Journal of Personal Finance Vol 14 issue2

Volume 14 Issue 2 2015 www.journalofpersonalfinance.com

Journal of Personal Finance

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

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Volume 14, Issue 2

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Journal of Personal Finance

Volume 14, Issue 2 2015 The Official Journal of the International Association of Registered Financial Consultants

Š2015, IARFC. All rights of reproduction in any form reserved.

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Journal of Personal Finance

CONTENTS

Editor’s Notes.........................................................................................................................................................................................8 Does Financial Sophistication Matter in Retirement Preparedness?..............................................................................9 Kyoung Tae Kim, Ph.D., Assistant Professor, Department of Consumer Sciences, The University of Alabama Sherman D. Hanna, Ph.D., Professor, Department of Human Sciences, The Ohio State University Lack of financial sophistication has been suggested as a cause of retirement plan failure. We extend previous studies of retirement adequacy by testing the effect of financial sophistication proxies on projected retirement adequacy, using the 2010 Survey of Consumer Finances (SCF) dataset. We found that only 44% of households with a full-time head aged 35 to 60 are adequately prepared for retirement in 2010, compared to 58% in 2007. Our multivariate analysis shows that college educated households are more likely to have an adequate retirement than those with less than a high school degree. Households using a financial planner are more likely to have an adequate retirement than those that do not use one.

What Do Subjective Assessments of Financial Well-Being Reflect?..............................................................................21 Steven A. Sass, Ph.D., Research Economist, Center for Retirement Research at Boston College Anek Belbase, M.P.A., Research Project Manager, Center for Retirement Research at Boston College Thomas Cooperrider, Associate, Berkeley Research Group, LLC Jorge D. Ramos-Mercado, Research Assistant, Center for Retirement Research at Boston College Households have become increasingly responsible for meeting distant financial needs – specifically paying down student loans, saving for retirement, and saving for college. This study, using data from the 2012 FINRA Foundation Financial Capability Survey, nevertheless finds that a household’s financial satisfaction is highly correlated with its ability to meet its day-to-day needs, with much more muted relationships with its protection against risk and accumulation of savings to meet future needs. Nor do subjective assessments become much more sensitive to distant deficits if the household’s day-to-day finances are in reasonably good shape or if assessment is made by a financially literate individual. Households thus cannot be expected to devote much effort to addressing distant deficits by themselves. They need initiatives that raise their awareness of distant financial deficits, such as broadcasting simple rules-of-thumb and providing ready access to quick financial check-ups; and compensate for their on-going lack of awareness, such as structures that make it easy and automatic to address such deficits.

Factors Related to Making Investment Mistakes in a Down Market...........................................................................34 Shan Lei, Ph.D., Assistant Professor of Finance and Economics, Department of Accounting, Economics and Finance, West Texas A&M University Rui Yao, Ph.D., CFP®, Associate Professor, Department of Personal Financial Planning, University of Missouri Using data from the 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study, this study identifies the factors related to making investment mistakes by moving assets into more of a cash position in a down market while having an adequate level of emergency funds. The results show that investors who are male, Asian, wealthier, overconfident, loss-averse, and reported an understanding of financial risks are more likely to make such investment mistakes during a down market. These findings have important implications for investors, their advisors, and financial planning professionals in general.

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A Quantitative Evaluation of Four Retirement Spending Models.................................................................................43 James S. Welch, Jr., Senior Application Developer, Dynaxys, LLC Traditional retirement planning assumes that disposable income is constant throughout retirement, before it is indexed to inflation.

Demographic retirement spending data indicate that retirees spend more early in retirement, while they are physically active, and voluntarily spend less later in retirement. Four researchers reviewed retiree demographic spending data and proposed retirement spending models which fit their observations. We added these spending models to a linear programming based retirement calculator that computes maximum disposable income for the first year of retirement and applies a spending model to the remainder of retirement. We defined a base scenario and examined how the spending models behaved compared with the traditional constant spending model and with each other. We ran a series of tests to observe how the spending models perform when an assumption of the base scenario was perturbed. We conclude that a retiree may safely choose higher spending early in retirement while budgeting for lower disposable income later in retirement.

Simplifying RIA Oversight..............................................................................................................................................................58 Guy Baker, Ph.D. Student at the American College, Wealth Team Solutions The Securities and Exchange Commission (SEC) provides oversight to RIAs managing at least $100 million of Regulatory Assets under Management (RAUM). This paper identifies and explores five key areas the SEC has marked as the most probable areas of failure, affecting the markets and consumers. It is questionable whether these major concerns highlighted in SEC releases, no-action letters and speeches are relevant to the smaller firms who do not take custody or provide true discretionary management of assets. The level of reporting and scrutiny placed on smaller RIAs using a simple business model is likely a waste of SEC resources and taxpayer money. If the SEC delegated oversight of these less complicated RIAs to trained, licensed and regulated compliance consultants, the SEC would reduce their regulatory workload and could refocus resources on larger firms which are susceptible to conflicts of interest and other compliance violations established in the 1940’s Act. This small change would actually enhance regulation of smaller firms, and would bring meaningful relief to them at the same time. The advantages are significant. This change would allow smaller firms to provide a higher level of service and build better relationships with their clients.

Financial Planning Research Needs--A Practitioner’s View.............................................................................................72 Joseph A. Tomlinson, FSA, CFP®, RFC®, Tomlinson Financial Planning, LLC We need a stronger connection between the community of financial planners and those doing research to support financial planning practice, particularly those in the academic world. Research can support changes and refinements in financial planning practice, the development of new investment and insurance products to better meet client needs, and improvements in financial planning software.

In this article I highlight a number of different areas where research can add significant value. I mention a number of the articles I have written that touch briefly on research issues, but often call for more in-depth research. I also often cite the work of co-editor Wade Pfau who has taken a lead role in doing research that applies to financial planning practice.

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CALL FOR PAPERS JOURNAL OF PERSONAL FINANCE (www.JournalofPersonalFinance.com)

OVERVIEW The Journal of Personal Finance is seeking high quality submissions that add to the growing literature in personal finance. The editors are looking for original research that uncovers new insights—research that will have an impact on advice provided to individuals. The Journal of Personal Finance is committed to providing high quality article reviews in a single-reviewer format within 60 days of submission.

Potential topics include: • Household portfolio choice • Retirement planning and income distribution • 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 Please check the “Submission Guidelines” on the Journal’s website (www.

JournalofPersonalFinance.com) for more details about submitting manuscripts for consideration.

CONTACT Wade Pfau and Joseph Tomlinson, Co-Editors Email: jpfeditor@gmail.com www.JournalofPersonalFinance.com

©2015, IARFC. All rights of reproduction in any form reserved.

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JOURNAL OF PERSONAL FINANCE VOLUME 14, ISSUE 2 2015 Co-Editors Wade Pfau, The American College Joseph Tomlinson, Tomlinson Financial Planning, LLC

Editorial Board Benjamin F. Cummings, Ph.D., Saint Joseph’s University Dale L. Domian, Ph.D., CFA, CFP™, York University Michael S. Finke, Ph.D., CFP™, RFC® Texas Tech Joseph W. Goetz, Ph.D., University of Georgia Michael A. Guillemette, Ph.D., University of Missouri Clinton Gudmunson, Ph.D., Iowa State University Sherman Hanna, Ph.D., The Ohio State University George W. Haynes, Ph.D., Montana State University Douglas A. Hershey, Ph.D., Oklahoma State University Karen Eilers Lahey, Ph.D., The University of Akron Douglas Lamdin, Ph.D., University of Maryland Baltimore County Jean M. Lown, Ph.D., Utah State University Angela C. Lyons, Ph.D., University of Illinois Carolyn McClanahan, MD, CFP™, Life Planning Partners Yoko Mimura, Ph.D., California State University, Northridge Robert W. Moreschi, Ph.D., RFC®, Virginia Military Institute Ed Morrow, CLU, ChFC, RFC®, IARFC David Nanigian, Ph.D., The American College Barbara M. O’Neill, Ph.D., CFP™, CRPC, CHC, CFCS, AFCPE, Rutgers Rosilyn Overton, Ph.D., CFP™, RFC®, New Jersey City University Alan Sumutka, CPA, Rider University Jing Jian Xioa, Ph.D., University of Rhode Island Rui Yao, Ph.D., CFP™, University of Missouri Tansel Yilmazer, Ph.D., CFP™, The Ohio State University Yoonkyung Yuh, Ewha Womans University Seoul, Korea Mailing Address:

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

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 requests should be addressed to: IARFC Journal of Personal Finance The Financial Planning Building 2507 North Verity Parkway Middletown, OH 45042 Info@iarfc.org 1-800-532-9060

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Journal of Personal Finance

EDITORS’ NOTES

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his issue begins with an article by Kyoung Tae Kim and Sherman Hanna that examines the relationship between financial sophistication and retirement preparedness. The authors’ multivariate analysis showed that college educated households are more likely to have an adequate retirement plan than those with less than a high school degree, and households using a financial planner are more likely to be adequately prepared than those who do not use one.

The second article by Steven Sass, Anek Belbase, Thomas Cooperrider, and Jorge Ramos-Mercado examines the relationship between subjective assessments of financial wellbeing and measures of ability to meet near-term financial needs and more distant financial needs. The authors found that subjective assessments were strongly influenced by the ability to meet day-to-day needs, and that the ability to meet more distant needs in terms of protection and savings had little impact on such subjective assessments. They found that, even when households’ day-to-day finances were in reasonably good shape or when the assessment was being made by a financially literate individual, the connection between subjective assessments and distant needs was still a tenuous one. They concluded that there exists a need to provide initiatives to raise awareness of distant financial deficits.

We next turn to a study by Shan Lei and Rui Yao identifying factors related to making investment mistakes in a down market.

The authors used an FPA-Ameriprise survey and specifically focused on the action of moving to cash in a down market, even with adequate emergency funds. The study found that investors who were male, Asian, wealthier, overconfident, loss averse, and reported an understanding of financial risks were more likely to make such investment mistakes in a down market.

The next article by James Welch examines retirement spending models. Traditional practitioner-based models assume constant real spending over the course of retirement. However, evidence based on actual spending patterns shows higher spending early in retirement and reduced spending later due to limitations of activities. The author cites analysis by four different researchers who have proposed retirement spending models that show declining spending, and the uses their proposed spending patterns

in a linear programming model to compute optimal lifetime spending patterns. The spending patterns developed using this analysis demonstrated that retirees could safely choose higher spending early in retirement.

The fifth article by Guy Baker provides a proposal for simplifying RIA oversight by the SEC. The article cites five areas that the SEC has identified as the most probable areas of failure affecting markets and consumers. The author then goes on to argue that these areas do not apply to smaller firms who do not take custody or provide true discretionary management of assets. The article then makes the case that the SEC should structure the oversight of smaller firms differently than the oversight of the large firms, and that such a change would improve the oversight of both large and small firms and improve overall efficiency.

The final article is a bit unusual for an academic journal in that it provides the view of a financial planning practitioner on what types of future research would be most valuable in improving financial planning practice. The article addresses needs in the areas of financial planning strategies, financial products, and financial software. The author, Joe Tomlinson, has served as coeditor of this journal for this issue and the prior two, and will be leaving after this issue.

Wade sincerely thanks Joe for his tremendous service to the journal over these past three issues and looks forward to Joe’s further contributions to helping shape and build research which is directly relevant to financial planning practitioners.

Starting with the Spring 2015 issue of the journal, Professor Walt Woerheide, Ph.D., ChFC®, CFP®, will join Wade as the new co-editor. Dr. Woerheide is the Vice President of Academic Affairs and Dean and a Professor of Investments at The American College in Bryn Mawr, PA. In his distinguished career as an academic at the intersection of finance and personal financial planning, he has held appointments at the University of Illinois at Chicago, the University of Michigan at Flint, and Rochester Institute of Technology. We welcome him to the journal.

©2015, IARFC. All rights of reproduction in any form reserved.

—Wade Pfau —Joe Tomlinson

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Does Financial Sophistication Matter in Retirement Preparedness?

Kyoung Tae Kim, Ph.D., Assistant Professor, Department of Consumer Sciences, The University of Alabama Sherman D. Hanna, Ph.D., Professor, Department of Human Sciences, The Ohio State University

Abstract Lack of financial sophistication has been suggested as a cause of retirement plan failure.

We extend previous studies of retirement adequacy by testing the effect of financial sophistication proxies on projected retirement adequacy, using the 2010 Survey of Consumer Finances (SCF) dataset. We found that only 44% of households with a fulltime head aged 35 to 60 are adequately prepared for retirement in 2010, compared to 58% in 2007. Our multivariate analysis shows that college educated households are more likely to have an adequate retirement than those with less than a high school degree.

Households using a financial planner are more likely to have an adequate retirement than those that do not use one.

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Introduction

Literature Review

“The U.S. is in the midst of three transitions that are changing the landscape of retirement planning: the imminent retirement of the baby boomers, increasing uncertainty over the future of Social Security and Medicare, and the replacement of traditional defined benefit (DB) pension plans with defined contribution (DC) plans” (Love, Smith, & McNair, 2007). Because retirement planning is becoming increasingly challenging, these factors motivate our research. First, many Americans expect Social Security to face financial insolvency. According to the 2012 Old-Age and Survivors Insurance and Federal Disability Insurance Trustees Report, the combined OASI and DI Trust Fund assets will diminish until they become exhausted in 2033 – “After trust fund exhaustion, continuing income is sufficient to support expenditures at a level of 75 percent of program cost for the rest of 2033, declining to 73 percent for 2086.” Moreover, employers have increasingly switched retirement plans to defined contribution (DC) pension plans, such as 401Ks and Individual Retirement Accounts where individuals are responsible for their own savings.

Increases in life expectancy require more financial resources for retirement. The U.S. retirement system does not provide as much adequacy as some other advanced economies (Greenhouse, 2013), although many countries face sustainability challenges.

Life Cycle Saving model. The standard model for analyzing retirement saving behaviors is the Life Cycle Saving (LCS) Model (Modigliani & Brumberg, 1954). For convenience, we will refer to the LCS Model. The LCS Model (Ando & Modigliani, 1963) posits that consumption and saving reflect an individual’s stage in the life cycle, which is generally proxied by age. Young households are expected to have negative saving since they typically have relatively low earnings and incur other higher expenses regarding education and housing. Households in the middle period of the life cycle begin to save their money for retirement and pay their debts. After retirement, dis-saving is expected to occur again. Harrod (1948) presented the U-shaped pattern based on the saving and dis-saving behaviors over time periods. The central tenet of the LCS Model is that individuals (or households) attempt to keep the marginal utility of consumption constant over time. According to the LCS Model, the consumption smoothing can be achieved by borrowing when earnings are low, saving for wealth accumulation when earnings are high, and dissaving in retirement (Browning & Crossley, 2001; Browning & Lusardi, 1996).

Our research extends previous retirement adequacy research by adding financial sophistication proxies as salient factors.

Lusardi and Mitchell (2011) concluded that lack of financial sophistication is one of the reasons for retirement plan failure.

The existing literature has focused on the link between financial sophistication and a plan for retirement, which assumes that workers with retirement planning have a propensity to save for retirement. Our study fills the missing piece that a link to financial sophistication and the projected level of retirement preparedness. Further, most of the past retirement studies on financial sophistication have focused on workers age 51 and older by using Health and Retirement Study (HRS) datasets.

Relatively little research on financial literacy in the U.S. has been conducted on younger workers. Therefore, the main purpose of this study is to investigate the impact of financial sophistication on the retirement adequacy of U.S. households, including those with heads under age of 51. Another purpose is to assess the retirement adequacy of U.S. households after the Great Recession began in December 2007. Munnell (2012) noted that the significant decline in the wealth-to-income ratio from the 2010 Survey of Consumer Finance (SCF) was a signal of even more serious economic problems for future retirees. The magnitude of the recent Great Recession and the associated wealth loss had the potential to significantly affect individual worker’s retirement decisions and their levels of adequacy. Lastly, this study provides some insights into factors related to retirement adequacy of U.S. households after the recent Great Recession, using the 2010 SCF dataset. Since the SCF does not provide a direct measurement of financial sophistication, we proposed three proxies based on previous literature.

Replacement rates have historically been used to determine the adequacy of retirement resources to maintain an individual’s standard of living. Retirement adequacy is normally defined as having resources that exceed the amount needed to finance desired retirement consumption. Palmer (1992; 1994) proposed the required replacement ratio as a proxy for retirement needs, assuming that pre-retirement spending is a proxy for adequate post-retirement spending. If post-retirement income is at least as high as needed post-retirement spending, that will indicate retirement adequacy, which is consistent with consumption smoothing implied by a normative life cycle model (Ando & Modigliani, 1963).

Retirement Adequacy. Hanna and Chen (2008) found that research on retirement adequacy of working households has produced a wide range of estimates, with estimates of the proportion of workers on track for an adequate retirement ranging from 31% to 80%. Yuh (2011) analyzed whether working households in the 2004 SCF could maintain their living standard in retirement, and estimated that 56% of pre-retired households would be able to maintain 70% of permanent income in retirement. Only 39% of households would not be ‘at risk’ of being financially unprepared for their retirement under a 100% standard of pre-retirement earning.

Kim, Hanna, & Chen (2014) estimated the projected retirement adequacy of U.S. households using a new concept, a retirement income stage defined as a period in which the projected number of income sources (Social Security benefit, defined benefit pension, and part-time wages) remained constant. Based on the 1995-2007 SCF datasets, about 73% of households with a head or spouse/partner aged 35 to 70 have more than one income stage.

When income stages were taken into account, the proportion of households with retirement adequacy overall increased from

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44% in 1995 to 58% in 2007, and in each year the proportion was much lower than estimates ignoring income stages.

Financial Sophistication in Household Finance. The need for financial sophistication has become increasingly important in household finance area. Since financial sophistication is defined as the ability of a household to avoid making financial mistakes, household finance researchers have become increasingly interested in the concept of financial sophistication (Calvet, Campbell, & Sodini, 2009).

Calvet et al. (2009) summarized some empirical studies relating to a correlation between household characteristics and investment mistakes. Those with better financial education had better diversified portfolios (Calvet, Campbell, & Sodini, 2007; Goetzmann & Kumar, 2008; Vissing-Jorgensen, 2003).

In contrast to households with less education, those with more education were less likely to hold losing and sell winning stocks (Calvet et al., 2009; Dhar & Zhu, 2006). The more financially sophisticated could make better financial decisions because they had less financial inertia (Agnew, Balduzzi, & Sundén, 2003; Bilias, Georgarakos, & Haliassos, 2008; Campbell, 2006; Calvet et al., 2009; Vissing-Jorgensen, 2002). In addition to Calvet et al.

(2009)’s review, we examined other studies clearly focusing on the relationship between financial sophistication and household financial decisions. Households with high financial literacy tended to invest in stocks (Van Rooij, Lusardi, & Alessie, 2011; Yoong, 2011; Christelis, Jappelli, & Padula, 2010). The less financially sophisticated were less likely to accumulate wealth (Stango & Zinman, 2009).

Financial Sophistication and Retirement Planning. Previous researchers such as Clark and d’Ambrosio (2002), Lusardi and Mitchell (2007, 2009, 2011), Van Rooij, Lusardi, and Alessie (2011) and Fornero and Monticone (2011) found that more financially knowledgeable people were more likely to plan for retirement. The existing literature has mainly focused on the link between financial sophistication and plan for retirement, assuming that retirement planning is a powerful indicator of actual saving behavior. For example, Lusardi and Mitchell (2007) reported that respondents they classified as “planners” reached retirement with much higher wealth levels and displayed higher financial literacy than non-planners. They used the 1992 and 2004 cohorts of the Health and Retirement Study (HRS), which includes questions about people’s capacity to handle basic financial literacy concepts (2007, 2011), and also the Rand American Life Panel (ALP), which offers several features for analyses of financial literacy and retirement planning (Lusardi & Mitchell, 2009). In addition, some researchers examined similar research questions with survey data from other countries. Van Rooij et al. (2011) examined the relationship between financial knowledge and retirement planning in the Netherlands by using De Nederlandsche Bank (DNB) Household Survey. Fornero and Monticone (2011) used the 2006 and 2008 waves of the Bank of Italy Survey on Household Income and Wealth (SHIW) to conduct empirical analysis of participation in pension plan, how workers contributed and how to respondents allocated retirement

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portfolios. The authors of these studies reached conclusions similar to those by Lusardi and Mitchell empirical results, that financial knowledge was related to having higher retirement wealth.

Methods Data and sample selection. In this study, the 2010 Survey of Consumer Finances (SCF) dataset was used to test the relationship between proxies for financial sophistication and projected retirement adequacy. The Federal Reserve Board has triennially released the SCF since 1983, and the 2010 SCF was released in April 2012. The SCF provides comprehensive and detailed information on the financial status of U.S. households (Bricker, Kennickell, Moore, & Sabelhaus, 2012). The SCF is designed to provide very detailed information on all aspects of households finances such as the assets, liabilities, and income of U.S. households and their investments in financial products.

Detailed demographic information is collected, including age, racial/ethnic identification, and education level. Our analytical sample was composed of households with a head who is age 35 to 60, and employed full time. Most past researchers assumed that respondents who are younger than 35 are more likely to have major changes in jobs or marital status, which would make the projection of retirement adequacy less accurate. Further, in order to avoid a selection effect for workers over 60 who have already retired, we used a different age restriction from some recent retirement adequacy studies such as Yuh (2011) and Kim et al. (2014), who included workers up to age 70 in the analyses.

Finally, 2,283 households met our sample criteria from the 2010 SCF. About 19% of the sample households responded that they would never retire. For those households, we assumed that their expected retirement age equals 70 (Kim et al., 2014).

Retirement Adequacy Projection and Dependent Variable. In order to project retirement resources, this research used the methods reported by Chen (2007) and Kim et al. (2014), with the calculation of retirement income from projected retirement assets combined with estimated income from Social Security, defined benefit pensions, and part-time wages. Moreover, our calculation of spending needed in retirement followed the method used by Chen (2007). From the 2010 Consumer Expenditure Survey published by the Bureau of Labor Statistics, we estimated benchmark replacement ratios by the published income categories1.

There is no direct measurement to calculate retirement resources in the SCF dataset. In this study, retirement resources included Social Security benefits, projected Defined Contribution (DC) account balances, projected Defined Benefit (DB) pensions, projected part-time wages after retirement from full-time jobs, and projected financial and non-financial assets. We generally followed Chen (2007) in calculations of retirement needs.

Projection of retirement assets used the historical mean real rates of return for each major investment category, applied to current balances in each investment category and projected contributions in each investment category. (Yuh, Montalto, & Hanna, 1998; Chen, 2007; Yuh, 2011). The asset allocation of the defined contribution accounts and other retirement assets was assumed 1

See Appendix 1

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to be constant until retirement. The dependent variable was set up as a dichotomous indicator of projected retirement adequacy coded as 1 if the replacement ratio was greater than the benchmark replacement ratio, otherwise it was coded as 0.

Financial Sophistication Proxies in the SCF. The SCF does not provide a direct measurement for financial literacy or sophistication. Based on research related to financial sophistication, we used three proxies for financial sophistication: (1) education, (2) use of financial planning services, and (3) understanding of the SCF survey questions. Education has been widely accepted as a good proxy for financial sophistication. Kyrychenko and Shum (2009), Stango and Zinman (2009) and Kennickell, Kwast and Starr-McCluer (1996) used education as a proxy for financial sophistication. In our study, the highest educational attainment of the household is coded as five dummy variables: less than high school, high school graduate, some college, bachelor degree and post-bachelor degree. Though previous studies focused only on the educational attainment of either the household head or the respondent, for couple households we considered the highest education level of the head or and the spouse/partner. For example, if the head’s highest attainment is a high school diploma and the spouse has a bachelor’s degree, household education is coded as bachelor’s degree.

Other Independent Variables. Demographic variables, economic status variables, and financial attitude variables were used as independent variables. The demographic variables included age, racial/ethnic status, and household type measured as a categorical variable. Age was classified using three categories: 35-44, 45-54 and 55-60. For multivariate analysis, age of head was used as a continuous variable. Racial/ethnic group was categorized by four dummy variables: White, Black, Hispanic, and Asia/other.

Marital status was categorized into four categories: married couple, single male, single female, and unmarried partner households. The economic status variables included normal income and retirement planning variables. Retirement planning variables consisted of expected retirement age, having a defined benefit pension, and having a defined contribution pension. The expected retirement age was coded into four categories based on Social Security pension calculation rules: before 62, between 62 and 65, over 65 and ‘never retire’. To capture the possible nonlinearity of the effect of income, household income was transformed into the natural log of normal income. The financial attitude variables were based on the respondent’s risk tolerance, measured as four dummy variables for no risk, average, above and substantial risk tolerance.

Analysis. For the multivariate analysis, we used logistic regression (logit), which is widely used for analyzing the relationship Moreover, the SCF has a variable with the interviewer’s assessbetween several explanatory variables and aadequacy binary outcome vari- to be a Empirical Model Specification.

For this study, retirement was assumed ment of how well the respondent understood the SCF questions, able. In order to adequately handle missing data, this study used with four levels, excellent, good, fair, and poor. Though little the Repeated-Imputation Inference (RII)characteristics technique, providing function of financial sophistication and other household such as an demographic research has focused on understanding of the SCF survey, it estimate of variances more closely representing the true variances would plausibly reflect financial sophistication. Huston, Finke, statusthan economic andestimates financial obtained attitudes. by only one implicate (Lindamood, and Smith (2012) used this variable as one of main financial Hanna, & Bi, 2007). Means tests were used with the RII techsophistication factors. For the purpose of this study, because of nique to examine differences of retirement preparedness between the distribution of responses, we coded responses of excellent households with different financial sophistication proxies. and good understanding as good understanding of the SCF đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘…đ?‘… đ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž survey, and responses of fair and poor understanding are coded as Empirical Model Specification. For this study, retirement = đ?‘“đ?‘“ (đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ â„Žđ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–đ?‘–, đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘đ?‘‘â„Žđ?‘–đ?‘–đ?‘–đ?‘–, đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’đ?‘’ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ , đ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“đ?‘“ đ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Žđ?‘Ž not having good understanding of the SCF survey. adequacy was assumed to be a function of financial sophistication and other household characteristics such as demographic, Lastly, use of a financial planner would imply a willingness to economic status and financial attitudes. take in further financial knowledge. Marsden, Zick, and Mayer The and empirical model for retirement adequacydemographic, was specifiedeconomics using a logistic regression mode (2011) found that using a financial planner has substantial =f (financial sophistication, status, and intangible benefits, such as goal setting, calculating retirement financial attitudes) expressed as the log odds function. needs, diversifying retirement accounts, using supplemental The empirical model for retirement adequacy was specified using retirement accounts, accumulating emergency funds, responding a logistic regression model, expressed as the log odds function. positively to the economic crisis, and possessing retirement confidence. Therefore, use of a financial planner may supplement an đ?‘?đ?‘? ) = đ?›˝đ?›˝0 + đ?‘Ľđ?‘Ľ1 đ?›˝đ?›˝1 + đ?‘Ľđ?‘Ľ2 đ?›˝đ?›˝2 + â‹Ż + đ?‘Ľđ?‘Ľđ?‘˜đ?‘˜ đ?›˝đ?›˝đ?‘˜đ?‘˜ = Xβ logit (p) = log ( individual’s financial sophistication. The SCF asks two questions 1 − đ?‘?đ?‘? about source of financial decisions, “What sources of information Where do you use to make decisions about saving and investments Where (borrowing or credit)?â€? Among two types of financial decisions, X = a vector of a household’s characteristics such as financial saving and investment decisions are more relevant to retirement X = a vector ofsophistication, a household’s characteristics as financial demographic, demographic, such economics status,sophistication, financial attitudes saving than credit or borrowing decisions. We created a dummy economics status, variable of financial planner usage based on the respondent Ă&#x; = financial a vector attitudes of coefficients to be estimated reporting that a financial planner was used for savings and đ?›˝đ?›˝ = a vector ofResearch coefficients to be estimated investment decisions.

Hypotheses.

We constructed research hypotheses based on previous literature. As previous studies suggested, it is expected that financially sophisticated households are more likely to have an adequate retirement. These are three research Š2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

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hypotheses for our financial sophistication proxies.

H1: Households with higher educational attainment are more likely to have adequate retirement preparedness.

H2: Households using a financial planner for savings and investment decisions are more likely to have adequate retirement preparedness.

H3: Households with good understanding of the SCF survey questions are more likely to have adequate retirement preparedness.

Limitations. As discussed in previous sections, the SCF does not provide a direct measurement for financial literacy or sophistication. Though this study selects three variables as proxies to the financial sophistication, there are some possible biases related to them.

Results Descriptive Results. The characteristics of sample households are presented in Table 1. Only 24% of households planned to retire before age 62. Over half of households (50.6%) had a defined contribution pension, while only 16% had a defined benefit pension, a pattern consistent with the trend toward replacement of traditional defined benefit (DB) pension plans with defined contribution (DC) plans (Love et al., 2007). Over 63% of households were willing to take some risks with their investments. Table 2 shows distributions of categories of the financial sophistication proxies. About 72% of the households had completed education beyond the high school level, and 6% of households did not have a high school diploma. Over 28% of households used a financial planner for saving and investment decision. More than 91% of the respondents were considered by the interviewer to have a good understanding of the SCF survey questions.

Table 2 also shows descriptive patterns of projected retirement adequacy by the three financial sophistication proxies. We calculated the mean income replacement ratio (IRR) by using Chen (2007)’s retirement income stage method, with benchmark ratios for different income levels estimated from the 2010 Consumer Expenditure Survey. Each household’s IRR was compared to the benchmark ratio for that household’s income category, and if the household’s IRR is at least as high as the benchmark, it was counted as having retirement adequacy. The proportion of retirement adequacy was highest, at 54%, for households having a post-bachelor degree, compared to 46% for households with a bachelor degree, 43% for households with some college, 32% for those with a high school diploma, and 17% for less than a high school degree. Households using a financial planner had higher projected retirement adequacy (50%) than those that did not use one (39%). Only 32% of households with poor understanding of the survey questions but 43% of households with good understanding of survey questions were adequately prepared for retirement. There were significant differences of mean retirement adequacy for each proxy. In order to easily compare the descriptive differences to the multivariate results, significance tests in Table 2 on mean differences are presented with the same reference categories used in the logit in Table 3. For comparison

with the 1995 to 2007 SCF, we also used the same methods to compute the adequacy proportion of households with a full-time working head aged 35 to 60, and the pattern including the 2010 adequacy rate is shown in Figure 1. The proportion of households projected to have retirement adequacy increased from 1995 to 2007, then dropped sharply. Only 44% of households in 2010 had projected retirement adequacy, which is 14 percentage points lower than overall adequacy in 2007.

Multivariate Results. The logit results of financial sophistication proxies, demographic, economic status, financial status affecting households’ projected retirement adequacy are presented in Table 3. College educated households were more likely to have retirement adequacy than those with less than a high school education.

Households using a financial planner were more likely to have an adequate retirement than otherwise similar households not using a financial planner for savings and investment decisions. The 2-tail p value shown in Table 3, 0.0988, is greater than the usual .05 threshold for significance, but because we tested a directional hypothesis, it is reasonable to divide the p value by 2 for a 1-tail test (c.f., Wang & Hanna, 2007). Therefore, the effect of using a financial planner can be judged to be significant. Interviewer assessment of the respondent as having good understanding of the SCF survey was not related to the likelihood of having an adequate retirement.

The likelihood of having an adequate retirement was lower for those who expected to retire before 62 than for those who expected to retire at 62 or after, and than those who do not expect to retire. Having a defined benefit pension and having a defined contribution pension were each positively related to the likelihood of adequate retirement. This result is consistent with empirical results reported by Yuh et al. (1998), Chen (2007), and Kim et al. (2014). The likelihood of retirement adequacy increases with normal household income. Single female households were less likely to have an adequate retirement than married couple households. Households willing to take average or above average risk were more likely to have retirement adequacy than those unwilling to take any risk, but those willing to take substantial risk were not significantly different in projected adequacy than those unwilling to take any risk. The 83.5% concordance shows the model does a good job of predicting retirement adequacy.

Discussion and implications We found that about 44% of working households aged 35 to 60 in 2010 were adequately prepared for retirement. The overall adequacy proportion dropped by 14 percentage points between 2007 and 2010, presumably because of the impact of the economic recession began in December 2007.

As the main focus of this research, we tested for the effects of three proxies for financial sophistication based on previous studies: (1) Education, (2) Use of financial planning services, and (3) Understanding of the SCF survey questions. Each of these proxies was related to increased likelihood of retirement adequacy in the bivariate analyses reported in Table 2. The first two proxies were related to retirement adequacy when controlling

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for the effects of other variables in the logit. Our multivariate analysis shows that households with college education were more likely to have an adequate retirement than those with less than a high school education. Households using a financial planner were somewhat more likely to have an adequate retirement than non-user households. However, good understanding of SCF survey was not significantly related to the likelihood of having an adequate retirement.

The strong effect of educational attainment suggests the importance of appropriate reading levels for financial education materials. Wiener, Baron-Donovan, Gross, and Block-Lieb (2005) discussed testing the effectiveness of financial management material for consumers who had filed for bankruptcy, and noted that the material was written at a 7th to 9th grade reading level. Our results imply that designing financial education materials for low reading levels may help households with low educational attainment. The effect of using a financial planner is more problematic, as many households cannot afford to hire a financial planner. The median flat fee charged by Certified Financial Planners™ for a comprehensive financial plan is $2,497 (King, 2012). If government programs and private employers could provide financial counselors at low or zero cost to households who cannot afford financial planners, perhaps those without a college education would be more likely to achieve retirement adequacy.

If our proxies for financial sophistication were related to time preferences of respondents, then some of the effects we attributed to financial sophistication differences might actually be due to differences in time preference. As Hanna and Kim (2014) noted, assumptions about personal discounting of the utility of consumption can have a substantial impact on assessments of retirement adequacy. It would be useful to perform a similar analysis of the projected retirement adequacy of households with datasets with more direct measures of financial sophistication and financial literacy, for instance, the Health and Retirement Study datasets. However, the SCF does contain the most comprehensive set of financial information of U.S. households of all ages, so our research does provide a contribution to the literature on factors affecting the projected retirement adequacy of U.S. households.

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Table 1. Selected Characteristics of Sample Households, 2010 SCF Age of head

Variable

35 – 44 45 – 54 55 – 60 Marital status Married Single male Single female Partner Racial-ethnic category White Black Hispanic Asian or others Expected retirement age Retirement age < 62 62 ≤ Retirement age ≤ 65 Retirement age > 65 Never Retire Retirement plan Have defined benefit plan Have defined contribution plan Risk tolerance No risk Average risk Above average risk Substantial risk

Percentage 39.3 42.2 18.5 62.2 12.6 18.9 6.3 68.6 13.0 13.4 5.0 23.7 38.9 18.8 18.6 16.4 50.6 36.9 42.5 17.9 2.7

Note: Restrictions are described in the Methods Section, and include head or spouse/partner being 35 or older, but no more than 60, and head and/or the spouse being in the labor force. N=2,283

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Table 2. Projected Retirement Adequacy by Proxies for Financial Sophistication, Bivariate Analysis (Means Test), 2010 SCF

Variable Overall

Education of household

Category a

Percent in Category

Retirement Adequacy

Mean Difference b

P-value c

All households

100.0

43.6%

N/A

N/A

Post-bachelor degree

19.4

53.7%

37.0%

<.0001

Bachelor degree

33.0

46.4%

29.7%

<.0001

Some college

19.3

42.5%

25.8%

<.0001

22.7

32.4%

15.7%

<.0001

5.6

16.7%

N/A

N/A

Yes

28.3

49.9%

10.7%

<.0001

No

71.7

39.2%

N/A

N/A

Yes

91.2

43.2%

11.3%

<.0001

No

8.8

31.9%

N/A

N/A

High school graduate Less than high school

Use of financial planner Good understanding of SCF survey question a The

reference category used in the mean test is indicated in bold face.

b

Weighted data; RII technique is used

c

Significance test is for mean difference from reference category for each variable.

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Table 3. Logistic Regression Analysis of Retirement Adequacy, 2010 SCF Variable

Coefficient

2-tail

Standard Error

Odds ratio

0.3104 0.3105 0.3066 0.3335

1.569 2.141 2.298 2.334

0.1289

1.237

0.2119

0.960

0.1457 0.1734 0.1763

1.928 2.281 1.880

0.1137

1.652

0.1502

1.605

<.0001

0.0655

1.866

0.0320

0.5677

0.955

0.0353 <.0001 0.7001

0.1697 0.1619 0.2445

0.700 0.487 1.099

0.4046 0.2709 0.1938

0.1934 0.1935 0.2685

0.852 0.809 0.706

0.0048 0.0028 0.4534

0.1433 0.1667 0.2978

1.497 1.644 1.250

p-value a Education of household (reference category: less than high school) High school 0.4505 0.1464 Some college 0.7611 0.0141 Bachelor degree 0.8320 0.0066 Post-bachelor degree 0.8476 0.0110 Use of financial planner 0.2129 0.0988 (reference category: No) Good understanding of the SCF survey question -0.0406 0.8466 (reference category: No) Expected retirement age (reference category: before 62) 62 ≤ Retirement age ≤ 65 0.6564 <.0001 65 < Retirement age ≤ 70 0.8245 <.0001 Never Retire 0.6312 0.0003 Have defined contribution plan 0.5023 <.0001 (reference category: No) Have defined benefit plan 0.4730 0.0017 (reference category: No) Log of Income

0.6238

Age of household head -0.00503 Marital status (reference category: married) Single male -0.3566 Single female -0.7190 Partner 0.0943 Racial-ethnic category (reference category: White) Black -0.1603 Hispanic -0.2125 Asian or others -0.3485 Risk tolerance (reference category: Take no risk) Average risk 0.4036 Above average risk 0.4973 Substantial risk 0.2231 Concordance (mean) 83.5% a

Unweighted RII analysis of 2010 SCF dataset, analysis of 2,283 households with full-time employed head or spouse age 35-60. ©2015, IARFC. All rights of reproduction in any form reserved.

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Appendix 1 Benchmark replacement ratio across income categories, Consumer Expenditure Survey, 2010 Income Category

1

2

3

4

5

6

Less

$10,000

$15,000

$20,000

$30,000

$40,000

than

to

to

to

to

to

$10,000

$14,999

$19,999

$29,999

$39,999

$49,999

Benchmark replacement ratio

2.26

1.58

1.43

1.17

1.02

0.91

Income Category

7

8

8

10

11

12

$50,000

$70,000

$80,000

$100,000

$120,000

$150,000

than

to

to

to

to

and

$69,999

$79,999

$99,999

$119,999

$149,999

more

0.81

0.76

0.71

0.69

0.68

0.51

Income Range

Income Range Benchmark replacement ratio

Note: We use the normal household income of the household categorized in the corresponding published income category in the BLS. And we set the benchmark ratio as the ratio of average annual expenditure divided by average pre-tax income in that BLS category. For example, a household with a normal income of $35,000 would be assumed to have expenditures equal to 102% of pre-tax income, while a household with a normal income of $90,000 would be assumed to have expenditures equal to 71% of pre-tax income. If the projected retirement replacement ratio is equal to or greater than the benchmark replacement ratio, this household would have adequate retirement resources to maintain retirement needs.

20

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Figure 1 Trends in Retirement Adequacy of U.S. Households, 1995-2010 SCF Datasets

Note: We computed the adequacy proportion of U.S. households from the 1995-2010 SCF by employing the retirement income stage method (c.f., Kim et al., 2014). The adequacy proportion means percentage of households with a head age 35-60 and with the head employed full-time which are projected to have retirement adequacy. Total sample size was 9,322.

Š2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

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What Do Subjective Assessments of Financial Well-Being Reflect?

Steven A. Sass, Ph.D., Research Economist, Center for Retirement Research at Boston College Anek Belbase, M.P.A., Research Project Manager, Center for Retirement Research at Boston College Thomas Cooperrider, Associate, Berkeley Research Group, LLC Jorge D. Ramos-Mercado, Research Assistant, Center for Retirement Research at Boston College 1* Abstract Households have become increasingly responsible for meeting distant financial needs – specifically paying down student loans, saving for retirement, and saving for college.

This study, using data from the 2012 FINRA Foundation Financial Capability Survey, nevertheless finds that a household’s financial satisfaction is highly correlated with its ability to meet its day-to-day needs, with much more muted relationships with its protection against risk and accumulation of savings to meet future needs. Nor do subjective assessments become much more sensitive to distant deficits if the household’s day-to-day finances are in reasonably good shape or if assessment is made by a financially literate individual. Households thus cannot be expected to devote much effort to addressing distant deficits by themselves. They need initiatives that raise their awareness of distant financial deficits, such as broadcasting simple rules-of-thumb and providing ready access to quick financial check-ups; and compensate for their on-going lack of awareness, such as structures that make it easy and automatic to address such deficits.

1* The research reported herein was performed pursuant to a grant from the FINRA Investor Education Foundation. The findings and conclusions expressed are solely those of the authors and do not represent the views of the FINRA Investor Education Foundation or Boston College. The authors thank Anthony Webb for his comments and suggestions.

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Journal of Personal Finance

Peace of mind is one of the great benefits that comes from having one’s financial house in order. Financial satisfaction is also often used as a measure of financial well-being. But bliss could be the fruit of ignorance. If so, subjective financial assessments would be imperfect measures of well-being and peace of mind hazardous to financial health.

Financial satisfaction is based on what one sees and values at a particular point in time. Financial well-being, however, involves protection against hard-to-see risks and the accumulation of savings to meet future needs. So it would not be surprising if subjective assessments overlook issues distant from day-to-day concerns. But households today are increasingly responsible for such issues, specifically paying off student loans, saving for college, and saving for retirement. To the extent subjective assessments overlook deficits in these areas, households could lack sufficient motivation to address these issues,2 the deficits are likely to grow, and peace of mind would significantly diminish financial well-being. Initiatives that raise awareness, and compensate for the lack of awareness, would then be needed to enhance the household’s financial well-being.

This study examines the relationship between subjective financial assessments and financial well-being by testing the following three hypotheses for working-age households: 1. That subjective financial assessments are significantly more sensitive to day-to-day concerns, such as the ability to cover current expenses and debt payments, than to distant concerns, such as protection against risk and having enough savings to meet future needs.

2. That subjective financial assessments follow a pecking order, with distant issues having a significantly greater effect on subjective assessments if the household’s day-to-day finances are in a reasonably good shape. To the extent that this is the case, motivation to address distant deficits should rise as concern over day-to-day problems declines.

3. That financial literacy significantly enhances the sensitivity of subjective financial assessments, and especially sensitivity to distant deficits. To the extent this is the case, initiatives that increase financial literacy would increase a household’s motivation, as well as its ability, to improve its financial well-being.

The discussion proceeds as follows. The first section reviews the literature on subjective assessments as a measure of financial well-being. The second section describes the data and methodology used to test the three hypotheses. The third section presents findings consistent with the first hypothesis, that subjective assessments largely reflect day-to-day concerns. The fourth section presents findings inconsistent with the second hypothesis, that distant issues have a significantly greater effect on subjective 2

Isen (1987), Foote (2000).

assessments once the household’s day-to-day finances are in reasonably good shape. The fifth section presents findings inconsistent with the third hypothesis, that financial literacy significantly enhances sensitivity to financial deficits, especially distant deficits. The final section concludes that financial satisfaction today is a poor measure of financial well-being; and that initiatives to improve well-being must raise the awareness, or compensate for the lack of awareness, of hard-to-see distant deficits.

Subjective assessments as a measure of financial well-being Financial well-being is measured not by income and wealth, but by the happiness and life satisfaction that income and wealth provide. Happiness and life satisfaction, however, are not easily measured. Researchers have thus used financial satisfaction — an individual’s subjective assessment of his or her financial condition — as a yardstick to assess financial well-being.3 An extensive body of research has shown that the relationship between happiness and life satisfaction and the household’s objective financial situation is rather indirect in prosperous economies such as the United States. Overall levels of happiness and life satisfaction have remained much the same in such economies, despite dramatic increases in per capita income and wealth.

Financial satisfaction has remained a major contributor to an individual’s overall happiness and life satisfaction. But the primary drivers of financial satisfaction are not income and wealth, but income and wealth relative to social reference groups, previous levels of income and wealth, and aspirations that rise more or less in line with increases in income and wealth.4 Subjective financial assessments would remain a reasonable yardstick for assessing financial well-being if they reflected the household’s ability to maintain or improve its income and wealth relative to its social reference group and personal financial benchmarks. Financial satisfaction would also be valuable as a motivator, with dissatisfaction an incentive to improve the household’s finances – relative to its social reference group and personal financial benchmarks.

An anomaly reported in Mugenda et al. (1993) and Xiao et al.

(2014), however, raises concerns about the value of subjective assessments as an indicator of financial well-being. These studies found financial literacy associated with a reduction in financial satisfaction. The researchers suggested that financially literate individuals do not have weaker finances, but are better equipped to see deficits. This hypothesis is consistent with the notion that financial rationality is limited; that subjective assessments can mask serious shortfalls; and that less literate households are 3

Another approach to identify household preferences from market behavior and uses these preferences to estimate the effect of actual or potential economic changes on happiness and life satisfaction.

4 Easterlin (1974 and 2006); Blanchflower and Oswald (2004); Stutzer (2004); Ferrer-i-Carbonell (2005); Luttmer (2005); Johnson and Kreuger (2006), Clark, Frijters, and Shields (2008); Joo and Grable (2008); Easterlin, et al.

(2010).

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

overly sanguine — and thus less likely to take action to improve their financial well-being. To the extent this is the case, financial satisfaction is less useful as a measure of financial well-being; and initiatives to improve well-being would need to correct, or otherwise accommodate, inaccurate subjective assessments of the household’s financial condition.5 This study contributes to the literature by testing the three hypotheses, listed above, for working-age adults: 1) that subjective financial assessments are primarily associated with day-today concerns rather than distant concerns that increasingly affect the household’s financial well-being; 2) that these assessments follow a pecking order, with more distant concerns having a significantly greater relationship with financial satisfaction after day-to-day concerns are addressed; and 3) that financial literacy significantly enhances the accuracy of subjective assessments, primarily by enhancing the relationship between subjective assessments and distant financial concerns.

The findings address the use of financial satisfaction as an indicator of financial well-being and impetus motivating households to improve their well-being. The findings also address the design of initiatives to improve well-being by identifying: 1) issues that subjective assessments reasonably reflect, and how that changes as day-to-day deficits subside; 2) the ability of financial literacy to improve the quality of subjective financial assessments, and thereby a household’s motivation to improve its financial well-being; and 3) issues that require initiatives that correct or otherwise accommodate inaccurate subjective assessments.

Data and Methodology

23

•

1,447 of the remaining respondents who say someone else in the household is more knowledgeable about saving, investing, and debt – as the study is interested in the quality of household assessments.

•

138 of the remaining respondents who indicated that they “don’t know” or “prefer not to say” when asked how satisfied they are with their current financial condition.

•

1,284 of the remaining respondents who answered “don’t know” or “prefer not to say” when asked about particular financial conditions.

•

1,105 of the remaining respondents who are ages 61 or older – as their work/retirement status is difficult to assess from the data collected in the Survey.

This leaves a sample of 9,473 respondents. The dataset includes population weights, which we use to make this very large sample representative of the nation at large.

There are various measures used to assess financial satisfaction.

This study uses responses to the question in the Survey: “Overall, thinking of your assets, debts and savings, how satisfied are you with your current personal financial condition? Please use a 10-point scale, where 1 means ‘Not At All Satisfied’ and 10 means ‘Extremely Satisfied.’” The Survey asked this question at the beginning of the interview, before respondents were asked any questions that would lead them to review their finances. Their responses can thus be taken as representative of such assessments in the population at large.7 The distribution of responses is given in Figure 1.

Data The study examines the relationship between the subjective financial assessments of working-age adults and their household’s objective financial condition. We use data collected in the 2012 FINRA Investor Education Foundation State-by-State Financial Capability Survey, an online survey of 25,509 American adults conducted from July to October 2012.6 The sample used excludes: •

•

Figure 1. Distribution of Subjective Financial Assessments Figure 1. Distribution of Subjective Financial Assessments

16%

Mugenda, Hira, and Fanslow (1990); Xiao, Chen, and Chen (2013). For other studies of financial satisfaction, see Hseih (2001); Plagnol (2010); Seghieri, Tanturri, and DeSantis (2006); Vera-Toscano, Ateca-Amestoy, and Serrano-Del-Rosal (2006); and Brown, et al. (2014).

6 The Survey sampled approximately 500 respondents in each state plus the District of Columbia, with each to approximating Census distributions by age, gender, ethnicity, education, and income. FINRA Foundation (2012a and 2012b).

10%

8%

10%

10%

12%

12%

6%

5%

5%

9

10

4%

0% 1

2

3

1 = “Not At All Satisfied”

4

5

6

7

8

10 = “Extremely Satisfied”

Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

7 5

15%

12%

5,414 respondents who are younger than 25, full-time students, or are living with parents, friends, or roommates – to exclude respondents not fully engaged in the work force or who have not established an independent household.

6,648 of the remaining respondents who are retired or disabled or whose spouse is retired or disabled – as their financial condition is difficult to assess from the data collected in the Survey.

16%

Different surveys ask different questions to assess financial satisfaction, for different analytic reasons. This study analyzes the relationship between financial satisfaction and day-to-day as opposed to distant financial conditions.

That the question in the FINRA Foundation Survey explicitly asks “thinking of your assets, debts and savings” prompts respondents to consider issues other than day-to-day concerns; that the question focuses on current stocks of “assets, debts and savings,” on the other hand, might prompt respondents to make a present-minded rather than forward-looking assessment of their financial condition. All in all, the question seems well suited for this study.

Journal of Personal Finance

24

Table 1. Household Financial Indicators Included in the Study

Table 1. Household Financial Indicators Included in the Study Day-to-Day Concerns

Self-Assessed Difficulty Covering Expenses Not difficult Moderately difficult Very difficult Unemployment Neither the respondent nor a spouse or partner is unemployed Unemployed: respondent and/or a spouse or partner is unemployed Self-Assessed Current Debt Burden * Not too much debt Moderate debt burden Heavy debt burden Ability to Access $2,000 Could certainly or probably access $2,000 Could not likely access $2,000

Incidence 42% 43% 15% 89% 11% 25% 38% 37% 62% 38%

Distant Concerns

Medical Insurance Has medical insurance No medical insurance Life Insurance Has life insurance No life insurance Life insurance not needed (no dependents or Social Security benefits deemed adequate) Retirement ** Active retirement plan Inactive retirement plan No retirement plan Saving for College Saving for college Not saving for college No need to save (no financially dependent children) Housing Own free and clear Own with a mortgage Own, underwater Rent Student Loans No student loans Not concerned about repaying Concerned might not be able to repay

80% 20% 63% 23% 14% 61% 10% 29% 21% 35% 44% 16% 37% 12% 35% 77% 11% 12%

* Self-Assessed Current Debt Burden: Based on responses to "How strongly do you agree or disagree with the statement ‘I have too much debt right now?” on a scale from 1 to 7, with a response of 3 to 5 classified as “Moderate Debt Burden.” ** Retirement: Respondents with “No Retirement Plan” have neither employer DB pension accruals nor a 401(k)/IRA type retirement savings; respondents with an “Inactive Retirement Plan” have only 401(k)/IRA type savings and no one in the household is currently making regular contributions to such plans.

Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

21 ©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

To assess relationships between this measure of financial satisfaction and the household’s financial well-being, we use the reasonably comprehensive set of financial indicators listed in Table 1, which also gives their incidence in the sample. Day-to-day issues are the household’s ability to meet current expenses and debt payments, whether anyone in the household is currently unemployed, and its ability to access $2,000 if need be. Distant issues are medical and life insurance (if the household has dependents)

Table 2. Control Characteristics Included Table 2. Control Characteristics Included in the Studyin

25

coverage, retirement saving, saving for college, home ownership and mortgage debt, and student loan debt.

Previous research has shown that financial satisfaction varies by age, income, and various personal characteristics. It can also be expected to vary with local labor market conditions. We thus control for personal and local labor market characteristics listed in Table 2.

the Study

Age Group 25-34 35-49 50-60 Adjusted Income Tertile * Lowest terciles Middle terciles Highest terciles Sex Female Male Marital Status Married Never married Divorced, separated, or widower Ethnicity White Not white Education College or more Some college High school or less Aversion to Investment Risk ** Willing to take risks Moderately risk averse Risk averse Seen a Financial Advisor in the Last 5 Years Has seen a financial advisor Has not seen a financial advisor Financial Literacy *** Not financially literate Financially literate County Unemployment Rate Less than 6.2% 6.2-8.0% (the U.S. rate for 2012) Greater than 8%

Incidence 28% 45% 27% 35% 33% 31% 53% 47% 65% 22% 13% 72% 28% 34% 35% 31% 21% 50% 29% 56% 44% 53% 47% 26% 50% 24%

* Adjusted Income Tercile: Each age group is divided into adjusted household income terciles using the OECD equivalence scale (OECD n.d.). Each tercile includes all respondents in each age-specific tercile: the lowest tercile includes all respondents in the lowest adjusted income tercile in each age group. ** Aversion to Investment Risk: Based on responses to “When thinking of your financial investments, how willing are you to take risks?” on a scale from 1 to 10, with a response of 4 to 7 classified as “Moderately Risk Averse.” *** Financial Literacy: Based on the number of correct answers to 5 standard financial literacy questions, with those answering 4 or 5 questions correctly coded as “Financially Literate.”

Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

26

Journal of Personal Finance

Methodology We do not attempt to identify causal relationships running from financial well-being to subjective assessments. Instead we attempt to identify statistically significant associations consistent or conflicting with the hypotheses listed above.

concerns has a statistically significant relationship with financial satisfaction. Should both estimates be significant, we will use a t-test to determine whether the difference in predictive power is statistically significant, and thus whether the set of concerns with greater predictive power can be said to have a stronger relationship with financial satisfaction.

ToToTo identify relationships between assessments and identify relationships between day-to-day and This test is affected by the incidence of day-to-day and distant identify relationships betweensubjective subjectiveassessments assessmentsand andthe thevarious various day-to-day anddistant distant the various day-to-day and distant financial conditions listed in deficits as well as the relationship between these deficits and To identify relationships between subjective assessments and the various day-to-day andand distant To identify relationships between subjective assessments the various day-to-day and distant financial conditions listed ininTable financial conditions listed Table1,1,we weestimate estimatethe themodel: model: Table 1, webetween estimatesubjective the model: financial identify dentify relationships assessments and the various day-to-day and distantsatisfaction. However, the incidence of the two sets of financial conditions listed in Table 1, weconditions estimate the model: financial listed in Table 1, we estimate the model: deficits issubjective much assessments theassessments same. Respondents, onday-to-day average, have 1.82distant Tođ?&#x2019;&#x2122;đ?&#x2019;&#x2122;identify To+identify relationships relationships between between subjective andand the the various various day-to-day andand distant + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; .

(1) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝ ncial conditions listed in Table 1, we estimate the model: 0 đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2018;Şđ?&#x2018;Ş + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; .

(1) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝ (1) 0 đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2018;Şđ?&#x2018;Ş day-to-day and 1.80 distant deficits.

Thus, the results essentially To identify relationship financial listed listed in Table we 1,+estimate we theđ?&#x153;ˇđ?&#x153;ˇthe model: model: đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; conditions + đ?&#x153;ˇđ?&#x153;ˇconditions + đ?&#x153;ˇđ?&#x153;ˇ= .đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;1, (1) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝0 +financial +inđ?&#x153;&#x20AC;đ?&#x153;&#x20AC;đ?&#x153;ˇđ?&#x153;ˇTable đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;estimate đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + .

(1) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x203A;˝đ?&#x203A;˝ đ?&#x2018;Şđ?&#x2018;Ş0 + đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2018;Şđ?&#x2018;Ş + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;and reflect differences in sensitivity indicate whether subjective + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; .

(1) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; =with đ?&#x203A;˝đ?&#x203A;˝0with subjective financial assessments SFA dependent on , the with subjective financial assessments SFA on đ?&#x203A;˝đ?&#x203A;˝ baseline assessment of those financial conditions lis 0 đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;Ş đ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;Ş subjective financial assessments SFA dependent on 0đ?&#x203A;˝đ?&#x203A;˝0, the baseline assessment those assessments areofsignificantly more sensitive to day-to-day than with no financial deficits â&#x20AC;&#x201D; indicating an â&#x20AC;&#x153;adequateâ&#x20AC;? state of financial well-being â&#x20AC;&#x201D; and no + đ?&#x153;ˇđ?&#x153;ˇ + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ + đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x153;ˇđ?&#x153;ˇ + đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; + . đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; .

(1) (1) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝ = đ?&#x203A;˝đ?&#x203A;˝ baseline assessment of those with no financial deficits â&#x20AC;&#x201D; indicat0 SFA đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;onassessments đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;Ş SFA đ?&#x2018;Şđ?&#x2018;Ş with no financial deficits indicating an â&#x20AC;&#x153;adequateâ&#x20AC;? state of0 financial well-being â&#x20AC;&#x201D;đ?&#x2018;Şđ?&#x2018;Ş baseline and no withâ&#x20AC;&#x201D; subjective financial assessments dependent đ?&#x203A;˝đ?&#x203A;˝0,đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;the assessmenton of đ?&#x203A;˝đ?&#x203A;˝those with subjective financial dependent 0 , the baseline assessment of those distant concerns control characteristics with reductions inand financial satisfaction; day-to-day and ing an â&#x20AC;&#x153;adequateâ&#x20AC;? state associated ofassociated financial well-being no assessment control with no financial â&#x20AC;&#x201D; indicating anwell-being â&#x20AC;&#x153;adequateâ&#x20AC;?â&#x20AC;&#x201D;state â&#x20AC;&#x201D;= and control characteristics with in financial satisfaction; day-to-day anddistant distant with no financial deficits â&#x20AC;&#x201D; indicating an â&#x20AC;&#x153;adequateâ&#x20AC;? state of financial and of nofinancial well-being h subjective financial assessments SFA dependent onreductions đ?&#x203A;˝đ?&#x203A;˝00, theâ&#x20AC;&#x201D; baseline ofdeficits those + đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x203A;˝đ?&#x203A;˝0no đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;â&#x20AC;&#x153;adequateâ&#x20AC;? and characteristics associated with reduced assessments financial đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; characteristics associated reductions in financial satisfaction; control associated with reductions in financial satisfaction; day-to-day and distant h no financial deficitsdeficits â&#x20AC;&#x201D; indicating anwith state of financial well-being â&#x20AC;&#x201D; and no and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;characteristics andcontrol control characteristics associated with reduced assessments financial deficits đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;and control characteristics associated with reductions in financial satisfaction; day-to-day and distan withwith subjective subjective financial financial assessments assessments SFA SFA dependent dependent on đ?&#x203A;˝đ?&#x203A;˝on đ?&#x203A;˝đ?&#x203A;˝ the baseline baseline assessment assessment of those of those 0 , that 0 , the The second hypothesis asserts subjective financial assessđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x201E;đ?&#x2019;&#x201E;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;; and đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , đ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇ , with and đ?&#x153;ˇđ?&#x153;ˇ reductions ininđ?&#x2019;&#x2122;đ?&#x2019;&#x2122; financial associated with these and trol characteristics associated reductions in financial satisfaction; day-to-day distant financial and and characteristics with reduced assessments day-to-day and financial deficits and and control control đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; financial deficits đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;and and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;associated and control characteristics associated with reduced with with nođ?&#x2019;&#x2122;đ?&#x2019;&#x2122;satisfaction financial no financial deficits deficits â&#x20AC;&#x201D; ments indicating â&#x20AC;&#x201D; indicating andeficits â&#x20AC;&#x153;adequateâ&#x20AC;? â&#x20AC;&#x153;adequateâ&#x20AC;? state state of financial of financial well-being well-being â&#x20AC;&#x201D; and â&#x20AC;&#x201D; and no assessments noa financia đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , and đ?&#x153;ˇđ?&#x153;ˇ reductions financial satisfaction associated with these deficits and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; ; and đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , distant with subjective follow aan pecking order, with financial satisfaction having To identify relationships between subjective assessm and đ?&#x153;ˇđ?&#x153;ˇ coefficients then indicate the relationship between characteristics.

The estimated ncial deficits đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; and control characteristics associated with reduced assessments control control characteristics characteristics associated associated with with reductions reductions in financial in financial satisfaction; satisfaction; day-to-day day-to-day and and distant distant characteristics associated with reduced assessments , đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; ; and đ?&#x153;ˇđ?&#x153;ˇ , đ?&#x153;ˇđ?&#x153;ˇ , and đ?&#x153;ˇđ?&#x153;ˇ reductions in financial satisfaction associated with these deficits and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; ; and đ?&#x153;ˇđ?&#x153;ˇ , đ?&#x153;ˇđ?&#x153;ˇ , and đ?&#x153;ˇđ?&#x153;ˇ reductions in financial satisfaction associated with these deficits and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; then đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; indicate the relationship between characteristics. The estimated đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; financial defici đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;significantly đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; stronger relationship with distant issues if theno houseeach day-to-day and distant deficit and a respondentâ&#x20AC;&#x2122;s overall subjective financial assessment. financial financial deficits deficits đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; and and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; and control control characteristics characteristics associated associated with with reduced reduced assessments assessments and đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , đ?&#x153;ˇđ?&#x153;ˇ , and đ?&#x153;ˇđ?&#x153;ˇ reductions in financial satisfaction associated with these deficits and , and reductions in financial satisfaction associated with characteristics.

The estimated đ?&#x153;ˇđ?&#x153;ˇ and đ?&#x153;ˇđ?&#x153;ˇ coefficients then indicate the relationship between characteristics.

The estimated đ?&#x153;ˇđ?&#x153;ˇ and đ?&#x153;ˇđ?&#x153;ˇ coefficients then indicate the relationship between financial conditions listed in Table 1, we estimate đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E;đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; controlTo characteristicstha each day-to-day and distant deficit and a respondentâ&#x20AC;&#x2122;sđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; overall subjective financial assessment. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; finances đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; are in a reasonably good shape. holdâ&#x20AC;&#x2122;s day-to-day đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; ; and ; and đ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;ˇđ?&#x153;ˇ , đ?&#x153;ˇđ?&#x153;ˇ , đ?&#x153;ˇđ?&#x153;ˇ , and , and đ?&#x153;ˇđ?&#x153;ˇ reductions đ?&#x153;ˇđ?&#x153;ˇ reductions in financial in financial satisfaction satisfaction associated associated with with these these deficits deficits and and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; a these andđ?&#x153;ˇđ?&#x153;ˇcharacteristics.

The estimated and and đ?&#x153;ˇđ?&#x153;ˇ coefficients then indicate the relationship between racteristics.

Thedeficits estimated each day-to-day and distant deficit and a respondentâ&#x20AC;&#x2122;s overall subjective financial assessment. each day-to-day and distant deficit and a respondentâ&#x20AC;&#x2122;s overall subjective financial assessment. deficits đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; test đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; this hypothesis, we construct a proxy measure of financial an individucoefficients then indicate relationship between each day-tocharacteristics. characteristics.

TheThe estimated estimated đ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;ˇđ?&#x153;ˇ and and đ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;ˇđ?&#x153;ˇ coefficients coefficients then then indicate indicate the the relationship relationship between between h day-to-day and distant deficit and athe respondentâ&#x20AC;&#x2122;s overall subjective financial assessment. + đ?&#x153;ˇđ?&#x153;ˇthe +, đ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇđ?&#x2018;Şđ?&#x2018;Şđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2018;Şđ?&#x2018;Ş, + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; =We đ?&#x203A;˝đ?&#x203A;˝0use đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x201E;đ?&#x2019;&#x201E;đ?&#x153;ˇđ?&#x153;ˇ ; and đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;day-to-day deficits. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;estimated đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; alâ&#x20AC;&#x2122;s concern over The regression estimating (1) relationships between financial satisfaction and day and distant deficit and equation a equation respondentâ&#x20AC;&#x2122;s overall subjective eacheach day-to-day day-to-day andfinanand distant distant deficit deficit andand ain respondentâ&#x20AC;&#x2122;s a respondentâ&#x20AC;&#x2122;s overall overall subjective subjective financial financial assessment. assessment.

The regression estimating (1)identifies identifies relationships between financial satisfaction and characteristics.

The est reductions subjective financial assessments associated with the various and distant concerns.

ToTo test the hypothesis, that subjective the variousday-to-day day-to-day andregression distantfinancial financial concerns. testidentifies thefirst first hypothesis, that subjective The estimating equation (1) relationships between financial satisfaction and â&#x20AC;&#x201D; cial assessment.

The regression estimating equation (1) identifies relationships between financial satisfaction eachcovering day-to-day andand di assessments SFA dependen dire day-to-day deficits inwith the subjective sample as financial a whole that assessments are significantly more sensitive to day-to-day than distant concerns, we compare the assessments are significantly more sensitive and tobetween day-to-day than day-to-day distant concerns, wethe compare the various day-to-day distant financial concerns.

To test first hypothesis, that subjective regression estimating equation (1)the identifies relationships satisfaction anddistant the financial various and financial concerns.

To test the first hypothesis, that subjectiv with no financial deficits â&#x20AC;&#x201D; indicating an â&#x20AC;&#x153;adequate day-to-day expenses is â&#x20AC;&#x153;very difficult,â&#x20AC;? current debt burdens are ability ofand set totopredict satisfaction.

We run two regressions fitting assessments are significantly more sensitive to day-to-day than distant concerns, we compare th ability ofeach each setoffinancial ofdeficits deficits predict financial satisfaction.

Wefirst first run two regressions fitting assessments are significantly more sensitive toequation day-to-day than distant concerns, we compare the various day-to-day distant concerns.

To testThe the first hypothesis, that subjective The â&#x20AC;&#x153;adequateâ&#x20AC;? financial conditions andfinancial control characteristics The regression regression estimating estimating equation (1) identifies (1) identifies relationships relationships between between financial financial satisfaction satisfaction and characteristics associated with and reductions in To identify relationships between subjective andcontrol theregressions various day-to-day distant â&#x20AC;&#x153;heavy,â&#x20AC;? atassessments least member of the household isand unemployed, Ě&#x201A;Ě&#x201A;one = subjective financial assessments as a function of only day-to-day or distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; ability of each set of deficits to predict financial satisfaction.

We first run two regressions fitting đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; ability of each set of deficits to predict financial satisfaction.

We first run two fitting essments ssments are significantly more sensitive to day-to-day than distant concerns, we compare the subjective financial a function of only day-to-day orand distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; the various various day-to-day day-to-day distant distant financial financial concerns. concerns.

To test To test the the firstfirst hypothesis, hypothesis, that that subjective subjective associated with greaterassessments satisfactionasare thethe first conditions or and đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; = financial The regression estimati deficits đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;$2,000 đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and control characteri đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and if and the respondent could not likely access be.

Our Ě&#x201A; essments and the day-to-day and hips subjective assessments and the various day-to-day distant Ě&#x201A;distant đ?&#x203A;˝đ?&#x203A;˝Ě&#x201A; đ?&#x203A;˝đ?&#x203A;˝deficits and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x203A;˝đ?&#x203A;˝Ě&#x201A; đ?&#x203A;˝đ?&#x203A;˝Ě&#x201A;item .distant We coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;function and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;of conditions the đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; Ě&#x201A;theđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; = Ě&#x201A;various Ě&#x201A;predict ity ofbetween eachcharacteristics set of to financial satisfaction.

We run two regressions fitting =concerns, financial assessments as asignificantly only day-to-day or distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; financial conditions listed in Table 1, we estimate the model: đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;= đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; assessments assessments are are more more sensitive sensitive to day-to-day than than distant weneed we compare compare the subjective financial assessments asto aday-to-day function of only day-to-day or distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; =đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;subjective đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; usethe the on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;significantly and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; conditions the đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; listed for each inuse Tables 1coefficients andfirst 2 and with the đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;concerns, đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; . We đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; the various day-to-day đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;of đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , reductions đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , and đ?&#x153;ˇđ?&#x153;ˇassociated reductions in financial sat đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; ; and proxy measure is the sum theWe with ips between subjective assessments and various day-to-day and distant Ě&#x201A; Ě&#x201A;each regressions produce two estimates ofđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; financial and use Ě&#x201A;estimates Ě&#x201A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;use Ě&#x201A; = jective ective financial as a to function of only day-to-day or deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; of of each set of set deficits of deficits to đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; predict tođ?&#x2018;&#x2039;đ?&#x2018;&#x2039;satisfaction, predict financial satisfaction. satisfaction.

We first run two regressions fitting fitting đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;respondentâ&#x20AC;&#x2122;s đ?&#x203A;˝đ?&#x203A;˝ and đ?&#x203A;˝đ?&#x203A;˝Ě&#x201A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; We use the coefficients onfirst đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;run andtwo đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2019;&#x201E;đ?&#x2019;&#x201E;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;regressions conditions theare signific đ?&#x203A;˝đ?&#x203A;˝Ě&#x201A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;the and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;ability đ?&#x203A;˝đ?&#x203A;˝ .distant We the coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;.đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;financial and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; conditions the đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;produce te the regressions provide two ofeach respondentâ&#x20AC;&#x2122;s financial satisfaction, and use đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; exception of provide 1) housing, where â&#x20AC;&#x153;own with a mortgageâ&#x20AC;? is used asđ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; isted inmodel: Table 1, assessments we estimate theto model: đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; ability đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;each đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; assessments đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; = đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; and đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coeffici characteristics.

The estimated đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2014; each of the individualâ&#x20AC;&#x2122;s dire day-to-day deficits.

For example, đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; â&#x2C6;&#x2014; Ě&#x201A; these estimates in a third regression: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;ž + đ?&#x153;&#x2013;đ?&#x153;&#x2013; .

In the Ě&#x201A; Ě&#x201A; = = setand subjective subjective financial financial assessments assessments as a as function a function of only of only day-to-day day-to-day or distant or distant deficits: deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; regressions provide to produce two estimates of each respondentâ&#x20AC;&#x2122;s financial satisfaction, đ?&#x203A;˝đ?&#x203A;˝ and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;˝đ?&#x203A;˝ .

We use the coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; conditions the đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? regressions provide to produce two estimates of each respondentâ&#x20AC;&#x2122;s financial satisfaction, and use +where đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;the + đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;+đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; .

(1) đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;ofđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;each đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x203A;˝đ?&#x203A;˝0 đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; these third regression: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;and = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;?đ?&#x2018;? + đ?&#x203A;žđ?&#x203A;žđ?&#x2018;?đ?&#x2018;? đ?&#x153;ˇđ?&#x153;ˇ +đ?&#x2018;Şđ?&#x2018;Şđ?&#x153;&#x2013;đ?&#x153;&#x2013;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2018;Şđ?&#x2018;Ş. +Inđ?&#x153;&#x20AC;đ?&#x153;&#x20AC;the isted 1, weestimates estimate thea model: theđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; baseline condition; 2) age income, đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; in Table đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; â&#x20AC;&#x153;adequateâ&#x20AC;? đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; in đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; ability of us de đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;= đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; each day-to-day and distant deficit and a respondent if the respondent indicated that covering day-to-day expenses is â&#x2C6;&#x2014; â&#x2C6;&#x2014; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; simplest case, the results would find only one estimate has statistically significant predictive Ě&#x201A; Ě&#x201A; these estimates in a third regression: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;ž + đ?&#x153;&#x2013;đ?&#x153;&#x2013; .

In the ressions essions provide to produce two estimates of each respondentâ&#x20AC;&#x2122;s financial satisfaction, and use đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;˝đ?&#x203A;˝ đ?&#x203A;˝đ?&#x203A;˝ and and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;˝đ?&#x203A;˝ đ?&#x203A;˝đ?&#x203A;˝ .

We .

We use use the the coefficients coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; and and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; conditions conditions the the these estimates in a third regression: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;ž + đ?&#x153;&#x2013;đ?&#x153;&#x2013; .

In the simplest case, the results would find only one estimate has statistically significant predictive subjective financial ass is đ?&#x153;ˇđ?&#x153;ˇset in the middle of the age and income distributions đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; +đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; +baseline (1) đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? đ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; .

(1) đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2018;Şđ?&#x2018;Ş â&#x20AC;&#x153;very difficultâ&#x20AC;? and their spouse ispredictive unemployed, the proxyĚ&#x201A; and meaâ&#x2C6;&#x2014;â&#x2C6;&#x2014; only one set ofof concerns has a astatistically significant relationship Ě&#x201A; Ě&#x201A; se eđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;estimates inpower, regression: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľmiddle đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; +results â&#x201E;ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; +statistically đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;provide + .significant In the regressions regressions provide tođ?&#x153;&#x2013;đ?&#x153;&#x2013;one produce to produce two two estimates estimates ofwith each ofđ?&#x203A;˝đ?&#x203A;˝significant each respondentâ&#x20AC;&#x2122;s respondentâ&#x20AC;&#x2122;s financial financial satisfaction, satisfaction, and use use simplest case, the results would find only one estimate has statistically significant predictive Ě&#x201A; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; simplest case, the would find estimate has statistically indicating only one set concerns has relationship with 35 to group and adjusted income tercile; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x203A;žđ?&#x203A;žonly đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? assessments đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; and đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + â&#x20AC;&#x201D; đ?&#x153;ˇđ?&#x153;ˇpower, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;ađ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;third +indicating đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2018;Şđ?&#x2018;Ş age + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;that .that (1) baseline assessment of those with subjective financial SFA dependent on đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;the đ?&#x2018;Şđ?&#x2018;Ş50 0 , the sure of their concern over day-to-day deficits would be the sum of with â&#x2C6;&#x2014; â&#x2C6;&#x2014; financial satisfaction.

Should both estimates be significant, we will use a t-test to determine Ě&#x201A; Ě&#x201A; Ě&#x201A; Ě&#x201A; financial satisfaction.

Should both estimates be significant, we will use a t-test to determine power, indicating that only one set of concerns has a statistically significant relationship with these these estimates estimates in a in third a third regression: regression: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľ = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľ + â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; + đ?&#x203A;žđ?&#x203A;ž đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; + đ?&#x203A;žđ?&#x203A;ž đ?&#x153;&#x2013;đ?&#x153;&#x2013; + . đ?&#x153;&#x2013;đ?&#x153;&#x2013; In . the In the plest case, the results would find only one estimate has statistically significant predictive power, indicating that only one set of concerns has a statistically significant relationship 3) baseline localdependent labor market which entered as a variđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; state đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; regression đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; well-being đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;?â&#x20AC;&#x201D;đ?&#x2018;?đ?&#x2018;? and regressions provide rela to p with no is financial â&#x20AC;&#x201D; indicating an â&#x20AC;&#x153;adequateâ&#x20AC;? of financial no (1) identifies , the assessment of those ndent on đ?&#x203A;˝đ?&#x203A;˝and estimating equation baseline assessment ofdeficits those cial assessments SFA oninđ?&#x203A;˝đ?&#x203A;˝conditions, 0whether 0 , the the difference predictive isisstatistically significant, and thus whether the set ofaofThe the two reductions in financial satisfaction associated with these whether the difference incondition. predictive power statistically significant, and thus whether the set financial satisfaction.

Should both estimates be significant, we will use t-test to determine wer, indicating that only one set of concerns has 8apower statistically significant relationship with financial satisfaction.

Should both estimates bevarious significant, we will use athese t-test to determine simplest simplest case, case, the the results results would would find find only only one one estimate estimate has has statistically statistically significant significant predictive predictive control characteristics associated with reductions in financial satisfaction; day-to-day and distant able without any baseline The model is estimated estimates in a thir uateâ&#x20AC;? of financial well-being â&#x20AC;&#x201D; and no the day-to-day and distant financial concerns icits â&#x20AC;&#x201D;state indicating an â&#x20AC;&#x153;adequateâ&#x20AC;? state of financial well-being â&#x20AC;&#x201D; and no cial assessments SFA dependent on đ?&#x203A;˝đ?&#x203A;˝ , the baseline assessment of those whole.

We then standardized this toof vary from 0 to 1,this withto1 vary represe 0 whole.

Wemeasure then standardized this measure fr concerns with predictive power can bewe toto have athat relationship with financial deficits for population as awhether whole.

We then standardized oh concerns withgreater greater predictive power can be said have astronger stronger relationship with financial whether the difference insaid predictive power isdetermine statistically significant, thus the set ncial satisfaction.

Should both estimates be significant, will use aand t-test to whether the difference inof predictive power is statistically significant, and thus whether power, power, indicating indicating that only only one one set set concerns ofthe concerns hasand has aassociated statistically ameasure statistically significant significant relationship relationship with financial deficits đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; control characteristics with reduced assessments whole. then standardized this to from 0 to 1, withwith 1sensitive aof 1vary using Ordinary Least Squares (OLS) with robust standard errors 1representing simplest case, the the resul in â&#x20AC;&#x201D; financial satisfaction; day-to-day andfinancial distant đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and We assessments are significantly more to set day-t scits associated with reductions in financial satisfaction; day-to-day and distant indicating an â&#x20AC;&#x153;adequateâ&#x20AC;? state of well-being â&#x20AC;&#x201D; and no with all four dire day-to-day deficits. with all four dire day-to-day deficits. satisfaction. measure to vary from 0 to 1, with 1 representing a household with 1 concerns with greater predictive power can be said to have a stronger relationship with financia satisfaction. concerns with greater predictive power can be said to have a stronger relationship with financial ether ther the difference in predictive power is statistically significant, and thus whether the set of financial financial financial satisfaction. satisfaction.

Should Should both both estimates estimates be significant, be significant, we will we will use use a t-test a t-test to determine to determine with all four dire day-to-day deficits. power, indicating that đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;weights , assessments đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;and , and in financial satisfaction associated with deficits and to correct for heteroskedasticity and with population to đ?&#x153;ˇđ?&#x153;ˇ correct associated with reduced assessments ability of each setthese of0deficits to predict financial scteristics associated with reductions in financial satisfaction; day-to-day and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;whole. and control characteristics associated reduced đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; ; and đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; reductions 10 We then standardized this measure to vary 0 to We then to vary from 1, with 1the representing a sati hou then standardized measure to vary from 0the todistant 1,whole. with representing apower household whole.

We then standardized this measure topower vary from 0this to measure 1, with 1significant, representing a to household satisfaction. fourstandardized dire day-to-day deficits. cerns with greaterWe predictive canthis be said to have a whether stronger relationship financial satisfaction. whether the difference difference in1with predictive inall predictive is statistically iswhole. statistically significant, and and thus thus whether whether the set of set offrom 9power financial satisfaction.

S 1 financial 1indicate subjective financial assessments as a function of onl for sampling bias. characteristics.

The estimated đ?&#x153;ˇđ?&#x153;ˇ and đ?&#x153;ˇđ?&#x153;ˇ coefficients then the relationship between đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; and control characteristics associated with reduced assessments 1 aland satisfaction associated with these deficits and 1 We then test whether subjective financial assessments follow a pecking order nd đ?&#x153;ˇđ?&#x153;ˇ reductions in financial satisfaction associated with these deficits and We then test whether subjective assessment đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; with all four dire day-to-day deficits. with allpredictive four dire day-to-day deficits. đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; with all four dire day-to-day deficits.with all four dire day-to-day deficits. sfaction. concerns concerns withwith greater greater predictive power power cansubjective can be said be said to financial have to have a stronger aassessments stronger relationship relationship with with financial financial We then test whether follow a pecking order by estim whether the difference Ě&#x201A; Ě&#x201A; Ě&#x201A; each day-to-day and distant andthen respondentâ&#x20AC;&#x2122;s overall subjective financial đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; financial and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = assessment. đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; .follow We use the coefficie đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; d đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x201E;đ?&#x2019;&#x201E; reductions insecond financial satisfaction associated these deficits and deficit fficients then indicate relationship between estimated đ?&#x153;ˇđ?&#x153;ˇThe and đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;the coefficients then indicate thewith relationship between second hypothesis asserts that financial assessments follow aaapecking order, đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; We test whether subjective assessments awith satisfaction. satisfaction.

The hypothesis asserts thatsubjective subjective financial assessments follow pecking order,with with following model: following model: đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; concerns greater p The regression estimating equation (1) identifies relationships regressions provide to produce two estimates offollo each following model: We then test whether subjective financial assessments and đ?&#x153;ˇđ?&#x153;ˇ coefficients then indicate the relationship between estimated đ?&#x153;ˇđ?&#x153;ˇ dentâ&#x20AC;&#x2122;s overall subjective financial assessment.

We then test whether subjective financial assessments follow a pecking order estimat financial satisfaction having a significantly stronger relationship with distant issues if the We then test whether subjective financial assessments follow a pecking order by estimating the distant deficit and a respondentâ&#x20AC;&#x2122;s overall subjective financial assessment.

We then test whether subjective financial assessments follow a pecking order by estimating the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; pecking orderthat by estimating the following model:follow second hypothesisstronger asserts The that subjective financial assessments follow afinancial pecking order, with financial satisfaction The having a significantly relationship with distant issues if the second hypothesis asserts subjective assessments a pecking by order, with satisfaction. between financial satisfaction and the various day-to-day and Ě&#x201A; estimates in householdâ&#x20AC;&#x2122;s day-to-day finances are To hypothesis, distant and a asserts respondentâ&#x20AC;&#x2122;s overall subjective financial đ?&#x2018;?đ?&#x2018;? we đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; these đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; regression: financial satisfaction having a significantly stronger with distant issues theâ&#x201E;ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? a third đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;if= đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; financial satisfaction having agood significantly stronger relationship with issues if = the householdâ&#x20AC;&#x2122;s finances areininaassessments areasonably reasonably goodshape.

Totest test this hypothesis, wedistant seconddeficit hypothesis that subjective financial follow afollowing pecking order, with whole.

We then standardized this assessment. measure toshape. vary from 0this to 1, with 1=đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;representing household following model: model: following model:day-to-day following model: â&#x2C6;&#x2122; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;abetween đ?&#x153;ˇđ?&#x153;ˇrelationship â&#x2C6;&#x2122;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x203A;˝đ?&#x203A;˝đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ??&#x192;đ??&#x192;satisfaction â&#x2C6;&#x2122;â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; +and đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x203A;˝đ?&#x203A;˝the đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; â&#x2C6;&#x2122;đ?&#x2018;Şđ?&#x2018;Şđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;+ + đ??&#x192;đ??&#x192;estima â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;+ đ?&#x2018;?đ?&#x2018;? distant financial concerns.

To test the first hypothesis, that sub0 + đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; simplest đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; one đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;financial đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; The regression estimating equation (1) good identifies relationships 0 + đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; construct a aproxy measure of an individualâ&#x20AC;&#x2122;s concern over day-to-day deficits.

We use 1householdâ&#x20AC;&#x2122;s case, the results would find only day-to-day finances are in a reasonably good shape.

To test this hypothesis, we (2) + đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ??&#x192;đ??&#x192; â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝ householdâ&#x20AC;&#x2122;s day-to-day finances are in a reasonably shape.

To test this hypothesis, we construct proxy measure of an individualâ&#x20AC;&#x2122;s concern over day-to-day deficits.

We use the ncial satisfaction having a with significantly stronger relationship with distant issues if the The The second second hypothesis hypothesis asserts asserts thatthat subjective financial assessments follow follow a pecking a pecking order, withwith đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; all four dire day-to-day deficits. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; 0 subjective đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2018;Şđ?&#x2018;Ş order, đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;financial đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; assessments đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; the various day-to-day and distant financial concerns.

To test the first hypothesis, that subjective jective assessments are significantly more sensitive to day-to-day reductions in subjective financial assessments associated with dire day-to-day estimated đ?&#x153;ˇđ?&#x153;ˇ indicating only one set of concerns sating relationships between financial satisfaction equation (1) identifies relationships between satisfaction and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; construct ađ?&#x2019;&#x2018;đ?&#x2019;&#x2018;and proxy measure of an individualâ&#x20AC;&#x2122;s concern over deficits.

We use seholdâ&#x20AC;&#x2122;s day-to-day finances aređ?&#x2019;&#x2018;đ?&#x2019;&#x2018; in a reasonably good shape.

To test hypothesis, we in subjective financial assessments with estimated đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2018;?đ?&#x2018;? đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; that đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; if the đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; construct aassociated proxy measure ofđ?&#x2019;&#x2018;đ?&#x2019;&#x2018;day-to-day an individualâ&#x20AC;&#x2122;s concern over day-to-day deficits.

We use the has a đ?&#x2018;?đ?&#x2018;? dire đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;day-to-day đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;power, đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; the đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; issues financial satisfaction having having significantly ađ?&#x203A;˝đ?&#x203A;˝significantly stronger stronger relationship with distant distant issues the đ?&#x2018;?đ?&#x2018;? reductions đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; financial đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;this đ?&#x2018;?đ?&#x2018;?satisfaction đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; a đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;relationship second hypothesis đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;(2) đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;â&#x2C6;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; +of + â&#x2C6;&#x2122; relationđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;ifđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; +ofđ??&#x192;đ??&#x192;changes đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;be +th đ?&#x153;ˇđ?&#x153;ˇ + đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; â&#x2C6;&#x2122;đ??&#x192;đ??&#x192;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; +đ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122;+ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;distant +đ?&#x203A;˝đ?&#x203A;˝satisfaction. đ??&#x192;đ??&#x192;0with â&#x2C6;&#x2122;of +we đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; +The đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;we â&#x2C6;&#x2122;that đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;acompare +between đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; â&#x2C6;&#x2122; that đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; +đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ??&#x192;đ??&#x192;day-to-day â&#x2C6;&#x2122; satisfaction đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; +set đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2018;Şđ?&#x2018;Şofđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;â&#x2C6;&#x2122; and + We đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;an+ + đ?&#x153;ˇđ?&#x153;ˇ +more đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122; the đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ??&#x192;đ??&#x192;difficult,â&#x20AC;? â&#x2C6;&#x2122;day-to-day đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛= (2) = đ?&#x203A;˝đ?&#x203A;˝ assessments are significantly sensitive to than concerns, compare the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;sign đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Ş than distant concerns, the ability of each defiđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Şđ?&#x2019;&#x2122;đ?&#x2019;&#x2122; 0+ đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; â&#x2C6;&#x2122;between financial Should both estimates đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;â&#x2C6;&#x2122;+ đ?&#x2018;Şđ?&#x2018;Şđ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; cerns.

To test the first hypothesis, subjective đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; is the vector changes in the relationship f In this model, deficits inin the as â&#x20AC;&#x201D; covering day-to-day expenses is â&#x20AC;&#x153;very current đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;householdâ&#x20AC;&#x2122;s 0đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Şgood y andequation financial concerns.

To test the first hypothesis, that subjective đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; ating (1) identifies relationships financial â&#x2C6;&#x2122;associated đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; is vector in In this model, đ??&#x192;đ??&#x192;đ?&#x153;ˇđ?&#x153;ˇđ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; is the vector changes inđ?&#x153;ˇđ?&#x153;ˇ the In this model, struct adistant proxy measure of individualâ&#x20AC;&#x2122;s concern over deficits. use đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reductions in subjective financial assessments associated with dire day-to-day estimated đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; reductions in subjective financial assessments with dire day-to-day estimated đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; householdâ&#x20AC;&#x2122;s day-to-day day-to-day finances finances are are a in reasonably a reasonably good shape. shape.

To test To test this this hypothesis, hypothesis, we we We then whether financial assessments follow a pecking order by estimating the deficits the0sample sample astest awhole whole â&#x20AC;&#x201D; that covering day-to-day expenses is â&#x20AC;&#x153;very difficult,â&#x20AC;? current đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; subjective đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; financial satisfaction ha â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;the is the vector of changes in regressions the relationship between financial Inisto this model,financial đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;and ability of each set of deficits predict satisfaction.

We first run two fitting đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; whether the difference in predictive power is statisti day-to-day than distant concerns, we compare the cits to predict financial satisfaction.

We first run two regressions debt burdens are â&#x20AC;&#x153;heavy,â&#x20AC;? at least one member of the household unemployed, ficantly more sensitive to day-to-day than distant concerns, we compare the y and distant financial concerns.

To test the first hypothesis, that subjective satisfaction and distant deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;day-to-day ,and as đ?&#x2018; đ?&#x2018; , the standardized measure ofstandar concer satisfaction distant deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;use , as between financial satisfaction distant deficits, in subjective financial assessments associated with mated đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; construct adeficits proxy aâ&#x20AC;&#x201D; proxy measure measure ofship an of individualâ&#x20AC;&#x2122;s an concern concern over over day-to-day deficits. deficits.

WeisWe use the theđ?&#x2018; đ?&#x2018; , the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;and deficits in the sample asconstruct a of whole that covering day-to-day expenses is â&#x20AC;&#x153;very difficult,â&#x20AC;? current debt burdens are â&#x20AC;&#x153;heavy,â&#x20AC;? at least one member the household isday-to-day unemployed, and the indire the sample as aindividualâ&#x20AC;&#x2122;s whole â&#x20AC;&#x201D; that covering day-to-day expenses â&#x20AC;&#x153;very curren đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reductions householdâ&#x20AC;&#x2122;s day-to-day Ě&#x201A; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;with satisfaction and deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;concerns đ?&#x2018; đ?&#x2018; , the standardized measure ofdifficult,â&#x20AC;? concern over dt đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;thedistant following model: đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; day-to-day 2, asor đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;be. = subjective financial assessments asof athe function of financial only day-to-day distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;of 2 đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; greater predictive power can be said satisfaction.

We first two regressions fitting respondent could not likely access need Our proxy measure is sum of the deficits to predict financial We first run two regressions fitting subjective financial assessments as aif if function of only dayficantly more sensitive than distant concerns, compare the deficits, varies from 0 to 1.

We model these changes as linear functions of â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; is the vector of changes in the rela In this model, đ??&#x192;đ??&#x192; â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; is the vector changes in the relationship between financial In this model, đ??&#x192;đ??&#x192; deficits, varies from 0 to 1.

We model these change s, standardized measure of concern over day-to-day deficits, â&#x2C6;&#x2122;satisfaction. đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; isdebt the vector ofare changes in the relationship between financial Infitting this model, đ??&#x192;đ??&#x192;tođ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; burdens â&#x20AC;&#x153;heavy,â&#x20AC;? at least one member household is unemployed, and the respondent could not likely access $2,000 need be.

Our proxy measure is the sum of the reductions reductions in subjective in subjective financial assessments assessments associated associated with with dire dire day-to-day day-to-day estimated estimated đ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; is the vector of changes in the relationship between financial In$2,000 this model, đ??&#x192;đ??&#x192;we cits in the sample as a run whole â&#x20AC;&#x201D; covering day-to-day expenses is â&#x20AC;&#x153;very difficult,â&#x20AC;? current debt burdens are â&#x20AC;&#x153;heavy,â&#x20AC;? at least one member of the household is unemployed, and the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; s đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;that đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; 2 đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; construct a proxy meas đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; Ě&#x201A; Ě&#x201A; Ě&#x201A; deficits, varies from 0 to 1.

We model these changes as linear functions of s, so đ??&#x192;đ??&#x192; i Ě&#x201A; satisfaction. Ě&#x201A; đ?&#x203A;˝đ?&#x203A;˝not and = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;= đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; We use the coefficients on and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;concern thelinear đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x153;ˇđ?&#x153;ˇasđ?&#x153;ˇđ?&#x153;ˇ with each of the day-to-day deficits.

For example, ifđ?&#x2018;&#x2039;đ?&#x2018;&#x2039; = ftssessments only day-to-day or distant đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; 11 deficits tosatisfaction predict financial satisfaction.

We first run two regressions fitting đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; d ađ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;reductions function ofassociated only day-to-day orcould distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;individualâ&#x20AC;&#x2122;s đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; to-day or distant deficits: đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; respondent could not likely access $2,000 need be.

Our proxy measure isfunctions the sum ofover thesatisf đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; of the relationship financial satisfaction and each distant satisfaction and distant deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;difficult,â&#x20AC;? ,between as the standardized respondent likely access $2,000 if be.

Our proxy measure is the sum ofconditions the satisfaction and distant đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; ,if as đ?&#x2018; đ?&#x2018; ,between the standardized measure ofđ?&#x2018; đ?&#x2018; , concern dayđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; burdens are â&#x20AC;&#x153;heavy,â&#x20AC;? at distant least one member of the household isdistant unemployed, and the reductions associated with each of the individualâ&#x20AC;&#x2122;s dire day-to-day deficits.

For example, of constants: the relationship financial from 0deficits, to 1.

We model these changes as and deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; as đ?&#x2018; đ?&#x2018; , standardized measure of concern over day-to-day deficits in in dire the sample sample as as whole whole â&#x20AC;&#x201D; that â&#x20AC;&#x201D; that covering covering day-to-day day-to-day expenses expenses is â&#x20AC;&#x153;very isday-to-day â&#x20AC;&#x153;very difficult,â&#x20AC;? current current andđ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;..aneed ,avaries as đ?&#x2018; đ?&#x2018; ,constants: standardized measure of over đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;if đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reductio estimated đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;?đ?&#x2018;?,satisfaction đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; thedeficits đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; the đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;of đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;the đ?&#x2018;?đ?&#x2018;? constants: the relationship between financial satisfaction and each distant deficit đ?&#x2018;Ľđ?&#x2018;Ľ đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; Ě&#x201A; đ?&#x2018;?đ?&#x2018;? đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; 2 đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; 2spouse provide toreductions produce two each respondentâ&#x20AC;&#x2122;s financial satisfaction, and use +be. đ?&#x153;ˇđ?&#x153;ˇđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; â&#x2C6;&#x2122; debt đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;varies + đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122; each đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + â&#x2C6;&#x2122; individualâ&#x20AC;&#x2122;s đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; +estimates đ?&#x153;ˇđ?&#x153;ˇleast đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;and + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ (2) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝ 2that 2đ??&#x192;đ??&#x192; = the day-to-day expenses is difficultâ&#x20AC;? their isthe ssessments asđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; arespondent function ofindicated only distant deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; ficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; conditions the = đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x203A;˝đ?&#x203A;˝Ě&#x201A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;could . We We use the coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;if and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;regressions conditions the use the coefficients on conditions the regresđ?&#x153;ˇđ?&#x153;ˇ associated each of the individualâ&#x20AC;&#x2122;s dire day-to-day deficits.

For example, đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;debt đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Ş pondent ondent not likely access need Our proxy measure is the sum of the đ?&#x153;ˇđ?&#x153;ˇ reductions associated with of the dire day-to-day deficits.

For example, ifWe đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; 0or modeled as the baseline reduction that deficit, đ?&#x2018;Şđ?&#x2018;Şso ,deficits when ischanges deficits, varies from 0isbaseline to 1. model these đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; deficits, varies from 0of to We model these changes as linear functions of s,equal sosample đ??&#x192;đ??&#x192;to is ađ?&#x2018;&#x2018;đ?&#x2018;&#x2018;ip burdens burdens are are â&#x20AC;&#x153;heavy,â&#x20AC;? â&#x20AC;&#x153;heavy,â&#x20AC;? at least at one one member member of the of household household isof unemployed, unemployed, and the thesin the respondent indicated that covering day-to-day expenses isâ&#x20AC;&#x153;very â&#x20AC;&#x153;very difficultâ&#x20AC;? and their isfunctions đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;covering modeled asfor the for that deficit, đ?&#x203A;˝đ?&#x203A;˝lin of s,s, so is avector vector of constants: the relationship between deficits, varies from 0 $2,000 today-to-day 1. model these changes as linear functions of đ??&#x192;đ??&#x192;with a1. deficits, from 0đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; to 1.

We model these changes asspouse linear s, so đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; isreduction ađ?&#x203A;˝đ?&#x203A;˝and vector đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;We đ?&#x2018;?đ?&#x2018;? đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;as0, the aif đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; is â&#x2C6;&#x2014;, when Ě&#x201A; Ě&#x201A; modeled as the baseline reduction that deficit, đ?&#x203A;˝đ?&#x203A;˝ s sum isofequal totheir 0, plus đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; a con đ?&#x2018;?đ?&#x2018;?đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; Ě&#x201A;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; . sions đ?&#x2018;?đ?&#x2018;?for unemployed, the proxy measure of their concern over day-to-day deficits would be the sum of these estimates in a third regression: = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;ž + đ?&#x153;&#x2013;đ?&#x153;&#x2013; .

In the đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; financial satisfaction, and use đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;produce We use the coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; and đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; conditions the đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? oeach two estimates of each respondentâ&#x20AC;&#x2122;s financial satisfaction, and use provide to produce two estimates of each respondentâ&#x20AC;&#x2122;s reductions associated with each of the individualâ&#x20AC;&#x2122;s dire day-to-day deficits.

For example, if the respondent indicated that covering day-to-day expenses is â&#x20AC;&#x153;very difficultâ&#x20AC;? and spouse respondent respondent could could not not likely likely access access $2,000 $2,000 if need if need be. be.

Our Our proxy proxy measure measure is the is the sum the of the the respondent indicated that covering day-to-day expenses is â&#x20AC;&#x153;very difficultâ&#x20AC;? and their spouse is unemployed, the proxy measure of their concern over day-to-day deficits would be the sum of đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x203A;˝đ?&#x203A;˝respondentâ&#x20AC;&#x2122;s đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; of constants: the between satisfaction constants: the relationship between financial satisfaction and distant deficit đ?&#x2018;Ľđ?&#x2018;Ľcoeff đ?&#x153;&#x2030;đ?&#x153;&#x2030;financial times asisđ?&#x2018; đ?&#x2018; varies from 0 to 1.đ?&#x2018; đ?&#x2018; ,relationship To extent that the estimated đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;â&#x20AC;&#x153;heav financial satisfaction and distant deficit is each of constants: the relationship between satisfaction andofeach deficit đ?&#x2018;Ľđ?&#x2018;Ľđ?&#x2018; đ?&#x2018; , offinancial constants: the relationship between satisfaction and each distant deficit đ?&#x2018;Ľđ?&#x2018;Ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; is đ?&#x153;&#x2030;đ?&#x153;&#x2030;each times as đ?&#x2018; đ?&#x2018; the varies from 0modeled tofinancial 1.

To extent thai đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; debt burdens are đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; asthe đ?&#x2018;?đ?&#x2018;? distant đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; â&#x2C6;&#x2014;financial Ě&#x201A; The second hypothesis asserts thatexample, subjective đ?&#x2018;?đ?&#x2018;? financa â&#x2C6;&#x2014; đ?&#x2018;?đ?&#x2018;? thewith đ?&#x2018;?đ?&#x2018;? that the two reductions inđ?&#x153;&#x2013;đ?&#x153;&#x2013;in satisfaction associated these deficits for the population as a times đ?&#x2018; đ?&#x2018; , as đ?&#x2018; đ?&#x2018; varies from 0 to 1.

To the extent estimated đ??&#x192;đ??&#x192; coefficients đ?&#x153;&#x2030;đ?&#x153;&#x2030; Ě&#x201A; regression: Ě&#x201A; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; satisfaction đ?&#x2018;?đ?&#x2018;? Ě&#x201A; simplest case, results would find only one estimate has statistically significant predictive đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; â&#x201E;ľthe đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;ž + .

In the ođ?&#x2018;&#x2018;đ?&#x2018;&#x2018;hird produce two estimates of each respondentâ&#x20AC;&#x2122;s financial satisfaction, and use đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; unemployed, the proxy measure of their concern over day-to-day deficits would be the sum of the two reductions financial associated with these deficits for the population as a đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;ž + đ?&#x153;&#x2013;đ?&#x153;&#x2013; .

In the financial satisfaction, and use these estimates in a third regresđ?&#x153;ˇđ?&#x153;ˇ đ?&#x153;ˇđ?&#x153;ˇ reductions reductions associated associated with with each each of the of the individualâ&#x20AC;&#x2122;s individualâ&#x20AC;&#x2122;s dire dire day-to-day day-to-day deficits. deficits.

For For example, if if đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; respondent indicated that covering day-to-day expenses is â&#x20AC;&#x153;very difficultâ&#x20AC;? and their spouse is unemployed, the proxy measure of their concern over day-to-day deficits would be the sum of đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; +modeled đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? modeled as the baseline forequal that deficit, đ?&#x203A;˝đ?&#x203A;˝significa , whe asthe theplus baseline for that deficit, đ?&#x203A;˝đ?&#x203A;˝ sand to a const than zero and statistically economically significant, the results would belic đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;In this đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;reduction đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;?as đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; baseline reduction for that deficit, ,awhen when s isis equal to0, plus đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; isđ?&#x2018;?đ?&#x2018;? the vector of changes in the relationship between financial model, as the baseline forâ&#x2C6;&#x2122;modeled that đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; , when s modeled is equal to 0, a constant baseline reduction for that deficit, đ?&#x203A;˝đ?&#x203A;˝reduction zero and economically when sthan isand equal to 0, statistically plus constant could not đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; deficit, đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;reduction đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; , and financial satisfaction having arespondent significantly stronger â&#x2C6;&#x2014; and statistically economically significant, the results would be consisten đ?&#x2018;?đ?&#x2018;? expenses indicating that only one set of concerns has a statistically significant relationship with Ě&#x201A; Ě&#x201A; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; population đ?&#x2018;?đ?&#x2018;? than đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;zero đ?&#x2018;?đ?&#x2018;?find the đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;â&#x20AC;&#x153;very đ?&#x2018;?đ?&#x2018;? power, the two reductions financial satisfaction associated with these deficits for the population as adeficits stimate has statistically significant predictive hird đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = â&#x201E;ľ đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; + â&#x201E;ľconcern đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; +the đ?&#x2018;&#x2039;đ?&#x2018;&#x2039; đ?&#x203A;žđ?&#x203A;žin + đ?&#x153;&#x2013;đ?&#x153;&#x2013;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; In mployed, the proxy measure of đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; their over day-to-day deficits would be the sum of two reductions in financial satisfaction associated with these for the as ade .đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; In the simplest ults regression: would only one estimate has statistically significant predictive the the respondent respondent indicated indicated that that covering covering day-to-day day-to-day expenses is isvaries â&#x20AC;&#x153;very difficultâ&#x20AC;? difficultâ&#x20AC;? and and their their spouse spouse istothat isassocia notion that subjective financial assessments become less sensitive distant đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;?extent đ?&#x2018;?đ?&#x2018;? đ?&#x2018; đ?&#x2018; , notion that subjective financial assessments become đ?&#x153;&#x2030;đ?&#x153;&#x2030; times đ?&#x2018; đ?&#x2018; , as đ?&#x2018; đ?&#x2018; from 0 to 1.

To the extent the 0, plus a constant times s, as s varies from 0 to 1.

To the satisfaction and distant deficits, , as đ?&#x2018; đ?&#x2018; , the standardized measure of concern over day-to-day đ?&#x153;ˇđ?&#x153;ˇ reductions times đ?&#x2018; đ?&#x2018; , as đ?&#x2018; đ?&#x2018; varies from 0 to 1.

To the extent that the estimated đ??&#x192;đ??&#x192; coefficients are đ?&#x153;&#x2030;đ?&#x153;&#x2030; đ?&#x153;&#x2030;đ?&#x153;&#x2030;sion: times đ?&#x2018; đ?&#x2018; , as đ?&#x2018; đ?&#x2018; varies from 0 to 1.

To that the estimated đ??&#x192;đ??&#x192; coefficients are greater đ?&#x153;&#x2030;đ?&#x153;&#x2030; times as varies from 0 to 1.

To the extent that the estimated đ??&#x192;đ??&#x192; coefficients are greater đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;distant đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; that householdâ&#x20AC;&#x2122;s finances to are in a reasonably đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; notion subjective financial assessments less sensitive deficits as as financial satisfaction.

Should both estimates be significant, we will usebecome aday-to-day t-test to determine đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; day-to-day has awould statistically significant relationship with two reductions in financial satisfaction associated with deficits for the population as a 2 these t only one set of concerns has a statistically significant relationship with ults find only one estimate has statistically significant predictive unemployed, unemployed, the the proxy proxy measure measure of their of their concern concern over over day-to-day deficits deficits would would be the be the sum sum of of over day-to-day deficits rise â&#x20AC;&#x201D; and more sensitive as concern over day-to-da case, the results would find only one estimate has statistically over day-to-day deficits rise â&#x20AC;&#x201D; and more sensitive than zero and and economically significant, thew than zero and statistically and economically significant, the results would be consistent extent that thewith estimated coefficients are greater than zero deficits, and varies from 0 tozero 1.significant, Westatistically model changes as linear functions of s, so đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;more is astatistically vector the respondent indicate than zero and statistically economically thethese results would bepower consistent the than and and economically significant, results would be consistent with the construct a proxy measure of an individualâ&#x20AC;&#x2122;s concern over day-to-day deficits rise â&#x20AC;&#x201D; and sensitive as concern over day-to-day deficits whether the difference in predictive is statistically significant, and thus whether the set of will use a has t-test to determine Should both estimates be significant, we will use that athat t-test to two determine the the two reductions reductions insatisfaction financial financial satisfaction satisfaction associated associated with these these deficits deficits forsensitive for the the population population as adeficits as the a less t. significant, only onenotion setwe of concerns afinancial statistically significant relationship with significant predictive power, indicating only one set of notion notion that subjective financial assessments become se that subjective financial assessments less to distant as co unemployed, proxy that subjective assessments become less sensitive toin distant deficits as concern and statistically and economically significant, the results would of constants: the relationship between financial and each distant deficit đ?&#x2018;Ľđ?&#x2018;Ľwith isbecome notion subjective financial assessments become less sensitive to distant as concern đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; reductions infinancial subjective financial asse estimated đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;deficits concerns with greater predictive can be said to have aThe stronger relationship with atistically significant, thus whether the setmore of đ?&#x2018;?đ?&#x2018;? power eShould in predictive powerand is be statistically significant, and thus whether the set of both estimates significant, we will use a t-test to determine The final hypothesis asserts that financial literacy increases sensitivity to fina final hypothesis asserts that financial literacy inc over day-to-day deficits rise â&#x20AC;&#x201D; and more sensitive as conce over day-to-day deficits rise â&#x20AC;&#x201D; and more sensitive as concern over day-to-day deficits dfi the two reductions in over day-to-day deficits rise â&#x20AC;&#x201D; and sensitive as concern over day-to-day deficits decline. over day-to-day deficits rise â&#x20AC;&#x201D; đ?&#x203A;˝đ?&#x203A;˝ and sensitive astoconcern over day-to-day deficits be consistent with the notion that subjective financial assessments modeled as the baseline reduction for that deficit, ,more when s is equal 0, plus a constant deficits in the sample as decline. a whole â&#x20AC;&#x201D; that coveringdef da final hypothesis asserts that financial literacy increases sensitivity to financial have apower stronger relationship with raid canis bestatistically said a financial strongersatisfaction. relationship withthe financial epredictive intopredictive power significant, and thus whether set ofTheđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;?đ?&#x2018;? to have especially deficits.

To test the follow đ?&#x153;&#x2030;đ?&#x153;&#x2030;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;to times đ?&#x2018; đ?&#x2018; ,aas đ?&#x2018; đ?&#x2018; variesrelationship from 0 to 1.with Tofinancial the extent that the estimatedtođ??&#x192;đ??&#x192;distant greater especially to this distant deficits.atwe To estimates test this hypothesis, debtare burdens arehypothesis, â&#x20AC;&#x153;heavy,â&#x20AC;? least one member of the 8 See đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients predictive power can beHira, said have Mugenda, and Fanslow (1990) andfinal Xiao, Chen, and Chen (2013) especially to literacy distant deficits.

To testhypothesis this hypothesis, estimates following mode Theconsistent final assertswe that financial literacy increases The final asserts that financial literacy increases sensitivity to financial defici The final hypothesis asserts thatstronger financial literacy increases sensitivity tohypothesis financial deficits, and The hypothesis asserts that financial increases sensitivity to financial deficits, and the 10 than zero and statistically and economically significant, the results would be with the respondent could not likely access $2,000 if need be The study tested more complex models but could not estimate many cofor studies that find financial literacy associated with reduced subjective đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;pecking notion that subjective financial become less sensitive to distant deficits as concern â&#x20AC;˛â&#x20AC;˛â&#x20AC;˛ es efficients due to colinearity.

These more complicated tests also provided the financial assessments.

For other characteristics see Hsieh (2001) and Joo and đ?&#x153;ˇđ?&#x153;ˇ reductions associated with each of the individua especially to distant deficits.

To test this hypothesis, we especially to distant deficits.

To test this hypothesis, we estimates the following model: especially to distant deficits.

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+ đ??&#x192;đ??&#x192; đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; = đ?&#x203A;˝đ?&#x203A;˝ đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;+ đ?&#x2018;Şđ?&#x2018;Ş +đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; .+ đ??&#x192;đ??&#x192; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ??&#x192;đ??&#x192;+ đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; â&#x20AC;˛â&#x20AC;˛â&#x20AC;˛ same simple Grable (2004). +day-to-day đ?&#x153;ˇđ?&#x153;ˇsignificant đ??&#x192;đ??&#x192;estimates +asđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x153;ˇđ?&#x153;ˇthis + đ??&#x192;đ??&#x192;model. + đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x153;ˇđ?&#x153;ˇcovering . đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; (3) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; statistically = over đ?&#x203A;˝đ?&#x203A;˝0stronger over day-to-day deficits rise â&#x20AC;&#x201D;satisfaction and more sensitive concern decline. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + deficits đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; theđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; respondent that ex financial having a as significantly with distant issues the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;relationship đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;indicated đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;if đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2018;Şđ?&#x2018;Ş + đ?&#x153;&#x20AC;đ?&#x153;&#x20AC; day-to-day nancial assessments follow a pecking order, with 9 Thesubjective 11The coefficients is asserts that financial assessments follow a pecking other models in this study were also estimated with OLS,order, robustwith stancarry the superscript â&#x20AC;&#x153;pâ&#x20AC;? as they will differ from the coeffiđ?&#x2019;&#x2021;đ?&#x2019;&#x2021; proxy đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; we of their đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; ove đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; this đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; test đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C;reasonably đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; unemployed, the measure concern đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; with đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; are đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; goodđ?&#x2019;&#x2021;đ?&#x2019;&#x2021; shape. đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2018;&#x201C;đ?&#x2018;&#x201C; householdâ&#x20AC;&#x2122;s day-to-day finances in a To hypothesis, â&#x20AC;˛â&#x20AC;˛â&#x20AC;˛ â&#x20AC;˛â&#x20AC;˛â&#x20AC;˛ â&#x20AC;˛â&#x20AC;˛â&#x20AC;˛ nger relationship with distant issues if the a that significantly stronger relationship with distant issues if the shaving asserts subjective financial assessments follow a pecking order, dard errors, model (1). đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2021;đ?&#x2019;&#x2021;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; 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đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;Şđ?&#x2018;Ş final hypothesis financial literacy sensitivity tođ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; deficits, are vectors marginal changes in the relationship In this model, đ?&#x153;ˇđ?&#x153;ˇ and đ?&#x153;ˇđ?&#x153;ˇuse are of marginal ch In this đ?&#x153;ˇđ?&#x153;ˇofđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; the twomodel, reductions in satisfaction associate đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; concern đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;financial a proxy measure of an individualâ&#x20AC;&#x2122;s over day-to-day deficits.

Wefinancial the vectors đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; in ably good shape. test this hypothesis, wewith ay finances are in To a reasonably good shape.

To construct test thisissues hypothesis, we having a significantly stronger relationship distant if the andform đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reserved. are vectors of marginal changes the relationship betwee In this model, đ?&#x153;ˇđ?&#x153;ˇin Š2015, IARFC. All rights of reproduction any đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reductions in subjective financial assessments associated with dire day-to-day estimated đ?&#x153;ˇđ?&#x153;ˇ ncern over deficits.

We use the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; asure of anday-to-day individualâ&#x20AC;&#x2122;s concern over day-to-day deficits. use ay finances are in a reasonably good shape.

To deficits. test this hypothesis, we especially to distant ToWe test thisthe hypothesis, wesubjective estimates theđ?&#x2019;&#x2021;đ?&#x2019;&#x2021;followingand model: đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;assessments đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; assessments day-to-day and đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;distant deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and distant đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;, fordef đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; subjective day-to-day đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x153;ˇđ?&#x153;ˇdeficits, areand vectors ofđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;marginal changes Inday-to-day this đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; are vectors of marginal changes in relationship between In whole this model, đ?&#x153;ˇđ?&#x153;ˇmarginal and đ?&#x153;ˇđ?&#x153;ˇ are vectors of marginal the relationship between In individualâ&#x20AC;&#x2122;s this model, đ?&#x153;ˇđ?&#x153;ˇconcern and vectors of changes in model, the relationship between in the sample as â&#x20AC;&#x201D; that covering day-to-day expenses isand â&#x20AC;&#x153;very In deficits this model, đ?&#x153;ˇđ?&#x153;ˇchanges subjective assessments and and distant đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;the and assessments associated with dire day-to-day asure ofsubjective an over day-to-day deficits.

We use theđ?&#x153;ˇđ?&#x153;ˇin đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;difficult,â&#x20AC;? ions in financial associated with dire day-to-day đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; assessments đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; current đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , for financiall đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; aare

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đ?&#x2018; , the standardized measure of concern over day-to-day đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; satisfaction satisfaction and and distant distant deficits, đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; , ,asin asđ?&#x2018; đ?&#x2018; ,the đ?&#x2018; đ?&#x2018; ,the the standardized standardized measure measure ofofconcern concern over overday-to-day day-to-day đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; deficits,deficits, varies from 0 to 1.2 0đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;We model these changes as functions of over s, so day-to-day đ??&#x192;đ??&#x192;of issoso a đ??&#x192;đ??&#x192;vector satisfaction and ,1. as the standardized measure of concern deficits, varies varies from from 0đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; to 1.2 2đ?&#x2018; đ?&#x2018; ,We We model thesechanges changes as linear linear functions of s, s, đ??&#x192;đ??&#x192; vector đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;?đ?&#x2018;?distant đ?&#x2018;?đ?&#x2018;?model đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; deficits, đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;to đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; these đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; as đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;functions đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; linear đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;isisaavector đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;+ đ?&#x153;ˇđ?&#x153;ˇ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;(2) +14, đ?&#x153;ˇđ?&#x153;ˇrelationship â&#x2C6;&#x2122; 1. đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;2đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;2We + đ?&#x153;ˇđ?&#x153;ˇbetween â&#x2C6;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; + đ?&#x153;ˇđ?&#x153;ˇ â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; â&#x2C6;&#x2122; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;as + đ?&#x153;ˇđ?&#x153;ˇ+đ?&#x2018;Şđ?&#x2018;Ş đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x153;ˇđ?&#x153;ˇđ?&#x2018;Şđ?&#x2018;Şđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;and + â&#x2C6;&#x2122; đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;each đ??&#x192;đ??&#x192;of â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; + đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x153;&#x20AC;đ?&#x153;&#x20AC;â&#x20AC;˛â&#x20AC;˛ (2) đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝from đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; = đ?&#x203A;˝đ?&#x203A;˝0đ??&#x192;đ??&#x192;+ Volume Issue đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; 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isđ?&#x2018;Ľđ?&#x2018;Ľa đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? modeledmodeled as the baseline reduction for that deficit, đ?&#x203A;˝đ?&#x203A;˝ , when s is equal to 0, plus a constant of constants: the relationship between financial satisfaction and each distant deficit đ?&#x2018;Ľđ?&#x2018;Ľ is modeled asasthe thebaseline baselinereduction reduction for forthat that deficit, deficit, đ?&#x203A;˝đ?&#x203A;˝đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; whens sisisequal equaltotođ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; 0,0,plus plusaaconstant constant đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;, ,when đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;?đ?&#x2018;?đ??&#x192;đ??&#x192; đ?&#x2018;?đ?&#x2018;? as â&#x2C6;&#x2122; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;varies isas the vector of changes â&#x2C6;&#x2122;1.đ?&#x2018; đ?&#x2018; đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; inTo isthat the vector between changes in the between financial nmodeled this model, In this0 model, đ??&#x192;đ??&#x192;to asđ?&#x153;&#x2030;đ?&#x153;&#x2030;the baseline reduction for deficit, đ?&#x203A;˝đ?&#x203A;˝the ,relationship when sestimated is of equal to 0,financial plus ađ?&#x2019;&#x2018;đ?&#x2019;&#x2018;relationship constant times đ?&#x2018; đ?&#x2018; , from to 1.that To the extent the đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients are greater đ?&#x153;&#x2030;đ?&#x153;&#x2030;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; times times đ?&#x2018; đ?&#x2018; , đ?&#x2018; đ?&#x2018; , as đ?&#x2018; đ?&#x2018; đ?&#x2018; đ?&#x2018; varies varies from from 0 0 to 1.

To the the extent extent that that the the estimated estimated đ??&#x192;đ??&#x192; đ??&#x192;đ??&#x192; coefficients coefficients are aregreater greater đ?&#x153;&#x2030;đ?&#x153;&#x2030; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; đ?&#x2018;?đ?&#x2018;? atisfaction distant deficits, satisfaction đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; , as đ?&#x2018; đ?&#x2018; , and the distant standardized deficits, measure đ?&#x2019;&#x2122;đ?&#x2019;&#x2122; , as đ?&#x2018; đ?&#x2018; , of the concern standardized over day-to-day measure of concern over day-to-day and statistically and economically significant, the results would be consistent with the timeszero đ?&#x2018; đ?&#x2018; ,and as đ?&#x2018; đ?&#x2018; varies from 0 to 1.

To the extent that the estimated đ??&#x192;đ??&#x192; coefficients are greater đ?&#x153;&#x2030;đ?&#x153;&#x2030;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;than đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; than thanzero zeroand andstatistically statistically and andeconomically economicallysignificant, significant, the the resultswould would be beconsistent consistentwith withthe the đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;results đ?&#x2019;&#x2018;đ?&#x2019;&#x2018;The second limitation is due đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; to the fact that the objective meabecome less sensitive toassessments distant deficits as 2 2concern over day-tothat subjective financial become less sensitive to distant deficits as concern eficits, varies from 0 to 1. deficits, We model varies these from changes 0 to 1. as linear We model functions these of changes s, so đ??&#x192;đ??&#x192; as is linear a vector functions of s, so hannotion zero and statistically and economically significant, the results would be consistent with the notion notionthat thatsubjective subjectivefinancial financialassessments assessmentsbecome becomeless lesssensitive sensitivetotodistant distant deficits deficitsasasconcern concern đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; is a vector đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; day deficits rise â&#x20AC;&#x201D; and more sensitive as concern over day-to-day sures in the dataset are imprecise. overthat day-to-day deficits rise â&#x20AC;&#x201D; and more sensitive asbetween concern over day-to-day deficits decline. notion subjective financial assessments become sensitive todistant distant deficits as and concern f constants: the of between constants: financial the relationship satisfaction and as each financial satisfaction deficit đ?&#x2018;Ľđ?&#x2018;Ľ is deficits each distant deficit đ?&#x2018;Ľđ?&#x2018;Ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; is The data do not allow us to over overrelationship day-to-day day-to-day deficits deficits rise rise â&#x20AC;&#x201D; â&#x20AC;&#x201D;and and more moreless sensitive sensitive asconcern concern over over day-to-day day-to-day deficits decline. decline. đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;? đ?&#x2018;?đ?&#x2018;? assess the adequacy of the householdâ&#x20AC;&#x2122;s employment, medical and deficits decline. over day-to-day deficitsreduction risemodeled â&#x20AC;&#x201D; and sensitive as day-to-day deficits modeled as the baseline formore as that thedeficit, baseline đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; reduction that deficit, đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; , concern when s for isover equal to 0, plus a ,constant whendecline. s is equal to 0, plus a constant đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; andtothe đ?&#x2019;&#x2018;đ?&#x2019;&#x2018; and distant đ?&#x2018;?đ?&#x2018;? The final hypothesis đ?&#x2018;?đ?&#x2018;? that financial life insurance asserts literacy increases sensitivity financial deficits, and To identify relationships between subjective assessments various day-to-day The The final final hypothesis hypothesis asserts asserts that that financial financial literacy literacy increases increases sensitivity sensitivity to to financial financial deficits, deficits, and and accumulation of home equity, or college times đ?&#x2018; đ?&#x2018; , as đ?&#x2018; đ?&#x2018; varies from 0 to times 1.

To đ?&#x2018; đ?&#x2018; , the as đ?&#x2018; đ?&#x2018; extent varies that from the 0 estimated to 1.

To the đ??&#x192;đ??&#x192; extent coefficients that the are estimated greater đ??&#x192;đ??&#x192;coverage, đ?&#x153;&#x2030;đ?&#x153;&#x2030; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; asserts that financial literacy increases đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; senđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients are greater The final hypothesis and retirement savings.

The regressions The final hypothesis asserts that financial literacy increases sensitivity to financial deficits, and han especially zero andespecially statistically and than economically zero andin statistically significant, and the economically results would significant, befollowing consistent the with results the would be consistent with the estimate differences to distant deficits.

To test this hypothesis, we estimates the model: financial conditions listed Table 1, we estimate the model: especially toto distant distant deficits. deficits.

To To test testthis this hypothesis, hypothesis, we we estimates estimates thefollowing following model: model: sitivity to financial deficits, and especially to distant deficits.

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đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ??&#x192;đ??&#x192; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x201E;đ?&#x2019;&#x201E; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; andfinancially đ??&#x192;đ??&#x192;The vectors ofđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; marginal changes in to these relationships iterate individuals; đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;for relationships literate individuals relative non-litcharacteristics. estimated andđ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; then indicate the for relationship between đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; are đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients financially literate individuals relative to non-literate The regression results and đ?&#x153;ˇđ?&#x153;ˇ vectors of marginal and to changes đ?&#x153;ˇđ?&#x153;ˇtođ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; areindividuals. vectors inbethe relationship of marginal between changes in thewould relationship between equation (1) are presented in n this model, đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; In The this model, đ?&#x153;ˇđ?&#x153;ˇrelative financially financially literate literate individuals individuals non-literate individuals. individuals.

The TheThe regression regression results results would would đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; are đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;results erate individuals. regression would consistent regression results estimating each day-to-day and distant relative deficit and anon-literate respondentâ&#x20AC;&#x2122;s overall subjective financial assessment. financially literatewith individuals relativethat to non-literate individuals.

The regression results would be consistent the hypothesis financially literate individuals are more sensitive to with the hypothesis that financially literate individuals more Table 3.sensitive They day-to-daynondeficits are associated with large bebeconsistent consistent with the the hypothesis hypothesis that that financially financially literate individuals individuals are are more more sensitive ubjective assessments andwith day-to-day subjective assessments and distant deficits, and day-to-day đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;literate and and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;are for financially deficits, nonđ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and đ?&#x2019;&#x2122;đ?&#x2019;&#x2122;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;show ,toto for financially đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; ,distant be consistent with the that financially literate individuals more sensitive to sensitive to hypothesis deficits, and especially distant deficits, tođ?&#x2019;&#x2021;đ?&#x2019;&#x2021;theare extent reductions in subjective financial assessments and distant deficits đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; coefficients, and especially the deficits,deficits, and especially distant deficits, to theđ?&#x2019;&#x2021;đ?&#x2019;&#x2021;extent that the that đ??&#x192;đ??&#x192;that đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;andespecially đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021;extent coefficients, coefficients, and andespecially the theand deficits, especially distant distant deficits, deficits, toand to the the the theđ??&#x192;đ??&#x192;ofđ??&#x192;đ??&#x192;relationships đ??&#x192;đ??&#x192;estimating vectors of marginal changes đ??&#x192;đ??&#x192;extent vectors inđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;these marginal changes for inespecially these relationships for terate individuals; đ??&#x192;đ??&#x192;and literate đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; The (1) identifies between financial satisfaction coefficients, andequation especially the coefficients, are that the regression with much milder reductions. đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; relationships đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; areindividuals; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; are coefficients, and especially the deficits, and especially distant deficits, to the extent that the đ??&#x192;đ??&#x192; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; the various day-to-day and distant financial concerns.

To test the first hypothesis, that subjective statistically and economically significant. significant. đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ??&#x192;đ??&#x192; coefficients, are statistically and economically đ??&#x192;đ??&#x192;đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients, coefficients, are are statistically statistically and and economically economically significant. significant. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; nancially literate individuals financially relative toliterate non-literate individuals individuals. relative The to non-literate regression individuals. results wouldin The regression results would assessments are significantly more sensitive to day-to-day than distant we compare the đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; Asconcerns, shown Table 3, significant difficulty in covering daily đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients, are statistically and economically significant. ability each set of deficits to predict financial satisfaction.

We firstexpenses run It is important tobenote various limitations to individuals the methodology outis associated with a 2.1 e consistent with the of hypothesis consistent that financially with theliterate hypothesis that financially are more literate sensitive individuals totwo regressions are morefitting sensitive to point reduction and heavy curĚ&#x201A; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; subjective financial a function financial of only day-to-day deficits: đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; lined above. First, the assessments reductions inassubjective assess- or distant rent debt a 1.2 point reduction. Unemployment, in đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; = đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; burdens with coefficients, and especially the coefficients, eficits, andments especially distant deficits, deficits, and to especially the extent distant that the deficits, đ??&#x192;đ??&#x192; to the extent that the đ??&#x192;đ??&#x192; Ě&#x201A; Ě&#x201A; Ě&#x201A; đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; . We use coefficients on đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; the it and đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; to making with financial arethe reductions relative toand đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;addition moreespecially difficult the to cover daily expenses and đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;associated đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; and đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; = đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x203A;˝đ?&#x203A;˝đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;deficits đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; conditions regressions baseline provide tostate. produce tworeductions estimates of each respondentâ&#x20AC;&#x2122;s financial satisfaction, and use reduces subjective assessments by đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; đ?&#x2019;&#x2021;đ?&#x2019;&#x2021; an â&#x20AC;&#x153;adequateâ&#x20AC;?

These include both 1) meet current debt payments, are statistically đ??&#x192;đ??&#x192;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients, and economically are statistically significant.andĚ&#x201A; economicallyĚ&#x201A;significant. đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; coefficients, these estimates in a third â&#x201E;ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; + â&#x201E;ľđ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; + additional đ?&#x2018;&#x2039;đ?&#x2018;&#x2039;đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x203A;žđ?&#x203A;žđ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;? + đ?&#x153;&#x2013;đ?&#x153;&#x2013; â&#x2C6;&#x2014;â&#x2C6;&#x2014;. 0.5 In the reductions in satisfaction byregression: those whođ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; have=the deficit; 2) points. And a lack of secure access to $2,000 is đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; and đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018; đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;đ?&#x2018;&#x2018;an simplest case, the results would find only one estimate has statistically significant predictive 1 associated with a 0.7more point reduction.12 (As financial satisfaction increases in satisfaction by those who address the issue and are in 1 1 tested more complex models but could not estimate many coefficients due to colinearity. These more The study The Thestudy studytested testedmore morecomplex complexmodels modelsbut butcould couldnot notestimate estimatemany manycoefficients coefficientsdue duetotocolinearity. colinearity.These Thesemore power, indicating that only one set of concerns has aestimates statistically significant relationship with complicated tests also provided the same statistically significant estimates as this model. an â&#x20AC;&#x153;adequateâ&#x20AC;? state.

The relative tosignificant the baseline state is measured complicated complicated tests tests also also provided provided the thesame same statistically significant estimates asthis this simple simple model. model.

The study tested more complex models butreduction could not statistically estimate many coefficients duesimple toascolinearity.

These more on a scale from 1 to 10, each 1 point reduction is 2 2 2 financial The coefficients carry the superscript â&#x20AC;&#x153;pâ&#x20AC;? as they will differ from the inwe model (1). satisfaction.

Should both estimates be coefficients significant, willin a t-test The The coefficients coefficients carry the theof superscript superscript â&#x20AC;&#x153;pâ&#x20AC;? â&#x20AC;&#x153;pâ&#x20AC;? asasthey they will will differ differ from the the coefficients coefficients inuse model model (1).

(1). to complicated tests also provided the same statistically significant estimates asfrom thisincludes simple model. associated with acarry lack college saving, for example, equivalent to determine an 11 percentage point reduction in subjective difference predictive iscoefficients statistically significant, and thus assessments.) whether the set of The coefficients carry the the superscript asinthey willamong differpower from the model (1). financial bothwhether the reduction in â&#x20AC;&#x153;pâ&#x20AC;? satisfaction those not savinginand concerns with greater predictive power can be said to have a stronger relationship with financial the increase in satisfaction among those who are. To the extent satisfaction.

Among distant concerns, the only deficit associated with more 1 the reductions thenot study are many due to anbut increase The study tested more complexidentified models The study butin could tested more estimate complex models coefficients could duenot toinestimate colinearity. many These coefficients more due to colinearity. These more than a half-point omplicated tests also provided thecomplicated sameabove statistically tests significant provided estimates the sameoverstate asstatistically this simple significant model. estimates as this simpledecline model. is the 0.7 point reduction associated with financial satisfaction an also initial state, they the 2 The coefficients carry the superscript Thegenerates â&#x20AC;&#x153;pâ&#x20AC;? coefficients as they will carry differ the superscript from the coefficients â&#x20AC;&#x153;pâ&#x20AC;? as they inwill model differ (1).fromathe coefficients in model (1). This â&#x20AC;&#x153;reduction,â&#x20AC;? however, could be lack of college saving. motivation a deficit to address a particular issue.

Such The second hypothesis asserts that subjective financial assessments follow a pecking order, with apparent than real. The 0.3 point reduction associated with overstatements are plausibly greater for distant deficits, whose withmore financial satisfaction having a significantly stronger relationship distant issues if the â&#x20AC;&#x153;no need to save,â&#x20AC;? for existence and impact are more difficult to see. householdâ&#x20AC;&#x2122;s day-to-day finances are in a reasonably good shape. To test this hypothesis, wehouseholds with no financially dependent children, suggests that construct a proxy measure of an individualâ&#x20AC;&#x2122;s concern over day-to-day deficits. We use the saving for college is associated with a 0.4 Twoestimated limitations on the accuracy of the results should also be point increase in satisfaction above an initial state, indicated by đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reductions in subjective financial assessments associated with dire day-to-day noted.

The first is due to the fact that three indicators of the theislevel satisfaction of those with â&#x20AC;&#x153;no need to save,â&#x20AC;? which deficits in the sample as a whole â&#x20AC;&#x201D; that covering day-to-day expenses â&#x20AC;&#x153;veryofdifficult,â&#x20AC;? current householdâ&#x20AC;&#x2122;s financial condition are subjective â&#x20AC;&#x201D;of thetherespondentâ&#x20AC;&#x2122;s raises the baseline debt burdens are â&#x20AC;&#x153;heavy,â&#x20AC;? at least one member household is unemployed, and the constant. Not saving for college would thus be subjective sense of difficulty covering every-day expenses, in measure associated a much respondent could not likelyinaccess $2,000 if need be.

Our proxy is the with sum of the milder reduction in the subjective finanahaving much current debt, andeach being to repay student cialdeficits. assessments of households with dependent children. đ?&#x153;ˇđ?&#x153;ˇđ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; reductions associated with of able the individualâ&#x20AC;&#x2122;s dire day-to-day For example, if đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026;đ?&#x2019;&#x2026; too debt. createsindicated potentialthat measurement error, as respondents theThis respondent covering day-to-day expenses is â&#x20AC;&#x153;very difficultâ&#x20AC;? and their spouse is The only other distant associated with a statistically withunemployed, the same objective level of financial could have the proxy measure of theirdifficulty concern over day-to-day deficits would be the sum deficits of significant reduction in subjective assessments greater than 0.2 the two reductions in financial satisfaction associated with these deficits for the population as a different subjective assessments. More troublesome, this reliance are renting (-0.4), a lack of medical insurance (-0.3), and having on subjective indicators could bias the results. This would be an inactive retirement plan (â&#x20AC;&#x201C;0.4).

The relationship between the case if objectively similar respondents have different disposifinancial satisfaction and all other distant deficits â&#x20AC;&#x201D; a lack of life tions; and those with â&#x20AC;&#x153;gloomyâ&#x20AC;? dispositions indicate greater difinsurance, no retirement plan, home ownership with a mortgage ficulty with these issues and less satisfaction with their finances; greater than the value of oneâ&#x20AC;&#x2122;s house, and concern about repaying and those with â&#x20AC;&#x153;sunnyâ&#x20AC;? dispositions report less difficulty with student loans â&#x20AC;&#x201D; is small and at best of marginal statistical these issues and greater satisfaction with their finances. We thus significance. include a variable that reflects the respondentâ&#x20AC;&#x2122;s financial disposition â&#x20AC;&#x201D; the respondentâ&#x20AC;&#x2122;s aversion to investment risk â&#x20AC;&#x201D; which 12 The very large reductions in financial satisfaction associated with risk should limit â&#x20AC;&#x153;dispositionalâ&#x20AC;? bias. But to the extent such bias aversion suggests that dispositional factors could indeed have a large effect on persists, the regression estimates would overstate the relationship subjective financial assessments, including assessments of difficulty in coverbetween financial satisfaction and these three issues, two of ing day-to-day expenses and the weight of current debt burdens. The inclusion of this variable in the regressions should reduce the bias dispositional which are day-to-day issues of central importance to this study. factors would otherwise introduce in estimates of the relationship between in financial satisfaction and these two day-to-day deficits.

Journal of Personal Finance

28

Table 3: Correlates of Subjective Financial Assessment TABLE 3. Correlates of Subjective Financial Assessment

Day-to-Day Concerns

Self-Assessed Ability to Cover Expenses Very difficult Moderately difficult Employment Unemployed Self-Assessed Current Debt Burden Heavy burden Moderate burden Access $2,000 Could not likely access $2,000

Coefficient

Standard Error

-2.101*** -1.172*** -0.505*** -2.101***

[0.094] [0.058] [0.081] [0.094]

-1.235*** -0.435***

[0.070] [0.059]

-0.687***

[0.064]

-0.344*** -0.113+ 0.083

[0.072] [0.060] [0.095]

0.039 -0.397***

[0.068] [0.073]

-0.336*** -0.672***

[0.064] [0.065]

0.269*** -0.025 -0.426***

[0.068] [0.077] [0.061]

-0.148+ -0.202**

[0.077] [0.069]

Distant Concerns

Insurance No medical insurance No life insurance Life insurance not needed Retirement No retirement plan Inactive retirement plan Saving for College No need to save Not saving for college Housing Own free and clear Own, underwater Rent Student Loans Concerned might not be able to repay Has loans, not concerned about repaying

Control Characteristics

Male -0.034 Marital status Never married -0.108+ Divorced, separated, or widowed -0.153* Non-white ethnicity 0.082 Education Some college -0.286*** High school or less -0.164* Aversion to investment risk Risk averse -1.645*** Moderately risk averse -0.742*** Has not seen a financial advisor -0.090+ Financially literate -0.481*** County unemployment rate 0.005 Age group Ages 25 to 34 0.483*** Ages 50 to 60 -0.031 Adjusted income tercile 0.08 Lowest tercile -0.036 Highest tercile 0.280*** Constant 8.616*** 23 R-squared 0.448 Constant 8.616*** Adjusted 0.446 R-squaredR-squared 0.448 N 9,473 Adjusted R-squared 0.446 * p<0.05; ** p<0.01; *** p<0.001 N 9,473 calculations using data from FINRA Investor Education Foundation (2012a) *Source: p<0.05;Authors’ ** p<0.01; *** p<0.001 Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

[0.046] [0.065] [0.069] [0.055] [0.053] [0.064] [0.077] [0.054] [0.047] [0.049] [0.012] [0.058] [0.055] [0.060] [0.067] [0.056] [0.130] [0.130]

Table 4. Predictive power of Day-to-Day and Distant Concern Fitted Values Table 4. Predictive power of Day-to-Day and Distant Concern Fitted Values Coefficient Standard Error Fitted Values ©2015, IARFC. All rights of reproduction in any form reserved.

Coefficient Standard Fitted Values Day-to-day concerns 0.882*** [0.023]Error Day-to-day concerns

0.882***

[0.023]

Volume 14, Issue 2

29

A surprising result is the relationship between subjective financial assessments and retirement saving. Households with no retirement plan — with neither traditional defined benefit pension accruals nor any 401(k)/IRA savings — are clearly in a more adverse condition than households with an inactive plan — households with 401(k)/IRA savings, and perhaps defined benefit pension accruals, but currently do not “regularly contribute” to those plans. Having an inactive plan is associated with a 0.4 point reduction in subjective assessments relative to having an active plan. But having no plan has no apparent effect.

This result is consistent with the notion that a lack of salience, just discounting, underlies the weak relationship between subjective assessments and distant financial concerns.13 Day-to-day deficits are not just “present,” they are also salient. Households are continually reminded of difficulty in covering current expenses, making current debt payments, wanting to work but lacking a job, and the financial fragility that lacking reliable access to $2,000 entails. This is not the case with deficits in retirement planning and other distant deficits. Respondents with an inactive plan are better off than respondents with no plan at all. But respondents with an inactive plan seem aware of having a deficit. Those without a plan do not.

Turning to the control variables, the results indicate that only two personal characteristics are associated with economically and statistically significant changes in subjective financial assessments — financial literacy and investment risk aversion. Consistent with the Mugenda/Xiao findings, financial literacy is associated with a 0.5 point decline in financial satisfaction. Strong risk aversion is associated with a large 1.6 point decline and moderate risk aversion with a 0.7 point decline. To the extent investment risk aversion reflects the respondent’s financial “disposition,” the results indicate a strong relationship between a “gloomy” financial disposition and a “gloomy” assessment of the household’s finances. The inclusion of this variable would then dampen the bias the respondent’s disposition introduces in estimates of the relationship between Constantfinancial satisfaction and the respondent’s subjective assessment R-squaredof the household’s ability to cover day-toAdjusted R-squared day expenses and current debt payments.

13

N

The regression results show day-to-day deficits are associated with much larger reductions in financial satisfaction than distant deficits. To test whether this difference is statistically significant, we compared the ability of each set of concerns to predict financial satisfaction. The results, presented in Table 3, show both sets of concerns have statistically significant predictive power.

However, day-to-day concerns have greater predictive power, which the t-statistic confirms at the 0.999 confidence level. This supports the hypothesis that subjective financial assessments place significantly greater weight on day-to-day than on distant financial concerns.

A complicating issue in assessing the relationship between distant concerns and financial satisfaction is that attending to distant concerns is costly. It takes income the household could use to meet their day-to-day needs — needs that the results indicate have an outsized effect on financial satisfaction. The relatively modest reductions in subjective assessments associated with distant deficits could be due, in part, to households with such deficits using their income to address day-to-day needs, which increases financial satisfaction. To the extent that this is the case, the results would underestimate the relationship between subjective assessments and distant concerns. To the extent that this is the case, subjective assessments would also be expected to follow a pecking order — to have a stronger relationship with distant issues as day-to-day deficits decline — and the gain in satisfaction that comes from shifting resources to address such deficits likewise declines.

Do subjective financial assessments follow a pecking order?

The pecking order hypothesis asserts that financial assessments are more sensitive to distant deficits when the household’s dayto-day finances are in reasonably good shape. It asserts that the reduction in financial satisfaction associated with distant deficits should rise as s, our measure of concern over dire day-to-day 8.616*** [0.130] deficits, declines; and that the reduction in satisfaction should 0.448 decline (i.e., become 0.446 less negative) as concern over dire day-today deficits rises.

9,473

The success of *auto-enrollment in dramatically raising 401(k) participation p<0.05; ** p<0.01; *** p<0.001 rates, especially among young and low-income workers, that Investor The Education results of Foundation the regression testing the pecking order hypothesis Source: Authors’ calculations using also datasuggests from FINRA (2012a) a lack of salience, not just discounting, is responsible for retirement saving did not find evidence that this is the case. The results, presented deficits.

Table 4: Predictive power of Day-to-Day and Distant Concern Fitted Values

Table 4. Predictive power of Day-to-Day and Distant Concern Fitted Values Fitted Values

Coefficient

Standard Error

Distant concerns

0.512***

[0.032]

t-statistic

8.396***

Day-to-day concerns

0.882***

[0.023]

* p<0.05; ** p<0.01; *** p<0.001 Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

TABLE 5. Change in the relationship between subjective financial assessments and distant deficits as proxy measure of concern over dire day-to-day deficits, s, rises from 0 to 1 Change in correlates

Distant concerns

30

0.512***

t-statistic

[0.032]

8.396***

Journal of Personal Finance

* p<0.05; ** p<0.01; *** p<0.001 Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

Table 5: Change in the relationship between subjective financial assessments and distant deficits as TABLE 5. Change in the relationship between subjective assessments and distant proxy measure of concern over dire day-to-day deficits, s, rises financial from 0 to 1

deficits as proxy measure of concern over dire day-to-day deficits, s, rises from 0 to 1 Change in correlates Distant Concerns Correlates, s=0 as coefficient of s Insurance No medical insurance No life insurance Life insurance not needed Retirement No retirement plan Inactive retirement plan Saving for College No need to save Not saving for college Housing Own free and clear Own, underwater Rent Student Loans Concerned might not be able to repay

N R-squared

-0.058 0.023 0.087

[0.129] [0.129] [0.158]

-0.165* -0.051 -0.033

[0.067] [0.051] [0.088]

0.303* -0.244

[0.127] [0.162]

-0.130* -0.059

[0.059] [0.065]

-0.793*** -0.936***

[0.166] [0.165]

1.185*** 1.104+

[0.055] [0.061]

0.142 0.176 -0.269*

[0.175] [0.165] [0.132]

0.044 -0.096 -0.075

[0.057] [0.073] [0.052]

0.049

[0.183]

-0.099

[0.068]

9,473 0.451

* p<0.05; ** p<0.01; *** p<0.001 Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

in Table 5, do not show individuals becoming more sensitive to 24 The results do not support for the hypothesis that subjective assessments become significantly more sensitive to distant finandistant deficits as concern over dire day-to-day deficits declines. cial conditions as day-to-day deficits and the concern they create Sensitivity to a lack of medical insurance and a retirement plan decline.15 Households, by themselves, thus cannot be expected actually increase as concern over dire day-to-day deficits rise, to address distant concerns once their day-to-day finances are in though the incremental reductions in satisfaction are small.

Saving for college could be one issue consistent with the pecking reasonably good shape. order hypothesis, but the results are difficult to interpret.14 The Does financial literacy enhance the accuracy of subjective study finds no other statistically significant changes in the relafinancial assessments? tionship between financial satisfaction and other distant deficits.

As concern over the household’s day-to-day finances subsides, subjective assessments have much the same relationship with life The results of the regression testing the effect of financial literacy insurance deficits, having an inactive retirement plan, renting, and are also not consistent with the third hypothesis, that financial literacy significantly enhances sensitivity to the household’s finanbeing concerned about repaying student loans. cial condition, and especially more disant financial conditions.

The results, presented in Table 6, find only two distant deficits 14 The results show “not saving for college” associated with a large 0.9 point associated with a greater reduction in the financial satisfaction of reduction in subjective assessments among those with no dire day-to-day deficits (s=0), with the reduction vanishing as s rises from 0 to 1. But the financialy litereate individuals — having no retirement plan and results also find a very similar relationship between subjective assessments having a mortgage greater than the value of one’s house. Current and “no need to save” — a 0.8 reduction in satisfaction when s=0, which also debt burdents, a day-to-day deficit, is also associated with greater vanishes as s rises from 0 to 1. If households with no dependent children and “no need to save” represent the “initial state,” the results are consistent with reductions in subjective assessments. saving for college producing a large increase in financial satisfaction when s = 0, which raises the baseline constant and creates the large gap in satisfaction between those who save and those with “no need to save,” with that increase in satisfaction vanishing as s rises from 0 to1. The relationship between financial satisfaction and saving for college would then be partially consistent with the pecking order hypothesis — the increase in satisfaction associated with saving for college rises as concern over dire day-to-day deficits declines, consistent with the pecking order hypothesis; but the reduction in satisfaction associated with “not saving for college” — relative to the “initial state” — would be much the same.

15 The study tested more complex models of dissatisfaction associated with day-to-day deficits that included moderate difficulty in covering day-to-day expenses and moderate debt burdens. The regressions, however, failed to estimate many coefficients due to colinearity; they also failed to identify any other statistically significant interactions.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

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TABLE 6. Difference in the relationship between financial satisfaction and household Table 6: . Difference in the relationship between financial satisfaction and household financial financial conditions, financially literate vs. financially not literate individuals conditions, financially literate vs. financially not literate individuals

Day-to-Day Concerns

Self-Assessed Ability to Cover Expenses Very difficult to cover expenses Moderately difficult to cover expenses Employment Unemployed Self-Assessed Current Debt Burden Heavy debt burden Moderate debt burden Access $2,000 Could not likely access $2,000

Correlates of nonliterate individuals

Difference, literate vs. non literate individuals

-2.066*** -1.174***

[0.123] [0.084]

-0.142 0.03

[0.183] [0.113]

-0.564***

[0.105]

0.173

[0.159]

-0.901*** -0.207*

[0.102] [0.090]

-0.699*** -0.433***

[0.138] [0.118]

-0.758***

[0.086]

0.212+

[0.124]

-0.318*** -0.229** 0.09

[0.090] [0.088] [0.113]

-0.032 0.259* -0.108

[0.147] [0.119] [0.198]

0.167+ -0.322**

[0.086] [0.112]

-0.289* -0.119

[0.136] [0.148]

-0.504*** -0.894***

[0.097] [0.097]

0.324** 0.476***

[0.118] [0.127]

0.283** 0.230* -0.406***

[0.104] [0.110] [0.087]

-0.05 -0.598*** -0.011

[0.135] [0.150] [0.118]

-0.206*

[0.104]

0.056

[0.134]

-0.028

[0.046]

-0.108+ -0.148* 0.079

[0.065] [0.069] [0.055]

-0.271*** -0.153*

[0.053] [0.064]

-1.612*** -0.733*** -0.083+ -0.313* 0.005

[0.077] [0.054] [0.047] [0.128] [0.012]

25 0.465***

[0.058] [0.055]

Distant Concerns

Insurance No medical insurance No life insurance Life insurance not needed Retirement No retirement plan Inactive retirement plan Saving for College No need to save Not saving for college Housing Own free and clear Own, underwater Rent Student Loans Concerned might not be able to repay

Control Characteristics

Male Marital status Never married Divorced, separated, or widower Non-white ethnicity Education Some college High school or less Aversion to investment risk Risk averse Moderately risk averse Has not seen a financial advisor Financially literate County unemployment rate

Age group Ages 25 to 34 Ages 50 to 60 Adjusted income tercile Lowest Tercile Highest Tercile

Constant Observations R-squared Adjusted R-squared

-0.037 -0.04 0.284***

8.469*** 9473 0.454 0.451

[0.068] [0.055]

[0.149]

* p<0.05; ** p<0.01; *** p<0.001 Source: Authors’ calculations using data from FINRA Investor Education Foundation (2012a)

Journal of Personal Finance

32

The results also indicate that financial literacy reduces sensitivity to one distant deficit, eliminating the reduction in financial satisfaction associated with a lack of life insurance among non-literate individuals. While the results also indicate a decline in sensitivity to a lack of college saving, the decline could be more apparent than real.16 We find no other statistically significant differences.

The relationship between subjective financial assessments and the ability to cover day-to-day expenses, unemployment, the ability to access $2,000, medical insurance coverage, having an inactive retirement plan, owning one’s home free and clear, renting, and being concerned about repaying student loans is much the same for financially literate and non-literate individuals.17 These results are consistent with the Mugenda/Xiao hypothesis that financial literacy reduces financial satisfaction because it increases sensitive to deficits. Financial literacy is associated with greater awareness of issues emphasized in financial education programs — current debt burdens, having a mortgage greater than the value of one’s house, and not having a retirement plan. But we find no significant effect on issues not generally addressed in such programs, including distant concerns such as life and medical insurance, saving for college, and paying down student debt.

Conclusions Financial well-being is properly measured by the happiness and life satisfaction that income and wealth provide — tomorrow as well as today. Our findings indicate that financial satisfaction has become less reliable as an indicator of financial well-being as households have become increasingly responsible for meeting distant financial needs. Peace of mind can actually impede the achievement of financial well-being.

16 The decline in sensitivity to a lack of college saving is matched by a reduction in the relationship between financial satisfaction and “no need to save.”

If households with no dependent children and “no need to save” represent the “initial state,” not saving for college would thus be associated with a similar, not lesser, reduction in financial satisfaction among financially literate individuals. Saving for college would also be associated with a smaller increase in satisfaction, which could be interpreted as a reduction in sensitivity to a distant financial concern.

17 As older and higher income individuals are more likely to be financially literate, the study tested for differences in the correlates by age and income.

It did that with a model consisting of a system of linear equations with each age or income group estimated individually. It then tested whether the correlates of financial literacy differ by age or income using a nested F-test. The results showed no statistically significant differences in the correlates by age or income.

Even when workers were explicitly asked “Overall, thinking of your assets, debts and savings, how satisfied are you with your current personal financial condition?” their assessments were highly correlated with day-to-day financial concerns, with much more muted relationships with protection against risk and saving to meet future needs. Nor did this significantly change if the household’s day-to-day finances were in reasonably good shape or if the worker making the assessment was financially literate.

Because of this weak relationship between financial assessments and distant concerns, households cannot be expected to devote much effort to addressing distant deficits. This could explain why distant needs are not generally addressed by households acting independently, but in a structured, institutionalized context.

Retirement saving is largely done in employer plans where saving is easy, automatic, and incented by tax preferences and matching employer contributions. The same is true for medical insurance and basic life and disability insurance. Student loans and mortgages are also paid down according to contractual schedules.

But employer plans are far from universal. Nor are mechanisms generally in place to help households avoid deficits in college or retirement saving or in carrying a substantial mortgage into retirement.

As households are increasingly responsible for managing issues distant from day-to-day concerns, the findings support the importance of initiatives that raise awareness, and compensate for the lack of awareness, of distant financial deficits. Initiatives that raise awareness include broadcasting simple rules-of-thumb and providing ready access to financial check-ups. Defaulting all workers into a retirement plan with an adequate default contribution, as is currently underway in the United Kingdom, would raise awareness of retirement saving deficits and reduce the need for households to continually act on that awareness. More broadly, the results support the greater use of defaults, mandates, or the transfer of responsibility from households to government or employers — to reduce the nation’s significantly increased reliance on individual household decision-making for basic financial well-being.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

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Journal of Personal Finance

Factors Related to Making Investment Mistakes in a Down Market Shan Lei, Ph.D., Assistant Professor of Finance and Economics, Department of Accounting, Economics and Finance, West Texas A&M University Rui Yao, Ph.D., CFPÂŽ, Associate Professor, Department of Personal Financial Planning, University of Missouri

Abstract Using data from the 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study, this study identifies the factors related to making investment mistakes by moving assets into more of a cash position in a down market while having an adequate level of emergency funds. The results show that investors who are male, Asian, wealthier, overconfident, loss-averse, and reported an understanding of financial risks are more likely to make such investment mistakes during a down market. These findings have important implications for investors, their advisors, and financial planning professionals in general.

Key Words: Behavioral bias, down market, investment behavior, recession

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Volume 14, Issue 2

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Introduction

Literature Review

Classic economics theory assumes individuals are completely informed and rational when making decisions. However, in reality, decision makers go through both a rational and an emotional process. In some situations, emotional elements dominate the decision-making process (James, 2011; Ozmete & Hira, 2011; Tilson, 2005). Emotionally driven investment behaviors could lead to the unnecessary realization of financial losses, which are obviously not optimal considering the investors’ financial situations. These behaviors can impede investors’ ability to accumulate wealth and jeopardize their financial goal achievement.

Understanding factors that affect investors’ decision-making processes is the first step into the solution to help them overcome behavior biases and avoid investment mistakes.

Underdiversification and non-participation in risky assets are two common investment mistakes made by investors. Underdiversification refers to investing in only a small subset of investable assets. Calvet et al. (2006) measured underdiversification by comparing the Sharpe Ratio of a household portfolio to the ratio’s benchmark index. They chose a currency-hedged world index as the benchmark in their research. Underdiversification leads to higher investment risks, which means an increased probability of financial losses. Loss-averse investors are more likely to make investment mistakes in a down market. Non-participation in risky assets is an investment mistake that leads to lower portfolio risks and lower portfolio returns. Most investors do not realize that non-participation gives up not only the downside investment risks but also the upside investment gains. Therefore, this leads to opportunity costs that hinder investors’ wealth accumulation (Calvet et al., 2006; Calvet, Campbell, & Sodini, 2009). Results from prior research showed that investors’ psychological factors, as well as demographic and economic characteristics, affected investment decisions such as non-participation (Agarwal, Gabaix, Driscoll, & Laibson, 2009; Barber & Odean, 2001; Goetzmann & Kumar, 2008). Warren Buffett once stated: “Investing is not a game where the guy with the 160 IQ beats the guy with the 130 IQ…. Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing” (Ro, 2014; Stone, 1999).

It is natural for human beings to make mistakes; however, mistakes have consequences. The direct impact of investment mistakes is the loss of wealth. According to Calvet, Campbell, and Sodini (2006), 5% of the Swedish population suffers losses that equal more than 5% of their financial wealth per year and 1% of Swedish investors lose nearly 10% of their financial wealth per year, due to underdiversification. Cocco (2005) predicted a welfare loss of nearly 2% of annual consumption due to investors making the non-participation mistake. In addition, sub-optimal investment decisions also lead to portfolio inefficiency, as reflected by low Sharpe Ratios (Calvet et al., 2006).

If an investor moves to more of a cash position that is not due to a consumption need, a need to reallocate the portfolio, or a need to take advantage of taxes, then the move is likely to be an investment mistake. Investors who make this mistake will fall farther behind in reaching their financial goals. They will end up with portfolio losses that will take them a longer time and more effort to get back to where they were before and then move forward.

In today’s economic environment, people are assuming more risks in preparing for their retirement, which is one of their most important goals, and while doing so they are expecting lower payouts from Social Security and defined benefit pension plans when they retire. This makes investment mistakes something they cannot afford to do.

During the past recession, almost all investors saw their portfolio balance go south (Smeeding, 2012). How did they react to this external economic change? Did they follow the standard investment rule of “buy low, sell high” or did they panic and sell low? Who moved to cash? What factors affected this investment behavior? Answers to all of these questions provide important implications to investors, their financial advisors, and financial professionals in general. Findings herein also provide insights on how to help these investors overcome behavioral biases and avoid investment mistakes. This paper empirically examines the characteristics of investors who made investment mistakes during the past recession in a down market, which is when most investment mistakes are likely to happen.

The Effects of Demographic and Economic Characteristics Demographic characteristics. Past research found that men were more likely to be overconfident (Mittal & Vyas, 2011) and trade more frequently than women (Barber & Odean, 2001).

“Greater overconfidence leads to greater trading and to lower expected utility.” This suggested that trading excessively was an investment mistake. Because of excessive trading, men achieved lower net returns than women (Barber & Odean, 2001). Past research did not agree on the effect of age on investment decision-making. Compared to older people, younger people tended to have financial portfolios that were underdiversified, and they tended to not invest in risky assets (Bertaut & Starr, 2000; Goetzmann & Kumar, 2008; King & Leape, 1987). For those who did invest in risky assets, their portfolios ran inefficiently (Calvet et al., 2006).

In contrast, Korniotis and Kumar (2011) conducted research on the effect of age on investment decisions using the data collected from a large brokerage house. The authors showed that older investors had more diversified portfolios, traded less, and were less prone to have a behavior bias. They also found that aging caused the deterioration of cognitive abilities and had a negative effect on their investment skills.

This result was consistent with the study conducted by Agarwal, Gabaix, Driscoll, and Laibson (2009), which showed that due to the decline of cognitive abilities among older adults, they were more likely to make financial mistakes.

36

Journal of Personal Finance

Prior research found that investors with a higher level of education and finance literacy were less prone to investment mistakes during the recession. Winchester, Huston, and Finke (2011) found that education had a positive effect on investors’ prudent behavior, defined as conducting a portfolio-rebalancing strategy without increasing the cash holdings. Bucher-Koenen and Ziegelmeyer (2011) stated that households with lower financial literacy were more likely to sell assets and realize losses, which reduced their total financial wealth. Klapper, Lusardi, and Panos (2013) analyzed the Russian households’ investment behavior during a recession and found that people with higher financial literacy were more capable of withstanding macroeconomic and income shocks. Goetzmann and Kumar (2008) found that less-educated investors tended to have underdiversified portfolios. Calvet, Campbell, and Sodini (2008) found that investors with more advaced education were more likely to buy and less likely to sell risky assets in down markets. According to Calvet et al. (2006; 2009), investors with a lower education level were more likely to make the nonparticipation investment mistake.

Furthermore, for those who owned some risky assets, their portfolios were more likely to be underdiversified.

Economic characteristics. Past research found that investors with more wealth and more income were less likely to make the non-participation investment mistakes (Calvet et al., 2009).

This negative relationship between the possiblity of making investment mistakes and wealth and income was confirmed

by Goetzmann and Kumar (2008) and Calvet et al. (2006). By analyzing the portfolio adjustment behavior of Sweden households between 1999 to 2002, Calvet et al. (2008) found that investors rebalanced their portfolio based on its return. Investors with a higher income and higher wealth had a more aggressive investment strategy and more diversified portfolios and therefore they were more likely to buy and less likely to sell risky assets in down markets.

A lot of research has examined the determinants of portfolio diversification and risky asset holdings. Younger investors with a low income and low wealth were more likely to have an underdiversified portfolio (Goetzmann & Kumar, 2008). Results from the studies conducted by Anderson (2013), Goetzmann, and Kumar (2008), and Roche, Tompaidis, and Yang (2013) confirmed the positive relationship between income and the level of portfolio diversification. Prior research also showed that wealthier investors tended to have more diversified portfolios (Goetzmann & Kumar, 2008; Roche, Tompaidis, & Yang, 2013).

The Effect of Psychological Factors Overconfidence. After analyzing portfolio data from a large brokerage house for six years, Goetzmann and Kumar (2008) found that, other than investors’ demographic and economic charateristics such as income, age, and education, psychological factors played an important role in explaining their underdiversified portfolios.

One of the main factors was overconfidence. The authors found that overconfident investors tended to have underdiversifed port-

folios. Park et al. (2010) studied investors in South Korea and found that the portfolios of overconfident investors performed worse. Trinugroho and Sembel (2011) conducted an experimental study to examine the effect of overconfidence on investment performance. They compared the trading frequency and returns between people with a high overconfidence level (as shown by a high miscalibration level) and a low overconfidence level. The results showed people with a high overconfidence level traded more agressively and excessively than those with a low overconfidence level. This result suggests that overconfidence led to excessive trading and a poorer portfolio performance.

Loss aversion. Past research found that loss aversion was related to non-participation in risky assets. According to Hofschire et al.

(2013), investors in general “allocated less capital” in equities because of the past recession and they have continued to focus their investment on bonds.

Due to loss aversion and the tendency to evaluate portfolios frequently, investors tended to trade excessively (Benartzi & Thaler, 1993; Shalev, 2000). After examining individual retirement saving decisions, Benartzi and Thaler (2007) concluded that one common behavior was to buy high and sell low. The authors argued that the underlying reason for this behavior was an overreation to short-term losses. Bucher-Koenen and Ziegelmeyer (2011) showed that if moving to cash resulted from myopia and loss aversion, it would be harmful to investors’ financial wealth.

Other psychological factors. Despite these aforementioned factors, prior research found that trend-following bias (Goetzmann & Kumar, 2008), confirmation bias (Park et al., 2010), regret aversion, and mental accounting (Beach & Rose, 2005) were related to making investment mistakes such as underdiversification and nonparticipation. Investors expecting an “extraordinary idiosyncratic gain” were found to focus on certain companies’ stocks and committed the underdiversification mistake (Polkovnichenko, 2003). Investors with behavioral biases were more likely to make investment mistakes such as underdiversification, nonparticipation, and “ buy high” and/or “sell low.” These mistakes can severely impede investors’ ability to accumulate wealth, which delays the realization of their financial goals.

Conceptual Framework Based on economic theory, most people prefer a smooth consumption pattern versus an irregular one. One major purpose of saving and investing is to smooth lifecycle consumption. Economic theory assumes that individuals are completely informed and rational when making decisions. Empirical evidence and anecdotal observations, however, show that some investors moved to cash or other safer assets during the past economic recession (Financial Planning Association & Ameriprise, 2010). If moving into more of a cash position was not driven by a consumption need or a need to reallocate the portfolio or take advantage of taxes, then it was a mistake. Past research found a relationship between this irrational reaction and several factors.

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Volume 14, Issue 2

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According to prospect theory, loss-averse investors are more likely to overreact to financial losses than to gains (Kahneman & Tversky, 1979). Their loss function is convex to the origin with a steeper slope, while their gain function is concave to the origin with a slope that is less steep. This means the magnitude of their utility decrease when they experience a financial loss is larger than the magnitude of their utility increase when they experience a financial gain. That said, loss-averse investors are then likely to sell in down markets.

Overconfidence is one factor that affects investment behavior.

Overconfidence refers to a decision-making bias that overrestimates one’s abilities to accomplish certain task (Lichtenstein & Fischhoff, 1977). Overconfidence prevails in many domains.

Brick (2014) reported that over 90% of drivers believed that they had above-average driving skills. Experimental studies by Hamermesh (1985) showed that participants were overconfident about their life expectancy. Montier (2006) found that 74% of the sample fund managers believed that their performance was above average. Glaser and Weber (2007) concluded that overconfidence coincided with higher trading volumes and might be more severe in certain groups of people.

Based on the above conceptual framework, we explored the following hypotheses: Loss aversion positively affects the likelihood of making investment mistakes.

Overconfidence positively affects the likelihood of making investment mistakes.

Methodology Data This study used data from the 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study. The data were collected online by an independent market research firm between June 27, 2008 and July 18, 2008. The total sample size was 3,022. According to the National Bureau of Economic Research’s Business Cycle Dating Committee, the recession was from December 2007 to June 2009. Thus, the respondents took part in the survey at least six months into the Great Recession.

The data provided information related to the demographic and economic characteristics of the respondents and their households, as well as information about respondents’ subjective attitude, such as self-confidence towards the future, self-reported financial knowledgeable level, and self-claimed understanding of risks. Most importantly, that study focused on factors related to making investment mistakes in a down market and the data included information about investors’ reaction to the Great Recession. Therefore, the data were particularly appropriate for this study.

Variables The dependent variable was whether respondents made an investment mistake during the down market (1=Yes, 0=No).

In this study, the mistake is moving assets into more of a cash position in a down market while having an adequate level in an emergency fund. The survey asked respondents their reaction to the market changes during the past year: “Since the market has changed over the past year, what actions, if any, have you taken?” “Moving assets into more of a cash position” was one of the choices. As previously stated, if cashing out of the market was not done due to a consumption need, then this non-participation behavior would be an investment mistake that led to the realization of financial losses and a reduction of total wealth (Calvet et al., 2006).

Based on the conceptual framework and the literature review, this study selected independent variables to be included in the analyses. This study used “travel less than previously anticipated” (1=Yes, 0=No) and “work during retirement” (1=Yes, 0=No) as proxies for the relative degree of loss aversion and used “confidence compared to five years ago” (less, the same, and more) as a proxy for overconfidence. This survey was conducted in 2008 during the Great Recession when investment values were highly volatile and declining quickly, and it compared that time with 2003, which was somewhat better in the financial markets (Smeeding, 2012). Investors expressing more confidence were likely to be overconfident. The question in the survey asked how the economic situation at that time (during the great recession) negatively affected respondents’ expectations about the future.

Selecting “travel less than anticipated” and/or “work during retirement” as an answer indicated that those respondents were unhappier than those who did not choose this answer. It also suggested that these investors reacted to the down market. Since more loss-averse investors are more likely to react in a down market (Hofschire et al., 2013), those who chose these items as answers were considered to be more loss-averse investors.

Other independent variables that serve as control variables are in four categories: 1) demographic characteristics of the respondents, 2) economic characteristics of the respondents’ households, 3) the respondents’ risk management, and 4) the respondent’s financial knowledge. Demographic variables included ages (younger than 35, 35–44, 45–54, 55–64, and 65 or older); gender (1=female, 0=male); education (high school or lower, some college and college degree or above); and race (non-Hispanic white, black/African-American, Asian, Hispanic, and others). Economic characteristics included business ownership (1=Yes, 0=No), household income (less than $50,000, $50,000–$99,999, $100,000–$149,999, $150,000–$249,999, and $250,000 or more), and household investable assets (less than $25,000, $25,000–$99,999, $100,000–$499,999, $500,000– $999,999, and $1,000,000 or more), debt ownership (short-term only, long-term only, both short- and long-term, and no debt).

Short-term debt included credit-card debt, payday loans, personal loans, and medical bills. Long-term debt included student loans, car loans or leases, a primary mortgage, and a home equity loan or a line of credit.

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Risk management variables included ownership of the following insurance products: 1) employer provided life insurance, 2) other life insurance, 3) disability insurance, 4) health insurance, 5) long-term care insurance, 6) personal liability insurance, 7) and property and casualty insurance. Financial knowledge variables included an understanding of financial-related issues (do not understand, not sure, and understand) and an understanding of financial risks (do not understand, not sure, and understand).

income groups. About two-thirds (64.9%) of the respondents reported household investable assets as being below $100,000.

The percentage of people making mistakes was generally higher for higher-investable asset groups with a slight drop from the third (15.3%) to the second highest (13.4%) asset groups. Nearly half of the respondents owned both short- and long-term debts (49.6%). Those who did not have any debts had the highest percentage (15.6%) of making the investment mistake.

Method of Analysis

Most respondents had employer-provided life insurance (55.6%), health insurance (82.8%), and property and casualty insurance (58.1%). A higher percentage of those who had a health insurance, long-term care insurance, personal liability insurance, and property and casualty insurance policy made the investment mistake, compared to those who did not have such insurance coverage (percentages were 11.9%, 14.8%, 18.9%, and 13.4%, respectively).

This study aimed to examine the factors related to making investment mistakes in down markets. The 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study research team provided weights to adjust for differential nonresponses from the online responses and to correct the error estimation.

Missing values accounted for 7.6% of the total sample. This study excluded respondents with missing values. This practice should not lead to a bias in statistical analysis (Bennett, 2001; Schafer, 1999). As a result, the total sample size in this study was 2,792.

This study conducted descriptive analysis to present sample characteristics overall and broken down by whether respondents made investment mistakes. After controlling for other variables, this study employed weights in the descriptive analysis and conducted a logistic regression analysis to examine factors related to making mistakes. The logistic regression did not use weights.

This study also employed correlation analysis to examine correlations among independent variables.

Almost half of the respondents (48.2%) reported a lower level of confidence about their financial future than five years ago.

Among those who reported more confidence, 12.8% made the investment mistakeâ&#x20AC;&#x201D;the highest among all confidence groups.

Most respondents reported that they understood financial-related issues (64.8%) and financial risks (74.0%). Among these respondents, about one-eighth made the investment mistake (percentages were 12.8% and 12.5%, respectively). Because of the market performance, some respondents anticipated traveling less in the future (35.6%) and working during retirement (14.0%). Among those who expected to travel less, 13.3% made the investment mistake.

Logistic Results

Results Sample Characteristics and Descriptive Results Table 1 shows the sample characteristics both in general and broken down by whether the respondents made investment mistakes. Overall, 11.2% of the total sample made investment mistakes by moving their assets into more of a cash position without a consumption need. The percentages for making a mistake were higher for older respondents (percentage ranging from 9.2% for the two youngest groups to 15.3% for the oldest group). More than half (59.1%) of the respondents were males, among whom 12.3% made this mistake. Among 40.9% of the total sample who were females, 9.7% made this mistake. Asians accounted for 2.6% of the sample. Among Asians, 26.9% made this mistake during the past recession, which is the highest among all race groups.

The majority of the respondents did not own a business (88.4%).

Among those who did, 16.4% made this investment mistake.

Most (59.1%) of the respondents reported having a household income of between $50,000 and $100,000, while 6.2% reported earning more than $250,000. However, the percentage of making mistakes was generally higher for higher-income groups with a slight drop from the third (12.5%) to the second highest (11.9%)

Logistic results showed that respondents who were more loss averse were also more likely to make investment mistakes. This is consistent with the first hypothesis and prior research findings.

Respondents who anticipated traveling less in the future because of the market performance during the recession were 1.4 times as likely to make investment mistakes as those who did not have such an expectation. Similarly, respondents who anticipated working during retirement because of the market performance during the recession were 1.5 times as likely to make investment mistakes as those who did not have such an expectation. Consistent with the the second hypothesis and findings from prior literature, the logistic results showed that respondents who were overconfident were more likely to make investment mistakes.

Compared with respondents who had the same level of confidence at present versus five years ago, those who expressed more confidence were 1.4 times as likely to make investment mistakes.

After controlling for other variables in the model, females were found to be less likely than males (odds ratio=0.726) to make the financial mistake of cashing out of the stock market during the recession (Table 2). Asians were 2.4 times as likely as non-Hispanic Whites to make such mistakes. Business owners were 1.3 times as likely to make the investment mistakes as respondents who did not own a business. Household investable assets had a

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Volume 14, Issue 2

positive relationship with the likelihood of making investment mistakes. Compared to respondents with total household investable assets of less than $25,000, those with a higher level of investable assets were more likely to make these investment mistakes. The odds ratio was 2:1 for the $25,000–$99,999 group and the $100,000–$499,999 group, 2.3 for the $500,000–$999,999 group, and 2.4 for the $1,000,000 or more group. Compared with respondents with no household debt, those who had long-term debts were 74.3% as likely and those who had both short- and long-term debts were 59.4% as likely to make investment mistakes.

Respondents who had personal liability insurance were 1.3 times as likely to make investment mistakes as those who did not have such insurance coverage. Respondents who reported that they understood financial risks were 1.8 times as likely to make investment mistakes as those who were not sure.

Discussion and Implications Using data collected from the 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study, this study examined factors related to making investment mistakes in the Great Recession. The mistakes referred to moving to more of a cash position while having an adequate level of emergency funds. Results showed that being a male, an Asian, a business owner, having investable assets more than $25,000, having personal liability insurance, and reporting an understanding about financial risks were associated with a higher likelihood of making investment mistakes. Having long-term debts was associated to a lower likelihood of making investment mistakes. This study allocated particular attention to how being more loss averse and overconfident affected the likelihood of making investment mistakes.

Our results confirmed both hypotheses in that higher degrees of loss aversion and overconfidence were associated with higher likelihoods of making investment mistakes.

It is likely that an overconfidence bias affected the respondents who reported a higher level of confidence for their financial future in a recession than five years ago. Respondents who anticipated travelling less in the future and working during retirement were likely to be more loss averse. If moving to more of a cash position in a downturn was not due to a consumption need, a need to reallocate the portfolio, or a need to take advantage of taxes, then it is likely to be an investment mistake and inconsistent with the standard investment rule of “buy low, sell high.”

The consequences of making this mistake are an unnecessary realization of portfolio losses, lower accumulated wealth, and falling behind in reaching one’s financial goals.

This study is among the first to examine the factors related to making investment mistakes in a down market. The implications of this work are potentially far reaching in the financial planning arena. In today’s economic environment as employers continue to switch from defined benefit plans to defined contribution plans, Social Security’s future payouts become questionable as

39

individuals’ longevity becomes longer, and so individuals and households are more responsible than ever for their financial future.

Making investment mistakes is something they cannot afford to do. This requires that individuals and households thoroughly understand the economic environment they face and their financial responsibilities, and they must make rational decisions about what they should do in order to have a successful financial future.

Although it is not easy for investors to overcome emotional urges, it is important to recognize their existence and understand how they affect investment decision-making. It is very important that financial educators and financial practitioners help investors better understand the challenges they face and overcome a projection bias in order to reduce the likelihood of making investment mistakes such as cashing out in a down market with adequate emergency funds.

The objective of financial advisors and financial planning professionals, in general, should be to make sure that individuals and households truly understand the risks and returns of financial products, the risks of their investment portfolio, and how that portfolio should perform over time not only during up markets but also during down markets. Moreover, it is important for financial advisors to understand their clients’ risk attitudes in both the gain domain and the loss domain. For those who are likely to suffer from behavioral biases such as overconfidence and loss aversion, it is important for financial advisors to explore investment products that will help their clients overcome the effects of such behavioral biases and prevent them from making investment mistakes such as moving into a cash position in a down market without a good reason.

Journal of Personal Finance

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Table 1: Making Investment Mistakes by Sample Characteristics Sample Characteristics

Making Investment Mistakes Overall

Overall

Yes

No

11.2

88.8

Demographic Characteristics

Making Investment Mistakes Overall

Yes

No

No

44.4

12.7

87.3

Yes

55.6

10.0

90.0

Risk Management Employer provided life insurance

Age < 35

Sample Characteristics

29.2

9.2

90.8

Other life insurance

35-44

18.3

9.2

90.8

45-54

19.2

10.1

89.9

No

54.6

10.3

89.7

55-64

18.1

14.3

85.7

Yes

45.4

12.3

87.7

≥ 65

15.3

15.3

84.7

No

68.3

11.1

88.9

Yes

31.7

11.4

88.6

Gender Male

59.1

12.3

87.7

Female

40.9

9.7

90.3

High school diploma or less

10.5

8.1

91.9

Some college

48.3

10.3

89.7

College degree or above

41.2

13.1

86.9

Education

Race Non-Hispanic White

81.4

10.7

89.3

Disability insurance

Health insurance No

17.2

8.1

92.0

Yes

82.8

11.9

88.1

No

82.6

10.5

89.5

Yes

17.4

14.8

85.2

Long-term care insurance

Personal liability insurance

Black/African American

6.0

14.4

85.6

No

73.1

8.4

91.6

Asian

2.6

26.9

73.1

Yes

26.9

18.9

81.1

Hispanic

7.0

8.4

91.6

Other

3.0

12.2

87.8

No

41.9

8.2

91.8

Yes

58.1

13.4

86.6

ago More

21.6

12.8

87.2

Same

30.2

10.4

89.6

Less

48.2

11.1

88.9

Do not understand

12.6

9.2

90.8

Not sure

22.6

7.8

92.2

Understand

64.8

12.8

87.2

Do not understand

10.2

9.2

90.9

Not sure

15.8

6.7

93.3

Understand

74.0

12.5

87.5

Economic Situations Business Ownership

Respondents’ Expectations

No

88.4

10.6

89.5

Yes

11.6

16.4

83.6

< $50,000

4.6

7.9

92.2

$50,000-100,000

59.1

9.8

90.2

20.5

12.5

87.5

9.7

11.9

88.1

6.2

22.3

77.7

Household income

$100,000-150,000 $150,000-250,000 ≥ $250,000 Household investable assets < $25,000

Property and casualty insurance

Confidence compared to 5 years

Understand financial-related issues

33.9

5.6

94.4

$25,000-99,999

31.0

13.0

87.0

$100,000-499,999

24.7

15.3

84.7

$500,000-999,999

5.8

13.4

86.6

≥ $1,000,000

4.6

16.2

83.8

Travel less than previously antic-

Short-term debt only

7.5

9.4

90.6

Short-term and long-term debt

49.6

9.7

90.3

ipated No

64.4

10.1

89.9

Long-term debt only

27.8

12.1

87.9

Yes

35.6

13.3

86.7

No debt

15.1

15.6

84.4

No

86.0

11.3

88.7

Yes

14.0

11.0

89.0

Debt ownership

Understand financial risks

Work during retirement

Note: Analysis of the 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study; weighted results; sample size=2,792. Numbers are in percentages. Column 1 shows the frequencies for each variable. Column 2 and column 3 show the frequencies of who did and did not make investment mistakes among each demographic characteristic, respectively. ©2015, IARFC. All rights of reproduction in any form reserved.

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Table 2: Logistic Analysis of Factors Related to Making Investment Mistakes in a Down Market Parameter

Coefficient

Intercept

-4.506***

Odds Ratio

Proxy of being more loss averse Travel less than previously anticipated

0.323**

1.381

Work during retirement

0.395*

1.484

More

0.315*

1.37

Less

0.244

1.277

35-44

0.301

1.351

45-54

0.184

1.201

55-64

0.249

1.283

≥ 65

0.396

1.486

Female (reference: Male)

-0.320**

0.726

Some college

0.356

1.428

College degree or above

0.401

1.494

Black/African American

0.576

1.78

Asian

0.876***

2.402

Hispanic

0.283

1.326

Other

0.055

1.057

0.268*

1.307

$50,000-99,999

0.444

1.558

$100,000-149,999

0.286

1.33

$150,000-249,999

0.502

1.652

≥ $250,000

0.618

1.855

$25,000-99,999

0.735**

2.086

$100,000-499,999

0.758***

2.134

$500,000-999,999

0.842***

2.32

≥ $1,000,000

0.888***

2.43

Short-term debt only

-0.208

0.812

Short-term and long-term debt

-0.522***

0.594

Long-term debt only

-0.298*

0.743

Employer provided life insurance

-0.047

0.954

Other life insurance

-0.018

0.982

Disability Insurance

-0.062

0.94

Health insurance

0.048

1.049

Long-term care insurance

0.133

1.142

Personal liability insurance

0.247*

1.28

Property and casualty insurance

0.235

1.265

Do not understand

-0.309

0.735

Understand

0.304

1.355

Do not understand

0.46

1.584

Understand

0.612*

1.844

Proxy of being overconfident Confidence compared to 5 years ago (reference: same)

Demographic Characteristics Age (reference: ≤ 35)

Education (reference: high school diploma or less)

Race (reference: White non-Hispanic)

Economic Characteristics Business ownership Household income (reference: < $50,000)

Household investable assets ( reference: < $25,000)

Debt ownership (reference: no debt)

Risk Management

Financial Knowledge Understand financial-related issues (reference: Not sure)

Understand financial risks (reference: Not sure)

Note: Analysis of the 2008 FPA-Ameriprise Financial Value of Financial Planning Research Study; unweight results; * p<.05; ** p<.01; *** p<.001.

Journal of Personal Finance

42 References

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Liu, H. (2008). A new explanation for underdiversification. Working Paper, Olin Business School, Washington University.

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Goetzmann, W. N., & Kumar, A. (2008). Equity portfolio diversification.

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Park, J., Konana, P., Gu, B., Kumar, A., & Raghunathan, R. (2010). Confirmation bias, overconfidence, and investment performance: Evidence from stock message boards. McCombs Research Paper Series No. IROM-07–10.

Polkovnichenko, V. (2003). Household portfolio diversification. University of Minnesota and Federal Reserve Bank of Minnesota, working paper.

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Roche, H., Tompaidis, S., & Yang, C. (2013). Why does junior put all his eggs in one basket? A potential rational explanation for holding concentrated portfolios. Journal of Financial Economics, 109(3), 775-796.

Schafer, J. L. (1999). Multiple imputation: A primer. Statistical methods in medical research, 8(1), 3-15.

Shalev, J. (2000). Loss aversion equilibrium. International Journal of Game Theory, 29(2), 269-287.

Stone, Amy. (1999, July 5). Homespun Wisdom from the “Oracle of Omaha.” Business Insider. Retrieved from http://www.businessweek. com/1999/99_27/b3636006.htm Tilson, W. (2005). Applying behavioral finance to value investing. Artikel T2 Partners LLC. Smeeding, T. (2012, October 12). Income, wealth, and debt and the great recession. Retrieved from https://web.stanford.edu/group/recessiontrends/ cgi-bin/web/sites/all/themes/ barron/pdf/IncomeWealthDebt_fact_sheet. pdf Trinugroho, I., & Sembel, R. (2011). Overconfidence and excessive trading behavior: An experimental study. International Journal of Business and Management, 6(7), p. 147.

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©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

43

A Quantitative Evaluation of Four Retirement Spending Models James S. Welch, Jr., Senior Application Developer, Dynaxys, LLC Abstract Traditional retirement planning assumes that disposable income is constant throughout retirement, before it is indexed to inflation. Demographic retirement spending data indicate that retirees spend more early in retirement, while they are physically active, and voluntarily spend less later in retirement. Four researchers reviewed retiree demographic spending data and proposed retirement spending models which fit their observations.

We added these spending models to a linear programming based retirement calculator that computes maximum disposable income for the first year of retirement and applies a spending model to the remainder of retirement. We defined a base scenario and examined how the spending models behaved compared with the traditional constant spending model and with each other. We ran a series of tests to observe how the spending models perform when an assumption of the base scenario was perturbed. We conclude that a retiree may safely choose higher spending early in retirement while budgeting for lower disposable income later in retirement.

Keywords: retirement planning, Roth IRA, tax-deferred savings, linear programming, optimal spending plan, retirement spending, disposable income, spending models.

Acknowledgements The author thanks Beverly Barker for her editing and formatting contributions, David S. Hirshfeld for his suggestions from his Operations Research perspective, and William Burdick for his observations as a computationally literate, recent retiree. Errors are solely the responsibility of the author.

Journal of Personal Finance

44

Introduction The dual mandate of retirement planning is to not spend so fast that your savings run out before you do and to not spend so slowly that you astonish your grateful heirs with your generosity. The optimal retirement plan matches retiree spending to the changing needs of the different phases of retirement while satisfying a Final Total Asset Balance (FTAB) requirement. In this paper we compare five retirement spending models that, if executed properly, meet the dual mandate.

The Computational Platform We used the Optimal Retirement Planner (ORP) as the computational platform for this study. ORP is a retirement calculator built on an off-the-shelf Linear Programming (LP) system [Welch 2015].1 ORP assumes three retirement savings accounts:

The Traditional Spending Model (TSM) assumes constant retirement spending, indexed to inflation, [Bengen 1994].

1. Tax-deferred account (IRA)2 contributions from wages are exempt from personal income taxes. Distributions are taxed as personal income.

Four researchers independently surveyed retiree spending data and proposed alternate spending models that produce spending plans that fit their observed spending more closely than does TSM.

2. Roth IRA contributions from wages are subject to personal income taxes. Asset returns and distributions are not taxed.

We present the results of computational experiments that compare alternate spending models’ disposable income to TSM’s and to each other. We define disposable income to be the nominal (i.e. indexed to inflation) amount of after-tax money available for personal consumption for the term of the retirement plan. Initial disposable income is the after-tax money available for spending for the first year of retirement.

We examine details of the computed plans to understand how they work. Our results are that the alternate spending models provide for significantly higher initial disposable income as compared to TSM. We conclude that it is reasonable for the retiree to adopt an alternate spending model that provides for 20% or more initial spending than TSM, provided that the retiree follows the alternate spending model’s full spending plan and spends less later in retirement.

Literature Review The alternate spending models that we review are: 1. The Age Banding Model (ABM) [Basu 2005], 2. The Changing Consumption Model (CCM) [Blanchett 2013], 3. The Lifecycle of Spending Model (LSM) [ Roy 2014], and 4. The Reality Retirement Planning (RRP) model [Bernicke 2005].

Bernicke [2005] and Blanchett [2013] reviewed the issue of whether the spending reductions that they observed later in retirement are voluntary or are forced on retirees by necessity.

Both researchers conclude that retirees choose to spend less later in retirement.

3. After-tax account contributions can be from any source and are assumed to be already taxed as appropriate. Profits are taxed as incurred. Distributions are not taxed.

We define retirement savings to be the sum of the account balances for these three accounts.

ORP models Federal income taxes and the Required Minimum Distribution (RMD)3.

A key feature of an LP model is its objective function. The objective function spans the model and yields the profit from doing the activities of the model. The LP optimizer maximizes the objective function value. ORP’s objective function is initial disposable income. ORP maximizes its objective function while satisfying the requirements (constraints) of the model. An example of constraints is satisfying the selected spending model’s requirements for each year of retirement while ending up with a zero FTAB. ORP maximizes initial disposable income and the spending model determines disposable income for the remainder of retirement.

We use the term income to connote maximum after tax disposable income. ORP computes income. Spending is what the retiree decides to do. Spending is less than income, for modeling purposes, in that ORP does not include credit card debt and home equity loans.

Maximum income is achieved by scheduling withdrawals from savings in a manner that minimizes income taxes on the IRA withdrawals plus 85% of Social Security benefits while maximizing compounded returns on all accounts.

We now briefly review the alternate spending models of this study.

1

ORP is available on the Internet at www.i-orp.com.

We refer to the collection of tax-deferred accounts as the IRA. 3 For a list of abbreviations, please see Appendix A. 2

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

Age Banding Model (ABM) The Age Banding Model for Planning Retirement Needs [Basu 2005] refines the application of inflation to retirement spending.

The Age Banding Model does these things: •

Divides spending into three categories (Basic Living, Leisure, and Healthcare)4 and assigns each category its own inflation rate. Our inflation rates for Leisure and Healthcare are 2.33 times5 the Basic Living inflation rate.

•

Each category is assigned a proportion of total spending, which, when multiplied times the category’s inflation rate, yields a weighted inflation rate for that category. The ABM inflation rate is the sum of the weighted category rates.

•

At ages 65, 75, and 85 category proportions are adjusted to provide for less Basic Living and Leisure spending, and for more Healthcare.

ORP uses ABM’s spending computation in maximizing income.

Changing Consumption Model (CCM) Blanchet [2013] uses the RAND Health and Retirement Study data set as the data source for his Changing Consumption Model (CCM). Using nonlinear curve fitting, he approximates the CCM’s annual, real change in consumption6 (Δage) with the function: Δage = 0.00008*age2 + 0.0125*age – 0.0066 * ln(ExpTar) + 54.6% where 59 < age < 96 ExpTar is the after-tax expenditure target for the first year of retirement. Our estimate of ExpTar is 5% of initial savings plus Social Security benefits. This is just a rough estimate because ExpTar is what ORP is maximizing. The definition of ExpTar means that Δage is not only a function of age but also of initial income.

Although Δage may be non-negative at the age boundaries (60 and 95), it is negative away from the boundaries. This means that consumption declines at a nonlinear rate for older retirees.

4 Basu models a fourth category; taxes. For modeling purposes there are two kinds of taxes: personal income taxes and real estate taxes. ORP already models personal income taxes outside of the spending model. We include real estate taxes in the Basic Living category.

5 Basu assumes a 3% Basic Living inflation rate and a 7% inflation rate for Leisure and Healthcare. The ratio of Healthcare or Leisure to basic living is 2.33. The Basic Living inflation rate is a settable parameter.

6 Blanchett uses the terms, spending, consumption, and expenditure as synonyms. We prefer spending as something the retiree chooses to do and income is that money that may or may not be spent.

45

ORP’s income is based on CCM’s consumption. An age’s disposable income (diage), except for the first year of retirement, is based on the income of the previous age for all ages in retirement: diage = diage-1* (1 + inflation + Δage)

The Lifecycle of Spending Model (LSM) In her paper, The Lifecycle of Spending, Roy [2014] surveyed the spending of 1.5 million retirement U.S. households who use Chase Bank mortgage, debit, and credit cards to do a majority of their spending. She concludes that in an environment with 2.5% inflation, actual retiree spending has a constant annual spending adjustment of 0.545%. This value reflects both inflation and age related reduced spending.

The Lifecycle of Spending Model (LSM) income (diage) adjustment is based on LSM’s rate of spending change: diage = diage -1*(1 + (inflation – 1.5)*0.00545) Inflation is an input parameter. 1.5 is a translation constant.

According to Roy’s results when inflation is 2.5%, then the LSM inflation index is 0.545%. We subtract 1.5 from inflation so that the above equation gives this result. Other inflation values are adjusted accordingly. The LSM spending adjustment is constant for all retirement ages.

Reality Retirement Planning (RRP) Bernicke’s Reality Retirement Planning (RRP) is based on data drawn from the Bureau of Labor Statistics’ Consumer Expenditure Survey. He categorizes retirement spending into 5 year intervals as shown in Table 1.

Journal of Personal Finance

46

Table 1: Consumer Expenditure Survey Broken Down into 5-Year Age Groups

Age 55-59 60-64 65-69 70-74 75+

Average Annual Age Expenditures $45,062 $38,218 $32,103 $27,517

5 Year Spending Decrease 16.7% 16.0% 14.3%

Annual Spending Decrease (δage) 3.34% 3.20% 2.86% 4.44% 0

• δage is the annual average of the percentage spending decrease over the 5 year interval.

• For ages 54 and younger, and ages 75 and older the spending reduction is zero and spending increases at the rate of inflation.

ORP’s income (di) adjustment for RRP is: diage = diage-1*(1 + inflation – δage) from the second year of retirement to age 75. ORP maximizes initial income and the remainder of retirement spending is derived from it.

Bernicke includes a numeric example of RRP in his paper. In Appendix B we compare Bernicke’s RRP to ORP’s RRP results and show that the two implementations agree on the shape of their spending curves but disagree on initial income (assumed by Bernicke, computed by ORP).

The Experiment The experiment is to define a base scenario and then run a series of tests to measure the alternate spending models’ performance.

The Base Scenario Defined Our base scenario is for a single, 65 year old retiree with $1,000,000 in retirement savings distributed across all three retirement savings accounts: 1. The IRA contains $400,000 2. The Roth IRA contains $350,000 3. The after-tax7 account balance is $250,000.

These proportions were chosen by computing accumulation phase savings for a 30 year old who allocates 1/3 of their annual retirement savings to each of the three accounts. The accumulated asset totals were evaluated at age 65. The Roth IRA account balance is lower than the IRA because of income taxes deducted from the Roth IRA contributions. The after-tax account balance is even lower due to income taxes deducted from contributions 7 The literature frequently uses the term taxable account for what we call the after-tax account. In our view all accounts are taxable because they are taxed either before the money enters, as it accrues, or as it is distributed.

and because the 15% capital gains tax paid on annual investment returns reduces compounding8 [Saftner and Fink, 2004].

We further assume: 1. A 27 year planning horizon (to age 92).

2. Zero FTAB, i.e. there is no estate. (Of course if the retiree does not live to age 92 any remaining savings are the estate.)

3. 2.5% annual inflation.

4. All three retirement savings accounts have the same 5% ROR9.

Inflation and ROR apply to separate parts of the process. Assets increase in value by their ROR, each year they are not distributed. Assets do not lose monetary value from inflation. Income is indexed to inflation.

With the exception of one test we do not model Social Security benefits in order to concentrate on spending issues.

The Base Scenario Examined Our first test is to solve the base scenario with each of the spending models and observe model income behavior. Figure 1 graphically contrasts TSM income to alternate spending models’ income.

8

Our simplifying assumption is that the after-tax account is invested exclusively in mutual funds which pass through capital gains annually with only minor unrealized capital gains. We assume there are no fixed income investments and thus negligible dividends. Since the after-tax account in depleted early in most plans after-tax tax consideration are safely ignored.

9 The focus of this study is on spending models without the need to address the confounding impact of different RORs for the three accounts. Coppersmith and Sumutka [2011] assumed different RORs for different accounts in their LP model with the outcome being transfers between accounts when their optimizer maximized asset returns.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

47

2 Page 47, Figure 1: Cosmetic changes.

Reposition Age to make it show properly.

Figure 1: Compare Traditional Spending Model Income to the Alternate Spending Models Assumptions:  The Base scenario with no embellishments.

Age Banding

$000 100 80

80

60

60

40 Age 65

70

75

80

85

90

Lifecycle of Spending

$000 100

40 Age 65

70

75

80

85

90

Reality Re3rement Planning

$000 100 80

80 60

Changing Consump3on

$000 100

LSM

TSM 40 Age 65 70

60 40 75

80

85

90

RRP

TSM

Age 65

70

Discussion: • Each panel contrasts one alternate spending model to TSM.

The alternate spending models show higher initial income and lower final income than does TSM. This gives the retiree the funds to enjoy a more active early retirement.

75

80

85

90

when the retirees are more active, reduced real income with flat nominal income at mid-plan, and increased income at the end to accommodate higher healthcare costs.

ABM’s saw tooth pattern is according to the model’s specificaLSM’s fixed 0.545% annual adjustment accounts for both tion. Between category adjustments ABM’s income growth is reduced spending and 2.5% inflation. Real income is being faster than TSM because Leisure and Healthcare expenses inflate reduced every year. faster than Basic Living’s inflation rate. ABM’s first adjustment occurs at age 65, the retirement age, but it doesn’t take effect 2 Early in retirement RRP’s income falls at a precipitous rate but, until age 66. Thus, initial income occurs on the last year of full at age 75, reverts to constant income, indexed to inflation. spending (age 65) before the first retirement adjustment.

Table 2 summarizes the application of the four spending models CCM’s income is nonlinear with higher income at the start to the base scenario.

Journal of Personal Finance

48

3 Page 48, Table 2: Remove stray black grid Table 2: Base Scenario Income Summary Assumptions:  Base scenario with no embellishments, the same test as Figure 1.

Inflation

TSM $000 Initial

ABM %

Total

Initial

CCM %

Cost

Initial

LSM %

Cost

Initial

RRP %

Cost

Initial

Cost

Nominal

47

1,891

18.6%

1.0%

10.2%

3.1%

24.6%

5.7%

29.3%

3.9%

Real

47

1,328

18.6%

0.5%

10.2%

1.5%

24.6%

2.7%

29.3%

1.9%

Discussion: • The Nominal row shows income with 2.5 % inflation.

• The Real row shows total income with inflation removed.

• The Traditional Spending Model (TSM) columns are in thousands of dollars.

• Initial is income for the first year of retirement; the optimizer’s objective function. Nominal initial income and real initial income are the same by definition.

• Total is the sum of all TSM income over the span of retirement. The Nominal Total value is misleading in some sense because it mixes valuable dollars early in the plan with less valuable dollars late in the plan. Nonetheless, it is useful for comparison purposes.

• The alternate spending models’ Initial entries are the percentage differences between the alternate spending models’ values and the corresponding TSM entries. For example, the LSM initial income is 24.6% greater than TSM’s initial income.

• The alternate spending models’ Cost entries are the percent reduction in total alternate spending models’ income relative to total TSM income. This is a measure of the percentage that total income is reduced by adopting alternate spending models. For example, ABM sacrifices 1.0% of total, nominal income by increasing initial income 18.6% above TSM.

4 Page 48: Change in text Alternate Spending Models’ Income Changes Conventional retirement planning is all about managing retirement savings and the assumed, initial withdrawal rate (first Change close parentheses “)”savings) in diasage (1 + βage)*di Forage all -1 retirement ages except the first, alternate spending model year withdrawals divided by beginning the=starting income (di) is computed as: point. The Initial column of Table 2 measures the after-tax, disposable funds available for personal consumption during the diage = (1 + βage)*diage-1 first year of retirement. In this scenario initial savings withdrawTo di = (1 + β ) *di : -1 agebecause taxes agehave toage als are greater than initial income be paid. where βage depends on the spending model being tested. Figure Initial income is not a surrogate for initial withdrawals or for the 2 illustrates the values of the multiplier βage as generated by the withdrawal rate. four alternate spending models. by taking off the footnote.

Since the alternate spending models show higher initial income and lower final income than TSM, we conclude that the alternate spending models transfer money from late retirement to early retirement where presumably the retiree is more able to enjoy the benefits. The positive Cost percentages indicate that it is not an exact transfer. Additional money spent early in retirement does not benefit from return compounding toward the end of retirement and may increase income taxes on IRA withdrawals.

3

We utilize the format of Table 2 throughout our paper without further explanation. ©2015, IARFC. All rights of reproduction in any form reserved.

J. Welch

27 Aug 2015

5 Page 49, Figure 2:

Volume 14, Issue 2

49

New lower right graph. Text change (60 to 55) and last line.

Figure 2: Income Rate Change for Each Year of the Base Scenario Assumptions:  Base scenario.  Retiree’s age and retirement age are set to 55 to show each alternate spending model’s active age range.

Age Banding

βage 5.0%

βage 4.0%

ABM

3.5%

TSM

0.0%

3.0%

-­‐5.0%

2.5%

TSM

2.0%

-­‐10.0%

1.5%

-­‐15.0% Age 55 60 65 70 75 80 85 90

βage

Changing Consump2on

βage

Lifecycle of Spending

3.0% 2.5%

-­‐0.5%

TSM

-­‐1.5%

1.5%

-­‐2.5%

1.0% 0.0% Age 55 60 65

Reality Re2rement Planning

0.5%

TSM

2.0%

0.5%

CCM

1.0% Age 55 60 65 70 75 80 85 90

LSM

RRP

-­‐3.5% -­‐4.5%

70 75 80 85 90

Age

55 58 61 64 67 70 73 76 79 82 85 88 91

Discussion: • The y-­‐axis is βage, the percent modification to the previous year’s disposable income. These values are from one of the optimizer’s internal tables.

• TSM‘s 2.5% constant inflation rate appears in every graph as a reference point.

• CCM is active from age 60 through age 92 and acts like TSM outside of this range, i.e. βage = 0.

• LSM is active throughout retirement. LSM is defined to be 0.545% at 2.5% inflation.

• RRP is active from age 55 through age 75 and acts like TSM outside of this range.

Because of the higher inflation rates for Leisure and Healthcare ABM’s inflation rate is higher than TSM’s. The re-proportioning of spending categories keeps ABM from running out of control.

At age 75, 15% is a substantial income reduction.

For early retirees CCM increases income above what it was entering retirement. The CCM smile [Blanchett 2013] decreases early in retirement, flattens out in mid plan as the retiree’s activity level decreases, and then increases in the latter part of the plan as healthcare expenses increase.

RRP’s spending-change graph reflects its stepwise definition where values are drawn from Table 1.

4

A Quantitative Evaluation of Four Retirement Journal of Personal Finance Spending Models

50 J. Welch

6 Page 50, Figure 3: Cosmetic, repositioned “Age”

27 Aug 2015

Figure 3: Savings Balance Deficits Assumptions:  The Base Scenario without change. $000 -­‐10 -­‐30 -­‐50 -­‐70 -­‐90 -­‐110 -­‐130 -­‐150

Age

ABM CCM LSM

RRP

65

70

75

80

85

90

Discussion: • The graph shows the alternate spending models’ savings balance minus the TSM savings balance. This is the estate deficit should the retiree not live to full term.

Inheritance Prospects

ed. At first IRA withdrawals are tax free as the after-tax account supplies most of the income. At age 68 after-tax account withdrawals decline and IRA withdrawals jump to the top of the 10% bracket. This is a consequence of LP’s balancing tax minimization against the maximization of asset returns. When the after-tax account is depleted the Roth IRA satisfies the remainder of income.

Life is uncertain and if the retiree should demise before the end of their plan whatever is left in their savings is their estate.

Figure 3 shows the difference between the TSM estate and the alternate spending models’ estates for each year of retirement.

Most retirees end their retirement plan with a termination age beyond age 90. This is a conservative choice because there is fair probability that they will live that long. But actuarial data show that the end will more than likely come sooner. If the retiree chooses to follow an alternate spending model, then the heirs’ bequests will be reduced for a major part of the plan. This makes sense as a consequence of accelerated spending early in retirement.

The RMD is of no consequence because IRA withdrawals, at the top of the 10% bracket, are well above the RMD. The Figure 4 withdrawal strategy showed how LP balances other activities as it maximizes income: 1. Distribute the after-tax account first because its ROR is reduced by the capital gains tax making the after-tax account ROR less than the untaxed 5% RORs of the other accounts. (If the capital gains tax is zero then our results will be different.)

Sources of Funds Maximizing income is all well and good but where does the money come from? In this section we look at how the alternate spending models differ in the way they withdraw from the savings accounts.

We first consider TSM withdrawals and then contrast them to alternate spending models’ withdrawal. Figure 4 shows TSM savings account withdrawals and how the IRA withdrawals are taxed.

The story being told here is with the IRA as the central character and with the others playing supporting roles. Distributions are made from the IRA for the entire term of the plan, at the top of either the No-tax or the 10% bracket. Remaining income requirements are filled in from either the after-tax account early in the plan or the Roth IRA after the after-tax account is deplet-

2. Preserve the IRA because of its tax deferred return compounding.

3. Distribute the IRA without taxes.

5

4. Keep the IRA distributions at the top of the 10% tax bracket.

5. Satisfy the RMD. 6. Preserve the Roth IRA because of its tax-free compounding and its tax-free distributions.

Satisfying these sometimes contradictory constraints is the essence of the application of LP to retirement planning.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

51

7 Page 51, Figure 4: Identify y-­axis, Repair Age label Figure 4: TSM Withdrawals and Federal Income Tax Brackets Assumptions:  Base scenario with no embellishments, same as Figure 1.  Y-­‐axis is in thousands of dollars.

100 80

g Spendin

60 40 20 0 Age 40 20

ATer-­‐tax

Roth IRA

IRA

65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 10% Bracket No-­‐tax

0 Age 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92

Discussion: • The top line of the upper panel is constant income increased each year by 2.5% inflation.

• The remaining lines of the upper panel show account withdrawals.

• In the absence of other sources, income is the sum of savings withdrawals minus taxes on IRA withdrawals.

• The lower panel shows income subject to taxes, distributed across income tax brackets. The bars are divided into segments. Each segment represents money that is taxed in a tax bracket. The No-­‐tax segment represents money that is excluded from taxes, i.e. the standard deduction, personal exemption and the age 65 or older exclusion. The 10% bracket segment represents IRA withdrawals that are taxed at the 10% rate.

6

J. Welch

27 Aug 2015 Journal of Personal Finance

52

8 Page 52, Figure 5: Cosmetic positioning of “Age” Figure 5: Alternate Spending Model Withdrawal Plans Assumptions:  Base scenario with no embellishments, the same as Figure 1.

Age Banding

$000 100

100

80

80

60 40

Changing Consump1on

$000

Income

60 AUer-­‐ta

40

x

0 Age 65 $000 100

20

IRA

20

70

75

80

85

90

Lifecycle of Spending

Roth IRA

tax

IRA

0 Age 65

70

75

80

85

90

Reality Re1rement Planning

$000

100

80

80

Income

60

60 Roth IRA

40 20 0 Age 65

AUer-­‐

20

IRA

70

75

80

Roth

40

85

90

IRA

IRA

0 Age 65

70

75

80

85

90

Discussion: • IRA withdrawals are at a steady rate at the top of the 10% tax bracket.

• Roth IRA withdrawals follow income for ABM and RRP. • The LSM panel shows Roth IRA withdrawals declining as the plan progresses even though income is increasing. This is because LSM’s revised “inflation rate” of 0.545% is much less than the IRA’s 5% ROR.

Figure 5 shows income and savings account withdrawals for the alternate spending models.

The alternate spending models’ withdrawal strategies are similar to TSM’s. Differences in spending models are reflected in the after-tax and the Roth IRA account withdrawals. IRA withdrawals are pegged to the top of the 10% tax bracket. As retirement progresses the changes in income are reflected in the Roth IRA distributions for all models.

Sensitivity Analysis With sensitivity analysis we modify one base scenario assumption and assess the impact on the alternate spending models relative to TSM and to each other.

The linear program’s optimizer minimizes taxes while it maximizes the compounding of asset returns. In the following tests tax minimization is mostly hidden from view. Asset compound7 ing is very much in evidence because increasing income in the early years of retirement reduces assets and thus reduces subseThe age that the after-tax account is depleted shows all three acquent years’ compounding of returns on those now spent assets. counts having distributions in the same year. This occurs in only one year but at different ages for different spending models.

The assumptions that we modify in this analysis are Social Security benefits, savings account balances, ROR, and longevity.

Social Security Social Security Benefits increase income subject to personal income taxes. This, in turn, affects the savings distribution plan because IRA withdrawals are sensitive to income taxes. ©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

53

9 Page 53, Table 3: C osmetic, black bar down right hand side.

Table 3: Social SecurityABenefits Effect onEvaluation Nominal Income Quantitative of Four

Retirement

Spending Models Assumptions: J.Welch 27 Aug 2015 Base scenario with the Social Security Primary Insurance Amount (PIA) as the independent variable. PIA is the amount of benefits due at Full Retirement Age (FRA), age 66 in this case.

The PIA is in thousands of dollars.  Social Security benefits begin at age 66, the retiree’s FRA. There are no Social Security Benefits 9 P age t5he 3, first Table C osmetic, black bar down right hand side. during year o3f : retirement.

PIA

TSM $000

ABM %

CCM %

LSM %

Table on Nominal $000 3: Social Initial Security Total Benefits Initial Effect Cost Initial Income Cost

Initial

RRP %

Cost

Initial

Cost

18.6% 1.0% 0 47 1,891 10.2% 3.1% 24.6% 5.7% 29.3% 3.9% Assumptions: 18.6% 1.0% 10  Base s56 2,225 11.7% 3.5% 24.6% 5.7% 29.3% cenario with the Social Security Primary Insurance Amount (PIA) as the independent 3.9% 18.6% of benefits 1.0% due 20 64 PIA 2,558 12.9% 3.8% 24.5% 29.2% variable. is the amount at Full Retirement Age (FRA), 5.8% age 66 in this case. 4.0%

The PIA housands of dollars. 1.0% 18.6% 30 73 is in t2,890 14.0% 4.1% 24.5% 5.8% 29.2% 4.0%

 Social Security benefits begin at age 66, the retiree’s FRA. There are no Social Security Benefits Discussion: during the first year obf enefits retirement.

• Adding Social Security increases income by an amount smaller than the benefit. 85% of PIA 000 to personal ABM % % reduce income.

LSM % RRP % benefits aTSM re s$ubject income taxes. TCCM axes • $000 Beginning b enefits a t a ge 6 6 c auses t he f irst y ear I RA w ithdrawals t o b e l arge e nough Initial Total Initial Cost Initial Cost Initial Cost Initial to cover Cost t 50% 1,891 above the amount 1.0% of the second ubsequent years. 5.7% 18.6% 0 spending a47 10.2% and s3.1% 24.6% 29.3% 3.9% 10

56

2,225

18.6%

1.0%

11.7%

3.5%

24.6%

5.7%

29.3%

3.9%

20

64

2,558

18.6%

1.0%

12.9%

3.8%

24.5%

5.8%

29.2%

4.0%

30

73

2,890

18.6%

1.0%

14.0%

4.1%

24.5%

5.8%

29.2%

4.0%

Discussion: Savings Account Size Of the alternate spending models, only CCM is sensitive to 10 3 TPIA able 4: Rthe emove black ars by an amount smaller than the benefit. 85% of • Adding Social Security benefits increases ncome increasing thePage PIA. As5the increases, gap between CCMib savings increase income and may push IRA and TSM widens. Recall thatare thesCCM function definition benefits ubject to personal income taxes. Larger Taxes retirement reduce income. withdrawals into higher includes natural log of ExpTar. ORP • Savings Beginning benefits aestimates t aEffect ge 66 cExpTar auses tas he 5% first year IRA withdrawals to tax be lbrackets. arge enough to cover Table 4: the Initial Account Sizes on Nominal Income of initial savings plus the PIA. Because of ExpTar, Blanchett’s spending at 50% above the amount of the second and subsequent years.

Except for CCM the Table 4 alternate spending model percent[2013] function generates curves that are not linearly related to Assumptions: ages arevsimilar to those in Table 3. As with Social Security TSM are the other alternate models.

For larger PIAs  asBase scenario except spending initial savings balance is the independent ariable. benefits CCM is sensitive to savings account balances. theCCM spending curves away from TSM accounting for Savings sizes are ishift n millions of dollars. increasing initial income. CCM is the most sensitive to Social  Account sizes are increased proportionally to the total saving size.

Security benefits.

Size

10 Page 53 Table 4: R emove black bars TSM $000 ABM % CCM % Initial

Total

Initial

Cost

Initial

LSM %

Cost

Initial

RRP %

Cost

Initial

Cost

Table Account 18.6% Sizes Effect on Nominal Income 3.1% $1M 4: Initial 1.0% 47 Savings1,891 10.2%

24.6%

5.7%

29.3%

3.9%

$2M

4.5%

24.6%

5.7%

29.3%

3.9%

10.2%

3.1%

24.6%

5.7%

29.3%

93

3,702

18.6%

1.0%

15.9%

Assumptions: $3M 1.0% 5,465 initial 18.6% 5.2% 5.7% 29.3% 3.9%  Base 137 scenario except savings balance is the 19.4% independent variable. 24.6% $4M 181 sizes are 7,198 21.8% 5.8% 24.6% 5.7% 29.3% 3.9%  Savings in millions 18.6% of dollars. 1.0%  Account increased 18.6% proportionally to the 23.8% total size. $5M 224 sizes are 8,921 1.0% 24.6% 5.7% 29.4% 3.9% saving 6.2% Discussion:

TSM $000 ABM % CCM % LSM % RRP % • Size Increasing savings Total increases tInitial he size of the IRA whose larger dCost istributions increase taxes, decreases Cost income.

Initial

Cost Initial Initial Cost which Initial $1M

47

1,891

18.6%

1.0%

3.9%

$2M

93

3,702

18.6%

1.0%

8 15.9%

4.5%

24.6%

5.7%

29.3%

3.9%

$3M

137

5,465

18.6%

1.0%

19.4%

5.2%

24.6%

5.7%

29.3%

3.9%

$4M

181

7,198

18.6%

1.0%

21.8%

5.8%

24.6%

5.7%

29.3%

3.9%

$5M 224 8,921 18.6% 1.0% 23.8% 6.2% 24.6% 5.7% 29.4% 3.9%

Discussion: • Increasing savings increases the size of the IRA whose larger distributions increase taxes, which decreases income.

8

Journal of Personal Finance

54

Table 5: Rates of Return EffectTable on Nominal 5: Rates Income of Return Effect on Nominal Income

A Quantitative Evaluation of Four Retirement o except for ROR  which is tshe independent variable.

Base cenario except for ROR which is the independent ariable.

SpendingvModels Assumptions:

J. Welch

$000

ROR ABM %

Total

Initial %

TSM $000

Initial Cost

CCM %

Initial Total

ABM %

Initial Cost

LSM %

Initial Cost

CCM %

Initial Cost

RRP % Initial Cost

27 Aug 2015

LSM %

Initial Cost

RRP %

Cost

Initial

Cost

999

19.8% 0

0.0% 25

13.8% 999

19.8% 0.0%

32.2% 0.0%

13.8% 0.0%

34.6% 0.0%

32.2% 0.0%

0.0%

34.6%

0.0%

1,155

19.6% 1

0.2% 29

13.0% 1,155

19.6% 0.6%

30.6% 0.2%

13.0% 1.2%

33.6% 0.6%

30.6% 0.7%

1.2%

33.6%

0.7%

1,891

18.6% 5

1.0% 47

10.2% 1,891

18.6% 3.1%

24.6% 1.0%

10.2% 5.7%

29.3% 3.1%

24.6% 3.9%

5.7%

29.3%

3.9%

3,033

17.3% 10

2.7% 76

3,033 7.2%

17.3% 5.8%

18.3% 2.7%

10.5% 7.2%

23.9% 5.8%

18.3% 7.9%

10.5%

23.9%

7.9%

11 Page 54, Table 5: Remove black bar on right side.

Table 4: Social Security Benefits Effect on Nominal Income Discussion: • income As the Rincreases.

OR increases ncreases initial TSM initial TSM income increases.

Assumptions: The income alternate sspending mith odel initial income dvantage SM declines arger aR ue to the loss of real e spending model •initial advantage over TSM dSeclines for laarger RORs odver ue Tto the lA oss of fror eal Base cenario w the ocial Security Primary Insurance mount (lPIA) s ORs the idndependent compounding o f r educed a sset r eturns a nd i ncreased t axes. g of reduced asset returns and i ncreased t axes. variable. PIA is the amount of benefits due at Full Retirement Age (FRA), age 66 in this case.

• The gcrows ost The of aes arly, distributions rows as ROR increases. arly, larger distributions R iarger ncreases.

POR IA ils in thousands of dgollars.  Social Security benefits begin at age 66, the retiree’s FRA. There are no Social Security Benefits during the first year of retirement.

PIA models TSM $000 ABM models % CCM % to ROR. of the retirement spending All of the areretirement sensitive to spending ROR. are sensitive

Rate of Return $000 (ROR)

Initial

Total

Initial

LSM %

RRP %

later in the plan, more so for higher than forCost low RORs. Initial Cost Initial Cost RORs Initial

Cost

Thus Cost3.1% increases24.6% as ROR increases.

18.6% 1.0% 1,891 10.2% 5.7% 29.3% 3.9% The ROR reflects0 the retiree’s 47 investment strategy. A low ROR her RORindicates increases income.

Higher As early ROR income increases increases income. the assets As early that income generate increases returns the assets that generate returns 10 56 asset 2,225 11.7% 3.5% 24.6% 5.7% 29.3% 3.9% a willingness to sacrifice returns18.6% to achieve 1.0% For the purpose of comparison, RORs are considered average portfolio stability.20 A high ROR a desire to achieve 18.6% 1.0% 64 indicates 2,558 12.9% 3.8% 24.5% 5.8% 29.2% rates and the volatility of the RORs would impact all4.0% spending his reduces compounding decrease. laterThis in athe reduces plan,level compounding more so for higher later RORs in the plan, than for more lowso for higher RORs than for low greater returns by30 tolerating higher 18.6% Since 1.0% 73 2,890 of volatility.

14.0% 4.1% 24.5% 5.8% 29.2% 4.0% models similarly. these modelsDiscussion: are deterministic, as opposed to probabilistic, their s Cost increases RORs. ROR increases.

Thus increases as ROR increases. results areasmore realistic forCost low RORs because low volatility Inflation • Adding Social Security benefits increases income by an amount smaller than the benefit. 85% of more closely approximates the constant ROR assumption. benefits are subject to personal income taxes. Taxes reduce income.

Table 6 shows the effect of inflation on the retirement spending • Beginning bconsidered enefits t age 66 tocauses the first year IRA withdrawals to band e large nough to of cover the purpose ofthe comparison, RORs the are purpose of acomparison, average rates RORs and are the considered volatility average of rates the evolatility All of retirement For spending models are sensitive ROR. models. spending at 50% above the amount of the second and subsequent years.

Higher increases income.

As earlyall income increases the similarly.

All of the models are sensitive to inflation. would impact allROR spending the RORs models would similarly. impact spending models assets that generate returns decrease. This reduces compounding

12 Inflation Page 54, Table 6: Remove black bar on right side.

n

Table 6: Inflation’s Effect on Nominal Income

Table 6 shows the effect of inflation Tableon 6 shows the retirement the effectspending of inflation models. on the retirement spending models.

Assumptions:  Standard scenario except inflation rate is the independent variable.  For ABM the inflation rate is the Basic Living inflation rate.

Inflation

TSM $000

ABM %

CCM %

LSM %

RRP %

% 6: Inflation’s Initial Total onInitial Cost Initial Cost Table Effect Nominal Table 6:Income Inflation’s Effect on NominalInitial Income Cost

Initial

Cost

0

62

1,735

31.6%

4.5%

8.7%

3.1%

8.8%

2.4%

27.1%

4.3%

2.5

47

1,891

25.0%

1.0%

10.2%

3.1%

24.6%

5.7%

29.3%

3.9%

5

35

2,053

3.2%

-­‐2.4%

11.7%

3.0%

45.9%

8.7%

31.3%

3.3%

10 18 2,356 -­‐28.8% -­‐6.8% 14.5% 2.4% 112.1% 12.9% 34.1% 2.0%

Discussion: • ABM’s negative values at higher inflation rates are a consequence of Healthcare’s inflation rate being 2.33 times the Basic Living inflation rate and by increasing Healthcare’s proportion of spending at age 85.

©2015, IARFC. All rights of reproduction in any form reserved.

9

A Quantitative Evaluation of Four Retirement Spending Models

Volume 14, 2 J. Issue Welch

13 Page 55, Table 7: Remove Vertical black bars

27 Aug 2015

55

Table 7: Longevity Effect on Income Assumptions:  Base scenario except for life expectancy. Retiree’s age at the end of the plan is the independent variable.

Term Age

TSM $000 Initial

Total

ABM % Initial

CCM %

Cost

Initial

LSM %

Cost

Initial

RRP %

Cost 1.9%

Initial 22.5%

Cost

80

72

1,393

17.0%

0.9%

5.1%

1.1%

14.0%

2.8%

90

50

1,798

18.6%

1.1%

10.8%

3.2%

22.9%

4.9%

53.1%

3.7%

95

44

2,038

18.4%

0.7%

13.1%

4.4%

27.1%

7.0%

60.9%

4.0%

100 40 2,307 17.5% -­‐0.3% 14.7% 5.1% 31.1% 9.2% 66.5% 4.2%

Discussion: • As the term of the plan increases, savings are spread over a longer period of time causing initial income to decrease.

• The compounding of asset returns over a longer time period will increase total income.

• ABM’s uneven results are a consequence of Healthcare’s inflation rate being 2.33 times the Base Living inflation rate and the increasing of Healthcare’s proportion of income at age 85.

This idiosyncratic behavior is not shared by the other alternate spending models because their ROR and inflation rate’s relative positions do not change over the span of retirement.

Conclusion

Increasing the inflation rate decreases TSM initial income because the same assets are consumed at a faster rate. Total spending rises because it includes inflated dollars at the end of the plan. The alternate spending models’ initial income also decrease but at a slower rate relative to TSM because, in a high inflationary environment, it is profitable to move spending forward from the end of the plan.

Financial advisors and retirees benefit from being aware of spending plan issues. Some factors to be considered when considering an alternative spending model are:

Longevity A common way to protect against outliving savings is to set the plan termination to an age which has little likelihood of being reached [Tresidder 2012]. Such a strategy trades lower initial income for a higher comfort factor. In Table 7 we view the consequences of this strategy.

All of the models are sensitive to changes in the length of the plan.

10

1.

Initial disposable income (Table 2): The purpose of the alternate spending models is to plan retirement spending to match spending levels to what the retiree typically needs. This may bring money forward from late in retirement to early retirement when the retiree is more active.

2.

Cost of the strategy (Table 2): Savings withdrawals early in retirement reduce the compounding of savings returns, and correspondingly reduce disposable income over the totality of retirement. Different alternate spending models have different costs.

3.

Estate size for ages in the mid 80’s (Figure 3): Most retirees plan their finances with the assumption that they will live into their 90’s. Retirees should consider that their mortality path may be similar to the general population making the size of the estate in the early 80’s something to be considered. The plan does not end at age 85 but the savings during the mid 80s are the estate.

If we accept the retirement survey data as credible, the fit of the spending models to the data as reasonable, and the optimizer as accurate, then younger retirees can benefit from increasing their spending early in retirement and then letting nature take its course.

Journal of Personal Finance

56 References

Basu, Somuath. (2005). Age Banding: A Model for Planning Retirement Needs. Financial Counseling and Planning, 16 (1), 29-36.

Bengen, William P. (1994). Determining Withdrawal Rates Using Historical Data. Journal of Financial Planning, 7 (4), October, 171-180.

Bernicke, Ty. (2005). Reality Retirement Planning: A New Paradigm for an Old Science. Journal of Financial Planning, 18 (6), 54-60.

Blanchett, David, Maciej Kowara, and Peng Chen. (2012). Optimal Withdrawal Strategy for Retirement Income Portfolios. Morningstar Report, Working Paper, May 22, 2012.

Blanchett, David. (2013). Estimating the True Cost of Retirement. Morningstar Report, Working Paper, November 5, 2013.

Pfau, Wade D. (2015). Making Sense Out of Variable Spending Strategies for Retirees. Social Research Network, March 16, 2015. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2579123 Raabe, William, and Richard B. Toolson. (2002). Liquidating Retirement Assets in a Tax-efficient Manner. AAII Journal, May. Roy, S. Katherine. (2014). The Lifecycle of Spending. J.P. Morgan, Retirement Insights, January.

Saftner, Don and Philip R. Fink. (2004). Review Tax Strategies to Ensure That Retirement Years are Golden. Practical Tax Strategies, May. Tresidder, Todd R. (2012). Are Safe Withdrawal Rates Really Safe? Journal of Personal Finance, 11 (1), 113-142.

Welch, James S. (2015). Mitigating the Impact of Personal Income Taxes on Retirement Savings Distributions. Journal of Personal Finance, 14 (1), 17-27.

Appendix A: Abreviations ABM: Basu’s [2005] Age Banding Model proportions annual spending into categories, assigns a different inflation rate to each category, and adjusts the category proportions based on age.

CCM: The Changing Consumption Model is Blanchett’s [2013] model varyies retirement spending using a non-linear function that tracks consumption over retirement.

ExpTar: The after-tax, total expenditure (spending) target for CCM’s first year of retirement. ORP’s estimate of ExpTar is 5% of the sum of initial saving account balances plus Social Security Benefits.

FTAB: Final Total Account Balance is the sum of all three savings accounts at the end of the plan. FTAB is also known as the plan’s estate. The FTAB is a settable parameter.

FRA: Full Retirement Age is the age at which a person first becomes entitled to full or unreduced retirement benefits.

LSM: The Lifecycle of Spending Model [Roy 2014] tracks retirement spending across retirement based on credit card data.

PIA: Principle Insurance Amount is the amount of Social Security benefits for which the retiree is eligible at FRA. ROR: is the profit on an investment expressed as a percentage of investment’s value.

RRP: Reality Retirement Planning is Bernicke’s [2005] model for varying retirement spending based on Bureau of Labor Statistics’ data.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

57

Appendix B: Bernicke’s Example Bernicke’s [2005] paper includes a Hypothetical Retirement Income Projection example. He uses the FTAB method to compare his RRP model to TSM. Figure 6 compares ORP’s TSM and RRP income to Bernicke’s TSM and RRP income. The purpose of this comparison is to validate ORP’s rendering of RRP. Bernicke assumes a tax rate of 15.3% before age 62 and 9.6%

thereafter. ORP reports that 37% of taxable income falls into 15% bracket throughout retirement with an average tax rate of 8.8%.

In the left panel ORP’s initial income is $55,000 which is less than Bernicke’s assumed $60,000. This is consistent with Bernicke’s TSM depleting savings five years early. $60,000 initial spending is too high. In the right panel ORP’s initial spending is $73,000 which is greater than Bernicke’s assumed $60,000 initial income. This underspending is consistent with Bernicke’s $2.3M FTAB.

Figure 6: Comparison of ORP Income to Bernicke’s Example Assumptions:  A 55 year old married couple,  Both are retiring at age 55,  30 year plan ending at age 85,  $12,000 Social Security benefits for each spouse beginning at age 62. (This translates to PIA of $16,000.),  2% per year increase in Social Security benefits,  3% spending inflation rate,  $800,000 in a 401K, with no other savings,  8% 401K ROR.  ORP assumes a zero FTAB; Bernicke computes the FTAB.  Bernicke assumes $60,000 initial income; ORP computes the maximum initial income.

Traditional Spending

$000 150

$000 150

130

130

110

110

90

90

70

70

50 Age 55

60

65

70

75

80

85

50 Age 55

Reality Retirement Planning

ORP Bernicke

60

65

70

75

80

85

Discussion: • The left panel compares ORP’s and Bernicke’s disposable income for the TSM spending model.

• The right panel compares ORP’s and Bernicke’s disposable income for Bernicke’s RRP spending model.

• Income is after personal income taxes have been paid.

Bernicke assumes a tax rate of 15.3% before age 62 and 9.6% thereafter. ORP reports that 37% of taxable income falls into 15% bracket throughout retirement with an average tax rate of 8.8%.

In the left panel ORP’s initial income is $55,000 which is less than Bernicke’s assumed

58

Journal of Personal Finance

Simplifying RIA Oversight Guy Baker, Ph.D. Student at the American College, Wealth Team Solutions Abstract The Securities and Exchange Commission (SEC) provides oversight to RIAs managing at least $100 million of Regulatory Assets under Management (RAUM). The number of Registered Investment Advisors (RIAs) managing these assets has increased significantly since 2004. In addition, the SEC has increased pressure to provide broader oversight of RIAs and Investment Advisor Representatives (IARs). The SEC and organizations such as the Investment Advisor Association (IAA) are lobbying Congress for more resources to provide adequate oversight. Even the smaller firms (managing less than 3.6% of RAUM) are held to the same level of oversight, expense and reporting requirements as the large, very complex investment advisors who manage 96.4% of the assets (NRS, 2014).

This paper identifies and explores five key areas the SEC has marked as the most probable areas of failure affecting the markets and consumers. It is questionable whether these major concerns highlighted in SEC releases, no-action letters and speeches are relevant to the smaller firms who do not take custody or provide true discretionary management of assets. The level of reporting and scrutiny placed on smaller RIAs using a simple business model is likely a waste of SEC resources and taxpayer money. If the SEC delegated oversight of these less complicated RIAs to trained, licensed and regulated compliance consultants, the SEC would reduce their regulatory workload and could refocus resources on larger firms which are susceptible to conflicts of interest and other compliance violations established in the 1940â&#x20AC;&#x2122;s Act. This small change would actually enhance regulation of smaller firms, and would bring meaningful relief to them at the same time. The advantages are significant. This change would allow smaller firms to provide a higher level of service and build better relationships with their clients.

Š2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

59

Simplifying RIA Oversight The two most significant laws governing RIAs are the Investment Advisers Act of 1940 (Act) and the Dodd-Frank Act passed in 2010. Dodd-Frank was significant because it redefined new categories of advisors under supervision by the SEC. It limited SEC oversight to firms with $100 million of RAUM or more.

Advisors with less than $100 million were forced to move to state supervision. According to the ADV Part 1 data filed by all RIAs, the exercise of discretionary authority over accounts has caused increased regulation and oversight of even smaller firms (IAA-NRS, 2014).

Even though Dodd-Frank defined small advisors, the number of Investment advisors under SEC supervision has continued to increase. Prior to the expulsion of these small advisors, the IAA reported there were 10,895 firms managing $61.7 trillion of assets at the end of 2014. Assets for SEC registered advisors had increased 25% since 2012. IAA determined the 112 largest advisors (1% of all SEC registered RIAs) accounted for more than 52.6% of all RAUM. Advisors with less than $1 billion of RAUM represented 71.5 percent of all advisors, collectively managing only 3.5 percent (NRS, 2104).

Congress’ primary purpose for implementing the Act was to eliminate abuse believed to have contributed to the 1929 stock market crash and eventual depression. The Commission ascertained advisers required scrutiny because transactions occurred in volume that could affect interstate commerce, national securities exchanges and other securities markets, the national banking system, and the national economy (Cornell Law, no date).

Before the Act passed, the SEC used protocols Congress eventually legislated into law. The SEC actions were based on a congressionally-mandated study of investment companies which included investment counsel and investment advisory services (NRS, 2014).

The NRS IAA report (2014) focused on how Congress approached fiduciary duty, concluding the advisor’s primary fiduciary duty is to disclose any and all conflicts of interest which could negatively impact a client’s decision and outcome.

A SEC commentary (2013) asserts, unless these conflicts are removed, the true fiduciary relationship between the advisor and the client is impossible. The SEC report stressed, if the advisor is faced with a conflict of interest, they will be unable to deliver unprejudiced advice and would likely favor their own financial interests. The Act reflected congressional recognition of the Most RIAs are small firms. The median RAUM for all SEC fiduciary relationship advisors have with clients. The Act sought their ADV showing the median firm RIAs is $331.2 million, with to expose potential and real conflicts of interest that might cause had nine non-clerical employees and 26–100 clients holding 100 advisers to either, consciously or unconsciously, render advice accounts under advisement. The SEC reports 70% have fewer that would benefit the advisor or the firm, rather than exclusively than 50 employees, while 57 percent have fewer than five.

A sigclient (Staff of SEC, 2013).

Simplifying RIA the Oversight nificant aspect of SEC scrutiny is the custodial role of the RIA. two most laws governing RIAs are the Investment Advisers Act of 1940 More than two thirds of the RIAsThe report theysignificant do not custody assets (NRS, 2014).

(Act) and the Dodd-Frank Act passed in 2010. Dodd-Frank was significant because it redefined

new categories of advisors under supervision by the SEC. It limited SEC oversight to firms with $100 million of RAUM or more. Advisors with less than $100 million were forced to move to

state supervision. According to the ADV Part 1 data filed by all RIAs, the exercise of discretionary authority over accounts has caused increased regulation and oversight of even

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Common Violations that may lead to SEC Investigations include: Misrepresentation or omission of important information about securities Manipulating the market prices of securities Stealing customers’ funds or securities Violating broker-dealer responsibility to treat customers fairly Insider trading (violating trust relationship by trading on material, non-public information about a security.)

Selling unregistered securities Source: SEC Release 2013

The SEC provides a roadmap to the kind of violations that lead to an investigation. Of these six, only misrepresentation or omission of information applies directly to RIAs which fit a simple model. These less complex firms should be exempt because of the improbability they would violate any of the other fundamental, statutory regulations the SEC is seeking to prevent (SEC release, 2013).

The current scrutiny and oversight burden simple RIAs endure because of SEC scrutiny is both expensive and overly strict.

The manpower costs and the resources and hours expended by both the SEC and these RIAs to identify and correct anticipated violations are unnecessary because these violations are virtually nonexistent. This paper seeks to demonstrate an alternative form of supervision and compliance for simple firms would be sufficient to protect the public.

For purposes of this paper, a “Direct” (or simple) RIA is defined as an independent, “fee only” or “fee based” firm, which deals directly with portfolio providers who perform basic portfolio management and relies on these companies to calculate returns which are then communicated to the public. These RIAs have no regulatory infractions on their ADV, do not trade individual securities, do not take custody of assets, have no discretionary control over the funds, and give only basic financial planning advice. They get paid a percentage of RAUM and do not participate in performance fees as a general practice.

Literature Review Several sections in the SEC codify a Registered Investment Advisor. Section 202 (a) (11) of the Act defines an Investment Advisor as “any person or entity who, 1) for compensation, 2) engages in the business of, and provides advice to others, and 3) issues reports or analyses regarding securities.” The SEC states a person or entity must satisfy all three elements to fall within the definition of “investment adviser.” The SEC staff also explains how the Act applies to financial planners, pension consultants

and other persons who provide investment advice as part of their services, as well as newsletter editors who give advice to subscribers (Staff of SEC, 2013).

Daniel Gallagher, Commissioner of the SEC since 2011, remarks to the annual FINRA conference, “The SEC lacks resources to oversee the roughly 11,000 registered investment advisers. The SEC annually examines about 9% of them each year.” Gallagher recommended the SEC require advisers to hire an examiner to review their operations annually (Schoeff, 2014).

A March 2015 article reported that Andrew Ceresney, Director of the SEC Division of Enforcement, testified before the House Subcommittee on Financial Services. He put the RIA industry on notice and warned them, the SEC is increasing regulation of industry practices and exploring new ways to identify and prosecute misconduct. Ceresney stated, “Investment advisors and the funds they manage also remain a focus of the enforcement division, and we regularly investigate and bring actions against investment advisors for conflicts of interest, misrepresentations regarding performance or investment strategies, breaches of their duties to their clients, and other fraudulent conduct” (Corbin, 2015).

Ceresney testified that in 2014 his division successfully prosecuted 755 enforcement actions garnering $4.16 billion in penalties. He said the SEC had won every case that was adjudicated in its administrative court, but only won 60% of the cases remanded to Federal Court. This hearing focused on SEC procedures used to prosecute enforcement actions and its fairness. Members of the Congressional committee questioned the use of internal administrative courts and argued the SEC did not give defendants a fair trial in those forums. Ceresney defended the SEC and said they would rather take the cases to Federal Court. The testimony included evidence the SEC is using more outside resources and needed to expand their staff to handle the oversight (Corbin, 2015).

David Tittsworth, President and CEO of the IAA wrote in an email, “Even a casual observer can figure out SEC Chairwoman Mary Jo White is continuing to make enforcement her primary priority at the SEC. There is really only one reasonable course of action for investment advisory firms and investment companies -- make sure you have a robust and dynamic compliance program in place and avoid even gray areas.” (Corbin, 2015) The SEC is focused on robust enforcement of the Act. Exactly how prevalent are these infractions? According to the 2014 ADVs, there were 9,380 registered investment advisers (86.1 percent) who reported they had no disciplinary history at all.

This was also true in 2013 when 9,063 advisers (86.0 percent) reported the same thing. Newly registered advisers number 8.9 percent of the total investment advisers registered with the SEC (973 of 10,895), but only 5.0 percent (76) of them reported disciplinary issues out of the 1,515 RIAs who admitted infractions.

Of those, 81 reported being charged with a felony, but only 23 firms (0.2 percent of all advisers) were convicted or pled guilty or nolo contendere to the charges (White, 2015).

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2 Top 6 Items on ADV

# of Advisors

61

% of total Infractions

% of total Advisors Nature of infraction

11C2

411

8.37%

3.77%

11C4

401

8.17%

3.68%

Had an order entered against them

11C5

403

8.21%

3.70%

Had a fine imposed

11D2

749

15.26%

6.87%

Involved in the violation of an investment-related activity

11D4

604

12.30%

5.54%

In the last 10 years had an order issued against them

11E2

609

12.41%

5.59%

Involved in an infraction

Had a SRO issue a violation against them for an investment-related activity

Source: National Registry Services

In item 11, the ADV has 24 required disclosures. An analysis of the ADV shows there were 159 (1.4 percent) RIAs who said they made a false statement or omission to the SEC and were found guilty. Another 411 firms (3.9 percent) reported the SEC found they had violated SEC regulations or statutes. Only 12 firms reported the SEC denied, suspended, revoked or restricted their ability to provide advisory services. Of the 1,515 advisers showing they committed at least one disciplinary event, 767 reported at least one of those events involved the firm or its supervised persons (as opposed to an affiliate). Since being first registered, 76 (7.8 percent of new advisers) were disciplined and only 33 acknowledged an event which involved them or a supervised person. Private fund advisers made up 303 of all newly registered advisers (31.1 percent) and accounted for 26 of the 76 new advisers (34.2 percent) reporting disciplinary events (NRS, 2014).

Although any infraction can impact public trust, only 3.68 percent reported an order entered against them which had to be disclosed on their ADV. Of those, one third were Private Funds.

With the growing number of RIAs putting significant strain on SEC resources to properly supervise advisors and prosecute violators, the SEC admitted they are searching for ways to delegate supervision (Corbin, 2015). This was part of the reason Dodd Frank demoted firms with less than $100 million RAUM to state supervision. Both the SEC and IAA have said regulation of the financial services industry will be difficult without an increased budget and personnel (Cummings and Finke, 2010). This problem is not new. J.A. Gray (1994) pointed out the problem in his 1994 American Bar Journal article.

To find violations, the SEC and FINRA conduct targeted exams, known as sweeps. Sweeps are used to identify emerging issues and determine how to regulate them. The number of firms examined varies, and are chosen based on a variety of factors, including the level and nature of business activity in particular areas, customer complaints and regulatory history, as well as, prior examination findings. Limiting their inquiry to a small number of firms, the sweeps reduce the regulatory burden on the majority of firms (FINRA, 2015). Here is a list of some of the recently identified problems emerging from the sweeps.

Problems Emerging from Sweeps Order Routing and Execution Quality of Customer Orders Cybersecurity High Frequency Trading Spot-Check of Social Media Communications Alternative Trading Systems Business Continuity Plans Order Protection Disclosure Practices Spot-Check of Non-Traded REIT Communications Alternative Trading Systems Conflicts of Interest Spread-Based Structured Products Spot-Check of Reverse Convertibles Advertising and Sales Literature Placement Agents Bank Broker-Dealer Services Direct Market Access, Naked Access, Electronic Access or Sponsored Access (“DMA”) Non-Investment Company Exchange Traded Products Communications Retail Forex Trading Structured Products Review Retail Municipal Securities Transactions Retail Sale of “Gas Bonds” Municipal Underwriting and Municipal Derivative Instruments Exchange Traded Funds Sale and Promotion of Non-Traded REITs Hedge Fund Advertisements and Sales Literature False and Misleading Rumors Information Barriers Collateralized Mortgage Obligations Use of Professional Designations Source: FINRA 2015

Date Announced July 2014 January 2014 July 2013 June 2013 May 2013 November 2012 October 2012 September 2012 September 2012 July 2012 November 2011 March 2011 October 2010 October 2010 August 2010 June 2010 November 2009 July 2009 June 2009 May 2009 May 2009 May 2009 Marcy 2009 January 2009 July 2008 January 2008 December 2007 September 2007

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Financial Industry Affiliations Related person is:

Number of Advisors

Percentage of Advisors

Broker-dealer, Municipal securities dealer government securities broker or dealer

2,323

21.3%

Other investment advisor (including financial planners)

4,090

37.5%

Registered municipal advisor

393

3.6%

Registered security-based swap dealer

61

0.6%

Major security-based swap participant

7

0.1%

Commodity pool operator/trading advisor (wheter registered or exempt

1,893

17.4%

Futures commision merchant

250

2.3%

Banking or thrift institution

824

7.6%

Trust company

745

6.8%

Accountant or accounting firm

725

6.7%

Lawyer or law firm

463

4.2%

Insurance company or agency

1,752

16.1%

Pension consultant

589

5.4%

Real estate broker or dealer

469

4.3%

Sponsor or syndicator of limited partnerships (or equivalent), excluding pooled

624

5.7%

3,856

35.4%

investment vehicles Sponsor, general partner, managing member (or equivalent) of pooled investment vehicles Source: National Registry Services - IAA Evolution Revolution

This IAA shows sixteen types of advisors. Of the 10,895 SEC advisors, 4,090 are classified as traditional, “fee only” RIAs and 2,323 who are dual registered RIAs affiliated with a BrokerDealer. The rest provide various services, including law, accounting and trust services. In 2014, 6,889 advisers (63.2%) reported they only engage in giving investment advice (NRS, 2014).

Dodd-Frank created several new exemptions: 1) Advisers Solely to Venture Capital Funds - Section 203(l). 2) Advisers Solely to Private Funds with less than $150 Million in AUM - Section 203(m) and, 3) Family Office exclusion, Section 202(a)(11).

These and other exemptions evidence the SEC is willingness to acknowledge there are circumstances and business models that should be exempt from the intent of the law (Muller, Baris & Chertok, 2011).

After reviewing the violations and SEC no-action letters, the SEC has intensified focus on the five areas: 1) breaches of RIA fiduciary duty, 2) conflicts of interest, 3) misrepresentations regarding performance or investment strategies, 4) custody under the 2009 amendment of the Act, and 5) discretionary control.

Breaches in Fiduciary Duty Broker-dealers and registered representatives are held to a suitability standard, but it is the RIA/IAR who is held to a much higher fiduciary standard. In 1963, the U.S. Supreme Court ruled Section 206 of the 1949 Act imposed a fiduciary duty on RIAs based on the SEC v. Capital Gains Research Bureau, Inc., 375 U.S. 180 (1963). It was the Supreme Court’s first interpretation of the Investment Advisers Act. This case is cited by lower courts and the SEC when taking enforcement action against RIAs (Abromovitz, 2012).

The SEC committee outlined five core principles which define the fiduciary standard: 1) Put the client’s best interests first; 2) Act with prudence, that is, with the skill, care, diligence and good judgment of a professional; 3) Do not mislead clients--provide conspicuous, full and fair disclosure of all important facts; 4) Avoid conflicts of interest, and 5) Fully disclose and fairly manage, in the client’s favor, unavoidable conflicts (Committee, 2015).

The fiduciary standard goes beyond making certain an investment is suitable for the client. The RIA has the absolute duty to do the right thing for their clients. If an RIA is sanctioned, the SEC and state regulators will issue press releases to announce litigation brought against the RIA for violations. The local news media might also report the story while posting it on the internet and social media. The ability and proclivity for prospective clients to search for information has increased the probability the firm will be damaged (Regmaven, 2015).

Even allegations must be included in the firm’s ADV. The RIA is required to make full disclosure of legal and disciplinary events such as: criminal or civil actions brought against firm personnel; violations of investment-related statutes or regulations, and any violation of self-regulatory rules. The breach of fiduciary duty can trigger regulatory scrutiny for many years to come. Previous mistakes can cause the firm to be viewed as a higher risk. This will result in more scrutiny and oversight. If there are clear and obvious violations of the fiduciary duty, the SEC will refer the matter to the Division of Enforcement. If the SEC has reason to think client money is at risk, the enforcement division will act swiftly (White, 2015).

The SEC has sanctioned RIAs for not having emergency or

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

contingency plans, in case of disaster. A plan should address: 1) Where employees will conduct business if the offices are destroyed or severely damaged, 2) how employees can be contacted, 3) how to contact clients, regulators, suppliers and other service providers, and 4) a succession plan if a key member of the firm dies or becomes disabled (SEC Action, 2015).

Under Rule 206(4)-7, SEC registered advisors are required to implement and audit their policies and procedures. Robust policies and procedures, if followed, can help RIAs live up to their obligation as fiduciaries (Corbin, 2015).

In extreme violations, IARs could be criminally prosecuted by the Justice Department for a breach of the standard. The SEC only has the authority to seek civil sanctions. Madoff and Sandford are well-publicized cases. They used influence and reputation to bilk investors by establishing trust through non-business dealings and affiliations.

A more common fiduciary breach is known as “cherry-picking.”

The SEC filed a civil complaint alleging a pattern of unjust enrichment and harm to clients. The advisor created a “cherry-picking” scheme and bought securities during the day in one single account. He then waited until later in the same day and al-

63

located those purchases based on which securities appreciated in value (SEC Press Release, 2013-168). This is just one example of numerous published deficiency letters.

The benefit of advisors communicating their fiduciary obligation demonstrates commitment to the client’s financial wellbeing. According to a TD Ameritrade Institutional survey, the top reason investors choose to work with an independent RIA is the requirement to offer advice that is solely in the best interest of their clients (Barratt, 2011). In addition, investors are being encouraged by journalists to use an RIA because they are fiduciaries. In an article published by Reuters columnist, Linda Stern, she called the fiduciary standard “a superior one” and said “a fiduciary is legally bound to put your interests above her own” (Stern, 2011).

Conflicts of interest The 1940s Act was written primarily to eliminate conflicts of interest that would materially and negatively impact clients making a decision as to whether or not to invest. In the instructions provided by the SEC for its ADV Part 2A requirement, the SEC provides specific instructions regarding an advisor’s fiduciary

Conflicts of Interest Appearing in ADV Part 2

Applicable to Simple RIAs?

Can you, in plain English, explain any conflicts you may have or are reasonably likely to occur during the services you provide?

Yes

As a fiduciary, do you make full disclosure of all material facts related to your advisory relationship? Advisors should seek to avoid conflicts, if at all possible. You must provide sufficient specific facts so the client is able to understand the conflict and the implications. This may require more information than required in the ADV and in more detail than in brochures or other handouts.

Yes

Even if the advisor is not required to give a brochure, they may be required to provide material information about any conflicts of interest and disciplinary information.

Yes

If there is compensation for the sale of securities, do you disclose if there is an incentive to recommend investment products based on the compensation received?

Not Applicable

If you are paid performance fees based on a share of capital gains or capital appreciation of assets (such as a hedge fund or other pooled investment vehicles), are you fully disclosing them?

Not Applicable

Do you get paid from other investment advisors when you recommend their services and do you receive compensation directly or indirectly from them?

Possible

If you have a material financial interest in securities which you or a related person recommends, do you benefit?

Not Applicable

Do you invest in the same securities you are recommending to your clients and is there a resulting conflict of interest because you invested?

Not Applicable

If you buy or sell for your own account at the same time you buy or sell for the account of a client, how does this impact you or the client?

Not Applicable

Is there reasonableness and procedures you follow when recommending a client utilize a broker-dealer for products or services other than the execution of trades?

Not Applicable since Simple RIAs are not affiliated with a BD

If clients are required to pay commissions, are the commissions higher than those charged to other clients because of soft dollar benefits?

Not Applicable

How are brokerage commissions used when buying products and services for clients to purchase research?

Not Applicable

Do you, as a result of the volume of business, receive referrals for directing transactions to a particular broker-dealer?

Not Applicable

Does your recommendation, request or requirement to execute trades through a specified Broker-Dealer create any revenue or material conflict of interest?

Not Applicable

Do you derive an economic benefit in the form of awards or trips from someone who is providing investment advice or advisory services to clients?

Not Applicable

If you vote the proxy for client securities, what is the procedure and policies you use for this process? How can clients obtain information regarding your vote?

Not Applicable

Do you or any of your related parties act as a portfolio manager for a wrap fee program? Are your related parties held to the same standard?

Not Applicable

Source: ADV Part 2

64

Journal of Personal Finance

responsibility to disclosure of conflicts to clients. This obligation requires the RIA to provide specific facts so “the client is able to understand the conflicts of interest you have and the business practices in which you engage, and can give informed consent to such conflicts or practices or reject them” (NRS, 2014).

The SEC acknowledges conflicts of interest are inherent in the financial services business. But they still must be disclosed.

Black’s Law Dictionary defines a conflict of interest as “a real or seeming incompatibility between one’s private interests and one’s public or fiduciary duties.” Chris Stanley (2013) puts it another way, “a conflict of interest exists if an advisor directly or indirectly benefits from a client’s particular course of action.

A “seeming” conflict of interest may exist even if a client’s particular course of action is, in fact, in the client’s best interest.”

The mere fact a RIA can provide both a fee-based management service and sell products for a commission, places both the client and the advisor in an awkward situation.

RIA in a Box points out there are basically only two types of RIAs. There is the independent RIA who is “fee only” and the “fee based.” Some IARs are affiliated with a broker-dealer. But there is a new “hybrid RIA” emerging. This model allows the RIA to have both a broker-dealer affiliation and be an independent RIA, not under the supervision of a BD. This model gives the advisor greater independence, control, and financial reward, while allowing them to broker products and provide advisory services. This form of doing business is under greater regulatory scrutiny because of the perceived potential for conflicts of interest (RIA in a Box, 2015).

The term, conflict of interest or conflict appears 21 times in the ADV Part 2. The SEC wants the RIA to clearly understand and then disclose any conflicts which may interfere with their independence and ability to serve the client as a fiduciary. Here are some of the common conflicts the SEC has identified and their applicability to Simple RIAs.

Performance Reporting and Strategies A third area is performance reporting. Rule 206(4)-1(a)(5) of the Advisers Act makes it a fraudulent, deceptive, or manipulative act to provide advertisements which contain an untrue statement of material fact or which is false or misleading. Rule 206(4)-8 makes it fraudulent, deceptive, or manipulative for an investment adviser to make any untrue statement of a material fact or omit a material fact to any prospective investor in a pooled investment vehicle. The Commission generally does not dictate calculation methodology or composite construction requirements, but what is generally expected is the results portrayed will not be misleading and will be accompanied by adequate disclosure. The calculations need to be made on a consistent basis. Calculation

methodologies should be documented in written policies and procedures (Committee, 2015).

Consistent with the SEC call for RIA ethical standards, the Chartered Financial Analyst Institute, governed by the Global Investment Professional Standards (GIPS®) committee, created standardized guidelines for reporting whether an investment firm is making profits for investors. GIPS is used for calculating and presenting investment performance for a firm to market its investment management services. At the GIPS Standards Annual Conference, Andrew Bowden, director of the SEC Office of Compliance Inspections and Examinations, discussed SEC enforcement activities when false claims of compliance with the Global Investment Performance Standards (GIPS®) are discovered. He acknowledged the GIPS standards are broadly accepted as voluntary global ethical standards. Bowden reported that “It is important to establish [false] claims of GIPS compliance, claims of verification or material misrepresentations, and false claims of performance can lead to civil and criminal penalties” (Detamore, 2014). The following eleven areas are considered by the SEC as issues RIAs should consider when attempting to adhere to both the SEC requirements and the GIPS standards.

Firms need to use a consistent, non-performance based criteria when choosing the representative account and not data which demonstrates good performance, but is not representative of actual portfolios. The Cloverdale no-action letter specifically outlined the acceptable methodology for using portfolio results in advertisements. In order to not violate 206(4), the RIA is required to disclose all material facts relevant to the comparison of the actual results to the benchmark (Cloverdale, 1986).

The example given in the Clover no-action letter compares actual results to an index, but does not disclose that the volatility of the index was materially different than the volatility of the model portfolio. In the Clover no-action letter, the SEC stated Rule 206(4)-1(a)(5) prohibits advertisements that include model or actual results, but do not show a deduction of advisory fees, brokerage or other commissions or expenses.

The Uniform Prudent Investor Act of 1995 (UPIA) is a useful benchmark for measuring RIA services. It prescribes Index funds as a safe harbor for trustees seeking to invest and manage trust assets as a prudent investor would choose to do. The trustee needs to exercise reasonable care, skill and caution. The Act details five specific duties the trustee must follow to be covered by the safe harbor. 1) Select risk and return objectives suitable to the client. 2) Provide diversification. 3) Evaluate the entire allocation of the portfolio. 4) Avoid unreasonable and avoidable costs, and 5) Consider the tax consequences (Simon, 1998). The Simon analysis details why the UPIA affords the investment advisor a range of services that are more beneficial to clients than stock selection or market timing. It also serves to address the issue of market performance reporting.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

Infraction Discretion

65

Description GIPS - The ability to implement the intended strategy. Portfolio is non-discretionary if the client dictates the allocation.

Applicable to Simple RIA Not Applicable – client dictates allocation through risk assessment.

SEC – an account is discretionary if the RIA can decide which securities to purchase and sell for the client.

Representative The performance reporting must use a consistent nonNot Applicable – only uses Account performance based criteria for choosing the representative defined benchmarks performance reporting to show results. The Clover no-action letter details the requirements.

Benchmark GIPS standard require disclosure of the benchmark used. Factors Not Applicable – depending Description like volatility are important points for comparison. Others on the benchmark used include mix of international and domestic securities, cash credit for comparison – usually quality of bonds, duration and liquidity. dictated by the fund company Applicable Net of Fees GIPS standards allows the use of gross-of-fees, but SEC requires the use net-of-fees in advertisements that include model or actual results.

Not Applicable – returns Total Return Total returns must disclose whether dividends are reinvested. provided by fund company Performance GIPS standards mandate TR include realized and unrealized gains and losses, plus income for the measuring period. Must follow the Clover no-action letter guidelines.

Performance Record Returns must reflect the term of the money manager. Horizon Not Applicable – information Portability no-action letter details the stipulated guidelines. provided by the fund company Not Applicable – returns Model: Hypothetical, These models cannot be linked to actual returns. The models can provided by the fund Backtested or be supplemental information only and does not represent actual company simulated returns trading of client accounts.

Supervised by Compliance Past Specific Presented as supplemental information only. The potential for Consultant Recommendations profit cannot be presented without presenting the potential for loss. Advertising must present a comprehensive analysis of all securities purchased during the period.

Not Applicable – returns Advertising Guidelines Excludes one-on-one presentations and individual client provided by fund company reporting from the advertising guidelines. Any performance must be shown net of fees. Responses to client inquiries are not considered advertisement.

Not Applicable – returns Significant Events Clover no-action stipulates results must disclose the impact of material market or economic conditions and the impact on provided by fund company the results. Public offerings may skew results and needs to be disclosed if it materially impacted the performance.

Recordkeeping Performance records must be maintained for yearly at least five Not Applicable – Records years. The inability to show performance records disqualifies the provided by fund company results from being used in advertising or client presentations.

Source: Global Investment Professional Standards

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Discretionary Management For GIPS, discretion means “the ability of the firm to implement its intended strategy.” The SEC definition of discretion is more explicit. In the Form ADV instructions, the SEC specifies “your firm has discretionary authority or manages assets on a discretionary basis, if it has the authority to decide which securities to purchase and sell for the client” (Form ADV). The “Simple” RIA would not have this ability.

This is an ongoing controversy regarding what constitutes discretionary authority and what assets should be included in the RAUM calculation. The SEC has not made this easy, according to Jack Waymire (2015) in a RIABiz article about the SEC and complying with the RAUM rules. He points out the SEC has not clearly defined either. There is a broad definition of AUM that includes discretionary and non-discretionary assets invested in securities, mutual funds and pooled or separately managed accounts. The proposed RAUM should be called AUMA (assets under management -discretionary assets, and advisement non-discretionary assets). Using this method, only assets covered by a fee agreement would be eligible. Assets under a Brokerdealer would not count.

Discretionary assets are assets where the management is continuous for a portfolio. This means a one-time service, such as selling assets would not count. Another term is supervisory or management. Supervisory refers to non-discretionary assets, while management refers to ongoing active management. These would be discretionary assets. The SEC requires the RIA to list non-discretionary and discretionary assets separately in the ADV. The distinction is that advisors provide advice and monitor performance of the assets, but they do not supervise. Discretionary refers to assets that are managed by advisory firms. What is management? Assets are still considered discretionary, if the advisor is the decision-maker, but has limited-trading-authority in their service agreement. Firms that conduct research, make buy/sell decisions, and execute transactions without obtaining client permission for each transaction has discretionary authority (Waymire, 2015).

Custodial Violations The SEC is vitally concerned about custody and promulgated an amendment with the 2009 Custody Rule. According to data from the ADV, two thirds of all SEC registered RIAs reported they do not custody assets (NRS, 2014). An advisor is considered to have custody if they physically hold an asset in their possession. Price Waterhouse Cooper (PWC, 2011), in one of their FS Regulatory Briefs, defined the Custody Rule by asking whether the advisor holds, directly or indirectly, client cash or securities, or has the authority to obtain possession while in the process of providing advisory services. Possession is not restricted to physical custody under the Rule. The determination of custody is based whether the advisor can direct funds for their own potential

benefit. Custody is considered to exist, if the adviser or a related persons has the “direct or indirect authority to gain possession of client cash or securities (Laurensen, 2013).

The SEC defines custody as: i) possession of client cash or securities (but not third party checks written by clients); ii) any arrangement where the advisor has the ability (under a power of attorney) to cause the custodian to withdraw cash or securities upon advisor instruction, and iii) the ability to access cash or securities through an entity, such as a pooled investment, LLC, or general partnership.

Private funds are considered to have custody even though they may not actually have access to the client funds. Importantly, advisors are considered a custodian because they can deduct fees from their client’s account. However, they may be exempt from the annual surprise audit, if this is their only authority related to those funds.

Since the SEC’s 2009 Custody Rule amendment, the number reporting custody increased from 2,493 in 2011, to 3,518 in 2014. This was a 41.1 percent increase. Only 86 advisors (0.7 percent) reported being a “qualified custodian” meaning, they actually take physical custody of client assets. The rest use a qualified custodian. The IAA reports Private Fund advisors act as both advisor and general partner to a limited partnership and are deemed a custodian.

If custody exists, the RIA must hire an independent accountant to conduct a surprise exam of the custodial arrangement annually. The auditor must verify client assets are accounted for. If the custodian is a related party, the accounting firm must register with the Public Accounting Oversight Board.

The vast majority of RIAs want to avoid being a custodian, but they may do so unintentionally. Usually, RIAs use qualified custodians like Charles Schwab or TD Ameritrade. But a RIA can take custody of client assets inadvertently. This is the result of being sloppy or ignorant of requirements. Here are some examples: 1. If the client gives their password and user ID for their 401(k) or other accounts to the RIA and they can trade assets.

2. If a RIA pays bills for clients.

3. If they have a power of attorney over assets.

4. If they take control of the client’s checkbook.

5. If they are a successor trustee on a client’s account and become a trustee at death, they are a custodian (Advisors4Advisors, 2013).

Evaluation The literature shows the SEC is making comments about increased regulatory oversight. In each of these five identified areas, the concern and regulation procedures put into place have promulgated extensive costs, utilization of resources and time.

©2015, IARFC. All rights of reproduction in any form reserved.

Volume 14, Issue 2

The question remains, is the SEC effort towards simple, small RIAs worth the time and energy. Are there enough infractions and violations to devote the resources to oversee this group?

Will the SEC and states yield enough in fines and penalties to be worth the effort? More important, will the investors be any more secure and safe from fraud, misrepresentation and malevolence?

The SEC admits auditing the smaller firms, requires significant SEC resources to properly supervise and regulate them. The SEC expends these resources, despite the fact nearly two thirds of all RIAs do not take custody, a prime area for abuse. The question needs to be asked, how much opportunity is there for any of these RIAs to violate the 1940s Act? Is it possible, many of these firms, due to the nature of their business model, are virtually exempt from many of the concerns and regulatory issues the SEC is seeking to manage?

Cost of Compliance. The 2016 Obama budget proposed $1.722 billion, an increased appropriation over the 2015 budget ($1.574 billion). The SEC hopes to add 225 additional examiners. Of those, 180 would focus on RIAs and investment companies. This is an increase over the 72 examiners added in 2015. Their goal is to examine 14% of all RIAs each year.

Neil Simon, the VP for government relations at IAA, endorsed the increase and said adequate oversight of advisors remains a problem. The financial planning coalition, and IAA, have been advocating the SEC receive greater resources to address the oversight (White, 2015).

The Dodd Frank legislation compounded the problem by bringing hedge funds, private funds and municipal advisors under the SEC jurisdiction. There will be 25 IARs for every one SEC examiner. The IAA opposes efforts by the SEC to allow self-regulatory organizations from supplementing the oversight activities (Corbin, 2015) Reducing costs. There is a clear line of demarcation between simple RIAs and complex ones. In 2014, 6,889 advisers (63.2%) reported they are not actively engaged in any business other than giving investment advice about securities. Among these, using the most common category of measure, there are approximately 4,090 RIAs (37.5%) who would be prime prospects for this non-audit category. There is a sub group of 6,085 firms with assets under $1 billion and they comprise only 3.5% of the total RAUM. Advisors with more than $5 billion represent 89% of the assets. It makes sense for the SEC to offload these smaller RIAs to outside, trained compliance consultants.

The literature review definitively showed the 4,090 “Simple RIAs”, have a different level of complexity than the larger firms.

They do not offer private funds, are not qualified custodians, they take no discretionary authority over client money (except to draw fees) or provide services to financial institutions, and they are not intermediaries. Yet, they are lumped into the same pool as the larger RIAs, private funds, and a myriad of non-traditional RIAs for scrutiny and oversight. SEC supervision depletes resources for no apparent gain.

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Violations and Penalties. With over 9,000 firms having no violations, the SEC has the ability to distinguish between those who have a proclivity for violations. The literature showed only 1,515 firms reported violations on their ADV. However, in 2014, there were only 23 felony convictions and the majority of the reported violations were minor infractions. These could easily have been handled by the outside consultants. There were 755 enforcement issues in 2014, accounting for more than $4.16 billion in disgorgement and penalties. From the description of these cases, in the SEC releases, the RIAs who were prosecuted or sanctioned would not have been considered “Simple RIAs” (White, M.J, 2015).

This is not to say compliance and regulatory oversight is unnecessary or simple. The more important question is whether the magnitude of the problem is worth the SEC spending a significant portion of their $1.7 billion budget to yield such nominal results.

The question has to be asked, “Should the SEC be the provider of oversight for these “simple” firms who are not likely targets to yield significant results from an SEC audit?”

There is precedent to reduce the number of firms under direct SEC scrutiny. The Dodd-Frank Act was an attempt to reduce the burden on the SEC. The act caused 1,701 firms to switch to state supervision, thus eliminating some of the SEC’s regulatory burden. By eliminating the “Simple RIAs” the burden could be further reduced.

Mr. Gallagher suggested the SEC write a regulation requiring advisers to hire an examiner to review their operations. This recommendation by Gallagher to force advisors to hire third-party contractors to conduct examinations is in line with the “Simple RIA” model. Andrew Ceresney stated the same thing. Since most RIAs hire compliance consultants to oversee their ADV filings and advertising, why not use them to oversee RIA activity deemed “simple.”

The literature shows one third or more of all RIAs are not tied to a Broker-dealer and are not trading securities. Why is there a need to scrutinize their trading? Yet, custody and trading are two of the biggest targets on the SEC emerging issues list. Most of the FINRA/SEC violations issues are not relevant to a “Simple RIA.” For instance, “cherry picking” or favoring one client over another, is nonexistent and cannot happen if the RIA uses portfolio models and does not offer individual securities. The research shows the bifurcation between the simple and complex is clear and easy to identify.

Another area of regulatory focus is performance reporting.

Since a “Simple RIA” uses packaged portfolios and limits their service to rebalancing and determining risk tolerance, there is no opportunity for them to misreport performance. Performance is defined and reported by the fund managers.

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outside consultants, it is clear there is room and precedent to change the regulatory model for “Simple RIAs.” To do so, the SEC would need to do three things.

Recommendations

Recommendations

outside outside outside consultants, outside consultants, consultants, consultants, it is clear ititisis itclear there clear is clear there isthere room there isisroom and room is room precedent and and and precedent precedent precedent to change totochange to change the change regulatory the thethe regulatory regulatory regulatory modelmodel model formodel for forfor First, the SEC would establish an objective criteria to identify candidates for this

“Simple “Simple “Simple RIAs.” “Simple RIAs.” RIAs.” To RIAs.” doTo so, Todo To the doso, do so, SEC the so, thewould the SEC SEC SEC would would need would need toneed do need to three todo to dothree things. do three three things. things. things.

“Simple” classification. The SEC would prioritize key factors that indicate whether a RIA coul

First, the SEC would needasto“Simple” establish an objective criteria to be classified Recommendations or not. Each factor could be given a weighting. The total score would Recommendations Recommendations Recommendations identify candidatesdetermine for this “Simple” classification.

The SEC whether the RIA was qualified or not, and could be administered by these trained First, First, the First, First, SEC the the would the SEC SEC SEC would would establish would establish establish establish an objective ananobjective an objective objective criteria criteria criteria tocriteria identify totoidentify to identify candidates identify candidates candidates candidates for this for forthis for this would need to prioritize key factors that indicate whether athisRIA consultants. Here are some suggestions.

“Simple” “Simple” “Simple” “Simple” classification. classification. classification. classification.

Theas SEC The The The would SEC SEC SEC would would prioritize would prioritize prioritize prioritize keynot. factors key key key factors factors that factors indicate that that that indicate indicate whether indicate whether whether awhether RIAa acould RIA RIA a RIA could could could could be classified “Simple” or Each factor could be given abeclassified weighting. total score would determine whether the be classified be be classified classified as “Simple” asas“Simple” as “Simple” “Simple” orThe not.oror Each not. or not. not.

Each factor Each Each factor could factor factor could be could could given bebegiven be agiven weighting. given a aweighting. weighting. a weighting.

The total The The The score total total total score would score score would would would RIA was qualified or not, and could be administered these determine determine determine determine whether whether whether the whether RIA the the was the RIA RIA RIA qualified was was was qualified qualified qualified or not, oror and not, or not, could not, and and and could be could could administered bebeadministered be administered administered by these bybyby these trained by these these trained trained trained trained consultants.

Here are some suggestions. consultants. consultants. consultants. consultants.

Here Here are Here Here some are areare some suggestions. some some suggestions. suggestions. suggestions.

Second, the SEC should change how these “Simple RIAs” are supervised. By training,

In the worst case, an advisor could be guilty of not accurately identifying suitability. While it is

Opportunities for the “Simple RIA” to be faced with conflicts of interest are minimized because of the nature of the investment accurately determining the correct risk tolerance. In the Act, it is assumed a fiduciary will adhere product they offer clients. Many RIAs provide model portfolios to the standard and will only make suitable recommendations. While the Act is concerned with which are constructed by third parties. While the RIA monitors fiduciary duty, which is defined as doing the right thing for the client, the fiduciary is to guard and manages these portfolios, there is no opportunity for them against being compromised any conflict of interest. to trade stocks tobythe disadvantage of their client. These RIAs rely on institutionally priced products and do not offer programs Based on the literature review, and the importance the SEC places on each of these five paying commissions. possible for an RIA to pick unsuitable solutions for their client, suitability usually boils down to

categories, the “Simple RIA”, would be virtually exempt from the any of these issues.

In the worst case, an advisor could be guilty of not accurately identifying suitability. While it is possible for an RIA to pick unsuitable solutions for their client, suitability usually boils down to accurately determining the correct risk tolerance. In the Act, it is assumed a fiduciary will adhere to the standard and will only make suitable recommendations. While the Act is concerned with fiduciary duty, which is defined as doing the right thing for the client, the fiduciary is to guard against being compromised by any conflict of interest.

Considering the Dodd Frank redefinition and Gallagher’s recommendation for advisors to hire

Based on the literature review, and the importance the SEC places on each of these five categories, the “Simple RIA”, would be virtually exempt from the any of these issues. Considering the Dodd-Frank redefinition and Gallagher’s recommendation for advisors to hire outside consultants, it is clear there is room and precedent to change the regulatory model for “Simple RIAs.” To do so, the SEC would need to do three things.

licensing and registering existing compliance consultants, organizations who currently serve th

“Simple RIA” market, could serve double duty. As compliance consultants, they already perfo

Second, the SEC should change how involved thesein“Simple RIAs” mock audits and are intimately the development of the ADV and review all Second, Second, Second, the Second, SEC the theshould the SEC SEC SEC should should change should change change how change these how how how these “Simple these these “Simple “Simple RIAs” “Simple RIAs” RIAs” areRIAs” supervised. are areare supervised. supervised. supervised.

By training, By Bytraining, By training, training, are supervised. Bynewsletters, training, licensing and existingwith the RIA and know as well as advertising.

Theyregistering have ongoing communication licensing licensing licensing licensing and registering and and and registering registering registering existing existing existing compliance existing compliance compliance compliance consultants, consultants, consultants, consultants, organizations organizations organizations organizations who currently who who who currently currently currently serveserve the serve serve the thethe compliance consultants, organizations who currently serve the “Simple “Simple “Simple RIA” “Simple RIA” market, RIA” RIA” market, market, could market, could serve could could serve double serve serve double double duty. double duty.

As duty. duty. compliance As Ascompliance As compliance compliance consultants, consultants, consultants, consultants, they already they they they already already perform already perform perform perform “Simple RIA” market, could serve double duty.

As compliance mock mock audits mock mock audits and audits audits are and and intimately and are are are intimately intimately intimately involved involved involved involved in the in development in the in the the development development development of the of ADV of the of the the ADV and ADV ADV review and and and review review all review all all all consultants, they already perform mock audits and are intimately newsletters, newsletters, newsletters, newsletters, asasas well as well advertising. well asasadvertising. as advertising. advertising.

They They have They They ongoing have have ongoing ongoing communication ongoing communication communication communication the with with RIA with the the and the RIA RIA RIA know and and and know know know involved inas well the development ofhave the ADV and with review all newsletters, as well as advertising. They have ongoing communication with the RIA and know their clients, better than the SEC ever could know a RIA through an audit. Properly trained and licensed, by the SEC, these registered compliance consultants (RCCs) would become the perfect foot soldiers to do most of the work with SEC guidance. As RCCs, the compliance consultants would work within a framework established by the SEC to prevent overreach. Using a set protocol for “Simple RIAs” would simplify compliance and reduce the costs and burdens currently placed on RIAs who would now be adhering to a more standardized model.

RCCs have access to RIA client files and knowledge of the RIA methodology. It would be much easier for a RCC to identify infractions and hold RIAs accountable to the fiduciary standard than an SEC auditor who is unfamiliar with the firm. If any question of fiduciary duty, conflict of interest or the myriad of other emerging issues arise, the RCC would by contract, be mandated to report violations to the SEC. This system of checks and balances would give consultants the authority to keep their clients under tight control. Ongoing Maintenance of the RIA relationship would require these RCCs to submit, under penalty of perjury, a prototype evaluation of each client annually. This evaluation would cover the key points the SEC needed to identify during an audit. Should the SEC find areas of concern targeted in the questionnaire, they would then select that RIA for an issue audit. Otherwise, they could accept the RCC’s report.

The question of additional cost could be raised as a possible objection. However, most of these RIAs who would be reclassified, are paying these consultants a monthly retainer to refile

©2015, IARFC. All rights of reproduction in any form reserved.

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their ADV and approve advertising and other basic SEC mandated oversight. Adding the audit would increase costs, but not substantially. It would be de minimis compared to the cost of a SEC audit.

Based on the RIA’s score, they would become eligible to apply for the streamlined regulation program. How does a RIA get in the program? Using the point system suggested earlier, here is a suggested scoring grid the RCC would apply to the criteria.

If the RCC determines the RIA is eligible, the recommendation would be submitted to the SEC for approval.

Over 75 pts – automatically included in program 74-50– Assessment by Compliance Consultant 50 and Under – Eligible for reconsideration.

The third suggestion is to change the definition of discretionary management. When a RIA becomes classified as a “Simple RIA”, the SEC should redefine discretionary management to exclude this category. The only reason many RIAs are considered discretionary managers is because they withdraw fees from the investment account. Since “Simple RIAs” all use qualified custodians, it would be very easy to put safeguards into the client agreements to protect them from having their accounts overcharged. The only access a “Simple RIA” has to an account is to deduct fees on a quarterly basis. Since the qualified custodian is responsible for withdrawing the fees, the RIA agreement would explicitly define limitations and the amount of the fee. This would be administered by institutional custodians and eliminate any risk of the misuse of funds for the advisor.

Implementing these three recommendations would eliminate nearly 4,000 RIAs currently under SEC supervision who are, by the SEC’s definition, unable to violate the established and emerging issues. Should the RIAs not adhere to the basic guidelines established by the SEC, the RCC would recognize the infractions and report them immediately for SEC action.

Conclusion The RIA world is growing rapidly in size. The number of RIAs is increasing at a steady pace and the amount of RAUM is increasing geometrically. There is strong evidence the SEC is overwhelmed and unable to provide the level of oversight and regulation investment advisors require, to maintain the trust of the investment world. With pressure on government spending, Congress is reluctant to expand the SEC budget to the levels the SEC needs to reduce their workload. Giving the states more responsibility, with no budget, only exacerbates the problem. The only viable solution is for the SEC to offload more of the RIAs. This could be done by using the private sector.

By licensing and training existing compliance consultants, who are already integral to the fabric and maintenance of these RIAs, it is a natural extension of their service and ability to report

violations to the SEC. The SEC could easily identify and register qualified consultants who have the desire, ability and capability to perform diligent supervision and management of these Simple RIAs. Doing so would remove a significant burden from the SEC and at the same time provide the level of oversight Congress was trying to create when it passed the security protection laws.

References Abromovitz, L. (2012). Recent changes in the regulatory landscape. The Advisors Professional Library. Think Adviser. Retrieved from http:// www.thinkadvisor.com/2012/01/01/scope-of-the-fiduciary-duty-owedby-investment-adv Advisors4Advisors. (2013). News Analysis: The real story behind the SEC warning that is has found widespread violations by RIAs of the custody rule. Advisor4Advisors. Retrieved from http://advisors4advisors.com/ compliance/registered-investment-advisors/article/17296-news-analysisthe-real-story-behind-todays-sec-warning-that-it-has-found-widespreadviolations-by-rias-with-the-custody-rule Barlyn, S. (2013). Comply – U.S. regulator intensifies scrutiny of fee based accounts. Reuters. Retreived from http://www.reuters.com/article/2013/12/12/sec-churning-idUSL1N0JP27I20131212 Barratt, L. (2011). Survey: Fiduciary duty number one reason investors choose RIAs. Financial Planning. Retreived from http://www.financial-planning.com/news/td-ameritrade-ria-survey-2675616-1.html Berrebi, C., Clancy, N., Dominitz, J., Hung, A., Suvankulov, F., & Talley, E. (2008). Investor and industry perspectives on investment advisers and broker-dealers. Rand Institute for Civil Justice Report Sponsored by the United States Securities and Exchange Commission SSRN Working Paper 1701181.

Clipperman. (2103). Compliance requirements for Registered Investment Advisers. Clipperman Compliance Services. Retrieved from http://www. cippermancs.com/Articles/RIA_Compliance_Requirements.pdf Committee for the Fiduciary Standard. (2015). Five core principles. Retrieved From http://www.thefiduciarystandard.org/2011/03/30/committee-initiatives/ Consumer Federation of America website. (2015). Save our retirement.

Retrieved from http://www.saveourretirement.com/ Corbin, K. (2015). “SEC not letting up on RIAs.” Financial Planning.

Retrieved from http://www.financial-planning.com/news/regulatory_compliance/sec-not-letting-up-on-rias-2692277-1.html?zkPrintable=1&nopagination=1 Corbin, K. (2015). “SEC wants to add 225 examiners.” Financial Planning.

Retrieved from http://www.financial-planning.com/news/regulatory_compliance/sec-wants-to-add-225-examiners-through-2016-budget-2691852-1.html Cornell Law. Legal Information Institute. 15 U.S. Code 80b-1 – Findings.

Retrieved from https://www.law.cornell.edu/uscode/text/15/80b-1 Dean, L. & Finke, M. (2011). Compensation and client wealth among U.S. Investment Advisors. Available at SSRN: http://ssrn.com/abstract=1802628 or http://dx.doi.org/10.2139/ssrn.1802628

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Detamore, C. (2014). SEC to Investment Firms: ‘Stakes high to get GIPS compliance right.’ Market Integrity Insights, CFA Institute. Retrieved from http://blogs.cfainstitute.org/marketintegrity/2014/09/19/sec-to-investment-firms-stakes-high-to-get-gips-compliance-right-video/

Schoeff, M, Jr. (2014). RIAS should be forced to hire outside examiners.

Investment News. Retrieved from http://www.investmentnews.com/ article/20140520/FREE/140529989/rias-should-be-forced-to-hire-outside-examiners-gallagher?CSReferrer=accessControl-investmentnews

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Securities and Exchange Commission. General Instructions for Part 2 of form. ADV. OMB 3235-0049. Retrieve from http://www.sec.gov/about/ forms/formadv-part2.pdf

FINRA, (2015). Targeted examination letters. FINRA. Retrieved from http:// www.finra.org/industry/targeted-examination-letters Frankel, T. (2013). “The failure of investor protection by disclosure” University of Cincinnati Law review, 81 (2), Winter 2012.

Frankel, T. (2011). “The Regulation of Brokers, Dealers, Advisors and Financial Planners.” Review of Banking and Financial Law (BU School of Law) 30, 123-139.

Frankel, T. (2001). “Let the Securities and Exchange Commission outsource enforcement by litigation: A proposal.” Journal of Business and Securities Law. Michigan State College of Law 11 Fall (1).

Frankel, T. (2008). “How should the Financial Markets be regulated” The Wall Street Lawyer, October 2008, 12 (10).

Gittleman, S. (2014). SEC bars, fines advisory owner for misrepresenting GIPS compliance. Thomson Reuters. Retrieved from http://blog.thomsonreuters.com/index.php/sec-bars-fines-advisory-owner-for-misrepresenting-gips-compliance/ Heath, D. (2010). “Too big to jail? Executive unscathed as regulators let banks report criminal fraud.” Huffington Post. May, 2010, http://www. huffingtonpost.com/2010/05/03/too-big-to-jail-executive_n_561961. html.

Holland, D. D. (2007). “How to become a Registered Investment Advisor.”

National Underwriter. October, 2007. http://www.lifeandheathinsurancenews.com Larsen, J. & Hinton, P. (2009). “SEC settlements in Ponzi scheme cases: putting Madoff and Stanford in context.” NERA Economic Consulting, March 2009. Retrieved from http://www.nera.com/extImage/PUB_ Ponzi_schemes3_0309_final.pdf.

Laurensen, E. (2013). “Frequent compliance issues under the SEC’s custody rule under the Act.” Practical Compliance & Risk Management for the Securities industry.

Muller, K.W., Baris, J.G., Chertok, S. (2011). The SEC’s New Dodd-Frank Advisers Act rulemaking: an analysis of the SEC’s Implementation of Title IV of the Dodd-Frank Act, Harvard Business Law Review. Online 57. Retrieved from http://www.hblr.org/2011/07/the-secs-new-doddfrank-advisers-act-rulemaking-an-analysis-of-the-secs-implementationof-title-iv-of-the-dodd-frank-act/ Murphy, M. E. M. (2013). Washington, DC 20549-1090 Re: File Number 4-606 Dear Ms. Murphy: fi360, Inc.(“fi360”) is pleased to respond to the Commission’s request for information set forth in Release No. 3469013/IA-3558 (the “Release”), regarding duties of Broker, Dealers, and Investment Advisers.

National Registry Services. (2014). Evolution revolution. Acuity. Retrieved from https://www.investmentadviser.org/eweb/docs/Publications_News/ Reports_and_Brochures/IAA-NRS_Evolution_Revolution_Reports/evolution-revolution_2014.pdf RIA in a Box. (2015). “Hybrid Registered Investment Advisor (RIA) Registration. RIA in a Box. Retrieved from http://www.riainabox.com/ hybrid-ria-registration

Simon, W. S. (1998). UPIA - Uniform Prudent Investor Act. State of California. Retrieved from http://www.indexmutualfunds.com/PrudentInvestorAct.pdf Stanley, C. (2013). “Advisor conflicts of interest: finding and mitigating them.” Think Advisor, December 23, 2013. Retrieved from http:// www.thinkadvisor.com/2013/12/24/advisor-conflicts-of-interest-finding-and-mitigati?page_all=1 Stern, L. (2011). Stern advice: Even fiduciaries can give bad advice.

Rueters. Retrieved from http://www.reuters.com/article/2011/11/02/column-personalfinance-idUSN1E7A015120111102 The Hill. (2015). White House readies crackdown on financial advisers.

The Hill. Retrieved from http://www.bloomberg.com/news/2015-01-22/ white-house-aide-calls-for-stricter-broker-Fregulrules-on-401-k-plans. html The Securities and Exchange Commission Action - New Rule. (2004).

Adopting a Code of Ethics. Retrieved from https://www.sec.gov/rules/ final/ia-2256.htm The Securities and Exchange Commission. (2012). SEC Charges Oregon-based firm with failure to disclose revenue sharing. Press Release (180). Retrieved from http://www.sec.gov/News/PressRelease/Detail/ PressRelease/1365171484512#.VRYOdfnF_dF The Securities and Exchange Commission. (2012). Carlo di Florio speech to National Association of Compliance Professionals. (October 22, 2012). Retrieve from http://www.sec.gov/News/Speech/Detail/ Speech/1365171491600#.VRYPTPnF_dF The Securities and Exchange Commission. (2013). SEC charges San Diego-based firm with cherry picking. Press Release (168). Retrieved from http://www.sec.gov/News/PressRelease/Detail/PressRelease/1370539795856#.VRqTIvnF98E The Securities and Exchange Commission Action. (2015). Regulation systems compliance and integrity. Retrieved from http://www.sec.gov/rules/ final/2014/34-73639.pdf The Staff of the Investment Adviser Regulation Office. (2013). Regulation of Investment Advisers by the U.S. Securities and Exchange Commission. Division of the Securities and Exchange Commission, (March, 2013). Retrieved from http://www.sec.gov/about/offices/oia/oia_investman/rplaze-042012.pdf The Staff of the Investment Adviser Regulation Office. (2010). Information for newly-Registered Investment Advisors. Division of the Securities and Exchange Commission. Modified November 2010. Retrieved from http://www.sec.gov/divisions/investment/advoverview.htm Touryalai, H. (2014). “Still booming top RIAs keep getting bigger. Forbes Magazine.” October 2014. Retrieved from http://www.forbes.com/sites/ halahtouryalai/2014/04/16/still-booming-top-rias-keep-getting-bigger/ United States Government Accountability Office. (2011). “Consumer Finance: Regulatory Coverage Generally Exists for Financial Planners, but Consumer Protection Issues Remain.” GAO-11-235.

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United States Investment Performance Committee. (no date). Performance Advertising â&#x20AC;&#x201C; Reconciling the GIPS Standards with the Investment Advisors Act of 1940. Retrieved From file:///C:/Users/Guy/Downloads/ sec_gips_white_paper_final.pdf Walter, E. B. (2009). Regulating Broker-Dealers and Investment Advisers: Demarcation or Harmonization? Journal of Corporation Law, 35(1), 1-10.

Waymire, J. (2015). The SEC needs to clean up its semantics before accusing RIAs of inflating AUM. RIABiz. Retrieved from http://www.riabiz. com/a/19621907/the-sec-needs-to-clean-up-its-semantics-before-accusing-rias-of-inflating-aum White, M. J. (2015). Testimony before the US House Committee on Financial Oversight. Retrieved from http://tabbforum.com/opinions/%27examining-the-sec%27s-agenda-operations-and-fy-2016-budget-request%27-chair-mary-jo-white%27s-testimony

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Financial Planning Research Needs—A Practitioner’s View Joseph A. Tomlinson, FSA, CFP®, RFC®, Tomlinson Financial Planning, LLC Abstract We need a stronger connection between the community of financial planners and those doing research to support financial planning practice, particularly those in the academic world. Research can support changes and refinements in financial planning practice, the development of new investment and insurance products to better meet client needs, and improvements in financial planning software. In this article I highlight a number of different areas where research can add significant value. I mention a number of the articles I have written that touch briefly on research issues, but often call for more in-depth research. I also often cite the work of co-editor Wade Pfau who has taken a lead role in doing research that applies to financial planning practice.

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Introduction

Generating retirement income

Financial planning involves making recommendations to clients about a wide variety of matters related to both the accumulation of savings during the working years and decumulation after retirement. Recommendations for the accumulation stage include such matters as--how much to save and how to budget expenses, how to allocate savings among asset classes, which particular investments to choose, how to manage risks by purchasing insurance, and how to manage taxes and utilize tax-advantaged accounts. For decumulation, recommendations include—developing a retirement spending plan, implementing a strategy for taking withdrawals from savings, asset allocation for retirement, when to claim Social Security, whether to utilize annuities and what sort, whether to utilize home equity (e.g., with a reverse mortgage) and how and when, and how to manage gifting and bequests.

Perhaps the biggest challenge for financial planners is working with retirees and those nearing retirement--trying to come up with the best strategies to generate retirement income, recognizing that different clients may need different strategies. Unlike the medical profession where standard practices have been developed for treating various conditions, there are no standard practices for generating retirement income—just a dizzying array of alternatives advocated by different researchers and practitioners. Professor Wade Pfau of the American College has inventoried and categorized the various methods and has also done in-depth analysis on a number of these alternatives. In 2015, he co-authored a paper with Jeremy Cooper, “The Yin and Yang of Retirement Income Philosophies,” that provided a framework for categorizing the different retirement income methods and discussed some of the different approaches.

Such recommendations are implemented utilizing the particular products that planners have available, both investment products and products offered by insurance companies—life insurance and annuities. In developing recommendations planners rely heavily on financial projection software.

This is an area where there is a split between academic economists and those doing practitioner research. Economists have based much of their work on life-cycle finance with roots going back nearly a century. Key concepts are utility maximization and consumption smoothing, and techniques employed include dynamic programming. There are a lot of insights that practitioners could potentially gain from this type of research, but unfortunately, the amount of math involved makes the articles and papers inaccessible to most practitioners. Adding further to the inaccessibility is that life-cycle finance has been studied for so long that those doing research today have developed an “in-club” shorthand for articles and papers. Phrases like, “We will assume Epstein-Zinn preferences,” with no further explanation are commonly found in the literature.

In an ideal world, financial planning would be directly supported by research. Such research would feed into the development of optimal solutions for clients in terms of the choice of strategies, available products, and software. There would also be a lot of feedback from those doing the planning to the research community. Unfortunately, we do not find ourselves in this ideal world.

In the field of financial planning, the connection between research and practice needs to be a closer one.

One particular issue for financial planning is that there is a split between academic research in economics, finance, and investments, and research that is more practitioner based. The academic research is mostly carried out by economists, and many of these researchers limit themselves to communicating with other academic economists. Also, the papers they produce often make heavy use of equations and are not accessible to most practitioners. The practitioner research is carried out by financial planners who have developed an interest in research, as well as academic researchers in personal financial planning departments at universities with advanced degrees in either economics or financial planning related fields. This research tends to be more accessible to the financial planning community, but to some extent, still suffers from a lack of feedback and direction from practicing financial planners.

The practitioner research on generating retirement income has descended from financial planner Bill Bengen who in the early 1990s developed the 4% rule, demonstrating that, based on historical returns, a portfolio of 50% to 75% stocks would have safely supported inflation-adjusted withdrawals of 4% of initial savings over a 30-year retirement period. Since then there have been numerous studies to fine tune the original 4% method, most prominently the 2006 Journal of Financial Planning contribution, “Decision Rules and Maximum Withdrawal Rates,” by Jonathan Guyton and William Klinger, which described and tested a proposed method that starts with inflation-adjusted withdrawals similar to Bengen’s original method, but adjusts withdrawals based on portfolio experience.

In this article, I will address this disconnection between research and practice by offering my views on areas that I as a practitioner see as needing more research focus. Over the past four years I have written numerous articles that combine research and practice recommendations. I will cite many of them in the discussion that follows where I feel the subject areas may merit more in-depth research. I also often cite the work of co-editor Wade Pfau who has taken a lead role in doing research that applies to financial planning practice

In the past few years, some researchers have expressed concerns that the 4% rule is no longer safe given today’s investment environment. In this 2013 Journal of Financial Planning contribution, “The 4 Percent Rule Is Not Safe in a Low-Yield World,” Michael Finke, Wade Pfau, and David Blanchett provided a detailed analysis raising questions about the viability of the 4% rule.

Both the academic and practitioner camps continue to generate research, but there needs to be more of an exchange of ideas between the two groups. I’ve attempted to encourage such com-

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munication in two published articles, but much more is needed.

In the 2013 Advisor Perspectives article, “Beware of Financial Planning’s Misguided Rules-of-Thumb,” I made the argument for more communication between economists and those doing practitioner research, and for applying economists’ life-cycle analysis to financial planning. In the 2014 article in the Journal of Retirement, “Retirement Income Research: What Can We Learn from Economics?” which I co-authored with Gordon Irlam, we extended the argument for more communication and demonstrated the application of the life-cycle approach and stochastic dynamic programming for retirement planning.

One potential area for research that could bring the two camps together involves the use of utility functions in the development of retirement income strategies. Utility functions often serve as the foundation for the economists’ approach to retirement strategies, but the particular functions employed do not adequately take into account actual retiree preferences. For example, the most popular utility functions assume time-separable utility—that the utility derived from current year consumption is independent of the amount of consumption in prior years. In the 1950s, economist James Duesenberry argued that this was an unrealistic assumption and came up with the concept of habit-forming utility, and researchers since have worked on the math needed to reflect habit formation in utility functions. This could be viewed as a positive step in introducing realism, but the research involved has focused more on speculation and hypotheses about consumer preferences rather than actual surveys to better understand real-world preferences.

As a research project, I suggest undertaking survey research focused on retirees and near-retirees with questions aimed at understanding tolerance for variability of year-to-year retirement consumption, and specifically tolerance for decreases in consumption.

The economists’ approach to generating retirement income utilizing dynamic programming can be tremendously powerful in developing strategies for withdrawals from savings and asset allocation, but the approach needs a foundation built from utility functions that better reflect real-world retiree preferences.

Behavioral economics In the dozen years since psychologist Daniel Kahneman was awarded the Nobel Prize for economics, there has been growing recognition of the importance of behavioral economics. The importance for economics transfers over to an importance for financial planning research and practice. Planners cannot assume that their clients are knowledgeable, rational, and free from behavioral biases. Nor should planners themselves assume that their recommendations are free from their own flawed decision making. Awareness is key.

Often the easy part of working with clients is coming up with a plan that meets specific financial goals for risk and reward. The hard part is getting the client to accept the plan, implement it, and stick with it. Planners need to make choices about whether to attempt to coach a client out of certain behavioral biases or

accommodate the biases and work around them. The idea is not to give up on attempting to make financially optimal recommendations to clients, but instead to get as close to financially optimal as possible, recognizing the various biases that have to be overcome or worked around.

Behavioral issues are particularly important in dealing with retirement planning and I’ll cite a couple examples to highlight the challenges that planners face. Survey research for the Center for Retirement Research 2015 Issue Brief, “Are Cognitive Constraints a Barrier to Annuitization?” involved authors Jeffrey Brown, Arie Kapteyn, Erzo Luttmer, and Olivia Mitchell utilizing a survey where individuals were asked how much they would be willing to pay for an additional $100 per month of Social Security. The median answer was $3,000. Based on current payout rates for inflation-adjusted immediate annuities, the cost of purchasing $100 per month of inflation-adjusted lifetime income is about $30,000. So it is clear that the average member of the general public is way off the mark in translating assets into income. Given that we are shifting more and more from pensions to retirement savings plans where individuals arrive at retirement with, hopefully, lots of savings, but little lifetime income, the biggest job for the retirement planner is making recommendations about how to turn the retirement savings into sustainable lifetime income. Given this assets-to-income translation issue, the planner’s job is much more challenging than just recommending that clients transfer substantial amounts of savings into annuities.

Another example is from a paper in this edition of this Journal.

In “What Do Subjective Assessments of Financial Well-Being Reflect?” co-authors Steven Sass, Anek Belbase, Thomas Cooperidder, and Jorge Ramos-Mercado found that, in personal assessments of financial well-being, there was no significant difference depending on whether individuals had a retirement plan or not. This is a clear indication that the average working person is quite clueless about how well prepared they are for retirement.

So where does research fit into this picture? There has been considerable success as reported by Richard Thaler and Cass Sunstein in their popular 2008 book “Nudge” about the success of the Save More Tomorrow program helping people to save for retirement. The research has basically involved testing the effect of introducing new program features in defined contribution plans, i.e. auto-enrollment, and auto-escalation. A potential “next frontier” for such research involves helping retirement plan participants turn their retirement savings into sustainable lifetime income. There has been a lot of research done on the financially optimal approaches for turning savings into income (both by economists and practitioners as described in the section above), but much less has been done on how to overcome the behavioral barriers standing in the way of effective implementation. The following papers have begun to deal with this issue: •

“Why Don’t People Insure Late-Life Consumption?”

by Jeffrey Brown, Jeffrey Kling, Sendhil Mullainathan, and Marian Wrobel (2008) in American Economic Review demonstrated the importance of framing in the

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annuitization decision. Subjects in a survey who were presented the annuity option in a consumption frame were much more likely to choose annuitization than those where annuities were presented as an investment.

•

“What Makes Annuitization More Appealing?”—a 2013 paper by John Beshears, James Choi, David Laibson, Brigitte Madrian, and Stephen Zeldes used surveys to test hypothetical annuitization choices. The way features were presented to those surveyed had a major impact on choices. For example, even though inflation-adjusted annuities are considerably less popular than level payment versions in actual sales, the authors demonstrated that by emphasizing the income to be received over multiple years, rather than just the initial payment, swung the demand from those surveyed heavily in favor of inflation-adjusted.

•

David Blake and Tom Boardman of the Cass Business School in the UK authored the 2014 paper “Spend More Today Safely: Using Behavioral Economics to Improve Retirement Expenditure Decisions with SPEEDOMETER Plans,” which recommended applying an approach similar to Save More Tomorrow to generate income from savings.

This research represents an important start, but there is much more to be done.

There are a number of other areas where behavioral biases get in the way of good planning. Optimism bias and associated overconfidence spawns hordes of investment managers and brokers who advocate market beating strategies, despite mountains of evidence that the return premium (if any) they generate is less than the additional expense charges the client pays. Present bias, which means that individuals tend to have a short planning horizon and heavily discount the future, results in a lack of attention to saving adequately for retirement and avoidance of thinking about the late-in-life risk of needing long-term care. Despite much research in the past few years that shows the value of deferred claiming of Social Security, the majority still claim Social Security as early as they can. Overconfidence about returns that can be earned by investing partially explains why so few defer claiming Social Security. Those are just a few examples. Daniel Kahneman’s 2013 book, “Thinking Fast and Slow” discusses the full array of behavioral biases that get in the way of optimal decision making. In reading the book, it’s easy to apply the discussion of the various behavioral biases directly to financial planning issues. A bit of further thought can generate ideas for research projects that might help steering clients (and planners) in the direction of better financial outcomes.

Other practice issues Asset allocation glide paths—There has been a significant amount of research in the past few years on asset allocation glide paths during retirement, i.e. how changing the stock/bond mix over the course of retirement can improve outcomes. Most of

the articles on this subject have been published in the Journal of Financial Planning. The initial study published in 2014 was “Reducing Retirement Risk with a Rising Equity Glide Path,”

by Wade Pfau and Michael Kitces. Their research showed that rising equity glide paths in retirement for client portfolios had the potential to reduce the probability and magnitude of failures. Luke Delorme’s 2015 article, “Conforming the Value of Rising Equity Glide Paths: Evidence from a Utility Model,” supported the Pfau and Kitces analysis with a somewhat different approach. In “Retirement Risk, Rising Equity Glide Paths, and Valuation-Based Asset Allocation,” (2015) Kitces and Pfau demonstrated the impact of an accelerated rising equity glide path in overvalued stock markets. In another 2015 article, “Revisiting the Optimal Distribution Glide Path,” David Blanchett found a decreasing glide path to be optimal. Wade Pfau took on the challenging task of reconciling these different findings in his blog post, “To Rise or Not To Rise: Stock Allocation During Retirement,” published at retirementresearcher.com on March 2, 2015.

These recent studies on glide paths have assumed inflation-adjusted withdrawals from savings similar to the original Bengen method, without adjustments in withdrawals for investment experience. An area for additional research would involve testing glide paths under more flexible withdrawal methods to see if the optimal glide paths change.

Money’s worth ratios (MWRs) for annuities—In trade publications, annuities are often described as illiquid, complicated and high priced. Such comments fail to recognize the different varieties of annuities with very different characteristics. The type of annuity that is favored by many economists and other researchers is the single-premium immediate annuity (SPIA). The basic structure of the product is “pay X dollars up front and receive Y dollars per month for life.” This product is indeed illiquid, but hardly complicated. With regard to the “high priced” comment, things are less clear.

About 25 years ago economist Mark Warshawsky began to analyze SPIA pricing in comparison with returns available on bonds. This type of analysis was formalized in a measure called the Money’s Worth Ratio (MWR), which compares the price of a SPIA with the present value of projected SPIA payments based on mortality tables. Such analysis has been done using mortality tables for the general population and for annuitants (lower mortality due to buyer selection) and using both government and corporate yield curves in the present value calculations. Because one would expect insurers offering annuities to earn a margin for risk and profit, it would be natural to expect MWRs to be less than 1.

The landmark study on MWRs was the 1999 paper “New Evidence on the Money’s Worth of Individual Annuities,” authored

by Olivia Mitchell, James Poterba, Mark Warshawsky, and Jeffrey Brown. Their analysis produced MWRs for SPIAs ranging from .74 to .94 depending on mortality table, Treasury versus corporate bond yields, and age of SPIA purchase. Analysis by others after this paper was published showed similar results.

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However, there hasn’t been much attention paid to MWRs after interest rates dropped substantially as a result of the financial crisis. I did a very rough analysis for an Advisor Perspectives article in 2014, “Why SPIAs are a Good deal Despite Low Rates,” which indicated that MWRs have improved substantially, but it would certainly be worth having a well-recognized economist do a study and make the results widely available. It would help introduce an element of fact to counter over-generalized speculation about excessive margins in annuity pricing that appears too frequently in articles about annuities.

Housing wealth—The average American approaching retirement has more housing wealth than savings, and it will become increasingly necessary to utilize housing wealth to support retirement. One way to do this is with a reverse mortgage, which offers different options such as line of credit and a tenure option that makes monthly payments. The common wisdom in the financial planning community is that the reverse mortgage should be thought of as a last resort and only considered after other financial resources have been depleted. But recent research has shown that the current low interest rate environment makes it particularly advantageous to take out a reverse mortgage—either line of credit or tenure payments—rather than waiting and losing out on this especially attractive opportunity if interest rates rise.

It may make sense for new retirees to move early rather than waiting.

The benefits of this strategy were demonstrated by Shaun Pfeiffer, John Salter, and Harold Evensky in the 2013 Journal of Financial Planning contribution, “Increasing the Sustainable Withdrawal Rate Using the Standby Reverse Mortgage.”

Pfeiffer, Salter, and Angus Schaal followed with a 2014 article in the same publication, “HECM Reverse Mortgage Now or Last Resort,” which focused on the advantage of setting up a reverse mortgage in the current low interest rate environment. Wade Pfau further supported this argument with the 2014 article, “The Hidden Value of a Reverse Mortgage Standby Line of Credit,” in Advisor Perspectives.

Another issue for planners related to the use of home equity is how best to coordinate between the use of reverse mortgages and annuities to generate retirement income. I examined this subject in the 2015 Advisor Perspectives article, “New Research: Reverse Mortgages, SPIAs and Retirement Income.”

The use of home equity to support retirement is definitely an area that merits more research.

Long-term care—The potential need for long-term care represents a daunting retirement risk, but the planning profession has lagged in being able to effectively address the risk with clients. Part of the problem is a lack of easy-to-understand statistical information on the probability of needing care of varying durations. There’s information buried in Society of Actuaries experience studies and other research documents, but it’s not easy to interpret. So analysis of the data is needed to make this information readily available for financial planners.

There are also more studies needed on long-term care insurance products, particularly comparisons of standard LTC insurance with hybrid products where LTC insurance is combined with life insurance or annuity products.

I’ve done some limited analysis on LTC experience which can be found in my 2013 Advisor Perspectives Article, “A New Tool to Calculate Long-Term Care Needs,” which highlights the work by actuary Jack Paul who has developed a system that can be used to estimate the potential probabilities and ranges of LTC costs that clients might face. In a 2012 Advisor Perspectives article, “Comparing Long-Term Care Alternatives,” I provided a comparison of regular LTC insurance versus the newer hybrid life/LTC policies.

Product needs Research can have a direct impact on financial advice, but it can also point out the need for new products. Here are a few ideas.

Social Security delay product—Vanguard or a similar low-cost provider could come up with a product where an individual pays X dollars at retirement (say 65), and the product pays a temporary inflation-adjusted annuity that smoothly transitions into delayed SS at age 70—one-stop shopping for an inflation-adjusted income stream. The product could also be customized for couples who utilize a coordinated claiming strategy to produce a smooth inflation-adjusted lifetime income stream.

A more attractive inflation-adjusted immediate annuity (SPIA)— My rough calculations indicate that money’s worth ratios (MWRs) for inflation-adjusted SPIAs are significantly less attractive than for level-pay versions or versions where payments step up at a fixed percentage each year. Insurers could likely develop more attractive pricing and protect themselves from inflation risk by doing inflation swaps (akin to selling Treasuries and buying TIPS) to effectively add inflation protection to the corporate bond investments supporting this line of business. This product is the most natural add-on to the floor income provided

by Social Security, and more attractive pricing might help the product get more attention and sales.

Making SPIAs more responsive to interest rate movements--Another potential enhancement to make SPIAs more attractive might be entitling purchasers to increase payments every 5 years or so if interest rates have risen. The objective would be to overcome reluctance to purchase because of the prospect of rising interest rates. It would be interesting to price out this provision to determine the reduction in the initial SPIA payout rates that would be required to cover the additional cost to insurers. This would give SPIA purchasers flexibility analogous to the flexibility to refinance fixed-rate mortgages.

Combination annuity and systematic withdrawals products—We are beginning to see studies that point to the benefits of combining single-premium immediate annuities (SPIAs) and systematic withdrawals. Mark Warshawsky’s 2015 paper, “Government Policy on Distribution Methods for Assets in Individual Accounts

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for Retirees—Life Income Annuities and Withdrawal Rates,” offered a general policy perspective and highlighted the benefits of promoting annuity and systematic withdrawal combinations. I’ve demonstrated the benefits of SPIAs combined with systematic withdrawals in two Advisor Perspectives articles, both in 2015, “The Advantages of a Dynamic Retirement Income Strategy” and “Are Managed-Payout Funds Better than Annuities?” Given the benefits of such combinations, it would make sense to develop products that do the combining (with proportions tailored to meet client needs) rather than leaving it to advisors and clients to do the combining and income management themselves.

Long-term care insurance that people will actually buy—Research might help support building more salable products. Michael Kitces, a planner and thought leader among practitioners, recently advocated developing LTC insurance policies with twoor three-year elimination periods before payments begin in order to lower the steep premium costs.

Hybrid policies mixing LTC and life insurance or annuities have gained some popularity, and development work continues, although the financial benefits of the life/LTC hybrids are questionable. I’d like to see more research on annuity/LTC hybrids.

An unfortunate barrier to moving forward with LTC product development is that few (if any) insurers in the LTC business are looking to increase sales or product offerings.

Software Financial planners mostly work with people who are well off.

They make more money that way and they can focus on investment management. However, for people of ordinary means, financial planning and retirement planning get more complicated, and planners are not well equipped to provide services to this segment of the population. They lack the necessary skills and knowledge, and the software they rely on is not up to the task.

Retirement planning for regular folks can be described as a complicated optimization problem requiring decisions about: •

When to retire

•

When to claim Social Security

•

How to allocate savings between equities and fixed income investments

•

Which asset classes to invest in within equities and fixed income

•

Whether to also invest in alternative asset classes (real estate, commodities)

•

How to manage expenses in retirement

•

How much to withdraw from savings, and how to plan withdrawals

•

Which accounts (taxable vs. tax-favored) to draw from in what sequence

•

Whether to purchase an annuity, and, if so, what type

•

Whether to utilize home equity to support retirement, and, if so, how—downsizing, reverse mortgage—what type

•

How to manage out-of-pocket medical expenses

•

How to deal with the potential risk of needing longterm care

When we consider that most financial planning software focuses on investment management, it becomes apparent that existing software doesn’t even begin to get the job done for regular folks.

Average individuals and couples don’t have a lot of savings, and hold most of their “wealth” is in the form of future Social Security payments and home equity. Also, most of these software packages are not capable of optimizing consumption, and their retirement cash flow projections don’t adjust consumption to respond to investment experience.

We are faced with a question of how to develop software that planners can use to address the planning needs of regular folks (or that people can use themselves). One approach would be to attempt to build a giant optimization system—set a goal of maximizing the utility of lifetime consumption and throw all of the above decisions into the system. This approach would surely have too many dimensions to handle.

Another approach would be to do research and establish rules or priorities to reduce the number of decisions. For example, it likely makes sense to delay Social Security before purchasing an annuity, and for those purchasing annuities, a particular type of annuity may dominate or come close enough so that other types can be ignored.

But despite this bleak state of affairs, there are initiatives aimed at heading things in the right direction. One example comes from the Financial Security Project at Boston College, where they have developed interactive “Target Your Retirement” software.

It provides a user-friendly way for individuals to test strategies for when to retire, when to claim Social Security, and whether to use home equity. However, for more detailed planning, much more development work is needed. The economists’ life cycle approach and stochastic dynamic programming has potential for application to real world planning. There is currently one commercial software product that uses this approach, “ESPLanner” developed by Professor Laurence Kotlikoff of Boston University. But it has not gained much traction with the planning community. More needs to be done.

Conclusion When one considers the number of economics journals, finance and investment journals, and journals dealing with personal finance, there is certainly an ample amount of material being produced to support advances in financial planning. However, there are a number of areas in financial planning that, as pointed out above, need more targeted research. We need more of a feedback loop from the practitioner community to the research community.

Hopefully this article offers a start, and the Journal of Personal Finance can be a strong outlet for this research.

References Beshears, J., Choi, J. J., Laibson, D., Madrian, B. C., & Zeldes, S. P. (2012).

What Makes Annuitization More Appealing? Cambridge, MA: NBER. Retrieved from http://www.nber.org/papers/w18575 Blake, D. P., & Boardman, T. (2014, Spring). Spend More Today Safely: Using Behavioral Economics to Improve Retirement Expenditure Decisions with SPEEDOMETER Plans. Risk Management and Insurance Review, 17(1), 83-112. Retrieved from http://ssrn.com/abstract=2404159 Blanchett, D. (2015). Revisiting the Optimal Distribution Glide Path.

Retrieved from Journal of Financial Planning: https://www.onefpa.org/ MyFPA/Journal/Documents/Feb2015_Contributions_Blanchett.pdf#search=equity%20glide%20path Brown, J. R., Kapteyn, A., Luttmer, E. F., & Mitchell, O. S. (2015). Are Cognitive Constraints a Barrier to Annuitization? Chesnut Hill, MA: Center for Retirement Research at Boston College.

Brown, J. R., Kling, J. R., Mullainathan, S., & Wrobel, M. V. (2008). Why Donâ&#x20AC;&#x2122;t People Insure Late-Life Consumption? A Framing Explanation of the Under-Annuitization Puzzle. American Economic Review, 98(2), 304309.

Delorme, L. (2015). Confirming the Value of Rising Equity Glide Paths: Evidence from a Utility Model. Retrieved from Journal of Financial Planning: https://www.onefpa.org/MyFPA/Journal/Documents/May2015_Contributions_Delorme.pdf#search=equity%20glide%20path Finke, M., Pfau, W. D., & Blanchett, D. M. (2013, June). The 4 Percent Rule is Not Safe in a Low-Yield World. Journal of Financial Planning, 26(6), 46-55.

Guyton, J. T., & Klinger, W. J. (2006). Decision Rules and Maximum Initial Withdrawals Rates. Retrieved from www.onefpa.org: https://www.onefpa. org/myFPA/journal/Documents/Decision%20Rules%20and%20Maximum%20Initial%20Withdrawal%20Rates.pdf#search=klinger Irlam, G., & Tomlinson, J. (2014, Spring). Retirement Income Research: What Can We Learn from Economics? Journal of Retirement, 1(4), 118-128.

Kahneman, D. (2011). Thinking Fast and Slow. New York: Penguin Group.

Kitces, M. (2015). Retirement Risk, Rising Equity Glide Paths and Valuation-Based Asset Allocation. Retrieved from Journal of Financial Planning: https://www.onefpa.org/MyFPA/Journal/Documents/March2015_Contribution_Kitces.pdf#search=equity%20glide%20path Mitchell, O. S., Poterba, J. M., Warshawsky, M. J., & Brown, J. R. (1999).

New Evidence on Moneyâ&#x20AC;&#x2122;s Worth of Individual Annuities. American Economic Review, 89(5), 1299-1318.

Pfau, W. D. (2014, December 9). The Hidden Value of a Reverse Mortgage Standby Line of Credit. Retrieved from Advisor Perspectives: http://www. advisorperspectives.com/articles/2014/12/09/the-hidden-value-of-a-reverse-mortgage-standby-line-of-credit

Pfau, W. D. (2015, March 2). To Rise or Not to Rise: Stock Allocation During Retirement. Retrieved from Retirement Researcher: http://retirementresearcher.com/rise-not-rise-stock-allocation-retirement/ Pfau, W., & Cooper, J. (2014). The Yin and Yang of Retirement Income Philosophies. SSRN. Retrieved from http://papers.ssrn.com/sol3/papers. cfm?abstract_id=2548114 Pfau, W., & Kitces, M. (2014). Reducing Retirement Risk with a Rising Equity Glide Path. Retrieved from Journal of Financial Planning: https://www. onefpa.org/MyFPA/Journal/Documents/Reducing%20Retirement%20 Risk%20with%20a%20Rising%20Equity%20Glide%20Path.pdf#search=Equity%20Glide%20Path Pfeiffer, S., Salter, J., & Evensky, H. (2013, December). Increasing the Sustainable Withdrawal Rate Using the Standby Reverse Mortgage. Retrieved from Journal of Financial Planning: https://www.onefpa.org/MyFPA/ Journal/Documents/Increasing%20the%20Sustainable%20Withdrawal%20 Rate%20Using%20the%20Standby%20Reverse%20Morgage.pdf#search=pfeiffer Pfeiffer, S., Schaal, C. A., & Salter, J. (2014, May). HECM Reverse Mortgage Now or Last Resort. Retrieved from Journal of Financial Planning: https:// www.onefpa.org/MyFPA/Journal/Documents/HECM%20Reverse%20 Mortgages%20Now%20or%20Last%20Resort.pdf#search=pfeiffer Sass, S. A., Belbase, A., Cooperrider, T., & Ramos-Mercado, J. D. (2015, Fall ). What Do Subjective Assessments of Financial Well-Being Reflect?

Journal of Personal Finance, 14(2).

Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. New Haven: Yale University Press.

Tomlinson. (2014, May 13). Why SPIAs are a Good Deal Despite Low Rates.

Retrieved from Advisor Perspectives: http://www.advisorperspectives.com/ articles/2014/05/13/why-spias-are-a-good-deal-despite-low-rates Tomlinson, J. (2012, December 18). Comparing Long-Term Care Alternatives.

Retrieved from Advisor Perspectives: http://www.advisorperspectives.com/ articles/2012/12/18/comparing-long-term-care-alternatives Tomlinson, J. (2013, September 10). A New Tool to Calculate Long-Term Care Needs. Retrieved from Advisor Perspectives: http://www.advisorperspectives.com/articles/2013/09/10/a-new-tool-to-calculate-long-term-careneeds Tomlinson, J. (2013, November 12). Beware of Financial Plannings Misguided Rules-of-Thumb. Retrieved from Advisor Perspectives: http://www.advisorperspectives.com/articles/2013/11/12/beware-of-financial-planningsmisguided-rules-of-thumb Tomlinson, J. (2015, July 28). Are Managed-Payout Funds Better than Annuities? Retrieved from Advisor Perspectives: http://www.advisorperspectives.com/articles/2015/07/28/are-managed-payout-funds-better-thanannuities Tomlinson, J. (2015, April 14). New Research: Reverse Mortgages, SPIAs and Retirement Income. Retrieved from Advisor Perspectives: http://www. advisorperspectives.com/articles/2015/04/14/new-research-reverse-mortgages-spias-and-retirement-income Tomlinson, J. (2015, January 15). The Advantages of a Dynamic Retirement Income Strategy . Retrieved from Advisor Perspectives: http://www. advisorperspectives.com/articles/2015/01/13/the-advantages-of-a-dynamic-retirement-income-strategy Warshawsky, M. J. (2015). Government Policy on Distribution Methods for Assets in Individual Accounts for Retirees--Life Income Annuities and Withdrawal Rules. Arlington: Mercatus Center George Mason University.

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