A Brief Money Management Scale and Its Associations With Personality, Financial Health, and Hypothetical Debt Repayment

Participants

Our initial sample consisted of 2,038 individuals who had begun the study after consenting (please refer to Table 1 for descriptive statistics). These participants were either students recruited from a public urban West Coast university (N = 347; 17.1%) in exchange for class credit or nonstudent volunteers recruited through social media websites (i.e., Craigslist and Facebook; N = 1,691; 82.9%). There existed no overlap in recruiting channels. Similar to working adults, students possess general consumer knowledge (Cole & Sherrell, 1995) and must make important money management decisions (Archuleta, Dale, & Spann, 2013).

Procedure

Additional Measures Included to Assess Validity of Items

Because a psychometrically sound money management scale should be correlated with previously established antecedents and consequences, we asked participants to report the following information. Specifically, previous research suggests that good money management is related to the Big Five personality traits (especially higher conscientiousness; see Donnelly et al., 2012), lower materialistic values (especially the striving-for-happiness dimension; e.g., Gardarsdottir & Dittmar, 2012), greater financial knowledge (e.g., Antonides et al., 2011), higher education (e.g., Donnelly et al., 2012), and better financial standing (e.g., income [Xiao et al., 2006], savings [Gardarsdottir & Dittmar, 2012], and lower debts [Lea et al., 1995]).

Participants completed the Big Five Inventory (BFI-10; Rammstedt & John, 2007) as a measure of personality traits, the Material Values Scale (MVS-15; Richins, 2004) as a measure of materialistic values (along three dimensions: seeing consumption as a central goal, a signal of success, and a way to achieve happiness), the Financial Products Knowledge Scale (Antonides et al., 2011) to assess knowledge of financial products as well as financial and demographic information. Participants reported their current economic standing (Howell, Kurai, & Tam, 2012) in terms of income (“What is your household income after taxes are taken out?”), savings (“What is the amount in all your savings and money market accounts?”), investments (“What is the value of all your investments [such as CD, stocks, or bonds]?”), credit card debt levels (“What is the balance on all your credit cards?” and “During the past year, how many of your credit cards have carried half or more of the maximum balance?”), and wealth (“Wealth, also known as net worth, is defined as the total value of everything someone owns minus any debt that he or she owes. A person’s net worth includes his or her bank account or cash savings.”). Lastly, participants provided demographic information including their gender, age, and education.

Results

Participants

One hundred fifty-four individuals (see Table 1) participated in a series of unrelated lab studies, including other surveys, pertaining to judgment and decision-making. Of the participants, 71 (46.1%) self-reported that they were students, and 83 (53.9%) identified as nonstudent community members. The median monthly income range was $1,000–$1,999 among students and $3,000–$3,999 among community members.

Procedure

Participants responded to the 18 BMMS items. They also completed the BFI-10 (Rammstedt & John, 2007) and the MVS (Richins, 2004). As in Study 1, participants were asked to report their current economic standing with a four-item proxy based on Howell et al. (2012). Lastly, participants reported demographic information including their gender, age, education, and wealth.

Results

Participants

First, 80 participants (see Table 1) completed the BMMS administered on Amazon’s Mechanical Turk, an online system of paid survey-takers. Two weeks after completing the survey, all participants were invited to take the BMMS again; however, only 45 participants did so (M_age = 37.51, SD = 12.14; 41.7% female; 83.3% White; see Table 1). Hence, we had a completion rate of 56% and report the results only relating to participants who completed the BMMS in both instances. Important to note is that the mean BMMS scores in the first survey did not significantly differ between participants who chose to participate in the second survey and those who chose not to participate. The median net worth reported by participants was $15,000, and their median level of education was a 4-year university degree.

Procedure and Measures

The participants completed the BMMS, with the items presented in random order. In addition, participants reported their income allocation: the approximate percentage of income they allocate toward essential purchases (e.g., food or house supplies), material goods (e.g., furniture or clothing), life experiences (e.g., vacations), charitable donations, and savings and investments. Two weeks after taking the first survey, participants again completed the BMMS and the income allocation task.

Results

In both instances, the BMMS displayed good internal consistency (α = .85 at Time 1 and α = .84 at Time 2). Participants’ mean money management scores at Time 1 (M = 3.33, SD = .71) and Time 2 (M = 3.36, SD = 0.69) had a strong correlation (r = .82, p < .001), and participants’ mean scores on the savings factor displayed a strong positive relationship with their respective savings scores 2 weeks later (r = .81, p < .001). Participants’ mean insurance and investments management scores at Time 1 correlated positively with scores at Time 2 (r = .75, p < .001), and their credit management scores were also temporarily stable (r = .75, p < .001). Similarly, mean cash management scores at Time 1 are positively related to mean cash management scores at Time 2 (r = .55, p < .001). Finally, all 18 BMMS items had a significant positive correlation (ps < .05) with their counterpart items at Time 2.

Moreover, people’s allocation of income demonstrated adequate temporal stability, with allocation correlations ranging from r = .34 to r = .70, ps < .05. People’s quality of money management was positively related to their percentage of income allocated toward the “savings and investments” category at Time 1 (r = .34, p < .05) and at Time 2 (r = .55, p < .001; controlling for the proportion of income allocated toward meeting basic needs). This positive relationship implies that good money managers, as defined by the BMMS, are more likely to allocate their income toward saving and investing, regardless of their income. Furthermore, income allocation toward material goods (e.g., jewelry) served as a complementary measure to their previously assessed materialistic values. As expected, people’s BMMS scores were negatively related to the percentage of income they reported spending on material items, marginally at Time 1 (r = −.26, p = .09) and significantly at Time 2 (r = −.46, p < .005). This finding suggests that good money managers are not only less materialistic in their values than people with low BMMS scores but may also spend proportionally less of their income on material goods.

Participants

A sample of 161 adults (see Table 1) participated in a series of unrelated lab studies at a private university in the Northeastern United States for a payment of $20 with the chance of additional bonuses (see “Procedure”). Eight (5%) participants did not report their occupation. Of those who did, 90 (58.8%) identified as students and 63 (41.2%) were nonstudent community members. As in Study 2a, students reported that their median monthly income range is $1,000–$1,999. The community members’ median monthly income range is $2,000–$2,999.

Procedure

First, participants completed the BMMS. As in Studies 2a and 2b, participants were asked to indicate how often they had engaged in the following activities on a 1 (never) to 5 (always) scale. Descriptively, the scores on the overall measure (M = 3.09, SD = 0.62; α = .78) as well as the savings (M = 2.84, SD = 1.16; α = .90), insurance (M = 2.13, SD = 1.02; α = .75), credit (M = 4.16, SD = 0.96; α = .88), and cash (M = 3.19, SD = 0.98; α = .77) subscales were similar to those found in our previous studies.

Next, respondents participated in the debt repayment simulator game (Amar et al., 2011). In this debt repayment game, participants were saddled with six debt accounts that varied in amount (ranging from $3,000 to $60,000) and annual interest rate (ranging from 2.0% annual percentage rate [APR] to 4.0% APR). The game lasted for 25 rounds, and each round represented 1 year. In each round, participants received a hypothetical $5,000 cash allotment to pay down on one or more debts. After participants made and confirmed their repayment choices, the program presented the updated balance of each debt and a graph displaying the past and current standing of each debt. More important, the game was incentive compatible—that is, participants were told that they would receive a bonus based on their performance. Specifically, they would receive $2 if their total debt was $30,000 or less, $1.50 if their total debt was between $30,001 and $35,000, $1 if their total debt was between $35,001 and $40,000, or $0.50 if their total debt was greater than $40,001. Overall, the median ending debt balance was $35,860.60.

In addition to this basic paradigm (i.e., the control condition; N = 74), we included an experimental condition (N = 87). This condition differed from the basic paradigm in that it allowed for repayment of specific purchases within the most problematic debt account of the six (i.e., $60,000 at 4.0% APR). In contrast, the basic paradigm did not allow for repayment of specific purchases in any of the six accounts. The intention of this experimental condition was to improve repayment decisions under the assumption people would pay off more hypothetical debt when paying for specific purchases, rather than for only portions of a large debt sum. However, although we expected those in the experimental group to perform better than those in the control group, we hypothesized that money management would predict better performance in both the control and experimental conditions.

Results

When examining the skewness of the distribution of ending balances, we determined that the distribution was negatively skewed: Participants generally performed well, finishing the game with low debts. Because the distribution was skewed, we treated the ending debt balance as an ordinal variable and followed the procedures of previous studies (e.g., Donnelly et al., 2013) that used optimal scaling models (i.e., the SPSS CATREG procedure) to allow for ordinal, scaled outcomes in regression.

We conducted a categorical regression, with bootstrapping procedures to estimate the standard errors, predicting ending debt balance from trait money management, condition (i.e., experimental = .50 and control = −.50), and the interaction between money management and the condition. The overall model was significant, F(3, 160) = 4.82, p < .005, R² = .084. As we hypothesized, participants in the experimental condition had lower debts than those in the control condition (β = −.22, SE = .10, p < .05), and good money managers had lower debt balances (β = −.19, SE = .09, p < .05). More important, because the interaction of condition (control vs. experimental) and money management was not significant (β = .04, SE = .09, p = .64), we concluded that the relation between money management and debt repayment was not significantly different between the two conditions. Thus, these results indicated that money management, as measured by the BMMS, predicted hypothetical debt repayment behavior beyond the influence of a circumstance conducive to good debt management (i.e., opportunity to repay specific purchases). Furthermore, these results involved a measure of financial behavior that was not self-reported and thus was a promising behavioral indication that good self-reported money management corresponds to responsible money management practices.

Limitations and Future Directions

A limitation of our research is that unlike Dew and Xiao (2011), we did not use nationally representative samples. We recruited through several channels, but these do not ensure geographical or socioeconomic diversity. A consequent limitation is that some financial behaviors included in our scale may not be appropriate to measure across all socioeconomic populations when assessing money management quality. For instance, scales that exclude higher access financial tools (e.g., investing behaviors and credit management) show low-income families scoring higher (Godwin & Koonce, 1992). Furthermore, active financial management is related to knowledge about one’s finances and the financial tools currently available (Hilgert et al., 2003; Kim et al., 2003). Thus, a more representative scale would assess money management based on whether or not individuals use the financial tools they can actually access. Consequently, we advise for future research to examine participants’ BMMS scores in the context of their financial opportunities and, of course, in an expanded pool of participants of diverse financial standing.

Furthermore, unlike Dew and Xiao’s (2011) scale, the BMMS contains a single factor entitled insurance and investments; in contrast, Dew and Xiao’s scale has a separate insurance factor and instead groups savings and investments together. Possibly, this deviation from Dew and Xiao may have occurred because our item-pruning procedure ultimately yielded only one investment item, referring to contribution toward savings plans (e.g., 401k). This item may be conceptually similar to insurance because it describes allocating money toward a future monetary benefit, which may be useful in a later unexpected financial hardship. Consequently, researchers should further investigate the conceptual gray areas between insurance, investments, and savings.

Another limitation to this research is that Study 3 examines money management behavior with a simulator game that spans only the period of one laboratory session and offers a monetary incentive immediately following its completion. A better test of external validity would be to track people’s money management behavior longitudinally.

In addition, it is unclear for how long the BMMS and its items will remain applicable to people’s money management practices. Increased atomization and electronic access to financial information may soon let researchers gather consumer data without relying on self-report scales, and such innovations may alter money management behavior. Just as check-writing has declined with the growing frequency of electronic payments (Hancock & Humphrey, 1997) and items pertaining to check-writing are concentrated in older money management measures (see Godwin & Koonce, 1992), items composing our newly developed BMMS may also become obsolete. In turn, self-report measurements of money management will need to undergo further refinement as financial practices change because of innovations in the financial industry.