Printer Friendly

Conscientiousness, financial literacy, and asset accumulation of young adults.

This study utilizes the 1997 National Longitudinal Survey of Youth to examine the relationship between financial literacy, conscientiousness, and asset accumulation among young adults. Findings indicate that both conscientiousness and financial literacy are consistent predictors of asset accumulation among young Americans. A one-standard-deviation increase in conscientiousness is correlated with a 40% increase in net worth, a 53% increase in illiquid asset holdings, and a 33% increase in liquid asset holdings. A one-standard-deviation increase in financial literacy is correlated with a 60% increase in illiquid asset holdings and a 30% increase in liquid asset holdings. Financial literacy moderates the effect of conscientiousness on net worth. These findings suggest that conscientiousness and financial literacy are important factors and that policies and programming with a dual emphasis on increasing conscientiousness and financial literacy are likely to have a positive impact on consumer savings and asset-building.

**********

Complex financial markets--combined with a growing trend of individual responsibility in recent years--have forced consumers to manage their money on their own in a precarious environment. As financial products have become more complex, consumers' inability to understand them has become increasingly apparent (Willis 2008). Research indicates that most individuals do not understand basic financial concepts such as risk diversification, nor can they perform simple economic calculations such as compounding interest (Lusardi 2008).

To help consumers with the daunting task of managing their money in the current marketplace, many policy tools have been proposed (Organisation for Economic Co-operation Development 2010). The most popular of these is financial education. The general argument is: if consumers are given full information, they will become financially literate and thus able to understand the complex financial marketplace, make sound decisions, and prosper. Increasing financial literacy is an agreeable option, as it simultaneously empowers consumers while allowing financial innovations to get more and more complex with limited oversight. However, there are complicating factors in the marketplace--such as information asymmetries (Kozup and Hogarth 2008), corporate greed and malfeasance (Cohen 2010), and individuals' own cognitive biases (Huston 2010; Willis 2008)--that all call into question the viability and effectiveness of financial literacy. Financial literacy is important, yes. But is it sufficient as a stand-alone solution to the complex problems consumers face with their personal finances?

When it comes to personal finances, young Americans are a demographic group that deserves special attention. The financial events of the last decade have not left much room for young adults to thrive financially. A sluggish economy with high unemployment, coupled with high student loan debt, is making the transition into financial independence challenging for young people. Total student loan debt (now a staggering $1 trillion) surpassed outstanding credit card debt in June 2010 (Federal Reserve Bank 2010). Two-thirds of college seniors graduating in 2010 with loans carried an average of $25,250 in debt (The Project on Student Debt 2011). The unemployment rate for Americans age 18-29 in July 2012 was 12.7%, almost four points higher than the national average (8.1%) (Generation Opportunity 2012). Adding to the woes, says Professor Lucia Dunn (2012), is that this generation tends to have a high rate of time preference, meaning that they have a preference for immediate versus delayed consumption. She notes that younger generations are encouraged to spend when they are young rather than delaying gratification until they are more financially stable.

These predicaments leave many young adults susceptible to financial difficulties. For those attempting to save, what factors predict a positive outcome? These questions are at the heart of this study. More specifically, this study addresses these questions by evaluating two key predictors of financial behaviors--conscientiousness and financial literacy.

According to the American Psychological Association Dictionary of Psychology (VandenBos 2007), conscientiousness is defined as "the tendency to be organized, responsible and hardworking." Conscientiousness is comprised of six lower-level facets which include: competence (efficiency); order (organization); dutifulness (thorough); achievement striving (ambitious); self-discipline (not lazy); and deliberation (not impulsive) (Costa and McCrae 1992). Some traits related to the factor of conscientiousness include perseverance, impulse control, task- and goal-orientation, propensity to plan, and delay of gratification (John and Srivastava 1999).

In order to understand how conscientiousness may impact the financial decisions of consumers, a psychological theory of self-control is utilized. While there are no theoretical studies directly linking conscientiousness to asset-building, there are a multitude of studies on self-control that can inform our hypotheses. Conscientiousness is a broader concept than self-control, with self-control an underlying facet (self-discipline and impulse control) (Gailliot, Mead, and Baumeister 2008; Jensen-Campbell et al. 2007; O'Gorman and Baxter 2002). Therefore, an assumption in this study is that those high in conscientiousness are more likely to recognize problems with self-control and to exercise self-control.

Everyone struggles with self-control to one extent or another, whether in the form of overindulgence, impulsivity, or procrastination. And a lack of self-control can have detrimental effects on various areas of one's life, from health to finances to relationships (Moffitt et al. 2011). The argument for financial literacy can be validated and strengthened if it is successful at helping consumers overcome issues of self-control. For example, is teaching someone the importance of savings as effective at increasing savings as defaulting people into an automatic savings or retirement plan? Most of the extant research in this area suggests that it is not. If financial literacy is not effective, then other means, such as "nudges" (Thaler and Sunstein 2008), should be explored in order to structure financial decision-making environments that help consumers prosper. This study will evaluate both conscientiousness (a proxy for self-control) and financial literacy and their respective associations with financial outcomes among a nationally representative sample of young adults.

This study will contribute to the existing literature on financial literacy, personality traits (specifically, conscientiousness), and financial well-being in two important ways. First, this study is unique in that it uses a personality trait, conscientiousness, in an economic model as a measure of self-control. There is research indicating that this is as robust a measure as traditional measurements such as time preference or discount rates (Anderson et al. 2011). This approach suggests that there is something inherent, yet measureable and malleable, that is directly associated with self-control behaviors. The integration of conscientiousness and standard economic models provides an alternative approach to financial decisions based on personality psychology, economic theory, and behavioral economics.

Second, this study uses models of self-control to inform how conscientiousness might predict financial behaviors. Everyone struggles with self-control in different capacities and any failing of self-control can have negative financial consequences. The current financial system is set up to exploit some of these shortcomings in a multitude of ways, such as instant in-store credit approvals and overdraft fees. Financial literacy may be helpful in these instances, but this has never been directly evaluated. While this study cannot directly test the effects of financial literacy, it is able to discern between financial literacy and conscientiousness and assess whether there is a moderating relationship between the two.

BACKGROUND

Interest in financial literacy by researchers and policymakers has grown considerably in the past decade. The United States launched a first-ever national strategy to improve financial literacy in 2006 after recognizing the growing problem of financial illiteracy. A number of comprehensive literature reviews on financial literacy have been published in recent years (Collins and O'Rourke 2010; Hathaway and Khatiwada 2008; Hogarth 2006; Martin 2007), and several academic journals (1) have dedicated special issues to the topic.

On the surface, much of the enthusiasm surrounding financial literacy seems warranted. A good deal of the research conducted over the past several years indicates that consumers have very low levels of financial literacy. Lusardi and Mitchell (2005) found that many adults (age 50+) cannot calculate simple interest-rate calculations and are unfamiliar with the concepts of inflation and risk diversification. A study of youth by Lusardi, Mitchell, and Curto (2010) found that less than one-third of respondents demonstrated an understanding of interest rates, inflation, or risk diversification. Levels of debt have been found to be negatively correlated with financial knowledge (Lusardi and Tufano 2009). Financial literacy has also been linked to increased retirement savings (Bernheim and Garrett 2003; Clark and D'Ambrosio 2002; Lusardi and Mitchell 2009) and investment in the stock market (Van Rooij, Lusardi, and Alessie 2011).

Self-control has been tied to various behaviors and outcomes, such as academic performance (Chowdhury and Amin 2006; Tangney, Baumeister, and Boone 2004); alcohol consumption (Tangney, Baumeister, and Boone 2004; Wills and Stoolmiller 2002); crime rates (Gottfredson and Hirschi 1990); weight control (Baumeister, Heatherton, and Tice 1994); substance use; and health behaviors (Baumeister and Vohs 2004). This list highlights the importance of self-control as a critical element of success. In fact, Duckworth and Seligman (2005) found that self-control was a better predictor of academic performance than intelligence (as measured by IQ score). Self-control has increasingly been linked to financial behaviors and outcomes, including retirement accounts and planning (Chatterjee, Palmer, and Goetz 2010; Howlett, Kees, and Kemp 2008; Moffitt et al. 2011); net worth and asset diversification (Chatterjee, Palmer, and Goetz 2010); and financial planfulness, savings, and home ownership (Moffitt et al. 2011). Those with low self-control are more likely to buy on impulse (Faber and Vohs 2004; Verplanken and Herabadi 2001) and have money management and credit problems (Moffitt et al. 2011).

The "Big Five" personality factors are five broad domains of personality that are used to describe human personality. Five-Factor Model (FFM) theory characterizes individuals in terms of enduring patterns of thoughts, feelings, and behaviors that can be quantitatively assessed (McCrae and Costa 1999). The Big Five factor of interest in this study is conscientiousness. It is a broader concept than self-control, with self-control considered an underlying facet (Gailliot, Mead, and Baumeister 2008; Jensen-Campbell et al. 2007; O'Gorman and Baxter 2002). Conscientiousness includes aspects of self-control because it involves the ability to adapt one's behavior to task demands. While they are not conceptually the exact same concept, an underlying assumption in this study is that highly conscientious people exercise self-control and those exercising self-control are conscientious.

Conscientiousness has been linked to a number of social outcomes, including mortality (Friedman et al. 1993); physical health (Goodwin and Friedman 2006); occupational attainment (Judge et al. 1999); job performance (Hogan and Holland 2003); marriage stability (Roberts and Bogg 2004); and drug abstinence (Walton and Roberts 2004). In terms of financial outcomes, conscientiousness was found to be negatively related to impulse buying (Verplanken and Herabadi 2001) and positively associated with the accumulation of net worth (Ameriks et al. 2007, 2004; Chatterjee, Palmer, and Goetz 2010). Ameriks et al. (2007, 2004) found the participants in their study who scored high in the trait of conscientiousness were more likely to consume in line with their expectations. Anderson et al. (2011) found that conscientiousness was successful in predicting credit risk and credit scores.

While there is evidence to suggest that financial literacy and self-control are both related to financial outcomes, it is unclear whether the two are related, whether one may trump the other, or whether they are both important but independent factors in predicting financial outcomes. This study considers the relationships between financial literacy and financial outcomes, and conscientiousness and financial outcomes. The goal is to ascertain whether financial literacy is able to moderate the effects of conscientiousness on financial decisions.

Saving and asset accumulation are important for young Americans. This is the stage in their lives where they may start to consider buying a new home or starting a family, two life events that require economic capital. Researchers have proposed that the impact of self-control problems on wealth accumulation may vary according to the nature of the liquidity of the underlying financial assets (Ameriks et al. 2004). Laibson (1997) suggests that illiquid assets, which are a class of assets held for future consumption, are a form of precommitment device, or a way for people to lock money away. Liquid assets, which are assets that are easily converted into cash, provide more of a temptation because the money is readily available for spending.

Economists postulate that all individuals suffer from a lack of self-control and that some are just more aware of this affliction (Laibson 1997; Strotz 1956). As such, those who are aware of their self-control problems, referred to as "sophisticates," are more likely to utilize illiquid asset accounts. Those unaware of their self-control problems, referred to as "naifs," are less likely to utilize illiquid asset accounts. It is probable that the more conscientious an individual is and the more financial knowledge she or he has, the more likely the individual will be to invest in illiquid assets. Liquid assets are held as a buffer against income shocks and a potential fall in income. A liquid asset buffer is used to smooth out consumption during one's working life (Angeletos et al. 2001). Highly conscientious individuals will be more likely to exercise self-control and thus maintain a reasonable balance of liquid assets.

There is some evidence that people recognize this issue and allocate their money accordingly. An experimental study by Beshears et al. (2011) asked a group of participants to allocate funds between illiquid and liquid accounts. When the interest rate offered in the accounts was the same, nearly half of the money was allocated to illiquid accounts. Even when interest rates offered in the illiquid accounts were lower, about one-quarter of the money was allocated, indicating that people will accumulate illiquid assets even when it is more costly for them to do so. These findings echo the findings of other researchers (Angeletos et al. 2001) that found hyperbolic consumers, those who report a gap between their long-term goals and short-term behaviors, hold more illiquid assets. The researchers suggest that the cost of illiquidity is partially offset for hyperbolic discounters, since they recognize their struggle with self-control and value the long-term dividends of illiquid assets.

These studies highlight what we may expect for individuals who recognize issues of self-control in terms of wealth and asset holdings. Those high in conscientiousness may be more able to exercise self-control and therefore simultaneously invest in illiquid assets while keeping liquid assets at a reasonable level. This study will look at the composition of assets for young adults, taking into consideration conscientiousness and financial literacy as factors impacting these financial behaviors.

CONCEPTUAL FRAMEWORK

The purpose of this study is to understand how financial literacy and conscientiousness function in models predicting asset-building among young Americans. The conceptual framework for this study integrates theories of self-control and personality psychology. The first part of this section explains how conscientiousness can be used in an economic model to help explain financial behaviors (Borghans et al. 2008). The second part describes empirical work in the area of self-control and asset-building, and proposes the hypotheses for the study.

Promising work has emerged in the fields of personality psychology and economics exploring personality traits as predictors of financial behaviors. Measured personality is being investigated as a viable construct, derived from the preferences, constraints, and information of an economic model (Almlund et al. 2011). Almlund et al. (2011) suggest that personality measures may resolve inconsistencies in observed consumer choices generally attributed to preferences.

A commonly used variable in economic studies concerned with self-control is time preference, measured by a discount rate. Economists have hypothesized that differences in consumers' choices-for example whether to smoke, save for retirement, or buy insurance--are directly related to their discount rate. Frederick, Lowenstein, and O'Donoghue (2002) suggest that time preference is tri-dimensional, comprised of three underlying motives: (1) impulsivity, the tendency to act without adequate forethought; (2) compulsivity, the tendency to stick to plans; and (3) inhibition, the ability to override inherent urges. Of these three motives, both impulsivity and inhibition are facets underlying conscientiousness, leading to the proposition that conscientiousness may be a useful measure of time preference when compared to existing measures.

Researchers have started studying the link between economic and psychological variables, such as time preference and personality measures. In a study by Daly, Harmon, and Delaney (2009), researchers find financial discounting is related to a range of psychological variables, most notably self-control, conscientiousness, and extraversion. Results from a study by Anderson et al. (2011) indicate that personality traits have stronger predictive power than traditional time-preference measures for things like credit score, job persistence, body mass index (BMI), and smoking habits. The measure of conscientiousness in this study, as a proxy for self-control, is expected to behave much like time preference. While it is not suggested that conscientiousness is a better measure than time preference, it is proposed as an alternate measure with promising empirical support that offers a behavioral perspective often not available with standard economic measurements.

Borghans et al. (2008) have integrated personality traits into economic models where traits are treated as a public good in the sense that traits are equally available to all tasks conducted by an individual. In other words, all agents are endowed with each of the Big Five personality traits and the availability of that trait is not dependent on other external factors, such as people or activities. In the case of private goods, individuals would each have a limited supply of a trait and would have to choose where to allocate the scarce resource. The use of a trait in one activity would lessen the amount left for use in another activity.

Given the theoretical framework of this study-that the level of conscientiousness and not the allocation will impact financial decisions traits are treated as a public good. Modeling traits as public goods allows the same psychological traits to be available to all individuals across all activities and tasks. This is an important assumption in that it allows us to relate conscientiousness to financial decision-making without controlling for other factors that could decrease the amount of conscientiousness available to this particular task. A trait available for use in an activity such as financial decision-making may, however, be augmented or overridden by supplying additional time, knowledge, or energy to that activity. In this study, financial literacy is considered as an additional factor that may impact the overall effects of conscientiousness. Therefore, it may be that one with a high level of financial literacy need not be as conscientious as one with a low level of financial literacy, in order to have similar economic outcomes.

Hypotheses

Researchers have suggested that the impact of self-control problems on wealth may vary according to the nature of the liquidity of the underlying financial assets and debt (Ameriks et al. 2004). In building wealth, both liquid and illiquid assets are necessary and must be managed responsibly.

Illiquid assets are a class of assets that may be held for future consumption. Illiquid assets generate a steady stream of benefits, but are difficult to sell quickly due to considerable transaction costs, information problems, or incomplete markets. Financial wealth is more liquid than nonfinancial wealth, for example, a house is difficult to convert into cash in the short term but a savings account can be liquidated quickly. Laibson (1997) proposes that illiquid assets are a form of precommitment or self-control and that most illiquid assets have the same property as the "goose that laid golden eggs.' In this sense, illiquid assets promise to generate considerable benefits long term, but these benefits are very difficult to realize immediately and attempts to realize the benefits sooner can result in a substantial capital loss. It is expected that individuals who are more conscientious will exercise more self-control and accumulate more illiquid assets early in life. Correspondingly, financially literate consumers should also understand the value and benefits inherent in illiquid assets and will accumulate more illiquid assets earlier in life. The expectation is that both conscientiousness and financial literacy will predict the value of illiquid assets, but that even less conscientious individuals will accumulate assets if they are financially literate. The hypotheses for illiquid assets are:

H1: Conscientiousness will be positively correlated with illiquid assets.

H2: Financial literacy will be positively correlated with illiquid assets.

H3: Financial literacy will moderate the effect of conscientiousness on illiquid assets.

Liquid assets, generally in the form of cash in the bank, are easily accessible and can provide too much temptation to those with low self-control. Individuals with self-control problems may have difficulty accumulating wealth outside of illiquid assets (such as a home or retirement account). Accordingly, Ameriks et al. (2004) found that those with low self-control have fewer liquid assets. Financial literacy has not been linked to liquid financial asset levels, though it may be implied that those with higher levels of financial literacy may be more prudent with finances and hold a certain level of liquid assets as a buffer or as emergency funds.

The expectation is that individuals with low self-control will have trouble maintaining liquid asset balances. Financially literate consumers should understand the benefits of maintaining a reserve of liquid assets and should therefore have higher account balances. Therefore, both conscientiousness and financial literacy should predict the value of liquid assets, but less conscientious individuals will hold more liquid assets if they are financially literate. The hypotheses for liquid assets are:

H4: Conscientiousness will be positively correlated with liquid assets.

H5: Financial literacy will be positively correlated with liquid assets.

H6: Financial literacy will moderate the effect of conscientiousness on liquid assets.

The expectations with respect to net worth are that individuals with high self-control or high financial literacy will accumulate and maintain more liquid and illiquid assets, which increase overall net worth. Conscientiousness (Ameriks et al. 2004, 2007; Chatterjee et al. 2010) and financial literacy (Lusardi and Mitchell 2011) are expected to be correlated with net worth, with high levels of conscientiousness and financial literacy associated with high levels of net worth. The hypotheses for net woith are as follows:

H7: Conscientiousness will be positively correlated with net worth.

H8: Financial literacy will be positively correlated with net worth.

H9: Financial literacy will moderate the effect of conscientiousness on net worth.

METHODOLOGY

Data

Data for this study come from the 1997 National Longitudinal Survey of Youth (NLSY97), a nationally representative panel survey sponsored by the US Department of Labor. The NLSY97 consists of approximately 9,000 youths who were 12-16 years old as of December 31, 1996 (Bureau of Labor Statistics 2010). Round one of the survey took place in 1997, and youths continue to be interviewed on an annual basis.

The financial variables used in the study are collected when the respondents are 25 years old. The dependent variables are net worth, illiquid assets, and liquid assets. Net worth is the value of assets minus debt and is a calculated value available in the NLSY97 for respondents at age 25. Assets are comprised of: the total value of housing (value of home minus mortgage amount owed); stocks or mutual funds; retirement plans, current checking and savings balance; certificates of deposits/bonds/bills; automobiles; household furnishings; and other assets. Debt is constructed based on values of loans ($200 or more), college loans, and amount owed on other types of debt such as credit cards or money owed directly to a business. To protect the identities of the participants, top-coded values are applied to the top 2% of respondents. The average value of the top 2% of cases is used as the value for these respondents.

Illiquid assets are comprised of home equity; value of equity in personal business; value of stocks; mutual funds; bonds; retirement accounts; and certificates of deposits. Liquid assets are constructed using the current, reported value of the respondent's checking and savings account balances. Due to the skewed nature of financial variables, all dependent variables are transformed using the natural log function, and results are interpreted accordingly.

The key independent variables in this study are financial literacy and conscientiousness. Financial literacy is measured using three questions on compound interest, inflation, and stock risk that were collected in Wave 11 (2007) of the survey when the subjects were between age 23 and 27. While the traditional approach to measuring financial literacy has been to assign a point value to each question and then calculate an overall percentage score, some more recent studies (see Behrman et al. 2010; Lusardi, Mitchell, and Curto 2012) have indicated that this approach is deficient in that it does not allow for distinctions among questions in either difficulty or information. Therefore, a financial literacy PRIDIT score is calculated for each respondent and used in the final analysis. A PRIDIT score is calculated by assigning weights to the questions and then conducting a principle components analysis to generate a final score. In a PRIDIT, the smallest weight and largest penalty are assigned to the question that was the easiest to answer (compound interest). This minimally rewards those who correctly answered the easiest question, and assigns the largest penalties to those who responded incorrectly to the easiest question. The opposite is the case for the most difficult question (stock risk). It should be noted that the results presented in this paper hold whether the raw, tabulated score or the PRIDIT score for financial literacy is used.

Conscientiousness is measured based on two questions from the Ten Item Personality Inventory (TIPI) that was included in Wave 12 (2008) of the survey when the subjects were age 24-28. Respondents were asked "Using a scale from 1 to 7, where 1 means disagree strongly and 7 means agree strongly, please rate how well each pair of traits applies to you, even if one characteristic applies more strongly than the other." The two trait pairs presented to respondents are: dependable/self-disciplined and disorganized/careless. In order to use the measure of conscientiousness in the model, the scores of the two survey items are averaged (after reverse coding disorganized/careless) (Gosling 2011).

The following independent variables will be included as control variables in the models: gender; race; education; income (in natural log form); marital status; family socioeconomic status (mother's education); ASVAB score (Armed Services Vocational Aptitude Battery Score); any inheritance received; any student loans owed; and a dummy variable to represent the year in which the asset information was collected (referred to as asset round). Categorical variables are created for respondent race based on the four categories collected by the NLSY. The NLSY asks respondents whether they identify as Black, Hispanic, non-Black/non-Hispanic or Mixed-Race (Non-Hispanic). As so few identified as Mixed-Race, these subjects were combined with those identifying as non-Black/non-Hispanic and will be referred to as non-Black/non-Hispanic throughout the paper. Three categorical variables were created for education, including: (1) did not graduate from high school; (2) GED or high school graduate; and (3) college graduate (associates, bachelors, or graduate degree). Three categorical variables are created for marital status: single (never-married); married; or other (divorced, separated, or widowed). Inheritance is a dichotomous variable, coded as one if the respondent reported ever receiving an inheritance. The variable for student loans is also a dichotomous variable and is coded as one if the respondent reported having student loan debt.

Analysis

Cases with missing data on financial literacy or conscientiousness or with values for more than two variables are excluded from analysis, resulting in a final sample size of 5,892. An analysis was carried out to check the pattern of missing data. Separate variance t-tests indicate that missing values are completely at random, which is necessary in order to avoid biased estimates. When only one independent variable was missing, values were imputed using the Markov Chain Monte Carlo (MCMC) algorithm provided by the software package SPSS 19.0. Five implicates were created for each case, and values were imputed for both independent and dependent variables, allowing for a more robust model for prediction. This process allows the largest amount of cases to be used with the most complete data. The actual amount of imputed data is very small when compared to the overall sample. Only 12% of the values for income, 16% of ASVAB score, 5% of mother's education, and less than 1% of the data for the remaining independent variables are imputed.

The analysis of the data is only performed on cases where the dependent variable has not been imputed, a method referred to as multiple imputation, then deletion (MID) (Von Hippel 2007). Using imputed dependent variables can add noise to the estimates. If there is something wrong with the imputed dependent values, MID protects the estimates from the problematic imputations. When the dependent variable values are not missing in excess (more than 15%), MID offers more efficient estimates than ordinary multiple imputation methods (Von Hippel 2007). The use of MID causes the sample size in each analysis to fluctuate given the prevalence of missing financial data. Analyses of net worth, illiquid assets, and liquid assets are conducted on a final sample size of 5,111, 5,853, and 4,763, respectively.

A multivariate linear regression model using moderated regression (Aiken and West 1991) is used to provide the analysis for net worth and liquid assets. A tobit regression is used to estimate coefficients for illiquid assets because a large proportion of respondents have no illiquid assets (3,203 of 5,853). A linear regression in this case would lead to biased and inconsistent estimates of the coefficients. Moderated regression is used to ascertain whether or not financial literacy moderates the relationship between conscientiousness and the dependent financial variables. An interaction term is used to test for the moderating effects of financial literacy on conscientiousness. The moderator variable is financial literacy. In the cases where the interaction is significant, simple slope analysis is conducted to interpret the interaction effects in each model (Aiken and West 1991).

In order to conduct the simple slope analysis, both conscientiousness and financial literacy (PRIDIT score) are standardized for the analysis. Standardization creates a mean of zero and a standard deviation of one for each of the variables. This procedure reduces the multicollinearity between predictors and any interaction terms among them and facilitates the testing of simple slopes. It does not alter the significance of the interaction, nor does it alter the values of the simple slopes.

RESULTS

Sample Description

The NLSY oversamples at-risk populations, which is why the proportions of Blacks and Hispanics are larger than the general population when unweighted. When applying weights from Wave 1, the weighted estimate of the population proportions is: 50% male; 50% female; 16% Black; 12.5% Hispanic; and 71.5% non-Black/non-Hispanic. In terms of education, the weighted percentage for those without a high school degree is approximately 10%; those who indicated they have either a high school degree or GED comprise 57% of the sample, and those with a college degree comprise approximately one-third of the sample. A large percentage of the sample indicate that they have never been married (68%); 27% indicate that they are married, and a small percentage (5%) indicate they are divorced, separated, or widowed. The weighted mean level of mother's education for the sample is 12.96 years.

The weighted mean score for conscientiousness is 5.75 with a standard deviation of 1.14. Women score slightly higher than men (5.81 vs. 5.68), and this difference is statistically significant (F = 12.99, p < .01). Conscientiousness is strongly associated with education. Those with a college degree have a mean of 5.89, those with just a high school degree have a mean 5.70, and those without a high school diploma or GED have a mean of 5.53. The difference in means for the three groups is significant at the p < .05 level. In terms of race, Blacks report the highest level of conscientiousness with a mean of 5.88, Hispanics have a mean of 5.75, and non-Black/non-Hispanics report a mean of 5.71. The difference in means is significant for Blacks and the rest of the population (F = 26.3, p < .01) and for non-Black/non-Hispanics (F= 18.62, p < .01), but not for Hispanics (F = 0.84, p > .1). The variable for conscientiousness was standardized prior to including it in the regression analysis. The standardized value has a mean of zero, a standard deviation of one, a minimum of -4.77, and a maximum of 1.23.

Financial literacy scores are comparable to other studies (Lusardi and Mitchell 2011; Lusardi, Mitchell, and Curto 2010). On the basis of weighted estimates, approximately 80% of respondents were able to answer the question on compound interest correctly; 55% correctly answered the question on inflation, and 47% were able to correctly answer the question on stock risk. Almost 9% were unable to correctly answer any of the three questions. For complete information on the demographic distribution including weighted and unweighted percentages, refer to Table 1.

The mean level of illiquid assets is $17,556, while the median is zero. The range of illiquid assets is zero to $702,000. The highest values contain top-coded values for housing. For further analysis, the data were divided into quartile ranges for both conscientiousness and financial literacy, and the means and medians were compared across quartiles. With one minor exception (Q2 and Q3 of conscientiousness), there is a positive relationship between both net worth and conscientiousness and net worth and financial literacy. A total of 4,764 respondents in the final sample have complete data for liquid assets. The mean value of liquid assets is $3,453, and the median value is $600. The values range from $0 to $300,000, with the top 2% of values top-coded to $300,000. When the data are compared across quartiles of conscientiousness and financial literacy, we observe a relatively positive relationship between the mean and median value of liquid assets and financial literacy. The mean of liquid assets increases from Q1 of conscientiousness to Q2, but then fluctuates between Q2, Q3, and Q4. The median of liquid assets increases through Q3, then decreases slightly in Q4.

Of the overall final sample, 5,111 respondents have complete data on net worth. The mean value of net worth is $25,643 and the median is $7,325. The upper range value of $600,000 amount represents the top-coded values applied to the top 2% of respondents. The lower range value is -$354,000 after trimming extreme outliers. The same comparison analysis for illiquid and liquid assets is also used for net worth. When net worth is compared across quartiles for financial literacy and conscientiousness, a positive relationship is observed between both net worth and conscientiousness and net worth and financial literacy. Refer to Table 2 for further details.

Multivariate Results

Illiquid Assets

Results from both the full and simplified models for illiquid assets are presented in Table 3. The interaction term is not significant in the full model (p < .05), leading to the conclusion that financial literacy does not have a moderating effect on the relationship between conscientiousness and illiquid assets. Thus, H3 is not confirmed. While results from both models are presented in the table, only the effects from Model 1 are discussed and interpreted.

A tobit was used to analyze illiquid assets, and therefore the results in this section should be interpreted to be the marginal effects on the latent, unobserved value of illiquid assets (y*) rather than the observed value (y). Both financial literacy and conscientiousness are significant predictors of illiquid asset holdings; therefore, H1 and H2 are confirmed. A one-standard-deviation increase in financial literacy increases illiquid asset holdings by 60% and a one-standard-deviation increase in conscientiousness increases illiquid asset holdings by 53%. Illiquid asset balances are positively correlated with ASVAB score, income, high school or college degree, marriage, and inheritance. Female and Hispanic respondents are predicted to have less illiquid assets than their counterparts.

Liquid Assets

Results from both the full and simplified models for liquid assets are presented in Table 4. The mean [R.sup.2] value is the same (.349) for both models, and the interaction term coefficient in Model 2 is not significant. We conclude that financial literacy does not moderate the effect of conscientiousness on liquid asset balances; thus, H6 is not confirmed. Financial literacy and conscientiousness are both significant and positively correlated with liquid assets; therefore, H4 and H5 are confirmed. A one-standard-deviation increase in conscientiousness is correlated with an increase in liquid assets by 33%, and likewise a one-standard-deviation increase in financial literacy is correlated with a 30% increase in liquid assets. Education, marital status, intelligence, gender, race, income, and inheritance are all significant predictors of liquid assets. Females, Black respondents, and divorced respondents are predicted to have less liquid assets than their counterparts.

Net Worth

Results from both the full and simplified models for net worth are presented in Table 5. The mean [R.sup.2] value across all five implicates increases slightly from Model 1 to Model 2 (from .187 to .188). In addition, the interaction term coefficient in Model 2 is significant (p < .05). The significance of the interaction term leads to the conclusion that financial literacy moderates the effect of conscientiousness on net worth; therefore, H9 is confirmed. Model 1 is interpreted for main effects and Model 2 is used to further probe the interaction.

Conscientiousness is significant in the model predicting net worth, but financial literacy, aside from the interaction effect, is not significant. Therefore, H7 is confirmed but H8 is not confirmed. A one-standard-deviation increase in conscientiousness results in a 40% increase in net worth. Income, gender, marital status, student loans, and inheritance are significant predictors in the model.

The interaction term in Model 2 is significant, so further probing of the interaction term is conducted using the simple slope method (Aiken and West 1991). The simple slope analysis is presented in Table 6 and Figure 1. The interaction was tested at the mean financial literacy score and also for low and high (minimum and maximum) financial literacy scores. There are two diagnostic steps in a simple slope analysis. The first is to determine if the interaction is ordinal or disordinal. This is an indication of whether the simple slope is zero with the range of observed values. Based on results from Model 2 presented in Table 5, the value of conscientiousness at which the simple slope is zero is 0.141 (- ([b.sub.2]/[b.sub.3]) = - (.049/.347)). This is within the range of possible values of conscientiousness, so the interaction is disordinal. Given that the scores for conscientiousness are centered with a mean of zero, this can be interpreted to mean that the relationship differs for those with low and high levels of conscientiousness. From this analysis, we can determine that financial literacy seems to have a buffering effect on those low in conscientiousness, but a magnifying effect for those with above average conscientiousness. The next diagnostic test is to determine whether or not the simple slopes at specific levels of financial literacy are significantly different from zero. The results of this analysis are presented in Table 6. The simple slopes are significant for values at or above the mean of financial literacy, but not below the mean. Therefore, we conclude that financial literacy enhances the relationship between conscientiousness and net worth, but at the lowest levels of financial literacy there is no significant effect.

Robustness

The chronology of data collection in the survey is a limitation that should be noted. Asset variables were collected when the respondents were 25 years old. This means that asset data was collected from 2005 to 2009. Data on personality traits were collected in 2008, and data on financial literacy were collected in 2007. Therefore, reverse causality may be a concern, particularly for the population that provided asset data before personality traits or financial literacy were measured. It is possible that wealth leads to greater financial literacy and conscientiousness rather than the other way around as is posited in this study.

To address this concern, two robustness checks were conducted. The first check divided the sample into two groups: those who responded to asset questions prior to the collection of personality traits; and those who responded after the collection of personality traits. In the models for illiquid and liquid assets, variable significance does not change and the coefficients change only slightly, with no real changes in magnitude. In analyzing net worth, the interaction term is not significant for the group who responded to asset questions before responding to asset questions and is only significant at the p < .1 level for those who responded to asset questions after personality traits were collected. This is deemed acceptable given the reduction in sample size for both groups. More importantly, the significance level of conscientiousness does not change between groups, and the coefficients for conscientiousness fluctuate only slightly in each of the three models. The robustness of the results among the split samples provides evidence that reverse causality is not a concern with respect to conscientiousness.

The second check was conducted between those who responded to asset questions before financial literacy was collected and those who responded to asset questions after financial literacy was collected. When comparing these two groups, the significance level and coefficients for financial literacy do not change substantially in each of the three models. Financial literacy remains insignificant in the model predicting net worth, regardless of when the data were collected, and significant in the models for liquid and illiquid assets. The interaction term is only significant in the group that responded to asset questions after answering the financial literacy question, but similar to the groups separated by personality traits. This is deemed reasonable given the reduced sample size. The relative stability of the results indicates that reverse causality of financial literacy is not a serious problem in the models. However, it should be noted that the limitation of chronology still exists. Therefore, we do not imply causality between the predictors and financial variables, but rather a correlation.

CONCLUSIONS AND IMPLICATIONS

The models examined in this paper provide some insight into early asset accumulation of young Americans. This study finds a strong and consistent effect of conscientiousness on wealth and asset accumulation. These findings are consistent with other studies finding a positive and significant relationship between conscientiousness and financial outcomes (Ameriks et al. 2004, 2007; Chatterjee, Palmer, and Goetz 2010). Holding all other variables constant, conscientiousness is positively related to wealth, with a one-standard-deviation increase associated with a 40% increase in net worth, a 53% increase in illiquid assets, and a 33% increase in liquid assets.

The second important finding is the positive effect financial literacy has on asset-building. Financial literacy is positively correlated with both illiquid and liquid assets, with a one-standard-deviation increase associated with 60% and 30% increases, respectively. These findings are consistent with other studies that find a positive relationship between financial literacy and financial outcomes, such as savings and investment practices (Hilgert, Hogarth, and Beverly 2003). While not a significant predictor of net worth as a main effect, financial literacy has some moderating effects on wealth. It acts as a moderator for net worth where the effect of conscientiousness on net worth is magnified as financial literacy increases.

The interesting conclusion regarding net worth is the importance of both conscientiousness and financial literacy. Net worth is essentially the result of decisions to take on less debt and accumulate assets--so it could be that conscientiousness is influencing this behavior, particularly with those who do not have a high school or college degree. Financial literacy by itself is not significant, but when paired with conscientiousness it seems to help those low in conscientiousness to increase net worth. When considering the effects of both key variables on all three financial variables, there is a strong education effect. Compared with respondents who do not hold a high school degree, those with a high school degree hold 3.3 times more illiquid assets and 6.7 times more liquid assets, and those with a college degree hold 34 times more illiquid assets and 34 times more liquid assets. The finding of student loan debt as a significant and negative predictor of net worth supports the assertion that many college-educated young adults may have more debt than those who did not go to college, but are savvy enough to start investing early. Alternatively, it could be the result of restricted credit market access for those without a college degree.

The findings in this study have interesting implications for policymakers, financial planners, and educators. The study finds clear support for the role of financial literacy in asset accumulation, as even minimal increases in financial literacy increase liquid and illiquid asset holdings. The study also finds that financial literacy moderates the effect of conscientiousness on net worth, but not on liquid or illiquid assets. This suggests that the "channel" of the moderating effect of conscientiousness on net worth may be through debt. This is a potential area of future research; if confirmed, it would have interesting implications, as it could expose some nuances of the beneficial effects of financial education.

There has been a fair amount of research aimed at the use of financial education to improve financial behaviors, but far less research investigating other possible paths to financial success (Willis 2008). The findings from the studies evaluating financial education and literacy have been mixed, but the findings presented here provide some support for financial literacy as a means to financial success. There has not been as much attention paid to other means of changing financial consumer behavior. The strong and positive effect that conscientiousness has on the financial variables studied here suggests that efforts directed toward increasing conscientiousness may have positive effects. Researchers have found that measured personality traits are correlated over the lifecycle. They are not fixed traits and can potentially be altered by experience and investments (Almlund et al. 2011). Interventions to change personality are promising avenues to explore with respect to improving financial decisions.

Keeping this in mind, perhaps the concept of "financial education" should be interpreted more broadly. Interventions to increase conscientiousness and self-control could be considered as more innovative approaches to "education." Moffitt et al. (2011) found that childhood self-control predicts adolescents' mistakes and that those who improved their self-control over time had better outcomes than their counterparts. This is reminiscent of studies conducted by Mischel and Ebbesen (1970) and Mischel, Shoda. and Peake (1988) that linked delay in gratification early in life to better life outcomes (such as higher SAT scores and lower instances of drug use). These findings led researchers to suggest that understanding the key ingredients in self-control is central to finding the most efficient means to enhance them.

The findings from both of these studies provide support for early childhood, adolescent, and adult interventions aimed at improving self-control. Moffitt and his colleagues found gradients of self-control, leading to the conclusion that a universal approach to improving self-control could benefit everyone. Social skills development programs, cognitive coping strategies, and relaxation or mindfulness training are some methods that should be considered when addressing self-control issues. Perhaps we should leverage those results to look more broadly at the issue of self-control: instead of focusing on the problem of poor financial management in young adults, we should consider whether starting interventions much earlier in life might have a significant effect. Understanding how to increase conscientiousness, and therefore self-control, should continue to be both a research and policy priority.

In addition to educating clients about personal finance issues, financial planners and educators can serve consumers by making them aware of their self-control shortcomings and providing tips and practices to help increase financial well-being. Suggesting practices such as cash-based spending, automatic savings plans, or automatic bill-pay may help consumers reign in some of their negative spending patterns. A study by Oaten and Cheng (2007) suggests that these methods can be successful. More stringent policy changes could also help thwart self-control issues. Current regulations on retirement accounts have penalties to dissuade people from making early withdrawals; however, a large amount of "leakage" still occurs in these accounts--an estimated $250 billion a year (Bovbjerg 2009). These accounts could be made more illiquid to discourage early withdrawals. The same concept could be applied for health savings accounts and more restrictions could be placed on home equity loans to discourage people from borrowing from their future selves.

While continued efforts in financial education and financial literacy are important and should continue to be pursued, the hope is that this study will contribute to broadening the concept of "financial education." Rather than focusing on traditional methods to increase financial literacy, "financial education" and consumer policies that directly account for the role of self-control and personality deserve consideration.

REFERENCES

Aiken, Leona S., and Stephan G. West. 1991. Multiple Regression: Testing and Interpreting Interactions. Thousand Oaks, CA: Sage Publications, Incorporated.

Almlund, Mathilde, Angela Lee Duckworth, James J. Heckman, and Tim Kautz. 2011. Personality Psychology and Economics. In Handbook of the Economics of Education, edited by Eric A. Hanushek, Stephen Machin, and Ludger Woessmann (1-182). Amsterdam: North Holland.

Ameriks, John, Andrew Caplin, John V. Leahy, and Tom Tyler. 2004. Measuring Self-Control. NBER Working Paper No. 10514. Cambridge, MA: National Bureau of Economic Research.

--. 2007. Measuring Self-Control Problems. The American Economic Review, 97 (3): 966-972.

Anderson, Jon, Stephen Burks, Colin DeYoung, and Aldo Rustichini. 2011. Toward the Integration of Personality Theory and Decision Theory in the Explanation of Economic Behavior. Paper Presented at the IZA Workshop: Cognitive and Non-Cognitive Skills, Bonn, Germany.

Angeletos, George-Marios, David Laibson, Andrea Repetto, Jeremy Tobacman, and Stephen Weinberg. 2001. The Hyperbolic Consumption Model: Calibration, Simulation, and Empirical Evaluation. The Journal of Economic Perspectives, 15 (3): 47-68.

Baumeister, Roy F. and Kathleen D. Vohs. 2004. Self-Regulation. In Character Strengths and Virtues: A Handbook and Classification, edited by Christopher Peterson and Martin Seligman (499-516). Oxford: Oxford University Press.

Baumeister, Roy E, Todd F. Heatherton, and Dianne M. Tice. 1994. Losing Control: How and Why People Fail at Self-Regulation. Waltham, MA: Academic Press.

Behrman, Jere R., Olivia S. Mitchell, Cindy Soo, and David Bravo. 2010. Financial Literacy, Schooling and Wealth Accumulation. NBER Working Paper No. 16452. Cambridge, MA: National Bureau of Economic Research.

Bemheim, B. Douglas, and Daniel M. Garrett. 2003. The Effects of Financial Education in the Workplace: Evidence from a Survey of Households. Journal of Public Economics, 87 (7-8): 1487-1519.

Beshears, John, James Choi, David Laibson, Brigitte Madrian, and Jung Sakong. 2011. Self Control and Liquidity: How to Design a Commitment Contract. RAND Working Paper Series WR-895-SSA. Santa Monica, CA: The RAND Corporation.

Borghans, Lex, Angela Lee Duckworth, James J. Heckman, and Bas Ter Week 2008. The Economics and Psychology of Personality Traits. NBER Working Paper No. 13810. Cambridge, MA: National Bureau of Economic Research.

Bovbjerg, Barbara D. 2009. 401 (K) Plans: Policy Changes Could Reduce the Long-Term Effects of Leakage on Workers' Retirement Savings. Washington, DC: United States Government Accountability Office.

Bureau of Labor Statistics. 2010. National Longitudinal Survey of Youth 1997 Codebook. Washington, DC: Bureau of Labor Statistics.

Chatterjee, Swam, Lance Palmer, and Joseph Goetz. 2010. Individual Wealth Accumulation: Why Does Dining Together as a Family Matter? Applied Economics Research Bulletin, 8(1): 1-22.

Chowdhury, Mohammed S., and Mohammed N. Amin. 2006. Personality and Students Academic Achievement: Interactive Effects of Conscientiousness and Agreeableness on Students Performance in Principles of Economics. Social Behavior and Personality: An International Journal, 34 (4): 381-388.

Clark, Robert, and Madeleine D'Ambrosio. 2002. Saving for Retirement: The Role of Financial Education. TIAA-CREF Institute Working Paper 4-070102. New York: TIAA-CREF Institute.

Cohen, Lizabeth. 2010. Colston E. Wame Lecture: Is It Time for Another Round of Consumer Protection? The Lessons of Twentieth Century US History. Journal of Consumer Affairs, 44 (1): 234-246.

Collins, J. Michael, and Collin Michael O'Rourke. 2010. Financial Education and Counseling--Still Holding Promise. Journal of Consumer Affairs, 44 (3): 483-498.

Costa, Paul T., and Robert R. McCrae. 1992. Professional Manual: Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI). Odessa, IT-: Psychological Assessment Resources.

Daly, Michael, Colm P. Harmon, and Liam Delaney. 2009. Psychological and Biological Foundations of Time Preference. Journal of the European Economic Association, 1 (2-3): 659-669.

Duckworth, Angela Lee, and Martin E.P. Seligman. 2005. Self-Discipline Outdoes IQ in Predicting Academic Performance of Adolescents. Psychological Science, 16 (12): 939-944.

Dunn, Lucia. 2012. Young Adult Credit Card Debt: It's Worse than You Think. Nerd Wallet. http://www.nerdwallet.com/blog/2012/young-adult-credit-card-debt-worse/.

Faber, Ronald J., and Kathleen D. Vohs. 2004. To Buy or Not to Buy?: Self-Control and Self-Regulatory Failure in Purchase Behavior. In Handbook of Self-Regulation: Research, Theory, and Applications, edited by Roy F. Baumeister and Kathleen D. Vohs. New York: Guilford Press.

Federal Reserve Bank. 2010. Consumer Credit. Washington, DC: Federal Reserve Bank.

Frederick, Shane, George Lowenstein, and Ted O'Donoghue. 2002. Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature, 40 (2): 351-401.

Friedman, Howard S., Joan S. Tucker, Carol Tomlinson-Keasey, Joseph E. Schwartz, Deborah L. Wingard, and Michael H. Criqui. 1993. Does Childhood Personality Predict Longevity? Journal of Personality and Social Psychology, 65 (1): 176-185.

Gailliot, Matthew T" Nicole L. Mead, and Roy F. Baumeister. 2008. Self-Regulation. In Handbook of Personality: Theory and Research, edited by Oliver P. John, Richard W. Robins, and Lawrence A. Pervin (472-491). New York: The Guilford Press.

Generation Opportunity. 2012. Young Americans Suffer 12.7 Percent Unemployment. Washington, DC: Generation Opportunity.

Goodwin, Renee D., and Howard S. Friedman. 2006. Health Status and the Five-Factor Personality Traits in a Nationally Representative Sample. Journal of Health Psychology, 11 (5): 643-654.

Gosling, Samuel D. 2011. Ten Item Personality Measure (TIPI), http://homepage.psy.utexas.edu/home page/faculty/gosling/scales_we.htm#Ten%20Item%20Personality%20Measure%20%28TIPI%29.

Gottfredson, Michael R., and Travis Hirschi. 1990. A General Theory of Crime. Palo Alto, CA: Stanford University Press.

Hathaway, Ian and Sameer Khatiwada. 2008. Do Financial Education Programs Work? Federal Reserve Board of Cleveland Working Paper No. 08-0329. Cleveland, OH: Federal Reserve Board of Cleveland.

Hilgert, Marianne A., Jeanne M. Hogarth, and Sondra G. Beverly. 2003. Household Financial Management: The Connection between Knowledge and Behavior. Federal Reserve Bulletin, July: 309-322.

Hogan, Joyce, and Brent Holland. 2003. Using Theory to Evaluate Personality and Job-Performance Relations: A Socioanalytic Perspective. Journal of Applied Psychology, 88 (1): 100-112.

Hogarth, Jeanne M. 2006. Financial Education and Economic Development. G8 International Conference on Improving Financial Literacy. Moscow: Russian Federation.

Howlett, Elizabeth, Jeremy Kees, and Elyria Kemp. 2008. The Role of Self-Regulation, Future Orientation, and Financial Knowledge in Long-Term Financial Decisions. Journal of Consumer Affairs, 42 (2): 223-242.

Huston, Sandra J. 2010. Measuring Financial Literacy. Journal of Consumer Affairs, 44 (2): 296-316.

International Journal of Consumer Studies. 2012. International Journal of Consumer Studies, 36 (5).

Jensen-Campbell, Lauri A., Jennifer M. Knack, Amy M. Waldrip, and Shaun D. Campbell. 2007. Do Big Five Personality Traits Associated with Self-Control Influence the Regulation of Anger and Aggression? Journal of Research in Personality, 41 (2): 403-424.

John, Oliver P, and Sanjay Srivastava. 1999. The Big Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives. In Handbook of Personality: Theory and Research 2, edited by Lawrence A. Pervin and Oliver P. John (102-138). New York: Guilford Press.

Journal of Consumer Affairs. 2010. Journal of Consumer Affairs, 44 (2).

Journal of Financial Services Marketing. 2013. Journal of Financial Services Marketing, 18 (3).

Judge, Timothy A., Chad A. Higgins, Carl J. Thoresen, and Murray R. Barrick. 1999. The Big Five Personality Traits, General Mental Ability, and Career Success across the Life Span. Personnel Psychology, 52 (3): 621-652.

Kozup, John, and Jeanne M. Hogarth. 2008. Financial Literacy, Public Policy, and Consumers' Self Protection--More Questions, Fewer Answers. Journal of Consumer Affairs, 42 (2): 127-136.

Laibson, David. 1997. Golden Eggs and Hyperbolic Discounting. Quarterly Journal of Economics, 112 (2): 443-477.

Lusardi, Annamaria. 2008. Financial Literacy: An Essential Tool for Informed Consumer Choice? NBER Working Paper No. 14084. Cambridge, MA: National Bureau of Economic Research.

Lusardi, Annamaria and Olivia S. Mitchell. 2005. Financial Literacy and Planning: Implications for Retirement Wellbeing. Michigan Retirement Research Center: Research Paper No. WP 2005-108. Ann Arbor: Michigan Retirement Research Center.

Lusardi, Annamaria, and Olivia S. Mitchell. 2009. How Ordinary Consumers Make Complex Economic Decisions: Financial Literacy and Retirement Readiness. NBER Working Paper No. 15350. Cambridge, MA: National Bureau of Economic Research.

--. 2011. Financial Literacy and Planning: Implications for Retirement Wellbeing. NBER Working Paper No. 17078. Cambridge, MA: National Bureau of Economic Research.

Lusardi, Annamaria and Peter Tufano. 2009. Debt Literacy, Financial Experiences, and Over-indebtedness. NBER Working Paper No. 14808. Cambridge, MA: National Bureau of Economic Research.

Lusardi, Annamaria, Olivia S. Mitchell, and Vilsa Curto. 2010. Financial Literacy Among the Young. Journal of Consumer Affairs, 44 (2): 358-380.

--. 2012. Financial Sophistication in the Older Population. NBER Working Paper No. w17863. Cambridge, MA: National Bureau of Economic Research.

Martin, Matthew. 2007. A Literature Review on the Effectiveness of Financial Education. Federal Reserve Bank of Richmond: Working Paper WP 07-3. Richmond, VA: Federal Reserve Bank of Richmond.

McCrae, Robert R., and Paul T. Costa Jr., 1999. A Five-Factor Theory of Personality. In Handbook of Personality: Theory and Research, vol. 2 (139-153). New York: Guilford Press.

Mischel, Walter, and Ebbe B. Ebbesen. 1970. Attention in Delay of Gratification. Journal of Personality and Social Psychology, 16 (2): 329-337.

Mischel, Walter, Yuichi Shoda, and Philip K. Peake. 1988. The Nature of Adolescent Competencies Predicted by Preschool Delay of Gratification. Journal of Personality and Social Psychology, 54 (4): 687-696.

Moffitt, Terrie E" Louise Arseneault, Daniel Belsky, Nigel Dickson, Robert J. Hancox, HonaLee L. Harrington, Renate Houts, Richie Poulton, Brent W. Roberts, and Stephen Ross. 2011. A Gradient of Childhood Self-Control Predicts Health, Wealth, and Public Safety. Proceedings of the National Academy of Sciences of the United States of America, 108 (7): 2693-2698.

Oaten, Megan, and Ken Cheng. 2007. Improvements in Self-Control from Financial Monitoring. Journal of Economic Psychology, 28 (4): 487-501.

O'Gorman, John G., and Elizabeth Baxter. 2002. Self-Control as a Personality Measure. Personality and Individual Differences, 32 (3): 533-539.

Organisation for Economic Co-operation Development. 2010. Consumer Policy Toolkit. Paris: OECD Publishing.

Roberts, Brent W., and Timothy Bogg. 2004. A Longitudinal Study of the Relationships between Conscientiousness and the Social Environmental Factors and Substance Use Behaviors That Influence Health. Journal of Personality, 72 (2): 325-354.

Strotz, Robert H. 1956. Myopia and Inconsistency in Dynamic Utility Maximization. The Review of Economic Studies, 23 (3): 165-180.

Tangney, June P" Roy F. Baumeister, and Angie L. Boone. 2004. High Self Control Predicts Good Adjustment, Less Pathology, Better Grades, and Interpersonal Success. Journal of Personality, 72 (2): 271-324.

Thaler, Richard, and Cass Sunstein. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness. London: Yale University Press.

The Project on Student Debt. 2011. Student Loan Debt and the Class of 2010. http://projecton studentdebt.org/files/pub/classof2010.pdf.

VandenBos, Gary R. 2007. APA Dictionary of Psychology. American Psychological Association. Washington, DC: American Psychological Association.

Van Rooij, Maarten, Annamaria Lusardi, and Rob Alessie. 2011. Financial Literacy and Stock Market Participation. Journal of Financial Economics, 101 (2): 449-472.

Verplanken, Bas, and Astrid Herabadi. 2001. Individual Differences in Impulse Buying Tendency: Feeling and No Thinking. European Journal of Personality, 15 (SI): S71-S83.

Von Hippel, Paul T. 2007. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data. Sociological Methodology, 37 (1): 83-117.

Walton, Kate E., and Brent W. Roberts. 2004. On the Relationship between Substance Use and Personality Traits: Abstainers Are Not Maladjusted. Journal of Research in Personality, 38 (6):515-535.

Willis, Lauren E. 2008. Against Financial Literacy Education. Iowa Law Review, 94 (1): 197-285.

Wills, Thomas A., and Mike Stoolmiller. 2002. The Role of Self-Control in Early Escalation of Substance Use: A Time-Varying Analysis. Journal of Consulting and Clinical Psychology, 70 (4):986.

(1.) A sample of journals with special issues dedicated to financial literacy include: Journal of Consumer Affairs (2010); International Journal of Consumer Studies (2012); Journal of Financial Services Marketing (2013).

Jodi Letkiewicz (jodilet@yorku.ca) is an Assistant Professor at York University and Jonathan Fox (jjfox@iastate.edu) is the Ruth Whipp Sherwin Professor in Human Development and Family Studies at Iowa State University.

DOI: 10.1111/joca.12040

TABLE 1
Descriptive Results

                                 Weighted (a)
                                       %         Conscientiousness (a)
Variable             Frequency   (Unweighted)        (Unweighted)

Sample mean                                           5.75 (5.77)
Gender
  Male                 2,888     49.9% (49.0%)        5.68 (5.72)
  Female               3,004     50.1% (51.0%)        5.81 (5.83)
Race
  Black                2,335     16.0% (26.0%)        5.88 (5.90)
  Hispanic             1,901     12.5% (21.2%)        5.75 (5.76)
  White (Other)        4,748     71.5% (52.8%)        5.71 (5.71)
Education
  Some high school       676      9.6% (11.5%)        5.53 (5.62)
  High school/GED      3,495     56.8% (59.3%)        5.70 (5.75)
  College graduate     1,708     33.6% (29.0%)        5.89 (5.89)
Marital status
  Single               4,141     67.8% (70.3%)        5.69 (5.73)
  Married              1,482     27.5% (25.5%)        5.86 (5.87)
  Divorced               262      4.7% (4.5%)         5.83 (5.89)

                     Financial Literacy (a)
Variable                  (Unweighted)

Sample mean               1.81 (1.74)
Gender
  Male                    1.96 (1.88)
  Female                  1.66 (1.61)
Race
  Black                   1.58 (1.56)
  Hispanic                1.63 (1.59)
  White (Other)           1.90 (1.90)
Education
  Some high school        1.28 (1.27)
  High school/GED         1.66 (1.62)
  College graduate        2.23 (2.18)
Marital status
  Single                  1.83 (1.74)
  Married                 1.82 (1.77)
  Divorced                1.59 (1.58)

(a) Weighted using NLSY97 Wave 1 weights; total sample size is 5,892.

TABLE 2
Asset Analysis

                        Illiquid Assets   Liquid Assets   Net Worth

Statistic                  n = 5,853        n = 4,764     n = 5,111

Mean                        $17,556          $3,453        $25,643
Median                        $0              $600         $7,325
Minimum                       $0               $0         -$354,000
Maximum                    $702,000         $300,000       $600,000

                         Illiquid Assets      Liquid Assets

Statistic                Mean     Median     Mean     Median

Q1 Conscientiousness    $14,359     $0      $2,442     $400
Q2 Conscientiousness    $18,620     $0      $3,845     $700
Q3 Conscientiousness    $18,242     $0      $4,016    $1,000
Q4 Conscientiousness    $19,431     $0      $3,642     $700
Q1 Financial literacy   $13,579     $0      $1,952     $135
Q2 Financial literacy   $15,241     $0      $2,032     $300
Q3 Financial literacy   $17,038     $0      $3,371     $650
Q4 Financial literacy   $21,959    $800     $5,388    $1,500

                            Net Worth

Statistic                Mean     Median

Q1 Conscientiousness    $19,264   $5,000
Q2 Conscientiousness    $26,209   $7,500
Q3 Conscientiousness    $27,673   $8,700
Q4 Conscientiousness    $30,806   $7,950
Q1 Financial literacy   $19,246   $5,700
Q2 Financial literacy   $22,168   $5,350
Q3 Financial literacy   $25,013   $7,000
Q4 Financial literacy   $32,299   $11,000

TABLE 3
Tobit Analysis Results for Ln Illiquid Assets

                                             Model 1

Variable                         B               Exp(B)           SE

Intercept                   -12.083 ***      0.000               1.151
ASVAB score                  0.021 ***       1.021               0.006
Log income                   0.926 ***       1.0921 ([dagger])   0.085
Mother education            -0.005           0.995               0.051
Female                      -0.805 **        0.447               0.261
High school/GED              1.209 **        3.351               0.458
College degree               3.535 ***      34.300               0.564
Black                       -0.525           0.592               0.341
Hispanic                    -1.371 ***       0.254               0.368
Married                      3.366 ***      28.949               0.294
Divorced/Other              -0.485           0.616               0.649
Round 9(2005)               -0.389           0.678               0.420
Round 10 (2006)              0.242           1.274               0.392
Round 11 (2007)              0.772 *         2.164               0.386
Round 13 (2009)             -0.206           0.814               0.396
Has student loans           -0.467           0.627               0.312
Inheritance                  2.011 **        7.467               0.614
Financial literacy           0.467 **        1.596               0.144
Conscientiousness            0.424 ***       1.528               0.114
Fin. lit. * Conscientious
Sigma                        8.527 ***    5047.11                0.136

                                         Model 2

Variable                         B           Exp(B)       SE

Intercept                   -12.088 ***      0.000       1.150
ASVAB score                   0.021 ***      1.021       0.006
Log income                    0.924 ***      1.092 (a)   0.084
Mother education             -0.004          0.997       0.051
Female                       -0.807 **       0.446       0.261
High school/GED               1.233 **       3.430       0.458
College degree                3.546 ***     34.669       0.564
Black                        -0.523          0.592       0.341
Hispanic                     -1.353 ***      0.258       0.368
Married                       3.356 ***     28.681       0.294
Divorced/Other               -0.495          0.610       0.649
Round 9(2005)                -0.389          0.678       0.420
Round 10 (2006)               0.239          1.270       0.391
Round 11 (2007)               0.769 *        2.159       0.386
Round 13 (2009)              -0.220          0.803       0.396
Has student loans            -0.461          0.631       0.312
Inheritance                   2.002 **       7.403       0.614
Financial literacy            0.464 **       1.590       0.144
Conscientiousness             0.420 ***      1.522       0.114
Fin. lit. * Conscientious     0.177          1.193       0.111
Sigma                         8.524 ***   5031.75        0.136

Note: Model 1 is the simplified model. Model 2 is the full model.
W = 5,853. Results averaged across all five implicates.

(a) Income is calculated per 10% increase in illiquid assets.

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

TABLE 4
Multiple Regression Analysis Results for Ln Liquid Assets

                                          Model 1

Variable                        B              Exp(B)         SE

(Constant)                  -1.891 ***    0.151              .362
ASVAB score                  0.019 ***    1.019              .002
Log income                   0.369 ***    1.036 ([dagger])   .028
Mother education             0.035        1.036              .017
Female                      -0.306 ***    0.737              .083
High school/GED              1.906 ***    6.728              .150
College degree               3.542 ***   34.539              .183
Black                       -0.923 ***    0.397              .111
Hispanic                     0.093        1.097              .118
Married                      0.637 ***    1.891              .094
Divorced/Other              -0.700 ***    0.496              .190
Round 9(2005)                0.009        1.009              .132
Round 10 (2006)              0.062        1.064              .125
Round 11 (2007)              0.151        1.163              .123
Round 13 (2009)             -0.006        0.994              .125
Has student loans           -0.068        0.934              .101
Inheritance                  0.421 *      1.523              .198
Financial literacy           0.261 ***    1.298              .047
Conscientiousness            0.286 ***    1.331              .037
Fin. lit. * Conscientious
Adjusted [R.sup.2] (b)                      .349

                                       Model 2

Variable                        B          Exp(B)      SE

(Constant)                  -1.890 ***    0.151       .362
ASVAB score                  0.019 ***    1.019       .002
Log income                   0.369 ***    1.036 (a)   .028
Mother education             0.035 *      1.036       .017
Female                      -0.306 ***    0.736       .083
High school/GED              1.907 ***    6.736       .150
College degree               3.541 ***   34.519       .183
Black                       -0.923 ***    0.397       .111
Hispanic                     0.096        1.101       .118
Married                      0.636 ***    1.888       .094
Divorced/Other              -0.703 ***    0.495       .190
Round 9(2005)                0.010        1.010       .132
Round 10 (2006)              0.061        1.063       .125
Round 11 (2007)              0.149        1.161       .123
Round 13 (2009)             -0.007        0.993       .125
Has student loans           -0.067        0.936       .101
Inheritance                  0.418 ***    1.519       .198
Financial literacy           0.261 ***    1.298       .047
Conscientiousness            0.285 ***    1.329       .037
Fin. lit. * Conscientious    0.031        1.032       .036
Adjusted [R.sup.2] (b)                   .349

Note: Model 1 is the simplified model. Model 2 is the full model.
N = 4,763.

(a) Income is calculated per 10% increase in liquid assets.

(b) Results averaged across all five implicates.

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

TABLE 5
Multiple Regression Analysis Results for Ln Net Worth

                                          Model 1

Variable                       B             Exp(B)          SE

(Constant)                  2.749 **    15.621              .798
ASVAB score                 0.001        1.001              .005
Log income                  0.381 ***    1.037 ([dagger])   .055
Mother education            0.010        1.010              .039
Female                     -0.962 ***    0.382              .200
High school/GED             0.418        1.520              .329
College degree              0.615        1.849              .429
Black                       0.174        1.191              .265
Hispanic                   -0.038        0.963              .282
Mamed                       1.717 ***    5.570              .236
Divorced/Other             -0.464        0.629              .468
Round 9 (2005)              0.677 *      1.967              .325
Round 10(2006)              0.395        1.484              .302
Round 11 (2007)             0.238        1.268              .300
Round 13 (2009)            -0.170        0.844              .300
Has student loans          -7.004 ***    0.001              .249
Inheritance                 1.311 **     3.708              .505
Financial literacy          0.047        1.048              .113
Conscientiousness           0.336 ***    1.399              .087
Fin lit. * Conscientious
Adjusted [R.sup.2] (b)                     .187

                                      Model 2

Variable                       B          Exp(B)      SE

(Constant)                  2.702 **    14.912       .799
ASVAB score                 0.001        1.001       .005
Log income                  0.380 ***    1.037 (a)   .055
Mother education            0.012        1.012       .039
Female                     -0.962 ***    0.382       .200
High school/GED             0.453        1.573       .329
College degree              0.621        1.860       .428
Black                       0.179        1.196       .265
Hispanic                   -0.014        0.986       .283
Mamed                       1.708 ***    5.520       .235
Divorced/Other             -0.487        0.615       .467
Round 9 (2005)              0.684 *      1.981       .325
Round 10(2006)              0.391        1.478       .302
Round 11 (2007)             0.229        1.257       .299
Round 13 (2009)            -0.186        0.830       .300
Has student loans          -6.994 ***    0.001       .249
Inheritance                 1.297 *      3.657       .505
Financial literacy          0.049        1.050       .113
Conscientiousness           0.347 ***    1.414       .087
Fin lit. * Conscientious    0.253 *      1.288       .086
Adjusted [R.sup.2] (b)                  .188

Note: Model 1 is the simplified model. Model 2 is the full model.
N = 5,111.

(a) Income is calculated per 10% increase in net worth.

(b) Results averaged across five implicates.

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

TABLE 6 Simple Slope Analysis for Ln Net Worth

                     Low Financial           High Financial
Level of Moderator     Literacy      Mean       Literacy

Intercept                2.614       2.702       2.769
Simple slope            -0.104       0.349       0.696
SE                       0.154       0.087       0.165
p-Value                  0.501       <.001       <.001

Note: Results averaged across all five implicates.
COPYRIGHT 2014 American Council on Consumer Interests
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2014 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Letkiewicz, Jodi C.; Fox, Jonathan J.
Publication:Journal of Consumer Affairs
Article Type:Statistical data
Geographic Code:1USA
Date:Jun 22, 2014
Words:11532
Previous Article:Financial literacy and neighborhood effects.
Next Article:Testing a measurement model of financial capability among youth in Ghana.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters