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. Author manuscript; available in PMC: 2026 Apr 15.
Published before final editing as: Neuropsychology. 2026 Apr 9:10.1037/neu0001082. doi: 10.1037/neu0001082

Monetary savings and neuropsychological functioning in older adults without dementia

Cassidy P Molinare 1, Daisy T Noriega-Makarskyy 1, Jordan T Williams 1, Emma Oyen 1, Paige Kim 1, Jenna Axelrod 1, S Duke Han 1,2,3,4
PMCID: PMC13078639  NIHMSID: NIHMS2139404  PMID: 41955054

Abstract

Objective:

Socioeconomic status (SES) has been associated with cognitive functioning across the lifespan, but specific neuropsychological and economic components were not considered by previous studies. Here, we investigated monetary savings as a measure of SES and its association with performance across multiple neuropsychological measures in older adults without dementia.

Method:

Participants included 111 individuals (M age = 68.5, SD = 7.2; M years of education = 16.7, SD = 2.2; 73% female; 75.7% White) recruited from the greater Los Angeles area. Demographic data, household income, and combined monetary savings across checking and savings accounts were self-reported through a questionnaire. Participants completed cognitive measures from the National Alzheimer’s Coordinating Center Uniform Data Set, version 3, and the NIH Toolbox Cognitive Battery, version 2. Multiple linear regression analyses were conducted to evaluate associations between monetary savings and each cognitive measure. All analyses controlled for age, sex, years of education, and income.

Results:

Greater monetary savings was associated with greater performance on a fluid cognition domain (b = 1.18, 95% CI [0.10, 2.25], p = 0.032), and more specifically on an inhibitory control task (b = 0.98, 95% CI [0.25, 1.70], p = 0.009). Post-hoc analyses demonstrated that this effect was driven by individuals with above-median savings (b = 2.02, 95% CI [0.04, 3.99], p = 0.045).

Conclusions:

Our findings suggest that greater inhibitory control is associated with greater monetary savings and that this relationship is more salient among older individuals with relatively high savings. Further research is needed to determine the underlying mechanisms of this association.

Keywords: aging, socioeconomic status, executive function, financial decision-making

Introduction

An individual’s socioeconomic status (SES), or their social and economic position in society, has been consistently associated with cognitive function across the life span (Farah et al., 2006; Y. Liu & Lachman, 2019; Noble et al., 2005, 2007; Zhang et al., 2020). Studies of older adults have demonstrated that individuals with lower SES in adulthood tend to exhibit lower global cognitive functioning in older adulthood as compared to individuals with higher SES (Krueger et al., 2025; Lyu & Burr, 2016; Migeot et al., 2022; Shi et al., 2023; Wang et al., 2023). Similarly, lower childhood SES has often been associated with lower cognitive functioning in older age across several domains including memory, executive functioning (e.g., working memory, verbal fluency), and processing speed (Aartsen et al., 2019; Brewster et al., 2014; Greenfield et al., 2021; Hurst et al., 2013; Jefferson et al., 2011). Researchers have considered several different theoretical frameworks to explain the associations between SES and cognition in older adulthood, such as the scarcity hypothesis (C. Liu & Li, 2023), the cognitive reserve hypothesis (Jefferson et al., 2011), the accumulation hypothesis (Lyu & Burr, 2016), and the pathway hypothesis (Greenfield et al., 2021). While there is no clear consensus, these frameworks offer insight into the potential mechanisms through which lower socioeconomic status may impact later-life cognition.

Previous research has operationalized SES in a number of ways, often using single variables to represent SES such as income, wealth, educational attainment, occupation, parental education, and social mobility (Freidl et al., 1996; Koster et al., 2005; Lövdén et al., 2020; Opdebeeck et al., 2016; Schrempft et al., 2023). Other studies have acknowledged the complexity of SES, using a composite score to represent their SES variable. For example, early-life SES has been defined based on parental education, paternal occupation, total number of children in the family, and community-level SES, while adult SES has been defined using individuals’ education, occupation, current household income, and household income at age 40 (Jefferson et al., 2011; Wang et al., 2023; Wilson et al., 2005, 2007). Alternatively, in the Health and Retirement Study, childhood SES has been defined using parental education, paternal occupation, and family financial well-being during childhood, and adult SES as education and annual household income (Lyu & Burr, 2016). Other SES composites have also included more nuanced factors that may contribute to SES, such as health insurance and food security, to create a more comprehensive measure (Wang et al., 2023).

While there are numerous factors that influence an individual’s SES, one component that has been understudied is household monetary savings. To account for SES, studies have typically measured income or wealth, with wealth representing household assets (e.g., investments, savings bonds, and items owned such as cars or property) minus total debt. While a higher income may indicate greater SES, income alone does not provide a complete picture of an individual’s financial situation as it overlooks factors like expenses or assets. Instead, examining savings may provide a better indicator of financial standing as it suggests the individual does not live paycheck-to-paycheck, relying on each to meet their immediate expenses, but rather is able to maintain a financial buffer. A report from the Bank of America Institute highlights the discrepancy between income and savings by demonstrating that not just low-income households live paycheck-to-paycheck, but that 20% of households with incomes greater than $150,000 also rely on each paycheck to meet their expenses (Tinsley, 2024). Unlike illiquid assets such as private investments or property that would contribute to wealth, funds held in checking and savings accounts are considered more liquid as they are more easily accessible in the form of cash (Campbell et al., 2024). Maintaining a greater number of savings in liquid form, rather than solely long-term savings such as retirement accounts, ensures an individual is well-prepared to cover both routine expenses and unexpected financial burden (Bhutta et al., 2023). Professional financial advisors typically recommend maintaining an emergency fund of 3 to 6 months of expenses in liquid assets to cover unanticipated costs (Bhutta et al., 2023). As such, a greater amount of money held in liquid assets may reflect stronger financial decision-making and higher SES. Analyzing individuals’ savings also offers a more dynamic assessment of SES as checking and savings account balances tend to fluctuate more fluidly, whereas traditional SES variables such as educational attainment, income, and retirement investments are generally more stable and less sensitive to short-term changes. Leveraging a more nuanced measure of SES that captures an individual’s current access to liquid funds may improve our understanding of how financial stressors may impact cognitive functioning.

In the present study, we aimed to examine monetary savings as a measure of SES and investigate its association with specific cognitive functions in older adulthood. Specifically, we hypothesized that total monetary savings, defined as the combined funds across an individual’s checking and savings accounts, would be positively associated with executive functioning measures as maintaining a financial buffer in savings requires skills such as planning for the future, inhibiting impulsive purchases, problem-solving, and organization. Our hypothesis aligns with the scarcity hypothesis which posits that individuals with limited savings may be experiencing chronic financial insecurity, whereby the stress of meeting basic needs diminishes their capacity for higher-order cognitive functions (Mullainathan & Shafir, 2013). However, given that this is one of the first studies to examine monetary savings in the context of cognition, we also wanted to investigate how savings related to performances across a comprehensive neuropsychological battery to consider other cognitive domains including memory, attention, language, and visuospatial abilities. We use the term “savings” in the present study because we believe it most accurately reflects the monetary holdings available to the individual after adjustment for income level in demographically adjusted models.

Methods

Transparency and Openness

We report all data exclusions, manipulations, and measures included in this study below, and we follow Journal Article Reporting Standards (Kazak, 2018). Data analysis and visualization were conducted in R Version 4.4.2 using the stats, ggplot2, and table1 packages (R Core Team, 2024; Rich, 2025; Wickham, 2016). This study’s design and analyses were not preregistered.

Participants

An a priori power analysis conducted using the R pwr package estimated that a sample size of 92 participants would be necessary for a multiple regression analysis with one primary predictor variable and four covariates, assuming a medium effect size (Cohen’s f2 = 0.15), alpha level of 0.05, and 80% power. Our sample included 111 older adults (mean age = 68.7±7.1; mean years of education = 16.7±2.2; 73% female; 75.7% White, 14.4% Asian, 7.2% Black or African American, 1% Native Hawaiian or other Pacific Islander, 2% Other) from the greater Los Angeles community. Individuals were recruited from community outreach events, local senior centers, and the Alzheimer’s Prevention Registry. To meet criteria for enrollment, participants had to be aged 50 or older, fluent in English, and free from dementia (as determined by a score ≥21 on a modified version of the 5-min Montreal Cognitive Assessment) (Wong et al., 2015). Study exclusion criteria included a history of significant neurological or psychiatric illness, alcohol use disorder in the last 5 years, or any history of other substance abuse. Participants were excluded from our study sample if they did not complete the monetary savings question (n=4).

Measures

Participants completed a series of self-report measures as well as a comprehensive battery of neuropsychological assessments as detailed below. Study procedures were approved by the University of Southern California Institutional Review Board. All participants indicated their understanding of study procedures and provided written consent at the time of enrollment.

Demographics.

All participants completed a questionnaire in which they were asked to report their date of birth, sex (binarily coded as male or female), years of education, and race. Participants were also asked to report the total combined income of the family in their household during the past 12 months, “including money from jobs, net income from business, farm or rent, pensions, dividends, interest, Social Security payments, and any other monetary income received by members of the family who are at 15 years of age or older”, using a 16-point scale. Response options included “1 – Less than $5,000”, “2 – $5,000 to $7,499”, “3 – $7,500 to $9,999”, “4 – $10,000 to $12,999”, “5 – $12,500 to $14,999”, “6 – $15,000 to $19,999”, “7 – “$20,000 to $24,999”, “8 – $25,000 to $29,999”, “9 – $30,000 to $34,999”, “10 – $35,000 to $39,999”, “11 – $40,000 to $49,999”, “12 – $50,000 to $59,999”, “14 – $60,000 to $74,999”, “15 – $100,000 to $149,999”, “16 – $150,000 or more”. This income question was drawn from the standard demographic variables collected regularly by the Understanding America Study (UAS), a large, nationally representative internet panel study on the daily lives of U.S. families and individuals (available at https://uasdata.usc.edu/index.php) that is maintained by the Center for Economic and Social Research (CESR) at the University of Southern California (Alattar et al., 2018; Kapteyn et al., 2024).

Monetary savings.

In addition to reporting their household income, participants were also asked to answer the question “About how much money do you have in all of your savings/checking accounts?” using an 8-point scale. Response options included “1 – less than $999”, “2 – $1,000 to $4,999”, “3 – $5,000 to $9,999”, “4 – $10,000 to $24,999”1, “5 – $25,000 to $49,999”, “6 – $50,000 to $99,999”, “7 – $100,000 to $149,999”, “8 – $150,000 or more”, and “9 – Do not know”. Participants who indicated “Do not know” (n=3) were excluded from this sample. As such, response options ranged from 1 to 8. This savings question was developed based on the various questions about financial resources, such as money held in savings and checking accounts, administered in the UAS (Alattar et al., 2018; Kapteyn et al., 2024).

Neuropsychological assessments.

Global cognition was characterized using the total score from the Mini-Mental State Examination (MMSE), a 30-item measure that assesses orientation, memory, attention, calculation, language, and visuospatial abilities and is often used to determine cognitive impairment (Folstein et al., 1975). Additionally, participants completed measures from the National Alzheimer’s Coordinating Center Uniform Data Set, version 3 (UDS-3) neuropsychological battery (Weintraub et al., 2018). The UDS-3 measures included the Craft Story 21 immediate and delayed recall (total scores), Benson complex figure copy and delayed recall (total scores), number span forward and backward (total correct trials and longest span), Multilingual Naming Test (MINT; total score), verbal fluency phonemic test (combined total of F-words and L-words), category fluency (animals total score; vegetables total score), and Trail Making Test Part A, and Trail Making Test Part B. Participants (n=94) also completed measures from the NIH Toolbox, version 2 (NIHTB V2) (www.NIHToolbox.org). The NIHTB V2 Cognition Battery included tasks that assess executive function, episodic memory, working memory, language, attention, and processing speed. The battery yields a Fluid Cognition Composite score (comprised of the Dimensional Change Card Sort, Flanker Inhibitory Control and Attention, Picture Sequence Memory, List Sorting Working Memory, and Pattern Comparison tests) and a Crystallized Cognition Composite score (comprised of the Picture Vocabulary and Oral Reading Recognition tests).

Statistical analyses

Regression assumptions were evaluated prior to analysis. All scatterplots were visually inspected for outliers. Residual plots were examined to verify that assumptions of linearity and homoscedasticity were met, and histograms of residuals were used to assess normality. Variance Inflation Factors (VIFs) were calculated to evaluate multicollinearity. Linear regression analyses were computed to investigate whether total monetary savings was associated with the NIHTB V2 Fluid Cognition Composite and the individual tasks from the NIHTB V2 that comprise the composite. Linear regression analyses were also used to evaluate whether total monetary savings was associated with executive functioning measures from the UDS-3 (number span backward, verbal fluency phonemic test, category fluency, Trail Making Test Parts A and B). To provide context and assess the specificity of the association with executive functioning, we examined the association between monetary savings and other cognitive domains. We conducted additional linear regression analyses using the NIHTB V2 Crystallized Cognition Composite score, the individual tasks included in the Crystallized Cognition Composite score, and the other neuropsychological measures from the UDS-3 (Craft Story 21 immediate and delayed recall, Benson complex figure copy and delayed recall, number span forward, and the Multilingual Naming Test). In each of the models, age, sex, years of education, and income were included as covariates.

Post-hoc analyses were utilized to further examine the significant associations found between monetary savings and specific neuropsychological scores. To better assess the utility of monetary savings as a measure of SES, we compared our measure of monetary savings to another commonly used SES variable, income. For the variables that were significantly associated with monetary savings, we repeated our linear regression analyses using income as the predictor variable and controlling for age, sex, and years of education. A median split was used to divide the sample into a “high savings” group, including individuals with savings greater than the median amount, and “low savings” group, including individuals with savings less than or equal to the median amount. Using the same covariates as were used in our primary analyses, age, sex, years of education, and income, linear regression models were computed to determine whether the same associations between total monetary savings and cognitive outcomes that were observed in the total sample were consistent in a group of individuals with lower savings and a group of individuals with higher savings. Higher savings may indicate more consistent saving behavior, and individuals may only exhibit greater executive functioning if they have consistent access to financial resources.

Results

Participant demographics

Sample characteristics are shown in Table 1. The full sample included 111 older adults (M age = 68.5, SD = 7.2; M years of education = 16.7, SD = 2.2; 73% female; 75.7% White) (Table 1). Demographic characteristics of participants who completed the NIHTB V2 (n=94) and group differences between this subset and the high- and low-savings groups are reported in the Supplementary Materials (Supplementary Table 1). The distribution of total monetary savings in our sample demonstrated a relatively uniform spread with participants broadly dispersed across the full range of savings levels (Figure 1).

Table 1.

Sample demographics

Characteristic Overall, N=111
Age, Mean (SD), Range 68.5 (7.2), 51–83
Sex, n (%) Female 81 (73.0)
Years of Education, Mean (SD), Range 16.7 (2.2), 11–20
Race, n (%)
 White 84 (75.7)
 Asian 16 (14.4)
 Black or African American 8 (7.2)
 Native Hawaiian or Pacific Islander 1 (0.9)
 Other 2 (1.8)
MMSE total, Mean (SD), Range 28.4 (1.5), 24–30
Income, Median, Range 14, 1–16
Savings, Median, Range 4, 1–8
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Note. Income was measured using a 16-point scale (14 – $60,000 to $74,999); Savings was measured using an 8-point scale (4 – $10,000 to $24,999, 5 – $25,000 to $49,999). MMSE = Mini Mental Status Examination.

Figure 1.

Figure 1.

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Associations between total monetary savings and the NIH Toolbox, version 2 (NIHTB V2) Fluid Cognition Composite and the Flanker Inhibitory Control and Attention task (n=94)

Note. The error bars represent the mean ± standard error.

Models

No outliers were identified upon visual inspection of scatterplots. Regression diagnostics verified that linearity, homoscedasticity, and normality assumptions were met. All VIFs were below 2, indicating no concerns for multicollinearity. None of the measures from the UDS-3 were associated with total monetary savings, as shown in Table 2. However, there was a trend-level association between total monetary savings and performance on both category fluency measures (total vegetables and total animals) (Table 2). Lower total monetary savings was associated with lower performance on the NIHTB V2 Fluid Cognition Composite (b = 1.18, 95% CI [0.10, 2.25], p = 0.032) and lower performance on the Flanker Inhibitory Control and Attention task (b = 0.98, 95% CI [0.25, 1.70], p = 0.009) (Table 3). Total monetary savings was not associated with performance on the NIHTB V2 Crystallized Cognition Composite or any of the other individual NIHTB V2 measures (Table 3). However, there was a trend-level association between total monetary savings and performance on the Dimensional Change Card Sort task (b = 1.00, 95% CI [−0.19, 2.18], p = 0.099) (Table 3). All models included age, sex, years of education, and income as covariates. Additional adjustment for financial literacy did not change our results (Supplementary Tables 2 and 3).

Table 2.

Results from linear regression analyses evaluating the association between monetary savings and outcome measures from the Uniform Data Set-3, version 3 (N=111)

Measure B 95%CI p R2
Craft Story 21 immediate recall 0.090
 Monetary Savings −0.12 −0.74 – 0.49 0.688
 Age −0.04 −0.20 – 0.12 0.607
 Sex [Male] 1.69 −0.95 – 4.34 0.207
 Years of Education 0.12 −0.45 – 0.69 0.672
 Income 0.39 0.09 – 0.69 0.011
Craft Story 21 delay recall 0.067
 Monetary Savings −0.25 −0.83 – 0.32 0.388
 Age −0.01 −0.16 – 0.14 0.859
 Sex [Male] 1.91 −0.57 – 4.39 0.129
 Years of Education −0.10 −0.63 – 0.44 0.718
 Income 0.34 0.06 – 0.62 0.016
Benson Complex Figure copy 0.050
 Monetary Savings 0.03 −0.03 – 0.09 0.399
 Age 0 −0.01 – 0.02 0.573
 Sex [Male] −0.24 −0.50 – 0.02 0.065
 Years of Education 0.02 −0.04 – 0.07 0.557
 Income 0.01 −0.02 – 0.04 0.557
Benson Complex Figure recall 0.067
 Monetary Savings −0.17 −0.51 – 0.16 0.309
 Age −0.07 −0.16 – 0.02 0.105
 Sex [Male] −0.05 −1.50 – 1.39 0.943
 Years of Education 0.25 −0.06 – 0.56 0.109
 Income 0.09 −0.08 – 0.25 0.294
Number Span Forward total 0.098
 Monetary Savings 0.04 −0.18 – 0.26 0.705
 Age −0.07 −0.13 – −0.02 0.012
 Sex [Male] 0.38 −0.57 – 1.33 0.430
 Years of Education 0.01 −0.20 – 0.21 0.936
 Income 0.07 −0.03 – 0.18 0.171
Number Span Forward longest span 0.107
 Monetary Savings 0.03 −0.10 – 0.16 0.655
 Age −0.04 −0.08 – −0.01 0.008
 Sex [Male] 0 −0.55 – 0.55 0.998
 Years of Education 0.02 −0.10 – 0.14 0.761
 Income 0.04 −0.02 – 0.10 0.183
Number Span Backward total 0.130
 Monetary Savings −0.06 −0.28 – 0.15 0.562
 Age −0.09 −0.14 – −0.03 0.002
 Sex [Male] 0.74 −0.19 – 1.67 0.119
 Years of Education 0.12 −0.08 – 0.32 0.223
 Income 0.04 −0.06 – 0.15 0.410
Number Span Backward longest span 0.113
 Monetary Savings −0.06 −0.18 – 0.07 0.371
 Age −0.04 −0.08 – −0.01 0.009
 Sex [Male] 0.40 −0.15 – 0.94 0.151
 Years of Education 0.10 −0.02 – 0.21 0.105
 Income 0.02 −0.04 – 0.08 0.529
Total Animals 0.196
 Monetary Savings −0.50 −1.03 – 0.02 0.059
 Age −0.27 −0.40 – −0.13 <0.001
 Sex [Male] 1.29 −0.96 – 3.54 0.259
 Years of Education 0.36 −0.13 – 0.84 0.145
 Income 0.31 0.06 – 0.56 0.017
Total Vegetables 0.153
 Monetary Savings −0.33 −0.72 – 0.06 0.096
 Age −0.16 −0.26 – −0.06 0.002
 Sex [Male] −0.95 −2.64 – 0.73 0.264
 Years of Education −0.07 −0.43 – 0.29 0.689
 Income 0.22 0.03 – 0.41 0.024
Trail Making Test part A 0.128
 Monetary Savings −0.62 −1.53 – 0.29 0.181
 Age 0.37 0.14 – 0.61 0.002
 Sex [Male] 0.65 −3.27 – 4.57 0.742
 Years of Education 0.09 −0.75 – 0.93 0.835
 Income −0.11 −0.55 – 0.33 0.628
Trail Making Test part B 0.135
 Monetary Savings 1.00 −2.00 – 4.00 0.511
 Age 0.98 0.19 – 1.76 0.015
 Sex [Male] −9.86 −22.73 – 3.02 0.132
 Years of Education −1.13 −3.89 – 1.62 0.416
 Income −1.85 −3.33 – −0.36 0.015
MINT total correct 0.173
 Monetary Savings −0.09 −0.28 – 0.10 0.340
 Age −0.06 −0.11 – −0.01 0.022
 Sex [Male] 0.94 0.12 – 1.76 0.024
 Years of Education 0.08 −0.09 – 0.26 0.343
 Income 0.14 0.04 – 0.23 0.004
Total F and L words 0.103
 Monetary Savings −0.34 −1.22 – 0.55 0.453
 Age −0.23 −0.46 – −0.00 0.049
 Sex [Male] −0.25 −4.06 – 3.56 0.895
 Years of Education 0.07 −0.75 – 0.89 0.868
 Income 0.57 0.14 – 1.00 0.010
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Note. Bold p values should be considered significant if they are less than 0.05. MMSE = Mini Mental Status Examination.

Table 3.

Results from linear regression analyses evaluating the association between monetary savings and outcome measures from the NIH Toolbox, version 2 Cognition Battery.

Measures B 95%CI p R2
Fluid Cognition Composite 0.163
 Monetary Savings 1.18 0.10 – 2.25 0.032
 Age −0.29 −0.56 – −0.02 0.037
 Sex [Male] −0.95 −5.24 – 3.34 0.662
 Years of Education −0.59 −1.50 – 0.33 0.207
 Income 0.30 −0.18 – 0.78 0.219
Crystallized Cognition Composite 0.062
 Monetary Savings −0.13 −1.08 – 0.82 0.787
 Age −0.15 −0.39 – 0.10 0.231
 Sex [Male] 0 −3.81 – 3.81 0.999
 Years of Education −0.55 −1.36 – 0.27 0.184
 Income 0.39 −0.04 – 0.81 0.075
Flanker Inhibitory Control 0.122
 Monetary Savings 0.98 0.25 – 1.70 0.009
 Age −0.06 −0.24 – 0.13 0.538
 Sex [Male] −1.02 −3.91 – 1.87 0.486
 Years of Education −0.60 −1.22 – 0.02 0.058
 Income 0.08 −0.25 – 0.40 0.642
Picture Sequence Memory 0.081
 Monetary Savings 0.52 −0.78 – 1.82 0.428
 Age −0.35 −0.68 – −0.02 0.037
 Sex [Male] 1.53 −3.67 – 6.73 0.561
 Years of Education −0.69 −1.80 – 0.43 0.224
 Income 0.22 −0.36 – 0.81 0.443
Dimensional Change Card Sort 0.056
 Monetary Savings 1.00 −0.19 – 2.18 0.099
 Age −0.06 −0.36 – 0.24 0.708
 Sex [Male] −0.79 −5.54 – 3.97 0.743
 Years of Education −0.78 −1.80 – 0.24 0.131
 Income 0.10 −0.43 – 0.63 0.713
List Sorting Working Memory 0.145
 Monetary Savings 0.09 −0.87 – 1.05 0.853
 Age −0.16 −0.40 – 0.08 0.189
 Sex [Male] 0.14 −3.70 – 3.97 0.944
 Years of Education −0.33 −1.15 – 0.49 0.422
 Income 0.7 0.27 – 1.13 0.002
Pattern Comparison 0.075
 Monetary Savings 1.14 −0.44 – 2.72 0.154
 Age −0.24 −0.64 – 0.16 0.239
 Sex [Male] −2.79 −9.10 – 3.52 0.382
 Years of Education 0.40 −0.95 – 1.75 0.554
 Income −0.01 −0.71 – 0.69 0.980
Picture Vocabulary 0.081
 Monetary Savings −0.49 −1.61 – 0.63 0.385
 Age −0.15 −0.43 – 0.13 0.295
 Sex [Male] 0.23 −4.24 – 4.70 0.920
 Years of Education −0.25 −1.21 – 0.71 0.606
 Income 0.66 0.16 – 1.16 0.010
Oral Reading Recognition 0.070
 Monetary Savings 0.26 −0.56 – 1.07 0.536
 Age −0.12 −0.33 – 0.08 0.241
 Sex [Male] −0.09 −3.36 – 3.17 0.955
 Years of Education −0.78 −1.48 – −0.08 0.029
 Income 0.04 −0.33 – 0.40 0.844
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Note. Bold p values should be considered significant if they are less than 0.05. MMSE = Mini Mental Status Examination.

Post-hoc analyses

To demonstrate the utility of monetary savings as a measure of SES, we repeated the linear regression analyses that yielded significant results using income as the predictor variable instead of monetary savings. We did find an association between income and the NIHTB Fluid Cognition Composite (b= 0.49, 95%CI [0.03, 0.94], p= 0.036). However, the model that included monetary savings as a predictor variable (b= 1.18, 95%CI [0.03, 0.94], p= 0.036) explains 4.6% more variance in participants’ fluid cognition performance than the model with income alone (an R2 value of 0.163 compared to an R2 value of 0.117), demonstrating that monetary savings has incremental predictive value. We did not see an association between income and performance on the Flanker Inhibitory Control and Attention task (b= 0.23, 95%CI [−0.08, 0.54], p= 0.142).

To further investigate the relationship between monetary savings and scores on the NIHTB V2 Fluid Cognition Composite and the Flanker Inhibitory Control and Attention task, and whether the associations differed amongst individuals with lower vs. high savings, we repeated the linear regression analyses using a median split that divided the sample into a high savings (n=49) and low savings group (n=44). These analyses included age, sex, years of education, and income as covariates. There was no longer an association between monetary savings and the Fluid Cognition Composite in the high savings group (b = 2.33, 95% CI [−0.67, 5.32), p = 0.125), or in the low savings group (b = −1.26, 95% CI [−4.26, 1.73], p = 0.397). Lower monetary savings was associated with lower performance on the Flanker Inhibitory Control and Attention task only in the high savings group (b = 2.02, 95% CI [0.04, 3.99], p = 0.045), not in the low savings group (b = 0.90, 95% CI [−1.45, 3.24], p = 0.443) (Figure 1).

Discussion

In the current study, we investigated the association between monetary savings and cognitive functioning in older adults without dementia. We found that higher savings was associated with higher fluid cognitive abilities (NIHTB V2 Fluid Cognition Composite), and specifically inhibitory control (NIHTB V2 Flanker Inhibitory Control and Attention), after controlling for covariates. We also found that monetary savings explained more variance in fluid cognition performance than income, and that income was not associated with inhibitory control. After stratifying our sample into a low savings and high savings group, the association between savings and inhibitory control remained for individuals with higher savings but became trend-level for individuals with lower savings. The association between savings and the fluid cognition composite did not remain in the low or high savings group. We did not see an association between savings and verbal fluency (total F and L words), working memory (number span backward), or task-switching (Trail Making Test Part B) using outcome measures from the UDS-3.

To our knowledge, no previous study has investigated specific neuropsychological performance associations with a measure of savings to represent SES, and thus the findings from this study may be novel. However, previous studies have found associations between lower SES across the lifespan and lower cognitive functioning in older age, with different theories emerging to explain these relationships. The scarcity hypothesis posits that individuals with lower SES experience a chronic scarcity of resources, which requires them to devote most of their cognitive effort to managing immediate financial needs and making short-term decisions, often at the cost of long-term planning and future-oriented thinking (Mullainathan & Shafir, 2013). This theory has been used to explain the tendency for lower SES individuals to develop poorer executive function, fluid intelligence, and decision-making (C. Liu & Li, 2023; Mani et al., 2013; Sheehy-Skeffington, 2020). Additionally, neuroimaging studies have supported this hypothesis by demonstrating that individuals from lower SES backgrounds evidence reduced activation in frontal brain regions responsible for executive and decision-making abilities, including the anterior cingulate cortex and frontoparietal executive network (Gianaros et al., 2011; Sheridan et al., 2012; Yaple & Yu, 2020). Our results are consistent with the scarcity hypothesis such that they implicate a positive relationship between SES, as defined by total monetary savings, and inhibitory control, a component of executive functioning. However, we did not find an association between our SES measure and memory or other executive functions, including verbal fluency and working memory, which have been previously observed in other studies of older adults (Avila-Rieger et al., 2022; Marden et al., 2017; Peterson et al., 2021; Steptoe & Zaninotto, 2020). A plausible explanation for the differences in our findings is that previous studies have used different indicators of SES such as education, income, and wealth. Savings may capture a distinct element of SES and, as such, have different cognitive correlates. Previous research has repeatedly demonstrated that executive functioning encompasses multiple distinct but related processes including inhibitory control, cognitive flexibility, working memory, and verbal fluency (Fisk & Sharp, 2004; Lehto et al., 2003; Miyake et al., 2000). Because executive functioning is non-unitary, it is possible that monetary savings relates to inhibitory control specifically, and not necessarily other executive functions. However, further research is needed to replicate these findings. Additionally, savings may be a reflection of financial decision-making, as it reflects individuals’ ability to inhibit spending and plan for the future. These findings underscore the importance of considering savings as a more nuanced component of SES and highlight the limitations of relying solely on traditional SES variables.

Our results also have practical implications for monitoring cognitive changes in aging populations. For instance, monitoring of older adults’ bank accounts from a trusted source or finance professional may be helpful for identifying early signs of cognitive decline, since frontal lobe executive functions may decline first in the context of aging (Braver & West, 2011; Spreng et al., 2017). Low balances across checking and savings accounts that are uncharacteristic for the individual could reflect lower executive functioning, suggesting that the older adult may be having difficulty inhibiting their impulses to spend. Supervising older individuals’ bank accounts to track changes in their financial decision making may facilitate earlier interventions and limit financial losses. However, future research is needed to further investigate the specific mechanisms through which savings may be related to cognitive functioning and financial decision-making.

This study is not without limitations. First, the individuals included in our sample were predominately highly educated, White, and female. To improve the generalizability of our findings, a larger, more demographically diverse sample is needed. Similarly, our sample only included individuals living in the greater Los Angeles area, a region known for having a high cost of living (Thomas et al., 2024; UCLA Luskin School of Public Affairs, 2025). The residential location of participants may impact their financial habits and capacity to save; therefore, our findings may not reflect the experiences of individuals living in areas with different economic conditions. Additional research is needed to examine whether savings behavior differs across other regions of the United States. Another limitation of our study is that we did not collect data about participants’ current expenses. Monetary savings can fluctuate in response to certain life events such as unexpected medical bills or major home repairs. Recent large expenditures may have impacted participants’ bank account balances at the time of the study. While the advantage of measuring monetary savings is that it captures the natural variability in individuals’ financial resources, future research could achieve a clearer picture of individuals’ current financial status by collecting information on participants’ current expenses and recent, atypical large payments. Similarly, our measure of monetary savings does not capture the full complexity of an individual’s financial profile, particularly for someone with a higher level of income. Future work will include additional measures of individual’s liquid assets (e.g., money held in stocks, bonds, certificates of deposit). Additionally, given the exploratory nature of our analyses, there is a potential for spurious findings. As such, future studies are needed to replicate these findings and further examine the association between monetary savings and executive functioning. Finally, our study did not include individuals with known cognitive impairment. However, in future work we hope to explore the relationships between monetary savings and cognitive functioning in a sample of older adults who meet criteria for mild cognitive impairment to determine whether these associations differ from those observed in our current sample.

In conclusion, the results of this study demonstrate an association between greater monetary savings and greater inhibitory control, especially for individuals with greater savings. Additional research is needed to better understand the mechanisms that contribute to this association. Broadly, these findings may support the notion that changes in financial accounts may reflect cognitive changes in older age.

Supplementary Material

Supplementary Materials

Key Points.

Question:

How do monetary savings, an underexplored aspect of socioeconomic status, relate to cognitive functioning in older adults without dementia?

Findings:

Greater total monetary savings were associated with better inhibitory control even after controlling for age, sex, years of education, and income.

Importance:

Monetary savings may provide insight into older adults’ financial behavior and cognitive functioning that is not captured by traditional socioeconomic variables.

Next Steps:

Additional research is needed to better understand the mechanisms behind these findings, and future work should examine these associations in larger, more diverse samples.

Acknowledgements

This work was supported by the National Institute on Aging (R01AG068166 and K24AG081325 to SDH). 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 (R01 AG063954). 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).

*This manuscript is the result of funding in whole or in part by the National Institutes of Health (NIH). It is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH

Footnotes

Conflict of Interest Statement

The authors declare that there are no conflicts of interest.

1

During a portion of data collection, a typographical error was identified in one of the response options, which displayed as “4 - $10,000 to $24,000”. This error was promptly corrected and did not impact the integrity of the data collection process. The item was never flagged or questioned by any participants.

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