The Cognitive Correlates of Financial Literacy in Older Adults

ABSTRACT

Objectives

This study examined the cognitive correlates of financial literacy using a comprehensive neuropsychological battery, and whether education modifies the relationship between cognition and financial literacy.

Methods

Sixty-six participants completed sociodemographic questionnaires, an assessment of financial literacy, and a neuropsychological assessment. Multiple linear regression models that controlled for age, sex, and education examined the main effects of cognitive measures that showed a significant bivariate association with financial literacy.

Results

After correcting for multiple comparisons, the Crystallized Composite score (p = .002) and the Picture Vocabulary test (p = .002) from the NIH Toolbox, and the Multilingual Naming Test (p > .001) from the Uniform Data Set 3 were associated with financial literacy. Contrary to our hypothesis, education did not interact with cognitive measures when considering financial literacy scores.

Conclusions

Findings suggest that vocabulary knowledge and semantic memory may play an important role in financial literacy in older age.

Clinical Implications

Assessing vocabulary knowledge and semantic processes may help to identify older adults with lower financial literacy skills. Additionally, financial literacy interventions may consider targeting individuals with lower vocabulary knowledge and semantic processing skills.

KEYWORDS:

• Cognition
• financial literacy
• neuropsychology
• older adults

Introduction

Individuals may face increasingly difficult financial decisions as they reach older adulthood, including those related to investment of retirement savings and allocation of assets. Additionally, most older adults live on fixed incomes, thereby rendering the recuperation from financial losses extremely challenging (Nerenberg, 2000). Financial literacy, or the ability to access, understand, and utilize information that promotes positive financial outcomes (Braunstein & Welch, 2002), is a skill crucial for successfully navigating a complex financial world. Research suggests that low financial literacy increases risk of experiencing financial exploitation (James et al. 2014; Nguyen et al., 2021). For example, in a recent qualitative study (Nguyen et al., 2021), participants partially attributed financial exploitation experiences to inexperience and lack of awareness, and many reported making changes to their financial and consumer behaviors following the experience (e.g., being more vigilant).

Numerous studies have shown that financial literacy is generally low amongst both young and older adults (e.g., de Bassa Scheresberg, 2013; Lusardi & Mitchell, 2011a, 2011b). Arguably, financial literacy is especially important for older adults facing difficult financial decisions associated with older adulthood (Lusardi, 2012). In this context, epidemiological studies of aging have shown financial literacy to decline with age (e.g., Finke et al., 2017; Lusardi & Mitchell, 2011b). This suggests that certain factors associated with aging, such as changes in cognitive ability, may be particularly relevant for understanding variations in financial literacy within older adults.

While the vast majority of older adults do not have dementia (e.g., Fratiglioni et al., 1999), subtle cognitive declines associated with normal aging processes may impact financial literacy in older adulthood. Consistent with this, previous research has associated financial literacy with better cognition (Bennett et al., 2012; Boyle et al., 2013), better physical and mental health status (Bennett et al., 2012), and better decision-making (James et al., 2012) in older adults without dementia. With regard to cognition, researchers from one group have reported associations of financial literacy with global cognition as well as a range of cognitive domains including episodic memory, perceptual speed, working memory, visuospatial abilities, and semantic memory in older adults without dementia (Bennett et al., 2012). Additionally, research by a separate group (Finke et al., 2017) reported steady declines in financial literacy after age 60 and demonstrated that declines in word recall and vocabulary accounted for 50% of the variance in financial literacy decline. However, few other groups, to our knowledge, have examined the relationship between cognitive abilities and financial literacy in older adults utilizing a comprehensive neuropsychological battery.

While cognitive abilities may account for some of the variance in financial literacy scores in older ages, not all individuals experience cognitive declines as they age. In this context, other sociodemographic and contextual factors have also been shown to be important predictors of financial literacy. We recently found significant discrepancies in performance on a combined measure of health and financial literacy and global cognitive ability in a subset of participants without dementia (Weissberger et al., 2019). Certain factors were associated with these discrepancies, including sex, income, and trust of others. In a study by Boyle and colleagues (Boyle et al., 2013), combined financial and health literacy was independently predicted by word knowledge and education.

Given the limited research regarding the cognitive correlates of financial literacy, we sought to examine the relationship between financial literacy and cognition in a sample of older adults without dementia who underwent a comprehensive neuropsychological battery. We additionally sought to examine whether education would moderate associations between literacy and cognition for those cognitive domains significantly associated with financial literacy. While education is related to cognition throughout the lifespan (Lövdén et al., 2020), research has demonstrated an independent relationship between educational attainment and financial literacy (Boyle et al., 2013; Lusardi et al., 2010, 2011), which may be due to various factors such as greater exposure to financial educational resources, basic mathematical or numeracy skills, or intergenerational transmissions of financial literacy (Lusardi et al., 2011). However, among those without access to such educational resources, financial literacy would need to be developed through other means, and one’s cognitive abilities may be important in this process. In support of a potential interactive effect of education, one study which examined the association of mild cognitive impairment (MCI) and literacy found that higher education attenuated the effect of MCI on financial literacy (Han et al., 2015). Thus, we hypothesized that the relationship between cognition and financial literacy would be strongest in those with lower levels of educational attainment.

Research design

Participants

Seventy-one adults aged 50 years or older were initially recruited to participate in a study involving finances, cognition, and health. Participants were recruited from senior centers, elder abuse awareness events, and community outreach programs in the greater Los Angeles area. Prior to study enrollment, participants were screened for eligibility via telephone. Exclusion criteria included known cognitive impairment or a diagnosis of dementia, a neurological or psychiatric illness, or current problems with drugs or alcohol. On the day of their visit, participants were further screened for significant cognitive impairment using the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) screening instrument. Consistent with study protocol (Weissberger, Mosqueda, Nguyen, Samek, et al., 2020), those who scored 23 or below on the MoCA (Carson et al., 2018) were excused from participation in the study and thanked for their time.

Procedure and measures

Participants completed a series of behavioral and self-report measures including a comprehensive neuropsychological assessment and a measure of financial literacy. Study procedures were approved by the institutional review board of the University of Southern California. All study participants provided written consent to participate in the study.

Financial Literacy measure

The Financial Literacy measure (James et al., 2012) consisted of 23-items adapted from the Health and Retirement Survey (Lusardi & Mitchell, 2006). The measure assesses knowledge of financial terms and institutions such as the Federal Deposit Insurance Corporation (FDIC), numeracy, and the ability to perform simple monetary calculations. Answer choices are either multiple choice or true/false. Scores were calculated based on the percentage of correct items out of the total 23 items.

Neuropsychological Assessment

Participants completed the California Verbal Learning Test (CVLT-II), measures within the Uniform Dataset 3 (UDS 3) neuropsychological battery (Weintraub et al., 2018), and the NIH Toolbox® version 1.10 (www.NIHToolbox.org). The CVLT-II involves reading a 16-item word list to participants who are then asked to freely recall as many words as they can from the list. These initial learning trials were repeated five times. The sum of these five immediate recall trials was considered in the present study (CVLT-II List A Total). Following the five learning trials, a distractor word list (List B) was read to participants who were then asked to recall words from the distractor list (CVLT-II List B Total). Following this, participants were asked to freely recall words from List A (Short Delay Free Recall) and then provided with category cues (Short Delay Cued Recall). Free and cued recall trials were repeated after a twenty-minute delay in which participants completed other cognitive assessments (Long Delay Free Recall; Long Delay Cued Recall).

Measures of the UDS 3 included the Montreal Cognitive Assessment (MoCA), Craft Story 21 immediate and delayed recall (total scores), Benson complex figure copy and recall (total scores), number span forward and backward (total correct trials, and longest span), total score on the Multilingual Naming Test (MINT; a 32-item measure of picture naming), verbal fluency phonemic test (combined total of F-words and L-words), category fluency (animals total score; vegetables total score), Trail Making Test part A and part B.

The NIH Toolbox® version 1.10 (www.NIHToolbox.org) Cognition Battery consists of measures that assess executive functioning, attention, episodic memory, language, processing speed, and working memory. The battery yields a global Cognitive Function composite score, a Fluid Cognition composite score (e.g., processing new information, problem solving; Harada et al., 2013), and a Crystallized Cognition composite score (e.g., overlearned, familiar, and well-practiced knowledge and skills; Harada et al., 2013). The Fluid Cognition composite score is comprised of Dimensional Change Card Sort, Flanker Inhibitory Control and Attention, Picture Sequence Memory, List Sorting Working Memory, and the Pattern Comparison Processing Speed tests. The Crystallized Cognition composite score is comprised of Picture Vocabulary and Oral Reading Recognition tests.

Statistical analyses

Bivariate associations between financial literacy, demographic variables, and neuropsychological measures were assessed using Pearson correlations or independent samples t-tests. Multiple linear regression models regressed financial literacy on those cognitive tests that were significantly associated with financial literacy in the bivariate analyses. Sex (1 for male, 0 for female), age, and education in years were included as covariates in regression models. Regression models were corrected for multiple comparisons using Bonferroni corrections. Cognitive measures that remained significantly associated with financial literacy were entered into a second set of hierarchical linear regression analyses, which investigated the interactive effect of education on the relationship between cognition and financial literacy.

Results

Participant demographics

Of the 71 participants enrolled in the study, 5 were excluded from analyses due to having incomplete financial literacy data (i.e., did not complete this portion of the study), leaving a total of 66 participants available for data analyses (M age = 68.85, SD = 10.92; M education years = 16.03, SD = 2.48; 57.6% female). Participant demographics and scores on each of the neuropsychological tests are reported in Table 1. With regard to race breakdown, 47 participants (71.2%) self-reported Non-Hispanic White race. Four participants reported Non-Hispanic Black race (6.1%), nine reported being of Asian descent (13.6%), one reported “other” (1.5%), and one reported “unknown” (1.5%). Three participants reported being of Hispanic ethnicity (4.5%).

Table 1. Participant characteristics, scores on neuropsychological tests, and bivariate associations with financial literacy.

		M	SD	Min	Max	Pearson’s r	p-value
	Age	68.85	10.92	50	92	−0.044	0.725
	Education	16.03	2.48	11	20	0.247	0.046
	Sex (% Female; Female = 1)	57.58%	-	-	-	^at = −2.895	0.005
	Race (% non-Hispanic White)	72.31%	-	-	-	^at = −3.238	0.002
	Financial Literacy Score (% items correct)	80.56%	15.01	34	100	-	-
Neuropsychological Tests
	MoCA Total Score	27.71	1.55	23	30	0.287	0.020
	CVLT Immediate Recall	41.41	9.01	25	63	−0.023	0.854
	CVLT Short Delay Free Recall	8.49	3.01	2	16	−0.004	0.974
	CVLT Short Delay Cued Recall	9.83	2.72	4	16	−0.012	0.924
	CVLT Long Delay Free Recall	8.92	3.14	1	15	−0.019	0.878
	CVLT Long Delay Cued Recall	9.83	2.91	3	15	0.009	0.944
NIH Toolbox (N = 60)
	Fluid Composite Score	46.92	8.20	34	69	−0.027	0.839
	Crystalized Composite Score	58.45	9.00	38	77	0.348	0.006
	Total Composite Score	53.13	8.45	38	70	0.206	0.114
	Picture Vocabulary	56.23	10.59	30	73	0.328	0.011
	Flanker Inhibitory Control and Attention Test	42.27	6.32	32	62	−0.040	0.759
	List Sorting Working Memory Test	52.80	9.19	37	75	0.110	0.401
	Dimensional Change Card Sort Test	52.80	10.23	33	75	−0.133	0.310
	Pattern Comparison Processing Speed Test	43.10	11.27	19	68	−0.079	0.551
	Picture Sequening Memory Test	49.55	9.54	26	81	0.101	0.443
	Oral Reading Recognition Test	59.20	8.13	44	81	0.267	0.039
Uniform Data Set
	Craft Story Immediate Recall	19.08	6.05	5	34	0.172	0.166
	Craft Story Delayed Recall	16.17	6.48	3	31	0.150	0.229
	Benson Figure Copy	16.86	0.55	14	17	0.177	0.156
	Besonon Figure Recall	12.23	3.36	0	17	0.017	0.892
	Numbers Forward	8.08	2.14	4	13	0.256	0.038
	Numbers Backward	6.89	1.65	4	11	0.208	0.094
	Total animals	21.30	4.18	14	33	0.089	0.477
	Total vegetables	14.44	3.74	6	25	−0.168	0.177
	Trail Making Test Part A	33.23	12.85	13	71	−0.141	0.259
	Trail Making Test Part B	84.41	39.86	33	241	−0.262	0.033
	MINT total correct	29.70	2.46	20	32	0.461	<0.001
	Verbal Fluency, total F and L words	27.65	7.11	8	43	0.189	0.128

Note: MoCA: Montreal Cognitive Assessment; CVLT: California Verbal Learning Test; MINT: multilingual naming test.

^aIndependent samples t-tests.

Bivariate associations

Results of the bivariate associations with financial literacy are reported in Table 1. The financial literacy score was positively associated with education. Independent samples t-tests revealed an association between the financial literacy score and sex, such that males achieved significantly higher scores on the measure (M = 88.64; SD = 10.68) than females (M = 79.34; SD = 15.39; p = .005). With regard to race, White non-Hispanic participants had significantly higher scores on the financial literacy measure (M = 86.28, SD = 12.20) compared to non-White participants (M = 78.22, SD = 13.47; p = .002).

With regard to cognition, significant positive correlations arose between financial literacy and various cognitive measures including the MoCA total score (p = .020), the NIH Toolbox Crystallized Composite score (p = .006), the Picture Vocabulary subtest (p = .011), and the Oral Reading Recognition subtest (p = .039). Within the UDS 3 battery, numbers forward (p = .038), Trail Making Test part B (p = .033), and the MINT (p < .001) were also significantly related to financial literacy.

Regression analyses

Multiple linear regression models were run on the measures significantly associated with the financial literacy score (with the exception of the MoCA, which was not further investigated since it was utilized as a screening measure in the study). Multiple comparisons were corrected for such that the main effects of the cognitive measures were considered significant at a Bonferroni corrected level of 0.008 (0.05/6). All models included covariates of age, education, and sex. Results of these analyses can be viewed in Table 2. Three tests demonstrated a main effect with financial literacy at the desired significance level of p = .008: the NIH Crystal Composite score (p = .002), the NIH Picture Vocabulary test (p = .002), and the MINT (p < .001).

Table 2. Multiple linear regression models for those measures significantly associated with financial literacy in bivariate associations. The dependent variable in each model was financial literacy score. Each model included one of the six cognitive measures significantly associated with financial literacy in previous bivariate associations.

			b	Beta	SE_b	p-value
Model 1	F = 6.86, p < 0.001, R^2adj = 0.28
	age		−0.22	−0.18	0.15	0.094
	sex		4.36	0.17	2.94	0.144
	education		2.11	0.43	0.57	<0.001
	NIH Crystalized Composite		0.55	0.39	0.17	0.002
Model 2	F = 6.52, p < .001, R^2adj = 0.27
	age		−0.27	−0.21	0.15	0.071
	sex		4.11	0.16	3.00	0.176
	education		2.09	0.43	0.57	<0.001
	NIH Picture Vocabulary		0.45	0.38	0.14	0.002
Model 3	F = 5.01, p = 0.002, R^2adj = 0.21
	age		−0.18	−0.14	−1.18	0.244
	sex		5.99	0.24	1.99	0.051
	education		1.86	0.38	3.17	0.002
	NIH Oral Reading		0.43	0.27	2.29	0.026
Model 4	F = 4.87, p = 0.002, R^2adj = 0.19
	age		−0.07	−0.05	0.16	0.679
	sex		9.25	0.31	3.44	0.009
	education		1.86	0.31	0.69	0.009
	Numbers Forward		1.63	0.23	0.81	0.047
Model 5	F = 4.22, p = .004, R^2adj = 0.17
	age		0.02	0.01	0.18	0.912
	sex		9.45	0.31	3.53	0.009
	education		1.54	0.26	0.71	0.033
	Trail Making Test part B		−0.07	−0.18	0.05	0.162
Model 6	F = 7.88, p < .001, R^2adj = 0.30
	age		−0.10	−0.07	0.15	0.490
	sex		7.71	0.26	3.25	0.021
	education		1.64	0.27	0.64	0.013
	MINT		2.43	0.40	0.65	< 0.001

Note: SE_b: standard error of the unstandardized regression coefficient; MINT: multilingual naming test.

To further investigate potential interactions of cognition with education on financial literacy scores, we conducted three hierarchical linear regressions for the three measures that were significantly associated with financial literacy scores in the main effect models. Step one of the regression models incorporated just the covariates (age, sex, education). Step two added the cognitive measure of interest. Step three added an interaction term of education * the cognitive measure. Results of the regression analyses can be viewed in Table 3. None of the interactions between cognitive scores and education were significant (all ps $\ge$ 0.18).

Table 3. Hierarchical linear regression models for each of the three cognitive measures that were significantly associated with financial literacy after controlling for covariates and correcting for multiple comparisons. Step 1 included covariates, step 2 added the cognitive measure, and step 3 added the interaction of education*cognitive measure.

	Variable	b	Beta	SE_b	p-value
MINT
Model 1	age	−0.09	−0.06	0.16	0.598
R^2adj = 0.15	sex (Male)	10.55	0.35	3.47	0.003
	education	1.70	0.28	0.71	0.019
Model 2	age	−0.10	−0.07	0.15	0.490
R^2adj = 0.30	sex (Male)	7.71	0.26	3.25	0.021
	education	1.64	0.27	0.64	0.013
	MINT	2.43	0.40	0.65	<0.001
Model 3	age	−0.08	−0.06	0.15	0.597
R^2adj = 0.31	sex (Male)	7.99	0.27	3.23	0.016
	education	12.60	1.99	7.64	0.120
	MINT	8.03	1.31	4.14	0.057
	MINT * education	−0.35	−1.97	0.26	0.176
NIH Crystalized Composite
Model 1	age	−0.23	−0.18	0.16	0.152
R^2adj = 0.15	sex (Male)	6.78	0.27	3.1	0.033
	education	1.66	0.34	0.6	0.008
Model 2	age	−0.2	−0.18	0.15	0.132
R^2adj = 0.28	sex (Male)	4.36	0.17	2.94	0.144
	education	2.11	0.43	0.57	<0.001
	NIH Crystalized Composite	0.55	0.39	0.17	0.002
Model 3	age	−0.22	−0.17	0.15	0.141
R^2adj = 0.29	sex (Male)	3.53	0.14	2.99	0.244
	education	6.97	1.43	3.77	0.070
	NIH Crystalized Composite	1.86	1.32	1.02	0.073
	NIH Crystalized Composite * education	−0.08	−1.18	0.06	0.198
NIH Picture Vocabulary
Model 1	age	−0.23	−0.18	0.16	0.152
R^2adj = 0.15	sex (Male)	6.78	0.27	3.10	0.033
	education	1.66	0.34	0.60	0.008
Model 2	age	−0.27	−0.21	0.15	0.071
R^2adj = 0.27	sex (Male)	4.11	0.16	3.00	0.176
	education	2.09	0.43	0.57	<0.001
	NIH Picture Vocabulary	0.45	0.38	0.14	0.002
Model 3	age	−0.25	−0.20	0.15	0.098
R^2adj = 0.27	sex (Male)	3.88	0.15	3.01	0.202
	education	5.15	1.05	3.1	0.102
	NIH Picture Vocabulary	1.31	1.09	0.86	0.135
	Vocabulary * education	−0.05	−0.84	0.05	0.319

Note: SE_b = standard error of the unstandardized regression coefficient.

Discussion

In this study, we examined the cognitive correlates of financial literacy and the effect of education on these relationships. We found crystallized functions, and specifically vocabulary knowledge (NIH Picture Vocabulary test), to be associated with financial literacy scores after accounting for covariates and multiple comparisons. Confrontation naming, measured using the MINT, was also positively associated with financial literacy, after accounting for age, sex, and education. Contrary to our hypothesis and previous research that found that the effect of MCI on financial literacy was weakened in those with higher education (Han et al., 2015), education did not modify the relationship between the cognitive measures examined and financial literacy scores. It is possible that education plays a modifying role specifically when cognition becomes compromised by underlying neuropathology. Since our sample was heterogeneous in that it included all individuals without dementia (i.e., some of whom may meet criteria for mild cognitive impairment, and some of whom are cognitively healthy), this may have reduced our ability to observe a modifying role of education specifically in those with cognitive impairment. Future research is needed to investigate whether this is indeed the case.

The finding that vocabulary knowledge is associated with financial literacy converges with previous studies that have examined the relationship of financial literacy and cognition in older adults. For example, Boyle et al. (2013) found that word knowledge (measured by vocabulary and word reading) was independently associated with a combined measure of financial and health literacy. Finke et al. (2017) found that in addition to declines in word recall, declines in vocabulary accounted for 50% of variance in decline in financial literacy scores. Vocabulary knowledge begins to develop early in life and continues to grow throughout life (Kavé & Halamish, 2015; Kavé & Yafé, 2014; Kavé et al., 2022). Vocabulary knowledge reflects one’s general ability to learn new concepts (Laumann Long & Lisa, 2000), and requires the efficient use of strategies to learn new words and their meanings (e.g., working memory; Daneman & Green, 1986). Such skills may also be critical for learning new concepts related to financial matters. Future research may examine the mechanisms by which vocabulary knowledge predicts financial literacy.

We also found confrontation naming (i.e., MINT) to be associated with financial literacy, a finding consistent with previous reports that examined the relationship of financial literacy with a composite score of semantic memory that included confrontation naming (e.g., Bennett et al., 2012; Han et al., 2015). For example, a study by Han and colleagues (Han et al., 2015) found that in participants with mild cognitive impairment, semantic memory accounted for the most variance in financial literacy scores. Confrontation naming reflects the retrieval of stored information from semantic memory (Hodges et al., 1992) and financial literacy requires knowledge of financial concepts (Kimiyaghalam & Safari, 2015). The relationship between confrontation naming and financial literacy may thus reflect a general difficulty in retrieving stored conceptual information, including information and concepts that are financial in nature.

Semantic memory is also a cognitive function known to decline early in the Alzheimer’s disease (AD) process (Hodges et al., 1992; Salmon et al., 1999). Recent work has shown that declines in financial decision-making and vulnerability to financial exploitation may be early markers of impending AD (Boyle et al., 2019; Fenton et al., 2022; Weissberger, Mosqueda, Nguyen, Axelrod, et al., 2020). Financial literacy is a construct that is crucially important for making informed financial decisions (Lusardi, 2012) and is known to decline with age (Finke et al., 2017; Lusardi & Mitchell, 2011b). It is possible that declines in financial literacy may also mark impending AD, possibly via early declines in semantic memory (perhaps specific to financial concepts). Consistent with this possibility, a recent study demonstrated that faster declines in financial and health literacy were associated with higher risks for incident Alzheimer’s dementia (Yu et al., 2018).

This study has several noteworthy limitations. First, our sample was made up of highly educated and predominantly non-Hispanic White participants. This may limit the generalizability of findings, and the interactive effect of education and cognition on financial literacy may differ when examined within lower education samples. A second limitation relates to the cross-sectional nature of the study. Longitudinal studies may help clarify the nature of the relationships observed. Another limitation relates to the small sample size in this study. Past studies that have examined financial literacy were comprised of very large samples (e.g., Boyle et al., 2013; Han et al., 2015). Thus, while we did not find an interactive effect of education, this may be due to lack of power to detect effects. Of note, power analyses indicate that a sample size of 66 participants is sufficient to detect medium effect sizes (f = 0.20), with power = 0.80 and alpha = 0.05. While education was normally distributed in this sample, all participants had at least a high school education (with only one participant reporting 11 years). Thus, findings may differ when examining relationships among individuals with less than a high school education. Future studies may also consider examining how other contextual factors (e.g., sex, race) may interact with cognition to predict financial literacy scores. Finally, future studies may consider investigating how relationships between cognition, education, and financial literacy differ for individuals with mild cognitive impairment versus cognitively healthy individuals, as we did not make this differentiation in the present study.

Nevertheless, this study has several important strengths and novel contributions to the literature on financial literacy. First, we included a comprehensive battery of neuropsychological tests, including use of the NIH Toolbox, a computerized assessment of cognition. We chose to examine test scores separately while controlling for Type 1 error in order to achieve a higher level of specificity with regard to the cognitive functions important for financial literacy. Additionally, while previous studies of financial literacy have focused data collection on older adults aged 60–65 and older (e.g., Bennett et al., 2012; Boyle et al., 2013), our findings included a younger cohort of older adults (age 50 and up), attesting to the importance of these cognitive functions even in early older adulthood.

Taken together, our findings highlight the importance of specific cognitive abilities, namely vocabulary knowledge and semantic processes, for financial literacy as adults approach older age. As such, findings of the study have important clinical implications. For example, assessing these cognitive skills may help to identify older adults with lower financial literacy skills. Additionally, cognitive tests involving vocabulary knowledge and semantic processes may be useful in screening older adults for financial vulnerability. Finally, targeting financial literacy interventions to individuals with lower vocabulary knowledge and semantic processing skills may prove effective.

Clinical implications

• Assessing vocabulary knowledge and semantic processes may help to identify older adults with lower financial literacy skills.

• Financial literacy interventions may consider targeting individuals with lower vocabulary knowledge and semantic processing skills.

Acknowledgement

The authors gratefully thank the Han Research Lab staff and study participants. Some participants from the present study were recruited with the help of the Alzheimer’s Prevention Registry. The Alzheimer’s Prevention Registry is supported by a grant from the National Institute on Aging (R01AG063954). The Alzheimer’s Prevention Registry has been supported by the Alzheimer’s Association, Banner Alzheimer’s Foundation, Flinn Foundation, Geoffrey Beene Gives Back Alzheimer’s Initiative, GHR Foundation, and the state of Arizona (Arizona Alzheimer’s Consortium).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

De-identified data can be made available from the study’s corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Institute on Aging [grant numbers 1RF1AG068166 and K24AG081325 to SDH, T32AG000037 to ACL, K01AG064986 to ALN]