1 ADDITIONS TO THE FIN ANCIAL BEHAVIORS SCO RE (FBS) IN ASSESSIN G NET WORTH OF COUPLES By RACHEL MARIE DORMAN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL F ULFILLMENT OF THE RE QUIREMENTS FO R THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2012
2 2012 Rachel M arie Dorman
3 To the wonderful women I was name d after, Mary Rachel King Dorman and Marie Davis Johnson, my grandmothers
4 ACKNOWLEDGMENTS I am thankful to many people for their supp ort during the complet ion of my for his support throughout my I would like to thank Dr. Heidi Radunovich and Dr. Jean Lown for their guidance Also, I would like to thank m y friends, colleagues, and family for their continued love and support
5 TABLE OF CONTENTS P age ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 2 LITERATURE REVIEW ................................ ................................ .......................... 17 Background ................................ ................................ ................................ ............. 17 Household Production Model ................................ ................................ .................. 18 Net Worth and Financial Management Behaviors ................................ ................... 21 Conceptualizing Financial Management as a Throughput ................................ ...... 25 The Financial Behaviors Score ................................ ................................ ............... 26 Hypotheses ................................ ................................ ................................ ............. 28 3 METHODS ................................ ................................ ................................ .............. 31 Sampling and Data Collection ................................ ................................ ................. 31 Dependent Variable ................................ ................................ .......................... 32 Independent Variables ................................ ................................ ..................... 33 Statistical Methods ................................ ................................ ................................ .. 36 4 ANALYSIS ................................ ................................ ................................ .............. 39 Sample Description ................................ ................................ ................................ 39 Demographic Variables ................................ ................................ .................... 39 Dependent Variable ................................ ................................ .......................... 40 Independent Variables ................................ ................................ ..................... 40 Statistical Analysis ................................ ................................ ................................ .. 41 Description of Models ................................ ................................ ....................... 41 Hypothesis Testing ................................ ................................ ........................... 47 Summary ................................ ................................ ................................ ................ 50 5 CONCLUSIONS AND IMPLICATIONS ................................ ................................ ... 67 Discussion of Findings ................................ ................................ ............................ 67 Limitations ................................ ................................ ................................ ............... 68 Conclusions ................................ ................................ ................................ ............ 72
6 Hypothesis 1 ................................ ................................ ................................ ..... 72 Hypothesis 2 ................................ ................................ ................................ ..... 73 Hypothesis 3 ................................ ................................ ................................ ..... 73 Hypothesis 4 ................................ ................................ ................................ ..... 73 Implications ................................ ................................ ................................ ............. 74 REFERENCES ................................ ................................ ................................ .............. 77 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 81
7 LIST OF TABLES Table page 4 1 Descriptive s tatistics of d emographic v ariables ................................ .................. 52 4 2 Descriptive s tatistics of d ependent v ariable ................................ ........................ 53 4 3 Descriptive s tatistics of independent v ariables ................................ ................... 54 4 4 Hayhoe and Gutter (2012) FBS OLSR o utput ................................ .................... 55 4 5 Hay hoe and Gutter (2012) FBS plus a dditional i ndependent variables OLSR o utput ................................ ................................ ................................ ................. 58 4 6 Newly a ggregated FBS OLSR o utput ................................ ................................ 61 4 7 Individ ual independent variables OLSR o utput ................................ ................... 64
8 LIST OF FIGURES Figure page 2 1 Household production m odel ................................ ................................ ............. 29 2 2 Household p roduction m odel adapted to the FBS with expanded behaviors ...... 30 3 1 Normal p robability p lot ................................ ................................ ........................ 38
9 Abstract o f T hesis P resent to the Graduate School of the University of Florida in P artial F ulfillment of the R equirements for the D egree of Master of Science ADDITIONS TO THE FIN ANCIAL BEHAVIORS SCO RE (FBS) IN ASSESSIN G NET WORTH OF COUPLES By Rac hel Marie Dorman December 2012 Chair: Michael Gutter Major: Family, Youth, and Community Sciences In the past few years, economic conditions have fostered financial hardship for American households. Households are struggling to maintain wealth in a fina ncial environment that is becoming more severe (Federal Reserve, 2011; Kennickell, 2009). Due to difficult economic times, the ability of households to plan for the future is of gro wing importance as they consider retirement, try to make ends meet, and pla n for The need to sustain or gain more wealth coupled with the challenging economic times has shed light on the growing importance of financial management for households Financial management is particularly important for lower income households that have less financial resources to accumulate wealth. For lower income households financial management is the cornerstone to their ability to grow net worth. This study use d the Household Production Model as a theoretical framework The Ho usehold Production Model frames how families manage their finances by examining inputs, throughputs, and outputs of households (Deacon & Firebaugh, 1988). The study use d 2) Financial Behaviors Score, based on (2011)
10 may be missing vital aspects that play a role in measuring financial behaviors, which could impact the determination of a on which this s cale might improve include understanding the extent to which households engage in advanced planning for the financial future whether household members combine incomes, and whether households accessibility to emergency funds The purpose of this study wa s to expand on the current FBS by including the extent of advanced planning for financial future of families, w hether spouses or partners combine their assets and whether families have access to emergency fund s These three new factors were predicted to affect household net wo rth. The study used the NC 1172 Data; the data were collected from low to moderate income households The NC 1172 instrument was designed to collect data regardi ng savings behavior The findings suggest that financial management is indeed an important predictor of net worth. Further, the model that included whether a household had a planning horizon over a year combine d assets, and accessibility to an emergency fund was accepted over the model which did not This demonstrated the s cope of financial management behaviors consider ed as important determinants of net worth may vary for couples. With the economy facing hard times and man y households becoming delinquent on loan payments; this instrument was designed to learn more about th e psychological and economic factors relate d to savings behavior. This study is pertinent to financial management because it addresses how far into the future families plan their finances, whether spouses or partners combine their assets and if families have an emergency fund.
11 CHAPTER 1 INTRODUCTION In the past few years, economic conditions have fostered financial hardship for American households. In the United States, total household net worth fell from $ 14.4 trillion in 2007 to $ 13.9 trillion in 2010 (Federal Reserve, 2011). Net worth, a valuable indicator of household financial well being, as well as household economic and psychological well being, is important to American households (Campbell & Henretta, 1980; Headey & Wooden, 2004; Kim, Aldrich, & Keister, Chapter 5, 2004; Mullins, 1992; Smith, Langa, Kabeto, & Ubel, 2005; USDA: Economic Research Service, 2004; Weisbrod & Hansen, 1968). The way households spend, save, and monitor finances has become an increasingly pertinent subject due to economi c hard times felt by households of almost all socioeconomic levels. It is important for households to understand how financial management behaviors affect net worth. According to the Federal Reserve (2011), households reported over $ 16.5 trillion in asse ts in 2010; the year the data for this study w ere collected. Total household assets in 2010, over $ 16.5 trillion, were more than $ 4 trillion less than total household assets in 2007 (Federal Reserve, 2011). According to the Federal Reserve (2011), the ty pe of liability found to increase at the greatest rate between 2007 and 2010 was bank loans, which totaled approximately $ 99 billion in 2007 and increased to well over $ 26 0 billion in 2010. According to Kennickell (2009), the Gini coefficient for wealth i n 2007, a scale used to measure inequality in income and wealth distribution, was estimated to be 0.8121. The Gini coefficient is a score ranging from 0 to 1 that measure s inequality of distribution. A Gini score of 0 would indicate there is perfect equal ity, whereas a score of 1 would indicate complete inequality. I n 1998, the Gini coefficient for wealth was
12 0.7935 (Kennickell, 2009) and according to Kennickell (2009) in 1989, the Gini coefficient was 0.7863. The Gini coefficie nt increased between the ye ars of 1998 and 2007. T he increase in the Gini coefficient for wealth in the United States is evidence of increasing inequality in wealth distribution. At the height of the economic bubble, data from the U.S. Census Bureau (2012) shows that the median hou sehold net worth increased almost $30,000 between 1998 and 2007, from $91,300 in 1998 to $120,300 in 2007. The difference between the mean ($359,700) and median ($91,300) household net worth in 1998, $268,400, almost doubled by 2007; the mean for 2007 was $556,300, the median was $120,300, and the difference was $436,000 (U.S. Census Bureau, 2012). This change illustrates became more unequal during this time. The largest decrease in household wealth, more than $ 3 trilli on, occurred between 2007 and 2008 (Federal Reserve, 2011). This decrease in household wealth illustrates how the harsh financial recession negatively impacted American households. For some, financial troubles could have been lightened or possibly avoide d altogether by engaging in positive financial management behaviors. According to the U.S. Census Bureau (2010), the median household income in 2 009 was $49,777, which was a 0.7 % decline in median income for households between 2008 and 2009. According to the U.S. Census Bureau (2011) the median household income in 2 010 continued to decline by 2.3% to $49,445, from the previous year. The U.S. Census Bureau (2011) explains that since the recession in 2007 the median household income has declined by 6.4%, w hich is a 7.1% decline from the economic peak in 1999. Some households are struggling to stay above the poverty
13 line. The poverty rate increased significantly between 2008 and 2009 from 13.2% to 14.3% (U.S. Census Bureau, 2010). According to the U.S. Cen sus (2011), poverty rates continued to increase in 2010 to 15.1 % This could mean severe financial consequences for household finances considerin g inflation increased by over 3% between July of 2009 and July of 2010, while income remained unchanged or dec reased for households (Bureau of Labor Statistics 2010) These changes can have a significant impact on low income households that have fewer financial resources to weather hard financial times The small percentage change can impact households on a sma ll and large scale The small and large scale financial changes that families will have to make can range from ho w households shop for groceries to whether households can afford to buy insur ance for their households, car or health This can even impact s avings behavior by not allowing enough money to sav e for emergencies Changes in the poverty and inflation rates can greatly impact a ability to build net worth the high unemployment rate In 2010, the year the data for this study w ere collected, the national unemployment rate hit a new high of 9.6 % (Bureau of Labor Statistics, 2011). The national unemployment rate impacts households for many reasons This cou ld mean that member s of households who are seeking work, were laid off or c ould not find work; resulting in less income for the household With a reduction of income due to unemployment, people may rely on multiple credit cards or dipping into saving s to pay bills, creating debt as well as financial strain Stress resulting from financial hardship has the ability to manifest psychologically in relationships and may affect how
14 household members interact (Becker, 1965; Liker & Elder, 1983; Ross & Hill, 200 0) With the high national unemployment rate over the past few years, it is clear that unemployment is a problem for American households High unemployment rates mean fewer people have sufficient income to cover their household costs Without insufficie nt income households run the risk of delet ing their net worth to pay for household costs Also, households without income will have an extremely difficult time maintaining or growing their net worth. Financial management is particularly important for low er income households who have fewer financial resources available to accumulate wealth. For lower income households financial management becomes the corne rstone to their ability to grow net worth. F or households to cope with the difficult economic climate it may be necessary to make small and large scale changes in financial management behaviors. These changes can range from the way households shop for groceries to large purchases. One way households cope with or avoid financial hardships is through bud geting and monitoring, or tracking spending (Conger et al., 1999; Hilgert, Hogarth, & Beverly, 2003; Kerkmann, Lee, Lown, & Allgood, 2000). The Household Production Model frames family financial management as a process of inputs, throughputs, and outputs (Deacon & Firebaugh, 1988). Some common inputs are financial goals and aspirations. Some examples of a ation of finances. The output households get from input s and throughputs are the goal s they set, su ch as no financial debt or an increase in net worth. People struggl e to properly assess inputs and through puts that can have an impact on outputs. A measure that considers the
15 three main constructs of the Household Production Model would help to assess f inancial behaviors relate d to net worth. Dew and Xiao (2011) created the Financial Management Behavior Scale (FMBS) to measure household financial behaviors using multi dimension al psychometrics to predict savings and consumer debt. The FMBS measures four domains of financial management behaviors: consumption, cash management, savings and investment, and credit management (Dew & Xiao, 2011) Dew and Xiao (2011) concluded the FMBS scale is valid, reliable (alpha = .81), efficient, and an accurate scale to m easure financial manage ment behaviors Hayhoe and Gutter (2012) took the concept of the FMBS created by Dew and Xiao (2011) and created their own score, the Financial Behaviors consisted of four behaviors: spen ding less tha n is earned planning spending, monitoring spending, and having written financial goals. However, the FBS scale may be missing important aspects of household financial management, which Three important var iables, if included, may improve this score These variable s include the extent to which households plan for their financial future, whether couples combine assets, and whether households have access to resources (outside of the household) in case of an emergency. The purpose of this study was to revise the FBS to create a more robust measure of the relationship between financial management and net worth. This study expand ed to include the extent to which couples plan thei r finances into the future whether spouses or partners combine their inco me, and whether they have access to emergency funds. The goal wa s to determine if these
16 three additional factors added to the FBS would indicate a stronger relationship with net wor th. These three factors wer e expected to increase the ability of the FBS to predict net worth. This study recognize d financial well being but not the only one. The results of this study have potential to help households better understand the impact of their financial behaviors on net worth. To accomplish the purpose of this research, the study use d the NC 1172 data collected by the project team. The NC 1172 instrument was designed to collect data regardi ng savings behavior of low to moderate income households. With the economy facing hard times, this instrument was designed to learn more about psy chological and economic factors relate d to savings behavior. This research addresse d the extent to which fami lies plan their finances into the future whether spouses or partners combine their assets, and whether households have access to resources in case of an emergency.
17 CHAPTER 2 LITERATURE REVIEW Background This study examine d financial management behaviors of households and the relationship of financial behaviors to a Seifert (2002) explain one of the primary objectives to financial management is to create and maintain a surplus for capital investments for a posi tive net worth. They continue to k man, & Seifert, 2007, p. 7 3). For low to moderate income households financial resources can be more dif ficult to obtain making financial goal s harder to attain. Thus financial management becomes extremely important for households that have few financial resources to expend on wealth accumulation. The lack of financial management in these households may ha ve serious implications for the househ is needed for households and those who work with them to easily assess their financial management behaviors relate d to household net worth. The focus of this section is the theore tical approach used in this study and research on household financial management. Theories previously employed to understand household financial management include Systems Theory, Attachment Theory, and the Household Production Model. Liker and Elder (19 83) studied how depression and large financial losses can result in preoccupation with budgeting and heighten frustration within a househo ld. Systems Theory was used to demonstrate when the income flow of a household decreases dramatically, a change must be made in the budget to account for this reduction. Liker and Elder (1983) also found household
18 members facing severe financial loss experienced an increase in relational tension and conflict. Systems Theory is appropriate for examining the functionalit y of households; yet, this theory lacks ability to take into account psychological factors that might influence the decision making process. Ross and Hill (2000) used Attachment Theory as a theoretical foundation for their research on family unpredictab ility Their research implies, within family finances, a lack of money management predicts family instability. Attachment theory was used to explain how children and adults learned from their ake into account inputs, (2009) used the Household Production Model as the theoretical frame work for his research on how work and leisure roles are currently defined. He used the Household Production Model to explain how household work is not a market item that can provide income, where wages are earned, but is performed within the home and can be done at the expense of time but produces a desired output. Weiss (2009) il lustrate d how taking care of a child can be a large time input but for some households the output of an (p. 7). The Household Production Model provides an economic point of view suited to this research, because it allows for the system of goods, or outputs, such as net worth, to be better understood by examining inputs and throughputs. Household Producti on Model This study use d the Household Production Model as its theoretical framework. The Household Production Model frames how families manage their finances into three different areas: inputs, throughputs, and outputs. The model does so by examining
19 the each area (Deacon & Firebaugh, 1988). Some common inputs that households consider regard ing finances are financial goals and aspirations, demands, time, preferences, and av ailable resources, budgeting, tracking expenses, and setting planning and organizing finances to account for demands or resources. The output that households gain from the f unction is the goal that is set, such as no financial debt or specific net worth amount. This study focus ed on a Figure 2 1 provides a visual explanation of the Household Production Model and shows the nature of how house holds use inputs in various processes (throughputs) to produce specific outputs (e.g. well being, net worth). Becker (1965) has been cited by many previous researchers as one of the originators, if not the originator, of the Household Production Model ( Goodwin, Ackerman, & Kiron, 1997; Weiss, 1997). In his research Becker expand ed on traditional household economic theory, stating that resources and constraints determine Becker (1964) diverge d from traditio nal household economics in his definition of resources T raditional theory suggests that resources are specifically income, which is made up from wages earned and other income (Becker, 1965). Becker (1965) developed the construct of non working time, or leis total utility. Becker explained how leisure time is valuable and cannot be ignored as it impacts the (Becker, 1965). Leisure time, time during the nights or weekends,
20 can be us ed in a profitable or non profitable way that impacts the overall output of the household, i.e. well being. Muth (1966) expand ed eholds wish to produce outputs. Muth (1966) implie d that for households to do so they must plan. Muth (1966) illustrate d how a household to achieve a production or output such as a dinner or a clean house, utilizes inputs such as goods or time More recent work ch on the was underscored the importa nce of recognizing how non wage time is spent. Apps and Rees (1997) expand ed on the traditional household production model in two ways They include d two members of a household who provid e income and they accounted for ways that members of a household can collectively combine their ed how this works when a couple, each earning their own income, combined their income and assets, thus creating a collective s upply to the household. average contribution to household income. He did so by using nine years of data from the British Household Panel Survey of women who recorded their w eekly hours of paid work, labor earnings, and non sampled 9,764 women ( 2,585 single s and 7,179 couple s)
21 showed tha t use of the Household Production Model in predicting female household income was a more accurate predictor of true income. Previous research has shown net worth is a valuable output to measure a being (Campbell & Henretta, 1980; Headey & Wooden, 2004; Kim, Aldrich, & Keister 2004; Mullins, 1992; Smith, Langa, Kabeto, & Ubel, 2005; USDA: Economic Research Service, 2004; Weisbrod & Hansen, 1968). The Household Production Model has been applied to household consumption (Baxter & Jermann, 1999), child care (Brink & Groot, 1997), food systems and demand (Huffman, 2010), and resource allocation (Rapoport, Sofer, & Solaz, 2011) T he overarching theme of the Household Production Model is the more time and effort put into something the greater the output This study focus ed on estim throughput of financial management to its output of net worth. Net worth provides an first comes to mind as the best measure of househ old well being but research has shown that net worth is a better measure of household well being than income (Kim, Aldrich, & Keister, 2004). Net Worth and Financial Management Behaviors Heady and Wooden (2004) measured household well being by examining income and net worth. Well In their research, Heady and Wooden (2004) examined whether income or net worth would show higher correlations providing a stronger relationship to well being. Net worth was found to have higher correlations than income in both item measurements of well being, indicating a stronger relationship (Heady & Wooden, 2004). Weisbrod a nd
22 being with a combination of income and net research they were able to provide a view of how households were doing financially and how increasing income and net worth could impact their life cycle and their ability to gain net worth (Weisbrod & Hansen, 1968). In fact, Lusardi (1998) researched how cycl e. U sing Health and Retir e ment Study and limit ing the age range of 51 61 years old, Lusardi (1998) found households with long planning horizons (5 10 years) accumulated more wealth than those with shorter planning horizons (next few months or next few y ears). Net worth is a general way to measure Aldrich, and Keister (2004), and Keister (2000a) found between the 1960s and 1990s the wealth of American households increased more than threefold, to over $23 trillion, an d in the 1980s the majority of wealth was accumulated by the richest 20 % Yet, despite dramatic increases in American household wealth, inequality in wealth distribution has increased (Keister, 2000b). Campbell and Henretta (1980) conducted research on st using ell & Henretta, 1980, p. 628). Hatcher (2000) examined whether household s that save for emergencies would have greater net worth at the end demonstrated that households with limited resources, such as low to moderate income households, could benefit fro m emergency savings to help promote greater net worth lat er in life.
23 Hatcher (2000) noted it could be more difficult for low to moderate income households to increase their net worth through saving for emergencies because of fewer resources. Xiao and Nori premise that households with lower levels of net worth had resources to save In fact, Xiao and Noring (1994) reported households with net worth that placed them in the lowest quarter repo This further supports the premise that households with lower levels of net worth have fewer resour ces with which to accumulate wealth. Deacon and Firebaugh (1988) defined net worth as the balance between the value more encompassing, long term image of how a househo ld is doing financially. Net worth includes debts, the value of an owned home, as well as any savings. A savings behaviors. For example, if a household is consuming above the amount it can afford the household may have to rely on credit that incurs debt and reduces overall net worth. In previous research, n et worth also has been found to be an indicator of a ities. For example individuals with a higher net worth are more likely to own stock (Gutter, Fox, & Montalto, 1999; Hong, Kubik, & Stein, 2004; Kennickell, 2009). It is clear that net worth is a better measure of the financial well being of a household than income. How households divide labor, whether it is earning income or household work is important. This study examine d whether couples combine and share their income and
24 other assets, as well as whether the budgeting and monitoring of spending and s aving has an impact on their overall financial wellbeing as measured by net worth. Rapoport, Sofer, and Solaz (2011) wanted to better understand how the classification of non wage earning time is spent and how the sharing rule affects household production The researchers used the French Time Use survey, in which 7,460 households were included with a total of 20,370 individuals. From this data set, Rapoport et al. (2011) conducted their research with a sample of 1,414 couples. The first issue of non wag e earning time often has been classified as pure leisure rather than time spent doing Rapoport et al. (2011) took the position that this time should not be oversimplified and household or domestic production should be accounted for as well. The second issue of the sharing rule is whether the sharing of household duties (such as earning an income or caring for children) and decision making (such as planning and preparation for meals or household bud geting), should be included for a more encompassing view of household production (Rapoport et al., 2011). According to Rapoport et al. (2011) the classification of all non wage earning time as leisure can be misleading, especially for women who work from home, by making partners who do not contribute as much time to wage earning look as though they spend larger amounts of time in leisure. These results are also misleading, in that the partner who does not spend as much earning wages appears as though the y are not contributing to the overall household output. The results ignore the possibility that a partner may be more engaged in domestic or household work, and not sharing as much of the household burden as the other partner (Rapoport
25 et al., 2011). Buil ding on their research, this study include d an indicator of whether couples combine their assets and plan and monitor their spending together. Conceptualizing Financial Management as a Throughput This study propose d to better understand financial behavio rs of households by examining the impact of adding additional variables to the FBS scale created by Hayhoe and Gutter (2012). The Financial Management Behavior Scale (FMBS) was developed and validated by Dew and Xiao (2011) to study multi dimensional psyc hometric financial management behaviors. Dew and Xiao (2011) created a scale founded on the idea that individuals engage in financial management behaviors daily which may impact their financial well being. They created the FMBS by incorporating dimensions of financial behaviors shown to be applicable and beneficial to financial well being. Some of the financial topics the FMBS measures are comparison shopping, savings and investments, credit use, saving for retirement, and insurance use (Dew & Xiao, 2011) The steps of scale constructi on were broken into three sections. First Dew and Xiao (2011) identified and developed measures for the most important domains of financial management behaviors : consumption, cash flow, credit, savings and investment, and insurance Secondly, Dew and Xiao (2011) sent a draft to financial planning and counseling professionals and scholars to review their measures. Third, Dew and Xiao (2011) implemented the suggestions to strengthen the FMBS. They then tested their newly de veloped 17 item scale for validation. They did this by using a stratified random sample from the National Center for Marriage and Family Research, collected in 2009 in an attempt to understand how families were coping with the 2007 2009 recession (Dew & X iao, 2011) Dew and Xiao (2011) had a 67% response rate wit h 1,011 participants. Fully 45% of respondents were married (Dew & Xiao, 2011).
26 Dew and Xiao (2011) sought to validate the five factor scale by asking participants how they rate themselves in ce rtain financial behaviors, such as saving money, and For example participant s were asked to indicate their level of consumer debt. After conducting a residual correlation Dew and Xiao (2011 ) drop ped impulsive buying from the five factor scale ( as it never scored above 0 .49). Dew and Xiao (2011) then employed a scree plot to determine if a four or five factor scale would fit best. This analysis resulted in Dew and Xiao ( 2011) selecting a fo ur factor scale. This revision shortened the FMBS from 17 items to 15 items. Dew and Xiao (2011) then employed reliability ( .81 ) Dew and Xiao (2011) established the validity of their scale through financial planners and professional agreement that the items measure what they are intended to measure. A Least Squares Regression of the mean of the 15 items, gave a positive association ( = .94, < .001 ) Criterion validity is determined by test ing a variable to see if it predicts another variable, which would be expected if the tested variable truly measures what it claims FMBS needs more work and refinement yet concluded this scale is one of a kind a way to measure financial management using nationally representative data. The Financial Behaviors Score In a similar effort, focusing on low and moderate income households, Hayhoe and Gutter (2012) created the Financial Behaviors Score on a newer data set, the NC 1172 T his same data set was used in the current study. Hayhoe and Gutter (2012)
27 considered the FMBS in creation of the FBS but since the data were collected before the FMBS was available their data set does not contain proxies for all the FMBS variables Hayhoe and Gutter (2012) state that the Financial Behaviors Score was have been From the NC 1172 data, 826 respondents were used, and like Dew and Xiao (2011), 45% Coefficient Alpha to t est reliability of the four item FBS. The four items were s pending income, plans for money, monitoring money, and written financial goals. Scores could range from 0 to 11 ; this is because some questions were weighted more than others The four items were found to have a mean score of 2.55, with a standard deviation of 3.14 (Hayhoe & Gutter, 2012). This study examine d the relationship of financial management behaviors on net worth using a modified measure of the Hayhoe and Gutter FBS (2012). This study at tempted to d the may be missing important aspects of household behaviors that could play an important role in how net worth is predicted. T he literature supports addition al important dimensions which may potentially play an important role in impacting the net worth of a household s including the extent to which households plan for their financial future (Lusard i, 1998), whether households have access to resources in case of an emergency (Hatcher, 2000; Xiao & Noring, 1994) and whether couples in the household combine their assets (Apps & Rees, 1997). Fortunately, the NC 1172 data
28 included items which measur ed a ll three of these areas and these were used as Below are the research hypotheses which guided this study. This study aim ed to low ed this study to determine if the newly added variable s would change the score compared to previous research Ultimately, this study attempted to expand on the original FBS and learn new information about a couple household financial behaviors as they relate to net worth. Hypotheses The following hypotheses guided this study: 1. T he additional variables to the Hayhoe and Gutter (2012) FBS having a planning horizon greater than a year, access to emergency funds, and combining of assets will significan t ly improve ability to predict net worth for coup les as compared to the Hayhoe and Gutter (2012) FBS 2. Th e aggregated version the Hayhoe and Gutter (2012) FBS with the additional variables, will significant ly improv e prediction of couples net worth as co mpared to the Hayhoe and Gutter (2012) FBS plus three additional variables 3. The aggregated measure of the FBS which includes the three additional variables, will be a better predictor of net worth than the seven variables individually 4. H ouseholds that exhibit greater levels of the financial behaviors used in the new scale will show higher levels of net worth
29 Figure 2 1. Household Production Model Possible Throughputs Fi nancial Management Possible Output s Well being Net worth Ability to retire early college Possible Inputs Time Demands Preferences Resources
30 Figure 2 2 Household Production Model adapted to the FBS with expanded behav iors Input Resources, Life Cycle Stage, Demands, and Number of Children Throughput Spending, Immediate Planning, Monitoring, Writing Goals, Planning for Future Combining Assets, and accessibility to Emergency Funds Output Net worth
31 CHAPTER 3 METHODS Sampling and Data Collection This study use d data collected by the NC multistate research project which was created to better understand consumer saving s behavior. The target population for this st udy was low to moderate income households (NC 1172, 2007). As stated in Hayhoe and Gutter (2011), the study was sponsored by the North Central Region of the Cooperative Extension Service through land grant universities. The instrument used wa s developed to measure savings behavior, various psychological factors economic factors financial knowledge, socialization, and available resources that could influence savings behavior (NC 1172, 2007). The survey was distributed and data were collected using an onl ine survey. The data were collected through Survey Sampling International LLC, which provides computers and Internet service for individuals who complete surveys Data were collected in late 2010 over a two week period until 1,000 surveys were completed Only respondents with a spouse or partner and household s with a gross income of less than $80,000 were in cluded in the data collection Th is study filter ed all participants based on an item that directed respondents to indicate whether or not they have a spouse or partner Th e researcher c hose to use this item as the most accurate indicator of whether the respondent has a spouse or partner because it provided the most conservative estimate After performing cross tabulations with other measures that in dicate whether the respondent ha d a spouse, the responses were found to be inconsistent on this measure The item used to filter this data wa s the most direct and conservative measure, making it the best choice for the data. This study also require d
32 eith er the respondent or spouse be under the age of 66. After these conditions were met, the sample size was reduced from 1,000 to 471. Over 65% of the respondents reported a household gross income of between $20,001 and $60,000; fourteen percent reported t heir household gross i ncome as $0 to $20,000 and 30.7% reported between $60,001 and $80,000, the top income for the sample. The average net worth for the sample wa s $23,960. Twelve percent of the sample reported an average negative $4 ,500 net worth and ju st over 22% h ad an average net worth of zero About 40% of the sample ha d a positive net worth. A complete summary of the variables, both dependent and independent, can be found in the following paragraphs or in Table 4 1, 4 2, and 4 3. Dependent Variab le Net Worth : The dependent variable in this study was net worth defined as assets minus liabilities. The level of assets w ere fina ncial accounts, and cash on hand to be at this time? Please include retirement savings. Please exclude the value of your home, business, vehicles, furniture, and clothing. Please do not were as fo 01
33 The level of liabilities w as the dollar amount of your or your fa was coded ere subtracted from their assets, resulting in the lowest net worth score possible w as negative $200,000 and the highest net worth score possible for this created variable w as $200,000. I ndependent Variables The first four independent variables were coded by Hayhoe and Gutter (2012) for their FBS to fit the NC 1172 Survey. Spending : The first independent variable used in this study wa s from the Hayhoe and Gutter (2012) FBS. The level of h ousehold spending was measured by the
34 they w ere was was was Plan : The second independent variable wa s frequency of financial planning which was measured by the follow as combined because they both describe infrequent practices. By giving these two responses the same score, the study avoid ed causing the weight of the measure to be distorted or giving more credit to one behavior than another before, this study gave these two responses the same score to avoid causing the weighting of the measure to be skewed. Monitor : The third independent variable wa s monitoring spending, which was we re combined because the answer choices were so similar in nature.
35 Written Goals : The last variable of this study w as whether the household has written goals based on the following quest Planning Horizon : Planning horizon wa s the first new variable created for the enhanced FBS. This variable measure d the time period that wa s most important to a wa y we re possible response s 10 immediacy of thei r financial planning horizon. All answer choices over one year and up finances. Emergency Fund was whether households have access to resources in case of an emergency. This w as s if you needed at least $3,000, who could you turn to for this money? Check all that
36 other choices determine if the household has the ability to obtain the emergency funds rather than focusing on the source of the money. As a result, anyone who answered any of the possible sources of funding was Combine Assets : The next independent variable that w as add ed improved FBS w as whether partners combine their assets and was measured by the following question The following we Q uestion s ere scored as 1 and were scored 0. The highest possible score wa s 7 and the lowest possible score was 0. Scores ranged from 0 (1% of the sample) to 7 (8 % ). The mean score for the sample was 4.179 with a standard deviation of 1.63209. Statistical Methods P reliminary statistics wer e employed to investigate the normality of the data. A Normal Probability Plot was used to determine whether the data we re normally distributed. According to de Vaus (2001), a probability test evaluates the likelihood th at data are a good representation of the general population to be measured. Depending on the amount of variance between the sample and t he actual population, the Normal Probability Plot shows if there is normality or abnormalities within the data. The Normal Probability Plot was performed on the dependent variable, net worth. The result showed that while the data we re not perfectly normal the data we re approximately normally distributed. The results of the Normal Probability Plot are shown in Figure 3 1.
37 The demographic variables we re age of the respondent, number of people in the family, job status of the respondent, job status of the re level of the respondent, and e Based on the approximate normality of the data and the type of score created, this study use d an Ordinary Least Squared Regression. To test the first hypo thesis the research selected a multiple Ordinary Least Squared Regression, and by doing thi s produced a coefficient Lewis Beck (1980) explains that a coefficient provides the proportion of variation in the dependent variable by all the independent variabl es. The OLSR provide d sum, which w as used for calculating the F statistic. This test compare d the sum of the squares for the two models, the composite measure of the FBS and the individual variables. For the second hypothes is, an OLSR w as employed on the two models to calculate an F statistic. For the third hypothesis, an OLSR w as employed on the two models to calculate an F statistic. Once again this statistic allow ed for th e study to compare two models. For the fourth hypo thesis, an OLSR w as employed to determine the significance of the model in predicting net worth.
38 Figure 3 1 Normal Probability Plot
39 CHAPTER 4 ANALYSIS Sample Description Demographic Variables The sample profile and descriptive statistics can be found in Table 4 1. The average number of people per household was 2.85, with a range of one to nine. The .0 e was 45 .3 These averages were at the midpoint of the range. A Pearson Chi Square test on correlation between the ages of respondents and their spouses indicated that these two demographics were highly correlated with a chi square of 3802.5 2 and a p = 0.00 0. Due to such a high correlation to avoid multicollinearity bias. Thirty two percent of respondents were employed full time and 51% nts reported either not working for pay (22.2%) or we re disabled, retired or out of the labor force and not currently seeking employment (27.8%). Almost three fourths (72.5%) of the sample ha d completed some college or more. L ess than 30% (27.4%) ha d earn ed a high school education or less. A m found to have completed some college or more, while just over 40% (41.2%) earned a high school degree or less. At 49.5 % almost half of the sample had a gross income of und er $40,000, and just fewer than 80 % had an income of under $60,000. With such a large percent of the sample making under $60,000 in gross income, the sample wa s ideal for th e demographic pr ofile describe d a sample that wa s well suited for this study.
40 Dependent Variable Net worth was estimated as assets minus liabilities. The mean household net worth was $23,960 with a standard deviation of $72,380; the median was 0. The minimum household n et worth was $ 182,500 and the maximum was $199,500. Twenty two percent of respondents had a net worth of zero and almost 60 % of respondents had a net worth of zero or less. Five percent of respondents reported having a net worth of $199,500 or more. For a detailed view of net worth see Table 4 2. Independent Variables There wa s some variation in the proportion of the households engaging in the this study contain ed on ly couple households, unlike Hayhoe and Gutter (2012). Spending : This study, of couple households, found 26.5 % of respondents reported spending more than their income while the majority, at 73.5 % reported to either spend equal to their income or spend le ss than their income. Hayhoe and Gutter (2012) found a third of participants spent less than what they made. Planning : Within this study, half of households do not regularly plan their spending (51.2%), while 48. 8% of households regularly plan their spen ding. Hayhoe and Gutter (2012) found only 15 % of respondents had a written spending plan. Monitoring : 8% of couple households reported regularly monitor ing their spending, while 42 % reported not regularly monitor ing their spending. Hayhoe and Gutter (2012) found that 25 % of households responded they usually monitored their spending.
41 Written Goals : One third (33.5%) of households reported having written financial goals, while 66.5 % of households do not have written goals. Meanwhile, H ayhoe and Gutter (2012) reported that 2 6% of respondents reported having written financial goals. Planning Horizon : Two thirds (67.5%) have a longer financial planning horizon than one year and 32.5 % indicated their financial planning horizon was one year or less. Accessibility to Emergency Funds: Sixty four percent of the households reported having access to emergency funds while 36 % of households do not. Combine Assets: About two third they combine their assets with their spouse or partner while 34.2 % do not. See T able 4 3 for all independent variables. Statistical Analysis Description of Models This study employed an Ordinary Least Squares Regression in four different models to assess the best model for examining the relat ionships between financial management behaviors and net worth U sing this regression allowed the researcher to regression. An OLSR also allowed the researcher to view the si gnificance of each of the four models as they relate to predicting net worth. After performing the regressions it was clear there was a problem with multicollinearity for the variables related to education. The Variance Inflation Factors (VIFs), which is a quantifica tion of the collinearity of biv ariates, of the education bivariates were high (Meyers, Gamst, & Guarino, 2006) ; for example 13.655 for spouses who completed some college. High levels of VIFs are an indicator of collinear ity bias. To remedy this problem, the researcher truncated education bivariates
42 into two groups, respondents who had completed high school or less and those who completed some college or more, out of concern for collinearity bias. The new bivariates had lower VIFs, thus remedying the multicollinearity and avoiding bias. Model 1 : Model one was comprised of the original Hayhoe and Gutter (2012) FBS. The Hayhoe and Gutte r (2012) FBS had a p = 0.000 in predicting couple household wealth. The age of the respondent wa s found to have a p = 0.000. As the respondents age increase d by one year, on average the net worth increased by $957. Respondents who we re unpaid volunteer s were found to have a significant relationship in predicting net worth, p < 0.045, as compared to being e mployed full time Households that had a r espondent who w as an unpaid volunteer were found to have $67,617 in net worth as compared to households that had a respondent that was employed full time Also, respondent s whose spouse wa s not currently working fo r pay ( p < 0.038 ) or wa s disabled, retired, or not seeking work ( p < 0.006) were found to have a significant relationship to net worth, as compared to respondents whose spouses who were employed full time Households that had a r espondent s spouse that wa s not working for pay had on average $21,680 more household net worth than households that had a that was employed full time Households that have a r espondent s spouse who was disabled, retired, or not seeing work had on average $27,34 0 more household net worth than a household that has a as employed full time Households that had a gross income between $20,001 40,000 had a significant relationship, at p < 0.011, to net worth as compared to households with an income between $0 20,000. Households that had a gross income within $20,001 40,000 on
43 average had $26,342 more household net worth than households that had a gross income between $0 20,000. Households that had a gross income between $40,001 60, 000 or $60,001 80,000 were found to have a significance of 0.000 in predicting net worth as compared to households with an income between $0 20,000. Households that had a gross income within $40,001 60,000 on average had $45,753 more household net wo rth than households that had a gross income between $0 20,000. Households that had a gross income within $60,001 80,000 on average had $70,168 more household net worth than households that had a gross income between $0 20,000. Model 2: Model two comp rise d the original Hayhoe and Gutter (2012) FBS plus the three additional variables. The additional independent variables that w ere added to the original Hayhoe and Gutter (2012) FBS we re planning horizon, availability of emergency funds, and the combining of assets. P l anning Horizon, availability of emergency funds, and the Hayhoe and Gutter (2012) FBS were all found to have a p < 0 .001 or better; while combining assets was not found to be significant. The age of the respondent was found to have a p = 0.00 0. As the respondents age increase d by one year, on average the net worth increased by $95 0 Respondents who were an unpaid volunteer were found to have a significant relationship in predicting household net worth, p < 0. 030, as compared to re spondents who were employed full time Households that had a r espondent that w as an unpaid volunteer were found to have $ 70,286 more household net worth as compared to households that had a respondent that was employed full time Also, respondents who we re not working for pay were found to have a significant relationship in p redicting household net worth, p <
44 0.038, as compared to respondents who were employed full time Households that had a r espondent not working for pay were found to have $18 572 more ho usehold net worth as compared to households that had a respondent who was employed full time R espondent s whose spouse s were not currently working for pay ( p < 0.008 ) or wa s disabled, re tired, or not seeking work ( p < 0.010 ) were found to have a significa nt relationship in predicting net worth, as compared to respondents whose spouse was employed full time Households where a r espondent s spouse was not working for pay had on average $2 6 7 8 6 more household net worth than households where a ouse was employed full time Households where a r espondent s spouse was disabled, retired, or not seeing work had on average $2 4 714 more household net worth than a household where a as employed full time Households that had a gross income between $40,001 6 0,000 were found to have a significant relationship, p < 0.0 5 1, in predicting net worth as compared to households with an income between $0 20,000. Households that h ad a gross income within $40,001 6 0,000 on average had $ 31,8 27 more household net worth than households that had a gross income between $0 20,000. Households that had a gross income between $6 0,001 8 0,000 were found to have a significant relationship, p = 0.0 00 in predicting net worth as compared to households with an income between $0 20,000. Households that h ad a gross income within $60,001 8 0,000 on average had $ 53,571 more household net worth than households that had a gross income between $0 20,000. This model was found to be the best measure of fin ancial behaviors in predicting net worth and will be discussed later in the chapter.
45 Model 3: Model three comprised the combination of all seven independent variables combined into one new score. The newly aggregated FBS was found to have a significance o f .000 in predicting couple household wealth The age of the respondent w as significant at p < 0.00 1 As the respondent s age increase d by one year, on average the Respondents who we re an unpaid volunteer were found to have a significant relationship in predicting net worth, p < 0.04 6 as compared to being employed full time Households that had a r espondent that w as an unpaid volunteer were found to have $6 5 534 more household net worth as compared to households tha t had a respondent who was employed full time Also, wa s not currently working for pay ( p < 0.015 ) or wa s disabled, retired, or not seeking work ( p < 0.00 5 ) were found to have a significant relationship in predicting net worth, a s compared to respondents whose spouse was not employed full time Households that had a r espondent s spouse who was not wo rking for pay had on average $24 9 0 8 more household net worth than households that had a that was employed full time Households with a spouse who was disabled, retired, or not seeing work had on average $27, 634 more household net worth than household s that had a as employed full time. Households that had a gross income between $20,001 40 ,000 were found to have a s ignificant relationship, p < 0.026 in predicting net worth as compared to households with an income between $0 20,000. Households that had a gross income within $20,001 40,000 on average had $2 2 333 more household net worth than households that had a gross income between $0 20,000. Households that had a gross income between $40,001 60,000 or $60,001 80,000 were found to have a
46 significance of 0.000 in predicting net worth as compared to households with an income betwee n $0 20,000. Households that had a gross income within $4 0,001 60,000 on average had $39,388 more household net worth than households that had a gross income between $0 20,000. Households that had a gross income within $60,001 80,000 on average had $ 62,681 more household net worth than households that had a gross income between $0 20,000. Model 4: Model four comprised all seven independent variables as individual factors rather than aggregated factors. The individual independent variables: spendin g ( p < 0.001) planning horizon ( p = 0.000) and availability of emergency funds ( p = 0.000) were all found to have a significance of .001 or better and planning was found to have a significant relationship of p < 0 .018. Two of those independent variables, spending and planning, are from the original Hayhoe and Gutter (2012) FBS and two, planning horizon and emergency funds, are from the newly aggregated FBS. Respondents who we re an unpaid volunteer were found to have a significant relationship in p redictin g household net worth, p < 0.025, as compared to respondents who were employed full time Households with a r espondent who w as an unpaid volunteer were found to have $ 72,610 more household net worth as compared to households with a respondent who was emplo yed full time Also, respondents who we re not working for pay were found to have a significant relationship in predicting household net worth, p < 0.037, as compared to respondents who were employed full time Households with a r espondent that w as not work ing for pay were found to have $18 624 more household net worth as compared to households with a respondent who was employed full time R wa s not currently working for pay ( p <
47 0.003 ) or wa s disabled, re tired, or not seeking work ( p < 0.009) were found to have a significant relationship in predicting net worth, as compared to respondents whose spouse was employed full time. Households where a r espondent s spouse was not working for pay had on average $ 30,023 more household net worth than households where a was employed full time Households where a r espondent s spouse was disabled, retired, or not seeing work had on average $2 5,119 more household net worth than a household where a as employed full time Households with a gross income between $40,001 6 0,000 were found to have a significant relationship, p < 0.0 07 in predicting net worth as compared to households with an income between $0 20,000. Households with a gross income of $40,001 6 0,000 on average had $ 30,625 more household net worth than households with a gross income between $0 20,000. Households with a gross income between $6 0,001 8 0,000 were found to have a significant relationship, p = 0.0 00 in predicting net worth as compared to households with an income between $0 20,000. Households with a gross income within $60,001 8 0,000 on average had $ 54,687 more household net worth than households with a gross income between $0 20,000. Please see T ables 4 5, 4 6, 4 7, an d 4 including their significance. Hypothesis Testing Hypothesis One: The first hypothesis conjectured the FBS plus the three additional variables w ould show a significant improvement in predicting net worth for couple s as compared to the FBS To test this hypothesis, the Hayhoe and Gutter (2012) FBS (Model 1) was compared to the Hayhoe and Gutter (2012) FBS plus the three
48 additional variables (Model 2). An Ordinary Least Squared Regression (OLSR) reported the Hayhoe an d Gutter (2012) FBS with the three additional variables and the original statistic was used to compare the model estimating net worth with each scale. This was calculated by compari freedom. This procedure allows for the analysis of the strongest measure to be used in testing the first hypothesis. After comparing the Hayhoe and Gutter (2012) FBS with the three additions to th e Hayhoe and Gutter (2012) FBS, the resulting F statistic was16.99 with a cumulative probability of 0.9999 ; because of the result the researcher reject ed the Hayhoe and Gutter (2012) FBS. This means that the Hayhoe and Gutter (2012) FBS with additions is a better measure for predicting net worth than the original Hayhoe and Gutter (2012) FBS. The adjusted R 2 of the Hayhoe and Gutter (2012) FBS with additions is .241, as seen in Table 4 5, while the original Hayhoe and Gutter (2012) FBS has an adjusted R 2 o f .178, as seen in Table 4 6. This comparison failed to reject this hypothesis, that the Hayhoe and Gutter (2012) FBS with the three additional variables is the strongest predictor of net worth. The results of the F statistic and the adjusted R squared fai l to reject th e first hypothesis. Please see T ables 4 5 and 4 6 for the OLSR output of these models. Hypothesis Two: The second hypothesis posed that the new version of the FBS (Model 3) w ould be a better predictor of net worth than the Hayhoe and Gutter ( 2012) FBS plus additional variables (Model 2). Th e F statistic was used to compare the Hayhoe and Gutter (2012) FBS plus additional three variables to the new aggregated FBS. After testing the two scores the result was an F statistic of 6.947 with a
49 cumul ative probability of .9999. This result confirm ed that the Hayhoe and Gutter (2012) FBS with additional variables wa s a better predictor of net worth than the newly aggregated FBS. This confirm ed the Hayhoe and Gutter (2012) FBS with the additional variabl es was a better predictor of net worth than the newly aggregated FBS. This study chose to look at the adjusted R 2 values to confirm this hypothesis. The Hayhoe and Gutter (2012) FBS with additional variables had an adjusted R 2 value of 0 .241, as seen in Ta ble 4 6, and the newly aggregated FBS had an adjust R 2 value of 0 .212, as shown in Table 4 7. These results provide d further support that the Hayhoe and Gutter (2012) FBS with additional variables wa s a better measure for predicting net worth for couples t han the newly aggregated FBS. Thus, the second hypothesis was rejected as shown in Tables 4 6 and 4 7. Hypothesis Three: To test the third hypothesis the study employed similar analysis, the OLSR and an F statistic. This hypothesis conjectured that the new ly aggregated FBS (Model 3) would be a better predictor of net worth than the individual (Model 4). An OLSR was used for analysis of the newly aggregated FBS and also for the individual variables. The newly aggregated FBS was compared using an F statistic to the individual variables. The F statistic was 5.039 with a cumulative probability of 0 .9999. The results show that the individual variables are a better predictor of net worth than the newly aggregated FBS. Examining each adjusted R 2 the newly aggrega ted FBS adjusted R 2 was .212, as shown in Table 4 7, and the individual variable adjusted R 2 was .250, as seen in Table 4 8, further supports the outcome. The results demonstrate the individual variables are a better predictor of net worth than the
50 aggrega ted score. As a result, the third hypothesis was rejected The OLSR output of these models is shown in Tables 4 7 and 4 8. Hypothesis Four: The fourth hypothesis proposed that households exhibiting greater levels of the financial behaviors used in the scor e would have higher net worth. This hypothesis was examined by performing an OLSR on t he Hayhoe and Gutter (2012) FBS plus the three additional variables (Model 2 ) to examine the aggregated oved to have a p value of 0.000 in predicting net worth, showing significance. The result of the analysis thus failed to reject the third hypothesis. Please see T able 4 7 for the OLSR output of this model. Summary The results of this analysis reveal intere sting findings about creation of the new FBS with the additional three independent variables, planning horizon, emergency funds, and combining assets, was selected by compar ing this approach to a reduced model and a model where all FBS scores are aggregated. As expected the age and the income of the respondent were important to consider when estimating net worth. The ycle stage. The study found that within this model, as age of the respondent increased by one year their net worth increased on average by $950. It is not surprising that the first hypothesis yielded a positive result. While failure to support the second h ypothesis was not expected, it is telling that there are variables included within the newly aggregated score that are not strong predictors of net worth. A failure to support hypothesis three was also a surprisin g result. The results of the
51 analysis concl uded in the rejection of the third hypothesis I t is clear that the newly aggregated FBS included some variables that were and some that were not predictive of net worth. Finally, with analysis supporting the fourth hypothesis it is clear that the financia l behaviors that were included in the newly aggregated FBS are significant in
52 Table 4 1. Descriptive Statistics of Demographic Variables Variable M SD Proportions Members in Household 2.85 1.29248 Age 45.04 15.005 45.27 14.123 Employment Status Full Time 32.5 Part Time 10.3 Temporarily Laid Off 6.2 Unpaid Volunteer 1.1 Not working for Pay 22.2 Disabled, Retired, or Not Seeking Work 27.8 ployment Status Full Time 51.6 Part Time 12.0 Temporarily Laid Off 5.4 Unpaid Volunteer 0.6 Not working for Pay 12.2 Disabled, Retired, or Not Seeking Work 18.2 Education High School or less 2 7.4 Some College or more 72.5 High School or less 41.2 Some College or more 58.3 Gross Income $0 20,000 14.2 $20,001 40,000 35.0 $40,001 60,000 30.4 $60,001 80,000 20.2
53 Ta ble 4 2. Descriptive Statistics of Dependent Variable Variable Descriptive Statistic Amount Net Worth M 23960.44 SD 72380.53 M d n 0 Mode 0 Minimum 182500 Maximum 199500 25% 4500 50% 0 75% 37000 Skewness 1.083 Kurtosis 1.066
54 Tabl e 4 3. Descriptive Statistics of Independent Variables Variables M SD Proportions Spending .7346 .44201 Spending exceeded income 26.5 Spending equaled income / Spending was less than income 73.5 Planning .4883 .50040 Never / Seldom / Occasi onally 51.2 Usually / Most of the Time 48.8 Monitoring .5801 .49408 Never / Seldom / Occasionally 42.0 Usually / Most of the Time 58.0 Written Goals .34 .473 Yes 33.8 No 66.2 Planning Horizon .6752 .46881 Next few months / Next year 32.5 Next 1 4 years / Next 5 10 years/ Longer than 10 years 67.5 Emergency Funds .6497 .47758 No one / Extended Family / Friends / Community / Get a loan from a bank or credit union / Use my credit cards / Other 65 No one 35 Combine Assets .7152 .45180 Yes 71.5 No 28.5
55 Table 4 4. Hayhoe and Gutter (2012) FBS OLSR Output Unstandardized Coefficients Variables St. Error t Sig. (Constant) 95093.476 19601.162 4.851 .000 Age ** 957.204 267.963 3.572 .000 Job: Full Time (Comparison) Job: Part Time 2852.883 11551.698 .247 .805 Job: Temp Laid Off 11838.891 14906.570 .794 .428 Job: Unpaid Volunteer 6761 7.191 33569.519 2.014 .045 Job: Not Working for Pay 14227.457 9233.481 1.541 .124 Job: Disabled, Retired or not Seeking Work 3503.702 9766.419 .359 .720 Spouse Job: Full Time (Comparison) Spouse Job: Part Time 8678.944 10276.668 .845 .399 Spouse Job: Temp Laid Off 13115.664 15686.946 .836 .404 Spouse Job: Unpaid Volunteer 19109.200 47965.160 .398 .691 Spouse Job: Not Working for Pay 21680.689 10393.156 2.086 .038
56 Table 4 4. Continued Unstandardized Coefficients Variables St. Error t Sig. Spouse Job: Disabl ed, Retired or not Seeking Work 27340.131 9991.287 2.736 .006 Education: High School or Less (Comparison) Education: Some College or More 11075.940 7848.978 1.411 .159 Spouse Education: High School or Less (Co mparison) Spouse Education: Some College or More 10263.159 7090.351 1.447 .148 Members in Household 3029.599 2614.665 1.159 .247 Income: $0 20,000 (Comparison) Income: $20,001 40,000 26342.983 10275.741 2.564 .011 Income: $40,001 60 ,0 00 ** 45753.104 11335.661 4.036 .000
57 Table 4 4. Continued Unstandardized Coefficients Variables St. Error t Sig. Income: $60,001 80,000 ** 70168.226 12333.710 5.689 .000 Hayhoe and Gutter (2012) FBS ** 9733.721 2662.626 3.656 .000 *<.05 **<.001 R R 2 Adjusted R Square Std. Error of the Estimate 460 .211 178 65784 .417 Su m of Squares Mean Square F Sig. Regression 492691410939.422 27371745052.190 6. 325 .000 Residual 1839225521493.007 4327589462.336
58 Table 4 5. Hayhoe and Gutter (2012) FBS plus Additional Independent Variables OLSR Output Unstandardized Coefficients Variables St. Error t Sig. (Constant) 120599.938 19348.132 6.233 .000 Age ** 950.034 259.937 3.655 .000 Job: Full Time (Comparison) Job: Part Time 5391.016 11127.703 .484 .628 Job: Temp Laid Off 16683.931 14443.194 1.155 .249 Job: Unpaid Volunteer 70 286.948 32234.964 2.180 .030 Job: Not Working for Pay* 18572.542 8932.969 2.079 .038 Job: Disabled, Retired or not Seeking Work 2135.835 9461.350 .226 .822 Spouse Job: Full Time (Comparison) Spouse Job: Part Time 8625.359 9892.572 .872 .384 Spouse Job: Temp Laid Off 11399.854 15040.439 .758 .449 Spouse Job: Unpaid Volunteer 1749.946 46647.513 .038 .970
59 Table 4 5 Continued Unstandardized Coefficients Variables St. Error t Sig. Education: High School or Less (Comparison) Education: Some College or More 12961.983 7563.504 1.714 .087 Spouse Education: High School or Less (Comparison) Spouse Education: Some College or More 12533.971 6812.268 1.840 .066 Members in Household 2641.700 2525.512 1.046 .296 Income: $0 20,000 (Comparison) Income: $20,001 40,000 18034.972 10047.558 1.795 .073 Income: $40,001 60,000 ** 31827.805 11379.954 2.797 .005 Income: $60,001 80,000 ** 53571.576 12475.906 4.294 .000 Planning Horizon** 26060.727 6651.791 3.918 .000
60 Table 4 5 Continued Unstandardized Coefficients Variables St. Error t Sig. Emergency Funds** 26866.213 6810.931 3.945 .000 Combine Assets 6697.300 7236.913 .925 .355 Hayhoe and Gutter (2012) FBS** 9051.723 2714.909 3.334 .001 *<.05 **<.001 R R 2 Adjusted R Square Std. Error of the Estimate 527 .277 .241 62972.006 Sum of Squares Mean Square F Sig. Regression 635730168335.462 30272865158.832 7.634 .000 Residual 1657567922573.627 3965473498.980
61 Table 4 6. Newly Aggregated FBS OLSR Output Unstandardized Coefficients Variables St. Error t Sig. (Constant) 113693.203 19477.403 5.837 .000 Age ** 916.038 262.872 3.485 .001 Job: Full Time (Comparison) Job: Part Time 4418.073 11292.613 .391 .696 Job: Temp Laid Off 13599.580 14473.516 .940 .348 Job: Unpaid Volunteer 655 34.775 32755.394 2.001 .046 Job: Not Working for Pay 16038.523 9031.170 1.776 .076 Job: Disabled, Retired or not Seeking Work 1016.311 9586.862 .106 .916 Spouse Job: Full Time (Comparison) Spouse Job: Part Time 10116.197 10033.397 1.008 .314 Spo use Job: Temp Laid Off 13355.891 15304.427 .873 .383 Spouse Job: Unpaid Volunteer 2727.195 46618.030 .059 .953 Spouse Job: Not Working for Pay 24908.855 10161.012 2.451 .015
62 Table 4 6. Continued Unstandardized Coefficients Variables St. Error t Sig. Spouse Job: Disabled, Retired or not Seeking Work* 27634.264 9761.322 2.831 .005 Education: High School or Less (Comparison) Education: Some College or More 9784.563 7661.120 1.277 .202 Spouse Education: High School or Less (Comp arison) Spouse Education: Some College or More 10751.184 6918.182 1.554 .121 Members in Household 3219.466 2561.593 1.257 .210 Income: $0 20,000 (Comparison) Income: $20,001 40,000 22333.997 9964.360 2.241 .026 Income: $40,001 60,00 0 ** 39388.165 11050.693 3.564 .000
63 Table 4 6. Continued Unstandardized Coefficients Variables St. Error t Sig. Income: $60,001 80,000** 62681.739 12008.618 5.220 .000 New FBS** 10692.670 1947.521 5.490 .000 *<.05 **<.001 R R 2 Adjusted R Square Std. Error of the Estimate .495 .245 .212 64139.676 Sum of Squares Mean Squa re F Sig. Regression 561347000968.648 31185944498.258 7.581 .000 Residual 1731951089940.442 4113898075.868
64 Table 4 7. Individual Independent Variables OLSR Output Unstandardized Coefficients Variables St. Error t Sig. (Constant) 128473.521 19641.515 6.541 .000 Age ** 971.699 260.105 3.736 .000 Job: Full Time (Comparison) Job: Part Time 4906.318 11075.667 .443 .658 Job: Temp Laid Off 20727.108 14479.377 1.431 .153 Job: Unpaid Volunteer 72610.515 32245.187 2.252 .025 Job: Not Work ing for Pay* 18624.295 8909.560 2.090 .037 Job: Disabled, Retired or not Seeking Work 2367.489 9419.191 .251 .802 Spouse Job: Full Time (Comparison) Spouse Job: Part Time 9536.014 9878.751 .965 .335 Spouse Job: Temp Laid Off 11158.089 14991.777 .74 4 .457 Spouse Job: Unpaid Volunteer 5708.392 46504.840 .123 .902
65 Table 4 7. Continued Unstandardized Coefficients Variables St. Error t Sig. Spouse Job: Not Working for Pay 30023.407 9999.773 3.002 .003 Spouse Job: Disabled, Retired or not See king Work* 25119.869 9582.738 2.621 .009 Education: High School or Less (Comparison) Education: Some College or More 13843.260 7546.145 1.834 .067 Spouse Education: High School or Less (Comparison) Spouse Education: Some College or More 13011.8 44 6786.519 1.917 .056 Members in Household 2147.298 2523.623 .851 .395 Income: $0 20,000 (Comparison) Income: $20,001 40,000 18283.763 10050.947 1.819 .070
66 Table 4 7. Continued Unstandardized Coefficients Variables St. Error t Sig. Income: $40,001 60,000 30625.083 11393.813 2.688 .007 Income: $60,001 80,000 ** 54687.668 12511.457 4.371 .000 Spending ** 22834.215 7103.161 3.215 .001 Planning 18145.639 7631.459 2.378 .018 Monitoring 6430.431 7474.485 .8 60 .390 Written Goals 5604.383 6948.344 .807 .420 Planning Horizon ** 24084.893 6653.087 3.620 .000 Emergency Funds** 26765.667 6776.373 3.950 .000 Combine Assets 5762.377 7223.576 .798 .425 *<.05 **<.001 R R 2 Adjusted R Square Std. Err or of the Estimate .539 .297 .250 62596.913 Sum of Squares Mean Square F Sig. Regression 667173105776.977 27798879407.374 7.094 .000 Residual 1626124985132.112 3918373458.150
67 CHAPTER 5 CONCLUSIONS AND IMPL ICATIONS Discussion of Findings T his study found many descriptive variables were significant predictors of net worth. The age of the respondent was found to have a significant relationship in predicting higher levels of net worth. This aligns with previous research on net worth relating t o Life Cycle (Weisbrod & Hansen, 1968). The age of a respondent is descriptive of the ir life stage, thus aligning with the L ife C ycle model. It is not surprising to find that the older the age of the respondent the greater the level of net worth, because the respondent has had more time to accumulate wealth. This study found income level of the respondent is a predictor of net worth. This finding also aligns with previous research (Weisbrod & Hansen 1968) As stated earlier in the thesis, previous works describe how income and net worth are very closely correlated. In this study, the higher the income level the more significant relationship there was with net worth. For example, households that have the highest income level in our sa mple, $60,001 80,00 0 were found to have a significant relationship to net worth. This is because households with greater levels of income have more resources available to build net worth. The r esults of this study found that job status was a lso a predictor of net worth. For the respondent as compared to full time employment, the job status of unpaid volunteer and not working for pay were both found to have a significant relationship in predicting higher levels of net worth. For the respondent s spouse as compared to full t ime employment, not currently working for pay and being disabled, retired, or not seeking work were found to have a significant relationship of predicting higher levels of net
68 worth. The results of the jobs status of both the respondent and the spouse were not expected. Two of the three variables added to the original FBS w ere found to have a si gnificant relationship in predicting higher levels of net worth. It was interesting to find that the variables independently were better predictor s of higher net wor th than the aggregated score. This c ould be due to the fact that one of the three of the independent variables, combining assets, did not have a significant relationship in predicting net worth. Limitations There are limitations to take into consideration when evaluating this study. First, this study only includes couples ( spouses or partners ) Therefore, the results of this study are limited to couple households and should not b e generalized to all households The way employment status answers were categ orized may have constituted another limitation of this study. For example, the results indicated that respondents who reported having a spouse who was disabled, retired, or not currently seeking work was found to predict a higher net worth when compared to respondents whose spouse worked full time. Logically, it does not seem likely that a respondent whose spouse was not seeking work to have a higher net worth than one whose spouse was employed full time. However, it may be plausible for a respondent with a retired spouse to have a higher net worth than one whose spouse was employed full time. This mixed result may be due spouse may have been stay at home parents, working but not for pay. Or the respondent or their spouse may be close to retirement or had recently retired, with a large amount of net worth, and currently volunteer. It would be beneficial for future
69 research to examine the life stage of the respondent and their spous e or partner. Also, the contradictory nature of the results may be due to the confusing and unclear answer choices. may have been too vague. This answer could be broken down into more categor ies such as Another answer choice that may have This answer choice wrongly assumed disabled individuals did not work. The answer also group ed job status es together (i.e., disabled, retired, and not currently seeking work) which should not have been grouped. It was not appropriate that someone who was disabled to have been categorized as retired or vice versa. F urther limitation s of this stud y were related to variable of net worth The first household in this study could have reporte d having a net worth of $200,000 but have a mortgage on a home that was currently underwater. If this was the case, the or even negative. This was called for in the survey, which specifically stated to exclude the value Another limitation of the dependent variables of net worth in this study was how the value was calculated and measured. Net worth was calculated as the midrange of assets minus the midrange of liabilities. Assets and liabilities were measured categorically, not on a continuous scale. An example of an answer choice A midrange was given to each answer category. For example, From
70 there the midrange asset value was subtracted by the midrange liability value which supplied the net worth value used in this study. By calculating net worth in this way, the study created a value that was limited to bein g an estimate rather than an exact number. As a result, net worth values in this study were not an exact representation of the value of the net worth of the respondents. This study was limited by the survey categorical answer choices. Future research can improve on this, by providing a continuous answer choice. Another limitation of this study was the wording of some of the question and answer choices of the independent variables. For example for two independent variables the answer choices included The answer choices were confusing and because they do not provide a numerical example, it was difficult to assess a value. For one household the choice of seldom could mean engaging in a beh avior once a year, but for other households seldom could mean engaging in a behavior once a month. Without an example or definition of the answer choices, they are open to broad interpretation. Also, the answer choices were too similar in nature, such as or indicate there was frequency but it was unclear as to how these choices differ, making the answer choices more confusing for a respondent. Future research coul d improve on the answer choices by providing specific examples with numerical values. The wording of the independent variable labeled "written goals" stated: or your family have written goals such as educatio n, or starting a b Through this variable, the study
71 was trying to capture whether households created financial goals, however it may have created a further limitation. Couples and household members may have financial goals tha retirement) but are not written down. Households that did this would not have been awarded a point value in this study Due to the wording of the question the interpretation of this variable was limited to households that had written goals and did not include households who discussed and planned but did not explicitly write down goals. An additional limitation of this study involved how the planning horizon was measured. For this study, households with a planning horizon of a year or greater were awarded one point. H ouseholds with a planning horizon of less than a year were not awarded points. This method of scoring did not allow the study to consider the life stage of househo lds. For example, a household comprised of a newlywed couple may have a planning horizon of less than a year because of their new life stage, not because of a lack of financial security. Failing to account for possibil ity of not fairly scoring households. Another limitation of this study centers on the wording of the independent variable combine as have been interpreted differently by respondents. Assets could mean income, ownership of a home, IRA, or other forms of retirement accounts. Also, because some nly, they leave a spouse or partner off of the account and thus may not be considered as a
72 combined asset. The wording of this question could have been confusing to a respondent and should be clarified in future surveys. Future researchers might ask about specific forms of assets or respondents might be provided a list of assets and asked to select all that applied. It is also important to note the data w ere collected during a difficult economic time. As discussed early households during the time the data were collected were struggling to maintain wealth. This means behavior during a healthy economy. Finally, this study is a cross sectional stud y, not longitudinal; therefore, is not representative of ho useholds over a long period of time This study only provides one point in time. It does not provide information on past behaviors, demographics, or levels of wealth It would be interesting to have a longitudinal study to view pre and post recession to observe changes in levels of net wort h. Conclusions Hypothesis 1 Hypothesis 1 stated the additional variables to the Hayhoe and Gutter (2012) FBS will indicate a significant improvement in predicting net worth for couples as compared to the Hayhoe and Gutt er (2012) FBS The researcher fail ed to reject this hypothesis after conducting analysis on Mode l 1 and Model 2. Results of the comparison of the two models supported the hypothe sis. As a result it is clear th at the additional variables had a positive imp act in predicting net worth of couples and the ability of the Hayhoe and Gutter (2012) FBS to predict a couple net worth was improved
73 Hypothesis 2 Hypothesis 2 stated the aggregated version will significantly improve prediction of s compared to the Hayhoe and Gutter (2012) FBS plus three additional variables The researcher rejected this hypothesis after conducting analysis on Model 2 and Model 3 The results of the F statistic led the researcher to reject this hypothesis. As a res ult the newly aggregated FBS was not a better predictor of a Hayhoe and Gutter (2012) FBS plus additional variables. From this result, it seems that the newly added variables and the Hayhoe and Gutter (2012) FBS are a better predic tor of net worth separated rather than aggregated into one large score. Hypothesis 3 Hypothesis 3 stated the aggregated measure of the FBS will be a better predictor of net worth than the seven variables individually Analysis was conducted using Model 3 a nd Model 4. The researcher rejected this hypothesis after calculating an F statistic comparing Models 3 and 4. The result of this comparison indic ated that individual variables are a better predictor of net worth than combin ing variables into one score. R ejecting one large score was also found to be the result in Hypothesis 2, and using the variables independently proved to be better way of predicting net worth. Hypothesis 4 Hypothesis 4 state d households that exhibited greater levels of the financial beh aviors used in the score will show higher levels of net worth A T test was used to analyze thi s hypothesis. The T test showed there was significant predicting power in the newly aggregated FBS for net worth. Th us the researcher failed to reject the hypoth esis
74 that households that exhibit ed greater levels of the financial behav iors comprised of the score would show higher levels of net worth. Implications The findings of this research study are interesting for many reasons. The results of the study affirm p ast research on net worth and give new insight on how to properly assess couple when predicting net worth. For educators the results of this study have some interesting implications. It is important for teachers to instruct about budgeting and planning spending, as well as income and planning how to spend money, were found to have a predictive relationship with higher net worth. Also, teachers should encourage students to consider long term when planning their finances and always be prepared with an emergency fund. These two behaviors, having a planning horizon of greater than a year and accessibility to an emergency fund, were found to have a p redictive relationship to higher net worth. Teaching students to save early and have a financial planning horizon of greater than a year may have a major It would allow the student to accumulate wealth at a young age that could help them pay for later important expenses, such as college or a car. For practitioners, planners, and counselors, th is research indicates that Hayhoe and Gutter (2012) FBS plus the three individual variables were a better predictor of net wor th than any of the scores created. This should be considered when evaluating h which shows that certain demographic variables are predictive of hi gher net worth, such as ag e or income level. Practitioners should continue to take those variables into
75 consideration. Also, from the results in this study it is clear that having a planning horizon of greater than a year was predictive of higher net worth. Practitioners should tak e this into consideration when helping households make financial plans, especially low to moderate income households, by planning over a year into the future and expanding their financial planning horizon. In addition, the financial behaviors of not over s pend ing income and ma k ing plans for money are predictive of higher net worth. Having accessibility to an emergency fund also was found to be predictive of higher net worth. Therefore it would be wise for practitioners to help household s make plans for the ir finances that include saving for an emergency fund and not over spending. Future research shoul d examine the employment status of both partners the role that debt plays and the how often people are engaging in monitoring their fi nances, such as once a week, month, or year Each of these may be significant predictors of net worth Future research could e xamine further the different aspects of financial behaviors related to net worth, such as how credit is used, how aggressively de bt is paid down, and how households invest their assets. Also, future research might eliminate current variables, such as whether household s monitor their money, have written financial goals, or combine their assets since none of these behaviors were predi ctive of net worth Eliminating these variables from the score may possibly produce a stronger score. Further research should consider also looking into h ow often households engage in different financial management behaviors. Considering the results of this study, the variables independently did just as well as a composite score F uture research could focus on specific behaviors rather than aggregating variables to make a score. It also would be interesting for future research to
76 consider whether h ouseho lds w ere single income or had more than one income. Future research also might consider expanding the research to include not only couples but also singles. Als o, it could be helpful to investigate in more detail the job status of both partners. This study showed that respondents with a spouse who was not working for pay or retired, disabled, or not seeking work was predictive of higher net worth as compared to respondents whose spouse was employed full time This result may be due to respondents who are cl ose to retirement or are already retired and have high levels of net worth. Further research is needed to better understand this relationship and what it means for household and their net worth. Further research of low to moderate income households should continue to better understand how to apply financial management behaviors to households where resources are less available
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81 BIOGRAPHICAL SKETCH Rachel Marie Dorman graduated from A&M Consolidated High School (College Station, TX) in 2006. She began her undergraduate coursework at the University of Florida in fall of 2006 and graduated with a Bachelor of Science in Busi ness Administration focused on m arketing in May of 2010. She began her graduate studies at the University of Florida in August of 2010, pursuing a Maste r of Science degree in Family, Youth, and Community Sciences, with a focus in family financial management.