 48% of Asians, and 34% of Hispanics are covered under an employer-sponsored plan versus 57% of whites.

RETIREMENT PLANNING AND GENDER

When assessing the differences between men and women in retirement plan participation, a study conducted by Vanguard produced interesting findings. The study found that women are more likely to participate in retirement plans than men. In addition, women tend to save more into their employersponsored plans than men. However, the study also found that men tended to have higher account balances because their salaries were higher than their female colleagues (Nelson, 2021).

RETIREMENT PLANNING AND AGE

When planning retirement, previous research has shown that patience is a key element in achieving this goal. Clark et al. (2019) found that individuals who are patient and take time to understand the timelines associated with retirement planning are more likely to achieve their financial goals. In addition, those who are more patient tend to save more in and outside their retirement plans (Clark et al., 2019). In a recent study published in the Journal of Economic Behavior and Organization, Burro et al. (2022) found that having patience is based on whether an individual is rich or poor. Hence, the more financially secure an individual is, the more patience they possess (Burro et al., 2022). Furthermore, it was found that age is positively related to the level of contributions when planning for retirement. Jiménez et al. (2019) found that as individuals become older, their disposable income increases. They are more likely to plan better and contribute more to their investments. Although inconclusive, age appears to be an important contributor to the retirement planning process.

FINANCIAL PLANNING AND EDUCATION LEVEL

Previous research has shown a positive association between educational level and retirement planning. Transamerica Institute (2016) surveyed over 4,000 workers and found that as educational attainment increased, so did the participation rates in retirement plans. In addition, there is a strong association between financial literacy (an understanding of basic financial terminology) and the level of planning for retirement. Additionally, Michelson and Schwartz (2018) examined academic faculty and their perception and ability to plan for retirement. The faculty were chosen because, in most cases, their educational and income levels were higher than

the average individual. In addition, the faculty actively saved more for retirement than the average employee. However, the faculty’s ability to save the correct amount still may not have been sufficient for retirement. This is yet another important item to note; saving for retirement is only a part of the process. It is important to save a sufficient amount of money for retirement, regardless of one’s education.

FINRA BASED RESEARCH

Previous research, funded by the FINRA Foundation, utilized the 2018 National Financial Capability Study (NFCS) dataset with various findings. In October 2019, researchers established that investors (primarily women) who only invested through their employer-based retirement programs were less likely to be able to manage their investments. This contrasts with investors who have investment accounts outside their employer-sponsored plans (Fisch et al., 2019). Another study compared investor knowledge between men and women. It was found that 40% of the women culled from the 2018 NFCS study, were deemed to have low investment knowledge, compared to 8% who were viewed as possessing high investment knowledge (Global Financial Literacy Excellence Center & FINRA Foundation, 2020). Finally, another study conducted in 2019 provided updates to previous research on the financial well-being of veterans. Findings from this study state that veterans continue to struggle with credit card behavior but fare better overall in relation to managing other aspects of their personal finances (Mottola & Skimmyhorn, 2019). In addition, female veterans appear to manage their finances less than their peers, and black veterans appear to manage their finances better than their counterparts who identify as either white or “Other” relative to race/ethnicity (Mottola & Skimmyhorn, 2019). As evidenced by the FINRA Foundation-sponsored projects, NFCS data can be used to analyze various financial behavior patterns.

This study aims to assist practitioners and small-to-medium sized businesses (SMB). In doing so, the researchers aim to inform practitioners and SMBs about understanding associations tied to participation rates as they relate to minority group, age, household income, educational level, and gender.

This study does not intend to determine whether auto enrollment or opting into an employer-sponsored plan is effective. Furthermore, for the purposes of this study, nonminorities are categorized as white and non-Hispanic. All other ethnic groups are categorized as minorities.

METHOD

Based on previous research, ethnicity was found to be negatively correlated with participation in a retirement plan. In addition, age, education, and household income were all positively correlated with participation in a retirement plan. Previous surveys found that although women accumulated less in their retirement accounts than men, women were more likely to participate in their employer-sponsored retirement plans than men. As a result, the following research question was created for this study:

Research Question Do minority group, age, household income, gender, and education contribute to the participation in employer-sponsored retirement plans?

In conjunction with the abovementioned research questions, the following hypotheses were developed to address this question:

H1: Minority group is negatively associated with participation in employer-sponsored retirement plans.

H2: Household income is positively associated with participation in employer-sponsored retirement plans.

H3: Age is positively associated with participation in employer-sponsored retirement plans.

H4: Education is positively associated with participation in employer-sponsored retirement plans.

H5: Gender is positively associated with participation in employer-sponsored retirement plans.

To answer the research question and test the hypotheses, this analysis was performed based on secondary data analysis on a large dataset representative of the population in the United States.

RESEARCH DESIGN

Frequency distributions, descriptive statistics, and binary logistic regression were used to analyze the dataset used in this study. For all pertinent survey questions culled from the dataset, those that contained unanswered or unknown responses were removed from the final dataset to provide researchers with data that were clear and concise when testing the hypotheses.

When testing the hypotheses within this study, a binary logistic regression analysis was conducted. This methodology was chosen because the dependent variable (participation in an

employer-sponsored retirement plan) is dichotomous and can be tested against variables of various data types.

POPULATION AND SAMPLE

This study utilized data from the 2018 NFCS. This survey consisted of approximately 27,091 adults aged 18 years or older who participated online. The NFCS estimates that, when including all states within the United States (including the District of Columbia), there is a representation of approximately 500 respondents per state. Participants in this study were offered incentives to participate (FINRA, 2018).

DATA COLLECTION

NFCS data were collected using non-probability quota sampling. This was done online with millions of potential participants solicited to participate in the study (FINRA, 2018). To verify the identification and demographic characteristics of the participants, the survey used Survey Sampling International (SSI), EMI Online Research Solutions, and Research Now as providers of sampling solutions for the study (FINRA, 2018). Overall, the dataset has an estimated margin of error of .05 (half a percent). It should also be noted that the data collection within the 2018 study replicates the 2009, 2012, and 2015 data in that it did not specifically target any specific household, for example, the head of household or the primary decision maker within a household (FINRA, 2018).

ANALYSIS

An associational design was used to understand the relationships between the variables in this study. Each independent variable was derived from the 2018 NFCS study (see Appendix). These survey questions were non-overlapping and directly related to the independent variables. All analyses were conducted using IBM® SPSS.

FREQUENCIES AND DISTRIBUTIONS

A data analysis was performed to understand the distribution of the data. Frequencies were performed on the predictor variable, retirement plan, and the independent variables gender, age, minority group, education, and household income.

Table 1 illustrates that the age groups appeared to be somewhat evenly distributed among the age brackets,

with 18–24 representing the lowest, 2,388 (9.4%) and 65+ representing the highest, 5,361 (21.0%). When reviewing level of education, the frequencies revealed that more than half of the respondents did not have a college degree: 6,822 (53.8%) and 619 (2.4%) had no high school education. 5,619 (22.0%) had a bachelor’s degree while 6,152 (24.1%) had either an associate or a postgraduate degree. Household income levels

Table 1. Frequency Distributions

show that for those households earning more than $50,000, 13,811 (54.2%), 4,990 (19.6%) earn between $50,000 and $75,000 while 1,793 (7.0%) earned $150,000 or more. Finally, retirement plan participation illustrates that of the 25,490 respondents, 10,069 (39.5%) did not have a plan and 15,421 (60.5%) did have an employer-sponsored retirement plan.

RESULTS

A binomial logistic regression model was used to test the hypotheses and the associations between the independent and dependent variables. The model correctly predicted nearly 76% of the cases with an R2 of .355 using the Nagelkerke measurement and had a significant association between the independent variables and employer-sponsored retirement plans (χ2(df = 5, N = 25,490) = 7744.51, p < .001). The unstandardized beta weight for the constant was B = -2.917, SE = 0.97, Wald = 909.828, p < .001.

The independent variables in the binomial logistic regression analysis were examined. The age group was found to positively contribute to the model. The unstandardized Beta weight for age groups was B = .051, Wald = 28.846, p < .001. In the model, every one-unit increase in the range of the age group would make it 1.05 times as likely that the respondent will participate

Table 2. Results of Binomial Logistic Regression

in an employer-sponsored retirement plan. Education level was also found to positively contribute to the model. The unstandardized Beta weight for educational level was B = .169, Wald = 221.829, p < .001. In the model, every one-unit increase in an individual’s level of education would make it 1.18 times as likely that the respondent would participate in an employersponsored retirement plan. Household income levels also contributed positively to the model. The unstandardized Beta weight for the household income level was B = .588, Wald = 4208.919, and p < .001. In the model, every one-unit increase in the range of household income level makes the odds 1.80 times as likely that the respondent will participate in an employer-sponsored retirement plan. Lastly, gender (p = .400) and minority groups (p = .518) did not appear to be significant contributors in predicting the likelihood of a consumer having an employer-sponsored retirement plan (see Table 2).

Note. Dependent variable is retirement plan.

Note. *Significant at the 0.05 level ** significant at the .01 level.

DISCUSSION

Overall, the results of the logit analysis revealed that age, education, and household income, are positively associated with the likelihood of a consumer having an employersponsored retirement plan. These results support hypotheses 2 (household income levels), 3 (age groups), and 4 (education level) and previous research. However, the results of the analysis do not support hypotheses 1 (minority groups) and 5 (gender). The results of this study can possibly assist practitioners and employers in increasing the participation rates in employersponsored plans. The results reveal that the older, more educated, and higher household income a consumer has, the more likely they are to enroll in their employer’s plan. Employers and practitioners should move beyond minority groupings and take a more in-depth look at the population they are dealing with when attempting to increase enrollment (Stern, 2020). In doing so, retirement plan education programs can be tailored to provide information to employees on the

primary reasons for taking advantage of their employer plans, while providing scenarios based on age, education, and household income.

With a decrease in the qualified-benefit plans and the increase in qualified contribution plans, consumers are given ownership of how much and where to invest their funds for retirement. When these plans are provided to employees, information like the results of this study should be provided to them explaining the information broken down by age, household income, and education. In doing so, employees can identify more with the data by categorizing themselves into the groups that are more familiar. Whether or not this additional information will increase participation rates is uncertain. However, as stated previously, prior research has shown that awareness can increase participation rates. By providing awareness of how age, household income, and education effects participation to employees, companies stand to increase their employee’s enrollment and further assist them in obtaining a successful retirement.

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Survey Questions Used in Study

Survey Questions Used in Study

A4A_new_w (The answers to this question were derived from question related to ethnicity)

1 White Alone (non-Hispanic) NH

2 Non-White

Bequest Expectations and Annuity Ownership

Ying Yan10

Russell N. James III11

Abstract

This paper uses data from the 9 waves of the Health and Retirement Study (HRS) to examine how bequest expectations impact decisions about annuitization. The estimations of a random-effects model show that people who have a higher expectation of leaving a bequest are more likely to have an annuity, even controlling for housing wealth, non-housing wealth, health, and other demographic characteristics. Previous studies have shown a negative association between bequest motivation and annuitization. The differing relationship of annuitization with bequest motives and bequest expectations reveals a practically and theoretically important distinction between these two types of bequest measurements. The implications of other research findings that use bequest expectations as a proxy for bequest motive may need to be reconsidered.

Key Words: Annuities, Estate Planning, Bequest Expectation, Bequest Motive

INTRODUCTION

In his 1985 Nobel Prize acceptance speech Franco Modigliani introduced the “annuitization puzzle” that few people annuitize a portion or all of their wealth. The “annuitization puzzle” is not only a “puzzle” in the United States, but it is also a “puzzle” across many developed countries.

Longevity risk is the risk that a person lives longer than expected and has insufficient wealth to support his or her lifestyle. Retirees in the United States face a significant longevity risk in part because of increasing life expectancy (Knell, 2018). The average life expectancy is expected to increase (Le Bourg, 2012). Therefore, retirees should be increasingly concerned about the need to hedge against longevity risk.

An annuity, also known as longevity insurance, is a financial product typically used by investors to generate regular income payments for life, helping to replace a paycheck in retirement. Often, investing in annuities is an optimal asset management strategy for retirees (Finke & Pfau, 2015; Pfau, 2012; Kitces & Pfau, 2014). The economic literature provides theoretical and empirical evidence that life annuities bring substantial welfare improvements to retirees (Fehr & Habermann; Davidoff et al., 2005). However, the annuitization rate is low in the United States. For example, among those 65 and older private annuities comprise roughly than 1 percent of total wealth (Johnson, Burman, & Kobes, 2004) with only about three to five percent having any annuities (Lockwood, 2018).

Many have tried to explain the reasons that cause the “annuitization puzzle.” A popular explanation is people desire to leave a bequest. Because standard annuity payments end at the death of annuity owner, they are not bequeathable. This feature has the potential to decrease or even eliminate the desire for annuitization. Several studies show that having a bequest motivation (a desire to leave a bequest) could prevent purchase of annuities because annuity wealth is not bequeathable (Yaari, 1965; Bernheim, 1991; Bütler & Teppa, 2007).

However, bequest motivation is difficult to measure. Generally, researchers have used children or the self-reported importance of leaving a bequest as proxies to measure such motives. Commonly, they do find a negative association between purchasing a life annuity and these bequest motive measurements (Yaari, 1965; Bernheim, 1991; Bütler & Teppa, 2007). Outside of an annuity context, others have used the bequest expectations measurements found in the Health and

Retirement Study as a proxy for bequest motive (Kim et al., 2012; Willis, 1999; Yilmazer & Scharff, 2014).

This paper uses data from the 2002 through 2018 Health and Retirement Study (HRS) to examine how bequest expectations impact decisions about annuitization. The estimations of a random-effects model show that people who have a higher expectation of leaving a bequest are more likely to have an annuity, even when controlling for housing wealth, nonhousing wealth, and other health and demographic variables. This paper uses a new measurement related to bequests (i.e., expectations), and finds an opposite association with this measurement as compared to prior studies using other proxies for bequest motives (Yaari, 1965; Bernheim, 1991; Bütler & Teppa, 2007). The implications of other research findings that use bequest expectations as a proxy for bequest motive may need to be reconsidered.

LITERATURE REVIEW

Retirees in the United States face significant longevity risk. About 20 percentage of 65-year people will live to age 90 and beyond (Lockwood, 2018). Annuities, which convert wealth into a lifetime income stream, can insure people against this longevity risk. Therefore, life annuities appear to be a potentially valuable part of retirees’ portfolios. However, the annuitization rate is low in the United States (Peijnenburg, et al., 2016). One possible reason that people do not hold annuities is that people have bequest motives which could eliminate the purchase of annuities.

This may be, in part, because an annuity is not bequeathable wealth (Lockwood, 2018). Many people report that the most important reason for their saving is to leave a bequest (Lockwood, 2018). Yaari (1965) points out that in a life-cycle framework, consumers could maximize their utility by holding only annuity wealth if they do not care about bequest (i.e., they do not have any bequest motive). However, when they have a bequest motive, they could hold a portfolio which combines annuity wealth and other bequeathable wealth to maximize the marginal utility of bequests and consumptions (Yaari, 1965). Nevertheless, many retirees do not use annuities at all, but instead chose to self-insure (Lockwood, 2012).

Bernheim (1991) confirms Yaari’s results and indicates that people who care about their bequest motives will convert a lower, or even zero, percentage of assets into annuities, even if annuities are available at actuarially fair rates. In addition,

Bütler and Teppa (2007) indicate that the presence of a bequest motivation will reduce or eliminate the demand for annuities, even when the return on them exceeds the real market interest rate. Davidoff (2009) suggests that the bequest motives can significantly reduce the demand for annuities, and can be viewed as a possible explanation for the low demand observed in the US annuity market. Purcal and Piggott (2008) also confirm that the bequest motive is the strongest single factor reducing the demand for annuities in the Japanese market.

The previous literature shows that bequest motives may explain why people are not willing to annuitize all of their wealth (because people want to leave a bequest), but cannot explain why people do not annuitize any of their wealth (Lockwood, 2012). Lockwood suggests that bequest motives would not prevent or eliminate the purchases of actuarially fair annuities, but it can reduce the demand for the annuities actually available in the market. Similar to other researchers, Lockwood (2012) also points out that bequest motives could reduce the demand for available annuities.

Thus, bequest motives are universally recognized as an important factor in the annuitization decision. However, bequest motives may be difficult to accurately identify. For example, the use of children as a proxy for bequest motives introduces other confounding factors. A previous study shows that many retirees use family as a form of self-insurance, which allows retirees to take advantage of the possibilities of joint consumption (VidalMeliá & Lejárraga-García, 2006). Sharing financial resources among the members of their family reduces the attractiveness of annuities, because it accomplishes risk sharing in a different way (Vidal-Meliá & Lejárraga-García, 2006). This feature of risk sharing makes family similar to an incomplete annuities market (Kotlikoff & Spivak, 1981). Bequest motives can thus become entangled with the valuation of annuities through this family support mechanism (Jousten, 2001).

Other factors may also impact the desire for annuitization. Holding excess wealth relative to income needs may obviate the need for the longevity protection provided by annuities (Pang & Warshawsky, 2009). However, this is diminished where that wealth is primarily held in a personal residence where the owners do not want to convert their housing wealth into annuities or reverse mortgages because of bequest or other motivations (Chiang & Tsia, 2016). Also, public and employer pension plans and concern over significant medical expenditure risk could explain why only a few people purchase the available annuities in the market.

HYPOTHESIS

As mentioned, several studies conclude and confirm that bequest motives reduce or eliminate the demand for purchasing life annuities. Nevertheless, there appears to be no literature that considers the influence of bequest expectations on decisions for purchasing life annuities. This paper explores that issue. It measures bequest expectations as the respondent’s predicted percentage chance of leaving a bequest. It further delineates this by measuring the predicted percentage chance of leaving a bequest between $10K and $100K, between $100K and $500K, and more than $500K. Several studies using this measurement of bequest expectations have presented it as a proxy for bequest motivation (Kim et al., 2012; Willis, 1999; Yilmazer & Scharff, 2014). This approach may be incorrect. For example, a person could have zero motivation to leave a bequest, but still expect that – simply due to uncertainty regarding timing of death – such a bequest would be highly likely due to unconsumed wealth. Further, to the extent that one’s expected bequest increases, it would actually satisfy the underlying bequest motivation. This reduces the marginal utility from each additional dollar devoted to bequests. Thus, assuming stable underlying bequest preferences, at the margin higher bequest expectations should predict lower marginal bequest motivations. If this is the case then, controlling for wealth and longevity factors, the following hypothesis would hold.

Hypothesis: A higher bequest expectation will be associated with a higher propensity to use annuities.

DATA AND VARIABLES

This paper uses RAND data including all waves from Wave 6 to Wave 14 (2002-2018) of the Health and Retirement Study (HRS). It includes data from nine total waves across sixteen years as the HRS is conducted every two years. The HRS is a panel study of Americans who are older than 50 years of age. To maintain this age representativeness, new younger panel members are added every six years.

The dependent variable in the following analysis is annuity ownership which is a dichotomous variable that equals to “1” if the respondent reports having an annuity and “0” otherwise. Explanatory variables including bequest expectations, number of children, marital status, self-perceptions of health, housing wealth, non-housing wealth, years of education, stock ownership, participation in a pension plan, and age.

There are three bequest expectation variables measuring the chances for leaving an inheritance of $10,000 or more, $100,000 or more, and $500,000 or more. When all three variables are included in the same regression, the coefficient for the first variable reflects the association with the percentage chance that respondents will leave a bequest between $10,000 and $100,000; the second reflects the association with the percentage chance that respondents will leave a bequest between $100,000 and $500,000; and the third reflects the association with the percentage chance that respondents will leave a bequest over $500,000. These are nominal amounts and do not change across the years in the study. However, these reflect the associations with the expectations of bequests at various relative levels in each year.

Number of children is a continuous variable which measures the actual number of children that respondents have. “Married” is a dichotomous variable which is equal to “1” if respondents are married or “0” otherwise.

Health is measured using the four dummy variables of excellent health, very good health, good health, and fair health with “poor health” being the omitted comparison category. Housing wealth and non-housing wealth are continuous variables scaled to $100,000 units. Life insurance ownership is a dichotomous variable equal to “1” if respondents have at least one life insurance policy or “0” otherwise. Years of education is a continuous variable which measures the actual years that respondents have of formal education. Stock ownership is dichotomous variable which equals “1” if respondents own stocks or “0” otherwise. Pension plan is a dichotomous variable which equals “1” if respondents have at least one pension plan or “0” otherwise. Lastly, age is a continuous variable which measures the actual age of respondents.

Table 1. Descriptive Statistics reporting means (standard errors)

Table 1 shows the descriptive statistics for the initial survey wave, Wave 6 (2002), and the final survey wave, Wave 14 (2018). Given the addition of new younger respondent waves in 2004, 2010, and 2016, and respondents lost due to death or dropping out of survey participation, only 58.61% of the households observed in the 2018 wave were also observed in 2002 wave. Most characteristics are roughly similar in these two waves although the effects of inflation may explain the

increase wealth and bequest expectations over $500,000. Both participation in pension plans and stock ownership are notably lower in the 2018 wave.

Bequest expectations are expected to be associated positively with having an annuity. Again, this hypothesis is the opposite of that which would be made for a measurement of bequest motives, for the reasons described previously. The number of children is expected to be associated negatively with having an annuity. The theory of social interaction indicates that people care about their children’s utility, such as financial safety, happiness, and future development (Becker, 1974). Therefore, most people are willing to leave wealth to their heirs. However, annuity wealth is not bequeathable wealth. The theory could explain why people who have children are less likely to have an annuity. Additionally, people may have more expenditures, such as children’s educational costs and other expenses, when they have more children. Those expenditures and costs may result in financial constraints and leave relatively less wealth available for annuitization. Finally, the presence of children may serve as a substitute for longevity insurance resulting from family insurance and joint consumption (Vidal-Meliá & Lejárraga-García, 2006). (Some prefer to reserve the term longevity insurance for deferred annuity products; however the current data do not separate deferred and immediate annuity products (Ezra, 2016)).

Housing wealth and non-housing wealth are expected to be associated positively with purchasing an annuity. Greater wealth indicates a higher purchasing ability. The theory of consumer demand shows that people will consume more normal goods when they have larger wealth or other forms of discretionary income (Slacalek, 2009). However, people may treat housing wealth differently due to mental accounting, liquidity constraints, or the desire to avoid indebtedness necessary to access equity, thus warranting its separate examination.

Marriage could impact decisions about purchasing annuities. The theory of social interaction shows that people will care about their spouse’ utility (Becker, 1974). Joint and survivor annuities are available for married couples in the annuity market, and they provide lifetime benefits to the last surviving spouse. Married couples could purchase joint and survivorship annuities to have a lifetime financial protection for the surviving spouse (Brown & Poterba, 2000). Thus, married couples may be more likely to annuitize part or all of their wealth. However, Inkmann et al., (2011) indicate that married couples are significantly less likely to have an annuity

compared to the single individuals and this is because of the intra-household hedging of longevity risk. Married couples could hedge the longevity risk through shared family resources instead of transferring the longevity risk to the capital market. Therefore, the relationship between being married and having an annuity remains ambiguous.

Additionally, a more positive health condition is expected to be associated positively with purchasing an annuity. Generally, heathier people will have a longer life expectancy than those in poorer health. People who have a longer life expectancy will receive more benefits from a life annuity. Also, people with a longer life expectancy will have larger need for hedging the longevity risk. Therefore, heathier people should be more likely to have an annuity.

Life insurance ownership is expected to be associated negatively with having an annuity. Life insurance involves a financial bet that a person will live shorter than expected while annuities involve a bet that a person will live longer than expected. However, life insurance and annuities could be considered complementary products for achieving risk reduction, because life insurance reduces the risk from premature death while an annuity reduces the risk from longevity.

There is an ambiguous association between years of education and annuity ownership. People who have more years of education may have better financial literacy (Sucuahi, 2013; Sekita, 2011; Lusardi et al., 2010; Lusardi & Mitchell, 2011). Thus, they may have a better understanding the value and appropriate use of annuities. This could result in a higher probability of having an annuity. However, when people have better financial literacy (Sucuahi, 2013; Sekita, 2011; Lusardi et al., 2010; Lusardi & Mitchell, 2011), they may select other higher return investment vehicles, such as stocks and mutual funds. In this scenario, they may be less likely to have an annuity.

Stock ownership is expected to be associated positively with having an annuity. Compared to the annuity market, the stock market has more volatility and higher risk. Thus, for people who invest in stocks, having an annuity could help offset income volatility in their retirement savings. Additionally, both stock ownership and annuity purchases are likely associated with financial sophistication. Therefore, people who have investments in stocks may have a higher probability of having an annuity.

Participating in a pension plan has an uncertain association with having an annuity. Dent & Sloss (1996) indicate that the major benefit of pension plan is the stable income stream.

Some employers allow selection of either a defined benefit or defined contribution account. In such cases, selecting a pension plan may reflect a preference for a stable income in the retirement period. However, in many cases participation in a pension plan is not a voluntary choice, either because it is not available or because opting out is not available. In either scenario, a pension plan may fulfill the desire to ensure a stable income for retirement planning, and thus reduce the need for a privately purchased annuity. Additionally, the low demand for private annuities could result from the public pension system which allows retirees to have hedged the longevity risk with annuities provided by the public pension system, such as Social Security (Brown, 2001; 2003).

Lastly, age is expected to be associated positively with having an annuity. Annuities typically pay for life. Thus, for one person, the probability of having an annuity cannot normally decrease with age. Simply put, once you have a life annuity, you always have a life annuity. Additionally, an annuity is a bet on one’s own longevity. Thus, those who expect to live longer are more likely to purchase an annuity. To the extent that such predictions are correct, the surviving population at each older age would include a larger share of annuity holders due to their relatively higher probability of surviving to that age.

MODEL AND RESULTS

This paper estimates the following random-effect model via maximum likelihood:

AO* = β1be(10k-100k) + β2be(100k-500k) + β3be(500k+) + βjDVj + e

AO = 1 if AO* > 0 (Have an annuity)

AO = 0 if AO* ≤ 0 (Do not have an annuity)

AO is the unobserved latent variable measuring the underlying demand for having an annuity. Bequest expectations are measured by the bequest percentage chance indicated by be. There are three different levels of expected bequests which are be(10k-100k), be(100k-500k) and be(500k+). The be(10k-100k) variable measures the percentage chance that a respondent will leave a bequest between $10K to $100K. The be(100k-500k) variable measures the percentage chance that a respondent will leave a bequest between $100K to $500K, and the be(500k+) variable measures the percentage chance that a respondent will leave a bequest more than $500K. The matrix DV contains demographic variables and includes number of children, housing wealth, non-housing wealth, marital status, life insurance ownership, self-perception of health, years of education, stock ownership, pension plan, and age. Marginal effects are calculated to measure the magnitude of the association with the observed variable (AO or Annuity Ownership). The error term (e) is assumed to follow a standard normal distribution.

Simple cross-sectional models, such as logit or probit, are not ideal for longitudinal datasets such as this one because they assume that observations are independent. However, this longitudinal dataset includes multiple observations from the same households across time and such observations are not independent. A random effects model allows for the incorporation of multiple waves of data while recognizing the interdependence of observations coming from the same households across time (Laird & Ware, 1982). The ability to observe such behavior over time provides a more complete picture of the underlying relationships as compared with simple cross-sectional approaches which can apply only to single year of data.

Table 2. Random-Effect Model Results Reporting Coefficients [Marginal Effects]

The key finding is that people who have the stronger (higher percentage chance) bequest expectation are more likely to have an annuity. This matches the hypothesis but could be explained by alternate pathways. One possible explanation is that purchasing annuities results in a higher bequest expectation given that annuities can be an effective and efficient way to hedge the longevity risk (Lin & Cox, 2005). When people live longer than expected, they might otherwise need to spend more of their wealth to support their lives which could potentially reduce their expected bequests. Having an annuity could provide a lifetime income stream for supporting daily expenses and consequently protect the bequest goals. Another factor explaining this result may be the development of combination annuity products with additional investment features that preserve or expand bequest benefits. At the beginning of annuity market, there was only one annuity option which was the single pure life annuity. With the developing annuity market, people have more choices such as a life annuity with a guaranteed minimum payment, an installment refund annuity, and a joint and survivor annuity. Annuities are now not only just for hedging the longevity risk, but also for making an investment. Such investment features could add to the annuity’s likelihood of, by itself, increasing bequest expectations.

Several other factors were associated with annuity ownership. For example, having a greater number of children leads to a decrease in the probability of having an annuity. This may be explained by the increase in other regular expenditures, increase in family risk sharing through joint consumption models, or increase in underlying bequest motives. This result also matches past research using children as a proxy for bequest motives.

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

Table 2 shows the coefficients, marginal effects, and coefficient standard errors. For all bequest expectation variables -between $10K and $100K, $100K and $500K and more than $500K -- the marginal effects indicate that people who increase their expectations of leaving a bequest will also increase the likelihood that they will own an annuity. The marginal effects for bequest expectations at each of the three levels are statistically significant.

People who are married are more likely to have an annuity. This may result from the attraction to joint and survivor annuities where the lifetime benefits will continue until the death of the last survivor.

The results indicate that when compared with people who report being in the poor health level, people who report being in excellent health or good health are more likely to have an annuity. This is expected as purchasing an annuity is, in part, a bet that one will live a long time and thus will likely be less attractive to those who perceive themselves to be in poor health.

People who have higher non-housing wealth also have a higher likelihood of owning an annuity. When people have more

wealth, they are more likely to be able to afford purchasing an annuity. People who have more years of education also have a higher probability of having an annuity. Such people are likely to have better financial literacy (Sucuahi, 2013; Sekita, 2011; Lusardi et al., 2010; Lusardi & Mitchell, 2011). Therefore, they may have a better understanding of the importance of annuities and a stable lifetime income stream in retirement.

People who own stocks have a higher probability of having an annuity. This may be because stock investments have more volatility and higher risk, and annuities may hedge against this volatility. Alternately, it may be because both financial products are relatively sophisticated and may be associated with an underlying understanding of and interest in complex financial investment strategies.

People who have pension plans have a higher probability of also having an annuity. People who select pension plans, where such a selection within or between employers is possible, likely prefer the stable income streams for retirement planning. Thus, they may be more likely to prefer an annuity, which also provides stable income for life.

Finally, older people have a higher probability of having an annuity, as expected by the lifetime nature of the product. Life insurance ownership is not statistically significantly related to annuity ownership.

For each variable Table 2 reports the marginal effects. This can be interpreted as the expected change in the likelihood of annuity ownership resulting from a one-unit increase in the associated variable. The marginal effects resulting from differences in bequest expectations are highly significant but are relatively small compared to the effects from major factors such as health, age, marital status, and number of children. Nevertheless, the importance of the main finding lies in the observation of a positive and highly significant relationship with bequest expectations in contrast to the negative and significant relationships found with bequest motive proxies from past research. The key insight is that bequest expectations appear not to be an appropriate proxy for bequest motivations, despite their usage as such in past research.

CONCLUSION

The major finding of this paper is that having a greater bequest expectation is associated positively with having an annuity. This is the opposite relationship found in previous research using measurements of bequest motivations. At least two

explanations match with this result. One is that higher bequest expectations result in an increased desire to annuitize. The other is that annuitization results in increased bequest expectations.

The first matches with standard economic assumptions and with previous findings connecting bequest motivations with a decreased likelihood of annuitization. If expected bequests are subject to diminishing marginal utility then – given a stable underlying level of bequest preferences (a.k.a. motivation) – as the predicted expectation for leaving a bequest (and leaving a larger bequest) grows, then the marginal utility from each extra dollar of a bequest should fall. If annuitization is viewed as a way to protect against longevity risk, but at the cost of bequest transference, then this relatively lower marginal utility from each additional dollar of bequest transference should reduce the opportunity costs from annuitization. In other words, annuitization comes at the cost of bequests, but the higher bequest expectation reflects that this bequest desire may have otherwise already been largely fulfilled. Thus, higher bequest expectations could result in an increased demand for annuitization. This explanation, and these empirical results, suggest that using bequest expectations as a measurement of bequest motive, as has been done in previous research, is inappropriate.

The second explanation matches with a framing argument. In this explanation, the causation is reserved. Annuitization results in increased bequest expectations. The argument here is that annuitization protects against longevity risk. In the presence of other assets intended to be left as a bequest, annuitization can be viewed as protecting those assets against consumption due to excessive longevity (Yan & James, 2021). Thus, in the absence of (partial) annuitization, these assets might be completely exhausted due to living expenses incurred during an exceptionally long life. But annuitization protects those assets against such risks, thus helping to ensure their eventual transfer to the next generation.

A final explanation relates to the type of products offered in the modern annuity market. Currently, retirees have many options with respect to annuitization such as joint annuities and other survivor benefits, which could themselves generate guaranteed bequest benefits for others. Modern annuities provide flexibility regarding the methods of paying premiums, number of lives covered, waiting period for benefits to begin, and nature of payouts (Ezra, 2016; Finke & Pfau, 2015; Scott, 2015). Those flexibilities for investors would encourage retirees to annuitize

their wealth and, in many cases, would also directly result in a guaranteed bequest transfer at death.

Given that both causal argument directions are consistent with the association found in this paper, future research may profitably explore each possibility separately. To explore the impact of the second argument based upon framing, it may be useful to conduct experiments that employ the proposed annuity framing argument to measure its impact on annuity decisions. Finally, it may be useful to reconsider the implications of previous findings that have used bequest expectations as a proxy for bequest motivations.

As a matter of practice, these results suggest the importance of simultaneously addressing both client bequest expectations and client bequest goals/motivations. If underlying bequest motivations are already sufficiently addressed by relatively high current bequest expectations, then this suggests no additional bequest enlargement is desirable. In such cases, financial plans emphasizing lifetime consumption, such as annuities, or charitable bequests would become more relevant. Conversely, if bequest expectations were low relative to underlying bequest goals/motivations, the emphasis on enlarging expected bequests would be an important financial objective. Focusing only on one piece, either bequest goals/ motives or bequest expectations, in the absence of the other could lead mistaken conclusions for the advisor.

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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. Previous studies have shown a negative association between ___________ and annuitization.

A. Bequest motivation

B. Wealth

C. Childlessness

D. Income

2. Bequest motive is difficult to measure, what is a common proxy for bequest motivation that has been used in previous research studies?

A. Homeownership

B. Pension plan eligibility

C. Children

D. Race

3. Based on the key finding in "Bequest Expectations and Annuity Ownership," people who have a higher bequest expectation are:

A. More likely to have an annuity

B. Less likely to have an annuity

C. There is no association between the bequest expectations and annuity ownership.

D. This paper does not mention this association.

4. Many researchers have tried to explain the reasons that cause the “annuitization puzzle.” Which of the following is not a potential explanation?

A. The annuity market is not actuarially fair for many consumers.

B. Annuity wealth is not bequeathable and therefore may conflict with bequest goals.

C. Investors must choose to either annuitize all of their wealth or none of it.

D. Actual annuitization choices available to consumers are more limited than theoretical options described in economic models

5. Some researchers use bequest expectation as a proxy for bequest motive. The results from "Bequest Expectations and Annuity Ownership" suggest:

A. Bequest expectation is a good proxy for bequest motive.

B. Bequest expectation is not an appropriate proxy for bequest motive.

C. There is no association between bequest expectation and bequest motive.

D. The article does not mention this issue.

6. In the article “The Role of Financial Advisors in Shaping Investment Beliefs", the financial advisors’ influence is determined by:

A. Financial advisors’ experience

B. The importance level of advice from the client’s perspective

C. Clients’ experiences of the investment

D. Volatility of the stock market

7. Based on the ordered probit results, authors of "The Role of Financial Advisors in Shaping Investment Beliefs" found that:

A. The financial advisor’s (FA) influence is associated positively only with stock market Ups & Downs investment belief.

B. Instead of the volatility of stock market investment belief (Ups & Downs), the financial advisor’s influence is associated with personal experiences of investment belief.

C. For both investment beliefs (Ups & Downs and personal experiences), the financial advisor’s influence is associated positively with an “Extremely important” response.

D. The financial advisor’s (FA) influence has an opposite association for two different investment beliefs (stock market and personal experience).

8. For both investment beliefs (stock market ups & downs vs. personal experience), besides the financial advisor’s influence, _____was also associated with investment beliefs.

A. Investable assets

B. Educational attainment

C. Race/ethnicity

D. Gender

9. The formation of ________ is a paramount consideration when examining the connection between financial advisors’ investment beliefs and the interpersonal communication they engage in with clients.

A. Financial advisors’ investment beliefs

B. Client behavior

C. Crisis-driven equity event

D. New equity experience

10. The goal of "The Role of Financial Advisors in Shaping Investment Beliefs" is to:

A. Address the influence of financial advisors on clients’ investments, specifically, by estimating the correlation between utilizing a financial advisor and stock market participation

B. Illustrate how a financial advisor’s influence can affect the investment beliefs of clients.

C. Demonstrate how the investable assets are associated with clients’ investment beliefs.

D. Show how the money beliefs, such as money status and money worship beliefs, associated with clients’ investment beliefs.

11. According to the implication of “The Role of Financial Advisors in Shaping Investment Beliefs", this study highlighted that:

A. Given the clients had varying investing biases, experiences, educational attainment, etc., clients were more likely to invest based on personal experience.

B. Financial advisors should be aware of their own beliefs, attitudes, and behaviors.

C. Both financial advisors and clients should keep an eye on the volatility of equity market (Ups & Downs).

D. As the financial advisors will maximize client’s interest, it is important to follow financial advisor’ investment beliefs.

12. Investors who made investments decisions partially or completely with a broker or professional adviser’s help were more likely to expect future stock market returns to _________________________.

A. Beat historical averages

B. Align with historical averages

C. Be lower than historical averages

D. Having nothing to do with historical averages

13. Results showed that those who had some form of professional assistance when making financial decisions, were more likely to be ______________________ about future stock market performance.

A. Highly optimistic and highly pessimistic

B. Highly pessimistic and cautious-realistic

C. Pessimistic and realistic optimistic

D. Cautious-realistic and realistic-optimistic

14. The key implication from the study "Investment Advisor Use and Stock Market Return Expectations" is that working with a financial professional is associated with having a more _____________view of future stock market returns.

A. Neutral

B. Realistic

C. Pessimistic

D. Overly optimistic

15. In this study "Investment Advisor Use and Stock Market Return Expectations," cautious-realistic investors expected the approximate average annual return of the S&P 500 stock index to be __________over the next 10 years (without adjusting for inflation).

A. 0 – 4.9%

B. 5% – 9.9%

C. 10% – 14.9%

D. Over 14.9%

16. What percentage of the sample used in this study "Investment Advisor Use and Stock Market Return Expectations" were pessimistic about future returns of the stock market?

A. 19%

B. 26%

C. 33%

D. 52%

17. In relation to gender and retirement plan participation, which of the following statements is true?

A. Men and women equally contribute to retirement plans regardless of their salary levels.

B. Women are more likely to participate in retirement plans than men.

C. Men are more likely to participate in retirement plans than women.

D. Women have higher account balances in their plans versus men.

18. When planning for retirement, which one of the following approaches is more likely to enable an investor to reach their goals?

A. Being patient and understanding timelines is associated with achieving retirement goals.

B. Having a high sense of urgency and little patience is associated with achieving retirement goals.

C. Understanding the stock market fully and trading in and out of stocks based on trends is associated with achieving retirement goals.

D. Being patient and understanding market trends is associated with achieving retirement goals.

19. Previous research has shown educational achievement and retirement planning are associated in what way?

A. Individuals who major in business are better at retirement planning.

B. Individuals with no college education do not plan for retirement.

C. Individuals with higher levels of education participate more in retirement planning.

D. Educational achievement has so association with retirement planning.

20. According to previous research, some individuals view working with financial advisors as:

A. A service only needed if you do not understand investing.

B. Beneficial only if you do not have access to an employer-sponsored plan.

C. Only available to wealthy individuals.

D. A service only needed when you are ready to retire.