Journal of Personal Finance Vol. 10 Issue 2 by IARFC

Journal of

Personal Finance Applying neuroscience to financial planning practice: A framework and review Russell N. James III, Texas Tech University

Mutual Fund Tax Efficiency and Investment Selection D.K. Malhotra, Philadelphia University Rand Martin, Bloomsburg University of Pennsylvania C. Andrew Lafond, The College of New Jersey

Student financial counseling: An analysis of a clinical and non-clinical sample Sonya L. Britt, Kansas State University John E. Grable, Kansas State University Julie Cumbie, Kansas State University Sam Cupples, Kansas State University Justin Henegar, Kansas State University Kurt Schindler, Kansas State University Kristy Archuleta, Kansas State University

Gender Differences in Risk Aversion: A developing nationâ&#x20AC;&#x2122;s case Binay K. Adhikari, University of Alabama Virginia Oâ&#x20AC;&#x2122;Leary, Auburn University

Tools, Techniques, Strategies & Research to Aid Consumers, Educators & Professional Advisors Volume 10 Issue 2

2011

Journal of Personal Finance

Volume 10, Issue 2 2011

The Official Journal of the International Association of Registered Financial Consultants

Volume 10, Issue 2

CONTENTS EDITOR’S NOTES ..................................................................................................8 RESEARCH & THEORY Applying neuroscience to financial planning practice: A framework and review .................................................................................................................10 Russell N. James III, Associate Professor, Texas Tech University This paper presents findings from neuroscience, neuro-finance, neuroeconomics, behavioral finance, and behavioral economics in the context of a two-system model of human decision-making, labeled as the “rider” and the “elephant”. The rational “rider” system is characterized by overconfidence and deficiencies in speed and endurance. The emotional “elephant” system is characterized by time preference myopia, emotional marker processing, and loss aversion. Application of this neural model of financial decisionmaking results in a variety of effective and practical suggestions for financial planners. Mutual Fund Tax Efficiency and Investment Selection ................................66 D.K. Malhotra, Professor of Finance, Philadelphia University Rand Martin, Associate Professor of Finance, Bloomsburg University of Pennsylvania C. Andrew Lafond, Assistant Professor of Accounting, The College of New Jersey We examine six factors that may be important when looking for a more tax efficient mutual fund. We consider the pre- and post-liquidation bases for over 4,000 mutual funds in logical groupings. Our results for turnover show that its effect on tax efficiency depends upon conditions in the securities markets. A falling market leads to greater tax efficiency due to security sales at depressed prices. We have a similar finding for expense categories probably because increased sales lead to higher expenses. We find greater tax efficiency if mutual funds have institutional status, no-loads, and no 12b-1plan.

Journal of Personal Finance

Student financial counseling: An analysis of a clinical and non-clinical sample ................................................................................................................95 Sonya L. Britt, Kansas State University John E. Grable, Kansas State University Julie Cumbie, Kansas State University Sam Cupples, Kansas State University Justin Henegar, Kansas State University Kurt Schindler, Kansas State University Kristy Archuleta, Kansas State University The purpose of this study was to determine what factors predict whether students will seek on-campus peer-based financial counseling. An attempt was made to determine if students who seek help differ significantly from students who do not seek help. Findings provide a profile of college student financial counseling help-seekers. College-age financial counseling help seekers tend to be older, less satisfied with their income, less knowledgeable, less wealthy, and more stressed. The results from this study suggest that college financial counseling centers appear to be on target in connecting with some of the students they were designed to reach. Continued efforts to assist students with high financial stress may be a way to increase financial well-being among college students. Gender Differences in Risk Aversion: A developing nationâ&#x20AC;&#x2122;s case .............122 Binay K Adhikari, Graduate Student, University of Alabama Virginia O'Leary, Professor emerita, Auburn University This study used Hanna and Lindamood (2004)â&#x20AC;&#x2122;s graphic-based survey instrument to examine whether women who are employed in the Nepalese banking sector show more risk aversion than men. Women indeed reported their intention to take less risk and invested less of their wealth in risky assets than men. However, the difference disappeared after controlling for other relevant variables, notably their perceived knowledge of financial markets. Our analyses suggested that women demonstrated more risk aversion than men because they considered themselves to be less knowledgeable about financial markets. Our findings support the need to educate female investors to increase their confidence in their abilities to succeed in the world of finance.

ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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CALL FOR PAPERS JOURNAL OF PERSONAL FINANCE (www.JournalofPersonalFinance.com) OVERVIEW The new Journal of Personal Finance is seeking high quality manuscripts in topics related to household financial decision making. The journal is committed to providing high quality article reviews in a single-reviewer format within 45 days of submission. JFP encourages submission of manuscripts that advance the emerging literature in personal finance on topics that include: -

Household portfolio choice Retirement planning and income distribution Individual financial decision making 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

EDITORIAL BOARD The journal is also seeking editorial board members. Please send a current CV and sample review to the editor. JPF is committed to providing timely, high quality reviews in a single reviewer format. CONTACT

Michael Finke, Editor Email: jpfeditor@gmail.com www.JournalofPersonalFinance.com

Journal of Personal Finance

JOURNAL OF PERSONAL FINANCE VOLUME 10, ISSUE 2 2011 EDITOR Michael S. Finke, Texas Tech University ASSOCIATE EDITOR Wade Pfau, National Graduate Institute for Policy Studies (GRIPS) EDITORIAL ASSISTANT Britini Peoples, Texas Tech University EDITORIAL BOARD Steve Bailey, HB Financial Resources Joyce Cantrell, Kansas State University Dale Domian, York University Monroe Friedman, Eastern Michigan University Joseph Goetz, University of Georgia Clinton Gudmunson, Iowa State University Sherman Hanna, The Ohio State University George Haynes, Montana State University Karen Eilers Lahey, University of Akron Doug Lambin, University of Maryland, Baltimore County Jean Lown, Utah State University Angela Lyons, University of Illinois Ruth Lytton, Virginia Tech University Lewis Mandell, University of Washington and Aspen Institute Yoko Mimura, University of Georgia Robert Moreschi, Virginia Military Institute Edwin P. Morrow, Financial Planning Consultants David Nanigian, The American College Barbara O’Neill, Rutgers Cooperative Extension Jing Xiao, University of Rhode Island Rui Yao, University of Missouri Tansel Yilmazer, University of Missouri Yoonkyung Yuh, Ewha Womans University Mailing Address:

IARFC Journal of Personal Finance The Financial Planning Building 2507 North Verity Parkway Middletown, OH 45042-0506

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Postmaster: Send address changes to IARFC, Journal of Personal Finance, The Financial Planning Building, 2507 North Verity Parkway, Middletown, OH 45042-0506 Permissions: Requests for permission to make copies or to obtain copyright permissions should be directed to the Editor. Certification Inquiries: Inquiries about or requests for information pertaining to the Registered Financial Consultant or Registered Financial Associate certifications should be made to IARFC, The Financial Planning Building, 2507 North Verity Parkway, Middletown, OH 45042-0506. 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 Advisory Board, the Institute of Personal Financial Planning, and the Board of the International Association of Registered Financial Consultants specifically disclaim any personal, joint, or corporate (profit or nonprofit) liability for loss or risk incurred as a consequence of the content of the Journal. General Editorial Policy: It is the editorial policy of this Journal to only publish content that is original, exclusive, and not previously copyrighted. Subscription Rates: Individual: Institution:

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Journal of Personal Finance

EDITORâ&#x20AC;&#x2122;S NOTES Individual investors exhibit a number of behaviors that confound scientists who expect people to make choices that are in their long-run best interest. The worst behaviors often involve risk. We seem to have an instinctive negative response to a loss and avoid many activities that promise to make us better off (from equity investing to flying) because we follow our emotions more than our reason. We also tend to focus too much on investment returns in the recent past, and are often motivated to take risks only after the value of assets have risen to the point where they are overvalued (for example housing in the 2000s and internet stocks in the late 1990s). It seems that our emotions get in the way of a reasonable approach to personal finance decisions. This isn't news to anyone who has studied personal finance or who has counseled families about making choices that give them a better chance of achieving financial goals. This issue of the Journal includes the most exhaustive review of an emerging literature that provides insight into how the brain works when processing complex financial decisions. After reading the article I now have a completely different perspective on why we observe so many puzzling financial decisions and why it can be so difficult to change our behavior. In "Applying neuroscience to financial planning practice: A framework and review," author Russell James explains that we are all involved in a constant conflict between parts of the brain that operate more quickly using instinct and emotion, and the slower, deliberate part of our brains that attempts to impose reason on the emotional (gut) cognitive responses. This simple but significant insight of reason attempting to control the much more powerful emotion and instinct is characterized by the author as an elephant ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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being controlled by the rider. The rider may have very clear ideas about what is rational, but he is no match for the elephant when there is a difference of opinion. Recognizing how and why the mind responds to financerelated stimuli can help explain any number of deviations from what economists term normatively rational behavior. I often encounter a dismissive attitude toward rigorous economic theoretical treatment of personal finance questions when normative theory does a poor job of predicting actual behavior. I don't find this perspective appealing. If our financial decisions really do involve a conflict between the rational and the emotional, then it is important for our rational side to know where to steer the elephant, and how to keep the elephant from getting distracted. Understanding how and when our mind is liable to pull us in the wrong direction gives us a better opportunity to anticipate our weaknesses and avoid succumbing to temptations that compromise our long-term goals. ~Michael Finke

Journal of Personal Finance

APPLYING NEUROSCIENCE TO FINANCIAL PLANNING PRACTICE: A FRAMEWORK AND REVIEW Russell N. James III * Texas Tech University This paper presents findings from neuroscience, neuro-finance, neuroeconomics, behavioral finance, and behavioral economics in the context of a two-system model of human decision-making, labeled as the “rider” and the “elephant”. The rational “rider” system is characterized by overconfidence and deficiencies in speed and endurance. The emotional “elephant” system is characterized by time preference myopia, emotional marker processing, and loss aversion. Application of this neural model of financial decision-making results in a variety of effective and practical suggestions for financial planners.

Introduction Technological advances in brain imaging and related sciences have resulted in a variety of new findings uncovering information about how humans make financial decisions. The task of translating these neuroscience findings into financial planning practice holds great promise, but is also highly problematic. The numerous related studies are scattered throughout academic journals from disparate fields. Incorporating these findings into actual financial planning practice requires not only accurate translation of individual findings, but also the construction of a practically applicable framework for use in practice. Neither of these steps is easily accomplished. Popular reports of individual studies can over-simplify the reality of sometimes complex and fragile results. The accurate communication of results can still leave readers without clear guidance regarding practical *

Russell N. James III, Associate Professor, Department of Personal Financial Planning, Texas Tech University, Box 41210, Lubbock, TX 79409-1210; (806)742-5050 x273; russell.james@ttu.edu ©2011, IARFC. All rights of reproduction in any form reserved.

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implications. Even when results and implications are both appropriately described, practitioners may be left with only a disjointed laundry list of scattered concepts. This paper attempts to bridge this gap by presenting recent findings within a specific framework that can be applied in financial planning practice. The understanding and application of this general decision-making framework can illuminate human behavior and provide practical insight for the financial planner. The paper is organized as follows: 1. Review of neuroscience methodologies 2. Introduction to the dual-self (“elephant and rider”) decisionmaking framework 3. Review of findings in the context of the framework 4. Application of the framework to financial planning practice Review of neuroscience methodologies The recent increasing interest in neural components of human decision-making, including financial decision-making, may be driven in large part by technological advances in our ability to measure brain activity. For many generations, the “black box” of decision-making in the brain was largely impenetrable to scientists. Decision-making models could be deduced only by observing actual behavior. But, the mechanics of such decisions were always hidden. Rare exceptions occurred in patients with damage to specific brain areas. By studying humans with naturally occurring brain damage, and creating corresponding damage in animals, neuroscientists gradually mapped the functions of several brain regions. Since then, a variety of technological advances have dramatically improved the ability to non-invasively observe brain activation during decision-making processes. One of the earliest

Journal of Personal Finance

was the Electroencephalograph (EEG) (Huettel, Song, & McCarthy, 2008). The EEG records electrical signals generated on the surface of the brain through electrodes attached to the subject’s scalp. The EEG is still widely used today. It is particularly important for its ability to track the timing of brain activation in micro-seconds. But, because readings are taken only on the surface of the brain, there is no way to determine exactly which internal brain structures are being activated. This lack of “spatial resolution” can be addressed with Positron Emission Tomography (PET) scans or functional magnetic resonance imaging (fMRI) (Huettel, Song, & McCarthy, 2008). In a PET scan, subjects are injected with a small amount of radioactive glucose. As different regions of the brain are activated, they use this glucose as fuel. The relative difference in radioactivity of different regions can be observed by the PET scan. In this way, specific activation of brain structures can be observed in three-dimensions. Although still used for some purposes today, PET scanning is now relatively rare compared with the widespread application of fMRI. Unlike PET scanning, fMRI requires no radioactive injections. The basic concept of fMRI is that it attempts to measure changes in blood oxygenation. It measures this change based upon the magnetic properties of tissue. When blood without oxygen is exposed to a magnet it becomes magnetic. When blood with oxygen is exposed to a magnet it does not become magnetic. By measuring these differences in magnetism, the fMRI machine can estimate the relative oxygenation of different parts of the brain. Because oxygen use is part of neural activity, the fMRI can reveal areas of the brain that are more or less active during various tasks. One would naturally think that oxygenation levels would be lower in active brain areas. Neuronal firing does, in fact, burn oxygen. But, in response to this small use of oxygen, the body sends a relatively large amount of oxygenated blood to the area. It is this ©2011, IARFC. All rights of reproduction in any form reserved.

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rush of oxygenated blood to the active brain regions that can be easily seen by the fMRI scanner (Huettel, Song, & McCarthy, 2008). Like PET scans, fMRI has excellent spatial resolution. Blood flow changes are measured in small cubed areas called voxels. These voxels are usually about 3 cubed millimeters (about the size of a peppercorn) but can be 1 millimeter or smaller. The blood response, however, is relatively slow, taking about 4 to 6 seconds for peak differences. So, unlike EEG recordings, fMRI scans precision is measured in seconds, not milliseconds. (However, new technology allows EEG recording simultaneously with fMRI scanning to get the best of both worlds.) Typical fMRI experiments allow subjects to see a computer screen while lying in the fMRI machine. Subjects make choices using buttons or a joystick. During these choice tasks, the fMRI is constantly collecting images of the brain. Later data analysis can reveal what regions were particularly active during different tasks or prior to particular kinds of choices. However, locating relevant active regions is always a matter of probability, not certainty. A variety of factors can cause difficulties in the process. The brain is always active in thousands of different areas. This makes brains â&#x20AC;&#x153;noisy.â&#x20AC;? The activation that occurs during a particular task may relate to that task or it may not. To overcome this, researchers typically use many subjects and have them repeat the decision task dozens of times. Ideally, as the number of task-related images increase the random noise elements wash out, leaving only consistent, relevant activations. An additional problem is that the same brain areas can perform many different, seemingly unrelated, tasks. Thus, it may not always be possible to clearly identify which function or emotion is involved.

Journal of Personal Finance

Although much financial decision-making neuroscience research involves fMRI analysis, there are a variety of other methods available. Lesion studies involve experiments with individuals who have damage to specific brain areas. The function of these areas can be identified by observing differences between normal subjects and patients with lesions in certain areas. Animal studies involving decision making in primates have made use of single-cell recording where electrodes can record the activation of a single neuron (Glimcher, 2004). Other techniques measure brain-relevant physiological changes outside the brain itself. For example, hormone studies have used blood draws before, during and after a task to measure the levels of hormones related to various human emotions such as stress or attachment. Other studies have introduced hormones to measure effects on subsequent tasks (Barrazza & Zak, 2009). A variety of studies have employed measurements of skin conductance response. Skin conductance response measures autonomic arousal, reflecting underlying emotional reactions (Buchel, Morris, Dolan, & Friston, 1998). As always, new methodologies are continually being developed in neuroscience. However, several of these have yet to be used with a specific focus on financial decision making. Thus, this review contains no studies using magnetoencephalography, single photon emission computed tomography, or transcranial magnetic stimulation. Introduction to the dual-self (â&#x20AC;&#x153;elephant and riderâ&#x20AC;?) decision-making framework Brain activation is inordinately complex. The human brain contains perhaps 100 billion neurons and 100 trillion synapses (Williams & Herrup, 1988). Thus, any useful model of brain ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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activation must necessarily be a simplistic abstraction from reality. Fortunately, there are some core realities that allow categorization of brain activity. Camerer, Loewenstein, and Prelec (2004) explained, “A crucial fact is that the human brain is basically a mammalian brain with a larger cortex. This means human behavior will generally be a compromise between… animal emotions and instincts, and… human deliberation and foresight.” This suggests the possibility of effectively modeling human decision-making as the outcome of an interaction between two different actors. Such a “dual-self” model differs from the traditional economic model of a single, rational, utility-maximizing actor. This simple distinction may be the most critical difference between standard economic approaches to financial decision making and neuroscience approaches. Sanfey, Loewenstein, McClure and Cohen (2006) concluded, “perhaps the single most important perspective that neuroscience brings is to challenge the core assumption in economics that behavior can be understood in terms of unitary evaluative and decision-making systems.” While this represents a challenge to traditional economic assumptions, a variety of economists have found such “dual-self” models particularly useful in explaining human decision-making. Economic Models Adam Smith is known as the author of The Wealth of Nations (published in 1776) and the founding father of economics. However, Adam Smith’s first book, The Theory of Moral Sentiments (published in 1758) provided the underpinnings for his later work. In it, Smith argued that the behavior was the outcome of a struggle between two internal actors. Smith labeled these actors the “passions” and the “impartial spectator.” Ashraf, Camerer, and Lowenstein (2005, p. 131) summarized Smith’s approach describing, “The passions included drives such as hunger and sex, emotions such as fear and anger, and motivational feeling

Journal of Personal Finance

states such as pain .… The spectator, in contrast, ‘does not feel the solicitations of our present appetites. To him the pleasure which we are to enjoy a week hence, or a year hence, is just as interesting as that which we are to enjoy this moment’.” In 1981, Thaler and Shefrin introduced the “first systematic, formal treatment of a two-self economic man” employing the titles of “planner” and “doer” to represent the two sides. Thaler and Shefrin (1981, p. 394) explained, “The planner is concerned with lifetime utility, while the doer exists only for one period and is completely selfish or myopic.” Similarly, Fudenberg and Levine (2006) proposed a dual-self model of impulse control. They described their model, “Our theory proposes that many sorts of decision problems should be viewed as a game between a sequence of short-run impulsive selves and a long-run patient self” (Fudenburg & Levine, 2006, p. 1449). Benhabib and Bisin (2005) developed a model of consumption-savings decisions in which the division is between automatic processes and control processes. They concluded, “Agents have the ability to invoke automatic processes that are susceptible to impulses or temptations, or alternative control processes which are immune to such temptations” (Benhabib & Bisin, 2005, p. 464). In a review of neuroeconomic theories, Sanfey, et al. (2006, p.111) described a similar dual system model, “System 1 is automatic and heuristicbased; quickly proposing intuitive answers to problems as they arise. System 2, which corresponds closely with controlled processes, monitors the quality of the answer provided by System 1 and sometimes corrects or overrides these judgments.” Like Adam Smith, other economists have colored their particular dual self-models with a strong emphasis on emotional components. Loewenstein and O’Donoghue (2004, p. 1) developed “a two-system model in which a person’s behavior is ©2011, IARFC. All rights of reproduction in any form reserved.

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the outcome of an interaction between… [an] affective system that encompasses emotions such as anger and fear and motivational drives such as those involving hunger and sex… [and a] deliberative system that assesses options with a broad, goal-based perspective (roughly along the lines of the standard economic conception).” The addition of neuroscience into the economic dual-self model holds the potential of showing that the models are not merely theoretically useful, but actually represent distinct biological systems. It is probably conceptually sufficient to think of the two systems as consisting of the more central mammalian brain and the higher cognitive functions of the prefrontal cortex. The exact location of associated functions generally follows this model, but is naturally more detailed and specific. A summary of these locational correlates by Sanfey, et al. (2006, p.112) described the current state of knowledge, “There is a general consensus that high-level, deliberative processes, such as problem-solving and planning, consistently engage anterior and dorsolateral regions of prefrontal cortex as well as areas of posterior parietal cortex. By contrast, automatic processes appear to rely heavily on more posterior cortical structures, as well as subcortical systems. Emotional processes, in particular, seem reliably to engage a set of structures classically referred to as the limbic system … and several other areas such as the amygdala and insular cortex.” Elephant and Rider Each of the previously discussed economic labels for the two actors in the dual-self model (e.g., planner-doer, spectatorpassions, affective-deliberative) emphasized characteristics particularly important to the topic at hand. Rather than employing

Journal of Personal Finance

one of these more formal academic labels, Haidt (2006) adopted an analogy that may be more useful for contemplating practical applications with clients. Haidt explained, “The image that I came up with… was that I was a rider on the back of an elephant. I’m holding the reins in my hands, and by pulling one way or the other I can tell the elephant to turn, to stop, or to go. I can direct things, but only when the elephant doesn’t have desires of his own. When the elephant really wants to do something, I’m no match for him” (Haidt, 2006, p. 4). A variety of neuroscience and behavioral results suggest that this elephant and rider analogy has much to recommend it. It visually communicates the concept of the dual-self model, and even some sense of the relative advantages and disadvantages of each player. At its core, the elephant and rider model is a neural analogy, corresponding with the description of the human brain as a mammalian brain with a larger cortex. The rider is rational, representing the larger human cortex. The elephant, representing the more central parts of the brain that are shared with other mammals, is focused only on the immediate emotional impact of any action. The following section reviews specific neurological and behavioral findings corresponding with the framework of a rational rider and emotional elephant. Review of findings in the context of the framework The rider Because the rider represents traditional rational cognition, there is no need to spend time defining the rider’s core decisionmaking processes. (For those desiring a full elaboration, any standard microeconomics textbook can provide a range of mathematical models for rational, utility-maximizing choice.) The elephant and rider analogy, however, provides additional ©2011, IARFC. All rights of reproduction in any form reserved.

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information. As in real life, elephants are much faster and stronger than their riders. Elephants can lift enormous weight with relatively little effort. In contrast, work done by the rider is relatively slow and effortful. Likewise, when compared with the amazing endurance of elephants, riders are easily exhausted. The following section reviews three relative imperfections of the ridersystem. 1. Speed deficiency (rider processes are slower than elephant processes). 2. Endurance deficiency (rider processes are easily exhaustible). 3. Overconfidence (the rider misprojects control). Speed deficiency (rider processes are slower than elephant processes) This deficiency can be most powerfully demonstrated in individuals with a specific type of brain lesion that essentially removes the elephantâ&#x20AC;&#x2122;s emotional input from decision-making processes. Antonia Damasio (1996) explained that the ventromedial prefrontal cortex (VMPC) records linkages between facts of a particular situation and emotions previously paired with the situation in memory. For example, when shown emotionally charged pictures (such as a social catastrophe or traumatic injury), normal subjects generated strong responses in skin conductance responses, indicating an emotional reaction. However subjects with damage to the VMPC region failed to generate any such changes in skin conductance response (Damasio, 1996). The emotional input was simply lost. Essentially, the VMPC serves as the communication bridge that allows the elephantâ&#x20AC;&#x2122;s emotions (generated in the more central parts of the brain) to influence the riderâ&#x20AC;&#x2122;s rational cognition (operating in the cortex). Working with patients who have damage in this VMPC region, Damasio (1996) found that this elimination

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of emotional input impaired only specific types of decision making. Such patients’ intellect remained normal as measured by conventional IQ tests. Learning and retention of facts and skills were also unimpaired, as was the ability to logically solve problems. However, patients had great difficulty in decision tasks involving a large number of potentially important considerations such as future planning or social interactions. Damasio (1994) provided an example of such behavior in attempting to schedule an appointment with such a patient. “I suggested two alternative dates, both in the coming month and just a few days apart from each other. The patient pulled out his appointment book and began consulting the calendar… For the better part of a half-hour, the patient enumerated reasons for and against each of the two dates: previous engagements, proximity to other engagements, possible meteorological conditions, virtually anything that one could reasonably think about concerning a simple date” (Damasio, 1994, p. 193). Without the emotional markers that allow people to quickly choose one option over another, these patients appeared to rely exclusively on the slow, grinding machinery of raw cognitive processing. In other words, decisions emanated purely from the rational rider without input from the emotional elephant. Maruamtsu and Hanoch (2005) explained that emotional decisions “are fast because they rely on few cues, thus dispensing with much computational effort.” According to the “somatic marker hypothesis”, these cues are stored as emotional markers (Damasio, 1996). When a similar scenario or scene emerges, the similarity triggers the marker, which causes the body to physically respond. These physical changes then influence the resulting decision making process. One result of the relative speed of these emotional markers (and the related physical changes) is that emotion-related physical ©2011, IARFC. All rights of reproduction in any form reserved.

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changes occur well before any conscious cognitive awareness. In other words, when developing strategies for complex choice tasks, there is evidence that the emotionally charged areas of the brain identify and engage in appropriate strategies even before the rational side is aware that a strategy is being employed. Bechara, Damasio, Tranel, and Damasio (1997) reported results from a card selection game. Subjects selected cards with financial rewards and penalties from four different decks. Unknown to them, two of the decks had larger rewards but also much larger penalties and were thus less desirable. After about the 10th card selection, normal participants began to generate high skin conductance responses prior to selecting from the disadvantageous stack, suggesting a high emotional response to the losing deck. Even after selecting 20 cards, however, none of the participants had any opinion about the nature of the differences in the decks. It was not until the 50th card that subjects, on average, began to express a “hunch” as to which decks were riskier. By the 80th card, many were able to clearly state why certain decks were undesirable. Apparently, the riskiness of the decks was being signaled very early by the emotions, but was not recognized by higher cognitive functions until much later. Accordingly, patients with VMPC damage never generated any increased skin conductance responses prior to selecting from the disadvantageous decks. Neither did such patients ever express a “hunch” about deck characteristics. The ultimate concept developed through a variety of such studies is that quickly and efficiently making potentially complex decisions requires more than the rider’s pure rational cognition. Instead, complex decision-making processes must rely on emotional markers developed by the more central parts of the brain. As Muramatsu and Hanoch (2005) argued, it is the “fast and frugal” nature of emotional decision mechanisms that make them so important and useful. Understanding the relatively slow and

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effortful nature of the rational rider’s processing helps to remove the notion that the behavioral goal is somehow to eliminate or quash the emotional elephant’s participation. Endurance deficiency (rider processes are easily exhaustible) Not only is the rational rider side relatively slow, but it is also easily exhaustible. The cognitive resources of the rider side can be quickly depleted with extended effort. During the rider’s interaction with the elephant, the rider can be thought of as a representation of self-regulation. Self-regulation is cognitively complex, but largely located in one brain region. As explained by Banfield, Wyland, Macrae, Munte, and Heatherton (2004, p. 62), “self-regulation refers not only to executive processes such as working memory, attention, memory, and choice and decision making, but also to the control of emotion (covering issues of affect, drive, and motivation). The primary brain region responsible for these control functions is the prefrontal cortex .…” A series of experiments suggests that the capacity of the prefrontal cortex to control basic emotional affective drives can be substantially diminished by prior or simultaneous effortful tasks involving the same brain region. In other words, the ability of the rider to regulate elephant-side diminishes quickly with repeated or simultaneous effort. Roberts, Hager, and Heron (1994) conducted an early experiment testing the ability of subjects to inhibit a natural reaction through conscious control. In this case, subjects attempted to avoid looking at the side of a computer screen on which a flashing box appeared. When subjects were simultaneously read single digit numbers and required to add them together, the ability to inhibit the natural eye movement reaction following the flashing box decreased substantially.

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Shiv and Fedorikhin (1999) conducted a related experiment with more relevance to real world choice. Subjects were asked to memorize either a 2-digit or 7-digit number. While maintaining that number in memory, subjects selected which desert they would receive for lunch – either a healthy fruit salad or chocolate cake. The group memorizing the longer number chose the chocolate cake much more frequently (41% v. 63%). The results again suggest that the memory tasks partially exhausted the control of the affective drive for immediate gratification. Ward and Mann (2000) conducted a similar experiment with undergraduate females pursuing a calorie restricted diet. During a 10 minute session participants were asked to sample from a variety of snack bowls while completing either a simple reaction time task or a challenging memorization task. The group of dieters engaged in the memorization task consumed substantially more grams of snacks than did the other dieters. The authors explain, “the high cognitive load succeeded in narrowing restrained eaters' attention away from normally present inhibitory pressures” (Ward & Mann, 2000, p. 758). This potential for environmental pressures to periodically exhaust rider control also explains a core phenomenon in negative addictions and substance abuse: that constant availability is a key predictor of consumption. Mann (2005, p.924) explained, “alcohol problems vary with alcohol availability; this body of evidence is among the strongest bodies of evidence in existence linking health problems to determinants.” Room, Babor, and Rehm (2005, p. 526) found that “Drinking and alcohol related problems can be affected by restriction of the hours and days of alcohol purchasing and of the numbers and types of alcohol outlets.” Similarly, Ahmed (2005, p. 11) found “drug availability represents a major risk factor. Increased drug availability can precipitate the transition to addiction.” A rational decision not to consume may be fulfilled for

Journal of Personal Finance

an extended period of time. But, various factors leading to periodic exhaustion of rider control, combined with persistent availability of a temptation, inevitably increase the likelihood of consumption. Cognitive exhaustion can also result from the effort involved in making multiple sequential decisions. This concept of cognitive exhaustion can be seen in a variety of sales techniques involving multiple sequential requests. Once earlier or preliminary requests have exhausted cognitive resources, people tend to fall back on more emotional or social oriented responses, often increasing the likelihood of compliance. Examples of this include a sequence of rejection-then-moderation requests, where a large initial request is gradually reduced in a series of subsequent requests (O’Keefe & Hale, 2001). In a similar approach, the “That’s-Not-All” technique begins with an initial request followed by a series of subsequent requests that are progressively more desirable (Pollock, Smith, & Knowles, 1998). Alternatively, the “Foot-In-The-Door” technique starts with a very small request, followed by more substantial ones (Burger, 1999). In a review of all of these techniques, Fennis and Janssen (2010) concluded, “We propose that all sequential request techniques essentially trigger one underlying psychological mechanism that accounts for their impact: that of self-regulatory resource depletion.” Consistent with this notion, a review of 30 years of research on the “Foot-In-The-Door” sequential request technique found that the approach was more effective whenever the initial request was more cognitively demanding (Burger, 1999). Such cognitive exhaustion techniques are effective in a context where the salesperson’s social pressure creates a default behavior of complying with a specific request. In the absence of a simple, socially-preferable default compliance path, however, the typical result of cognitive exhaustion is inaction. For example, ©2011, IARFC. All rights of reproduction in any form reserved.

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Iyengar and Lepper (2000) found that consumers sampling premium jams purchased 30% of the time when there were 6 jams to choose from, but only 3% of the time when there were 24 jams. More relevant for financial planning, Botti and Iyengar (2006) studied 401(k) participation among 800,000 eligible employees at 657 companies. They found the highest rate of participation at companies with only two fund choices. As fund offerings increased, participation rates generally fell. For example, in companies offering 60 fund options, participation was nearly 15 percentage points lower than in companies with only 2 options. Overconfidence (the rider misprojects control) One of the greatest challenges in managing the relative limitations of the rider side in terms of speed, strength, and endurance, is that these limitations are persistently misperceived. Retrospectively, the rider may have a tendency to construct stories explaining actions in cognitively rational terms, regardless of the original motivations. This behavior was seen in early studies of patients following brain bisection where split visual screens could give instructions exclusively to one side of the brain (Gazzaniga, 1983). For example, the non-language right hemisphere would be given an instruction (such as “laugh”), followed by a request to verbally explain why the action was taken (a left hemisphere function). The language-producing left hemisphere would construct a rational, but inaccurate, story to explain the behavior (such as “because you said something funny”). Gazzaniga (1983, p.536) explained this as “the left hemisphere’s unrelenting need to explain actions taken from any one of a multitude of mental systems that dwell within us.” Although this retrospective evaluation of behavior is of interest, a more problematic issue is that when unemotionally predicting the future, people persistently over-project rational

Journal of Personal Finance

control relative to elephant-side desires and emotions. People project that they will prefer things that are good for them in the future, but just not right now. So, for example, when asked to choose between a healthy snack and chocolate for delivery in one week, 74% of subjects in Reed and van Leeuwen’s 1998 study chose the healthy snack. But, when given the chance to change their selection at the point of delivery, only 30% stayed with the healthy choice. Similarly, only 15% of light smokers (less than one cigarette per day) in high school predicted that they might be smoking in 5 years, but 5 years later 43% were still smoking (Johnston, O’Malley, & Bachman, 1993). Haidt (2007, p. 990) summarizes a wide variety of studies by explaining “people are generally more accurate in their predictions of what others will do than in their (morally rosier) predictions about what they themselves will do.” In a similar vein, Epley and Dunning (2000) report, “Researchers have repeatedly demonstrated that people on average tend to think they are more charitable, cooperative, considerate, fair, kind, loyal, and sincere than the typical person but less belligerent, deceitful, gullible, lazy, impolite, mean, and unethical---just to name a few.” The greatest mechanism by which the rider can influence future choices is to change the future decision environment, usually by a form of pre-commitment. However, to the extent that people mis-project their own future rational control, such devices will seem unnecessary and undesirable. So, it is not only the weaknesses of the rider side that create undesirable choices, but it is also the tendency to ignore these weaknesses when planning for the future. The elephant In contrast to the rider’s cognitive deliberation, the elephant, representing the more central parts of the brain shared with other ©2011, IARFC. All rights of reproduction in any form reserved.

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mammals, weighs only the immediate emotional impact of any action. There is, of course, an extensive range of factors associated with these elephant-side processes. However, for purposes of application to decision making, there are three crucial distinctions that characterize elephant-system thinking. 1) Myopia (the elephant focuses on now) 2) Somatic marker processing (the elephant activates by tangible emotional/social imagery) 3) Loss aversion (the elephant fears relative loss) The following sections review some evidence implications for each of these three characteristics.

and

Myopia (the elephant focuses on now) A key characteristic of the elephant-side system is its focus on immediate, rather than future, rewards. The issue of trading current for future rewards, or time discounting, has long been of interest to economists (Samuelson, 1937). In the traditional model, consumers have a consistent discount rate, modeled as an exponential decay function. This consistent discount rate means that all future delays of the same length are treated the same. Behavioral results, however, do not support this consistency when immediate rewards are available (Laibson, 1997; Prelec & Loewenstein, 1991; Thaler, 1981). Typically people will choose $10 now rather than $11 in a month. But, those same individuals will prefer $11 in 13 months rather than $10 in 12 months. Both cases involve a one-month delay, but the willingness to wait differs significantly depending upon whether or not an immediate reward is available. One popular way to represent this reality is to employ a beta-delta (a.k.a., quasi-hyperbolic) model of time discounting (Laibson, 1997). This beta-delta model represents the interaction of two time discounting functions, one sharply focused on immediate rewards, the other representing the time consistent

Journal of Personal Finance

rational discounting of earlier economic models. Such a two-part model corresponds with a dual-self model of decision-making. McClure, Laibson, Loewenstein, and Cohen (2004) conducted the first fMRI study attempting to identify whether or not separate brain regions actually represented the two parts of the proposed dual-self interactive system. In their study, subjects repeatedly chose between two different payments. All payoff sets involved a choice between an earlier (smaller) and later (larger) monetary payment. In some of the payoff sets the earlier payoff was immediate. In others, the earlier payoff was in two weeks or a month. For those payoff sets including an immediate potential payoff, the fMRI showed much greater activation in the classic limbic structures generally associated with emotional reward. (These areas have heavy neuronal connections with the brain’s dopamine system.) In contrast, the areas involved in higher-level cognitive functioning were equally engaged in all decision sets. This is consistent with a dual-self model where one side cares only about immediate rewards. This immediacy focused side essentially doesn’t participate in decisions comparing two future rewards. Conversely, the higher-level rational side is equally concerned about all tradeoffs, regardless of when the earliest payoff occurs. As further evidence of the interacting dual-self model, McClure, et al. (2004) found that the higher cognitive areas were relatively more active than the immediate reward areas when the delayed choice was selected, and relatively less active than the immediate reward areas when participants chose the earlier payoff. McClure, et al. (2004, p. 503) concluded that “separate neural systems value immediate and delayed monetary rewards.” A later study found the same phenomenon to be true when the rewards presented were not monetary but “primary”, i.e. fruit juice or water given to thirsty subjects (McClure, Ericson, Laibson, ©2011, IARFC. All rights of reproduction in any form reserved.

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Loewenstein, & Cohen, 2007). Again, the limbic reward area activation was greater for choices between an immediate reward and delayed reward than for choices between two delayed rewards. Higher cognitive areas responded similarly to either type of choice sets. As before, relative activation of the limbic system as compared with the higher cognitive system predicted whether the earlier or later reward would be selected. McClure, et al., (2007, p. 5796), pointed out “Humans share this limbic reward system with many other mammalian species, none of which respond to costs and benefits delayed more than a few minutes, except in a rigid, preprogrammed manner.” This coincides with a dual-self analogy based upon the shared mammalian brain (“elephant”) and the uniquely human higher-cognitive faculties (“rider”). The beta-delta model is not universally accepted among fMRI studies of time discounting. Although employing a betadelta model, Witmann, Leland and Paulus (2006) found no significantly greater brain activation when subjects chose immediate rewards over delayed. However, in that study all rewards were purely hypothetical, and subjects knew the choices were not real. Others have argued for a unitary representation of reward valuation in the brain (Kable & Glimcher, 2007). Carter, Meyer, and Huettel (2010), in a review of findings from all such fMRI studies, argue that the evidence supports an intermediate perspective, validating both views. In essence, this is possible in a system where a beta-delta type model is engaged in the valuation process, but the process later ends with a single value signal representation. Taking a different approach, Bechara and Damasio (2005) argue for greater emotional involvement of immediate outcomes based upon the structure of the previously discussed VMPC. They explain, “The organization of the VM[PC] region in relation to

Journal of Personal Finance

time (i.e., “near future” is processed more posteriorly, whereas “distant future” is more anterior) explains why information conveying immediacy trigger stronger somatic [emotional] responses, and therefore exert a stronger bias on decisions, than information conveying delayed outcomes.” In summary, there is neurological support for the use of a dual-self model with a shared mammalian side responding strongly to immediate visceral or emotional experiences competing with a higher cognitive rational side coolly weighing all outcomes. Somatic marker processing (the elephant activates by tangible emotional/social imagery) As discussed previously, elephant-side responses are dramatically faster than the relatively slow processes of higher level cognition. This speed comes from the use of emotional markers. According to the “somatic marker hypothesis,” emotions attach to particular scenarios or images (Damasio, 1996). With relatively minimal cues linked to the marked scenario, a complex assortment of emotions is instantly retrieved and physiologically experienced (Muramatsu & Hanoch, 2005; Damasio, 1996). Naturally, these emotions easily attach to scenarios based upon actual experience. Bechara and Damasion (2005) labeled such experiences as “primary inducers.” It comes as no surprise that one’s personal life experiences can dramatically influence subsequent decision making on an emotional level. But, of critical importance is the existence of “secondary inducers” (Damasio, 1995; Bechara & Damasio, 2005). These secondary inducers include imagination of hypothetical emotional events. For example, “The imagination of being attacked by a bear, winning an award, or losing a large sum of money, are also examples of secondary inducers” (Bechara & Damasio, 2005, p. 340). Emotional imagery, and corresponding emotional markers, ©2011, IARFC. All rights of reproduction in any form reserved.

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can thus be generated through the conscious effort of imagination. Consequently a goal, such as a financial goal, can be consciously built into an emotional goal through the generation of emotional imagery. Such images, however, must be of concrete or tangible scenarios rather than of abstract concepts. It is instructive here to conceptualize the “elephant” side as being shared mammalian components. Other mammals have little capacity for language, mathematics, or text. Instead, animal cognition likely relates to images and circumstances, rather than abstract concepts. The significance of tangibility may also relate to neural structures. Again, returning to the importance of the VMPC, a bridging structure incorporating emotional markers into decision making, Bechara and Damasio (2005, p. 358) explained, The organization of the VM cortex along the axis of “concrete/tangible” to “abstract” may also explain why, for instance, people have an easier time spending money on credit cards as opposed to spending real money. Similarly, spending money becomes no object when a disease threatens the life of a loved one, and so on. This is because credit is more abstract than money, and money is more abstract than losing a “bond” from a loved one. One might even think of tangibility in terms of translating it to the experience of other mammals. In a laboratory setting monkeys have been trained to use simple currency markers that can be exchanged for food (Chen, Lakshminarayanan, & Santos, 2006), suggesting that tangible money is within the realm of mammalian (at least primate) understanding. No such accomplishment has been achieved with analogies to credit cards, retirement plans, insurance, or other sophisticated financial

Journal of Personal Finance

devices. Thus, such abstract concepts are unlikely to automatically generate emotional meaning unless tied to more tangible (“mammal-friendly”) imagery. Perhaps the most plentiful source of tangible emotional scenarios across mammalian experience is social relationships. In the natural world, this is no small issue. For most social mammals, access to food and reproductive opportunities depends largely upon the individual’s relative social status within the group, and the group’s relative social standing among other groups. In humans, social relationships are the most frequent instigators of emotion. Kemper (1991) explained, “when respondents are asked for instances in which they experienced certain emotions, invariably and with very high frequency they report contexts involving social relations.” Consequently, the most powerful imagined scenarios influencing elephant-side behavior are likely to be those involving significant others. Such social influences can have powerful effects on financial outcomes. Social pressure has been used with dramatic effect in microfinance programs in poorer regions of the world. Microfinance deals mainly with poor borrowers who could not qualify for conventional loans. Borrowers function in groups, each being responsible both for their own and for other group members’ payments. Individually, these borrowers may have irregular sources of income and may lack any meaningful collateral to protect against default. Yet, the social nature of these obligations often results in unexpectedly high repayment rates. Smets and Bähre (2004, p. 216) explained, “Such conventional collateral has to a large extent, if not completely, been replaced by nonconventional or social collateral. The strength lies in the social constraint mechanism among participants; they exert pressure on each other or on specific participants to encourage payments to be ©2011, IARFC. All rights of reproduction in any form reserved.

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made on time.” Similarly, Woolcock (1999) provides evidence that microfinance repayment success is not simply a matter of lending policies, but is instead dependent on the nature and extent of social relations among and between group members and staff members. Less formal peer relationships can still exert significant influence, even on more sophisticated financial choices. Duflo and Saez (2002) studied tax deferred accounts of faculty members at a large university and found substantial peer effects in investment decisions. Controlling for other factors, when participation in a retirement savings program by other members of a department increased by 1.0 percent, this peer influence, on average, resulted in a 0.2 percent increase in one’s own retirement savings. Similarly, when the average share of the contribution invested in one particular vendor increased by 1.0 percent among other members of the department, the peer influence alone resulted in a 0.5 percent increase in one’s own usage of that vendor. In sum, the elephant side appears to process information through emotional markers attached to images or scenarios. Most emotional content comes from social connections and relationships. Scenarios or images that are more tangible (such as social relationships and physical objects) will have greater impact than abstract concepts that are not common to general mammalian experience Loss aversion (the elephant fears relative loss) Having an aversion to losses can be perfectly rational. However, an excessive aversion to losses occurs when the risk of loss is heavily overvalued compared to an equivalent potential for gain. (For the remaining paper, “loss aversion” will refer to this myopic overvaluation of the pain from loss as compared with the benefit from an equivalent gain.) For example, Benartzi and

Journal of Personal Finance

Thaler (1995) showed that when gambles involving possible loss are presented one at a time, most people exhibit myopic loss aversion. Thus, most people will not accept a 50-50 chance to gain $200 or lose $150. This core asymmetry in valuation of equivalent gains and losses results in a variety of persistent, non-maximizing choice behaviors. Loewenstein, Weber, Hsee, and Welch (2001) suggest that this behavior is rooted in emotional, rather than cognitive, processing. If so, then excessive loss aversion may have origins in related dual-system neural circuitry. As mentioned previously, the elephant system represents shared mammalian features. As such, it is not surprising that fear of loss appears as the single most powerful emotion for the elephant. An animal regularly giving precedence to other emotions over well-founded fear will very quickly become someone else’s lunch. (This fear may center on the potential for loss of life, loss of bodily integrity, loss of social standing, loss of resources, or other losses, but, in most circumstances fear is centered on the potential for some loss relative to the status quo.) In support of the shared mammalian nature of this elephant-system characteristic, Chen, et al. (2006) reported loss aversion bias in Capuchin monkeys that had been taught to trade small coin-like disks in exchange for food rewards. Fear of loss is urgent, powerful, and tends to dominate other considerations. As Muramatsu and Hanoch (2005, p. 204) explained, “elicitation of fear prompts agents to focus their attention on the importance of the incoming stimulus (while ignoring all other pieces of information).” Fear reactions are automatic, coming from neural circuitry centered on the amygdala (Öhman & Mineka, 2001). As discussed above in the somatic marker hypothesis, this neural circuitry communicates to decision making processes through the VMPC. ©2011, IARFC. All rights of reproduction in any form reserved.

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Shiv, Loewenstein, Bechara, Damasio, and Damasio (2005) tested the origins of loss aversion with a group of individuals having damage to the VMPC, amygdala, or other components of the neural circuitry necessary to engage emotion in decision tasks. In this experiment, subjects were give $20. In each of 20 rounds they could decide to play a dollar or keep a dollar. If a subject played a dollar, the experimenter flipped a coin in front of the subject. If the coin landed on heads, the subject lost the dollar, otherwise the subject received $2.50. The compensation maximizing behavior was to play every round. Normal participants played in 57.6% of the rounds, exhibiting strong loss averse behavior. In contrast, subjects with damage to emotional neural circuitry played in 83.7% of rounds, showing less loss aversion. Although there was no rational reason to do so, normal subjects were much more likely to play immediately after a win (61.7%) than immediately after a loss (40.5%). Patients with damage to emotional circuitry exhibited much less of this nonrational behavior, investing almost identically after a win (85.2%) as after a loss (84.0%). For normal subjects the likelihood of playing each round decreased consistently as the game progressed. Presumably, as normal subjects accumulated more loss experiences, they became less likely to play. Thus, it appears that without the emotional elements of experiencing loss, the resulting choices by patients with neural damage were more rational. Results from Damasio (1994) support this difference in felt emotional experiences resulting from a loss. Subjects selected cards from different decks with various loss and gain outcomes. To measure emotional, autonomic system activity, researchers recorded subjectsâ&#x20AC;&#x2122; skin conductance responses. Normal subjects responded with high amplitude skin conductance responses whenever selecting from a deck with greater previous loss experiences. VMPC-damaged participants showed no such

Journal of Personal Finance

changes in skin conductance responses. Later, Bechara and Damasio (2005) reported that amygdala damaged patients performed similarly to VMPC-damaged patients on the card selection task. These amygdala-damaged patients also failed to generate high amplitude skin conductance responses when selecting from a deck with greater previous loss experiences. De Martino, Camerer, and Adolphs (2010) also tested patients with damage to the amygdala. Although the patients retained a normal ability to respond to changes in risk and expected value, they displayed dramatically lower loss aversion than normal comparison players. In contrast, in a subsequent test involving a series of risky choices with no potential for loss, there were no significant differences between the amygdala-damaged and control groups. One fMRI study did not find a detectable BOLD signal in the amygdala with loss-averse behavior (Tom, Fox, Trepel, & Poldrack, 2007). However, this may have been caused by the much smaller and less frequent losses in this study as compared with the previous examples. Another suggestion is that quick spiking in the amygdala is less likely to be observed in the fMRI, which more easily detects the longer processing in structures receiving input from the amygdala (De Martino, Camerer, and Adolphs, 2010). Physiological differences in emotional reactions to equivalent gains and losses were demonstrated in a study by Sokol-Hessner, et al. (2009). They found, using normal subjects, that the average skin conductance response per dollar lost was significantly greater than the average skin conductance response per dollar gained. Similar results were obtained by Hochman and Yechiam (2011) who found that losses led to greater autonomic responses as measured by pupil dilation and heart rate than did equivalent gains. ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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This core issue of loss aversion can be used to explain a wide range of inconsistent behaviors demonstrated in behavioral studies. For example, loss aversion can help to explain behaviors such as the endowment effect (Thaler, 1980). The endowment effect is the tendency for people to value items more highly once they have taken ownership of them. Once a person has ownership of an item, giving that item up involves a feeling of relative loss. In a trade, the gain may ultimately outweigh the loss, but if equivalent gains (e.g., the new item) are less important than losses (e.g., the owned item), the tendency will be to keep the owned item. If instead, a person is simply choosing between two items not yet owned, the selection is between two potential gains and does not involve loss aversion. Kahneman, Knetsch, and Thaler (1990) reported results where students in every other seat were given university labeled mugs to keep. All students were later asked to value the mugs. Those who had been given the mugs to keep valued them, on average, at twice the price of those who had not received the mugs. Similarly, Knetsch (1989) reported that when students were given a coffee mug at the beginning of class and had the opportunity to trade for a chocolate bar at the end of class, 89% kept the coffee mug. But, when students were given a chocolate bar at the beginning of class and allowed to trade for a coffee mug at the end of class only 10% traded for the coffee mug. Corresponding with the concept that this phenomenon is driven by shared mammalian characteristics, a similar endowment effect has also been demonstrated in chimpanzees, in that case trading peanut butter and popsicles (Brosnan, et al., 2007). Loss aversion also relates to the sunk-cost effect. In this phenomenon, people allow previous unrecoverable expenditures (i.e., sunk costs) to influence subsequent decisions. If a previous investment is abandoned, then it must be framed as a loss. However, if additional resources are put into the same investment,

Journal of Personal Finance

then it is still possible that eventually the combined net result will no longer be a loss. So, for example, Thaler and Johnson (1990) found, “in the presence of prior losses, outcomes which offer a chance to break even are especially attractive.” This desire to avoid accepting a loss can influence an investor’s decision to sell or hold stocks. Odean (1998), tracking 10,000 brokerage accounts and 162,948 trades, found that in any one year 14.8% of all gaining stocks were sold, but only 9.8% of all losing stocks. This occurred even though such action was detrimental to investor gains. (The subsequent performance of losing stocks held was 1.06% worse than the market, while the subsequent performance of winning stocks sold was 2.35% better than the market.) In a similar fashion, loss aversion can be used to explain status quo bias. Status quo bias is the tendency for people to choose to maintain the current state of affairs, even if it wasn’t chosen by them. A deviation from the status quo typically brings with it the risk of loss or gains (compared with the current path). But, to the extent that losses are felt more strongly than equivalent gains, the potential gains must be much more substantial than potential losses in order to motivate a deviation from the status quo. Thus, individuals may be willing to maintain the status quo even if the expected outcome is somewhat less advantageous than another alternative. Status quo bias is not necessarily an issue of risk aversion – the status quo may contain a good deal of risk. Instead, the key issue is using the present path as the comparison point for defining gains and losses. If the alternative path is selected and it ends up being worse that the status quo, then the decision results in a relative loss. But, if the status quo is selected and the alternative path ends up with a better outcome, then the decision results only in a missed relative gain. Because an experienced loss is much ©2011, IARFC. All rights of reproduction in any form reserved.

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more distressing than a missed opportunity for gain, the status quo is more emotionally safe. Kahneman and Tversky (1982) reported that people subjectively expected this emotional difference. Where stock A subsequently outperformed stock B, most believed that an investor who traded stock A for stock B would feel worse than an investor who decided not to trade stock B for stock A. The missed opportunity was the same in both cases. However, trading out of stock A can be perceived as a loss from the prior status quo, where holding stock B was only a missed gain opportunity. Nicolle, Fleming, Bach, Driver and Dolan (2011) examined this link between status quo bias and feelings of regret in an fMRI study. Using a visual detection task with a default response, they found subjects reported greater regret when making an error by rejecting the status quo (default) than when making an error by staying with the status quo (default). Analysis of fMRI data indicated that the anterior insula and medial prefrontal cortex showed increased activation after status quo rejection errors than after status quo acceptance errors. Both areas are typically involved in error processing, but the anterior insula is particularly important for subjective feeling states. Additionally, other research has suggested that the insula plays a role in both fear and anxiety (Shin and Liberzon, 2010). As such, the fMRI results are consistent with a greater emotional or fear response to status quo deviation errors than to status quo maintenance errors. As further examples of status quo bias, Samuelson and Zeckhauser (1988) had subjects choose between a moderate risk investment and a high risk investment for hypothetical inherited money. If the inherited money was already in the high-risk company, most (56%) opted to leave it in the high risk company. If the inherited money was already in a moderate-risk company,

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most (63%) opted to leave it in the moderate risk company. This difference occurred even though the tax and brokerage charges for changes were inconsequential and the precise nature of the risk (percentage likelihood of various outcomes) was known. In the context of insurance selection, Harvard employees who started when there were few health insurance options tended to stay with initial choices, even when new options became available. Employees who began work later, and were thus not subject to this same status quo bias, were much less likely to choose the older plans (Samuelson & Zeckhauser, 1988). Similarly, employees often accept a company’s default retirement plan even when it leads to poorer investments (Thaler & Sunstein, 2008). Perhaps more startling, Ritov and Baron (1990) presented subjects with a vaccination scenario in which their child had a 10 in 10,000 chance of dying from the flu without vaccination. If the proposed vaccine eliminated this risk, but had a 6 in 10,000 chance of causing the child’s death, most subjects would choose not to vaccinate. One possible explanation is that the vaccination had too great of a chance of killing a child who would otherwise have been healthy – a clear loss compared to the status quo. The potential for saving a child with the vaccine might be viewed as a useful opportunity for a gain as compared to the status quo, but not as emotionally impactful as the opportunity for a loss. (Additionally, this scenario could also relate to the time preference of the elephant side, as death from vaccination would presumably be more immediate.) The general bias towards inaction also makes sense in the context of the somatic marker hypothesis. If a proposed action is connected with a loss image, then contemplating taking the action will immediately trigger the loss image and related bodily responses. But attaching emotion to inaction may be more ©2011, IARFC. All rights of reproduction in any form reserved.

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difficult. Inaction is a general default state, not specific to any one scenario. Perhaps doing nothing cannot be meaningfully attached to a specific emotion. Thus, loss aversion might be thought of more specifically as an aversion to identifiable actions that generate loss. In an fMRI study, Fleming, Thomas, and Dolan (2010) tested whether or not status quo preference increased with cognitive complexity. (Such a result would be expected where a loss-averse elephant interacts with an easily exhaustible rider.) Using a visual detection task, they found that subjects tended to favor the default only when making difficult decisions, but not when making easy ones. Their neuroimaging analysis suggested that overcoming this status quo bias in a high complexity scenario required increased drive from the inferior frontal cortex to the subthalamic nucleus. In a sense, overcoming the elephantâ&#x20AC;&#x2122;s status quo bias in a complex task required special effort on the part of the rider (here representing activity in the inferior frontal cortex). In reviewing the various permutations of loss aversion on human choice, it is clear that the conceptual framing of a scenario is important. Behavior may change dramatically depending upon whether or not a choice is framed in terms of potential gain or potential loss. A classic example of this framing effect was provided by Tversky and Kahneman (1981). Subjects were asked to imagine that the U.S. was preparing for an outbreak of a disease expected to kill 600 people. When the two options were characterized as either (A) 200 people will be saved or (B) 1/3 chance that 600 people will be saved with 2/3 chance that no people will be saved, 72% of subjects chose option A. When the two options were framed as either (A) 400 people will die or (B) 1/3 chance that nobody will die with 2/3 chance that 600 people will die, only 22% of subjects chose option A. In both cases,

Journal of Personal Finance

options A and B were identical (e.g, if 600 were expected to die, saving 200 is the same as 400 dying). Only the framing of the options changed. But that framing change led to a strong behavioral shift. Loss aversion led to a preference for even a small chance that no loss would be encountered, but only when the options were described in terms of losses. Reframing identical gambles as losses or gains was shown to impact Capuchin monkey trading behavior as well. In Chen, et al. (2006), monkeys could trade tokens with one of two experimenters. In both cases monkeys had an equal probability of getting either one or two apple pieces. The “gain frame” experimenter always showed monkeys one apple piece. After receiving the coin, the experimenter would give the monkey one apple piece and, with 50% probability, also add a second apple piece. The “loss frame” experimenter always showed two apple pieces. With 50% probability this experimenter would only give one of the displayed pieces to the monkey, dropping the other into an opaque box. All monkeys strongly preferred trading with the “gain frame” experimenter. (In the immediately prior experiment, monkeys had strongly preferred the other experimenter, suggesting the effect was not due to a general preference for one experimenter). Application of the framework to financial planning practice A persistent and recurring issue in financial planning practice is the need to successfully motivate clients to take actions in their own long term interests and to avoid actions that sabotage their long-term goals and plans. The rider-elephant framework can be used to create and analyze specific strategies to accomplish this sometimes daunting task. Implementing this framework means dropping the notion that clients are always perfectly rational actors ©2011, IARFC. All rights of reproduction in any form reserved.

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who will persistently do what is in their long-term interests. Instead, client actions and decisions are understood as the outcome of an interaction between a rational rider side and an emotional elephant side. The rational rider side characteristics are speed deficiency, endurance deficiency, and overconfidence. The emotional elephant side characteristics are myopia, somatic marker processing, and loss aversion. Discussed below are a variety of potential strategies resulting from the application of this framework. Use the outsider’s advantage Financial planners begin with an advantage that clients don’t have. This advantage is separate from any advanced technical knowledge the planner has acquired. Planners have a major advantage in identifying appropriate courses of action based simply on the fact that they are not the clients. Each of us will have elephant-system involvement in our own losses. Each of us will have elephant-system temptations for our own immediate consumption. Each of us will be emotionally influenced in our decision for ourselves. However, these elephant-system influences have little effect on our decisions on behalf of someone else. It is easy for us to remain emotionally detached regarding someone else’s loss. We can be completely objective and rational about another person’s temptations. This is not to say that planners should be unempathetic. The point is that planners are in the rationally advantageous position of being removed from the first person experiences. In other words, you are a better planner for me than I am simply by virtue of the fact that you are not me. Taking advantage of this position, planners not only can reduce the negative influences of elephant excesses, but can also recognize the potential fragility of a client’s stated rider-system intentions. Fortunately, the rider system’s persistent overconfidence does not apply to our evaluations of others. Planners can design for

Journal of Personal Finance

scenarios that will help clients even in moments of cognitive exhaustion and temptation. Seek illiquidity in investments Seeking illiquidity, of course, contradicts all traditional assumptions about investment choice. In traditional finance, illiquidity is undesirable and requires offsetting compensation. However, in the context of the elephant-rider interaction, illiquidity can be extraordinarily valuable. Accomplishing long-term goals requires that invested funds not be consumed prematurely. But, highly accessible funds are a constant temptation for the elephantsystem, which is always focused on immediate, emotionallyinteresting experiences. Given that the rider system is easily exhaustible, constant availability of the elephantâ&#x20AC;&#x2122;s temptation increases the likelihood of large and small unplanned expenditures. Two sources of illiquidity will help reduce elephant temptation. First, delayed access makes funds less tempting. The elephantsystem is intently focused on immediate experience. Funds that require administrative delays of several days to access, perhaps with a series of forms to be completed, are of little interest to the elephant. Second, attaching losses to withdrawals can also protect against elephant intrusion. One of the elephantâ&#x20AC;&#x2122;s core characteristics is a fear of losses. Attaching an immediate loss to the act of withdrawing funds prevents the elephant from being tempted. The immediate emotional experience of taking an unnecessary loss is not attractive to the elephant. Consider how common it is for people to build wealth in illiquid assets. The personal residence (30% of total wealth), closely-held businesses (18%), other real estate (11%), and retirement plans (9%) are dominant sources (68%) of household wealth (Wolff, 1998). These assets make up an even larger portion (83%) of all household wealth for those outside the top 20% of all ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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wealth holders (Wolff, 1998). Yet, none of these assets can be quickly liquidated without taking major penalties (at least until certain ages with retirement plans). None of these assets generate elephant-system temptation to consume because of their illiquidity. Laibson (1997) goes further to suggest that financial market innovations that increased liquidity are partially to blame for the decline in the U.S. savings rates. Seek illiquidity in personal debt When financial products allow immediate access to borrowing against otherwise illiquid assets, such as the personal residence, the advantage of illiquidity is damaged and temptation for immediate consumption grows. As Laibson (1997) explains, By enabling the consumer to instantaneously borrow against illiquid assets, financial innovation eliminates the possibility for partial commitment. This has two effects on the welfare of the current self. First, the current self no longer faces a self-imposed liquidity constraint and can therefore consume more in its period of control. Second, future selves are also no longer liquidity constrained and may also consume at a higher rate out of the wealth stock that they inherit. (p. 645) When personal credit is instantly available for use in consumption, it can become a persistent attractive temptation for the elephant system. For some clients, reducing the total number of credit cards and lines of credit can be a positive step in this direction. However, so many sources of instant credit exist that this may be of only limited help. A more effective approach can be the use of a security freeze. A security freeze (a.k.a. credit freeze) prevents access to the consumerâ&#x20AC;&#x2122;s credit file. No new credit lines of any source will be

Journal of Personal Finance

available while the freeze is in place (although current credit lines can still be used). The access can be lifted by using a PIN or, if the PIN is lost, by mailing in proof of identity. For clients needing intervention with ongoing consumer credit use problems, consider keeping half of the PIN in your office and half with the client. Or in extreme cases consider intentionally destroying the PIN, so that any new access to credit requires the arduous process of gathering and mailing in a variety of personal identification documents. The delay created by a security freeze is not problematic for planned credit transactions such as the purchase of a house or car. But, especially where access to the PIN requires additional steps, it prevents immediate elephant-side temptation for new, easy-credit expenditures and additional credit cards. Further, such a technique may be especially helpful in the case of married couples where one partner has substantially greater problems with overusing consumer credit. In some cases, the rider-side will tend to over-project future control and may not find these barriers necessary. A more palatable approach can be to argue for protection against identity theft. Security freezes prevent anyone, regardless of the amount of detailed information they have about a person, from applying for credit. Indeed, a security freeze where the client does not have access to the full PIN would prevent even a well-informed burglar from being able to engage in identity theft. The use of an identity theft argument to justify the security freeze has the added advantage of protecting the client from an elephant-motivating loss scenario. Seek imagined illiquidity Real illiquidity can be imposed by contractual agreements such as those discussed above. However, imagined illiquidity can be created by the use of mentally separate accounts for saving and ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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spending. The classic economic assumption is that wealth is fungible, i.e. all money is treated the same. Bertrand, Mullainathan, and Shafir (2004) found that this did not fit with common behavior. They explained, â&#x20AC;&#x153;Also, contrary to standard fungibility assumptions, people compartmentalize wealth and spending into distinct budget categories, such as savings, rent, and entertainment, and into separate mental accounts, such as current income, assets, and future incomeâ&#x20AC;? (p. 420). Obviously, setting up three identical bank accounts for current spending, medium term savings goals, and retirement savings actually adds no real liquidity constraints. It does, however, substantially affect spending behavior. The tendency to spend money out of these different accounts will differ widely (Betrand, et al., 2004). One advantage of separate accounts is that they can alert the rider side to stop spending. If all money was held in a single account, there is no natural stopping point to consider the cumulative effect of immediate spending. Mental accounts, however, create stop signs that trigger rider attention. Also, separate accounts allow for an attachment of different emotional markers to different pools of funds. If the elephant has attached a somatic (emotional) marker to the tangible savings goal associated with the account (such as a car or vacation) and if breaking into the account is seen as losing that emotionally-salient scenario, the lossaverse elephant will be less tempted. Even a piggy bank can serve as an excellent barrier to liquidity if it is mentally categorized for a particular expenditure. (Consider the visceral impact on the lossaverse, emotional elephant of having to actually break the piggy bank!) In a similar way, expenditure budgets create stop signs that alert the rider to intervene in spending behavior within a particular category. An additional $20 expense in an exhausted budget

Journal of Personal Finance

category can be reframed as threatening the stability of the budget, the plan, and associated long-term goals. Use “opt out” defaults It is possible for financial planners to pre-select a wide range of appropriate choices for a client’s comprehensive financial plan and deliver it as one completed package. However, without client participation in building each component, there is a danger that clients will not see the plan as theirs. If the plan is not mentally “owned” by the client, future commitment to plan guidelines may be at risk. Conversely, clients who are heavily involved in making detailed choices for every point in the planning process will be more likely to take ownership of the plan as something they have built. The advantage of this approach, however, is offset by two problems. First, the process of considering a long range of cognitively challenging questions is exhausting for clients. Second, financial planners are experts who should provide guidance to clients, not simply list all available options. Providing wisely-selected default options allows the planner to influence outcomes while still maintaining client participation. The power of default options comes from the elephant-side status quo bias. As discussed above, the elephant prefers the status quo because deviations are seen as having potential for loss. To the extent that the default options are viewed as the standard, normal choices, i.e., the status quo, then deviations will require special rider-side cognitive effort to overcome the elephant’s loss aversion. As discussed previously, because the rider is easily exhausted, default options will become progressively more likely in a long series of questions. Perhaps the most powerful version of a default is the “opt out” provision. In this scenario a single, implicitly-recommended ©2011, IARFC. All rights of reproduction in any form reserved.

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path is provided, and clients are given the choice to opt out of that path. This sends the strongest signal that the provided path is the status quo. The requirement to opt out builds the perception that such actions are a bold violation of the accepted status quo. This perception can be further heightened by the use of a detailed opt out process highlighting the potential losses resulting from rejecting the standard approach. Requiring physical effort such as filling out a form may also increase the tendency to accept the default (Samuelson & Zeckhauser, 1988). Johnson, Hershey, Meszaros, and Kunreuther (1993) studied this phenomenon by providing an auto insurance feature (regarding the right to sue for pain and suffering) as either an “opt out” from the full policy, or an “opt in” from the limited policy. Over half (53%) choose to keep the right by not opting out of the full policy, but less than a quarter (23%) chose to add the right by opting in from the limited policy. (A common implementation of this can be seen when renting a car as contracts typically require that insurance be refused, and that the refusal be separately signed.) In another example, organ donation rates across countries increase by almost 60 percentage points when comparing an “opt in” nation with an “opt out” nation (Gimbel, Storsberg, Lehrmen, Gefenas & Taft, 2003; Johnson & Goldstein, 2003). A more subtle form of creating defaults is by adding an option that is similar to, but clearly inferior to, the desired choice. Consumers are attracted to the value represented by easy relative comparison through a process known as anchoring (Simonson & Tversky, 1992; Simonson, 1999). Consider the following three approaches to encouraging a client to include disability insurance as part of a financial plan. Using knowledge of the elephant and rider allows an evaluation of the relative likelihood of success.

Journal of Personal Finance

Approach 1: Client is given full information including proposals from 10 companies. Each includes pricing for every available short-term and long-term alternative including income replacement percentages from 40% to 70%, varying delays until payments start from two weeks to 24 months, varying lengths of total coverage, several alternative cost of living adjustment riders, varying definitions of disabilities and when each will result in payments, and the relative tax advantages and disadvantages for company paid or employee voluntary approaches. Comment: Because there are so many options, the risk of choosing the “wrong” one is high. The elephant would prefer to avoid the embarrassment and perceived loss of making the wrong choice by simply postponing the process (indefinitely) and sticking with the status quo (doing nothing). This choice scenario requires enormous rider-side effort. Even with the best of intentions, given the volume and complexity of required choices the rider’s endurance will be quickly exhausted allowing the elephant’s status quo bias (doing nothing) to prevail. Approach 2: The client is provided with five options representing different levels of protection with associated prices, one of which is recommended by the planner. Comment: The cognitive complexity of the decision is substantially reduced making it manageable for the rider. The elephant side is less concerned about making the wrong choice as there are fewer options and the recommendation provides a comfortable default.

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Approach 3: The client is provided with a recommended plan, with the knowledge that features could be adjusted if any seem inappropriate. If the client prefers not to have disability insurance, the planner requires a signed “release of liability” form. The planner explains that when families and children are left with no income, lose their house to foreclosure, and have nowhere else to turn, they may turn to a plaintiff’s lawyer to sue the financial planner for malpractice. The planner explains that the form indicates that she has tried to the best of her abilities to add disability insurance to the client’s plan and that the client is knowingly refusing that protection. Comment: The cognitive complexity of the decision is reduced to a simple yes or no. The client knows that features could be adjusted, but has no specific prompts to do so on any particular issue. Because of the “opt out” structure, doing nothing requires positive action. Opting out requires reviewing a separate document that is intentionally designed to alert the elephant to emotionally relevant loss. The action (signing the “release of liability”) is associated with concrete loss images (children losing their house and income) making it odious to the elephant. Build imagination capital As discussed previously, the elephant system processes through somatic markers, i.e. attaching emotions to images. When working with a client’s long-term goals it is important to make sure that these goals are translated into elephant-friendly images. Understanding the elephant system as shared mammalian characteristics may help to clarify what concepts are or are not relevant. Abstract financial and mathematical concepts are not relevant here. For example, instead of just setting an abstract goal

Journal of Personal Finance

- like 80% of current income available at age 65 from retirement assets - have clients identify potential emotional experiences associated with the goal. Ask clients a question about what they envision doing after their career. Where do they want to go? With whom do they want to spend time? What activities would they like to do together? Through this process, the planner can identify emotionally-relevant goals. More importantly, the planner actually helps clients build those goals, not by interjecting ideas but by asking questions that cause clients to construct their own imagery. Nobel prize winning economist Gary Becker used the term “imagination capital” to explain this process (Becker, 1996, p. 11). Becker and Mulligan (1997) presented an economic model based on the standard idea that consumers maximize their lifetime consumption experience. So, a simplified model of consuming now (period 0) and later (period 1) is that people maximize the function V = f0(c0) + β(S)∙f1(c1) In words, the formula indicates that consumers value both current consumption and future consumption. However, future consumption is less valuable than current consumption by the factor of β. This β factor is the traditional time discounting of standard economics. Contrary to the traditional economic approach, Becker and Mulligan (1997) suggested the time discount rate could be altered through spending effort (S) on becoming more future-focused. In other words, a consumer could “make future pleasures less remote by spending resources (S) on imagining them” (p. 734). Becker and Mulligan (1997) expanded on this process by explaining, “How can a person improve his capacity to appreciate the future? What exactly is S? First, S is partially determined ©2011, IARFC. All rights of reproduction in any form reserved.

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by time and effort spent appreciating future pleasures. While forming a mental picture of one’s future pleasures may not be incredibly difficult, the process of anticipation is not merely one of image formation but also one of scenario simulation. Even image formation may not be cheap because images of future pleasures have to be repeatedly refreshed in one’s mind in order to compete with current pleasures” (p. 734). Far from being a fanciful notion irrelevant to hard financial choices, Becker and Mulligan (1997) suggested that the most central concept in all financial decision making, the rate of future discounting, resulted from imagination. Further, the effort spent imagining these future experiences is cumulatively valuable, building up in the form of imagination capital. From the perspective of the elephant-rider model, the ideal way to develop elephant support of a long-term financial plan is to (1) associate relevant emotional imagery with the goals, and (2) conceptualize deviations from the plan in terms of risk of loss of the emotional goals. The elephant side is focused on immediate emotional experience, but the immediate emotional experience from (potentially) losing a closely held future dream is quite unpleasant. Conversely, if the goal is simply to hit some abstract numerical target for lifetime income smoothing the elephant will be unmoved. Build social networks As discussed previously, social connections and peer influences are strongly felt by the elephant side. Strong social relationships between clients and planners, and between clients and office staff, can help to emphasize the elephant’s perceived social importance of staying on track with planning goals. Where possible, bringing together groups of clients with similar goals in a

Journal of Personal Finance

social setting may help to provide mutual encouragement. Similar techniques are commonly used in nonprofit fundraising where social events and board meetings are valued for building a community of like-minded supporters. In the fundraising context, the natural reinforcement from peer group influence encourages donations more than any appeal from a development officer ever could. Likewise, much of the success of mutual support groups like Weight Watchers or Alcoholics Anonymous come from counteracting temptation with the elephant-side sensitivity to social approval. Although this sometimes puts the planner in loco parentis (in the place of a parent), it can make a difference to the emotional elephant that someone else is monitoring whether or not the client is successfully pursuing long term goals. Remove felt losses from good choices through precommitment A dominant characteristic of the elephant system is fear of loss. To encourage positive choices it is important that these choices, as much as possible, not be associated with the felt experience of loss. Otherwise, the elephant will strongly resist taking these loss-associated actions. Removing felt losses can be accomplished through pre-commitment that removes felt losses associated with future choices or through reframing scenarios from a loss perspective to a non-loss perspective. One example of removing felt losses through precommitment is the Save More Tomorrow program (Thaler & Benartzi, 2004). In this plan, people are allowed to commit in advance to allocating a portion of their future salary increases toward retirement savings. The timing and framing of this decision avoids any elephant-system interference. First, the decision is about two future options (saving more in the future versus not saving more in the future). This avoids the hyperbolic ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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discounting problem arising when one of the options is immediate consumption. Additionally, the decision poses no risk of loss from the status quo. Only gains, i.e. future salary increases, are being allocated here. The results are dramatic. Thaler and Benartzi (2004) reported that program participants increased their savings rate from an initial 3.5% all the way to 13.6% over the course of 40 months. A similar device was used by Chiou, Roe, & Wozniak (2005) for low income families by using pre-commitments to put a portion of the tax refund check into a savings accounts. In this pilot program, participants saved 236% more than they projected they would prior to hearing about the program. The pain of savings (lost consumption) was never felt by the elephant side in these programs because income never dropped from the status quo. Also, in the Save More Tomorrow program, because the savings deductions were made by the employer, transfers occurred automatically. This concept of automatic savings was made popular by David Bachâ&#x20AC;&#x2122;s (2005) Automatic Millionaire series. The basic idea of making savings occur automatically so that it does not require recurring conscious decisions to postpone consumption fits well within the elephantrider framework. In essence, the elephant does not feel the lost consumption due to the automatic nature of the savings. The initial pre-commitment to begin automatic savings in the future is of less interest to the elephant because it is a choice between two future outcomes. Remove felt losses from good choices through reframing There are, however, circumstances where avoiding felt losses is more difficult and pre-commitment may not provide a solution. A particular challenge for financial planners is helping clients deal with the experience of market volatility. Over the long-term, investment in more volatile assets tends to produce greater returns.

Journal of Personal Finance

However, volatility may result in some interim loss experiences. The elephant-system reaction to these losses may be extreme, often leading to market withdrawal at precisely the wrong times. A series of results suggests that these loss experiences are subject to reframing based upon broad or narrow bracketing (Read, Lowenstein & Rabin, 1999). Bracketing is the idea of thinking about experiences separately (narrow bracketing) or in large sets (broad bracketing). When investors focus on returns in narrow brackets, they will inevitably experience many felt losses. In particular for investments in publicly traded equity, many individual days will produce negative results. Even on overall positive days, many individual stocks or funds within a portfolio will have losses. The probability of felt losses diminishes to the extent that investors think in broader brackets. Today’s loss in one stock is not felt as a loss if the investor focuses on today’s positive results for the entire portfolio. Today’s loss in the entire portfolio is not felt as a loss if the investor is focused on the overall positive returns for the last 30 days. This month’s loss on the entire portfolio is not felt as a loss if the investor is focused on the overall returns for the last 12 months. As investors focus on larger and larger brackets, the likelihood for an emotionally felt loss typically becomes less. Conceptually, the only bracket that should matter to an investor is the one starting at the introduction of new savings and ending at the planned time of expenditure. However, in the midst of a down market, it is difficult to maintain this sanguine view. Indeed, investors would typically do better by simply not following their returns during the interim period. As Read, et al. (1999) explained, Over brief periods, stock prices are almost as likely to fall as to rise. For loss averse investors, the falls will be extremely painful and the rises only mildly enjoyable, so ©2011, IARFC. All rights of reproduction in any form reserved.

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the overall experience might not be worth undertaking. By this logic, if people could resist looking at their portfolios for longer periods—i.e., bracket their investment choices more broadly—the likelihood that they would see such losses would diminish, and the clear benefits of stocks would emerge. (p. 180) Thaler, Tversky, Kahneman and Schwartz (1997) had subjects make investment decisions between stocks and bonds simulating returns that would be experienced based upon making those allocation decisions twice per quarter, once per year, or once every five years. Those making reallocation decisions twice per quarter inevitably invested primarily in bonds, whereas those with a longer time frame invested mostly in stocks. Reallocation decisions made by the planner based upon client goals can be an effective strategy. But reallocation decisions made by the investor based on recent experiences of loss will rarely be strategically sound. Sokol-Hessner, et al. (2009), tested a form of reframing in a study measuring physiological changes and loss aversion. Subjects made a series of choices between certain gains and uncertain outcomes with a potential for loss. In one choice series, subjects were told to focus on each choice “as if it was the only one.” In the other, subjects were told to think of all choices together, “as if creating a portfolio.” The use of the larger “portfolio” bracket resulted in a dramatic reduction in loss aversion in the subject’s choices. Further, the experience of losses generated substantially more skin conductance response arousal than the experience of gains in the narrow-focus choice series, but not in the “portfolio” scenario. Even though the choices remained the same in both scenarios, a simple reframing led to both behavioral and physiological changes reflecting reduced loss aversion.

Journal of Personal Finance

Although planners may not be able to convince clients not to look at their returns for long periods of time, other forms of framing using broad bracketing may be effective. During down periods, planners may point to either the past (returns since original investment) or the future (time remaining until the money will be spent) to attempt a broadening of the client’s return bracketing. Focusing on the future, a planner responding to distress about a recent market downturn might ask a series of reframing questions such as: “Were you planning on spending the money today? Then why do we care what it did this month? We only care where it will be at the end of the next X years, right?” Planners may also consider reporting returns on a longer term basis (e.g., cumulative returns since initial investment) to reduce the likelihood of showing overall negative returns. Add felt losses to undesirable choices Because the elephant side experiences substantial fear of loss, this reality can be used to discourage negative behaviors that undermine long-term success. A prime example of adding felt losses to undesirable choices is intentionally seeking illiquidity as described above. Characterizing divergence from a plan as the loss of a cherished and emotionally-salient future dream is another powerful example. However, the same concept can be used in a variety of other contexts. Suppose a client has a long investment time horizon but has no interest in deviating from short-term secure investments such as bank certificates of deposit. It is likely that this mismatch of volatility and time horizon is being driven by underlying fear of loss. Simply pointing to the long-term difference in overall returns is unlikely to be convincing to the immediacy-focused, loss-averse elephant side. Instead the planner could present two plans, both with the same long-term financial goal. The difference in the two plans is presented as the monthly amount of savings that must be set aside to reach the goal based ©2011, IARFC. All rights of reproduction in any form reserved.

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upon projected returns. The elephant-system clearly experiences the reality of less money now as compared with more money now. This reframes the choice in terms relevant to the elephant-system and encourages the optimal long-term behavior. For clients with over-spending problems, especially related to the use of credit cards, shifting to an all cash system will dramatically influence spending behavior. The “mammalian brain” elephant-side clearly feels the visceral loss associated with handing over valuable cash and leaving the store without the money. Swiping a credit card provides no such sense of loss as perceived by the mammalian brain. This simple difference has been shown to dramatically affect spending behavior. Prelec and Simester (2001) reported, “In studies involving genuine transactions of potentially high value we show that willingness-topay can be increased when customers are instructed to use a credit card rather than cash. The effect may be large (up to 100%).” Similarly, Soman (2003) analyzed receipts from a grocery store and found the percentage of the total expenditure spent on nonessential treats and luxuries was 27% for those paying with cash and 43% for those paying with a credit card. There are, of course, a variety of other examples and scenarios that one might construct to add felt losses to bad choices (e.g., the making of a public pledge and the desire to avoid loss of social standing by breaking the pledge). The goal of adopting the elephant and rider model, however, is not to construct the perfect list of techniques. Instead, it is to provide a neurologicallyinformed perspective to aid in the ongoing creation and evaluation of strategies that will ultimately prove effective for financial planning practice. These strategies are made effective by incorporating the known characteristics of both elephant and rider. Thus, effective strategies will not seek to consistently overpower

Journal of Personal Finance

the elephant. (Such attempts are difficult, exhausting and, over extended periods of constant temptation, ultimately doomed.) Instead, effective strategies will seek to harness the elephant. By creating plans that incorporate known rider weaknesses with elephant-sensitive incentives, the financial planner can help clients to accomplish their goals, providing long-term satisfaction to both elephant and rider. References Ahmed, S. (2005) Imbalance between drug and non-drug reward availability: A major risk factor for addiction. European Journal of Pharmacology, 526, 920. Ashraf, N., Camerer, C., & Loewenstein, G. (2005). Adam Smith, behavioral economist. The Journal of Economic Perspectives, 19(3), 131-145. Bach, D. (2005). The automatic millionaire: A powerful one-step plan to live and finish rich. New York: Crown Business. Banfield, J., Wyland, C. L., Macrae, C. N., Munte, T. F., & Heatherton, T. F. (2004). The cognitive neuroscience of self-regulation. In R. F. Baumeister & K. D. Vohs (Eds.), Handbook of self-regulation: research, theory, and applications (pp. 62-83). New York: Guilford Publications, Inc. Barrazza, J. A., & Zak, P. J. (2009). Empathy toward strangers triggers oxytocin release and subsequent generosity. Annals of the New York Academy of Science, 1167(1), 182-189. Bechara, A., & Damasio, A. R., (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52, 336-372. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 274, 1293-1295. Becker, G. (1996). Accounting for tastes. Cambridge, Massachusetts: Harvard University Press. Becker, G., & Mulligan, C. (1997). The endogenous determination of time preference. Quarterly Journal of Economics, 112(3), 729-758. Benartzi, S. & Thaler, R. H. (1995). Myopic loss aversion and the equity premium puzzle. Quarterly Journal of Economics, 110(1), 73-92. Benhabib, J., & Bisin, A. (2005). Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption-savings decisions. Games and Economic Behavior, 52(2), 460-492. Bernheim, B. D. & Rangel, A. (2004). Addiction and cue-triggered decision processes. American Economic Review, 94(5), 1558-1590. ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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Bertrand, M., Mullainathan, S., & Shafir, E. (2004). A behavioral-economics view of poverty. American Economic Review, 94, 419-423. Botti, S., & Iyengar, S. S. (2006). The dark side of choice: When choice impairs social welfare. Journal of Public Policy & Marketing, 25(1), 24-38. Brosnan, S. F., Jones, O. D., Lambeth, S. P., Mareno, M. C., Richardson, A. S., Schapiro, S. J. (2007). Endowment effects in chimpanzees. Current Biology, 17, 1704-1707. Buchel C., Morris, J., Dolan, R., Friston, K. (1998). Brain systems mediating aversive conditioning: an event-related fMRI study. Neuron, 20, 947–957. Burger, J. M. (1999). The foot-in-the-door compliance procedure: a multiple process analysis and review. Personality and Social Psychology Review, 3(4), 303–325. Camerer, C., Loewenstein, G., & Prelic D. (2004). Neuroeconomics: Why economics needs brains. Scandinavian Journal of Economics, 106(3), 555579. Carter, R. M., Meyer, J. R., & Huettel, S. A. (2010) Functional neuroimaging of intertemporal choice models: A review. Journal of Neuroscience, Psychology, and Economics, 3(1), 27-45. Chen, M. K., Lakshminarayanan, V., & Santos, L. R. (2006). How basic are behavioral biases? Evidence from Capuchin monkey trading behavior. Journal of Political Economy, 114(3), 517-537. Chiou, A., Roe, S., & Wozniak, E. (2005) An evaluation of tax-refund splitting as an asset-building tool for low-to-middle income individuals. Retrieved November, 21, 2011, from http://www.d2dfund.org/system/files/publications/PAE+R2A2+FINAL.pdf Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the human brain. New York: HarperCollins. Damasio, A. R. (1995). Toward a neurobiology of emotion and feeling: operational concepts and hypotheses. Neuroscientist, 1, 19–25. Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 351, 1413-1420. De Martino, B., Camerer, C. F., & Adolphs, R. (2010). Amygdala damage eliminates monetary loss aversion. Proceedings of the National Academy of Science, 107(8), 3788-3792. Duflo, E. & Saez, E. (2002). Participation and investment decisions in a retirement plan: The influence of colleagues’ choices. Journal of Public Economics, 85(1), 121-148. Epley, N., & Dunning, D. (2000). Feeling ‘holier than thou’: Are self-serving assessments produced by errors in self- or social prediction? Journal of Personality and Social Psychology, 79(6), 861-875. Fennis, B. M. & Janssen, L. (2010). Mindlessness revisited: Sequential request techniques foster compliance by draining self-control resources. Current Psychology, 29, 235-246.

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Fleming, S. M., Thomas, C. L, & Dolan, R. J. (2010). Overcoming status quo bias in the human brain. Proceedings of the National Academy of Sciences, 107(13), 6005-6009. Fudenburg, D., & Levine, D. K. (2006). A dual-self model of impulse control. American Economic Review, 96(5), 1449-1476. Gazzaniga, M. S. (1983). Right hemisphere language following brain bisection. American Psychologist, 38(5), 525-537. Gimbel, R. W., Strosberg, M. A., Lehrman, S. E., Gefenas, E., & Taft, F. (2003). Presumed consent and other predictors of cadaveric organ donation in Europe. Progress in Transplantation, 13(1), 17-23. Glimcher, P. W. (2004). Decisions, uncertainty, and the brain: The science of neuroeconomics. Cambridge, MA: The MIT Press. Haidt, J. (2006). The happiness hypothesis, New York: Basic Books. Haidt, J. (2007). The new synthesis in moral psychology. Science, 316, 9981002. Hochman, G., & Yechiam, E. (2011). Loss aversion in the eye and in the heart: The autonomic nervous system’s response to losses. Journal of behavioral decision making, 24, 140-156. Huettel, S., Song, A. W., & McCarthy, G. (2008). Functional magnetic resonance imaging (2nd ed.). Sunderland, MA: Sinanauer Associates, Inc. Iyendgar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995-1006. Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302, 1338-1339. Johnson, E. J., Hershey, J, Meszaros, J, & Kunreuther, H. (1993). Framing, probability distortions, and insurance decisions. Journal of Risk and Uncertainty, 7(1), 35-51. Johnston, L., O’Malley, P., & Bachman J. (1993). Illicit drug use, smoking, and drinking by America’s high school students, college students, and young adults, 1975-1987. Rockville, Maryland: National Institute on Drug Abuse. Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Neuroscience, 10, 1625–1633. Kahneman, D., Knetsch, J., & Thaler, R. (1990). Experimental tests of the endowment effect and the Coase theorem. Journal of Political Economy, 98(6), 1325-1348. Kahneman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American, 246, 160-173. Kemper, T. (1991). Predicting emotions from social relations. Social Psychology Quarterly, 54, 330-342 Knetsch, J. (1989). The endowment effect and evidence of nonreversible indifference curves. American Economic Review, 79, 1277-1284. Laibson, D. (1997). Golden eggs and hyperbolic discounting. Quarterly Journal of Economics, 112, 443–477. ©2011, IARFC. All rights of reproduction in any form reserved.

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Loewenstein, G. (2000). Emotions in economic theory and economic behavior. American Economic Review, 90(2), 426-432. Loewenstein, G., & O’Donoghue T. (2004). Animal spirits: Affective and deliberative processes in economic behavior. Retrieved November 21, 2011 from http://ssrn.com/abstract=539843 Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267-286. Mann, R. E. (2005). Availability as a law of addiction. Addiction, 100(7), 924925. McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2007). Time discounting for primary rewards. Journal of Neuroscience, 27, 5796–5804. McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306, 503–507. Muramatsu, R. & Hanoch, Y. (2005). Emotions as a mechanism for boundedly rational agents: The fast and frugal way. Journal of Economic Psychology, 26(2), 201-221. Nicolle, A., Fleming, S. M., Bach, D. R., Driver, J., & Dolan, R. J. (2011). A regret-induced status quo bias. The Journal of Neuroscience, 31(9), 33203327. Odean, T. (UC-Davis). (1998). Are investors reluctant to realize their losses? Journal of Finance, 53, 1775-1798. Öhman, A., Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108, 483– 522. O’Keefe, D. J., & Hale, S. L. (2001). An odds-ratio-based meta-analysis of research on the door-in-the-face influence strategy. Communication Reports, 14(1), 31–38. Pollock, C. L., Smith, S. D., & Knowles, E. S. (1998). Mindfulness limits compliance with the that’s-not-all technique. Personality and Social Psychology Bulletin, 24(11), 1153–1157. Prelec, D., & Loewenstein, G. (1991). Decisionmaking over time and under uncertainty: A common approach. Management Science, 37, 770–786. Prelec, D. & Simester, D. (2001). Always leave home without it: A further investigation of the credit-card effect on willingness to pay. Marketing Letters, 12, 5-12. Read, D., Loewenstein, G., & Rabin, M. (1999). Choice bracketing. Journal of Risk and Uncertainty, 19, 171-197. Read, D., & van Leeuwen, B. (1998). Predicting hunger: The effects of appetite and delay on choice. Organizational behavior and human decision processes, 76, 189-205. Ritov, I., & Baron, J. (1990). Reluctance to vaccinate: Omission bias and ambiguity. Journal of Behavioral Decision Making, 3, 263-277.

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Roberts, R. J., Hager, L. D., & Heron, C. (1994). Prefrontal cognitive processes: Working memory and inhibition in the antisaccade task. Journal of Experimental Psychology: General, 123, 374-393. Room, R. P., Babor, T. & Rehm, J. (2005). Alcohol and public health. The Lancet, 365, 519-530. Samuelson, P. A. (1937). A note on measurement of utility. The Review of Economic Studies, 4, 155-161. Samuelson, W. & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1, 7-59. Sanfey, A. G., Loewenstein, G., McClure, S. M, & Cohen, J. D. (2006). Neuroeconomics: Cross-currents in research on decision-making. Trends in Cognitive Sciences, 10(3), 108-116. Shin, L. M., & Liberzon, I. (2010). The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology, 35, 169-191. Shiv, B. & Fedorikhin, A. (1999). Heart and mind in conflict: The interplay of affect and cognition in consumer decision making. Journal of Consumer Research, 26(2), 278-292. Shiv, B., Loewenstein, G., Bechara, A., Damasio, H., & Damasio, A. R. (2005). Investment behavior and the negative side of emotion. Psychological Science, 16(6), 435-439. Simonson, I. (1999). The effect of product assortment on buyer preferences. Journal of Retailing, 75(3), 347-370. Simonson, I., & Tversky, A. (1992) Choice in context: Tradeoff contrast and extreme aversion. Journal of Marketing Research, 29, 281-295. Smets, P., & Bähre, E. (2004). When coercion takes over: The limits of social capital in microfinance schemes. In H. B. Lont & O. Hospes (Eds.) Livelihood and microfinance: Anthropological and sociological perspectives on savings and debt (215-236). Delft, The Netherlands: Eburon Publishers. Sokol-Hessner, P., Hsu, M., Curley, N. G., Delgado, M. R., Camerer, C. F.,& Phelps, E. A. (2009). Thinking like a trader selectively reduces individuals’ loss aversion. Proceedings of the National Academy of Sciences of the United States of America, 106(13), 5035-5040. Soman, D. (2003). The effect of payment transparency on consumption: Quasiexperiments from the field. Marketing Letters, 14, 173-183. Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior and Organization, 1, 39-60. Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. Economics Letters, 8, 201-207. Thaler, R. H., & Benartzi, S. (2004). Save More Tomorrow: Using behavioral economics to increase employee saving. Journal of Political Economy, 112(1), S164-S187. Thaler, R. H., & Johnson, E.J. (1990). Gambling with the house money and trying to break even: The effects of prior outcomes on risky choice. Management Science, 36, 643-660. ©2011, IARFC. All rights of reproduction in any form reserved.

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Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control, Journal of Political Economy, 89(2), 392-406. Thaler, R. H., Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth and happiness. New Haven, CT: Yale University Press. Thaler, R., Tversky, A., Kahneman, D., & Schwartz, A. (1997). The effect of myopia and loss aversion on risk taking: An experimental test. Quarterly Journal of Economics, 112, 647-661. Tom, S. M, Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315, 515-518. Tversky, A. & Kahneman, D., (1981). The framing of decisions and the psychology of choice. Science, 211, 453-458. Ward, A., & Mann, T. (2000). Donâ&#x20AC;&#x2122;t mind if I do: Disinhibited eating under cognitive load. Journal of Personality and Social Psychology, 78(4), 753763. Williams, R. W. & Herrup, K. (1988). The control of neuron number. Annual Review of Neuroscience, 11, 423-453. Wittmann, M., Leland, D. S., & Paulus, M. P. (2007). Time and decision making: Differential contribution of the posterior insular cortex and the striatum during a delay discounting task. Experimental Brain Research, 179(4), 643-653. Wolff, E. N. (1998). Recent trends in the size distribution of household wealth. Journal of Economic Perspectives, 12(3), 131-150. Woolcock, M. J. (1999). Learning from failures in microfinance: What unsuccessful cases tell us about how group based programs work. American Journal of Economics and Sociology, 58(1), 17-42.

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MUTUAL FUND TAX EFFICIENCY AND INVESTMENT SELECTION D.K. Malhotra * Philadelphia University Rand Martin Bloomsburg University of Pennsylvania C. Andrew Lafond The College of New Jersey We examine six factors that may be important when looking for a more tax efficient mutual fund. We consider the pre- and post-liquidation bases for over 4,000 mutual funds in logical groupings. Our results for turnover show that its effect on tax efficiency depends upon conditions in the securities markets. A falling market leads to greater tax efficiency due to security sales at depressed prices. We have a similar finding for expense categories probably because increased sales lead to higher expenses. We find greater tax efficiency if mutual funds have institutional status, no-loads, and no 12b-1 plan.

Introduction Mutual funds continue to be popular as investment vehicles. Evidence of this is the fact that, according to the Investment Company Institute, the total dollars invested through mutual funds increased from $3.028 trillion in 1995 to $10.701 trillion as of May 2009. Since 1981 when the Internal Revenue Service (IRS) proposed regulations that opened the doors for companies to use 401(k) plans, many companies have moved away from pension plans towards self-directed 401(k) type retirement plans fueling the *

D.K. Malhotra, School of Business Administration, Philadelphia University, School House Lane and Henry Avenue, Philadelphia, PA 19144; (215)-9512813; MalhotraD@philau.edu ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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tremendous growth in the mutual fund industry (Gilliam, Dass, Durband, and Hampton 2010). This popularity results largely from the low cost diversification afforded by mutual funds (Weiss, 2005). But, with so many mutual funds available, investors need the best criteria for making selections that fit their needs. Investors usually select mutual funds based on performance, risk, and investment objective. Two other selection criteria have recently been touted as important. One is the expense ratio which has been the subject of extensive academic research (McLeod and Malhotra, 1994, Malhotra and McLeod, 1997, Bogle, 2006, Barber, Odean, and Zheng, 2005). In recent years tax efficiency has also emerged as a selection criterion of great interest. Tax efficiency is the subject of this study. Is the attention paid to tax efficiency justified? It may be for investors who are not invested in a tax-exempt fund or in a taxdeferred account such as an IRA. To answer the question for those investors, consider some facts about the taxation of mutual fund returns. Bruce (2003) reports that “the Securities and Exchange Commission says that more than 2.5 percent of the average stock fund's total return is lost each year to taxes, significantly more than the amount lost to fees. The tax bite varies from zero percent for the most tax-efficient funds to 5.6 percent for the least efficient.” Bernard (2006) says that mutual fund investors paid an estimated $15.2 billion in taxes according to a study conducted by data tracker Lipper, Inc. According to Roger Ibbotson, “roughly 2 percent of pre-tax returns of mutual funds are lost to taxes for those taxpayers in the highest tax bracket” (Tuve Investments, 2007). Discussion of tax efficiency in the popular press has become so common that in Kiplinger’s Personal Finance it was said that “taxefficient investing is the most over-covered topic in the financial

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pressâ&#x20AC;? (Gregory and Savage, 2003). Even with that coverage in the popular press, this topic has received very little attention in academic research. We think tax efficiency is an important topic and that investors should have a better idea of how to look for tax efficient mutual funds (Bergstresser & Poterba, 2002, IvkovicÂ´ & Weisbenner, 2009, Sialm & Starks, 2011). Our purpose in this study is to investigate the likely characteristics of tax-efficient mutual funds that an investor can easily use to select funds. Taxation of Returns and Avoidance Of Taxes Taxes can be incurred on shares of mutual funds in three ways. The first is when a shareholder sells shares for more than the purchase price and thus realizes a capital gain. Incurrence of this tax is under the control of shareholders since they decide when to sell shares. The second way is taxation of interest and dividends received when shares are not held in a tax-deferred retirement savings account. Mutual funds could be selected that do not pay interest or dividends due to their investment objective. But, if an investor selects funds that generate interest and dividends, tax liability will be incurred. The third type of taxation is caused by mutual fund managers as they turn over the assets of the fund and create capital gains income which may be distributed to shareholders. This is out of the control of the shareholder and is often overlooked by investors as a cause of taxation. Qualified dividends and long-term capital gains get preferential tax treatment at the maximum 15% tax rate; however, interest, nonqualified dividends, and short-term capital gains can be taxed as ordinary income. Since mutual fund rules require that all income earned by a fund be distributed each year to the shareholders, those funds that earn more income through interest, dividends, or capital gains are ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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less tax efficient and create a higher tax liability for the investor. But, there are three things that investors can do to avoid taxes besides holding shares in tax-deferred retirement savings accounts. One is using index funds. Index funds are more tax efficient than actively managed mutual funds because their objective to invest according to the representation of securities in indexes requires less frequent trading of securities. Other attributes of index funds is that they tend to have lower fees but lower earnings perspectives (Loibl, Lee, Fox and Mentel-Gaeta 2007). Over a long period of time, this buy and hold approach will enhance the overall return on an index fund (Ellentuck, 2004). The second means for avoiding tax is to use mutual funds that have tax efficiency as an objective. These funds reduce a shareholder’s taxes by having a lower turnover rate, by investing in non-dividend paying stocks, and by using a strategy of matching losses with gains during a year to minimize the amount of capital gains. These funds also attempt to reduce the incurrence of taxes by discouraging share sales through charging a redemption fee if shares are sold before a specified time period elapses after they are purchased (Ellentuck, 2004). The third means is by using exchange-traded funds. Gardner and Welch (2005) describe these funds by saying that “exchangetraded funds are a basket of securities owned by a mutual fund company, similar to a mutual fund, but differ in how their shares are issued, traded, and redeemed.” These funds use a strategy of creating and redeeming shares through in-kind transactions so that sales aren’t made and capital gains and other taxable transactions are not created. In the current study, we do not limit ourselves to an examination of mutual funds that are designed for tax efficiency. Instead, we examine all mutual funds for which we have adequate data in an effort to determine whether certain characteristics can be used to select tax efficient funds.

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Literature Review We could find only one academic study that examines aftertax mutual fund returns. Peterson, et al (2002) study the pre-tax returns, after-tax returns, and tax efficiency of 1,170 mutual funds that had diversified U.S. equity investment objectives over the years 1981 to 1998. The authors hypothesize that the important determinants of pre-tax returns would also be the important determinants of after-tax returns. They find that historically tax efficient funds outperform historically tax inefficient funds on an after-tax basis. Funds that experience net redemptions, especially in the case of large cap value funds, subsequently perform worse after-tax than comparable funds that do not have net redemptions. They find that the important determinants of after-tax and pre-tax returns are the level of risk taken, investment style, past pre-tax performance, and expenses. They find that turnover is not related to after-tax returns. Finally, they find that funds with large cash inflows, high expense ratios, or an emphasis on small cap stocks tend to be more tax efficient and to have lower pre-tax returns. The authors take their findings to mean that investors should focus on after-tax returns rather than tax efficiency. Our study differs from that of Peterson, et al (2002) in three ways. First, we include funds with debt as well as equity investment objectives. Second, we study tax-efficiency on the basis of earnings distributed and on the basis of earnings after distribution and sale of fund shares. And, third, we use a different sample period. Method of Investigation Tax efficiency is typically measured in one of two ways. One is by dividing the after-tax return by the pre-tax return to ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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obtain a tax-efficiency ratio. The other is computation of the return lost to taxes by subtracting the fundâ&#x20AC;&#x2122;s after-tax return from its pre-tax return. This second method is more relevant since the tax-efficiency ratio is not meaningful when the numerator or denominator is negative (Riepe, 2000). As a result we use the second method. When using this method, a positive difference (pre-tax return minus after-tax return) will mean less tax efficiency since more return is lost to taxes. A negative result can be had and will be discussed in our empirical results section below. The most desirable result would be a difference of zero, meaning that no return is lost to taxes. We use a three-pronged approach to analyze the tax efficiency of mutual funds. Firstly, we calculate tax efficiency on a pre-liquidation status and on a post-liquidation status for six fund characteristics. They are (1) turnover, (2) investment style, (3) expense category, (4) institutional or retail status, (5) load status, and (6) 12b-1 plan status. Many institutional investors such as endowment funds and pension funds are tax exempt and would not care about tax efficiency. But, we investigate the effect of the institutional/retail variable because purchase of institutional fund shares is not limited to tax exempt institutions. The only requirement for purchase of those shares is meeting the minimum investment amount. Pre-liquidation tax efficiency is the total return for 12 months minus the return after tax on distribution. Post-liquidation tax efficiency is the total return for 12 months minus the return after tax on distribution and sale. To calculate return after tax on distribution and sale, Morningstar says that it â&#x20AC;&#x153;applies the appropriate historical tax rate

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based on the date of distribution.” The tax rates that Morningstar uses are as follows. • 35 percent for interest income and dividends that do not qualify for taxation at the lower long-term capital gains tax rate • 15 percent for dividends that do qualify for the lower long-term capital gains tax rate • 35 percent for short-term capital gains • 15 percent for long-term capital gains We calculate tax efficiency for what we consider to be logical groupings of mutual funds under each of the six data items mentioned above. Listed below is an explanation of how we placed mutual funds into the groupings. 1. Turnover Level (Table 2): According to Morningstar, the turnover ratio “loosely represents the percentage of the portfolio’s holdings that have changed over the past year.” We use five turnover percentage ranges to categorize mutual funds: Very Low ≤ 20%, Low > 20% and ≤ 40%, Average > 40% and ≤ 80%, High > 80% and ≤ 150%, Very High > 150%. 2. Investment Style (Table 3): Morningstar says that mutual funds with an equity style invest in stocks and mutual funds with a fixed-income style invest in bonds. Those with a style of “equity and fixed income” invest in both stocks and bonds and are sometimes called balanced funds. 3. Expense Category (Table 4): Morningstar defines an expense ratio as “the percentage of fund assets paid for operating expenses and management fees, including 12b-1 fees, administrative fees, and all other asset-based costs incurred by the fund, except brokerage costs.” They use expense ratios to put mutual funds into four expense categories as follows.

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•

• • •

Category A: Mutual funds whose expense ratios fall within the cheapest quintile of its type of share class within its comparison group Category B: Mutual funds in the second-cheapest category Category C: Mutual funds with expense ratios falling between the 40th and 60th percentiles of the peer group Category F: Mutual funds with higher expense ratios than the funds in category C

We combine the Morningstar expense category classifications A and B to form our Low expense ratio category and we combine the C and F classifications to form our High expense ratio category. 4. Institutional / Retail Status (Table 5): Morningstar defines an institutional fund as one that meets one of three criteria: (1) has the word "institutional" in its name, (2) has a minimum initial purchase of $100,000 or more, or (3) states in its prospectus that it is designed for institutional investors or those purchasing on a fiduciary basis. We consider all other funds to be retail funds. 5. Load and No-Load Groupings (Table 6): Morningstar describes sales fees in this way: “Also known as loads, sales fees represent the maximum level of initial (front-end) and deferred (back-end) sales charges imposed by a fund.” To form our Load status categories, we combine the front-end and back-end sales charges reported by Morningstar. 6. 12b-1 Plan Status (Table 7): Morningstar defines a 12b-1 fee as “a fee used to pay for a mutual fund’s distribution costs. It is often used as a commission to brokers for selling the fund.”

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For each group of funds, we calculate the mean and variance of the tax efficiency results. Then, we compute t-statistics for twotailed tests of the difference in sample means. We compare the means of tax efficiency measures for each group of funds to the mean tax efficiency of the group that is likely to have the highest tax efficiency. In effect we are testing the following hypotheses about the mean tax efficiency of groups of funds.

Secondly, in order to evaluate the impact of fund characteristics on tax efficiency of a fund, we model tax efficiency (pre-liquidation as well as post liquidation) as a function of ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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investment style, load, 12b-1 plan, fees, share class, and turnover ratio. Equation 1 describes our regression model: Tax Efficiency = ß0 + ß1 Equity Style + ß2 Fixed Style + ß3 Fees + ß4 Load + ß5 12b-1 Plan + ß6 Share Class + ß7 Turnover Ratio + e (1) Where • •

• • • • •

Tax Efficiency refers to pre-liquidation and post-liquidation tax efficiency of a mutual fund. Equity Style is a dummy variable that equals 1 if the mutual fund follows equity style of investing and 0 otherwise. Fixed Style is a dummy variable that equals 1 if the mutual fund follows fixed style of investing and 0 otherwise. Load is the actual percentage load (front-end or deferred) of a fund. 12b-1 Plan is a dummy variable that equals 1 if the mutual fund has a 12b-1 plan charge and zero otherwise. Fees is a dummy variable that equals 1 if the mutual fund got a rating of A or B on expense ratio and zero otherwise. Turnover ratio is the actual percentage turnover ratio of the mutual fund.

We estimate the model specified in equation 1 using linear regression for each of the four years from 2006 to 2009. According to Peterson et al. (2002) mutual funds typically do not distribute capital gains more often than once a year and this can create a problem in identifying tax inefficient mutual funds. For instance, a mutual fund may decide not to distribute capital gains in a particular year due to poor performance. Such a fund’s after-tax return will be roughly the same as its pretax return, which makes it tax efficient for that particular year. Peterson et al. also argue that a

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fund can manage the realization of capital gains and, therefore, taxable distributions and after-tax returns in the short run. Therefore, in order to analyze the true impact of fund characteristics on tax efficiency of a fund, we need to lengthen the measurement period for after-tax returns beyond one year. Therefore, the third part is an estimation of coefficients for the above tax efficiency models using the panel data approach, which allows for pooling of observations on a cross-section of open-end funds over four years. The most obvious advantage of panel data is that the number of observations is typically much larger in panel data, which will produce more reliable parameter estimates and thus enable us to test the robustness of our linear regression results. Panel data also alleviate the problem of multicollinearity, because when the explanatory variables vary in two dimensions (cross-section and time series), they are less likely to be highly correlated. The most intuitive way to account for individual and/or time differences in the context of panel data regression is to use the fixed effects model. The fixed effect model assumes that difference across mutual funds can be captured in differences in the constant term. The regression coefficients (the slope parameters) across groups in this model are unknown but fixed parameters. It is also known as least square dummy variable (LSDV) model, and we use LSDV fixed-effect model to estimate cost efficiencies in the mutual fund industry. Furthermore, we include a dummy variable to account for time trend in the tax efficiency of mutual funds since 2006.

ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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Data Our data is from Morningstar Principia and it covers the years 2006 through 2009. We use nine Morningstar data items to investigate tax-efficiency: turnover, investment style, expense ratio, status as an institutional or retail fund, front-end load, deferred load, 12b-1 plan status, after-tax return on distribution for one year, and after-tax return on distribution and after sale of shares on a one-year basis. We combine front-end load and deferred load in order to put funds in one of two categories: load and no-load. Our data is summarized in Table 1. *

We are using live funds in this paper and, as a result, our study does suffer from survivorship bias. However, when we use panel data approach by combining the data for four years, we address the survivorship bias issue to some extent.

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Table 1: Summary Statistics for the Data of this Study

2006

2007

2008

2009

12Month Total Return

Retur n after Tax on Distri bution 1Year

Return after Tax on Distrib ution and Sale 1Year

2.63

7.35

3.99

4.72

2.52

2.22

5.88

2.93

Std.Dev.

2.61

3.62

8.87

Mean

1.73

-10.51

Median

1.09

Std.Dev.

PreLiqui dation Tax Effici ency

PostLiquid ation Tax Efficie ncy

Mean

3.36

Median

FrontEnd Load % Plus

12b-1 Plan %

Turnov er Ratio

1.64

0.42

79.02

3.66

0.00

0.25

53.00

9.04

6.02

2.19

0.38

116.98

-30.94

-32.67

-20.43

1.56

0.40

102.31

-12.07

-35.74

-37.31

-23.51

0.00

0.25

57.00

1.67

5.75

16.03

15.64

10.46

2.16

0.38

292.18

Mean

2.55

11.59

30.27

27.72

18.68

1.50

0.39

105.53

Median

1.17

11.18

29.43

26.65

17.87

0.00

0.25

59.00

Std.Dev.

2.84

6.08

16.00

16.04

10.34

2.13

0.38

62.00

Mean

2.69

16.73

44.71

42.03

27.99

1.49

0.39

105.65

Median

1.18

17.71

47.93

45.27

29.81

0.00

0.25

60.00

Std.Dev.

3.21

8.51

22.79

22.68

14.62

2.13

0.38

160.43

Deferred Load %

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Volume 10, Issue 2

Empirical Results Our findings are as follows for the six variables that we investigate with regard to tax efficiency. Turnover Ratio Table 2 gives our tax efficiency results by turnover level.

Journal of Personal Finance

Inspection of Table 2 reveals that mutual funds with lower turnover rates generally lose smaller percentages of return to taxes. For 2006, 2008, and 2009, almost every turnover category has a smaller return loss due to taxes than the next higher turnover category on both the pre-liquidation and post-liquidation bases. We use t-statistics to compare the mean tax efficiency measures for each of the low, average, high, and very high turnover groups to the tax efficiency measures for the very low turnover group for the same year. For these three years, this requires calculating 24 tstatistics. Twenty of those t-statistics show statistically significant differences in means. Each of the higher turnover groups is less tax efficient than the very low turnover group. This result is consistent with our expectation that the fewer the transactions a mutual fund does, the lower the total capital gains tax. For 2007, we have negative tax efficiency measures on the post-liquidation basis for all levels of turnover. Negative tax efficiency measures mean that there is a gain when shares are sold. This was triggered because the stock market fell in 2007. These capital losses reduced taxes. Therefore, in years where the stock market goes down, funds with higher turnover ratios can be more tax efficient than funds with lower turnover ratios. We find that funds with low, average, and high turnover levels in 2007 have tax benefits that are great enough to produce a statistically significant difference compared to the mean tax inefficiency measure of the very low turnover group. The very high turnover group goes in the opposite direction with a loss compared to the very low group. This difference is also statistically significant.

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Volume 10, Issue 2

Investment Style Table 3 gives our tax efficiency results by investment style.

Journal of Personal Finance

The results in Table 3 show that for all years on the pre-liquidation basis, funds with a fixed income style are more tax efficient than funds with the equity and fixed income style and two of those differences are statistically significant. But, funds with a fixed income style are less tax efficient than funds with the equity style for 2007, 2008, and 2009. All three of those differences are statistically significant. On the post-liquidation basis, funds with a fixed income style are more tax efficient than funds with the equity and fixed income style for 2006, 2008, and 2009 and those differences are statistically significant. Compared to funds with the equity style, those with the fixed income style are even more efficient for those same three years. For 2007, the results are reversed with both differences being statistically significant. To explain these results, consider that equity style funds can generate both dividend and capital gain income that must be distributed to investors. The dividends, if qualified and the capital gains distributions are taxed at a maximum preferential tax rate of 15 percent, result in less tax for the shareholder. Fixed income funds typically hold bonds that generate interest income. Interest income is taxed at the ordinary income tax rate which typically results in a higher tax liability and therefore lower tax efficiency. This last fact would explain the greater tax efficiency of equity funds on a pre-liquidation basis. We expect that the lower tax efficiency of equity funds on the post-liquidation basis results from having to pay tax on both dividend income and capital gains income. The reversal of tax efficiency results for 2007 compared to the other years resulted from the decline in the stock market that year. Capital losses on shares that were sold reduced taxes due.

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Volume 10, Issue 2

Expense Category Table 4 gives tax efficiency results for our low and high expense ratio categories. Table 4: Tax Efficiency of Mutual Funds by Expense Category Groupings Morningstar defines an expense ratio as â&#x20AC;&#x153;the percentage of fund assets paid for operating expenses and management fees, including 12b-1 fees, administrative fees, and all other asset-based costs incurred by the fund, except brokerage costs.â&#x20AC;? We combine the Morningstar expense category classifications A and B to form our Low expense category and we combine the C and F classifications to form our High expense category. Mean values are percentages of return lost due to taxes. They are computed by subtracting after-tax return from total return. T-statistics are for two-tailed tests for the difference in sample means assuming that the sample variances are unequal.

*Statistically significant at the 5 percent level

Journal of Personal Finance

Tax efficiency results for the expense ratio categories are mixed. One would expect that funds with low expense ratios would have lower turnover in their portfolios and therefore would be more tax efficient. That seems to be true for 2006 on the pre- and post-liquidation bases, for 2008 on the pre-liquidation basis, and for 2009 on the post-liquidation basis. T-statistics show that all of these differences in efficiency are statistically significant. For 2007 on the pre- and post-liquidation bases and for 2008 on the post-liquidation basis, high expense ratio funds are more efficient than low expense ratio funds and the t-statistics show that all of those differences are statistically significant. We believe this reversal compared to 2006 is also the result of the fall in the stock market in 2007. Institutional or Retail Status Table 5 gives our tax efficiency results by the institutional and retail status of groups of mutual funds. Table 5: Tax Efficiency of Mutual Funds by Institutional / Retail Status

Morningstar defines an institutional fund as one that meets one of three criteria: (1) has the word "institutional" in its name, (2) has a minimum initial purchase of $100,000 or more, or (3) states in its prospectus that it is designed for institutional investors or those purchasing on a fiduciary basis. We consider all other funds to be retail funds. Mean values are percentages of return lost due to taxes. They are computed by subtracting after-tax return from total return. T-statistics are for two-tailed tests for the difference in sample means assuming that the sample variances are unequal.

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Volume 10, Issue 2

*Statistically significant at the 5 percent level

Our results show that retail funds are less tax efficient than institutional funds on both the pre- and post-liquidation bases for all years of the sample period. Those differences in efficiency are statistically significant for all but the 2007 post-liquidation basis. A likely explanation for our results is found in the differences in retail and institutional funds especially as they affect turnover of securities in their portfolios. These funds usually have a minimum investment requirement and their investment strategy typically involves buying and holding securities. This works to the advantage of their customers which are pension funds, endowments, and high net worth individuals. Those investors will

Journal of Personal Finance

redeem shares less frequently than the public in general. Thus the buy-and-hold approach leads to a lower turnover rate, lower operating costs, and thus greater tax efficiency. Retail funds cater to individuals and financial advisors. They are characterized by a more active management style, higher risk, a higher turnover rate, and thus lower tax efficiency. Load Status Table 6 gives our tax efficiency results according to the load status of groups of mutual funds. We separate mutual funds into two broad groups here: load and no-load. A load fund is one for which the buying and/or selling of its shares causes the investor to incur a sales fee which is called a load. For a no-load fund there is no sales fee involved in transacting its shares. Table 6: Tax Efficiency of Mutual Funds for Load and NoLoad Groupings

Morningstar describes sales fees: “Also known as loads, sales fees represent the maximum level of initial (front-end) and deferred (back-end) sales charges imposed by a fund.” To form our Load status categories, we combine the frontend and back-end sales charges reported by Morningstar. Mean values are percentages of return lost due to taxes. They are computed by subtracting after-tax return from total return. T-statistics are for two-tailed tests for the difference in sample means assuming that the sample variances are unequal.

Volume 10, Issue 2

*Statistically significant at the 5 percent level

Inspection of Table 6 shows that mutual funds with loads are on average less tax efficient than no-load funds for every year during the sample period on both the pre- and post-liquidation bases. The t-statistics for differences in sample means are all statistically significant. Apparently, mutual funds with sales charges are on average more willing to engage in turnover of their portfolios which in turn reduces tax efficiency. 12b-1 Plan Status Table 7 gives our tax efficiency results for funds grouped according to whether they have a 12b-1 plan. If a mutual fund has a 12b-1 plan, it charges customers for marketing expenses which are often commissions to brokers who sell shares of the fund.

Journal of Personal Finance

Table 7: Tax Efficiency of Mutual Funds by 12b-1 Plan Status Morningstar defines a 12b-1 fee as “a fee used to pay for a mutual fund’s distribution costs. It is often used as a commission to brokers for selling the fund.” Mean values are percentages of return lost due to taxes. They are computed by subtracting after-tax return from total return. T-statistics are for two-tailed tests for the difference in sample means assuming that the sample variances are unequal.

*Statistically significant at the 5 percent level

Our results show that mutual funds that do not have a 12b-1 plan are more tax efficient than funds with a 12b-1 plan on both the pre- and post-liquidation bases for all years of the sample period. The t-statistics for differences in sample means are all statistically significant. Mutual funds that are willing to pay this additional marketing expense apparently have a higher turnover of securities and thus lower tax efficiency.

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Table 8 summarizes regression results for the model specified in equation 1. Table 8: Summary of regression results for tax efficiency of mutual funds.

Dependent variable is pre-liquidation tax efficiency and post-liquidation tax efficiency. Independent variables are equity-style, fixed-income style, fees, load charges, 12b-1 plan charges, turnover ratio, and share class (institutional or retail).

Journal of Personal Finance

On a pre-liquidation basis, mutual funds with equity style of investing are more tax efficient in three out of four years. Panel data also shows that there is lower return lost due to taxes if the fund is an equity style. Mutual funds with a fixed income style of investing are tax efficient in all the five regressions. On a postliquidation basis, mutual funds with an equity style of investing lose more return due to taxes and, therefore, are less tax efficient. Mutual funds with a fixed style of investing continue to be more tax efficient even on a post-liquidation basis and panel data analysis confirms this result. According to panel data analysis, mutual funds with lower fees (rated A or B on fees) are more tax efficient on a postliquidation basis and less tax efficient on a pre-liquidation basis. On a pre-liquidation basis, the relationship between fees and tax efficiency is statistically weak. Load mutual funds report significant loss return due to taxes and therefore are less tax efficient on a pre- and post-liquidation basis for each of the four years. Panel data analysis also confirms that load mutual funds are less tax efficient. Funds with a 12b-1 plan lose less return due to taxes. Panel data shows that on a pre-liquidation basis, 12b-1 plans are more tax efficient. On a year to year basis, the relationship between 12b-1 plan and tax efficiency is negative, but either it is weakly statistically significant [2007 (pre- and post-liquidation), 2008 (pre-liquidation), and 2009 (pre-liquidation)] or not significant (2006). On a pre- and post-liquidation basis, higher turnover ratio results in higher loss of return due to taxes for each of the four years except for the year 2007. Mutual funds with a higher ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

Volume 10, Issue 2

turnover ratio report higher loss of return due to taxes in 2006, 2008, and 2009 on a pre- as well as post-liquidation basis. For the year 2007, higher turnover ratio results in more tax efficiency on a pre-liquidation basis and the results are weakly statistically significant. On a post-liquidation basis also, higher turnover results in less loss of return due to taxes, but the results are not statistically significant. Panel data analysis shows that higher turnover ratio funds are less tax efficient on a pre-liquidation basis and the results are highly statistically significant. On a post-liquidation basis, higher turnover funds are more tax efficient and the results are weakly statistically significant. Institutional share class funds have a lower loss of return due to taxes, but this relationship is either not statistically significant or weakly statistically significant. The last two columns in Table 8 capture the time trend in mutual fund tax efficiency. On a pre-liquidation basis, tax efficiency shows improvement in 2007, 2008, and 2009 with reference to 2006. On a post-liquidation basis, tax efficiency shows improvement for the year 2007 in comparison to the year 2006. However, in 2008 and 2009 tax efficiency has deteriorated in comparison to 2006. Implications for Financial Planners The findings of this study provide advice for financial planners in selecting tax efficient mutual funds for their clients. Financial planners need to consider tax efficiency in building their clientâ&#x20AC;&#x2122;s mutual fund portfolio if they are going to have a successful financial plan. In building a portfolio of mutual funds, financial planners need to manage the portfolios of their clients by taking a comprehensive approach and examining all retirement accounts, nonretirement accounts, and nontaxable accounts at the same time.

Journal of Personal Finance

By taking a more comprehensive view, financial planners should employ a strategy of putting more tax-efficient mutual funds in nonretirement accounts and less tax efficient mutual funds in retirement accounts. Financial planners must understand that asset allocation is very important in employing a tax efficient strategy and mutual funds should be selected based upon after-tax return not pre-tax return. The study finds that more tax efficient funds include those funds that have a lower turnover rate, fixed income investment style, institutional status, and no-load status. Financial planners desiring tax efficiency should seek mutual funds with these attributes. In evaluating mutual funds that have low expense ratios, financial planners need to keep in mind that these funds will be more tax efficient when the securities markets are rising. However, if markets fall, funds that are more active and, therefore, have a higher expense ratio will be more tax efficient as they will create more capital losses selling shares at depressed prices, thus creating a tax advantage. Other tax-efficient strategies that can be taken include investing in index funds which employ a buy and hold strategy, receive qualified dividends taxed at 15%, and generate little if any capital gains. On the contrary, investing in high yield bond funds which generate interest income which is taxed at ordinary income tax rates is not a tax-efficient strategy. The study also finds that the turnover rate of a fundâ&#x20AC;&#x2122;s impact on tax efficiency depends upon conditions in the securities markets. In a rising market the opportunity is obvious - realize profits/capital gains and the investor accumulates wealth. Although there is less tax efficiency in a rising market, as taxable income is generated there is potentially a return for the investor, which is the primary objective of investing in the market. On the other hand, no financial planner or investor desires a falling ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

Volume 10, Issue 2

market. However, in a falling market there is opportunity for a financial planner or investor to create capital losses and offset capital gains, if any, and reduce the tax burden or increase tax efficiency. At the same time the investor benefits by reinvesting back in the market at lower prices. All of these implications need to be prefaced by stating that no investment decision should be solely based upon the tax impact of a decision or tax efficiency. The tax impact of an investment decision is just one criterion that needs examination in the selection of a particular mutual fund. At the end of the day, the only return that matters to an investor is the return that he or she has left over after taxes. Summary and Conclusions We investigate six factors which we have reason to believe could characterize or affect mutual funds as to their tax efficiency. Our measure of tax efficiency here is the percentage of return lost to taxes. One of the factors we investigate, turnover, is actually a determinant of tax efficiency since a higher turnover of securities causes the incurrence of more taxes which in turn reduces return. We evaluate the impact of the other five factors on tax efficiency. We find tax efficiency to be greater on average for mutual funds that have a lower turnover rate, a fixed income investment style, institutional status, and no-load status. Mutual funds with low expense ratios will be more tax efficient when the securities markets are rising. This will reverse if markets fall because a tax advantage will be created by capital losses incurred when investors decide to sell shares at depressed prices. Mutual funds whose investment style is a combination of equity and fixed income will

Journal of Personal Finance

have tax efficiency between those of the fixed income and equity styles. References Barber, B.M., Odean, T. and Zheng, L. (2005). Out of Sight, Out of Mind: The Effects of Expenses on Mutual Fund Flows, Journal of Business, 78 (6), 2095-2119. Bergstresser, D. and Poterba, J. (2002). Do after-tax returns affect mutual fund inflows? Journal of Financial Economics 63, 381–414. Bernard, T.S. (2006). Fund Investors can expect a tax increase. Lipper Inc. study Bogle, J. (2010). On mutual funds, cheaper is better, The Wall Street Journal. August 27 Bogle, J. (2006). Whose capital is it? Putting owners back in control. Business Economics, 41(2), 47-52. Bruce, L. (2003). Mutual fund turnover and taxes. Retrieved on January 4, 2010 from http://www.Bankrate.com. Ellentuck, A.B. (2004). Investing in tax efficient funds. The Tax Adviser, 35(11), 716-717. Gardner, R. and J. Welch, J. (2005). Increasing after-tax return with exchangetraded funds. Journal of Financial Planning, 18(6), 30-35. Gilliam, J., Dass, M., Durband, D., & Hampton, V. (2010). The role of assertiveness in portfolio risk and financial risk tolerance among married couples. Financial Counseling and Planning, 21(1), 53-78. Gregory, K. & Savage, S. (2003). Invest with an edge. Kiplinger’s Personal Finance, 57(2), 60. Investment Company Institute, Statistics, Mutual Fund Trends, Retrieved on December 31, 2009 from http://www.ici.org. Ivkovic´, Z. and Weisbenner, S. (2009). Individual investor mutual fund flows, Journal of Financial Economics 92, 223–237. Loibl, C., Lee, J., Fox, J., & Mentel-Gaeta, E. (2007). Women's HighConsequence Decision Making: A Nonstatic and Complex Choice Process. Financial Counseling and Planning, 18(2), 35-47, 98-100. Malhotra, D. K. and McLeod, R. (1997). An empirical analysis of mutual funds expenses, Journal of Financial Research, 20(2), 175-190. McLeod, R. and Malhotra, D.K. (1994). A re-examination of the effect of 12b1plans on mutual fund expense ratios, Journal of Financial Research, 17(2), 237-244. Peterson, J.D., Pietranico, P.A., Riepe, M.W., Xu, F. (2002). Explaining aftertax mutual fund performance, Financial Analysts Journal, 58(1), 75-86 Tuve Investments Inc., 2007, What makes for a tax efficient mutual fund? http://www.tuveinvestments.com . Sialm, C. and Starks, L. (2011). Mutual Fund Tax Clienteles, Journal of Finance, forthcoming. Weiss, Ira S. (2002). An empirical examination of tax factors and mutual funds’ stock sales decisions, Review of Accounting Studies, Boston, 7(2), 343-347. ©2011, IARFC. All rights of reproduction in any form reserved.

Volume 10, Issue 2

STUDENT FINANCIAL COUNSELING: AN ANALYSIS OF A CLINICAL AND NON-CLINICAL SAMPLE Sonya L. Britt* John E. Grable ** Julie Cumbie* Sam Cupples* Justin Henegar* Kurt Schindler* Kristy Archuleta* Kansas State University* The purpose of this study was to determine what factors predict whether students will seek on-campus peer-based financial counseling. An attempt was made to determine if students who seek help differ significantly from students who do not seek help. Findings provide a profile of college student financial counseling help-seekers. Collegeage financial counseling help seekers tend to be older, less satisfied with their income, less knowledgeable, less wealthy, and more stressed. The results from this study suggest that college financial counseling centers appear to be on target in connecting with some of the students they were designed to reach. Continued efforts to assist students with high financial stress may be a way to increase financial well-being among college students.

Contact Author: John Grable, Institute of Personal Financial Planning, School of Family Studies and Human Services, 318 Justin Hall, Kansas State University, Manhattan, Kansas; Phone: (785)532-1486; Fax: (785)532-5505; Email: jgrable@k-state.edu

Journal of Personal Finance

Introduction Financial literacy is an issue of national importance. The relevance of the issue was highlighted in 2009 when the U.S. Department of Treasury convened a meeting of researchers and policy makers to help establish a national agenda for financial literacy and education. The general consensus concerning outcomes from the meeting, and resulting research priorities, was that financial well-being appears to be strongly associated with educational outcomes. It was also concluded that helping consumers become more financially literate should result in a decline in financial stress at the aggregate and household level. Federal and state policy makers and educators have known this for years, although formal preparation on personal financial matters has only recently been integrated into the public education system. Today, 44 states offer some form of mandated or recommended personal financial education in elementary and high schools, although only 13 states require a course for graduation (Council for Economic Education, 2009). Albeit proportionally small in number, some American universities have begun to address the association between financial literacy and overall well-being by establishing campusbased financial education/counseling centers (Chamberlin, 2011). Students have increasingly voiced their opinion that they want and would utilize financial education services if offered at their university (Cude, Lawrence, Lyons, Metzger, LeJeune, Marks, & Machtmes, 2006; Goetz, Cude, Nielsen, Chatterjee, & Mimura, 2011). Over the past two decades, student financial counseling centers have developed as higher education administrators and educators have come to realize that college students may not be as financially astute as necessary in the modern financial marketplace. According to Chamberlinâ&#x20AC;&#x2122;s research, 14% of large ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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four-year public American colleges and universities have websites devoted entirely to financial literacy. Many of these websites also allow students to schedule an appointment to meet with a financial counselor. One-third of large public universities and schools (33%) offer a link on their financial aid website directing students to external sources of financial education, such as CashCourse速. Another 21% of schools provide financial education web links on a different website besides the financial aid office site. Although these statistics are impressive, it is important to note that slightly over one-third (38%) of large public universities and colleges provide no financial education to students. Cunningham (2000) made the case for preventive financial education based on her findings that nearly 70% of college students carry at least one credit card in their own name with the majority of those students being solely responsible for making payments on the credit card. Students with more debt spend additional time at work and consequently less time in school related activities. Had students received preventive financial education, they might have learned how to live within their means without going into credit card debt or other forms of predatory lending debt. More recent research adds support to this assertion in that student dropout rates may be related to financial problems they are experiencing (Joo, Durband, & Grable, 2008-2009). Perhaps to make matters worse, universities and colleges have jumped on the credit and debt bandwagon by issuing branded credit cards. Often, these credit offers have been extended to current students. Some have even argued that colleges or universities have become accomplices to the growing student debt problem by focusing on the portion of transaction fees transferred to the university, rather than assessing the true cost to students (Johnson, 2005). Easy credit is often used by students to fulfill

Journal of Personal Finance

immediate consumer needs and desires, thus creating financial stress for many students. In extreme cases, this debt can result in students taking their own lives (Johnson). New debt is in addition to student loans taken to complete studies. The Credit CARD Act of 2009 may have addressed some of these issues for college students by establishing stricter rules under which credit may be issued to college students (The White House, 2009), but for many students the Credit CARD Act of 2009 came too late, and there really is little evidence to suggest that the Act will stem the growing tide of student consumer debt in the future. Credit card debt, as suggested above, is a major factor of stress in the lives of some students (Grable & Joo, 2006). Although certain types of stress can be beneficial (i.e., eustress), few students are prepared to effectively manage their debt situation (Palmer, Bliss, Goetz, & Moorman, 2010). This often leads to distress or negative stress (Borden, Lee, Serido, & Collins, 2008; Goetz, Durband, Halley, & Davis, 2011). Students who report high levels of financial stress are known to be more likely to report other negative outcomes such as poor academic performance (Ross, Cleland, & MacLeod, 2006) and physical and mental health problems (Northern, Oâ&#x20AC;&#x2122;Brien, & Goetz, 2010). There are other issues that affect college student stress and financial capacities beyond credit and debt issues. For example, college students tend to be caught in an inflationary environment (particularly with the rising costs of tuition) while concurrently facing weaker employment outlooks. Low incomes and the ever changing job market are factors college students face on a daily basis. The result is potential stress among college students. This is a worry for college and university administrators and those who oversee public and private education systems. Specifically, there is evidence to indicate that high levels of stress, specifically related ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

Volume 10, Issue 2

to financial difficulties, can drive students either to drop out of school or to reduce their yearly credit load (Grable & Joo, 2006). Both are negatives when viewed from an institutional perspective. Not only do colleges and universities lose revenue but society loses the human capital gain derived from a higher education degree. Given the high level of financial sophistication required by college students to effectively manage daily financial matters (Lusardi, Mitchell, & Curto, 2010), in conjunction with maintaining healthy relationships and academic achievement, it is reasonable to conclude that some degree of financial knowledge is needed among college students so that they can meet the demands of the marketplace. This, in addition to the growing availability of on-campus financial counseling across the U.S., provides the impetus for this research. The purpose of this study was to determine what factors predict whether students will seek peerprovided assistance as a way to reduce financial stress. In other words, an attempt was made to determine if students who seek help differ significantly from students who do not seek help. The results from this study can be used to develop a profile of college students who are likely to seek help for financial questions and problems. The results also provide an insight into the types of financial stressors college students face today. As a preview, findings lend support to the hypothesis that there are groups of students who desperately need consumer-focused financial interventions as a way to reduce stress and maximize gains of a college education. Literature Review Through the identification of financially at-risk students, college financial counseling centers can design marketing strategies to meet the needs of these students. Further, both campus and legislative policies can be designed to ensure that the most at-

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risk students are provided assistance. The following review of literature addresses some of the most important consumer financial issues facing college students today and how these issues influence a college studentâ&#x20AC;&#x2122;s likelihood of seeking help. Financial Resources There is evidence to suggest that college students worry about their financial resource situation. Joo and her associates (2008-2009) used a convenience sample of college students at a large southwestern university to estimate that (a) 38% of college students worry about their debt load, (b) greater than 50% of the students are somewhat (34%) to extremely (17%) stressed about their financial situation, and (c) 42% of students feel that financial issues interfere with their academic performance. Certain characteristics can be used to identify college students who have financial problems. According to Lyons (2004), financially at-risk college students hold $1,000 or more of debt other than credit cards, are of a minority status (i.e., female, black, and/or Hispanic), and receive need-based financial assistance. These characteristics can also be used to predict who may be more likely to seek financial help. For instance, Worthy, Jonkman, and Blinn-Pike (2010) identified sensation-seeking, risky behavior, and use of credit as characteristics predicting which college students were likely to exhibit financial problems. Adams and Moore (2007) found that driving after consuming alcoholic beverages, taking amphetamines, experiencing depression in the past 30 days, having a high body mass index, and a low grade point average were all associated with high-risk credit behavior as measured by the amount of unpaid credit card debt. Given these findings, it seems apparent that about half of college students may actually be in a position of needing financial counseling while in college to help lessen their financial stress. Whether or not a student will ever seek ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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help or how consumer financial educators can predict who might seek help are questions as yet unanswered in the literature. Financial Attitudes Financial attitudes, including related factors such as financial knowledge and stress, are known to influence college studentsâ&#x20AC;&#x2122; ability to function in the financial marketplace (Lusardi et al., 2010). Recent studies reporting on the financial knowledge of college students indicate that students, in general, lack comprehension of basic financial concepts, although there is some evidence to suggest that current college students outperform high school students on tests of financial literacy (Mandell, 2008). According to results from the 2008 Jump$tart Coalition survey, high school studentsâ&#x20AC;&#x2122; financial literacy remains lowâ&#x20AC;&#x201D;in fact, it is the lowest level ever. Financial literacy does appear to increase for college students and continues to increase as students progress through college. This may not be attributable solely to increased classroom instruction, since the Jump$tart Coalition found that high school students who took a personal finance course did no better on financial literacy assessments than students who did not take a course. Rather, results appear to indicate that experiential learning is at least as important in increasing financial literacy as classroom instruction, although it is important to note that students have an interest in both learning approaches. According to Masud, Husniyah, Laily, and Britt (2004), using a sample of college students enrolled internationally, students stated that they wanted to learn more about personal finance topics. In their study, 90% of college students desired more information about saving, investing, insurance products, budgeting, and general financial management. As this brief review of literature suggests, there are large groups of college students who face financial resource constraints. Few of these students have the necessary skills to effectively

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manage both long- and short-term financial stressors. As Mandell (2008) pointed out, financial literacy on America’s college campuses is low. Fortunately, many leading colleges and universities have stepped into the fray to help students obtain information, knowledge, and resources in a way that both reduces stress and increases financial management skills (Chamberlin, 2011). The development and growth of on-campus financial counseling centers is, on the one hand, an improvement in helping students; on the other hand, little is known about the types of students who use financial counseling services. The remainder of this paper describes the conceptual framework used to address this important question and subsequent findings and implications. Conceptual Framework This study’s methodology was based on a modified conceptual framework conceptualized by Suchman (1966), which was developed for assessing health care help-seeking behavior and decision-making processes. Grable and Joo (1999) later modified and refined the model to fit into a consumer finance framework as a way to incorporate help-seeking behavior within consumer behavior studies. According to Grable and Joo, individuals go through five decision-making stages to determine whether they will seek financial help (see Figure 1). During Stage 1 of the help-seeking process, a person may exhibit various behaviors—both positive and negative—related to their financial situation. Possible positive behaviors for college students include paying bills on time and balancing checking accounts. Examples of negative behaviors might be over drafting checking accounts or using payday loans. Joo (1998) found that these types of financial behaviors may be influenced by demographic and socioeconomic characteristics, such as gender, ©2011, IARFC. All rights of reproduction in any form reserved.

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age, education, and income. Financial stressors and financial knowledge may also influence a personâ&#x20AC;&#x2122;s financial behavior (Grable & Joo, 1999). Stage 2 of the help-seeking process consists of a self-evaluation of financial behavior to determine what actions may result in positive and/or negative outcomes. In general, women are within the model predicted to evaluate their behavior more harshly than men (Lytton & Grable, 1997). Next, in Stage 3 of the process, a person identifies the cause(s) of certain financial behaviors. At Stage 4, a person must make the decision to seek help or not to seek help related to their financial behavior. The choice to seek help ought to be associated with a positive outcome. Elliehausen, Lundquist, and Staten (2007) found that participating in counseling was associated with a positive change in borrowersâ&#x20AC;&#x2122; credit profiles. This decision to seek help may be influenced by the same factors associated with Stage 1 of the process (i.e., demographic and socioeconomic characteristics, financial stressors, and financial knowledge). If a person decides to seek help the process moves to Stage 5 where different options for help are explored (e.g., financial counselor, financial planner, retirement specialist, or friend). Again, the expected outcome associated with seeking help is an increase in economic, social, and emotional well-being, which has been defined as contentment with oneâ&#x20AC;&#x2122;s material and non-material financial situation (Joo, 1998; Williams, 1983). The purpose of this study was to examine Stage 4 of the framework. Specifically, the choice to seek help via an on-campus financial counseling center was tested. The choice dilemma at Stage 5 was, by default, defined as seeking on-campus financial counseling or not seeking on-campus financial counseling.

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Figure 1 Framework of college student help-seeking behavior Method Data Data used for this study were obtained from two sources. Each dataset was comprised of college students from one large midwestern U.S. university. The first dataset included students

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who sought free financial counseling from an on-campus financial counseling center (i.e., the clinical group). The counseling services were offered to all students at the university and marketed through the campus school newspaper, posters around campus, and word of mouth. Some of the students in this sample may have made a follow-up appointment after listening to a group presentation. The second dataset was obtained from a research study designed to evaluate financial planning behavior and attitudes of college students (i.e., the non-clinical group). This random group of students learned about the research study through posters around campus and word of mouth. Students in the non-clinical sample ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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volunteered their time to complete a survey in return for a cash incentive. Outcome Variable Seeking financial counseling at the on-campus financial counseling center was the outcome variable of interest in this study. The variable was measured dichotomously where a student was classified as seeking financial counseling help (coded 1) or participating in a university research study but not seeking financial counseling (coded 0). There were no overlaps between the datasets; that is, each data point was an independent observation. Independent Variables Financial Resources. Several financial variables were available in the clinical and non-clinical datasets, including selfperceived net worth, amount of credit card debt, amount of noncredit card installment debt, and net income. The net worth variable was measured by asking respondents to answer the following question: â&#x20AC;&#x153;Suppose you were to sell off your major possessions (including your home), turn all of your investments and other assets into cash, and pay all your debts. Would you be in debt, break even, or have something left over?â&#x20AC;? A 10-point stairstep scale was used, with a response category of 1 indicating the student would be in serious debt, a response of 5 or 6 indicating the student would about break even, and a response of 10 indicating the student would have money left over. It was hypothesized that students with a lower level of net worth would be more likely to seek financial counseling. The amounts of credit card and installment debt were measured continuously for the clinical sample, but categorically for the non-clinical sample. The clinical sample was recoded to match the non-clinical dataset with the following categories for

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credit card debt: 1 = $0; 2 = less than $500; 3 = $500 – $999; 4 = $1,000 – $1,499; 5 = $1,500 – $1,999; 6 = $2,000 – $2,499; 7 = $2,500 – $2,999; 8 = $3,000 – $3,499; 9 = $3,500 – $3,999; and 10 = $4,000 or higher. Installment debt was coded in the following categories of 1 = $0; 2 = less than $1,000; 3 = $1,000 – $4,999; and 4 = $5,000 or higher. It was hypothesized that students with higher levels of credit card and installment debt and a lower level of income would be more likely to seek financial counseling given their reduced discretionary cash flow. Net monthly income data were coded continuously for both data sets. Missing income scores were replaced with the sample median income level. Financial Attitudes. The following attitudinal variables were used to assess common characteristics thought to influence a student’s decision to seek financial counseling: (a) confidence in one’s ability to meet a financial emergency, (b) satisfaction with one’s income, (c) level of financial stress, and (d) self-perceived financial knowledge. All financial attitude items were measured on a 10-item Likert-type scale with 10 being the most confident, satisfied, stressed, and knowledgeable, respectively. Students’ confidence to meet a financial emergency was measured with the following question: “How confident are you that you could find the money to pay for a financial emergency that costs about $1,000?” The satisfaction question asked respondents to rate their satisfaction with their “present financial situation.” The financial stress question asked, “How stressed do you feel about your personal finances, in general?” The knowledge question asked, “How knowledgeable do you think you are about personal finances compared to others?” It was hypothesized that students with less ability to meet a financial emergency and those who were less satisfied with their income would be more likely to seek financial counseling. Students with lower levels of financial stress and ©2011, IARFC. All rights of reproduction in any form reserved.

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higher levels of perceived financial knowledge were hypothesized to be less likely to seek financial counseling. Mental Health Status. Questions were asked in the clinical dataset as to students’ level of anxiety, sleeping difficulty, ability to concentrate on school and/or work, level of irritability, difficulty in controlling worries, level of muscle tension, and level of fatigue experienced as a result of their financial situation. Questions were asked using a scale of 1 to 7, where 1 meant they never experienced the above symptoms and 7 meant they always experienced the above symptoms. The non-clinical sample was asked similar questions about their level of distress; however, the questions were asked in a general sense, not merely related to students’ financial situation. Moreover, the response categories were coded 1 = not at all, 2 = several days, 3 = more than half the days, and 4 = nearly every day. To address the problems associated with different methods of measurement, a principal component analysis was separately conducted for the clinical and non-clinical samples to obtain a standardized mental health score for each respondent. A principal component analysis score always has a mean of 0 with a standard deviation of 1, eliminating the problem of different measurements between the two data sets. The unique score for each participant was then determined to be representative of their level of distress, with a higher scores representing greater distress. For the purposes of this study, the variable was coded “mental health.” Demographic Characteristics. Two demographic variables were measured in the clinical and non-clinical datasets and used for this study: age and sex of the student. These control factors were chosen based on their use in previous studies (e.g., Borden et al., 2008; Goetz et al., 2011; Palmer et al., 2010). Age was

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measured as a continuous variable. Sex was coded 1 = male and 0 = female. Data Analysis Method Based on the exploratory nature of this study, a non-parametric classification and regression tree (CR&T), using AnswerTree in SPSS, was used to determine the key characteristics separating the clinical and non-clinical group. CR&T trees are a useful exploratory tool because the method can accommodate nominal, ordinal, and continuous data in the same analysis to return the statistically significant predictors of the dependent variable. CR&T works on a binary tree algorithm system. The algorithm partitions data and produces accurate homogeneous subsets. According to SPSS (1998), “C&RT partitions data into two subsets so that the cases within each subset are more homogeneous than in the previous subset” (p. 184). The algorithm works on a recursive process by repeating until a “homogeneity criterion is reached or until some other stopping criterion is satisfied” (p. 184). It is possible for one variable to be used multiple times in a model. In this study, C&RT was chosen as a mechanism for splitting each node “such that each child node is more pure than its parent node. Here purity refers to the values of the target variable” (SPSS, p. 187). In a completely pure node, all of the cases have the same value for the target variable. C&RT measures the impurity of a split at a node by defining an impurity measure. Statistically significant predictors from the CR&T tree model were then used in a post-hoc confirmatory logistic regression model to determine each item’s impact in predicting whether a student sought financial help.

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Results The full sample characteristics are shown in Table 1. The sample was fairly evenly split between the clinical and non-clinical groups (48% and 52%, respectively). Forty-seven percent were female and most students did not hold any credit card or installment loans. For the most part, respondents said they would nearly break even if they were to sell all of their major possessions and convert all of their assets into cash to pay off debt. On average, students were moderately satisfied with their current financial situation and net income. Table 1 Sample Characteristics (n=140) Sample Characteristic and Code Clinical Sample Non-Clinical Sample Age Gender Female =0 Male =1 Monthly Income Credit Card Debt Installment Debt Loans Net Worth Emergency Preparedness Income Satisfaction Financial Situation Satisfaction Financial Stress Perceived Financial Knowledge Mental Health Status

% 47.86 52.14 52.86 47.14

Mean

Range

23.95

18-60

$752 <$500 $0 4.80 5.78 4.19 4.86 5.42 5.28 0.00

$0-$7,440 $0-$4,000 $0-$15,000 1-10 1-10 1-10 1-10 1-10 1-10 -1.24-3.10

When the clinical and non-clinical samples were compared (Table 2), the clinical sample was older and carried higher credit card debt but similar amounts of installment debt. The clinical

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sample had lower net worth, was less prepared for a $1,000 financial emergency, had lower financial and income satisfaction, and reported lower levels of perceived financial knowledge. In regards to financial stress, the clinical sampleâ&#x20AC;&#x2122;s (M = 5.66) stress level was only slightly higher than that of the non-clinical sample (M = 5.21). Table 2 Clinical vs. non-clinical sample characteristics Sample Characteristic and Code Age Female Male Monthly Income Credit Card Debt Installment Debt Loans Net Worth Emergency Preparedness Income Satisfaction Financial Situation Satisfaction Financial Stress Perceived Financial Knowledge Mental Health Status

Clinical Mean 25.12 53.73% 46.27% $754.66 $500-$999 $0 3.15 4.78 3.84 4.49 5.66 4.54 0.00

Non-Clinical Mean 22.84 52.05% 47.95% $749.89 <$500 $0 6.29 6.70 4.51 5.19 5.21 5.96 0.00

Statistical Differences Between Samples Independent t tests were performed to determine if there was a statistically significant difference between groups, based on each of the variables included in the study. Four variables were found to be significantly different between the two groups. First, age of the clinical group (M = 25.12, SD = 6.67) was significantly higher than the non-clinical group (M = 22.84, SD = 6.09), t140 = -2.07, p < .05. Second, self-reported net worth of the clinical group (M = 3.15, SD ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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= 1.58) was significantly lower than the non-clinical group (M = 6.23, SD = 2.94), t140 = 7.68, p < .001. Third, the ability of the clinical group (M = 4.78, SD = 2.43) to be able to pay for an emergency of $1,000 was significantly less than the non-clinical group (M = 6.70, SD = 3.00), t140 = 7.68, p < .001. Finally, the perceived financial knowledge of the clinical group (M= 4.54, SD = 1.76) was found to be significantly lower than the nonclinical group (M = 5.96, SD = 1.92), t140 = 4.61, p < .001. Classification and Regression Tree Results A classification and regression tree (C&RT) and logistic regression analysis were used to address the research question stated at the outset of this paper (i.e., to determine what factors predict whether students will seek financial counseling). C&RT was used to predict the most prominent characteristics associated with seeking services in a clinical setting. Demographic variables, such as gender and age, as well as all other variables assessed, including financial resources, financial attitudes, and mental health status, were entered into the model. The CR&T, illustrated in Figure 2, shows that net worth was the most important variable influencing those who sought financial help. Net worth produced a level of prediction improvement of .02. If a respondent scored 5.5 or lower (Node 1) on net worth (maximum of 10), then they were predicted to be more likely to seek services from the counseling center than those who scored 5.5 or higher. For those who scored 5.5 or lower on the net worth measure, mental health status became the next most important factor, producing a prediction improvement of .04. Those who scored more than 1.23 on mental health status (Node 3), indicating some type of mental health distress such as a depressive symptom, were more likely than those who scored less than 1.23 (Node 4) to seek help from a counseling center. For those who were more distressed, age became the next important predictor of seeking help from a counseling center. Age

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resulted in a prediction improvement of .03. Those who were over the age of 22.5 years (Node 6) were more likely to seek services from the counseling center than those who were under the age of 22.5 (Node 5). For those who were under the age of 22.5 years, perceived financial knowledge became an important predicting factor with a prediction improvement of .03. Those who scored less than 6.5 (maximum score of 10) were more likely than those who scored above 6.5 (Node 8) to seek help. Income satisfaction was also an important predicting factor of those who were likely to seek financial counseling. Income satisfaction had a prediction improvement of .02. Those who scored lower than 6.5 (maximum score of 10) on income satisfaction were more likely to seek financial help than those who scored above 6.5 (Node 10). Post-Hoc Confirmation. A logistic regression was used to confirm results from the C&RT model by utilizing the significant predictors found in the C&RT output. Net worth, perceived financial knowledge, and age were found to be significant variables associated with whether a person would seek financial help. In confirmation with the C&RT, the logistic regression results showed that net worth was the most important determinant explaining who sought financial counseling, with students reporting a higher level of net worth being 39% less likely to seek financial counseling (OR = .61, SE = -.77, p < .001). The next two important determinants associated with financial help seeking included reporting a lower level of perceived financial knowledge and being older, respectively. Students who reported higher levels of perceived financial knowledge were 28% less likely to seek financial counseling services (OR = .72, SE = -.35, p < .01). Older students were 9% more likely to seek financial counseling services (OR = 1.09, SE = .31, p < .05). Table 3 summarizes the results from the logistic regression model. ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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Figure 2 Decision tree predicting who sought financial counseling (1 = sought help) Table 3 Logistic regression results predicting who will seek financial counseling CLINICAL Node 0 Category % n 1 51.27 81 0 48.73 77 Total (100.00) 158 NW Improvement=0.2093

<=5.5

>5.5

Node 1 Category % n 1 71.68 81 0 28.32 32 Total (71.52) 113

Node 2 Category % 1 0.00 0 100.00 Total (28.48)

n 0 45 45

MH Improvement=0.0351

<=-1.2329241500000001 Node 3 Category % 1 0.00 0 100.00 Total (3.16)

>-1.2329241500000001 Node 4 Category % n 1 75.00 81 0 25.00 27 Total (68.35) 108

n 0 5 5

AGE Improvement=0.0300

<=22.5

>22.5

Node 5 Category % 1 59.62 0 40.38 Total (32.91)

Node 6 Category % 1 89.29 0 10.71 Total (35.44)

n 31 21 52

FINKNOW Improvement=0.0311

<=6.5

>6.5

Node 7 Category % 1 67.39 0 32.61 Total (29.11)

Node 8 Category % 1 0.00 0 100.00 Total (3.80)

n 31 15 46

INCOME Improvement=0.0223

<=6.5 Node 9 Category % 1 76.32 0 23.68 Total (24.05)

>6.5

n 29 9 38

Node Category 1 0 Total

10 % 25.00 75.00 (5.06)

n 2 6 8

n 0 6 6

n 50 6 56

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Variable

Beta

Perceived Net Worth Mental Health Status Age Perceived Financial Knowledge Income Satisfaction

-.49*** -.20 .09* -.32** -.08

Standardized Beta Estimate (SE) -.77 -.11 .31 -.35 -.10

Odds Ratio (OR) .61 .82 1.09 .72 .93

*p < .05, **p < .01, ***p < .001 Pseudo R2 = .48

Discussion This study was conducted to evaluate the characteristics of students who sought financial counseling in comparison to the general student population (in this study, those who were likely to volunteer for a university sponsored research project). Once the comparison was complete, five significant factors were identified for use in anticipating who was likely to seek on-campus financial counseling: (a) perceived net worth, (b) mental health, (c) age, (d) perceived financial knowledge, and (e) income. In general, help seekers were older, had a lower asset base, less satisfaction with income, less knowledge, and elevated levels of mental health distress. These findings add to the literature that describes at-risk students (e.g., Bliss, 2010; Palmer et al., 2010). Results from the logistic regression analysis confirmed these general themes; however, mental health and income were not found to be statistically significant in the post-hoc test. Overall, the clinical sample demonstrated a much lower perceived net worth than the non-clinical sample. Perception of net worth could be influenced by the overall amount of debt a student holds, including credit cards, installment loans, and student loans. Naturally, students accrue more debt as they progress through ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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school with the accumulation of additional student loans. Therefore, targeting senior seminar classes to deliver financial education may prove beneficial in reaching those most in need, as well as prompting some students to seek additional financial counseling (Goetz et al., 2011). Identifying students who receive student loans is another way to target the potentially low net worth students. In this study, students who sought out financial counseling felt as though they had a lesser understanding of financial concepts than their peers. The level of objective knowledge of participants or their previous course experience was not known, therefore it was not possible to test whether increased objective knowledge (possibly through a personal finance course) results in an increased likelihood of seeking financial counseling, as suggested by Goetz and his associates (2011). Rather, the perceived lack of financial knowledge among help seekers does suggest that financial counseling centers may be attracting some students who feel they need more information regarding personal finances. This study also found that those seeking financial counseling services were older than the non-clinical sample by an average of two years. This seems counterintuitive, as older students would seem to be more mature and responsible with their spending and would need less financial counseling. Several suggestions come to the forefront to explain the age difference. First, as students get closer to graduation, they may begin to grasp their personal financial realities. In other words, these students might be reevaluating their personal financial position, including debt, savings, and income, as they look to enter the â&#x20AC;&#x153;real world.â&#x20AC;? It may be this reevaluation that drives them to seek help. Other external factors might also be motivating help-seeking behavior, such as self-reflection. As they begin to interview for jobs, separate from

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parental support, or perhaps begin to get married and take on the financial dependency of a family, the realization that there is more to be learned may come to mind. Additionally, age may simply be associated with experience. It is likely that some older students have amassed more debt in the latter part of their post-secondary education than the former and thus have more debt to pay off, creating a sense of urgency to seek financial guidance. That is, they have gained experience through behavior that prompts a helpseeking response. In either case, targeting marketing efforts toward senior seminar classes may aid in reaching students who have much to benefit from financial education. Although not statistically significant predictors in the logistic regression, the C&RT results showed that students seeking financial counseling were less satisfied with their income situation and more likely to exhibit mental health distress. When a financial emergency arises the tendency to use credit increases in response to the liquidity crisis, especially if sufficient cash is not available to cover the necessary expenses. These factors may work in tandem since a person who is not able to meet a financial emergency must find alternative cash sources (which are likely to charge high rates of interest and fees), leading to a dissatisfaction with their financial situation. This, in turn, may cause mental health distress in the form of anxiety, fear, and depression. Financial and emotional issues tend to be highly related, as confirmed by Aniol and Snyder (1997) who found that individuals struggling with a similar issue sometimes sought help from a financial counselor and sometimes from a mental health counselor with no apparent reason for choosing one over the other. As such, it is important for those working in on-campus centers to engage in joint marketing efforts with university mental health/counseling centers, since contact with these outreach offices may prove the catalyst that prompts students decide to seek help. ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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Conclusion The results from this study are important for three reasons. First, the results provide a profile of college students who are likely to seek help for financial questions and problems via oncampus financial counseling. Student help seekers tend to be older, less satisfied with their income, less knowledgeable, less wealthy, and more stressed. Second, findings provide insights into the types of financial stressors college students face today. Specifically, issues related to debt, new job benefit information, sources of increased income, and other factors that directly impact young adults seem to be variables underlying search behavior. This is somewhat of a conjecture however. Additional research is needed to test Stage 5 of the theoretical help-seeking framework to determine what alternatives individuals evaluate in their decision to seek financial help. Finally, results lend support to the hypothesis that there are groups of students who need consumerfocused financial counseling as a way to reduce stress and maximize gains associated with a college education. Future research efforts are needed to expand upon the results from this study. It would be useful to document who, among those seeking help, have completed a personal finance course while in college. It is possible that financial awareness is a factor that influences a studentâ&#x20AC;&#x2122;s choice to seek help. Other areas to be explored include conducting a longitudinal study over several years to include years of school enrollment and post-graduation years to assess whether or not help-seeking characteristics change over time and to determine if the outcomes associated with counseling are effective. Such a study would be beneficial in testing the effectiveness of free on-campus financial counseling centers versus financial counseling assistance that must be paid for outside of the college environment. Additionally, replications of

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this study at other universities that provide on-campus financial counseling are needed to ensure the validity of findings. The limitations of the current study should also be considered. First, it would be ideal to have an identical measure of mental health distress available for clinical and non-clinical samples. This study used a principal component analysis to obtain a usable score for each respondent, although the clinical sample was asked specifically about mental health distress caused by financial issues whereas the non-clinical sample was asked about general mental health distress. The sequence of the mental health distress questions followed several financial questions, so it is possible that non-clinical sample respondents were thinking about finances when answering the mental health distress questions. Secondly, all students were sampled from one midwestern university. The students self-selected into either the help seeking group by receiving free financial counseling, or into the nonclinical group by participating in the research study in exchange for a small cash incentive. Students from other more liberal or conservative schools may report different responses. Also, as an exploratory study, the sample size was relatively small, although representative of the university in which the sample was taken and sufficiently large enough for the statistical analyses used in this study. For example, Field (2009) noted that a sample size of 85 is needed to detect medium effect sizes, whereas 10-15 participants are needed for coefficient stability in a regression. Under both rules, this studyâ&#x20AC;&#x2122;s sample size was suitably large. In summary, the results from this study suggest that college financial counseling centers are reaching some of the students they were designed to reachâ&#x20AC;&#x201D;i.e., those with fewer financial resources and lower levels of perceived financial knowledge. An interesting ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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finding is that as students get older (thus closer to graduation) they become more likely to seek financial counseling. Further research should be conducted to determine what motivates this behavior. These behavioral concepts could help colleges and universities understand the emotional and financial stress students face and lead to constructive solutions. References Adams, T., & Moore, M. (2007). High risk health and credit behavior among 18 to 25 year old college students. Journal of American College Health, 56(2), 101-108. Aniol, J. C., & Snyder, D. K. (1997). Differential assessment of financial and relationship distress: Implications for couples therapy. Journal of Marital and Family Therapy, 23, 347-352. Borden, L. M., Lee, S., Serido, J., & Collins, D. (2008). Does participation in a financial workshop change financial knowledge, attitudes, and behavior of college students? Journal of Family and Economic Issues, 29, 23-40. Chamberlain, L. (2011). Dollars and sense: How colleges and universities promote financial literacy. NASFAA Student Aid Transcript, 22(1). Council for Economic Education (2009). Survey of the states, economic, personal finance and entrepreneurship education in our nationâ&#x20AC;&#x2122;s schools in 2009: A report card. Retrieved from http://www.councilforeconed.org/about/survey2009/CEE_2009_Survey.pdf Lawrence, F., Cude, B., Lyons, A., Marks, L., & Machtmes, K. (2006). College studentsâ&#x20AC;&#x2122; financial practices: A mixed methods analysis. Journal of Consumer Education, 23, 13-26. Cunningham, J. (2000). College student credit card usage and the need for oncampus financial counseling and planning services. Undergraduate Research Journal for the Human Sciences. Retrieved from http://www.kon.org/urc/cunningham.html. Elliehausen, G. E., Lundquist, E. C., & Staten, M. E. (2007). The impact of credit counseling on subsequent borrower behavior. Journal of Consumer Affairs, 41(1), 1-28. Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles: Sage. Goetz, J., Cude, B. J., Nielsen, R. B., Chatterjee, S., & Mimura, Y. (2011). College-based personal finance education: Student interest in three delivery methods. Journal of Financial Counseling and Planning, 22(1), 27-42. Goetz, J., Durband, D., Halley, R., & Davis, K. (2011). A peer-based financial planning & education service program: An innovative pedagogic approach. Journal of College Teaching & Learning, 8(4), 7-14.

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Grable, J. E., & Joo, S. (1999). Financial help-seeking behavior: Theory and implications. Financial Counseling and Planning, 10(1), 14-25. Grable, J. E., & Joo, S. (2006). Student racial differences in credit card debt and financial behaviors and stress. College Student Journal, 40, 400-408. Gross, K. (2009, September 20). New credit card rules may hurt financially insecure students. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/New-Credit-Card-Rules-May-Hurt/47039/ Johnson, C. (2005). Maxed out college students: A call to limit credit card solicitations on college campuses. New York University Journal of Legislation and Public Policy, 8. Retrieved from http://ssrn.com/abstract=925234. Joo, S. (1998). Personal financial wellness and worker job productivity. Unpublished doctoral dissertation. Virginia Polytechnic Institute and State University, Blacksburg. Joo, S., Durband, D., & Grable, J. (2008-2009). The academic impact of financial stress on college students. Journal of College Student Retention, 10(3), 287-305. Lusardi, A., Mitchell, O. S., & Curto, V. (2010). Financial literacy among the young. Journal of Consumer Affairs, 44(2), 358–380. Lyons, A. C. (2004). A profile of financially at risk college students. The Journal of Consumer Affairs, 38(1), 56-80. Lytton, R., & Grable, J. E. (1997). A gender comparison of financial attitudes. Proceedings from the annual meeting of the Eastern Family Economics and Resource Management Association, Athens, GA: 1-14. Mandell, L. (2008). The financial literacy of young American adults: Results of the 2008 National Jump$tart Coalition Survey of High School Seniors and Students. Retrieved from College http://www.jumpstart.org/assets/files/2008SurveyBook.pdf Markovich, C. A., & DeVaney, S. A. (1997). College seniors' personal finance knowledge and practices. Journal of Family and Consumer Sciences, 21-28. Masud, J., Husniyah, A., Laily, P., & Britt, S. (2004). Financial behavior and problems among university students: Need for financial education. Journal of Personal Finance, 3(1), 82-96. National Institute of Health (2009, July 23). Clinical research & clinical trials. Retrieved from http://www.nichd.nih.gov/health/clinicalresearch Palmer, L., Bliss, D. L., Goetz, J. W., & Moorman, D. (2010). Helping undergraduates discover the value of a dollar through self-monitoring. American Journal of Business Education, 3, 103-108. Suchman, E. A. (1966). Health orientation and medical care. American Journal of Public Health, 56(1), 97-105. SPSS (1998). AnswerTree 2.0 User’s Guide. Retrieved from http://www.uic.edu/classes/idsc/ids422/trees.pdf The White House (2009). Fact sheet: Reforms to protect American credit card holders. Retrieved from http://www.whitehouse.gov/the_press_office/FactSheet-Reforms-to-Protect-American-Credit-Card-Holders/ ©2011, IARFC. All rights of reproduction in any form reserved.

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Williams, F. L. (1983). Money income, nonmoney income, and satisfaction as determinants of perceived adequacy of income. In M. Dunsing (Ed.), Proceedings of the Symposium of Perceived Economic Well-Being, Urbana, IL: University of Illinois at Urbana: 106-125. Worthy, S., Jonkman, J., & Blinn-Pike, L. (2010). Sensation-seeking, risktaking, and problematic financial behaviors of college students. Journal of Family Economic Issues, 31(2), 161-170.

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GENDER DIFFERENCES IN RISK AVERSION: A DEVELOPING NATION’S CASE Binay K Adhikari * University of Alabama Virginia O’Leary, Ph.D. Auburn University This study used Hanna and Lindamood (2004)’s graphic-based survey instrument to examine whether women who are employed in the Nepalese banking sector show more risk aversion than men. Women indeed reported their intention to take less risk and invested less of their wealth in risky assets than men. However, the difference disappeared after controlling for other relevant variables, notably their perceived knowledge of financial markets. Our analyses suggested that women demonstrated more risk aversion than men because they considered themselves to be less knowledgeable about financial markets. Our findings support the need to educate female investors to increase their confidence in their abilities to succeed in the world of finance.

Introduction Risk aversion is a concept in economics, finance, and psychology related to the behavior of consumers and investors faced with uncertainty. In the context of personal financial decisions, Dalton and Dalton (2008, p. 898, as cited in Hanna, Waller & Finke, 2008) define risk tolerance as “the level of risk exposure with which an individual is comfortable; an estimate of the level of risk an investor is willing to accept in his or her *

Binay K Adhikari, University of Alabama; bkadhikari@ua.edu The authors want to thank Kathmandu University School of Management for partially funding this study and James Ligon, an anonymous referee and Michael Finke, the editor for useful comments. Remaining errors are our own. ©2011, IARFC. All rights of reproduction in any form reserved.

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investment portfolio.” Risk tolerance is the inverse of risk aversion (i.e. a higher risk aversion implies lower risk tolerance). Extensive empirical evidence suggests that women are more risk averse than men. One study conducted by Weber, Blais and Betz (2002) involved a survey to quantify five distinct risk domains: financial risks, health and safety risks, recreational, ethical and social risks. Their results indicated that women were more risk-averse in all domains except social risk. Hinz, McCarthy and Turner (1997), Sunden and Surette (1998), and Olsen and Cox (2001) found that increased risk aversion affects the investment choices of women. In this paper we examine gender differences in financial risk aversion among employees of the Nepalese banking sector using Hanna and Lindamood (2004)’s graphic-based survey instrument. To our knowledge, ours is the first study of this kind in the context of a developing country. We find that knowledge of financial markets and products explains the difference in risk aversion more than the participants’ gender. Evidence of seemingly higher risk aversion exhibited by women than men is muted once the participants’ perceived level of knowledge of financial markets and products is introduced as an explanatory variable. Literature Review Several studies have sought to determine whether the difference in risk preferences between men and women found in the psychological literature translates into a difference in investment choices. Two of the early empirical studies to identify women as more risk averse than men when making investment choices were conducted by Cohn, Lewellen, Lease and Schlarbaum (1975) who obtained data from a mail questionnaire survey among

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customers of a large retail brokerage firm, and Riley and Chow (1992) who used data on actual asset allocation of a random sample of the US population. Later studies by Hinz et al. (1997) and Bajtelsmit and Vanderhei (1997) specifically examined the pension choices of U.S. investors and concluded that, even after controlling for income and age, women generally chose less risky pension fund options. Similarly, Sunden and Surette (1998) used household data from Survey of Consumer Finances (SCF) of 1992 and 1995 and reported that even after marital status was added to their list of control variables women still chose less risky pension funds. Using the 1983 SCF data, Hawley and Fujii (1993) found that education, income and debt were positively related to risk tolerance. Married couples and households headed by a single male were more risk tolerant than otherwise similar households headed by a single female. Age was not statistically significant in the analysis. Warner and Cramer (1995) explored the saving behaviors of baby boomers and fond similar results. Using the 1983 SCF risk tolerance data, Sung and Hanna (1996) found that income and education were positively related to risk. Generally, risk tolerance decreased with age after 45. Selfemployed persons were significantly more willing to take financial risks than those employed by others. In a theoretical paper Jaggia and Thosar (2000) noted an inverse relationship between age and risk taking. However, Sunden and Surette (1998) did not find any impact of age on risk preferences in their empirical research. A number of studies have investigated the role of knowledge/education on risk taking. In a study using data from a ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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national survey of nearly 2000 mutual fund investors’ investing behavior, Dwyer, Gilkeson and List (2002) found the impact of gender on risk taking is significantly weakened when investor knowledge of financial markets and investments is statistically controlled. Other studies that find significant role of knowledge/education include those by Hawley and Fujii (1993), Sung and Hanna (1996) and Haliassos and Bertaut (1995). For instance, in a theory cum empirical paper, Haliassos and Bertaut (1995) showed that those who have not attended college are significantly less likely to hold stock than those with at least a college degree. However, using US sample data of household holding of risky assets, Jianakoplos and Bernasek (1998) found no relationship between knowledge and risk taking. Daly and Wilson (2001) suggested that the increased responsibilities accompanying marriage and children will make a man less tolerant of risk. Supportive of this theory is the finding by Sunden and Surette (1998) that marriage makes both men and women more risk averse in their choice of pension plans. Similarly, Yao and Hanna (2005) found that risk tolerance was highest for single males, followed by married males, then unmarried females, then married females. Säve-Söderbergh (2003) argued, however, that marriage might encourage a couple to invest in riskier assets because each person now has a second income stream insuring against the loss of his or her own income. Furthermore, Säve-Söderbergh suggested that marriage has the potential to affect the risk preferences of men and women differently. Jianakoplos and Bernasek (1998) also found that the presence of children significantly increases the risk tolerance of married couples in their investments but significantly decreases the risk tolerance of single women. Therefore, the literature indicates that marriage may significantly affect the risk preferences of

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individuals, even though there are potentially offsetting theories explaining the direction of such effects. In summary, age, income, knowledge, and marital status are the four variables most commonly controlled for in the literature concerned with the relative risk preferences of men and women. Other variables, of course, may also be influencing the results. For example, race has been shown to influence risk perceptions (Flynn et al. 1994). Finucane et al. (2000) found white men to be the most risk tolerant. Moreover, Riley and Chow (1992) noted that the geographical location of an investor might influence risk preferences, although they suggest that income is probably driving this result. Rationale for Studying Risk Aversion in a Developing Country like Nepal Studies related to gender and risk in the Western world are abundant. It is of interest to explore whether the gender differences observed in the developed world relate to the developing world. This becomes especially interesting since studies including Asian population suggest that there exists an evident cultural difference in overconfidence. For example, Hsee and Weber (1999) found that Chinese were significantly more risk-seeking than Americans (in investment domain). They explained this phenomenon in terms of â&#x20AC;&#x153;cushion hypothesisâ&#x20AC;? which suggests people in a collectivist society such as China are more likely to receive financial help if they are in need, and they are consequently less risk averse than those in an individualistic society such as the USA. A few other studies led by Yates and his associates (e.g. Yates et al., 1996) indicated that respondents in Asian cultures (e.g. in China and Taiwan) exhibited markedly higher degrees of overconfidence than did respondents in Western cultures (e.g. in the United States). ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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Consequently, the question that whether there are cultural differences in the structure of risk aversion (such as gender difference) is pertinent. Despite Asia’s increasing influence in the world’s trade and financial markets, very few studies on risk aversion have been conducted in Asian countries and especially in south Asia. In fact, Yao (2008) noted that risk aversion studies that include Asian population are very limited. This paper attempts to fill this gap in the literature. Nepal is a developing country situated between the emerging economic powers, India and China. All of these countries have an increasing presence of woman in the workforce. Women, because they usually have lower working-life incomes than men (Bajtelsmit & Bernasek, 1996), are likely to have less wealth than men when they retire. The potential effect of this difference on the retirement benefits of men and women is compounded by the fact that women typically have longer life spans over which their retirement benefits must be allocated and they also tend to retire earlier than men (Blondal & Scarpetta 1998 as cited in Watson & McNaughton 2007). If women are indeed more risk averse in their investment choices, this characteristic will magnify the problems associated with their lower work-life incomes, lower retirement ages, and longer life expectancies. The importance of women as financial decision makers is highlighted by a World Bank (2001)’s report that focused on gender issues and their broad economic and social implications. This study concluded, among other things, that it is critically important to take gender into account in the field of social protection and the design of public programs. The positive effect of increased household income on the children’s welfare - their education, health, and nutrition - is stronger if that increase is controlled by, or channeled through, the mother. There may be a

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case, from a development effectiveness perspective, for targeting larger funds to women or designing investment products and schemes targeted to the attitudes and values of women, who are likely to be more productive mobilizers of their households’ resources than men. The worldwide literature has repeatedly found evidence for the greater financial conservatism of women compared to men. A conservative investment strategy results in less income on average than a more aggressive strategy since higher returns generally come at the cost of higher risk. This relationship between risks and returns implies that women, who choose to bear lower risk, will generally earn lower returns - in the long run. Life expectancy at birth has been increasing for both males and females in Nepal. It has increased from 42 years for males and 40 years for females in 1971i to 62 years for both males and females in 2005. Women’s life expectancy is projected to exceed that of the men in the near future (World Health Report, 2005 and population projection for Nepal 2001-2021). As put by Bajtelsmit and Bernasek (1996, p. 1) “Women’s greater longevity implies that, even with the same investment strategy and pension accumulation, retirement wealth must support a longer period of retirement. Women have lower lifetime earnings, less growth in earnings, less wealth, less pension coverage and lower pension participation rates.” To date, no empirical study of gender differences in financial risk preference in Nepal has been conducted. This research was undertaken to provide baseline information of value to those working to establish forward looking policies and procedures to accommodate the financial interests and needs of working women and men.

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Hypothesis We hypothesize that after other factors (age, marital status, income, investment knowledge) known to influence an individual's risk preferences are controlled, Nepalese women will demonstrate, on average, more risk aversion than Nepalese men. Method Hanna, Gutter and Fan (2001) observed that there are at least four methods of measuring risk tolerance: asking about investment choices, asking a combination of investment and subjective questions, assessing actual behavior, and asking questions based on hypothetical scenarios. They argue that inferring risk aversion based on observing actual portfolio allocations has many limitations, including the fact that many households have no portfolio to allocate so that nothing can be inferred about their risk aversion from their allocation. We employed hypothetical questions because it has been shown to be the firmest link to the theoretical concept of risk aversion (see e.g. Hanna et al., 2001). Barsky, Juster and Shapiro (1997) showed that if the respondent chooses to take an uncertain risk that could result in a decrease in income (or a significant gain) instead of one that is certain although less advantageous, the expected utility of the income resulting from the riskier choice exceeds the expected utility of having the current income stream with certainty.ii Instrument We used a modified version of the Hanna et al. (2001) pension choice measure of risk aversion which is an improvement over Barsky et al. (1997) that follows the expected utility model. The modified pension choice questions (Hanna & Lindamood,

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2004) include graphical illustrations to represent the quantity of the change in the pension. These illustrations increase the respondentsâ&#x20AC;&#x2122; understanding of the consequences of the hypothetical alternative outcomes and thus more accurately estimate their true risk level.iii In addition to the series of pension choice questions, the survey also included the SCF Investment Risk question for comparison purposes.iv Participants Two hundred and six employees of 12 banks and financial institutions in Nepal participated in this study. The sample size reported varies in different tables since participants were free to skip any questions they chose not to answer. Employees of the banking sector were chosen because the questions demanded a certain level of financial knowledge and a sense of probability and risk. Participants could choose either Nepali or English version of the questionnaire. MBA students of Kathmandu University pretested the instrument. Model and Variable Definition Respondentsâ&#x20AC;&#x2122; subjective risk tolerance ranks were derived from the risk tolerance questions and assigned a numeric rank in ascending order coded 1 (extremely low), 2 (very low), 3 (moderately low), 4 (moderate), 5 (high), 6 (very high) and 7 (extremely high) risk tolerance. Similarly, Investment Risk Tolerance (IRT) responses were also obtained using Survey of Consumer Finances questions. Since both dependent variables of interest, SRT and IRT, are ordered responses, an ordered probit regression model was applied for the analysis. The central idea of the model is that, underlying the ordered response is a latent, continuously distributed random variable representing relative risk aversion. Additionally, we also employed OLS regression as follows since OLS estimates have a more straightforward ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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interpretation and well-known model diagnosis tools are available for it.

where SRT is the respondent’s subjective risk tolerance rank derived from Hanna and Lindamood’s risk tolerance questions; coded from 1 for extremely low to 7 for extremely high risk tolerance; IRT is investment risk tolerance as it appeared in the SCF ranging from 1 to 4 (reverse coded to so that higher number represents higher risk tolerance); FEMALE is a dummy variable coded 1 for women and 0 for men; AGE is the respondent’s age range coded 1 (24 and below), 2 (25-29), 3 (30-34), 4 (35-39), 5 (40-44), 6 (45-49), 7 (50 and above); MARRIED is a dummy variable coded 1 for married and 0 for not married; INCOME is respondent’s monthly income range coded 1 (Up to Rs.19,999), 2 (Rs. 20,000-39,999) , 3 (Rs. 40,000-59,999), 4 (Rs. 60,00079,999), 5 (Rs. 80,000-99,999), 6 (Rs. 100,000-119,999) and 7 (Rs. 120,000 and above); KNOWLEDGE is the respondent’s selfreported rating of knowledge of investment market and products ranging from 1 to 7. 1= very little knowledge, 7 = very good knowledge; AGE*FEMALE is the interaction of age and sex; FEMALE*MARRIED is the interaction of sex and marital status; ε and v are independent random disturbance terms. In addition, the respondents also indicated an approximate percentage of wealth they had invested in stock and business (STOCK) which we used to tentatively assess their actual investment risk taking behavior.

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Results and Discussion Table 1 shows the frequency distribution and means of all variables of interest. About 37 per cent of our respondents were females. Seventy per cent were married. The average age was about 30 years and most made between Rs. 20,000 to 40,000 (about US $370 to $740, not adjusted for purchasing power parity) per month. The average IRT and SRT scores were 2.64 and 4.71 respectively both of which are on the upper half of the respective rating scales. Most of the respondents indicated that they had invested some share of their wealth in risky assets (stock and business). However, there were many (30) who had not invested anything on stock or businesses. This is not uncommon in Nepal where the stock market is small and stocks are not popular vehicles of investment. Table 5 shows correlations among these variables. Table 2 presents a quick comparison of the findings of Hanna et al. (2001) and the present study. Two obvious differences emerge. First the average relative risk aversion is lower for both males and females in this study. This is primarily driven by more men and women reporting extreme risk tolerance.v Second, unlike the results of Hanna et al. the difference in risk taking propensity of male and female is statistically significant (p-value of chisquared <.01) in this study. Table 3 reports the results of univariate OLS regressions for both SRT (Panel A) and IRT (Panel B). The results suggested that sex and knowledge were significantly associated with the level of risk tolerance (for both SRT and IRT). In particular, women demonstrated the intention to take less risk than men and participants with more perceived knowledge of investment markets tend to take more risk than those with less knowledge. However, the multivariate analysis in table 4 shows that the gender effect ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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disappeared after controlling for other variables. The effect of knowledge remained significant for both SRT and IRT (from both OLS and ordered probit models). The dy/dx columns indicate that the probability of being in the highest risk tolerant group (SRT=7, IRT=4) increased as knowledge increased. Age was not significant for SRT but was weakly significant for IRT and had the expected sign for IRT suggesting older employees intended to take less risk than younger ones. We found no significant problem of heteroscedasticity (chi-squared statistic is not significantly large) and multicollinearity (tolerance for none of the variables is less than .10) with OLS. Although our findings mainly suggest that only knowledge has a significant impact on risk tolerance, the univariate analysis indicates that sex also might also have impact on risk aversion. Thus, gender affects risk taking propensity although not in the magnitude hypothesized based on the review of the literature. Directionality of the coefficient is consistent with the hypothesis: men tend to be more risk tolerant than women. Perceived knowledge of the investment market and financial products has been shown to have a significant impact on risk aversion. The positive sign of the regression coefficient suggests that risk tolerance increases with the increase in perceived knowledge of investment related information. The cross correlations presented in table 5 explain some of the findings further. The significant positive relation between age and knowledge (r = .28, p<.01) indicates that older bank employees are more knowledgeable about financial matters than their younger colleagues. Likewise, there is a positive relationship between knowledge and risk tolerance (SRT: r = .28, p < .01, IRT: r = .284, p<.01), which suggests that people with more investment knowledge endorse more risky options. However, the direct

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relationship between age and risk tolerance is at most weakly significant both in univariate and multivariate settings. Furthermore, both reported risk tolerance measures, the IRT (r = .18, p <.05) and the SRT (r = .13, p <.10), for the employees are positively related to the proportion of wealth kept in risky assets (STOCK) i.e. stocks and direct investment in business. Those who rate themselves as high risk takers invest a larger portion of their wealth in risky assets (stocks and business) than those who rate themselves as more risk averse. Table 6 shows that there is a significant difference in the proportion of risky investment held by men and women. Women, who generally consider themselves less knowledgeable about finance than men exhibit greater risk aversion. However, the relationship is stronger between knowledge and risk tolerance than between gender and risk tolerance.vi It is therefore not surprising that in a review of a wide range of literature (see e.g. Lyons et al., 2008) conclude that the research on risk tolerance indicating women are more risk averse than men is far from clear. All in all, our findings are consistent with the stream of literature which concludes that knowledge, not gender, might be the key determinant of risk-taking propensity. Consistent with this, Elder and Rudolph (2003) employed the â&#x20AC;&#x153;final sayâ&#x20AC;? question in the Health and Retirement Study (HRS) to identify the sources of decision-making power within households. They found that decisions were more likely to be made by the spouse with greater financial knowledge, more education, and a higher wage, irrespective of gender.

ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

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We did not obtain significant interaction effect of either sex and marital status or sex and age on risk tolerance. This is plausible since traditionally, in Nepalese households like in most South Asian households, husbands and/or fathers make most of the financial decisions so wives and daughters remain less knowledgeable about finances and less active in financial decisionmaking before and after marriage. Conclusion The major finding of the current study, that women exhibit less financial risk tolerance than men is apparently occasioned by a disparity in perceived knowledge about investments. Our results are consistent with the stream of existing literature that argue that the difference in risk aversion is attributable to task familiarly and knowledge rather than gender. In fact, Deaux and Emswiller (1994) and Beyer and Bowden (1997) have noted that women are less confident and more risk averse in domains considered masculine, regardless of their (equal) ability to perform. Men are represented in greater numbers in financial markets than women (Merrill Lynch, 1996). In addition, women are likely to be perceived as more conservative investors and therefore offered less risky investments by brokers (Wang, 1994). Psychological research has shown that men tend to be more overconfident than women in areas like finance. Theory predicts that overconfident people trade excessively. Barber and Odean (2001) tested this prediction using account data from a discount brokerage firm and found that men traded 45% more than women. Trading reduced men's (risky) net returns by 2.65 percentage points a year compared to 1.72 percentage points for women.

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Our results from a developing nation reinforce the need of specific and targeted financial education to help men and women with their investment decisions. Men may need to be cautioned about the pitfalls of overconfidence, while women may need guidance on how to make investment choices that carry a calculated risk to obtain adequate growth. Endnotes: _____________ i

Source: Country Health System Profile, http://www.searo.who.int/EN/Section313/Section1523_6868.htm

Nepal

Let U be the utility function and C be permanent consumption. An expected utility maximizer will choose the 50-50 gamble of doubling lifetime income as opposed to having it fall by factor 1 - λ if: .5 U (2C) + .5 U (λ C) ≥ U(C) Assuming the utility function is constant relative risk aversion (CRRA), the following shows the relation between the Arrow-Pratt measure of relative risk aversion A and λ: λ = (2 – 2(1-A))[1/(1-A)]. This equation holds if A ≠ 1, and λ = .5 when A = 1. Therefore, by asking questions with different levels of λ, the Arrow-Pratt coefficient of relative risk aversion can be directly estimated. iii

Illustrative of the questions contained in the questionnaire is the following: Suppose that you are about to retire, and have two choices for a pension. Pension A gives you an income equal to your pre-retirement income. Pension B has a 50% chance your income will be double your pre-retirement income, and a 50% chance that your income will be 20% less than your pre-retirement income. You will have no other source of income during retirement, no chance of employment, and no other family income ever in the future. All incomes are after-tax. Which pension would you choose? Subsequent questions pose different percentage reductions in income. There were six questions out of which the respondent is required to answer a maximum of four questions. The respondent who accepts the possibility of largest cut in income for a possibility of doubling the income gets the higher possible point (on a scale of 1 to 7) indicating an extremely high subjective risk tolerance (SRT). iv

The Survey of Consumer Finances (SCF) is used to gather data on assets, liabilities, financial attitudes, and financial behaviors of individuals and families. The SCF risk assessment item has been widely used as a proxy for financial risk tolerance. The question appears as follows: ©2011, IARFC. All rights of reproduction in any form reserved.

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Which of the following statements on this page comes closest to the amount of financial risk that you are willing to take when you save or make investments? 1. Take substantial financial risk expecting to earn substantial returns 2. Take above average financial risks expecting to earn above average returns 3. Take average financial risks expecting to earn average returns 4. Not willing to take any financial risks (Note: In this paper we reverse coded the above responses (4 being substantial financial risk to make it comparable to SRT measure) v

It seems unlikely that such a large portion of respondents (24.7%) had a true relative risk aversion (RRA) of less than 1. RRA of 1 or less suggests that the household should invest more than their wealth in risky assets and buy on margin (see e.g. Hanna and Chen, 1997) â&#x20AC;&#x201C; an implication contrary to most empirical evidence. Precise calculation of RRA is not critical for our purpose. However, for robustness, we repeated the analyses that follow by discarding the observations with implied RRA of less than 1. Our key findings were not different from the ones with full data. We, therefore, decided to report the results that come from all available data. We thank a reviewer for pointing out this important issue. vi

It is credible that knowledge is endogenous since a personâ&#x20AC;&#x2122;s attitude towards risk may influence the willingness to acquire knowledge about financial assets. We realized that it is a very important issue and decided to do a second round of analysis to see if there exits an endogenity problem. For the regression with SRT, we used age as the instrumental variable for knowledge and for IRT, used marital status as the instrument. These variables were carefully chosen so that we have an instrument which is correlated with knowledge but is not likely to be correlated with error terms and does not have any independent explanatory power on the dependent variables (SRT and IRT). Marital status is likely to be endogenous in western cultural context but unlikely to be so in Nepalese context where most marriages are arranged and happen within a certain age range. Data limitations prevented us from doing more rigorous analysis. The Hausman test obtained no evidence of endogenity for our model.

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References Bajtelsmit, V. L. & Bernasek, A. (1996). Why do women invest differently than men? Financial Counseling and Planning, 7, 1-10. Bajtelsmit, V.L., & Vanderhei, J.L. (1997). Risk aversion and pension investment choices. In M.S. Gordon, O.S. Mitchell, & M. M. Twinney (Eds.), Positioning pensions for the twenty-first century (pp. 55-66). Philadelphia: University of Pennsylvania Press. Barber, B. & Odean, T. (1999). Boys will be boys: Gender overconfidence and common stock investment. Quarterly Journal of Economics, 116, 261-292 Barsky, R.B., Juster, F.T., Kimball, M.S. & Shapiro, M.D. (1997). Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study. Quarterly Journal of Economics, 112 (2), 537579. Bernasek, A., & Shwiff S. (2001). Gender, risk, and retirement. Journal of Economic Issues, 35(2), 345-356. Beyer, S & Bowden, E. (1997). Gender differences in self-perceptions: Convergent evidence from three measures of accuracy and bias. Personality and Social Psychology Bulletin, 23, 157-172. Bruce, A. & Johnson, J. (1994). Male and female betting behavior: new perspectives. Journal of Gambling Studies, 10, 183-98. Byrnes, J.P., Miller, D.C. & Schäfer, W.D. (1999). Gender differences in risk taking: A meta- analysis. Psychological Bulletin, 125(3), 367-383. Cohn, R. A., Lewellen, W.G., Lease, R.C., & Schlarbaum, G.G. (1975). Individual investor risk aversion and investment portfolio composition. Journal of Finance, 30(2), 605-620. Daly, M., & Wilson, M. (2001). Risk-taking, intra-sexual competition, and homicide. Nebraska Symposium on Motivation, 47, 1-36. De Bondt, W.F.M. (1998). A portrait of the individual investor. European Economic Review, 42(3-5), 831-844. Deaux, K., & Emswiller, T. (1974). Explanations of successful performance on sex-linked tasks: What is skill for the male is luck for the female. Journal of Personality and Social Psychology, XXIX, 80–85 Dwyer, P.D., Gilkeson, J.H., & List, J.A. (2002). Gender differences in revealed risk taking: Evidence from mutual fund investors. Economics Letters, 76( 2), 151-158. Elder, H. W., & Rudolph, P. M. (2003). Who makes the financial decisions in the households of older Americans? Financial Services Review, 12(4), 293308. Finucane, M.L., Flynn, J., Mertz, C.K., Satterfield, T.A. & Slovic, P. (2000). Gender, race, and perceived risk: The 'White Male' effect. Health Risk and Society, 2(2), 159-1 72. Flynn, J., Slovic, P. & Mertz, C.K. (1994). Gender, race and perception of environmental health risks. Risk Analysis, 14(6 ), 1101-1108. ©2011, IARFC. All rights of reproduction in any form reserved.

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Goldsmith, E. B., & Goldsmith, R. E. (1997). Gender differences in perceived and real knowledge of financial investments. Psychological Reports, 80(11), 236-238. Goldsmith, E. B., Goldsmith, R. E., & Heaney, J.-G. (1997). Sex differences in financial knowledge: A replication and extension. Psychological Reports, 81, 1169-1170. Grable, J. E., & Lytton, R. H. (1999). Financial risk tolerance revisited: The development of a risk assessment instrument. Financial Services Review, 8, 163-181 Hanna, S. & Chen, P. (1997). Subjective and objective risk tolerance: Implications for optimal portfolios. Financial Counseling and Planning, 8(2), 17-25. Hanna, S. D. & Lindamood, S. (2004). An improved measure of risk aversion. Financial Counseling and Planning, 15 (2), 27-38. Hanna, S.D., Gutter, M.S., & Fan, J. X. (2001). A measure of risk tolerance based on economic theory. Financial Counseling and Planning, 12 (2), 5360. Hanna, S. D., Waller, W., & Finke, M. (2008). The concept of risk tolerance in personal financial planning. Journal of Personal Finance, 7 (1), 96-108 Haliassos, M. & Bertaut, Carol C, 1995. Why Do So Few Hold Stocks?, Economic Journal, Royal Economic Society, 105(432). 1110-29 Hawley, C.B. & Fujii, E.T. (1993). An empirical analysis of preferences for financial risk: Further evidence on the Friedman-Savage model. Journal of Post Keynesian Economics, 16(2). 197-204. Hersch, J. (1996). Smoking, seat belts, and other risky consumer decisions: Differences by gender and race. Managerial and Decision Economics, 17(5), 471-481. Hinz, R. P., McCarthy, D. D., & Turner, J. A. (1997). Are women conservative investors? Gender differences in participant-directed pension investments. In Mitchell, O. (Ed.) Positioning Pensions for the Year 2000 (pp. 91â&#x20AC;&#x201C;103). Philadelphia: University of Pennsylvania Press. Hsee, C. K. & Weber, E. U. (1999). Cross-national differences in risk preferences and lay predictions for the differences. Journal of Behavioral Decision Making, 12, 165-179 Jaggia, S., & Thosar, S. (2000). Risk aversion and the investment horizon: A new perspective on the time diversification debate. Journal of Psychology and Financial Markets, 1(3-4), 211-215. Jianakoplos, N.A., & Bernasek, A. (1998). Are women more risk averse? Economic Inquiry, 36(4), 620-630. Johnson, J. E.V. & Powell, P. L., (1994). Decision making, risk and gender: Are managers different? British Journal of Management 5 (2), 123-38. Lewellen, W., Lease, R., Schlarbaum, G. (1977). Patterns of investment strategy and behavior among individual investors, Journal of Business, 50(3), 296333.

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Lyons, A. C., Neelakantan, U. & Scherpf, E. (2008). Gender and marital differences in wealth and investment decisions: Implications for researchers, financial professionals, and educators. Networks Financial Institute Working Paper No. 2008-WP-02. Merril Lynch (1996). You and your money. A financial handbook for women investors, New York. Olsen, R.A., & Cox, C.M. (2001). The influence of gender on the perception and response to investment risk: The case of professional investors. Journal of Psychology and Financial Markets, 2(1), 29-36. Powell, M. & Ansic, D. (1997). Gender differences in risk behaviour in financial decision-making: An experimental analysis. Journal of Economic Psychology. 18, 605 – 628. Riley, W., & Chow, K.V. (1992). Asset allocation and individual risk aversion. Financial Analysts Journal, 48(6), 32-37. Säve-Söderbergh, J. (2003). Essays on gender differences in economic decisionmaking. PhD thesis, Stockholm University. Sunden, A.E., & Surette, B.J. (1998). Gender differences in the allocation of assets in retirement savings plans. American Economic Review, 88(2), 207211. Sung, J. & Hanna, S. (1996). Factors related to household risk tolerance: An ordered probit analysis. Proceedings of the 42nd Annual Conference of the American Council on Consumer Interests, 221-228. Wang, P. (1994). Brokers still treat men better than women. Money, 23 (6), 10810. Warner, N. & Cramer, S. (1995). Saving behaviours. Journal of Consumer Studies and Home Economics, 19, 57-67. Watson, J., & McNaughton, M. (2007). Gender differences in risk aversion and expected retirement benefits. Financial Analysts Journal. 63(4) Watson, J., & Robinson, S. (2003). Adjusting for risk in comparing the performances of male- and female-controlled SMEs. Journal of Business Venturing, 18(6), 773-788. Weber, E.U., Blais, A.R. & Betz, N.E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making, 15, 263–290 World Bank (2001a). Engendering Development: Through Gender Equality in Rights, Resources, and Voice. Policy Research Report: Oxford University Press. Yao, R. (2008). Financial issues of Asian Americans. In J. J. Xiao (Ed.) Handbook of consumer finance research (pp. 303-316). NY, NY: Springer. Yao, R. & Hanna, S.D. (2005). The effect of gender and marital status on financial risk tolerance. Journal of Personal Finance, 4 (1), 66-85. Yates, J. F., Lee J. W., & Shinotsuka, H. (1996). Beliefs about overconfidence, including its cross-national variation. Organizational Behavior and Human Decision Processes, 65,138-147. ©2011, IARFC. All rights of reproduction in any form reserved.

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Table 1: Frequency Distribution of Key Variables

Male (0)

126

Age (years) 24 and below (1)

Female (1)

25-29 (2)

30-34 (3)

35-39 (4)

40-44 (5)

45-49 (6) 50 and Above (7)

IRT

Freq.

SRT

Freq.

Sex

Freq.

Freq. 20

Total

182

Total

194

Total

201

Total

201

Mean

2.64

Mean

4.71

Mean

0.37

Mean

3.03

Freq.

Knowledge Rating

Marital Status Not Married (0) Married (1)

71 129

Freq.

Monthly Income (in Rs.)

Freq.

%Invest Stock & Business

None (0)

Up to Rs.19,999 (1)

Freq.

20,000-39,999 (2)

1 - 20% (1)

3 4

8 31

40,000-59,999 (3) 60,000-79,999 (4)

28 11

21 - 40% (2) 41 - 60% (3)

48 31

61 - 80% (4) 80 -100% (5)

6 7

80,000-99,999 (5) 100,000-119,999 (6) 120,000 and above (7)

Total

190

Total

194

Total

190

Total

184

Mean

0.70

Mean

4.35

Mean

2.57

Mean

2.73

Numbers in parenthesis represent coded values for analysis. Means should be interpreted based on the coded values See the text (model and variable description section) for more details about the key variables. Totals do not match because respondents were free to skip any question they did not want to answer.

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Table 2: Comparison of Preliminary Findings with Hanna et al. (2001) Hanna et al.(2001)**

Our sample* Male n Extremely High, accept 50% cut (A< 1.0) Very High, reject 50% cut (1.0≤A < 2.0) Moderately High, reject 33% cut (2.0≤A< 3.8) Moderate, accept 10% cut (3.8 ≤A< 7.5) Low, accept 8% cut (7.5 ≤A< 9.3) Very Low, accept 5% cut (9.3 ≤A 14.5) Extremely Low, reject 5% cut (A>14.5) Entire sample Mean Relative Risk Aversion

Female %

Male

Female

33.3%

9.9%

0.0%

1.4%

17.9%

15.5%

5.8%

4.1%

13.8%

29.6%

23.2%

20.5%

13.0%

21.1%

45.4%

41.1%

3.3%

5.6%

6.8%

14.4%

3.3%

1.4%

8.7%

6.8%

15.4%

16.9%

10.1%

11.6%

123

100.0%

207

100.0%

146

100.0%

4.84a

5.72a

6.55b

6.88b

*This study: n = 194. ** Web Survey: n = 353. a. Weighted average based on midpoints of each range, except value of 0.9 used for “Extremely High” category, and 16.0 used for “Extremely Low” category. Chi-square for difference in male and female significant at .01. b. Weighted average based on midpoints of each range, except value of 0.9 used for “Extremely High” category, and 16.0 used for “Extremely Low category”. Notice, Hanna et al. say they used 16.0 used for “Very Low category” which is a typo (We confirmed with one of the coauthors). Chi square statistic not significant.

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Table 3: Univariate Analysis Unstandardized Coefficients Variables (Univariate)

Std. Error

Sig.

SRT and FEMALE

-.625

.303

-2.062

.041

SRT and AGE

.110

.097

1.139

.256

SRT and MARRIED

-.147

.235

-.625

.533

SRT and KNOWLEDGE

.538

.125

4.322

.000

SRT and INCOME

-.024

.087

-.281

.779

IRT and FEMALE

-.243

.121

-2.013

.046

IRT and AGE

-.022

.039

-.561

.575

IRT and MARRIED

.038

.093

.407

.685

IRT and KNOWLEDGE

.191

.048

3.979

.000

IRT and INCOME

.011

.034

.316

.752

Panel A: SRT

Panel B: IRT

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Journal of Personal Finance

ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

Volume 10, Issue 2

145

146

Journal of Personal Finance

ÂŠ2011, IARFC. All rights of reproduction in any form reserved.

Volume 10, Issue 2

147

Table 6: Independent Samples Test for Differences of Men and Women Levene's Test for Equality of Variances

AGE

STOCK

Equal variances assumed Not assumed Equal variances assumed Not assumed

t-test for Equality of Means Sig. (2-tailed)

Sig.

27.64

0.000

5.533 6.133

204 203

0.000 0.000

2.27

0.000

2.127 2.315

182 182

0.0348 0.0217

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