Benefits of Experience and Knowledge for Older Adults’ Monetary Sequence Preferences

Abstract

Financial literacy and financial experience may be important for understanding age differences in financial decisionmaking. Older adults generally have more financial experience than younger adults do, and some studies suggest they also have better financial literacy. We investigated associations among age (N = 594, aged 20–88, M _age = 46.48), financial experience, financial literacy, and preferences for receiving larger (versus smaller) amounts of money sooner (versus later). Older age was correlated with preferences for receiving larger amounts of money sooner and smaller amounts later, but this association was no longer significant after accounting for financial experience and financial literacy. Financial experience was the only significant contributor. We discuss implications for improving financial decision-making across adulthood.

Keywords

decision-making temporal sequences financial literacy financial experience age differences

Increases in population age (He et al., 2015) and the greater net worth of older adults relative to that of younger cohorts (Lusardi & Mitchell, 2007) make it important to understand age differences in financial decision making. When deciding how to receive money, people may prefer increasing or decreasing installments (i.e., monetary sequence preferences; Loewenstein & Sicherman, 1991). Normative economic principles dictate that one should take decreasing installments when receiving money (i.e., larger amounts of money sooner and smaller amounts later) to maximize the current value of available liquid funds (Loewenstein & Sicherman, 1991). Yet, people often prefer to “save the best for last” as shown in waiting to receive larger amounts later (e.g., Duffy & Smith, 2013; Loewenstein & Prelec, 1993). Older adults, however, tend to make economically correct choices when deciding how to receive money (Loewenstein & Sicherman, 1991; Löckenhoff & Samanez-Larkin, 2019; Strough et al., 2018). Older adults’ greater financial knowledge and experience relative to that of younger adults have been suggested to explain their monetary sequence preferences (Loewenstein & Sicherman, 1991), but this idea has not been tested. In the current study, we first sought to replicate prior research showing that older age was associated with sequence preferences for receiving money that conformed to normative economic principles. Second, we aimed to advance current knowledge by assessing the roles of financial experience and financial literacy in understanding age differences in such preferences.

Receiving Money: Sequence Preferences

To date, four studies have investigated age differences in sequence preferences for receiving money (Löckenhoff & Samanez-Larkin, 2019; Löckenhoff et al., 2017; Loewenstein & Sicherman, 1991; Strough et al., 2018).¹ Three studies used hypothetical scenarios about winning money, inheriting it, or receiving wages and showed that older age was associated with reporting preferences that conformed to the normative principle of “taking the biggest first” (Löckenhoff & Samanez-Larkin, 2019; Loewenstein & Sicherman, 1991; Strough et al., 2018). Across these studies, the time frame over which the money was to be received varied from being unspecified to 10 months to 10 years. The fourth study investigated preferences for receiving real monetary earnings over 30 minutes and did not find age differences (Löckenhoff et al., 2017). Thus, studies that use hypothetical scenarios have consistently found that older age is associated with economically better decisions about receiving money.

Financial Literacy and Experience

Age differences in financial decision making may be associated with age-related differences in financial literacy and experience, although few studies have examined this proposition (Loewenstein & Sicherman, 1991). Financial literacy refers to understanding economic principles and the ability to apply this understanding to make informed financial decisions, such as those related to debt and wealth accumulation (Lusardi & Mitchell, 2014). This knowledge may come from personal experience with financial decisions; indeed, financial literacy and experience are moderately correlated (r = .47; Li et al., 2015). However, financial literacy can also come from formal education or observational learning, without practical experience. Financial experience, like other experience, accumulates over time (Ericsson et al., 2007). Exposure to and use of financial products, such as credit cards and loans, provide financial experience. Although financial experience may lead to financial literacy, the two constructs are separate. A person may have experience with financial tasks without understanding or learning financial principles. For example, a person may gain experience by receiving advice from a financial advisor, but not understand the reasoning underlying the advice.

Older adults tend to have better financial literacy than younger adults (Li et al., 2013, 2015), though middle-aged adults outperform both younger and older adults (Lusardi & Mitchell, 2014). Financial literacy involves numeracy and capacity to do calculations. Older age is associated with lower numeracy (Bruine de Bruin et al., 2015), so age-related declines in fluid cognitive abilities (Salthouse, 2014) may contribute to older adults’ lower literacy relative to middle-aged adults. Financial literacy has been associated with better financial decisions. Specifically, greater financial literacy was associated with higher real-world credit scores and better hypothetical decisions about repaying debt (Li et al., 2015). No studies to date have examined the association between financial literacy and monetary sequence preferences. If older adults are more financially literate than younger adults, this may account for age differences in monetary sequence preferences.

Theories of aging and decision-making posit that older adults’ decisions may benefit from their personal experiences accrued over time (Strough, de Bruin, et al., 2015; Strough, Parker, et al., 2015). According to life-span theory (Baltes et al., 2006), cognitive “mechanics,” such as fluid reasoning, peak in early adulthood and then decline, whereas cognitive “pragmatics,” or knowledge from life experience, peak around age 60 and are maintained before gradually declining late in life (Salthouse, 2000; Tucker-Drob, 2019). Because cognitive reasoning becomes physiologically more taxing with age, older adults may sidestep effortful cognitive processing if they already know what to do based on their experience (Hess, 2014). Despite the theoretical importance attributed to experience, relatively few studies of aging and decision making measure it (see Strough et al., 2020, for a review).

Research that used crystalized intelligence as a proxy for financial experience showed that age-related increases in experience were associated with more patience in a temporal discounting task and higher financial and debt literacy (Li et al., 2013). A study that used a composite measure of financial experience and financial literacy showed that it partially accounted for older adults’ better self-reported money management (Eberhardt et al., 2018). These studies did not directly assess financial experience or distinguish it from financial literacy. Theoretically, older adults will generally have accrued more financial experience than younger adults, which may account for age differences in sequence preferences for receiving money.

Research Questions

We aimed to extend research on aging and financial decision making by assessing the extent to which financial experience and financial literacy uniquely accounted for variability in sequence preferences for receiving money. Both financial experience and financial literacy are associated with better financial decision making, suggesting they may each be associated with normatively correct sequence preferences for receiving money. Because sequence preferences have a temporal component, we included measures of time perspective to examine the roles of financial experience and financial literacy, above and beyond potential age-related shifts in time perspective. Our study addressed four research questions:

Is age associated with sequence preferences for receiving money?

Hypothesis 1: Older age will be correlated with preferences to receive larger amounts of money sooner than smaller amounts.

Is age associated with financial experience and financial literacy?

Hypothesis 2: Older age will be correlated with greater financial experience and financial literacy.

Are financial experience and financial literacy associated with sequence preferences for receiving money?

Hypothesis 3: Both greater financial experience and financial literacy will be correlated with sequence preferences to receive larger amounts of money sooner than smaller amounts.

Do financial experience and financial literacy statistically account for the association between age and monetary sequence preferences?

Hypothesis 4: Age will no longer be significantly associated with monetary sequence preferences when accounting for financial experience and financial literacy.

Method

Participants

An adult U.S. sample (N = 888) was recruited as part of a larger study through Amazon’s Mechanical Turk (MTurk) using a stratified sampling strategy to recruit 322 younger adults (aged 20–39), 304 middle-aged adults (aged 40–59), and 262 older adults (aged 60–88). Fifty-eight participants who inconsistently reported their age and 236 participants who did not report any demographic information except age were excluded.² The final sample (N = 594, aged 20–88, M _age = 46.48, SD = 15.16) consisted of 215 younger (M = 30.13, SD = 5.11), 200 middle-aged (M = 47.42, SD = 5.86), and 179 older adults (M = 65.06, SD = 5.04). Participants were mostly female (54%) and White (83.8%). A power analysis (G*Power; Erdfelder et al., 1996) indicated that a sample of 594 participants was sufficient to detect a small-medium effect in a multiple regression model testing for significant R ² increase, assuming power was .80 and α = .05.

Procedure

The authors’ university Institutional Review Board approved all study procedures. Participants provided electronic consent before completing the online survey. Measures were presented in a randomized order, except for demographic questions which appeared last (see Supplemental Table S1). Each participant received a $1.00 honorarium, a typical MTurk payment (Buhrmester et al., 2011).

Measures

Monetary Sequences

Participants read four vignettes adapted from prior research (Loewenstein & Sicherman, 1991; Strough et al., 2018) about receiving money (see Supplemental material).³ Following each, participants rated their sequence preference (1 = start with larger amounts first, end with the smaller; 6 = start with smaller amounts first, end with the larger). Normatively correct preferences were indicated by lower average scores.

Financial Literacy

Thirteen items were used to assess understanding of economic principles (Fernandes et al., 2014; for example, “Do you think that the following statement is true or false? Bonds are normally riskier than stocks”). Responses were scored as correct (1) or incorrect (0). Participants had the option of selecting “don’t know” or “refuse to answer,” which were scored as incorrect (0).⁴ Higher average scores indicated greater financial literacy.

Financial Experience

Twenty items assessed participants’ personal experience with a broad range of financial instruments (Li et al., 2015). Items (e.g., checking account, student loan, mortgage, credit card, car loan, mutual fund; for all items see bdm2097-sup-0001-SI.docx) were rated from 1 (never heard of it) to 6 (have a lot of personal experience with it). Higher average scores indicated greater experience.

Time Perspective

Two measures assessed time perspective. Zimbardo’s time perspective inventory (ZTPI; Zimbardo & Boyd, 1999) assessed behavioral tendencies. Fifty-six statements were rated from 1 (very untrue) to 5 (very true). There are five subscales: past negative (e.g., “things rarely work out as I expected”), past positive (e.g., “I get nostalgic about my childhood”), present fatalistic (e.g., “fate determines much in my life”), present hedonic (e.g., “it’s important to put excitement in my life”), and future (e.g., “I make lists of things to do”). Appropriate items were reverse-scored and mean scores were calculated for each subscale, with higher scores representing greater respective time perspective.

The Thinking About the Past/Present/Future (Grühn et al., 2016) measure consists of three items. Participants were asked, “How often during the past week did you spend time thinking about the past/present/future?” on a scale from 1 (rarely or never) to 4 (most or all the time). Higher scores indicated greater thought about the respective time perspective.

Demographics included age, gender, race/ethnicity, education, income, employment, and marital status.

Results

Means, standard deviations, and Cronbach’s alphas for all study variables are presented in Table 1. To examine the simple associations among age, financial experience, financial literacy, and sequence preferences for receiving money (Research Questions 1–3), bivariate correlations were estimated (Table 1). Older age, greater financial experience, and greater financial literacy were correlated with monetary sequence preferences of receiving larger amounts of money sooner than smaller amounts. Older age was correlated with greater financial experience and greater financial literacy. Greater financial experience was correlated with greater financial literacy.

Table 1

Means, Standard Deviations, Cronbach’s alphas, and Bivariate Correlations Among Key Variables.

	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.
1. Age	-
2. Sequence preferences	−.14**	-
3. Financial literacy	.19***	−.15***	-
4. Financial experience	.30***	−.17***	.51***	-
5. Thinking about past	−.02	.11**	−.19***	−.09*	-
6. Thinking about present	.12**	−.12**	.20***	.21***	−.20***	-
7. Thinking about future	−.13**	.03	−.05	.05	.12**	.10*	-
8. Past negative	−.11**	−.04	−.11**	−.16***	.28***	−.16***	.04	-
9. Past positive	.06	.001	.12**	.18***	.02	.15***	.14**	−.34***	-
10. Present hedonic	−.09*	.10*	−.16***	.04	.11**	.05	.15***	.06	.22***	-
11. Present fatalistic	.03	.04	−.39***	−.24***	.30***	−.23***	−.04	.47***	−.22***	.30***	-
12. Future	−.01	−.07	.20***	.16***	−.13**	.24***	.13**	−.03	.33***	−.24***	−.43***	-
Mean	46.48	3.18	0.63	4.16	2.27	3.13	2.83	2.96	3.56	2.52	3.04	3.66
SD	15.16	1.49	0.27	0.64	0.77	0.75	0.77	0.78	0.72	0.72	0.56	0.57
α	-	0.83	0.84	0.88	-	-	-	0.85	0.82	0.89	0.81	0.81

Note. Lower scores for sequence preferences reflect preferences for receiving larger amounts sooner than smaller amounts. Items 5–7 came from the Thinking About the Past/Present/Future scale (Grühn et al., 2016). Items 8–12 came from the Zimbardo time perspective inventory (Zimbardo & Boyd, 1999). ***p < .001; **p < .001; *p < .05.

To examine whether financial experience and financial literacy statistically accounted for the association between age and monetary sequence preferences (Research Question 4), a parallel mediation analysis using 5000 bootstrapped samples with the PROCESS macro (Hayes, 2013) for SPSS was conducted (Figure 1). Because thinking about the present and present hedonic time perspective were each significantly related to both age and monetary sequence preferences (Table 1), they were included as covariates.⁵ Further, because prior research showed that financial literacy differs based on demographic characteristics (Lusardi & Mitchell, 2007, 2011), gender, race/ethnicity, education, employment, marital status, and income were entered as covariates.

Figure 1

Parallel mediation model examining the extent to which financial experience and financial literacy accounted for the association between age and monetary sequence preferences, while controlling for demographics and time perspective. All coefficients are unstandardized bootstrapped effects. Normatively correct preferences for receiving money correspond to lower scores. Note. *p < .05, **p < .01, ***p < .001.

The overall model explained 7.30% of the variance in monetary sequence preferences, F(11, 581) = 4.16, p < .001. There was a significant indirect effect of age on monetary sequence preferences through financial experience (b = −.004, 95% CI [−.007, −.001]), but not financial literacy (b = −.001, 95% CI [−.003, .0002]). After including the significant indirect path, the direct effect (b = −.010, p = .02) of age on preferences for sequences to receive money was no longer significant (b = −.006, p = .21), suggesting that the association between older age and sequence preferences to receive larger amounts of money sooner than later was statistically accounted for by greater financial experience.⁶

In the final model, financial experience (b = −.29, p = .01) but not financial literacy (b = −.41, p = .14) was significantly associated with monetary sequence preferences. Thus, we further explored associations among financial literacy, financial experience, and age. Partial correlations showed that the association between older age and greater literacy was no longer significant after accounting for financial experience (r_p = .04, p = .37). In contrast, the association between older age and greater financial experience remained after accounting for financial literacy (r_p = .25, p < .001).

Discussion

The current study aimed to advance the inadequate literature on age differences in monetary sequence preferences by assessing the roles of financial experience and financial literacy. When asked how they would prefer to receive money, older adults reported a preference for receiving larger amounts of money sooner than smaller amounts—a choice that conforms to normative economic principles by maximizing the availability of liquid funds. Other research has also shown age-related benefits for financial decisions (e.g., Eberhardt et al., 2018; Strough et al., 2018). Our findings advance prior research (Strough et al., 2018) by showing that older age was no longer significantly associated with sequence preferences after accounting for financial experience. Our findings begin to unpack the meaning of age differences in financial decision making by identifying variables for which age serves as a proxy.

In our age-diverse U.S. sample, older age was associated with reporting greater financial experience; similar findings have been reported using samples from the UK (Eberhardt et al., 2018). Older age was also associated with greater financial literacy, which is consistent with research that compares older adults to younger adults (e.g., Li et al., 2013). However, the association between older age and greater literacy was no longer significant after accounting for experience, suggesting that older adults’ understanding of financial principles may reflect a lifetime of experience with loans and other financial products. Older people’s greater experience was not synonymous with understanding financial principles—age-related increases in financial experience remained even after accounting for literacy. These findings are novel, such that prior research on age and financial literacy has not accounted for the role of financial experience. These findings suggest that financial experience may be important for understanding why older age is often found to be associated with greater financial literacy, both in our study and in prior research (e.g., Li et al., 2015). In future research, measuring both financial experience and literacy will help to clarify the relative roles of each in aging and financial decision making.

Financial experience and financial literacy were each correlated with monetary sequence preferences that aligned with normative economic principles. However, in the mediation model that simultaneously accounted for both constructs, financial experience, but not financial literacy, was significantly associated with monetary sequence preferences. Potentially, the measure of financial literacy may have tapped only a limited range of knowledge about economic principles that were less relevant to preferences for sequences to receive money. Future work should explore both financial experience and financial literacy in relation to other financial tasks to gain insight into the types of financial decisions that benefit more from either experience or literacy. Also, because it can take many years of practice to cultivate expertise based on experience (see Strough et al., 2020, for a review), interventions should focus on providing people with the chance to gain hands-on experience through early and continued exposure to financial products.

The current findings also accounted for age differences in time perspective. Older age was associated with a greater orientation toward the present, in accord with socioemotional selectivity theory (Carstensen, 2006). One might then expect that older adults preferences for receiving larger sums of money sooner than smaller ones reflected motivation to spend the money in the “here and now” versus a more distant future. However, even though thinking about the present and a present hedonic time perspective were associated with monetary sequences, age was still significantly associated with sequence preferences after controlling for time perspective, consistent with previous research (Strough et al., 2018).

While our findings suggest that older adults made better financial decisions than younger adults, this may be limited to common financial tasks that they have experience with. The financial environment continues to transform rapidly. New rules and developments in financial technology, such as mobile payment services (e.g., Venmo), may increase the complexity of financial decisions (Lusardi, 2019). Older adults may encounter financial situations they have not experienced before. Potentially, this may disadvantage them relative to young adults for whom such technological changes are more familiar. Therefore, it is important to design interventions that are tailored to age-related strengths and weakness (Strough, de Bruin, et al., 2015; Strough, Parker, et al., 2015). For example, interventions for younger adults could provide experience with financial products whereas interventions for older adults could provide experience with financial technology.

Limitations and Future Directions

The present research has strengths and limitations. First, our use of hypothetical vignettes may not have tapped into emotional and motivational processes elicited by actual financial decisions (e.g., Löckenhoff et al., 2017). Although hypothetical scenarios have been shown to predict real-world behaviors (Bruine de Bruin et al., 2007), research on monetary sequence preferences would benefit from field studies investigating age differences in people’s decisions about actually receiving money from a bonus or winning the lottery (e.g., Larsson, 2011). Second, other measures of financial experience may differentially relate to age. For example, younger adults may have greater experience with student loans. Third, the majority of older adults in our study were in their sixties. Future work is necessary to determine whether benefits of experience extend to older ages. Fourth, participants were not screened for cognitive impairments, and therefore, it is unknown how pathological versus nonpathological aging may relate to monetary sequence preferences or financial literacy. Finally, our cross-sectional design and correlational methods do not address cohort differences or developmental changes (Lindenberger et al., 2011).

Conclusion

In conclusion, older adults’ sequence preferences for receiving money aligned with normative economic principles—as seen in preferring to receive larger amounts of money sooner than smaller amounts. Importantly, older age was no longer significantly associated with sequence preferences after accounting for financial experience and financial literacy, but experience remained a significant contributor. By building on these findings, interventions can be designed to facilitate sound financial decision-making across adulthood.

Supplemental Material

Supplementary Material 1 - Supplemental material for Benefits of Experience and Knowledge for Older Adults’ Monetary Sequence Preferences

Supplemental material, Supplementary Material 1, for Benefits of Experience and Knowledge for Older Adults’ Monetary Sequence Preferences by Jenna M. Wilson, JoNell Strough and Natalie J. Shook in The International Journal of Aging and Human Development

Footnotes

Acknowledgments

This report is based on a project conducted by the first author, under the supervision of the second and third authors, in partial fulfillment of the requirements for a Master of Science degree at West Virginia University. We thank Julie Hicks Patrick as a member of the thesis committee for comments on a prior version of this manuscript. This research was not preregistered. Data and study materials can be made available from the first author upon request.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Science Foundation (Award Number 1459021). The funding organization was not involved in designing the study, collecting and analyzing the data, or preparing the manuscript.

Notes

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Jenna M. Wilson, MS, is a doctoral student in the Life-Span Developmental Psychology program at West Virginia Unviersity. Her research broadly focuses on decision making and well-being. She examines how different mechanisms (e.g., experience, emotion regulation) are associated with decision making in various domains.

JoNell Strough is a professor of psychology and coordinator of the life-span developmental doctoral program at West Virginia University. Her research aims to facilitate healthy aging by identifying cognitive, emotional, and motivational mechanisms that underlie age differences in decision making.

Natalie J. Shook is an Associate Professor in the School of Nursing at the University of Connecticut. She earned her PhD from Ohio State University in 2007 and previously held faculty positions at Virginia Commonwealth University and West Virginia University. Dr. Shook’s research focuses on the extent to which cognitive and affective processes influence psychological and physical health.

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